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TheAlgorithms/Python | 5,795 | [mypy] Type annotations for `graphs/finding_bridges.py` and `graphs/random_graph_generator.py` | ### Describe your change:
Related to #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-08T10:00:47Z" | "2021-11-08T17:18:33Z" | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | a8aeabdf1891397a4a55988f33ac435ae0313c55 | [mypy] Type annotations for `graphs/finding_bridges.py` and `graphs/random_graph_generator.py`. ### Describe your change:
Related to #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def get_1s_count(number: int) -> int:
"""
Count the number of set bits in a 32 bit integer using Brian Kernighan's way.
Ref - http://graphics.stanford.edu/~seander/bithacks.html#CountBitsSetKernighan
>>> get_1s_count(25)
3
>>> get_1s_count(37)
3
>>> get_1s_count(21)
3
>>> get_1s_count(58)
4
>>> get_1s_count(0)
0
>>> get_1s_count(256)
1
>>> get_1s_count(-1)
Traceback (most recent call last):
...
ValueError: the value of input must be positive
>>> get_1s_count(0.8)
Traceback (most recent call last):
...
TypeError: Input value must be an 'int' type
"""
if number < 0:
raise ValueError("the value of input must be positive")
elif isinstance(number, float):
raise TypeError("Input value must be an 'int' type")
count = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| def get_1s_count(number: int) -> int:
"""
Count the number of set bits in a 32 bit integer using Brian Kernighan's way.
Ref - http://graphics.stanford.edu/~seander/bithacks.html#CountBitsSetKernighan
>>> get_1s_count(25)
3
>>> get_1s_count(37)
3
>>> get_1s_count(21)
3
>>> get_1s_count(58)
4
>>> get_1s_count(0)
0
>>> get_1s_count(256)
1
>>> get_1s_count(-1)
Traceback (most recent call last):
...
ValueError: the value of input must be positive
>>> get_1s_count(0.8)
Traceback (most recent call last):
...
TypeError: Input value must be an 'int' type
"""
if number < 0:
raise ValueError("the value of input must be positive")
elif isinstance(number, float):
raise TypeError("Input value must be an 'int' type")
count = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 5,795 | [mypy] Type annotations for `graphs/finding_bridges.py` and `graphs/random_graph_generator.py` | ### Describe your change:
Related to #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-08T10:00:47Z" | "2021-11-08T17:18:33Z" | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | a8aeabdf1891397a4a55988f33ac435ae0313c55 | [mypy] Type annotations for `graphs/finding_bridges.py` and `graphs/random_graph_generator.py`. ### Describe your change:
Related to #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Gaussian Naive Bayes Example
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import plot_confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
def main():
"""
Gaussian Naive Bayes Example using sklearn function.
Iris type dataset is used to demonstrate algorithm.
"""
# Load Iris dataset
iris = load_iris()
# Split dataset into train and test data
X = iris["data"] # features
Y = iris["target"]
x_train, x_test, y_train, y_test = train_test_split(
X, Y, test_size=0.3, random_state=1
)
# Gaussian Naive Bayes
NB_model = GaussianNB()
NB_model.fit(x_train, y_train)
# Display Confusion Matrix
plot_confusion_matrix(
NB_model,
x_test,
y_test,
display_labels=iris["target_names"],
cmap="Blues",
normalize="true",
)
plt.title("Normalized Confusion Matrix - IRIS Dataset")
plt.show()
if __name__ == "__main__":
main()
| # Gaussian Naive Bayes Example
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import plot_confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
def main():
"""
Gaussian Naive Bayes Example using sklearn function.
Iris type dataset is used to demonstrate algorithm.
"""
# Load Iris dataset
iris = load_iris()
# Split dataset into train and test data
X = iris["data"] # features
Y = iris["target"]
x_train, x_test, y_train, y_test = train_test_split(
X, Y, test_size=0.3, random_state=1
)
# Gaussian Naive Bayes
NB_model = GaussianNB()
NB_model.fit(x_train, y_train)
# Display Confusion Matrix
plot_confusion_matrix(
NB_model,
x_test,
y_test,
display_labels=iris["target_names"],
cmap="Blues",
normalize="true",
)
plt.title("Normalized Confusion Matrix - IRIS Dataset")
plt.show()
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 5,795 | [mypy] Type annotations for `graphs/finding_bridges.py` and `graphs/random_graph_generator.py` | ### Describe your change:
Related to #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-08T10:00:47Z" | "2021-11-08T17:18:33Z" | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | a8aeabdf1891397a4a55988f33ac435ae0313c55 | [mypy] Type annotations for `graphs/finding_bridges.py` and `graphs/random_graph_generator.py`. ### Describe your change:
Related to #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #
| #
| -1 |
TheAlgorithms/Python | 5,795 | [mypy] Type annotations for `graphs/finding_bridges.py` and `graphs/random_graph_generator.py` | ### Describe your change:
Related to #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-08T10:00:47Z" | "2021-11-08T17:18:33Z" | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | a8aeabdf1891397a4a55988f33ac435ae0313c55 | [mypy] Type annotations for `graphs/finding_bridges.py` and `graphs/random_graph_generator.py`. ### Describe your change:
Related to #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # A naive recursive implementation of 0-1 Knapsack Problem
This overview is taken from:
https://en.wikipedia.org/wiki/Knapsack_problem
---
## Overview
The knapsack problem is a problem in combinatorial optimization: Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible. It derives its name from the problem faced by someone who is constrained by a fixed-size knapsack and must fill it with the most valuable items. The problem often arises in resource allocation where the decision makers have to choose from a set of non-divisible projects or tasks under a fixed budget or time constraint, respectively.
The knapsack problem has been studied for more than a century, with early works dating as far back as 1897 The name "knapsack problem" dates back to the early works of mathematician Tobias Dantzig (1884–1956), and refers to the commonplace problem of packing the most valuable or useful items without overloading the luggage.
---
## Documentation
This module uses docstrings to enable the use of Python's in-built `help(...)` function.
For instance, try `help(Vector)`, `help(unit_basis_vector)`, and `help(CLASSNAME.METHODNAME)`.
---
## Usage
Import the module `knapsack.py` from the **.** directory into your project.
---
## Tests
`.` contains Python unit tests which can be run with `python3 -m unittest -v`.
| # A naive recursive implementation of 0-1 Knapsack Problem
This overview is taken from:
https://en.wikipedia.org/wiki/Knapsack_problem
---
## Overview
The knapsack problem is a problem in combinatorial optimization: Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible. It derives its name from the problem faced by someone who is constrained by a fixed-size knapsack and must fill it with the most valuable items. The problem often arises in resource allocation where the decision makers have to choose from a set of non-divisible projects or tasks under a fixed budget or time constraint, respectively.
The knapsack problem has been studied for more than a century, with early works dating as far back as 1897 The name "knapsack problem" dates back to the early works of mathematician Tobias Dantzig (1884–1956), and refers to the commonplace problem of packing the most valuable or useful items without overloading the luggage.
---
## Documentation
This module uses docstrings to enable the use of Python's in-built `help(...)` function.
For instance, try `help(Vector)`, `help(unit_basis_vector)`, and `help(CLASSNAME.METHODNAME)`.
---
## Usage
Import the module `knapsack.py` from the **.** directory into your project.
---
## Tests
`.` contains Python unit tests which can be run with `python3 -m unittest -v`.
| -1 |
TheAlgorithms/Python | 5,795 | [mypy] Type annotations for `graphs/finding_bridges.py` and `graphs/random_graph_generator.py` | ### Describe your change:
Related to #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-08T10:00:47Z" | "2021-11-08T17:18:33Z" | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | a8aeabdf1891397a4a55988f33ac435ae0313c55 | [mypy] Type annotations for `graphs/finding_bridges.py` and `graphs/random_graph_generator.py`. ### Describe your change:
Related to #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def is_palindrome(s: str) -> bool:
"""
Determine whether the string is palindrome
:param s:
:return: Boolean
>>> is_palindrome("a man a plan a canal panama".replace(" ", ""))
True
>>> is_palindrome("Hello")
False
>>> is_palindrome("Able was I ere I saw Elba")
True
>>> is_palindrome("racecar")
True
>>> is_palindrome("Mr. Owl ate my metal worm?")
True
"""
# Since Punctuation, capitalization, and spaces are usually ignored while checking
# Palindrome, we first remove them from our string.
s = "".join([character for character in s.lower() if character.isalnum()])
return s == s[::-1]
if __name__ == "__main__":
s = input("Enter string to determine whether its palindrome or not: ").strip()
if is_palindrome(s):
print("Given string is palindrome")
else:
print("Given string is not palindrome")
| def is_palindrome(s: str) -> bool:
"""
Determine whether the string is palindrome
:param s:
:return: Boolean
>>> is_palindrome("a man a plan a canal panama".replace(" ", ""))
True
>>> is_palindrome("Hello")
False
>>> is_palindrome("Able was I ere I saw Elba")
True
>>> is_palindrome("racecar")
True
>>> is_palindrome("Mr. Owl ate my metal worm?")
True
"""
# Since Punctuation, capitalization, and spaces are usually ignored while checking
# Palindrome, we first remove them from our string.
s = "".join([character for character in s.lower() if character.isalnum()])
return s == s[::-1]
if __name__ == "__main__":
s = input("Enter string to determine whether its palindrome or not: ").strip()
if is_palindrome(s):
print("Given string is palindrome")
else:
print("Given string is not palindrome")
| -1 |
TheAlgorithms/Python | 5,795 | [mypy] Type annotations for `graphs/finding_bridges.py` and `graphs/random_graph_generator.py` | ### Describe your change:
Related to #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-08T10:00:47Z" | "2021-11-08T17:18:33Z" | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | a8aeabdf1891397a4a55988f33ac435ae0313c55 | [mypy] Type annotations for `graphs/finding_bridges.py` and `graphs/random_graph_generator.py`. ### Describe your change:
Related to #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/bin/sh
#
# An example hook script to verify what is about to be committed.
# Called by "git commit" with no arguments. The hook should
# exit with non-zero status after issuing an appropriate message if
# it wants to stop the commit.
#
# To enable this hook, rename this file to "pre-commit".
if git rev-parse --verify HEAD >/dev/null 2>&1
then
against=HEAD
else
# Initial commit: diff against an empty tree object
against=$(git hash-object -t tree /dev/null)
fi
# If you want to allow non-ASCII filenames set this variable to true.
allownonascii=$(git config --bool hooks.allownonascii)
# Redirect output to stderr.
exec 1>&2
# Cross platform projects tend to avoid non-ASCII filenames; prevent
# them from being added to the repository. We exploit the fact that the
# printable range starts at the space character and ends with tilde.
if [ "$allownonascii" != "true" ] &&
# Note that the use of brackets around a tr range is ok here, (it's
# even required, for portability to Solaris 10's /usr/bin/tr), since
# the square bracket bytes happen to fall in the designated range.
test $(git diff --cached --name-only --diff-filter=A -z $against |
LC_ALL=C tr -d '[ -~]\0' | wc -c) != 0
then
cat <<\EOF
Error: Attempt to add a non-ASCII file name.
This can cause problems if you want to work with people on other platforms.
To be portable it is advisable to rename the file.
If you know what you are doing you can disable this check using:
git config hooks.allownonascii true
EOF
exit 1
fi
# If there are whitespace errors, print the offending file names and fail.
exec git diff-index --check --cached $against --
| #!/bin/sh
#
# An example hook script to verify what is about to be committed.
# Called by "git commit" with no arguments. The hook should
# exit with non-zero status after issuing an appropriate message if
# it wants to stop the commit.
#
# To enable this hook, rename this file to "pre-commit".
if git rev-parse --verify HEAD >/dev/null 2>&1
then
against=HEAD
else
# Initial commit: diff against an empty tree object
against=$(git hash-object -t tree /dev/null)
fi
# If you want to allow non-ASCII filenames set this variable to true.
allownonascii=$(git config --bool hooks.allownonascii)
# Redirect output to stderr.
exec 1>&2
# Cross platform projects tend to avoid non-ASCII filenames; prevent
# them from being added to the repository. We exploit the fact that the
# printable range starts at the space character and ends with tilde.
if [ "$allownonascii" != "true" ] &&
# Note that the use of brackets around a tr range is ok here, (it's
# even required, for portability to Solaris 10's /usr/bin/tr), since
# the square bracket bytes happen to fall in the designated range.
test $(git diff --cached --name-only --diff-filter=A -z $against |
LC_ALL=C tr -d '[ -~]\0' | wc -c) != 0
then
cat <<\EOF
Error: Attempt to add a non-ASCII file name.
This can cause problems if you want to work with people on other platforms.
To be portable it is advisable to rename the file.
If you know what you are doing you can disable this check using:
git config hooks.allownonascii true
EOF
exit 1
fi
# If there are whitespace errors, print the offending file names and fail.
exec git diff-index --check --cached $against --
| -1 |
TheAlgorithms/Python | 5,795 | [mypy] Type annotations for `graphs/finding_bridges.py` and `graphs/random_graph_generator.py` | ### Describe your change:
Related to #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-08T10:00:47Z" | "2021-11-08T17:18:33Z" | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | a8aeabdf1891397a4a55988f33ac435ae0313c55 | [mypy] Type annotations for `graphs/finding_bridges.py` and `graphs/random_graph_generator.py`. ### Describe your change:
Related to #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 301: https://projecteuler.net/problem=301
Problem Statement:
Nim is a game played with heaps of stones, where two players take
it in turn to remove any number of stones from any heap until no stones remain.
We'll consider the three-heap normal-play version of
Nim, which works as follows:
- At the start of the game there are three heaps of stones.
- On each player's turn, the player may remove any positive
number of stones from any single heap.
- The first player unable to move (because no stones remain) loses.
If (n1, n2, n3) indicates a Nim position consisting of heaps of size
n1, n2, and n3, then there is a simple function, which you may look up
or attempt to deduce for yourself, X(n1, n2, n3) that returns:
- zero if, with perfect strategy, the player about to
move will eventually lose; or
- non-zero if, with perfect strategy, the player about
to move will eventually win.
For example X(1,2,3) = 0 because, no matter what the current player does,
the opponent can respond with a move that leaves two heaps of equal size,
at which point every move by the current player can be mirrored by the
opponent until no stones remain; so the current player loses. To illustrate:
- current player moves to (1,2,1)
- opponent moves to (1,0,1)
- current player moves to (0,0,1)
- opponent moves to (0,0,0), and so wins.
For how many positive integers n <= 2^30 does X(n,2n,3n) = 0?
"""
def solution(exponent: int = 30) -> int:
"""
For any given exponent x >= 0, 1 <= n <= 2^x.
This function returns how many Nim games are lost given that
each Nim game has three heaps of the form (n, 2*n, 3*n).
>>> solution(0)
1
>>> solution(2)
3
>>> solution(10)
144
"""
# To find how many total games were lost for a given exponent x,
# we need to find the Fibonacci number F(x+2).
fibonacci_index = exponent + 2
phi = (1 + 5 ** 0.5) / 2
fibonacci = (phi ** fibonacci_index - (phi - 1) ** fibonacci_index) / 5 ** 0.5
return int(fibonacci)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 301: https://projecteuler.net/problem=301
Problem Statement:
Nim is a game played with heaps of stones, where two players take
it in turn to remove any number of stones from any heap until no stones remain.
We'll consider the three-heap normal-play version of
Nim, which works as follows:
- At the start of the game there are three heaps of stones.
- On each player's turn, the player may remove any positive
number of stones from any single heap.
- The first player unable to move (because no stones remain) loses.
If (n1, n2, n3) indicates a Nim position consisting of heaps of size
n1, n2, and n3, then there is a simple function, which you may look up
or attempt to deduce for yourself, X(n1, n2, n3) that returns:
- zero if, with perfect strategy, the player about to
move will eventually lose; or
- non-zero if, with perfect strategy, the player about
to move will eventually win.
For example X(1,2,3) = 0 because, no matter what the current player does,
the opponent can respond with a move that leaves two heaps of equal size,
at which point every move by the current player can be mirrored by the
opponent until no stones remain; so the current player loses. To illustrate:
- current player moves to (1,2,1)
- opponent moves to (1,0,1)
- current player moves to (0,0,1)
- opponent moves to (0,0,0), and so wins.
For how many positive integers n <= 2^30 does X(n,2n,3n) = 0?
"""
def solution(exponent: int = 30) -> int:
"""
For any given exponent x >= 0, 1 <= n <= 2^x.
This function returns how many Nim games are lost given that
each Nim game has three heaps of the form (n, 2*n, 3*n).
>>> solution(0)
1
>>> solution(2)
3
>>> solution(10)
144
"""
# To find how many total games were lost for a given exponent x,
# we need to find the Fibonacci number F(x+2).
fibonacci_index = exponent + 2
phi = (1 + 5 ** 0.5) / 2
fibonacci = (phi ** fibonacci_index - (phi - 1) ** fibonacci_index) / 5 ** 0.5
return int(fibonacci)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 5,795 | [mypy] Type annotations for `graphs/finding_bridges.py` and `graphs/random_graph_generator.py` | ### Describe your change:
Related to #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-08T10:00:47Z" | "2021-11-08T17:18:33Z" | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | a8aeabdf1891397a4a55988f33ac435ae0313c55 | [mypy] Type annotations for `graphs/finding_bridges.py` and `graphs/random_graph_generator.py`. ### Describe your change:
Related to #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from typing import Any
class Node:
def __init__(self, data: Any):
"""
Create and initialize Node class instance.
>>> Node(20)
Node(20)
>>> Node("Hello, world!")
Node(Hello, world!)
>>> Node(None)
Node(None)
>>> Node(True)
Node(True)
"""
self.data = data
self.next = None
def __repr__(self) -> str:
"""
Get the string representation of this node.
>>> Node(10).__repr__()
'Node(10)'
"""
return f"Node({self.data})"
class LinkedList:
def __init__(self):
"""
Create and initialize LinkedList class instance.
>>> linked_list = LinkedList()
"""
self.head = None
def __iter__(self) -> Any:
"""
This function is intended for iterators to access
and iterate through data inside linked list.
>>> linked_list = LinkedList()
>>> linked_list.insert_tail("tail")
>>> linked_list.insert_tail("tail_1")
>>> linked_list.insert_tail("tail_2")
>>> for node in linked_list: # __iter__ used here.
... node
'tail'
'tail_1'
'tail_2'
"""
node = self.head
while node:
yield node.data
node = node.next
def __len__(self) -> int:
"""
Return length of linked list i.e. number of nodes
>>> linked_list = LinkedList()
>>> len(linked_list)
0
>>> linked_list.insert_tail("tail")
>>> len(linked_list)
1
>>> linked_list.insert_head("head")
>>> len(linked_list)
2
>>> _ = linked_list.delete_tail()
>>> len(linked_list)
1
>>> _ = linked_list.delete_head()
>>> len(linked_list)
0
"""
return len(tuple(iter(self)))
def __repr__(self) -> str:
"""
String representation/visualization of a Linked Lists
>>> linked_list = LinkedList()
>>> linked_list.insert_tail(1)
>>> linked_list.insert_tail(3)
>>> linked_list.__repr__()
'1->3'
"""
return "->".join([str(item) for item in self])
def __getitem__(self, index: int) -> Any:
"""
Indexing Support. Used to get a node at particular position
>>> linked_list = LinkedList()
>>> for i in range(0, 10):
... linked_list.insert_nth(i, i)
>>> all(str(linked_list[i]) == str(i) for i in range(0, 10))
True
>>> linked_list[-10]
Traceback (most recent call last):
...
ValueError: list index out of range.
>>> linked_list[len(linked_list)]
Traceback (most recent call last):
...
ValueError: list index out of range.
"""
if not 0 <= index < len(self):
raise ValueError("list index out of range.")
for i, node in enumerate(self):
if i == index:
return node
# Used to change the data of a particular node
def __setitem__(self, index: int, data: Any) -> None:
"""
>>> linked_list = LinkedList()
>>> for i in range(0, 10):
... linked_list.insert_nth(i, i)
>>> linked_list[0] = 666
>>> linked_list[0]
666
>>> linked_list[5] = -666
>>> linked_list[5]
-666
>>> linked_list[-10] = 666
Traceback (most recent call last):
...
ValueError: list index out of range.
>>> linked_list[len(linked_list)] = 666
Traceback (most recent call last):
...
ValueError: list index out of range.
"""
if not 0 <= index < len(self):
raise ValueError("list index out of range.")
current = self.head
for i in range(index):
current = current.next
current.data = data
def insert_tail(self, data: Any) -> None:
"""
Insert data to the end of linked list.
>>> linked_list = LinkedList()
>>> linked_list.insert_tail("tail")
>>> linked_list
tail
>>> linked_list.insert_tail("tail_2")
>>> linked_list
tail->tail_2
>>> linked_list.insert_tail("tail_3")
>>> linked_list
tail->tail_2->tail_3
"""
self.insert_nth(len(self), data)
def insert_head(self, data: Any) -> None:
"""
Insert data to the beginning of linked list.
>>> linked_list = LinkedList()
>>> linked_list.insert_head("head")
>>> linked_list
head
>>> linked_list.insert_head("head_2")
>>> linked_list
head_2->head
>>> linked_list.insert_head("head_3")
>>> linked_list
head_3->head_2->head
"""
self.insert_nth(0, data)
def insert_nth(self, index: int, data: Any) -> None:
"""
Insert data at given index.
>>> linked_list = LinkedList()
>>> linked_list.insert_tail("first")
>>> linked_list.insert_tail("second")
>>> linked_list.insert_tail("third")
>>> linked_list
first->second->third
>>> linked_list.insert_nth(1, "fourth")
>>> linked_list
first->fourth->second->third
>>> linked_list.insert_nth(3, "fifth")
>>> linked_list
first->fourth->second->fifth->third
"""
if not 0 <= index <= len(self):
raise IndexError("list index out of range")
new_node = Node(data)
if self.head is None:
self.head = new_node
elif index == 0:
new_node.next = self.head # link new_node to head
self.head = new_node
else:
temp = self.head
for _ in range(index - 1):
temp = temp.next
new_node.next = temp.next
temp.next = new_node
def print_list(self) -> None: # print every node data
"""
This method prints every node data.
>>> linked_list = LinkedList()
>>> linked_list.insert_tail("first")
>>> linked_list.insert_tail("second")
>>> linked_list.insert_tail("third")
>>> linked_list
first->second->third
"""
print(self)
def delete_head(self) -> Any:
"""
Delete the first node and return the
node's data.
>>> linked_list = LinkedList()
>>> linked_list.insert_tail("first")
>>> linked_list.insert_tail("second")
>>> linked_list.insert_tail("third")
>>> linked_list
first->second->third
>>> linked_list.delete_head()
'first'
>>> linked_list
second->third
>>> linked_list.delete_head()
'second'
>>> linked_list
third
>>> linked_list.delete_head()
'third'
>>> linked_list.delete_head()
Traceback (most recent call last):
...
IndexError: List index out of range.
"""
return self.delete_nth(0)
def delete_tail(self) -> Any: # delete from tail
"""
Delete the tail end node and return the
node's data.
>>> linked_list = LinkedList()
>>> linked_list.insert_tail("first")
>>> linked_list.insert_tail("second")
>>> linked_list.insert_tail("third")
>>> linked_list
first->second->third
>>> linked_list.delete_tail()
'third'
>>> linked_list
first->second
>>> linked_list.delete_tail()
'second'
>>> linked_list
first
>>> linked_list.delete_tail()
'first'
>>> linked_list.delete_tail()
Traceback (most recent call last):
...
IndexError: List index out of range.
"""
return self.delete_nth(len(self) - 1)
def delete_nth(self, index: int = 0) -> Any:
"""
Delete node at given index and return the
node's data.
>>> linked_list = LinkedList()
>>> linked_list.insert_tail("first")
>>> linked_list.insert_tail("second")
>>> linked_list.insert_tail("third")
>>> linked_list
first->second->third
>>> linked_list.delete_nth(1) # delete middle
'second'
>>> linked_list
first->third
>>> linked_list.delete_nth(5) # this raises error
Traceback (most recent call last):
...
IndexError: List index out of range.
>>> linked_list.delete_nth(-1) # this also raises error
Traceback (most recent call last):
...
IndexError: List index out of range.
"""
if not 0 <= index <= len(self) - 1: # test if index is valid
raise IndexError("List index out of range.")
delete_node = self.head # default first node
if index == 0:
self.head = self.head.next
else:
temp = self.head
for _ in range(index - 1):
temp = temp.next
delete_node = temp.next
temp.next = temp.next.next
return delete_node.data
def is_empty(self) -> bool:
"""
Check if linked list is empty.
>>> linked_list = LinkedList()
>>> linked_list.is_empty()
True
>>> linked_list.insert_head("first")
>>> linked_list.is_empty()
False
"""
return self.head is None
def reverse(self) -> None:
"""
This reverses the linked list order.
>>> linked_list = LinkedList()
>>> linked_list.insert_tail("first")
>>> linked_list.insert_tail("second")
>>> linked_list.insert_tail("third")
>>> linked_list
first->second->third
>>> linked_list.reverse()
>>> linked_list
third->second->first
"""
prev = None
current = self.head
while current:
# Store the current node's next node.
next_node = current.next
# Make the current node's next point backwards
current.next = prev
# Make the previous node be the current node
prev = current
# Make the current node the next node (to progress iteration)
current = next_node
# Return prev in order to put the head at the end
self.head = prev
def test_singly_linked_list() -> None:
"""
>>> test_singly_linked_list()
"""
linked_list = LinkedList()
assert linked_list.is_empty() is True
assert str(linked_list) == ""
try:
linked_list.delete_head()
assert False # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
assert False # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10):
assert len(linked_list) == i
linked_list.insert_nth(i, i + 1)
assert str(linked_list) == "->".join(str(i) for i in range(1, 11))
linked_list.insert_head(0)
linked_list.insert_tail(11)
assert str(linked_list) == "->".join(str(i) for i in range(0, 12))
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9) == 10
assert linked_list.delete_tail() == 11
assert len(linked_list) == 9
assert str(linked_list) == "->".join(str(i) for i in range(1, 10))
assert all(linked_list[i] == i + 1 for i in range(0, 9)) is True
for i in range(0, 9):
linked_list[i] = -i
assert all(linked_list[i] == -i for i in range(0, 9)) is True
linked_list.reverse()
assert str(linked_list) == "->".join(str(i) for i in range(-8, 1))
def test_singly_linked_list_2() -> None:
"""
This section of the test used varying data types for input.
>>> test_singly_linked_list_2()
"""
input = [
-9,
100,
Node(77345112),
"dlrow olleH",
7,
5555,
0,
-192.55555,
"Hello, world!",
77.9,
Node(10),
None,
None,
12.20,
]
linked_list = LinkedList()
for i in input:
linked_list.insert_tail(i)
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(linked_list) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
result = linked_list.delete_head()
assert result == -9
assert (
str(linked_list) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
result = linked_list.delete_tail()
assert result == 12.2
assert (
str(linked_list) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
result = linked_list.delete_nth(10)
assert result is None
assert (
str(linked_list) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("Hello again, world!"))
assert (
str(linked_list)
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(None)
assert (
str(linked_list)
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(linked_list)
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def main():
from doctest import testmod
testmod()
linked_list = LinkedList()
linked_list.insert_head(input("Inserting 1st at head ").strip())
linked_list.insert_head(input("Inserting 2nd at head ").strip())
print("\nPrint list:")
linked_list.print_list()
linked_list.insert_tail(input("\nInserting 1st at tail ").strip())
linked_list.insert_tail(input("Inserting 2nd at tail ").strip())
print("\nPrint list:")
linked_list.print_list()
print("\nDelete head")
linked_list.delete_head()
print("Delete tail")
linked_list.delete_tail()
print("\nPrint list:")
linked_list.print_list()
print("\nReverse linked list")
linked_list.reverse()
print("\nPrint list:")
linked_list.print_list()
print("\nString representation of linked list:")
print(linked_list)
print("\nReading/changing Node data using indexing:")
print(f"Element at Position 1: {linked_list[1]}")
linked_list[1] = input("Enter New Value: ").strip()
print("New list:")
print(linked_list)
print(f"length of linked_list is : {len(linked_list)}")
if __name__ == "__main__":
main()
| from typing import Any
class Node:
def __init__(self, data: Any):
"""
Create and initialize Node class instance.
>>> Node(20)
Node(20)
>>> Node("Hello, world!")
Node(Hello, world!)
>>> Node(None)
Node(None)
>>> Node(True)
Node(True)
"""
self.data = data
self.next = None
def __repr__(self) -> str:
"""
Get the string representation of this node.
>>> Node(10).__repr__()
'Node(10)'
"""
return f"Node({self.data})"
class LinkedList:
def __init__(self):
"""
Create and initialize LinkedList class instance.
>>> linked_list = LinkedList()
"""
self.head = None
def __iter__(self) -> Any:
"""
This function is intended for iterators to access
and iterate through data inside linked list.
>>> linked_list = LinkedList()
>>> linked_list.insert_tail("tail")
>>> linked_list.insert_tail("tail_1")
>>> linked_list.insert_tail("tail_2")
>>> for node in linked_list: # __iter__ used here.
... node
'tail'
'tail_1'
'tail_2'
"""
node = self.head
while node:
yield node.data
node = node.next
def __len__(self) -> int:
"""
Return length of linked list i.e. number of nodes
>>> linked_list = LinkedList()
>>> len(linked_list)
0
>>> linked_list.insert_tail("tail")
>>> len(linked_list)
1
>>> linked_list.insert_head("head")
>>> len(linked_list)
2
>>> _ = linked_list.delete_tail()
>>> len(linked_list)
1
>>> _ = linked_list.delete_head()
>>> len(linked_list)
0
"""
return len(tuple(iter(self)))
def __repr__(self) -> str:
"""
String representation/visualization of a Linked Lists
>>> linked_list = LinkedList()
>>> linked_list.insert_tail(1)
>>> linked_list.insert_tail(3)
>>> linked_list.__repr__()
'1->3'
"""
return "->".join([str(item) for item in self])
def __getitem__(self, index: int) -> Any:
"""
Indexing Support. Used to get a node at particular position
>>> linked_list = LinkedList()
>>> for i in range(0, 10):
... linked_list.insert_nth(i, i)
>>> all(str(linked_list[i]) == str(i) for i in range(0, 10))
True
>>> linked_list[-10]
Traceback (most recent call last):
...
ValueError: list index out of range.
>>> linked_list[len(linked_list)]
Traceback (most recent call last):
...
ValueError: list index out of range.
"""
if not 0 <= index < len(self):
raise ValueError("list index out of range.")
for i, node in enumerate(self):
if i == index:
return node
# Used to change the data of a particular node
def __setitem__(self, index: int, data: Any) -> None:
"""
>>> linked_list = LinkedList()
>>> for i in range(0, 10):
... linked_list.insert_nth(i, i)
>>> linked_list[0] = 666
>>> linked_list[0]
666
>>> linked_list[5] = -666
>>> linked_list[5]
-666
>>> linked_list[-10] = 666
Traceback (most recent call last):
...
ValueError: list index out of range.
>>> linked_list[len(linked_list)] = 666
Traceback (most recent call last):
...
ValueError: list index out of range.
"""
if not 0 <= index < len(self):
raise ValueError("list index out of range.")
current = self.head
for i in range(index):
current = current.next
current.data = data
def insert_tail(self, data: Any) -> None:
"""
Insert data to the end of linked list.
>>> linked_list = LinkedList()
>>> linked_list.insert_tail("tail")
>>> linked_list
tail
>>> linked_list.insert_tail("tail_2")
>>> linked_list
tail->tail_2
>>> linked_list.insert_tail("tail_3")
>>> linked_list
tail->tail_2->tail_3
"""
self.insert_nth(len(self), data)
def insert_head(self, data: Any) -> None:
"""
Insert data to the beginning of linked list.
>>> linked_list = LinkedList()
>>> linked_list.insert_head("head")
>>> linked_list
head
>>> linked_list.insert_head("head_2")
>>> linked_list
head_2->head
>>> linked_list.insert_head("head_3")
>>> linked_list
head_3->head_2->head
"""
self.insert_nth(0, data)
def insert_nth(self, index: int, data: Any) -> None:
"""
Insert data at given index.
>>> linked_list = LinkedList()
>>> linked_list.insert_tail("first")
>>> linked_list.insert_tail("second")
>>> linked_list.insert_tail("third")
>>> linked_list
first->second->third
>>> linked_list.insert_nth(1, "fourth")
>>> linked_list
first->fourth->second->third
>>> linked_list.insert_nth(3, "fifth")
>>> linked_list
first->fourth->second->fifth->third
"""
if not 0 <= index <= len(self):
raise IndexError("list index out of range")
new_node = Node(data)
if self.head is None:
self.head = new_node
elif index == 0:
new_node.next = self.head # link new_node to head
self.head = new_node
else:
temp = self.head
for _ in range(index - 1):
temp = temp.next
new_node.next = temp.next
temp.next = new_node
def print_list(self) -> None: # print every node data
"""
This method prints every node data.
>>> linked_list = LinkedList()
>>> linked_list.insert_tail("first")
>>> linked_list.insert_tail("second")
>>> linked_list.insert_tail("third")
>>> linked_list
first->second->third
"""
print(self)
def delete_head(self) -> Any:
"""
Delete the first node and return the
node's data.
>>> linked_list = LinkedList()
>>> linked_list.insert_tail("first")
>>> linked_list.insert_tail("second")
>>> linked_list.insert_tail("third")
>>> linked_list
first->second->third
>>> linked_list.delete_head()
'first'
>>> linked_list
second->third
>>> linked_list.delete_head()
'second'
>>> linked_list
third
>>> linked_list.delete_head()
'third'
>>> linked_list.delete_head()
Traceback (most recent call last):
...
IndexError: List index out of range.
"""
return self.delete_nth(0)
def delete_tail(self) -> Any: # delete from tail
"""
Delete the tail end node and return the
node's data.
>>> linked_list = LinkedList()
>>> linked_list.insert_tail("first")
>>> linked_list.insert_tail("second")
>>> linked_list.insert_tail("third")
>>> linked_list
first->second->third
>>> linked_list.delete_tail()
'third'
>>> linked_list
first->second
>>> linked_list.delete_tail()
'second'
>>> linked_list
first
>>> linked_list.delete_tail()
'first'
>>> linked_list.delete_tail()
Traceback (most recent call last):
...
IndexError: List index out of range.
"""
return self.delete_nth(len(self) - 1)
def delete_nth(self, index: int = 0) -> Any:
"""
Delete node at given index and return the
node's data.
>>> linked_list = LinkedList()
>>> linked_list.insert_tail("first")
>>> linked_list.insert_tail("second")
>>> linked_list.insert_tail("third")
>>> linked_list
first->second->third
>>> linked_list.delete_nth(1) # delete middle
'second'
>>> linked_list
first->third
>>> linked_list.delete_nth(5) # this raises error
Traceback (most recent call last):
...
IndexError: List index out of range.
>>> linked_list.delete_nth(-1) # this also raises error
Traceback (most recent call last):
...
IndexError: List index out of range.
"""
if not 0 <= index <= len(self) - 1: # test if index is valid
raise IndexError("List index out of range.")
delete_node = self.head # default first node
if index == 0:
self.head = self.head.next
else:
temp = self.head
for _ in range(index - 1):
temp = temp.next
delete_node = temp.next
temp.next = temp.next.next
return delete_node.data
def is_empty(self) -> bool:
"""
Check if linked list is empty.
>>> linked_list = LinkedList()
>>> linked_list.is_empty()
True
>>> linked_list.insert_head("first")
>>> linked_list.is_empty()
False
"""
return self.head is None
def reverse(self) -> None:
"""
This reverses the linked list order.
>>> linked_list = LinkedList()
>>> linked_list.insert_tail("first")
>>> linked_list.insert_tail("second")
>>> linked_list.insert_tail("third")
>>> linked_list
first->second->third
>>> linked_list.reverse()
>>> linked_list
third->second->first
"""
prev = None
current = self.head
while current:
# Store the current node's next node.
next_node = current.next
# Make the current node's next point backwards
current.next = prev
# Make the previous node be the current node
prev = current
# Make the current node the next node (to progress iteration)
current = next_node
# Return prev in order to put the head at the end
self.head = prev
def test_singly_linked_list() -> None:
"""
>>> test_singly_linked_list()
"""
linked_list = LinkedList()
assert linked_list.is_empty() is True
assert str(linked_list) == ""
try:
linked_list.delete_head()
assert False # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
assert False # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10):
assert len(linked_list) == i
linked_list.insert_nth(i, i + 1)
assert str(linked_list) == "->".join(str(i) for i in range(1, 11))
linked_list.insert_head(0)
linked_list.insert_tail(11)
assert str(linked_list) == "->".join(str(i) for i in range(0, 12))
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9) == 10
assert linked_list.delete_tail() == 11
assert len(linked_list) == 9
assert str(linked_list) == "->".join(str(i) for i in range(1, 10))
assert all(linked_list[i] == i + 1 for i in range(0, 9)) is True
for i in range(0, 9):
linked_list[i] = -i
assert all(linked_list[i] == -i for i in range(0, 9)) is True
linked_list.reverse()
assert str(linked_list) == "->".join(str(i) for i in range(-8, 1))
def test_singly_linked_list_2() -> None:
"""
This section of the test used varying data types for input.
>>> test_singly_linked_list_2()
"""
input = [
-9,
100,
Node(77345112),
"dlrow olleH",
7,
5555,
0,
-192.55555,
"Hello, world!",
77.9,
Node(10),
None,
None,
12.20,
]
linked_list = LinkedList()
for i in input:
linked_list.insert_tail(i)
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(linked_list) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
result = linked_list.delete_head()
assert result == -9
assert (
str(linked_list) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
result = linked_list.delete_tail()
assert result == 12.2
assert (
str(linked_list) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
result = linked_list.delete_nth(10)
assert result is None
assert (
str(linked_list) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("Hello again, world!"))
assert (
str(linked_list)
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(None)
assert (
str(linked_list)
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(linked_list)
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def main():
from doctest import testmod
testmod()
linked_list = LinkedList()
linked_list.insert_head(input("Inserting 1st at head ").strip())
linked_list.insert_head(input("Inserting 2nd at head ").strip())
print("\nPrint list:")
linked_list.print_list()
linked_list.insert_tail(input("\nInserting 1st at tail ").strip())
linked_list.insert_tail(input("Inserting 2nd at tail ").strip())
print("\nPrint list:")
linked_list.print_list()
print("\nDelete head")
linked_list.delete_head()
print("Delete tail")
linked_list.delete_tail()
print("\nPrint list:")
linked_list.print_list()
print("\nReverse linked list")
linked_list.reverse()
print("\nPrint list:")
linked_list.print_list()
print("\nString representation of linked list:")
print(linked_list)
print("\nReading/changing Node data using indexing:")
print(f"Element at Position 1: {linked_list[1]}")
linked_list[1] = input("Enter New Value: ").strip()
print("New list:")
print(linked_list)
print(f"length of linked_list is : {len(linked_list)}")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 5,795 | [mypy] Type annotations for `graphs/finding_bridges.py` and `graphs/random_graph_generator.py` | ### Describe your change:
Related to #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-08T10:00:47Z" | "2021-11-08T17:18:33Z" | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | a8aeabdf1891397a4a55988f33ac435ae0313c55 | [mypy] Type annotations for `graphs/finding_bridges.py` and `graphs/random_graph_generator.py`. ### Describe your change:
Related to #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Normalization Wikipedia: https://en.wikipedia.org/wiki/Normalization
Normalization is the process of converting numerical data to a standard range of values.
This range is typically between [0, 1] or [-1, 1]. The equation for normalization is
x_norm = (x - x_min)/(x_max - x_min) where x_norm is the normalized value, x is the
value, x_min is the minimum value within the column or list of data, and x_max is the
maximum value within the column or list of data. Normalization is used to speed up the
training of data and put all of the data on a similar scale. This is useful because
variance in the range of values of a dataset can heavily impact optimization
(particularly Gradient Descent).
Standardization Wikipedia: https://en.wikipedia.org/wiki/Standardization
Standardization is the process of converting numerical data to a normally distributed
range of values. This range will have a mean of 0 and standard deviation of 1. This is
also known as z-score normalization. The equation for standardization is
x_std = (x - mu)/(sigma) where mu is the mean of the column or list of values and sigma
is the standard deviation of the column or list of values.
Choosing between Normalization & Standardization is more of an art of a science, but it
is often recommended to run experiments with both to see which performs better.
Additionally, a few rules of thumb are:
1. gaussian (normal) distributions work better with standardization
2. non-gaussian (non-normal) distributions work better with normalization
3. If a column or list of values has extreme values / outliers, use standardization
"""
from statistics import mean, stdev
def normalization(data: list, ndigits: int = 3) -> list:
"""
Returns a normalized list of values
@params: data, a list of values to normalize
@returns: a list of normalized values (rounded to ndigits decimal places)
@examples:
>>> normalization([2, 7, 10, 20, 30, 50])
[0.0, 0.104, 0.167, 0.375, 0.583, 1.0]
>>> normalization([5, 10, 15, 20, 25])
[0.0, 0.25, 0.5, 0.75, 1.0]
"""
# variables for calculation
x_min = min(data)
x_max = max(data)
# normalize data
return [round((x - x_min) / (x_max - x_min), ndigits) for x in data]
def standardization(data: list, ndigits: int = 3) -> list:
"""
Returns a standardized list of values
@params: data, a list of values to standardize
@returns: a list of standardized values (rounded to ndigits decimal places)
@examples:
>>> standardization([2, 7, 10, 20, 30, 50])
[-0.999, -0.719, -0.551, 0.009, 0.57, 1.69]
>>> standardization([5, 10, 15, 20, 25])
[-1.265, -0.632, 0.0, 0.632, 1.265]
"""
# variables for calculation
mu = mean(data)
sigma = stdev(data)
# standardize data
return [round((x - mu) / (sigma), ndigits) for x in data]
| """
Normalization Wikipedia: https://en.wikipedia.org/wiki/Normalization
Normalization is the process of converting numerical data to a standard range of values.
This range is typically between [0, 1] or [-1, 1]. The equation for normalization is
x_norm = (x - x_min)/(x_max - x_min) where x_norm is the normalized value, x is the
value, x_min is the minimum value within the column or list of data, and x_max is the
maximum value within the column or list of data. Normalization is used to speed up the
training of data and put all of the data on a similar scale. This is useful because
variance in the range of values of a dataset can heavily impact optimization
(particularly Gradient Descent).
Standardization Wikipedia: https://en.wikipedia.org/wiki/Standardization
Standardization is the process of converting numerical data to a normally distributed
range of values. This range will have a mean of 0 and standard deviation of 1. This is
also known as z-score normalization. The equation for standardization is
x_std = (x - mu)/(sigma) where mu is the mean of the column or list of values and sigma
is the standard deviation of the column or list of values.
Choosing between Normalization & Standardization is more of an art of a science, but it
is often recommended to run experiments with both to see which performs better.
Additionally, a few rules of thumb are:
1. gaussian (normal) distributions work better with standardization
2. non-gaussian (non-normal) distributions work better with normalization
3. If a column or list of values has extreme values / outliers, use standardization
"""
from statistics import mean, stdev
def normalization(data: list, ndigits: int = 3) -> list:
"""
Returns a normalized list of values
@params: data, a list of values to normalize
@returns: a list of normalized values (rounded to ndigits decimal places)
@examples:
>>> normalization([2, 7, 10, 20, 30, 50])
[0.0, 0.104, 0.167, 0.375, 0.583, 1.0]
>>> normalization([5, 10, 15, 20, 25])
[0.0, 0.25, 0.5, 0.75, 1.0]
"""
# variables for calculation
x_min = min(data)
x_max = max(data)
# normalize data
return [round((x - x_min) / (x_max - x_min), ndigits) for x in data]
def standardization(data: list, ndigits: int = 3) -> list:
"""
Returns a standardized list of values
@params: data, a list of values to standardize
@returns: a list of standardized values (rounded to ndigits decimal places)
@examples:
>>> standardization([2, 7, 10, 20, 30, 50])
[-0.999, -0.719, -0.551, 0.009, 0.57, 1.69]
>>> standardization([5, 10, 15, 20, 25])
[-1.265, -0.632, 0.0, 0.632, 1.265]
"""
# variables for calculation
mu = mean(data)
sigma = stdev(data)
# standardize data
return [round((x - mu) / (sigma), ndigits) for x in data]
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
|
## Arithmetic Analysis
* [Bisection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/bisection.py)
* [Gaussian Elimination](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/gaussian_elimination.py)
* [In Static Equilibrium](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/in_static_equilibrium.py)
* [Intersection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/intersection.py)
* [Lu Decomposition](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/lu_decomposition.py)
* [Newton Forward Interpolation](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_forward_interpolation.py)
* [Newton Method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_method.py)
* [Newton Raphson](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_raphson.py)
* [Secant Method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/secant_method.py)
## Audio Filters
* [Butterworth Filter](https://github.com/TheAlgorithms/Python/blob/master/audio_filters/butterworth_filter.py)
* [Iir Filter](https://github.com/TheAlgorithms/Python/blob/master/audio_filters/iir_filter.py)
* [Show Response](https://github.com/TheAlgorithms/Python/blob/master/audio_filters/show_response.py)
## Backtracking
* [All Combinations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_combinations.py)
* [All Permutations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_permutations.py)
* [All Subsequences](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_subsequences.py)
* [Coloring](https://github.com/TheAlgorithms/Python/blob/master/backtracking/coloring.py)
* [Hamiltonian Cycle](https://github.com/TheAlgorithms/Python/blob/master/backtracking/hamiltonian_cycle.py)
* [Knight Tour](https://github.com/TheAlgorithms/Python/blob/master/backtracking/knight_tour.py)
* [Minimax](https://github.com/TheAlgorithms/Python/blob/master/backtracking/minimax.py)
* [N Queens](https://github.com/TheAlgorithms/Python/blob/master/backtracking/n_queens.py)
* [N Queens Math](https://github.com/TheAlgorithms/Python/blob/master/backtracking/n_queens_math.py)
* [Rat In Maze](https://github.com/TheAlgorithms/Python/blob/master/backtracking/rat_in_maze.py)
* [Sudoku](https://github.com/TheAlgorithms/Python/blob/master/backtracking/sudoku.py)
* [Sum Of Subsets](https://github.com/TheAlgorithms/Python/blob/master/backtracking/sum_of_subsets.py)
## Bit Manipulation
* [Binary And Operator](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_and_operator.py)
* [Binary Count Setbits](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_count_setbits.py)
* [Binary Count Trailing Zeros](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_count_trailing_zeros.py)
* [Binary Or Operator](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_or_operator.py)
* [Binary Shifts](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_shifts.py)
* [Binary Twos Complement](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_twos_complement.py)
* [Binary Xor Operator](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_xor_operator.py)
* [Count 1S Brian Kernighan Method](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/count_1s_brian_kernighan_method.py)
* [Count Number Of One Bits](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/count_number_of_one_bits.py)
* [Gray Code Sequence](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/gray_code_sequence.py)
* [Reverse Bits](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/reverse_bits.py)
* [Single Bit Manipulation Operations](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/single_bit_manipulation_operations.py)
## Blockchain
* [Chinese Remainder Theorem](https://github.com/TheAlgorithms/Python/blob/master/blockchain/chinese_remainder_theorem.py)
* [Diophantine Equation](https://github.com/TheAlgorithms/Python/blob/master/blockchain/diophantine_equation.py)
* [Modular Division](https://github.com/TheAlgorithms/Python/blob/master/blockchain/modular_division.py)
## Boolean Algebra
* [Quine Mc Cluskey](https://github.com/TheAlgorithms/Python/blob/master/boolean_algebra/quine_mc_cluskey.py)
## Cellular Automata
* [Conways Game Of Life](https://github.com/TheAlgorithms/Python/blob/master/cellular_automata/conways_game_of_life.py)
* [Game Of Life](https://github.com/TheAlgorithms/Python/blob/master/cellular_automata/game_of_life.py)
* [Nagel Schrekenberg](https://github.com/TheAlgorithms/Python/blob/master/cellular_automata/nagel_schrekenberg.py)
* [One Dimensional](https://github.com/TheAlgorithms/Python/blob/master/cellular_automata/one_dimensional.py)
## Ciphers
* [A1Z26](https://github.com/TheAlgorithms/Python/blob/master/ciphers/a1z26.py)
* [Affine Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/affine_cipher.py)
* [Atbash](https://github.com/TheAlgorithms/Python/blob/master/ciphers/atbash.py)
* [Baconian Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/baconian_cipher.py)
* [Base16](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base16.py)
* [Base32](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base32.py)
* [Base64](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base64.py)
* [Base85](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base85.py)
* [Beaufort Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/beaufort_cipher.py)
* [Bifid](https://github.com/TheAlgorithms/Python/blob/master/ciphers/bifid.py)
* [Brute Force Caesar Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/brute_force_caesar_cipher.py)
* [Caesar Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/caesar_cipher.py)
* [Cryptomath Module](https://github.com/TheAlgorithms/Python/blob/master/ciphers/cryptomath_module.py)
* [Decrypt Caesar With Chi Squared](https://github.com/TheAlgorithms/Python/blob/master/ciphers/decrypt_caesar_with_chi_squared.py)
* [Deterministic Miller Rabin](https://github.com/TheAlgorithms/Python/blob/master/ciphers/deterministic_miller_rabin.py)
* [Diffie](https://github.com/TheAlgorithms/Python/blob/master/ciphers/diffie.py)
* [Diffie Hellman](https://github.com/TheAlgorithms/Python/blob/master/ciphers/diffie_hellman.py)
* [Elgamal Key Generator](https://github.com/TheAlgorithms/Python/blob/master/ciphers/elgamal_key_generator.py)
* [Enigma Machine2](https://github.com/TheAlgorithms/Python/blob/master/ciphers/enigma_machine2.py)
* [Hill Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/hill_cipher.py)
* [Mixed Keyword Cypher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/mixed_keyword_cypher.py)
* [Mono Alphabetic Ciphers](https://github.com/TheAlgorithms/Python/blob/master/ciphers/mono_alphabetic_ciphers.py)
* [Morse Code](https://github.com/TheAlgorithms/Python/blob/master/ciphers/morse_code.py)
* [Onepad Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/onepad_cipher.py)
* [Playfair Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/playfair_cipher.py)
* [Polybius](https://github.com/TheAlgorithms/Python/blob/master/ciphers/polybius.py)
* [Porta Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/porta_cipher.py)
* [Rabin Miller](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rabin_miller.py)
* [Rail Fence Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rail_fence_cipher.py)
* [Rot13](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rot13.py)
* [Rsa Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_cipher.py)
* [Rsa Factorization](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_factorization.py)
* [Rsa Key Generator](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_key_generator.py)
* [Shuffled Shift Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/shuffled_shift_cipher.py)
* [Simple Keyword Cypher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/simple_keyword_cypher.py)
* [Simple Substitution Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/simple_substitution_cipher.py)
* [Trafid Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/trafid_cipher.py)
* [Transposition Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/transposition_cipher.py)
* [Transposition Cipher Encrypt Decrypt File](https://github.com/TheAlgorithms/Python/blob/master/ciphers/transposition_cipher_encrypt_decrypt_file.py)
* [Vigenere Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/vigenere_cipher.py)
* [Xor Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/xor_cipher.py)
## Compression
* [Burrows Wheeler](https://github.com/TheAlgorithms/Python/blob/master/compression/burrows_wheeler.py)
* [Huffman](https://github.com/TheAlgorithms/Python/blob/master/compression/huffman.py)
* [Lempel Ziv](https://github.com/TheAlgorithms/Python/blob/master/compression/lempel_ziv.py)
* [Lempel Ziv Decompress](https://github.com/TheAlgorithms/Python/blob/master/compression/lempel_ziv_decompress.py)
* [Peak Signal To Noise Ratio](https://github.com/TheAlgorithms/Python/blob/master/compression/peak_signal_to_noise_ratio.py)
## Computer Vision
* [Cnn Classification](https://github.com/TheAlgorithms/Python/blob/master/computer_vision/cnn_classification.py)
* [Harris Corner](https://github.com/TheAlgorithms/Python/blob/master/computer_vision/harris_corner.py)
* [Mean Threshold](https://github.com/TheAlgorithms/Python/blob/master/computer_vision/mean_threshold.py)
## Conversions
* [Binary To Decimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/binary_to_decimal.py)
* [Binary To Hexadecimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/binary_to_hexadecimal.py)
* [Binary To Octal](https://github.com/TheAlgorithms/Python/blob/master/conversions/binary_to_octal.py)
* [Decimal To Any](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_any.py)
* [Decimal To Binary](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_binary.py)
* [Decimal To Binary Recursion](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_binary_recursion.py)
* [Decimal To Hexadecimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_hexadecimal.py)
* [Decimal To Octal](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_octal.py)
* [Hex To Bin](https://github.com/TheAlgorithms/Python/blob/master/conversions/hex_to_bin.py)
* [Hexadecimal To Decimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/hexadecimal_to_decimal.py)
* [Length Conversion](https://github.com/TheAlgorithms/Python/blob/master/conversions/length_conversion.py)
* [Molecular Chemistry](https://github.com/TheAlgorithms/Python/blob/master/conversions/molecular_chemistry.py)
* [Octal To Decimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/octal_to_decimal.py)
* [Prefix Conversions](https://github.com/TheAlgorithms/Python/blob/master/conversions/prefix_conversions.py)
* [Pressure Conversions](https://github.com/TheAlgorithms/Python/blob/master/conversions/pressure_conversions.py)
* [Rgb Hsv Conversion](https://github.com/TheAlgorithms/Python/blob/master/conversions/rgb_hsv_conversion.py)
* [Roman Numerals](https://github.com/TheAlgorithms/Python/blob/master/conversions/roman_numerals.py)
* [Temperature Conversions](https://github.com/TheAlgorithms/Python/blob/master/conversions/temperature_conversions.py)
* [Volume Conversions](https://github.com/TheAlgorithms/Python/blob/master/conversions/volume_conversions.py)
* [Weight Conversion](https://github.com/TheAlgorithms/Python/blob/master/conversions/weight_conversion.py)
## Data Structures
* Binary Tree
* [Avl Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/avl_tree.py)
* [Basic Binary Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/basic_binary_tree.py)
* [Binary Search Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_search_tree.py)
* [Binary Search Tree Recursive](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_search_tree_recursive.py)
* [Binary Tree Mirror](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_tree_mirror.py)
* [Binary Tree Traversals](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_tree_traversals.py)
* [Fenwick Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/fenwick_tree.py)
* [Lazy Segment Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/lazy_segment_tree.py)
* [Lowest Common Ancestor](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/lowest_common_ancestor.py)
* [Merge Two Binary Trees](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/merge_two_binary_trees.py)
* [Non Recursive Segment Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/non_recursive_segment_tree.py)
* [Number Of Possible Binary Trees](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/number_of_possible_binary_trees.py)
* [Red Black Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/red_black_tree.py)
* [Segment Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/segment_tree.py)
* [Segment Tree Other](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/segment_tree_other.py)
* [Treap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/treap.py)
* [Wavelet Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/wavelet_tree.py)
* Disjoint Set
* [Alternate Disjoint Set](https://github.com/TheAlgorithms/Python/blob/master/data_structures/disjoint_set/alternate_disjoint_set.py)
* [Disjoint Set](https://github.com/TheAlgorithms/Python/blob/master/data_structures/disjoint_set/disjoint_set.py)
* Hashing
* [Double Hash](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/double_hash.py)
* [Hash Table](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table.py)
* [Hash Table With Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table_with_linked_list.py)
* Number Theory
* [Prime Numbers](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/number_theory/prime_numbers.py)
* [Quadratic Probing](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/quadratic_probing.py)
* Heap
* [Binomial Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/binomial_heap.py)
* [Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/heap.py)
* [Heap Generic](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/heap_generic.py)
* [Max Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/max_heap.py)
* [Min Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/min_heap.py)
* [Randomized Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/randomized_heap.py)
* [Skew Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/skew_heap.py)
* Linked List
* [Circular Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/circular_linked_list.py)
* [Deque Doubly](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/deque_doubly.py)
* [Doubly Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/doubly_linked_list.py)
* [Doubly Linked List Two](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/doubly_linked_list_two.py)
* [From Sequence](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/from_sequence.py)
* [Has Loop](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/has_loop.py)
* [Is Palindrome](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/is_palindrome.py)
* [Merge Two Lists](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/merge_two_lists.py)
* [Middle Element Of Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/middle_element_of_linked_list.py)
* [Print Reverse](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/print_reverse.py)
* [Singly Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/singly_linked_list.py)
* [Skip List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/skip_list.py)
* [Swap Nodes](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/swap_nodes.py)
* Queue
* [Circular Queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/circular_queue.py)
* [Circular Queue Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/circular_queue_linked_list.py)
* [Double Ended Queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/double_ended_queue.py)
* [Linked Queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/linked_queue.py)
* [Priority Queue Using List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/priority_queue_using_list.py)
* [Queue On List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/queue_on_list.py)
* [Queue On Pseudo Stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/queue_on_pseudo_stack.py)
* Stacks
* [Balanced Parentheses](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/balanced_parentheses.py)
* [Dijkstras Two Stack Algorithm](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/dijkstras_two_stack_algorithm.py)
* [Evaluate Postfix Notations](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/evaluate_postfix_notations.py)
* [Infix To Postfix Conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/infix_to_postfix_conversion.py)
* [Infix To Prefix Conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/infix_to_prefix_conversion.py)
* [Next Greater Element](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/next_greater_element.py)
* [Postfix Evaluation](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/postfix_evaluation.py)
* [Prefix Evaluation](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/prefix_evaluation.py)
* [Stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stack.py)
* [Stack With Doubly Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stack_with_doubly_linked_list.py)
* [Stack With Singly Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stack_with_singly_linked_list.py)
* [Stock Span Problem](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stock_span_problem.py)
* Trie
* [Trie](https://github.com/TheAlgorithms/Python/blob/master/data_structures/trie/trie.py)
## Digital Image Processing
* [Change Brightness](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/change_brightness.py)
* [Change Contrast](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/change_contrast.py)
* [Convert To Negative](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/convert_to_negative.py)
* Dithering
* [Burkes](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/dithering/burkes.py)
* Edge Detection
* [Canny](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/edge_detection/canny.py)
* Filters
* [Bilateral Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/bilateral_filter.py)
* [Convolve](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/convolve.py)
* [Gabor Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/gabor_filter.py)
* [Gaussian Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/gaussian_filter.py)
* [Median Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/median_filter.py)
* [Sobel Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/sobel_filter.py)
* Histogram Equalization
* [Histogram Stretch](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/histogram_equalization/histogram_stretch.py)
* [Index Calculation](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/index_calculation.py)
* Morphological Operations
* [Dilation Operation](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/morphological_operations/dilation_operation.py)
* [Erosion Operation](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/morphological_operations/erosion_operation.py)
* Resize
* [Resize](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/resize/resize.py)
* Rotation
* [Rotation](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/rotation/rotation.py)
* [Sepia](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/sepia.py)
* [Test Digital Image Processing](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/test_digital_image_processing.py)
## Divide And Conquer
* [Closest Pair Of Points](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/closest_pair_of_points.py)
* [Convex Hull](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/convex_hull.py)
* [Heaps Algorithm](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/heaps_algorithm.py)
* [Heaps Algorithm Iterative](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/heaps_algorithm_iterative.py)
* [Inversions](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/inversions.py)
* [Kth Order Statistic](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/kth_order_statistic.py)
* [Max Difference Pair](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/max_difference_pair.py)
* [Max Subarray Sum](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/max_subarray_sum.py)
* [Mergesort](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/mergesort.py)
* [Peak](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/peak.py)
* [Power](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/power.py)
* [Strassen Matrix Multiplication](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/strassen_matrix_multiplication.py)
## Dynamic Programming
* [Abbreviation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/abbreviation.py)
* [All Construct](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/all_construct.py)
* [Bitmask](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/bitmask.py)
* [Catalan Numbers](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/catalan_numbers.py)
* [Climbing Stairs](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/climbing_stairs.py)
* [Edit Distance](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/edit_distance.py)
* [Factorial](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/factorial.py)
* [Fast Fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fast_fibonacci.py)
* [Fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fibonacci.py)
* [Floyd Warshall](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/floyd_warshall.py)
* [Fractional Knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fractional_knapsack.py)
* [Fractional Knapsack 2](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fractional_knapsack_2.py)
* [Integer Partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/integer_partition.py)
* [Iterating Through Submasks](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/iterating_through_submasks.py)
* [Knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/knapsack.py)
* [Longest Common Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_common_subsequence.py)
* [Longest Increasing Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence.py)
* [Longest Increasing Subsequence O(Nlogn)](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence_o(nlogn).py)
* [Longest Sub Array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_sub_array.py)
* [Matrix Chain Order](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/matrix_chain_order.py)
* [Max Non Adjacent Sum](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_non_adjacent_sum.py)
* [Max Sub Array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sub_array.py)
* [Max Sum Contiguous Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sum_contiguous_subsequence.py)
* [Minimum Coin Change](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_coin_change.py)
* [Minimum Cost Path](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_cost_path.py)
* [Minimum Partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_partition.py)
* [Minimum Steps To One](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_steps_to_one.py)
* [Optimal Binary Search Tree](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/optimal_binary_search_tree.py)
* [Rod Cutting](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/rod_cutting.py)
* [Subset Generation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/subset_generation.py)
* [Sum Of Subset](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/sum_of_subset.py)
## Electronics
* [Carrier Concentration](https://github.com/TheAlgorithms/Python/blob/master/electronics/carrier_concentration.py)
* [Coulombs Law](https://github.com/TheAlgorithms/Python/blob/master/electronics/coulombs_law.py)
* [Electric Power](https://github.com/TheAlgorithms/Python/blob/master/electronics/electric_power.py)
* [Ohms Law](https://github.com/TheAlgorithms/Python/blob/master/electronics/ohms_law.py)
## File Transfer
* [Receive File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/receive_file.py)
* [Send File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/send_file.py)
* Tests
* [Test Send File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/tests/test_send_file.py)
## Financial
* [Interest](https://github.com/TheAlgorithms/Python/blob/master/financial/interest.py)
* [EMI Calculation](https://github.com/TheAlgorithms/Python/blob/master/financial/equated_monthly_installments.py)
## Fractals
* [Julia Sets](https://github.com/TheAlgorithms/Python/blob/master/fractals/julia_sets.py)
* [Koch Snowflake](https://github.com/TheAlgorithms/Python/blob/master/fractals/koch_snowflake.py)
* [Mandelbrot](https://github.com/TheAlgorithms/Python/blob/master/fractals/mandelbrot.py)
* [Sierpinski Triangle](https://github.com/TheAlgorithms/Python/blob/master/fractals/sierpinski_triangle.py)
## Fuzzy Logic
* [Fuzzy Operations](https://github.com/TheAlgorithms/Python/blob/master/fuzzy_logic/fuzzy_operations.py)
## Genetic Algorithm
* [Basic String](https://github.com/TheAlgorithms/Python/blob/master/genetic_algorithm/basic_string.py)
## Geodesy
* [Haversine Distance](https://github.com/TheAlgorithms/Python/blob/master/geodesy/haversine_distance.py)
* [Lamberts Ellipsoidal Distance](https://github.com/TheAlgorithms/Python/blob/master/geodesy/lamberts_ellipsoidal_distance.py)
## Graphics
* [Bezier Curve](https://github.com/TheAlgorithms/Python/blob/master/graphics/bezier_curve.py)
* [Vector3 For 2D Rendering](https://github.com/TheAlgorithms/Python/blob/master/graphics/vector3_for_2d_rendering.py)
## Graphs
* [A Star](https://github.com/TheAlgorithms/Python/blob/master/graphs/a_star.py)
* [Articulation Points](https://github.com/TheAlgorithms/Python/blob/master/graphs/articulation_points.py)
* [Basic Graphs](https://github.com/TheAlgorithms/Python/blob/master/graphs/basic_graphs.py)
* [Bellman Ford](https://github.com/TheAlgorithms/Python/blob/master/graphs/bellman_ford.py)
* [Bfs Shortest Path](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs_shortest_path.py)
* [Bfs Zero One Shortest Path](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs_zero_one_shortest_path.py)
* [Bidirectional A Star](https://github.com/TheAlgorithms/Python/blob/master/graphs/bidirectional_a_star.py)
* [Bidirectional Breadth First Search](https://github.com/TheAlgorithms/Python/blob/master/graphs/bidirectional_breadth_first_search.py)
* [Boruvka](https://github.com/TheAlgorithms/Python/blob/master/graphs/boruvka.py)
* [Breadth First Search](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search.py)
* [Breadth First Search 2](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search_2.py)
* [Breadth First Search Shortest Path](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search_shortest_path.py)
* [Check Bipartite Graph Bfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_bfs.py)
* [Check Bipartite Graph Dfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_dfs.py)
* [Check Cycle](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_cycle.py)
* [Connected Components](https://github.com/TheAlgorithms/Python/blob/master/graphs/connected_components.py)
* [Depth First Search](https://github.com/TheAlgorithms/Python/blob/master/graphs/depth_first_search.py)
* [Depth First Search 2](https://github.com/TheAlgorithms/Python/blob/master/graphs/depth_first_search_2.py)
* [Dijkstra](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra.py)
* [Dijkstra 2](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra_2.py)
* [Dijkstra Algorithm](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra_algorithm.py)
* [Dinic](https://github.com/TheAlgorithms/Python/blob/master/graphs/dinic.py)
* [Directed And Undirected (Weighted) Graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/directed_and_undirected_(weighted)_graph.py)
* [Edmonds Karp Multiple Source And Sink](https://github.com/TheAlgorithms/Python/blob/master/graphs/edmonds_karp_multiple_source_and_sink.py)
* [Eulerian Path And Circuit For Undirected Graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/eulerian_path_and_circuit_for_undirected_graph.py)
* [Even Tree](https://github.com/TheAlgorithms/Python/blob/master/graphs/even_tree.py)
* [Finding Bridges](https://github.com/TheAlgorithms/Python/blob/master/graphs/finding_bridges.py)
* [Frequent Pattern Graph Miner](https://github.com/TheAlgorithms/Python/blob/master/graphs/frequent_pattern_graph_miner.py)
* [G Topological Sort](https://github.com/TheAlgorithms/Python/blob/master/graphs/g_topological_sort.py)
* [Gale Shapley Bigraph](https://github.com/TheAlgorithms/Python/blob/master/graphs/gale_shapley_bigraph.py)
* [Graph List](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_list.py)
* [Graph Matrix](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_matrix.py)
* [Graphs Floyd Warshall](https://github.com/TheAlgorithms/Python/blob/master/graphs/graphs_floyd_warshall.py)
* [Greedy Best First](https://github.com/TheAlgorithms/Python/blob/master/graphs/greedy_best_first.py)
* [Greedy Min Vertex Cover](https://github.com/TheAlgorithms/Python/blob/master/graphs/greedy_min_vertex_cover.py)
* [Kahns Algorithm Long](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_long.py)
* [Kahns Algorithm Topo](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_topo.py)
* [Karger](https://github.com/TheAlgorithms/Python/blob/master/graphs/karger.py)
* [Markov Chain](https://github.com/TheAlgorithms/Python/blob/master/graphs/markov_chain.py)
* [Matching Min Vertex Cover](https://github.com/TheAlgorithms/Python/blob/master/graphs/matching_min_vertex_cover.py)
* [Minimum Spanning Tree Boruvka](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_boruvka.py)
* [Minimum Spanning Tree Kruskal](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_kruskal.py)
* [Minimum Spanning Tree Kruskal2](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_kruskal2.py)
* [Minimum Spanning Tree Prims](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_prims.py)
* [Minimum Spanning Tree Prims2](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_prims2.py)
* [Multi Heuristic Astar](https://github.com/TheAlgorithms/Python/blob/master/graphs/multi_heuristic_astar.py)
* [Page Rank](https://github.com/TheAlgorithms/Python/blob/master/graphs/page_rank.py)
* [Prim](https://github.com/TheAlgorithms/Python/blob/master/graphs/prim.py)
* [Random Graph Generator](https://github.com/TheAlgorithms/Python/blob/master/graphs/random_graph_generator.py)
* [Scc Kosaraju](https://github.com/TheAlgorithms/Python/blob/master/graphs/scc_kosaraju.py)
* [Strongly Connected Components](https://github.com/TheAlgorithms/Python/blob/master/graphs/strongly_connected_components.py)
* [Tarjans Scc](https://github.com/TheAlgorithms/Python/blob/master/graphs/tarjans_scc.py)
* Tests
* [Test Min Spanning Tree Kruskal](https://github.com/TheAlgorithms/Python/blob/master/graphs/tests/test_min_spanning_tree_kruskal.py)
* [Test Min Spanning Tree Prim](https://github.com/TheAlgorithms/Python/blob/master/graphs/tests/test_min_spanning_tree_prim.py)
## Greedy Methods
* [Optimal Merge Pattern](https://github.com/TheAlgorithms/Python/blob/master/greedy_methods/optimal_merge_pattern.py)
## Hashes
* [Adler32](https://github.com/TheAlgorithms/Python/blob/master/hashes/adler32.py)
* [Chaos Machine](https://github.com/TheAlgorithms/Python/blob/master/hashes/chaos_machine.py)
* [Djb2](https://github.com/TheAlgorithms/Python/blob/master/hashes/djb2.py)
* [Enigma Machine](https://github.com/TheAlgorithms/Python/blob/master/hashes/enigma_machine.py)
* [Hamming Code](https://github.com/TheAlgorithms/Python/blob/master/hashes/hamming_code.py)
* [Luhn](https://github.com/TheAlgorithms/Python/blob/master/hashes/luhn.py)
* [Md5](https://github.com/TheAlgorithms/Python/blob/master/hashes/md5.py)
* [Sdbm](https://github.com/TheAlgorithms/Python/blob/master/hashes/sdbm.py)
* [Sha1](https://github.com/TheAlgorithms/Python/blob/master/hashes/sha1.py)
* [Sha256](https://github.com/TheAlgorithms/Python/blob/master/hashes/sha256.py)
## Knapsack
* [Greedy Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/greedy_knapsack.py)
* [Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/knapsack.py)
* Tests
* [Test Greedy Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/tests/test_greedy_knapsack.py)
* [Test Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/tests/test_knapsack.py)
## Linear Algebra
* Src
* [Conjugate Gradient](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/conjugate_gradient.py)
* [Lib](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/lib.py)
* [Polynom For Points](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/polynom_for_points.py)
* [Power Iteration](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/power_iteration.py)
* [Rayleigh Quotient](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/rayleigh_quotient.py)
* [Schur Complement](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/schur_complement.py)
* [Test Linear Algebra](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/test_linear_algebra.py)
* [Transformations 2D](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/transformations_2d.py)
## Machine Learning
* [Astar](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/astar.py)
* [Data Transformations](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/data_transformations.py)
* [Decision Tree](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/decision_tree.py)
* Forecasting
* [Run](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/forecasting/run.py)
* [Gaussian Naive Bayes](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gaussian_naive_bayes.py)
* [Gradient Boosting Regressor](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gradient_boosting_regressor.py)
* [Gradient Descent](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gradient_descent.py)
* [K Means Clust](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_means_clust.py)
* [K Nearest Neighbours](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_nearest_neighbours.py)
* [Knn Sklearn](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/knn_sklearn.py)
* [Linear Discriminant Analysis](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/linear_discriminant_analysis.py)
* [Linear Regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/linear_regression.py)
* Local Weighted Learning
* [Local Weighted Learning](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/local_weighted_learning/local_weighted_learning.py)
* [Logistic Regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/logistic_regression.py)
* Lstm
* [Lstm Prediction](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/lstm/lstm_prediction.py)
* [Multilayer Perceptron Classifier](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/multilayer_perceptron_classifier.py)
* [Polymonial Regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/polymonial_regression.py)
* [Random Forest Classifier](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_classifier.py)
* [Random Forest Regressor](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_regressor.py)
* [Scoring Functions](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/scoring_functions.py)
* [Sequential Minimum Optimization](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/sequential_minimum_optimization.py)
* [Similarity Search](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/similarity_search.py)
* [Support Vector Machines](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/support_vector_machines.py)
* [Word Frequency Functions](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/word_frequency_functions.py)
## Maths
* [3N Plus 1](https://github.com/TheAlgorithms/Python/blob/master/maths/3n_plus_1.py)
* [Abs](https://github.com/TheAlgorithms/Python/blob/master/maths/abs.py)
* [Abs Max](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_max.py)
* [Abs Min](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_min.py)
* [Add](https://github.com/TheAlgorithms/Python/blob/master/maths/add.py)
* [Aliquot Sum](https://github.com/TheAlgorithms/Python/blob/master/maths/aliquot_sum.py)
* [Allocation Number](https://github.com/TheAlgorithms/Python/blob/master/maths/allocation_number.py)
* [Area](https://github.com/TheAlgorithms/Python/blob/master/maths/area.py)
* [Area Under Curve](https://github.com/TheAlgorithms/Python/blob/master/maths/area_under_curve.py)
* [Armstrong Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/armstrong_numbers.py)
* [Average Mean](https://github.com/TheAlgorithms/Python/blob/master/maths/average_mean.py)
* [Average Median](https://github.com/TheAlgorithms/Python/blob/master/maths/average_median.py)
* [Average Mode](https://github.com/TheAlgorithms/Python/blob/master/maths/average_mode.py)
* [Bailey Borwein Plouffe](https://github.com/TheAlgorithms/Python/blob/master/maths/bailey_borwein_plouffe.py)
* [Basic Maths](https://github.com/TheAlgorithms/Python/blob/master/maths/basic_maths.py)
* [Binary Exp Mod](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exp_mod.py)
* [Binary Exponentiation](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exponentiation.py)
* [Binary Exponentiation 2](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exponentiation_2.py)
* [Binary Exponentiation 3](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exponentiation_3.py)
* [Binomial Coefficient](https://github.com/TheAlgorithms/Python/blob/master/maths/binomial_coefficient.py)
* [Binomial Distribution](https://github.com/TheAlgorithms/Python/blob/master/maths/binomial_distribution.py)
* [Bisection](https://github.com/TheAlgorithms/Python/blob/master/maths/bisection.py)
* [Ceil](https://github.com/TheAlgorithms/Python/blob/master/maths/ceil.py)
* [Check Polygon](https://github.com/TheAlgorithms/Python/blob/master/maths/check_polygon.py)
* [Chudnovsky Algorithm](https://github.com/TheAlgorithms/Python/blob/master/maths/chudnovsky_algorithm.py)
* [Collatz Sequence](https://github.com/TheAlgorithms/Python/blob/master/maths/collatz_sequence.py)
* [Combinations](https://github.com/TheAlgorithms/Python/blob/master/maths/combinations.py)
* [Decimal Isolate](https://github.com/TheAlgorithms/Python/blob/master/maths/decimal_isolate.py)
* [Double Factorial Iterative](https://github.com/TheAlgorithms/Python/blob/master/maths/double_factorial_iterative.py)
* [Double Factorial Recursive](https://github.com/TheAlgorithms/Python/blob/master/maths/double_factorial_recursive.py)
* [Entropy](https://github.com/TheAlgorithms/Python/blob/master/maths/entropy.py)
* [Euclidean Distance](https://github.com/TheAlgorithms/Python/blob/master/maths/euclidean_distance.py)
* [Euclidean Gcd](https://github.com/TheAlgorithms/Python/blob/master/maths/euclidean_gcd.py)
* [Euler Method](https://github.com/TheAlgorithms/Python/blob/master/maths/euler_method.py)
* [Euler Modified](https://github.com/TheAlgorithms/Python/blob/master/maths/euler_modified.py)
* [Eulers Totient](https://github.com/TheAlgorithms/Python/blob/master/maths/eulers_totient.py)
* [Extended Euclidean Algorithm](https://github.com/TheAlgorithms/Python/blob/master/maths/extended_euclidean_algorithm.py)
* [Factorial Iterative](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_iterative.py)
* [Factorial Recursive](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_recursive.py)
* [Factors](https://github.com/TheAlgorithms/Python/blob/master/maths/factors.py)
* [Fermat Little Theorem](https://github.com/TheAlgorithms/Python/blob/master/maths/fermat_little_theorem.py)
* [Fibonacci](https://github.com/TheAlgorithms/Python/blob/master/maths/fibonacci.py)
* [Find Max](https://github.com/TheAlgorithms/Python/blob/master/maths/find_max.py)
* [Find Max Recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/find_max_recursion.py)
* [Find Min](https://github.com/TheAlgorithms/Python/blob/master/maths/find_min.py)
* [Find Min Recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/find_min_recursion.py)
* [Floor](https://github.com/TheAlgorithms/Python/blob/master/maths/floor.py)
* [Gamma](https://github.com/TheAlgorithms/Python/blob/master/maths/gamma.py)
* [Gamma Recursive](https://github.com/TheAlgorithms/Python/blob/master/maths/gamma_recursive.py)
* [Gaussian](https://github.com/TheAlgorithms/Python/blob/master/maths/gaussian.py)
* [Greatest Common Divisor](https://github.com/TheAlgorithms/Python/blob/master/maths/greatest_common_divisor.py)
* [Greedy Coin Change](https://github.com/TheAlgorithms/Python/blob/master/maths/greedy_coin_change.py)
* [Hardy Ramanujanalgo](https://github.com/TheAlgorithms/Python/blob/master/maths/hardy_ramanujanalgo.py)
* [Integration By Simpson Approx](https://github.com/TheAlgorithms/Python/blob/master/maths/integration_by_simpson_approx.py)
* [Is Ip V4 Address Valid](https://github.com/TheAlgorithms/Python/blob/master/maths/is_ip_v4_address_valid.py)
* [Is Square Free](https://github.com/TheAlgorithms/Python/blob/master/maths/is_square_free.py)
* [Jaccard Similarity](https://github.com/TheAlgorithms/Python/blob/master/maths/jaccard_similarity.py)
* [Kadanes](https://github.com/TheAlgorithms/Python/blob/master/maths/kadanes.py)
* [Karatsuba](https://github.com/TheAlgorithms/Python/blob/master/maths/karatsuba.py)
* [Krishnamurthy Number](https://github.com/TheAlgorithms/Python/blob/master/maths/krishnamurthy_number.py)
* [Kth Lexicographic Permutation](https://github.com/TheAlgorithms/Python/blob/master/maths/kth_lexicographic_permutation.py)
* [Largest Of Very Large Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/largest_of_very_large_numbers.py)
* [Largest Subarray Sum](https://github.com/TheAlgorithms/Python/blob/master/maths/largest_subarray_sum.py)
* [Least Common Multiple](https://github.com/TheAlgorithms/Python/blob/master/maths/least_common_multiple.py)
* [Line Length](https://github.com/TheAlgorithms/Python/blob/master/maths/line_length.py)
* [Lucas Lehmer Primality Test](https://github.com/TheAlgorithms/Python/blob/master/maths/lucas_lehmer_primality_test.py)
* [Lucas Series](https://github.com/TheAlgorithms/Python/blob/master/maths/lucas_series.py)
* [Matrix Exponentiation](https://github.com/TheAlgorithms/Python/blob/master/maths/matrix_exponentiation.py)
* [Max Sum Sliding Window](https://github.com/TheAlgorithms/Python/blob/master/maths/max_sum_sliding_window.py)
* [Median Of Two Arrays](https://github.com/TheAlgorithms/Python/blob/master/maths/median_of_two_arrays.py)
* [Miller Rabin](https://github.com/TheAlgorithms/Python/blob/master/maths/miller_rabin.py)
* [Mobius Function](https://github.com/TheAlgorithms/Python/blob/master/maths/mobius_function.py)
* [Modular Exponential](https://github.com/TheAlgorithms/Python/blob/master/maths/modular_exponential.py)
* [Monte Carlo](https://github.com/TheAlgorithms/Python/blob/master/maths/monte_carlo.py)
* [Monte Carlo Dice](https://github.com/TheAlgorithms/Python/blob/master/maths/monte_carlo_dice.py)
* [Nevilles Method](https://github.com/TheAlgorithms/Python/blob/master/maths/nevilles_method.py)
* [Newton Raphson](https://github.com/TheAlgorithms/Python/blob/master/maths/newton_raphson.py)
* [Number Of Digits](https://github.com/TheAlgorithms/Python/blob/master/maths/number_of_digits.py)
* [Numerical Integration](https://github.com/TheAlgorithms/Python/blob/master/maths/numerical_integration.py)
* [Perfect Cube](https://github.com/TheAlgorithms/Python/blob/master/maths/perfect_cube.py)
* [Perfect Number](https://github.com/TheAlgorithms/Python/blob/master/maths/perfect_number.py)
* [Perfect Square](https://github.com/TheAlgorithms/Python/blob/master/maths/perfect_square.py)
* [Pi Monte Carlo Estimation](https://github.com/TheAlgorithms/Python/blob/master/maths/pi_monte_carlo_estimation.py)
* [Pollard Rho](https://github.com/TheAlgorithms/Python/blob/master/maths/pollard_rho.py)
* [Polynomial Evaluation](https://github.com/TheAlgorithms/Python/blob/master/maths/polynomial_evaluation.py)
* [Power Using Recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/power_using_recursion.py)
* [Prime Check](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_check.py)
* [Prime Factors](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_factors.py)
* [Prime Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_numbers.py)
* [Prime Sieve Eratosthenes](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_sieve_eratosthenes.py)
* [Primelib](https://github.com/TheAlgorithms/Python/blob/master/maths/primelib.py)
* [Proth Number](https://github.com/TheAlgorithms/Python/blob/master/maths/proth_number.py)
* [Pythagoras](https://github.com/TheAlgorithms/Python/blob/master/maths/pythagoras.py)
* [Qr Decomposition](https://github.com/TheAlgorithms/Python/blob/master/maths/qr_decomposition.py)
* [Quadratic Equations Complex Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/quadratic_equations_complex_numbers.py)
* [Radians](https://github.com/TheAlgorithms/Python/blob/master/maths/radians.py)
* [Radix2 Fft](https://github.com/TheAlgorithms/Python/blob/master/maths/radix2_fft.py)
* [Relu](https://github.com/TheAlgorithms/Python/blob/master/maths/relu.py)
* [Runge Kutta](https://github.com/TheAlgorithms/Python/blob/master/maths/runge_kutta.py)
* [Segmented Sieve](https://github.com/TheAlgorithms/Python/blob/master/maths/segmented_sieve.py)
* Series
* [Arithmetic](https://github.com/TheAlgorithms/Python/blob/master/maths/series/arithmetic.py)
* [Geometric](https://github.com/TheAlgorithms/Python/blob/master/maths/series/geometric.py)
* [Geometric Series](https://github.com/TheAlgorithms/Python/blob/master/maths/series/geometric_series.py)
* [Harmonic](https://github.com/TheAlgorithms/Python/blob/master/maths/series/harmonic.py)
* [Harmonic Series](https://github.com/TheAlgorithms/Python/blob/master/maths/series/harmonic_series.py)
* [Hexagonal Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/series/hexagonal_numbers.py)
* [P Series](https://github.com/TheAlgorithms/Python/blob/master/maths/series/p_series.py)
* [Sieve Of Eratosthenes](https://github.com/TheAlgorithms/Python/blob/master/maths/sieve_of_eratosthenes.py)
* [Sigmoid](https://github.com/TheAlgorithms/Python/blob/master/maths/sigmoid.py)
* [Simpson Rule](https://github.com/TheAlgorithms/Python/blob/master/maths/simpson_rule.py)
* [Sock Merchant](https://github.com/TheAlgorithms/Python/blob/master/maths/sock_merchant.py)
* [Softmax](https://github.com/TheAlgorithms/Python/blob/master/maths/softmax.py)
* [Square Root](https://github.com/TheAlgorithms/Python/blob/master/maths/square_root.py)
* [Sum Of Arithmetic Series](https://github.com/TheAlgorithms/Python/blob/master/maths/sum_of_arithmetic_series.py)
* [Sum Of Digits](https://github.com/TheAlgorithms/Python/blob/master/maths/sum_of_digits.py)
* [Sum Of Geometric Progression](https://github.com/TheAlgorithms/Python/blob/master/maths/sum_of_geometric_progression.py)
* [Sylvester Sequence](https://github.com/TheAlgorithms/Python/blob/master/maths/sylvester_sequence.py)
* [Test Prime Check](https://github.com/TheAlgorithms/Python/blob/master/maths/test_prime_check.py)
* [Trapezoidal Rule](https://github.com/TheAlgorithms/Python/blob/master/maths/trapezoidal_rule.py)
* [Triplet Sum](https://github.com/TheAlgorithms/Python/blob/master/maths/triplet_sum.py)
* [Two Pointer](https://github.com/TheAlgorithms/Python/blob/master/maths/two_pointer.py)
* [Two Sum](https://github.com/TheAlgorithms/Python/blob/master/maths/two_sum.py)
* [Ugly Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/ugly_numbers.py)
* [Volume](https://github.com/TheAlgorithms/Python/blob/master/maths/volume.py)
* [Zellers Congruence](https://github.com/TheAlgorithms/Python/blob/master/maths/zellers_congruence.py)
## Matrix
* [Count Islands In Matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/count_islands_in_matrix.py)
* [Inverse Of Matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/inverse_of_matrix.py)
* [Matrix Class](https://github.com/TheAlgorithms/Python/blob/master/matrix/matrix_class.py)
* [Matrix Operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/matrix_operation.py)
* [Nth Fibonacci Using Matrix Exponentiation](https://github.com/TheAlgorithms/Python/blob/master/matrix/nth_fibonacci_using_matrix_exponentiation.py)
* [Rotate Matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/rotate_matrix.py)
* [Searching In Sorted Matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/searching_in_sorted_matrix.py)
* [Sherman Morrison](https://github.com/TheAlgorithms/Python/blob/master/matrix/sherman_morrison.py)
* [Spiral Print](https://github.com/TheAlgorithms/Python/blob/master/matrix/spiral_print.py)
* Tests
* [Test Matrix Operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/tests/test_matrix_operation.py)
## Networking Flow
* [Ford Fulkerson](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/ford_fulkerson.py)
* [Minimum Cut](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/minimum_cut.py)
## Neural Network
* [2 Hidden Layers Neural Network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/2_hidden_layers_neural_network.py)
* [Back Propagation Neural Network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/back_propagation_neural_network.py)
* [Convolution Neural Network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/convolution_neural_network.py)
* [Perceptron](https://github.com/TheAlgorithms/Python/blob/master/neural_network/perceptron.py)
## Other
* [Activity Selection](https://github.com/TheAlgorithms/Python/blob/master/other/activity_selection.py)
* [Alternative List Arrange](https://github.com/TheAlgorithms/Python/blob/master/other/alternative_list_arrange.py)
* [Check Strong Password](https://github.com/TheAlgorithms/Python/blob/master/other/check_strong_password.py)
* [Davisb Putnamb Logemannb Loveland](https://github.com/TheAlgorithms/Python/blob/master/other/davisb_putnamb_logemannb_loveland.py)
* [Dijkstra Bankers Algorithm](https://github.com/TheAlgorithms/Python/blob/master/other/dijkstra_bankers_algorithm.py)
* [Doomsday](https://github.com/TheAlgorithms/Python/blob/master/other/doomsday.py)
* [Fischer Yates Shuffle](https://github.com/TheAlgorithms/Python/blob/master/other/fischer_yates_shuffle.py)
* [Gauss Easter](https://github.com/TheAlgorithms/Python/blob/master/other/gauss_easter.py)
* [Graham Scan](https://github.com/TheAlgorithms/Python/blob/master/other/graham_scan.py)
* [Greedy](https://github.com/TheAlgorithms/Python/blob/master/other/greedy.py)
* [Least Recently Used](https://github.com/TheAlgorithms/Python/blob/master/other/least_recently_used.py)
* [Lfu Cache](https://github.com/TheAlgorithms/Python/blob/master/other/lfu_cache.py)
* [Linear Congruential Generator](https://github.com/TheAlgorithms/Python/blob/master/other/linear_congruential_generator.py)
* [Lru Cache](https://github.com/TheAlgorithms/Python/blob/master/other/lru_cache.py)
* [Magicdiamondpattern](https://github.com/TheAlgorithms/Python/blob/master/other/magicdiamondpattern.py)
* [Nested Brackets](https://github.com/TheAlgorithms/Python/blob/master/other/nested_brackets.py)
* [Password Generator](https://github.com/TheAlgorithms/Python/blob/master/other/password_generator.py)
* [Scoring Algorithm](https://github.com/TheAlgorithms/Python/blob/master/other/scoring_algorithm.py)
* [Sdes](https://github.com/TheAlgorithms/Python/blob/master/other/sdes.py)
* [Tower Of Hanoi](https://github.com/TheAlgorithms/Python/blob/master/other/tower_of_hanoi.py)
## Physics
* [N Body Simulation](https://github.com/TheAlgorithms/Python/blob/master/physics/n_body_simulation.py)
* [Newtons Second Law Of Motion](https://github.com/TheAlgorithms/Python/blob/master/physics/newtons_second_law_of_motion.py)
## Project Euler
* Problem 001
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol3.py)
* [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol4.py)
* [Sol5](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol5.py)
* [Sol6](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol6.py)
* [Sol7](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol7.py)
* Problem 002
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol3.py)
* [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol4.py)
* [Sol5](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol5.py)
* Problem 003
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_003/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_003/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_003/sol3.py)
* Problem 004
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_004/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_004/sol2.py)
* Problem 005
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_005/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_005/sol2.py)
* Problem 006
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_006/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_006/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_006/sol3.py)
* [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_006/sol4.py)
* Problem 007
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_007/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_007/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_007/sol3.py)
* Problem 008
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_008/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_008/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_008/sol3.py)
* Problem 009
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_009/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_009/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_009/sol3.py)
* Problem 010
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_010/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_010/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_010/sol3.py)
* Problem 011
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_011/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_011/sol2.py)
* Problem 012
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_012/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_012/sol2.py)
* Problem 013
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_013/sol1.py)
* Problem 014
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_014/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_014/sol2.py)
* Problem 015
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_015/sol1.py)
* Problem 016
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_016/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_016/sol2.py)
* Problem 017
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_017/sol1.py)
* Problem 018
* [Solution](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_018/solution.py)
* Problem 019
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_019/sol1.py)
* Problem 020
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_020/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_020/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_020/sol3.py)
* [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_020/sol4.py)
* Problem 021
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_021/sol1.py)
* Problem 022
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_022/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_022/sol2.py)
* Problem 023
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_023/sol1.py)
* Problem 024
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_024/sol1.py)
* Problem 025
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_025/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_025/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_025/sol3.py)
* Problem 026
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_026/sol1.py)
* Problem 027
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_027/sol1.py)
* Problem 028
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_028/sol1.py)
* Problem 029
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_029/sol1.py)
* Problem 030
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_030/sol1.py)
* Problem 031
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_031/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_031/sol2.py)
* Problem 032
* [Sol32](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_032/sol32.py)
* Problem 033
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_033/sol1.py)
* Problem 034
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_034/sol1.py)
* Problem 035
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_035/sol1.py)
* Problem 036
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_036/sol1.py)
* Problem 037
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_037/sol1.py)
* Problem 038
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_038/sol1.py)
* Problem 039
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_039/sol1.py)
* Problem 040
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_040/sol1.py)
* Problem 041
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_041/sol1.py)
* Problem 042
* [Solution42](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_042/solution42.py)
* Problem 043
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_043/sol1.py)
* Problem 044
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_044/sol1.py)
* Problem 045
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_045/sol1.py)
* Problem 046
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_046/sol1.py)
* Problem 047
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_047/sol1.py)
* Problem 048
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_048/sol1.py)
* Problem 049
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_049/sol1.py)
* Problem 050
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_050/sol1.py)
* Problem 051
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_051/sol1.py)
* Problem 052
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_052/sol1.py)
* Problem 053
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_053/sol1.py)
* Problem 054
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_054/sol1.py)
* [Test Poker Hand](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_054/test_poker_hand.py)
* Problem 055
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_055/sol1.py)
* Problem 056
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_056/sol1.py)
* Problem 057
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_057/sol1.py)
* Problem 058
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_058/sol1.py)
* Problem 059
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_059/sol1.py)
* Problem 062
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_062/sol1.py)
* Problem 063
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_063/sol1.py)
* Problem 064
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_064/sol1.py)
* Problem 065
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_065/sol1.py)
* Problem 067
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_067/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_067/sol2.py)
* Problem 069
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_069/sol1.py)
* Problem 070
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_070/sol1.py)
* Problem 071
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_071/sol1.py)
* Problem 072
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_072/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_072/sol2.py)
* Problem 074
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_074/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_074/sol2.py)
* Problem 075
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_075/sol1.py)
* Problem 076
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_076/sol1.py)
* Problem 077
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_077/sol1.py)
* Problem 078
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_078/sol1.py)
* Problem 080
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_080/sol1.py)
* Problem 081
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_081/sol1.py)
* Problem 085
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_085/sol1.py)
* Problem 086
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_086/sol1.py)
* Problem 087
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_087/sol1.py)
* Problem 089
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_089/sol1.py)
* Problem 091
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_091/sol1.py)
* Problem 092
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_092/sol1.py)
* Problem 097
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_097/sol1.py)
* Problem 099
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_099/sol1.py)
* Problem 101
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_101/sol1.py)
* Problem 102
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_102/sol1.py)
* Problem 107
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_107/sol1.py)
* Problem 109
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_109/sol1.py)
* Problem 112
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_112/sol1.py)
* Problem 113
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_113/sol1.py)
* Problem 119
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_119/sol1.py)
* Problem 120
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_120/sol1.py)
* Problem 121
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_121/sol1.py)
* Problem 123
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_123/sol1.py)
* Problem 125
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_125/sol1.py)
* Problem 129
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_129/sol1.py)
* Problem 135
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_135/sol1.py)
* Problem 144
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_144/sol1.py)
* Problem 173
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_173/sol1.py)
* Problem 174
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_174/sol1.py)
* Problem 180
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_180/sol1.py)
* Problem 188
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_188/sol1.py)
* Problem 191
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_191/sol1.py)
* Problem 203
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_203/sol1.py)
* Problem 206
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_206/sol1.py)
* Problem 207
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_207/sol1.py)
* Problem 234
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_234/sol1.py)
* Problem 301
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_301/sol1.py)
* Problem 493
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_493/sol1.py)
* Problem 551
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_551/sol1.py)
* Problem 686
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_686/sol1.py)
## Quantum
* [Deutsch Jozsa](https://github.com/TheAlgorithms/Python/blob/master/quantum/deutsch_jozsa.py)
* [Half Adder](https://github.com/TheAlgorithms/Python/blob/master/quantum/half_adder.py)
* [Not Gate](https://github.com/TheAlgorithms/Python/blob/master/quantum/not_gate.py)
* [Quantum Entanglement](https://github.com/TheAlgorithms/Python/blob/master/quantum/quantum_entanglement.py)
* [Ripple Adder Classic](https://github.com/TheAlgorithms/Python/blob/master/quantum/ripple_adder_classic.py)
* [Single Qubit Measure](https://github.com/TheAlgorithms/Python/blob/master/quantum/single_qubit_measure.py)
## Scheduling
* [First Come First Served](https://github.com/TheAlgorithms/Python/blob/master/scheduling/first_come_first_served.py)
* [Round Robin](https://github.com/TheAlgorithms/Python/blob/master/scheduling/round_robin.py)
* [Shortest Job First](https://github.com/TheAlgorithms/Python/blob/master/scheduling/shortest_job_first.py)
## Searches
* [Binary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/binary_search.py)
* [Binary Tree Traversal](https://github.com/TheAlgorithms/Python/blob/master/searches/binary_tree_traversal.py)
* [Double Linear Search](https://github.com/TheAlgorithms/Python/blob/master/searches/double_linear_search.py)
* [Double Linear Search Recursion](https://github.com/TheAlgorithms/Python/blob/master/searches/double_linear_search_recursion.py)
* [Fibonacci Search](https://github.com/TheAlgorithms/Python/blob/master/searches/fibonacci_search.py)
* [Hill Climbing](https://github.com/TheAlgorithms/Python/blob/master/searches/hill_climbing.py)
* [Interpolation Search](https://github.com/TheAlgorithms/Python/blob/master/searches/interpolation_search.py)
* [Jump Search](https://github.com/TheAlgorithms/Python/blob/master/searches/jump_search.py)
* [Linear Search](https://github.com/TheAlgorithms/Python/blob/master/searches/linear_search.py)
* [Quick Select](https://github.com/TheAlgorithms/Python/blob/master/searches/quick_select.py)
* [Sentinel Linear Search](https://github.com/TheAlgorithms/Python/blob/master/searches/sentinel_linear_search.py)
* [Simple Binary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/simple_binary_search.py)
* [Simulated Annealing](https://github.com/TheAlgorithms/Python/blob/master/searches/simulated_annealing.py)
* [Tabu Search](https://github.com/TheAlgorithms/Python/blob/master/searches/tabu_search.py)
* [Ternary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/ternary_search.py)
## Sorts
* [Bead Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bead_sort.py)
* [Bitonic Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bitonic_sort.py)
* [Bogo Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bogo_sort.py)
* [Bubble Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bubble_sort.py)
* [Bucket Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bucket_sort.py)
* [Cocktail Shaker Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/cocktail_shaker_sort.py)
* [Comb Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/comb_sort.py)
* [Counting Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/counting_sort.py)
* [Cycle Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/cycle_sort.py)
* [Double Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/double_sort.py)
* [Dutch National Flag Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/dutch_national_flag_sort.py)
* [Exchange Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/exchange_sort.py)
* [External Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/external_sort.py)
* [Gnome Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/gnome_sort.py)
* [Heap Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/heap_sort.py)
* [Insertion Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/insertion_sort.py)
* [Intro Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/intro_sort.py)
* [Iterative Merge Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/iterative_merge_sort.py)
* [Merge Insertion Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/merge_insertion_sort.py)
* [Merge Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/merge_sort.py)
* [Msd Radix Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/msd_radix_sort.py)
* [Natural Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/natural_sort.py)
* [Odd Even Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_sort.py)
* [Odd Even Transposition Parallel](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_transposition_parallel.py)
* [Odd Even Transposition Single Threaded](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_transposition_single_threaded.py)
* [Pancake Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pancake_sort.py)
* [Patience Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/patience_sort.py)
* [Pigeon Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pigeon_sort.py)
* [Pigeonhole Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pigeonhole_sort.py)
* [Quick Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/quick_sort.py)
* [Quick Sort 3 Partition](https://github.com/TheAlgorithms/Python/blob/master/sorts/quick_sort_3_partition.py)
* [Radix Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/radix_sort.py)
* [Random Normal Distribution Quicksort](https://github.com/TheAlgorithms/Python/blob/master/sorts/random_normal_distribution_quicksort.py)
* [Random Pivot Quick Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/random_pivot_quick_sort.py)
* [Recursive Bubble Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/recursive_bubble_sort.py)
* [Recursive Insertion Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/recursive_insertion_sort.py)
* [Recursive Mergesort Array](https://github.com/TheAlgorithms/Python/blob/master/sorts/recursive_mergesort_array.py)
* [Recursive Quick Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/recursive_quick_sort.py)
* [Selection Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/selection_sort.py)
* [Shell Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/shell_sort.py)
* [Slowsort](https://github.com/TheAlgorithms/Python/blob/master/sorts/slowsort.py)
* [Stooge Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/stooge_sort.py)
* [Strand Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/strand_sort.py)
* [Tim Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/tim_sort.py)
* [Topological Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/topological_sort.py)
* [Tree Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/tree_sort.py)
* [Unknown Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/unknown_sort.py)
* [Wiggle Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/wiggle_sort.py)
## Strings
* [Aho Corasick](https://github.com/TheAlgorithms/Python/blob/master/strings/aho_corasick.py)
* [Alternative String Arrange](https://github.com/TheAlgorithms/Python/blob/master/strings/alternative_string_arrange.py)
* [Anagrams](https://github.com/TheAlgorithms/Python/blob/master/strings/anagrams.py)
* [Autocomplete Using Trie](https://github.com/TheAlgorithms/Python/blob/master/strings/autocomplete_using_trie.py)
* [Boyer Moore Search](https://github.com/TheAlgorithms/Python/blob/master/strings/boyer_moore_search.py)
* [Can String Be Rearranged As Palindrome](https://github.com/TheAlgorithms/Python/blob/master/strings/can_string_be_rearranged_as_palindrome.py)
* [Capitalize](https://github.com/TheAlgorithms/Python/blob/master/strings/capitalize.py)
* [Check Anagrams](https://github.com/TheAlgorithms/Python/blob/master/strings/check_anagrams.py)
* [Check Pangram](https://github.com/TheAlgorithms/Python/blob/master/strings/check_pangram.py)
* [Credit Card Validator](https://github.com/TheAlgorithms/Python/blob/master/strings/credit_card_validator.py)
* [Detecting English Programmatically](https://github.com/TheAlgorithms/Python/blob/master/strings/detecting_english_programmatically.py)
* [Frequency Finder](https://github.com/TheAlgorithms/Python/blob/master/strings/frequency_finder.py)
* [Indian Phone Validator](https://github.com/TheAlgorithms/Python/blob/master/strings/indian_phone_validator.py)
* [Is Contains Unique Chars](https://github.com/TheAlgorithms/Python/blob/master/strings/is_contains_unique_chars.py)
* [Is Palindrome](https://github.com/TheAlgorithms/Python/blob/master/strings/is_palindrome.py)
* [Jaro Winkler](https://github.com/TheAlgorithms/Python/blob/master/strings/jaro_winkler.py)
* [Join](https://github.com/TheAlgorithms/Python/blob/master/strings/join.py)
* [Knuth Morris Pratt](https://github.com/TheAlgorithms/Python/blob/master/strings/knuth_morris_pratt.py)
* [Levenshtein Distance](https://github.com/TheAlgorithms/Python/blob/master/strings/levenshtein_distance.py)
* [Lower](https://github.com/TheAlgorithms/Python/blob/master/strings/lower.py)
* [Manacher](https://github.com/TheAlgorithms/Python/blob/master/strings/manacher.py)
* [Min Cost String Conversion](https://github.com/TheAlgorithms/Python/blob/master/strings/min_cost_string_conversion.py)
* [Naive String Search](https://github.com/TheAlgorithms/Python/blob/master/strings/naive_string_search.py)
* [Palindrome](https://github.com/TheAlgorithms/Python/blob/master/strings/palindrome.py)
* [Prefix Function](https://github.com/TheAlgorithms/Python/blob/master/strings/prefix_function.py)
* [Rabin Karp](https://github.com/TheAlgorithms/Python/blob/master/strings/rabin_karp.py)
* [Remove Duplicate](https://github.com/TheAlgorithms/Python/blob/master/strings/remove_duplicate.py)
* [Reverse Letters](https://github.com/TheAlgorithms/Python/blob/master/strings/reverse_letters.py)
* [Reverse Long Words](https://github.com/TheAlgorithms/Python/blob/master/strings/reverse_long_words.py)
* [Reverse Words](https://github.com/TheAlgorithms/Python/blob/master/strings/reverse_words.py)
* [Split](https://github.com/TheAlgorithms/Python/blob/master/strings/split.py)
* [Upper](https://github.com/TheAlgorithms/Python/blob/master/strings/upper.py)
* [Wildcard Pattern Matching](https://github.com/TheAlgorithms/Python/blob/master/strings/wildcard_pattern_matching.py)
* [Word Occurrence](https://github.com/TheAlgorithms/Python/blob/master/strings/word_occurrence.py)
* [Word Patterns](https://github.com/TheAlgorithms/Python/blob/master/strings/word_patterns.py)
* [Z Function](https://github.com/TheAlgorithms/Python/blob/master/strings/z_function.py)
## Web Programming
* [Co2 Emission](https://github.com/TheAlgorithms/Python/blob/master/web_programming/co2_emission.py)
* [Covid Stats Via Xpath](https://github.com/TheAlgorithms/Python/blob/master/web_programming/covid_stats_via_xpath.py)
* [Crawl Google Results](https://github.com/TheAlgorithms/Python/blob/master/web_programming/crawl_google_results.py)
* [Crawl Google Scholar Citation](https://github.com/TheAlgorithms/Python/blob/master/web_programming/crawl_google_scholar_citation.py)
* [Currency Converter](https://github.com/TheAlgorithms/Python/blob/master/web_programming/currency_converter.py)
* [Current Stock Price](https://github.com/TheAlgorithms/Python/blob/master/web_programming/current_stock_price.py)
* [Current Weather](https://github.com/TheAlgorithms/Python/blob/master/web_programming/current_weather.py)
* [Daily Horoscope](https://github.com/TheAlgorithms/Python/blob/master/web_programming/daily_horoscope.py)
* [Download Images From Google Query](https://github.com/TheAlgorithms/Python/blob/master/web_programming/download_images_from_google_query.py)
* [Emails From Url](https://github.com/TheAlgorithms/Python/blob/master/web_programming/emails_from_url.py)
* [Fetch Bbc News](https://github.com/TheAlgorithms/Python/blob/master/web_programming/fetch_bbc_news.py)
* [Fetch Github Info](https://github.com/TheAlgorithms/Python/blob/master/web_programming/fetch_github_info.py)
* [Fetch Jobs](https://github.com/TheAlgorithms/Python/blob/master/web_programming/fetch_jobs.py)
* [Get Imdb Top 250 Movies Csv](https://github.com/TheAlgorithms/Python/blob/master/web_programming/get_imdb_top_250_movies_csv.py)
* [Get Imdbtop](https://github.com/TheAlgorithms/Python/blob/master/web_programming/get_imdbtop.py)
* [Get Top Hn Posts](https://github.com/TheAlgorithms/Python/blob/master/web_programming/get_top_hn_posts.py)
* [Get User Tweets](https://github.com/TheAlgorithms/Python/blob/master/web_programming/get_user_tweets.py)
* [Giphy](https://github.com/TheAlgorithms/Python/blob/master/web_programming/giphy.py)
* [Instagram Crawler](https://github.com/TheAlgorithms/Python/blob/master/web_programming/instagram_crawler.py)
* [Instagram Pic](https://github.com/TheAlgorithms/Python/blob/master/web_programming/instagram_pic.py)
* [Instagram Video](https://github.com/TheAlgorithms/Python/blob/master/web_programming/instagram_video.py)
* [Nasa Data](https://github.com/TheAlgorithms/Python/blob/master/web_programming/nasa_data.py)
* [Random Anime Character](https://github.com/TheAlgorithms/Python/blob/master/web_programming/random_anime_character.py)
* [Recaptcha Verification](https://github.com/TheAlgorithms/Python/blob/master/web_programming/recaptcha_verification.py)
* [Reddit](https://github.com/TheAlgorithms/Python/blob/master/web_programming/reddit.py)
* [Search Books By Isbn](https://github.com/TheAlgorithms/Python/blob/master/web_programming/search_books_by_isbn.py)
* [Slack Message](https://github.com/TheAlgorithms/Python/blob/master/web_programming/slack_message.py)
* [Test Fetch Github Info](https://github.com/TheAlgorithms/Python/blob/master/web_programming/test_fetch_github_info.py)
* [World Covid19 Stats](https://github.com/TheAlgorithms/Python/blob/master/web_programming/world_covid19_stats.py)
|
## Arithmetic Analysis
* [Bisection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/bisection.py)
* [Gaussian Elimination](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/gaussian_elimination.py)
* [In Static Equilibrium](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/in_static_equilibrium.py)
* [Intersection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/intersection.py)
* [Lu Decomposition](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/lu_decomposition.py)
* [Newton Forward Interpolation](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_forward_interpolation.py)
* [Newton Method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_method.py)
* [Newton Raphson](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_raphson.py)
* [Secant Method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/secant_method.py)
## Audio Filters
* [Butterworth Filter](https://github.com/TheAlgorithms/Python/blob/master/audio_filters/butterworth_filter.py)
* [Iir Filter](https://github.com/TheAlgorithms/Python/blob/master/audio_filters/iir_filter.py)
* [Show Response](https://github.com/TheAlgorithms/Python/blob/master/audio_filters/show_response.py)
## Backtracking
* [All Combinations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_combinations.py)
* [All Permutations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_permutations.py)
* [All Subsequences](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_subsequences.py)
* [Coloring](https://github.com/TheAlgorithms/Python/blob/master/backtracking/coloring.py)
* [Hamiltonian Cycle](https://github.com/TheAlgorithms/Python/blob/master/backtracking/hamiltonian_cycle.py)
* [Knight Tour](https://github.com/TheAlgorithms/Python/blob/master/backtracking/knight_tour.py)
* [Minimax](https://github.com/TheAlgorithms/Python/blob/master/backtracking/minimax.py)
* [N Queens](https://github.com/TheAlgorithms/Python/blob/master/backtracking/n_queens.py)
* [N Queens Math](https://github.com/TheAlgorithms/Python/blob/master/backtracking/n_queens_math.py)
* [Rat In Maze](https://github.com/TheAlgorithms/Python/blob/master/backtracking/rat_in_maze.py)
* [Sudoku](https://github.com/TheAlgorithms/Python/blob/master/backtracking/sudoku.py)
* [Sum Of Subsets](https://github.com/TheAlgorithms/Python/blob/master/backtracking/sum_of_subsets.py)
## Bit Manipulation
* [Binary And Operator](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_and_operator.py)
* [Binary Count Setbits](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_count_setbits.py)
* [Binary Count Trailing Zeros](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_count_trailing_zeros.py)
* [Binary Or Operator](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_or_operator.py)
* [Binary Shifts](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_shifts.py)
* [Binary Twos Complement](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_twos_complement.py)
* [Binary Xor Operator](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_xor_operator.py)
* [Count 1S Brian Kernighan Method](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/count_1s_brian_kernighan_method.py)
* [Count Number Of One Bits](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/count_number_of_one_bits.py)
* [Gray Code Sequence](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/gray_code_sequence.py)
* [Reverse Bits](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/reverse_bits.py)
* [Single Bit Manipulation Operations](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/single_bit_manipulation_operations.py)
## Blockchain
* [Chinese Remainder Theorem](https://github.com/TheAlgorithms/Python/blob/master/blockchain/chinese_remainder_theorem.py)
* [Diophantine Equation](https://github.com/TheAlgorithms/Python/blob/master/blockchain/diophantine_equation.py)
* [Modular Division](https://github.com/TheAlgorithms/Python/blob/master/blockchain/modular_division.py)
## Boolean Algebra
* [Quine Mc Cluskey](https://github.com/TheAlgorithms/Python/blob/master/boolean_algebra/quine_mc_cluskey.py)
## Cellular Automata
* [Conways Game Of Life](https://github.com/TheAlgorithms/Python/blob/master/cellular_automata/conways_game_of_life.py)
* [Game Of Life](https://github.com/TheAlgorithms/Python/blob/master/cellular_automata/game_of_life.py)
* [Nagel Schrekenberg](https://github.com/TheAlgorithms/Python/blob/master/cellular_automata/nagel_schrekenberg.py)
* [One Dimensional](https://github.com/TheAlgorithms/Python/blob/master/cellular_automata/one_dimensional.py)
## Ciphers
* [A1Z26](https://github.com/TheAlgorithms/Python/blob/master/ciphers/a1z26.py)
* [Affine Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/affine_cipher.py)
* [Atbash](https://github.com/TheAlgorithms/Python/blob/master/ciphers/atbash.py)
* [Baconian Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/baconian_cipher.py)
* [Base16](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base16.py)
* [Base32](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base32.py)
* [Base64](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base64.py)
* [Base85](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base85.py)
* [Beaufort Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/beaufort_cipher.py)
* [Bifid](https://github.com/TheAlgorithms/Python/blob/master/ciphers/bifid.py)
* [Brute Force Caesar Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/brute_force_caesar_cipher.py)
* [Caesar Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/caesar_cipher.py)
* [Cryptomath Module](https://github.com/TheAlgorithms/Python/blob/master/ciphers/cryptomath_module.py)
* [Decrypt Caesar With Chi Squared](https://github.com/TheAlgorithms/Python/blob/master/ciphers/decrypt_caesar_with_chi_squared.py)
* [Deterministic Miller Rabin](https://github.com/TheAlgorithms/Python/blob/master/ciphers/deterministic_miller_rabin.py)
* [Diffie](https://github.com/TheAlgorithms/Python/blob/master/ciphers/diffie.py)
* [Diffie Hellman](https://github.com/TheAlgorithms/Python/blob/master/ciphers/diffie_hellman.py)
* [Elgamal Key Generator](https://github.com/TheAlgorithms/Python/blob/master/ciphers/elgamal_key_generator.py)
* [Enigma Machine2](https://github.com/TheAlgorithms/Python/blob/master/ciphers/enigma_machine2.py)
* [Hill Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/hill_cipher.py)
* [Mixed Keyword Cypher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/mixed_keyword_cypher.py)
* [Mono Alphabetic Ciphers](https://github.com/TheAlgorithms/Python/blob/master/ciphers/mono_alphabetic_ciphers.py)
* [Morse Code](https://github.com/TheAlgorithms/Python/blob/master/ciphers/morse_code.py)
* [Onepad Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/onepad_cipher.py)
* [Playfair Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/playfair_cipher.py)
* [Polybius](https://github.com/TheAlgorithms/Python/blob/master/ciphers/polybius.py)
* [Porta Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/porta_cipher.py)
* [Rabin Miller](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rabin_miller.py)
* [Rail Fence Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rail_fence_cipher.py)
* [Rot13](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rot13.py)
* [Rsa Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_cipher.py)
* [Rsa Factorization](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_factorization.py)
* [Rsa Key Generator](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_key_generator.py)
* [Shuffled Shift Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/shuffled_shift_cipher.py)
* [Simple Keyword Cypher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/simple_keyword_cypher.py)
* [Simple Substitution Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/simple_substitution_cipher.py)
* [Trafid Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/trafid_cipher.py)
* [Transposition Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/transposition_cipher.py)
* [Transposition Cipher Encrypt Decrypt File](https://github.com/TheAlgorithms/Python/blob/master/ciphers/transposition_cipher_encrypt_decrypt_file.py)
* [Vigenere Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/vigenere_cipher.py)
* [Xor Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/xor_cipher.py)
## Compression
* [Burrows Wheeler](https://github.com/TheAlgorithms/Python/blob/master/compression/burrows_wheeler.py)
* [Huffman](https://github.com/TheAlgorithms/Python/blob/master/compression/huffman.py)
* [Lempel Ziv](https://github.com/TheAlgorithms/Python/blob/master/compression/lempel_ziv.py)
* [Lempel Ziv Decompress](https://github.com/TheAlgorithms/Python/blob/master/compression/lempel_ziv_decompress.py)
* [Peak Signal To Noise Ratio](https://github.com/TheAlgorithms/Python/blob/master/compression/peak_signal_to_noise_ratio.py)
## Computer Vision
* [Cnn Classification](https://github.com/TheAlgorithms/Python/blob/master/computer_vision/cnn_classification.py)
* [Flip Augmentation](https://github.com/TheAlgorithms/Python/blob/master/computer_vision/flip_augmentation.py)
* [Harris Corner](https://github.com/TheAlgorithms/Python/blob/master/computer_vision/harris_corner.py)
* [Mean Threshold](https://github.com/TheAlgorithms/Python/blob/master/computer_vision/mean_threshold.py)
* [Mosaic Augmentation](https://github.com/TheAlgorithms/Python/blob/master/computer_vision/mosaic_augmentation.py)
## Conversions
* [Binary To Decimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/binary_to_decimal.py)
* [Binary To Hexadecimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/binary_to_hexadecimal.py)
* [Binary To Octal](https://github.com/TheAlgorithms/Python/blob/master/conversions/binary_to_octal.py)
* [Decimal To Any](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_any.py)
* [Decimal To Binary](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_binary.py)
* [Decimal To Binary Recursion](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_binary_recursion.py)
* [Decimal To Hexadecimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_hexadecimal.py)
* [Decimal To Octal](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_octal.py)
* [Hex To Bin](https://github.com/TheAlgorithms/Python/blob/master/conversions/hex_to_bin.py)
* [Hexadecimal To Decimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/hexadecimal_to_decimal.py)
* [Length Conversion](https://github.com/TheAlgorithms/Python/blob/master/conversions/length_conversion.py)
* [Molecular Chemistry](https://github.com/TheAlgorithms/Python/blob/master/conversions/molecular_chemistry.py)
* [Octal To Decimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/octal_to_decimal.py)
* [Prefix Conversions](https://github.com/TheAlgorithms/Python/blob/master/conversions/prefix_conversions.py)
* [Pressure Conversions](https://github.com/TheAlgorithms/Python/blob/master/conversions/pressure_conversions.py)
* [Rgb Hsv Conversion](https://github.com/TheAlgorithms/Python/blob/master/conversions/rgb_hsv_conversion.py)
* [Roman Numerals](https://github.com/TheAlgorithms/Python/blob/master/conversions/roman_numerals.py)
* [Temperature Conversions](https://github.com/TheAlgorithms/Python/blob/master/conversions/temperature_conversions.py)
* [Volume Conversions](https://github.com/TheAlgorithms/Python/blob/master/conversions/volume_conversions.py)
* [Weight Conversion](https://github.com/TheAlgorithms/Python/blob/master/conversions/weight_conversion.py)
## Data Structures
* Binary Tree
* [Avl Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/avl_tree.py)
* [Basic Binary Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/basic_binary_tree.py)
* [Binary Search Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_search_tree.py)
* [Binary Search Tree Recursive](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_search_tree_recursive.py)
* [Binary Tree Mirror](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_tree_mirror.py)
* [Binary Tree Traversals](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_tree_traversals.py)
* [Fenwick Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/fenwick_tree.py)
* [Lazy Segment Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/lazy_segment_tree.py)
* [Lowest Common Ancestor](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/lowest_common_ancestor.py)
* [Merge Two Binary Trees](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/merge_two_binary_trees.py)
* [Non Recursive Segment Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/non_recursive_segment_tree.py)
* [Number Of Possible Binary Trees](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/number_of_possible_binary_trees.py)
* [Red Black Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/red_black_tree.py)
* [Segment Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/segment_tree.py)
* [Segment Tree Other](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/segment_tree_other.py)
* [Treap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/treap.py)
* [Wavelet Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/wavelet_tree.py)
* Disjoint Set
* [Alternate Disjoint Set](https://github.com/TheAlgorithms/Python/blob/master/data_structures/disjoint_set/alternate_disjoint_set.py)
* [Disjoint Set](https://github.com/TheAlgorithms/Python/blob/master/data_structures/disjoint_set/disjoint_set.py)
* Hashing
* [Double Hash](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/double_hash.py)
* [Hash Table](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table.py)
* [Hash Table With Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table_with_linked_list.py)
* Number Theory
* [Prime Numbers](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/number_theory/prime_numbers.py)
* [Quadratic Probing](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/quadratic_probing.py)
* Heap
* [Binomial Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/binomial_heap.py)
* [Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/heap.py)
* [Heap Generic](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/heap_generic.py)
* [Max Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/max_heap.py)
* [Min Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/min_heap.py)
* [Randomized Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/randomized_heap.py)
* [Skew Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/skew_heap.py)
* Linked List
* [Circular Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/circular_linked_list.py)
* [Deque Doubly](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/deque_doubly.py)
* [Doubly Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/doubly_linked_list.py)
* [Doubly Linked List Two](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/doubly_linked_list_two.py)
* [From Sequence](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/from_sequence.py)
* [Has Loop](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/has_loop.py)
* [Is Palindrome](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/is_palindrome.py)
* [Merge Two Lists](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/merge_two_lists.py)
* [Middle Element Of Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/middle_element_of_linked_list.py)
* [Print Reverse](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/print_reverse.py)
* [Singly Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/singly_linked_list.py)
* [Skip List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/skip_list.py)
* [Swap Nodes](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/swap_nodes.py)
* Queue
* [Circular Queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/circular_queue.py)
* [Circular Queue Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/circular_queue_linked_list.py)
* [Double Ended Queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/double_ended_queue.py)
* [Linked Queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/linked_queue.py)
* [Priority Queue Using List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/priority_queue_using_list.py)
* [Queue On List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/queue_on_list.py)
* [Queue On Pseudo Stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/queue_on_pseudo_stack.py)
* Stacks
* [Balanced Parentheses](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/balanced_parentheses.py)
* [Dijkstras Two Stack Algorithm](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/dijkstras_two_stack_algorithm.py)
* [Evaluate Postfix Notations](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/evaluate_postfix_notations.py)
* [Infix To Postfix Conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/infix_to_postfix_conversion.py)
* [Infix To Prefix Conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/infix_to_prefix_conversion.py)
* [Next Greater Element](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/next_greater_element.py)
* [Postfix Evaluation](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/postfix_evaluation.py)
* [Prefix Evaluation](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/prefix_evaluation.py)
* [Stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stack.py)
* [Stack With Doubly Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stack_with_doubly_linked_list.py)
* [Stack With Singly Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stack_with_singly_linked_list.py)
* [Stock Span Problem](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stock_span_problem.py)
* Trie
* [Trie](https://github.com/TheAlgorithms/Python/blob/master/data_structures/trie/trie.py)
## Digital Image Processing
* [Change Brightness](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/change_brightness.py)
* [Change Contrast](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/change_contrast.py)
* [Convert To Negative](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/convert_to_negative.py)
* Dithering
* [Burkes](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/dithering/burkes.py)
* Edge Detection
* [Canny](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/edge_detection/canny.py)
* Filters
* [Bilateral Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/bilateral_filter.py)
* [Convolve](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/convolve.py)
* [Gabor Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/gabor_filter.py)
* [Gaussian Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/gaussian_filter.py)
* [Median Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/median_filter.py)
* [Sobel Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/sobel_filter.py)
* Histogram Equalization
* [Histogram Stretch](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/histogram_equalization/histogram_stretch.py)
* [Index Calculation](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/index_calculation.py)
* Morphological Operations
* [Dilation Operation](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/morphological_operations/dilation_operation.py)
* [Erosion Operation](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/morphological_operations/erosion_operation.py)
* Resize
* [Resize](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/resize/resize.py)
* Rotation
* [Rotation](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/rotation/rotation.py)
* [Sepia](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/sepia.py)
* [Test Digital Image Processing](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/test_digital_image_processing.py)
## Divide And Conquer
* [Closest Pair Of Points](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/closest_pair_of_points.py)
* [Convex Hull](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/convex_hull.py)
* [Heaps Algorithm](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/heaps_algorithm.py)
* [Heaps Algorithm Iterative](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/heaps_algorithm_iterative.py)
* [Inversions](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/inversions.py)
* [Kth Order Statistic](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/kth_order_statistic.py)
* [Max Difference Pair](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/max_difference_pair.py)
* [Max Subarray Sum](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/max_subarray_sum.py)
* [Mergesort](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/mergesort.py)
* [Peak](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/peak.py)
* [Power](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/power.py)
* [Strassen Matrix Multiplication](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/strassen_matrix_multiplication.py)
## Dynamic Programming
* [Abbreviation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/abbreviation.py)
* [All Construct](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/all_construct.py)
* [Bitmask](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/bitmask.py)
* [Catalan Numbers](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/catalan_numbers.py)
* [Climbing Stairs](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/climbing_stairs.py)
* [Edit Distance](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/edit_distance.py)
* [Factorial](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/factorial.py)
* [Fast Fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fast_fibonacci.py)
* [Fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fibonacci.py)
* [Floyd Warshall](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/floyd_warshall.py)
* [Fractional Knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fractional_knapsack.py)
* [Fractional Knapsack 2](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fractional_knapsack_2.py)
* [Integer Partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/integer_partition.py)
* [Iterating Through Submasks](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/iterating_through_submasks.py)
* [Knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/knapsack.py)
* [Longest Common Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_common_subsequence.py)
* [Longest Increasing Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence.py)
* [Longest Increasing Subsequence O(Nlogn)](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence_o(nlogn).py)
* [Longest Sub Array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_sub_array.py)
* [Matrix Chain Order](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/matrix_chain_order.py)
* [Max Non Adjacent Sum](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_non_adjacent_sum.py)
* [Max Sub Array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sub_array.py)
* [Max Sum Contiguous Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sum_contiguous_subsequence.py)
* [Minimum Coin Change](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_coin_change.py)
* [Minimum Cost Path](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_cost_path.py)
* [Minimum Partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_partition.py)
* [Minimum Steps To One](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_steps_to_one.py)
* [Optimal Binary Search Tree](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/optimal_binary_search_tree.py)
* [Rod Cutting](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/rod_cutting.py)
* [Subset Generation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/subset_generation.py)
* [Sum Of Subset](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/sum_of_subset.py)
## Electronics
* [Carrier Concentration](https://github.com/TheAlgorithms/Python/blob/master/electronics/carrier_concentration.py)
* [Coulombs Law](https://github.com/TheAlgorithms/Python/blob/master/electronics/coulombs_law.py)
* [Electric Power](https://github.com/TheAlgorithms/Python/blob/master/electronics/electric_power.py)
* [Ohms Law](https://github.com/TheAlgorithms/Python/blob/master/electronics/ohms_law.py)
## File Transfer
* [Receive File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/receive_file.py)
* [Send File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/send_file.py)
* Tests
* [Test Send File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/tests/test_send_file.py)
## Financial
* [Equated Monthly Installments](https://github.com/TheAlgorithms/Python/blob/master/financial/equated_monthly_installments.py)
* [Interest](https://github.com/TheAlgorithms/Python/blob/master/financial/interest.py)
## Fractals
* [Julia Sets](https://github.com/TheAlgorithms/Python/blob/master/fractals/julia_sets.py)
* [Koch Snowflake](https://github.com/TheAlgorithms/Python/blob/master/fractals/koch_snowflake.py)
* [Mandelbrot](https://github.com/TheAlgorithms/Python/blob/master/fractals/mandelbrot.py)
* [Sierpinski Triangle](https://github.com/TheAlgorithms/Python/blob/master/fractals/sierpinski_triangle.py)
## Fuzzy Logic
* [Fuzzy Operations](https://github.com/TheAlgorithms/Python/blob/master/fuzzy_logic/fuzzy_operations.py)
## Genetic Algorithm
* [Basic String](https://github.com/TheAlgorithms/Python/blob/master/genetic_algorithm/basic_string.py)
## Geodesy
* [Haversine Distance](https://github.com/TheAlgorithms/Python/blob/master/geodesy/haversine_distance.py)
* [Lamberts Ellipsoidal Distance](https://github.com/TheAlgorithms/Python/blob/master/geodesy/lamberts_ellipsoidal_distance.py)
## Graphics
* [Bezier Curve](https://github.com/TheAlgorithms/Python/blob/master/graphics/bezier_curve.py)
* [Vector3 For 2D Rendering](https://github.com/TheAlgorithms/Python/blob/master/graphics/vector3_for_2d_rendering.py)
## Graphs
* [A Star](https://github.com/TheAlgorithms/Python/blob/master/graphs/a_star.py)
* [Articulation Points](https://github.com/TheAlgorithms/Python/blob/master/graphs/articulation_points.py)
* [Basic Graphs](https://github.com/TheAlgorithms/Python/blob/master/graphs/basic_graphs.py)
* [Bellman Ford](https://github.com/TheAlgorithms/Python/blob/master/graphs/bellman_ford.py)
* [Bfs Shortest Path](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs_shortest_path.py)
* [Bfs Zero One Shortest Path](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs_zero_one_shortest_path.py)
* [Bidirectional A Star](https://github.com/TheAlgorithms/Python/blob/master/graphs/bidirectional_a_star.py)
* [Bidirectional Breadth First Search](https://github.com/TheAlgorithms/Python/blob/master/graphs/bidirectional_breadth_first_search.py)
* [Boruvka](https://github.com/TheAlgorithms/Python/blob/master/graphs/boruvka.py)
* [Breadth First Search](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search.py)
* [Breadth First Search 2](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search_2.py)
* [Breadth First Search Shortest Path](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search_shortest_path.py)
* [Check Bipartite Graph Bfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_bfs.py)
* [Check Bipartite Graph Dfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_dfs.py)
* [Check Cycle](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_cycle.py)
* [Connected Components](https://github.com/TheAlgorithms/Python/blob/master/graphs/connected_components.py)
* [Depth First Search](https://github.com/TheAlgorithms/Python/blob/master/graphs/depth_first_search.py)
* [Depth First Search 2](https://github.com/TheAlgorithms/Python/blob/master/graphs/depth_first_search_2.py)
* [Dijkstra](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra.py)
* [Dijkstra 2](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra_2.py)
* [Dijkstra Algorithm](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra_algorithm.py)
* [Dinic](https://github.com/TheAlgorithms/Python/blob/master/graphs/dinic.py)
* [Directed And Undirected (Weighted) Graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/directed_and_undirected_(weighted)_graph.py)
* [Edmonds Karp Multiple Source And Sink](https://github.com/TheAlgorithms/Python/blob/master/graphs/edmonds_karp_multiple_source_and_sink.py)
* [Eulerian Path And Circuit For Undirected Graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/eulerian_path_and_circuit_for_undirected_graph.py)
* [Even Tree](https://github.com/TheAlgorithms/Python/blob/master/graphs/even_tree.py)
* [Finding Bridges](https://github.com/TheAlgorithms/Python/blob/master/graphs/finding_bridges.py)
* [Frequent Pattern Graph Miner](https://github.com/TheAlgorithms/Python/blob/master/graphs/frequent_pattern_graph_miner.py)
* [G Topological Sort](https://github.com/TheAlgorithms/Python/blob/master/graphs/g_topological_sort.py)
* [Gale Shapley Bigraph](https://github.com/TheAlgorithms/Python/blob/master/graphs/gale_shapley_bigraph.py)
* [Graph List](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_list.py)
* [Graph Matrix](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_matrix.py)
* [Graphs Floyd Warshall](https://github.com/TheAlgorithms/Python/blob/master/graphs/graphs_floyd_warshall.py)
* [Greedy Best First](https://github.com/TheAlgorithms/Python/blob/master/graphs/greedy_best_first.py)
* [Greedy Min Vertex Cover](https://github.com/TheAlgorithms/Python/blob/master/graphs/greedy_min_vertex_cover.py)
* [Kahns Algorithm Long](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_long.py)
* [Kahns Algorithm Topo](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_topo.py)
* [Karger](https://github.com/TheAlgorithms/Python/blob/master/graphs/karger.py)
* [Markov Chain](https://github.com/TheAlgorithms/Python/blob/master/graphs/markov_chain.py)
* [Matching Min Vertex Cover](https://github.com/TheAlgorithms/Python/blob/master/graphs/matching_min_vertex_cover.py)
* [Minimum Spanning Tree Boruvka](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_boruvka.py)
* [Minimum Spanning Tree Kruskal](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_kruskal.py)
* [Minimum Spanning Tree Kruskal2](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_kruskal2.py)
* [Minimum Spanning Tree Prims](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_prims.py)
* [Minimum Spanning Tree Prims2](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_prims2.py)
* [Multi Heuristic Astar](https://github.com/TheAlgorithms/Python/blob/master/graphs/multi_heuristic_astar.py)
* [Page Rank](https://github.com/TheAlgorithms/Python/blob/master/graphs/page_rank.py)
* [Prim](https://github.com/TheAlgorithms/Python/blob/master/graphs/prim.py)
* [Random Graph Generator](https://github.com/TheAlgorithms/Python/blob/master/graphs/random_graph_generator.py)
* [Scc Kosaraju](https://github.com/TheAlgorithms/Python/blob/master/graphs/scc_kosaraju.py)
* [Strongly Connected Components](https://github.com/TheAlgorithms/Python/blob/master/graphs/strongly_connected_components.py)
* [Tarjans Scc](https://github.com/TheAlgorithms/Python/blob/master/graphs/tarjans_scc.py)
* Tests
* [Test Min Spanning Tree Kruskal](https://github.com/TheAlgorithms/Python/blob/master/graphs/tests/test_min_spanning_tree_kruskal.py)
* [Test Min Spanning Tree Prim](https://github.com/TheAlgorithms/Python/blob/master/graphs/tests/test_min_spanning_tree_prim.py)
## Greedy Methods
* [Optimal Merge Pattern](https://github.com/TheAlgorithms/Python/blob/master/greedy_methods/optimal_merge_pattern.py)
## Hashes
* [Adler32](https://github.com/TheAlgorithms/Python/blob/master/hashes/adler32.py)
* [Chaos Machine](https://github.com/TheAlgorithms/Python/blob/master/hashes/chaos_machine.py)
* [Djb2](https://github.com/TheAlgorithms/Python/blob/master/hashes/djb2.py)
* [Enigma Machine](https://github.com/TheAlgorithms/Python/blob/master/hashes/enigma_machine.py)
* [Hamming Code](https://github.com/TheAlgorithms/Python/blob/master/hashes/hamming_code.py)
* [Luhn](https://github.com/TheAlgorithms/Python/blob/master/hashes/luhn.py)
* [Md5](https://github.com/TheAlgorithms/Python/blob/master/hashes/md5.py)
* [Sdbm](https://github.com/TheAlgorithms/Python/blob/master/hashes/sdbm.py)
* [Sha1](https://github.com/TheAlgorithms/Python/blob/master/hashes/sha1.py)
* [Sha256](https://github.com/TheAlgorithms/Python/blob/master/hashes/sha256.py)
## Knapsack
* [Greedy Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/greedy_knapsack.py)
* [Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/knapsack.py)
* Tests
* [Test Greedy Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/tests/test_greedy_knapsack.py)
* [Test Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/tests/test_knapsack.py)
## Linear Algebra
* Src
* [Conjugate Gradient](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/conjugate_gradient.py)
* [Lib](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/lib.py)
* [Polynom For Points](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/polynom_for_points.py)
* [Power Iteration](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/power_iteration.py)
* [Rayleigh Quotient](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/rayleigh_quotient.py)
* [Schur Complement](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/schur_complement.py)
* [Test Linear Algebra](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/test_linear_algebra.py)
* [Transformations 2D](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/transformations_2d.py)
## Machine Learning
* [Astar](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/astar.py)
* [Data Transformations](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/data_transformations.py)
* [Decision Tree](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/decision_tree.py)
* Forecasting
* [Run](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/forecasting/run.py)
* [Gaussian Naive Bayes](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gaussian_naive_bayes.py)
* [Gradient Boosting Regressor](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gradient_boosting_regressor.py)
* [Gradient Descent](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gradient_descent.py)
* [K Means Clust](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_means_clust.py)
* [K Nearest Neighbours](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_nearest_neighbours.py)
* [Knn Sklearn](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/knn_sklearn.py)
* [Linear Discriminant Analysis](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/linear_discriminant_analysis.py)
* [Linear Regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/linear_regression.py)
* Local Weighted Learning
* [Local Weighted Learning](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/local_weighted_learning/local_weighted_learning.py)
* [Logistic Regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/logistic_regression.py)
* Lstm
* [Lstm Prediction](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/lstm/lstm_prediction.py)
* [Multilayer Perceptron Classifier](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/multilayer_perceptron_classifier.py)
* [Polymonial Regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/polymonial_regression.py)
* [Random Forest Classifier](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_classifier.py)
* [Random Forest Regressor](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_regressor.py)
* [Scoring Functions](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/scoring_functions.py)
* [Sequential Minimum Optimization](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/sequential_minimum_optimization.py)
* [Similarity Search](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/similarity_search.py)
* [Support Vector Machines](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/support_vector_machines.py)
* [Word Frequency Functions](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/word_frequency_functions.py)
## Maths
* [3N Plus 1](https://github.com/TheAlgorithms/Python/blob/master/maths/3n_plus_1.py)
* [Abs](https://github.com/TheAlgorithms/Python/blob/master/maths/abs.py)
* [Abs Max](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_max.py)
* [Abs Min](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_min.py)
* [Add](https://github.com/TheAlgorithms/Python/blob/master/maths/add.py)
* [Aliquot Sum](https://github.com/TheAlgorithms/Python/blob/master/maths/aliquot_sum.py)
* [Allocation Number](https://github.com/TheAlgorithms/Python/blob/master/maths/allocation_number.py)
* [Area](https://github.com/TheAlgorithms/Python/blob/master/maths/area.py)
* [Area Under Curve](https://github.com/TheAlgorithms/Python/blob/master/maths/area_under_curve.py)
* [Armstrong Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/armstrong_numbers.py)
* [Average Mean](https://github.com/TheAlgorithms/Python/blob/master/maths/average_mean.py)
* [Average Median](https://github.com/TheAlgorithms/Python/blob/master/maths/average_median.py)
* [Average Mode](https://github.com/TheAlgorithms/Python/blob/master/maths/average_mode.py)
* [Bailey Borwein Plouffe](https://github.com/TheAlgorithms/Python/blob/master/maths/bailey_borwein_plouffe.py)
* [Basic Maths](https://github.com/TheAlgorithms/Python/blob/master/maths/basic_maths.py)
* [Binary Exp Mod](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exp_mod.py)
* [Binary Exponentiation](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exponentiation.py)
* [Binary Exponentiation 2](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exponentiation_2.py)
* [Binary Exponentiation 3](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exponentiation_3.py)
* [Binomial Coefficient](https://github.com/TheAlgorithms/Python/blob/master/maths/binomial_coefficient.py)
* [Binomial Distribution](https://github.com/TheAlgorithms/Python/blob/master/maths/binomial_distribution.py)
* [Bisection](https://github.com/TheAlgorithms/Python/blob/master/maths/bisection.py)
* [Ceil](https://github.com/TheAlgorithms/Python/blob/master/maths/ceil.py)
* [Check Polygon](https://github.com/TheAlgorithms/Python/blob/master/maths/check_polygon.py)
* [Chudnovsky Algorithm](https://github.com/TheAlgorithms/Python/blob/master/maths/chudnovsky_algorithm.py)
* [Collatz Sequence](https://github.com/TheAlgorithms/Python/blob/master/maths/collatz_sequence.py)
* [Combinations](https://github.com/TheAlgorithms/Python/blob/master/maths/combinations.py)
* [Decimal Isolate](https://github.com/TheAlgorithms/Python/blob/master/maths/decimal_isolate.py)
* [Double Factorial Iterative](https://github.com/TheAlgorithms/Python/blob/master/maths/double_factorial_iterative.py)
* [Double Factorial Recursive](https://github.com/TheAlgorithms/Python/blob/master/maths/double_factorial_recursive.py)
* [Entropy](https://github.com/TheAlgorithms/Python/blob/master/maths/entropy.py)
* [Euclidean Distance](https://github.com/TheAlgorithms/Python/blob/master/maths/euclidean_distance.py)
* [Euclidean Gcd](https://github.com/TheAlgorithms/Python/blob/master/maths/euclidean_gcd.py)
* [Euler Method](https://github.com/TheAlgorithms/Python/blob/master/maths/euler_method.py)
* [Euler Modified](https://github.com/TheAlgorithms/Python/blob/master/maths/euler_modified.py)
* [Eulers Totient](https://github.com/TheAlgorithms/Python/blob/master/maths/eulers_totient.py)
* [Extended Euclidean Algorithm](https://github.com/TheAlgorithms/Python/blob/master/maths/extended_euclidean_algorithm.py)
* [Factorial Iterative](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_iterative.py)
* [Factorial Recursive](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_recursive.py)
* [Factors](https://github.com/TheAlgorithms/Python/blob/master/maths/factors.py)
* [Fermat Little Theorem](https://github.com/TheAlgorithms/Python/blob/master/maths/fermat_little_theorem.py)
* [Fibonacci](https://github.com/TheAlgorithms/Python/blob/master/maths/fibonacci.py)
* [Find Max](https://github.com/TheAlgorithms/Python/blob/master/maths/find_max.py)
* [Find Max Recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/find_max_recursion.py)
* [Find Min](https://github.com/TheAlgorithms/Python/blob/master/maths/find_min.py)
* [Find Min Recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/find_min_recursion.py)
* [Floor](https://github.com/TheAlgorithms/Python/blob/master/maths/floor.py)
* [Gamma](https://github.com/TheAlgorithms/Python/blob/master/maths/gamma.py)
* [Gamma Recursive](https://github.com/TheAlgorithms/Python/blob/master/maths/gamma_recursive.py)
* [Gaussian](https://github.com/TheAlgorithms/Python/blob/master/maths/gaussian.py)
* [Greatest Common Divisor](https://github.com/TheAlgorithms/Python/blob/master/maths/greatest_common_divisor.py)
* [Greedy Coin Change](https://github.com/TheAlgorithms/Python/blob/master/maths/greedy_coin_change.py)
* [Hardy Ramanujanalgo](https://github.com/TheAlgorithms/Python/blob/master/maths/hardy_ramanujanalgo.py)
* [Integration By Simpson Approx](https://github.com/TheAlgorithms/Python/blob/master/maths/integration_by_simpson_approx.py)
* [Is Ip V4 Address Valid](https://github.com/TheAlgorithms/Python/blob/master/maths/is_ip_v4_address_valid.py)
* [Is Square Free](https://github.com/TheAlgorithms/Python/blob/master/maths/is_square_free.py)
* [Jaccard Similarity](https://github.com/TheAlgorithms/Python/blob/master/maths/jaccard_similarity.py)
* [Kadanes](https://github.com/TheAlgorithms/Python/blob/master/maths/kadanes.py)
* [Karatsuba](https://github.com/TheAlgorithms/Python/blob/master/maths/karatsuba.py)
* [Krishnamurthy Number](https://github.com/TheAlgorithms/Python/blob/master/maths/krishnamurthy_number.py)
* [Kth Lexicographic Permutation](https://github.com/TheAlgorithms/Python/blob/master/maths/kth_lexicographic_permutation.py)
* [Largest Of Very Large Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/largest_of_very_large_numbers.py)
* [Largest Subarray Sum](https://github.com/TheAlgorithms/Python/blob/master/maths/largest_subarray_sum.py)
* [Least Common Multiple](https://github.com/TheAlgorithms/Python/blob/master/maths/least_common_multiple.py)
* [Line Length](https://github.com/TheAlgorithms/Python/blob/master/maths/line_length.py)
* [Lucas Lehmer Primality Test](https://github.com/TheAlgorithms/Python/blob/master/maths/lucas_lehmer_primality_test.py)
* [Lucas Series](https://github.com/TheAlgorithms/Python/blob/master/maths/lucas_series.py)
* [Matrix Exponentiation](https://github.com/TheAlgorithms/Python/blob/master/maths/matrix_exponentiation.py)
* [Max Sum Sliding Window](https://github.com/TheAlgorithms/Python/blob/master/maths/max_sum_sliding_window.py)
* [Median Of Two Arrays](https://github.com/TheAlgorithms/Python/blob/master/maths/median_of_two_arrays.py)
* [Miller Rabin](https://github.com/TheAlgorithms/Python/blob/master/maths/miller_rabin.py)
* [Mobius Function](https://github.com/TheAlgorithms/Python/blob/master/maths/mobius_function.py)
* [Modular Exponential](https://github.com/TheAlgorithms/Python/blob/master/maths/modular_exponential.py)
* [Monte Carlo](https://github.com/TheAlgorithms/Python/blob/master/maths/monte_carlo.py)
* [Monte Carlo Dice](https://github.com/TheAlgorithms/Python/blob/master/maths/monte_carlo_dice.py)
* [Nevilles Method](https://github.com/TheAlgorithms/Python/blob/master/maths/nevilles_method.py)
* [Newton Raphson](https://github.com/TheAlgorithms/Python/blob/master/maths/newton_raphson.py)
* [Number Of Digits](https://github.com/TheAlgorithms/Python/blob/master/maths/number_of_digits.py)
* [Numerical Integration](https://github.com/TheAlgorithms/Python/blob/master/maths/numerical_integration.py)
* [Perfect Cube](https://github.com/TheAlgorithms/Python/blob/master/maths/perfect_cube.py)
* [Perfect Number](https://github.com/TheAlgorithms/Python/blob/master/maths/perfect_number.py)
* [Perfect Square](https://github.com/TheAlgorithms/Python/blob/master/maths/perfect_square.py)
* [Pi Monte Carlo Estimation](https://github.com/TheAlgorithms/Python/blob/master/maths/pi_monte_carlo_estimation.py)
* [Pollard Rho](https://github.com/TheAlgorithms/Python/blob/master/maths/pollard_rho.py)
* [Polynomial Evaluation](https://github.com/TheAlgorithms/Python/blob/master/maths/polynomial_evaluation.py)
* [Power Using Recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/power_using_recursion.py)
* [Prime Check](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_check.py)
* [Prime Factors](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_factors.py)
* [Prime Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_numbers.py)
* [Prime Sieve Eratosthenes](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_sieve_eratosthenes.py)
* [Primelib](https://github.com/TheAlgorithms/Python/blob/master/maths/primelib.py)
* [Proth Number](https://github.com/TheAlgorithms/Python/blob/master/maths/proth_number.py)
* [Pythagoras](https://github.com/TheAlgorithms/Python/blob/master/maths/pythagoras.py)
* [Qr Decomposition](https://github.com/TheAlgorithms/Python/blob/master/maths/qr_decomposition.py)
* [Quadratic Equations Complex Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/quadratic_equations_complex_numbers.py)
* [Radians](https://github.com/TheAlgorithms/Python/blob/master/maths/radians.py)
* [Radix2 Fft](https://github.com/TheAlgorithms/Python/blob/master/maths/radix2_fft.py)
* [Relu](https://github.com/TheAlgorithms/Python/blob/master/maths/relu.py)
* [Runge Kutta](https://github.com/TheAlgorithms/Python/blob/master/maths/runge_kutta.py)
* [Segmented Sieve](https://github.com/TheAlgorithms/Python/blob/master/maths/segmented_sieve.py)
* Series
* [Arithmetic](https://github.com/TheAlgorithms/Python/blob/master/maths/series/arithmetic.py)
* [Geometric](https://github.com/TheAlgorithms/Python/blob/master/maths/series/geometric.py)
* [Geometric Series](https://github.com/TheAlgorithms/Python/blob/master/maths/series/geometric_series.py)
* [Harmonic](https://github.com/TheAlgorithms/Python/blob/master/maths/series/harmonic.py)
* [Harmonic Series](https://github.com/TheAlgorithms/Python/blob/master/maths/series/harmonic_series.py)
* [Hexagonal Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/series/hexagonal_numbers.py)
* [P Series](https://github.com/TheAlgorithms/Python/blob/master/maths/series/p_series.py)
* [Sieve Of Eratosthenes](https://github.com/TheAlgorithms/Python/blob/master/maths/sieve_of_eratosthenes.py)
* [Sigmoid](https://github.com/TheAlgorithms/Python/blob/master/maths/sigmoid.py)
* [Simpson Rule](https://github.com/TheAlgorithms/Python/blob/master/maths/simpson_rule.py)
* [Sock Merchant](https://github.com/TheAlgorithms/Python/blob/master/maths/sock_merchant.py)
* [Softmax](https://github.com/TheAlgorithms/Python/blob/master/maths/softmax.py)
* [Square Root](https://github.com/TheAlgorithms/Python/blob/master/maths/square_root.py)
* [Sum Of Arithmetic Series](https://github.com/TheAlgorithms/Python/blob/master/maths/sum_of_arithmetic_series.py)
* [Sum Of Digits](https://github.com/TheAlgorithms/Python/blob/master/maths/sum_of_digits.py)
* [Sum Of Geometric Progression](https://github.com/TheAlgorithms/Python/blob/master/maths/sum_of_geometric_progression.py)
* [Sylvester Sequence](https://github.com/TheAlgorithms/Python/blob/master/maths/sylvester_sequence.py)
* [Test Prime Check](https://github.com/TheAlgorithms/Python/blob/master/maths/test_prime_check.py)
* [Trapezoidal Rule](https://github.com/TheAlgorithms/Python/blob/master/maths/trapezoidal_rule.py)
* [Triplet Sum](https://github.com/TheAlgorithms/Python/blob/master/maths/triplet_sum.py)
* [Two Pointer](https://github.com/TheAlgorithms/Python/blob/master/maths/two_pointer.py)
* [Two Sum](https://github.com/TheAlgorithms/Python/blob/master/maths/two_sum.py)
* [Ugly Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/ugly_numbers.py)
* [Volume](https://github.com/TheAlgorithms/Python/blob/master/maths/volume.py)
* [Zellers Congruence](https://github.com/TheAlgorithms/Python/blob/master/maths/zellers_congruence.py)
## Matrix
* [Count Islands In Matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/count_islands_in_matrix.py)
* [Inverse Of Matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/inverse_of_matrix.py)
* [Matrix Class](https://github.com/TheAlgorithms/Python/blob/master/matrix/matrix_class.py)
* [Matrix Operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/matrix_operation.py)
* [Nth Fibonacci Using Matrix Exponentiation](https://github.com/TheAlgorithms/Python/blob/master/matrix/nth_fibonacci_using_matrix_exponentiation.py)
* [Rotate Matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/rotate_matrix.py)
* [Searching In Sorted Matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/searching_in_sorted_matrix.py)
* [Sherman Morrison](https://github.com/TheAlgorithms/Python/blob/master/matrix/sherman_morrison.py)
* [Spiral Print](https://github.com/TheAlgorithms/Python/blob/master/matrix/spiral_print.py)
* Tests
* [Test Matrix Operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/tests/test_matrix_operation.py)
## Networking Flow
* [Ford Fulkerson](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/ford_fulkerson.py)
* [Minimum Cut](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/minimum_cut.py)
## Neural Network
* [2 Hidden Layers Neural Network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/2_hidden_layers_neural_network.py)
* [Back Propagation Neural Network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/back_propagation_neural_network.py)
* [Convolution Neural Network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/convolution_neural_network.py)
* [Perceptron](https://github.com/TheAlgorithms/Python/blob/master/neural_network/perceptron.py)
## Other
* [Activity Selection](https://github.com/TheAlgorithms/Python/blob/master/other/activity_selection.py)
* [Alternative List Arrange](https://github.com/TheAlgorithms/Python/blob/master/other/alternative_list_arrange.py)
* [Check Strong Password](https://github.com/TheAlgorithms/Python/blob/master/other/check_strong_password.py)
* [Davisb Putnamb Logemannb Loveland](https://github.com/TheAlgorithms/Python/blob/master/other/davisb_putnamb_logemannb_loveland.py)
* [Dijkstra Bankers Algorithm](https://github.com/TheAlgorithms/Python/blob/master/other/dijkstra_bankers_algorithm.py)
* [Doomsday](https://github.com/TheAlgorithms/Python/blob/master/other/doomsday.py)
* [Fischer Yates Shuffle](https://github.com/TheAlgorithms/Python/blob/master/other/fischer_yates_shuffle.py)
* [Gauss Easter](https://github.com/TheAlgorithms/Python/blob/master/other/gauss_easter.py)
* [Graham Scan](https://github.com/TheAlgorithms/Python/blob/master/other/graham_scan.py)
* [Greedy](https://github.com/TheAlgorithms/Python/blob/master/other/greedy.py)
* [Least Recently Used](https://github.com/TheAlgorithms/Python/blob/master/other/least_recently_used.py)
* [Lfu Cache](https://github.com/TheAlgorithms/Python/blob/master/other/lfu_cache.py)
* [Linear Congruential Generator](https://github.com/TheAlgorithms/Python/blob/master/other/linear_congruential_generator.py)
* [Lru Cache](https://github.com/TheAlgorithms/Python/blob/master/other/lru_cache.py)
* [Magicdiamondpattern](https://github.com/TheAlgorithms/Python/blob/master/other/magicdiamondpattern.py)
* [Nested Brackets](https://github.com/TheAlgorithms/Python/blob/master/other/nested_brackets.py)
* [Password Generator](https://github.com/TheAlgorithms/Python/blob/master/other/password_generator.py)
* [Scoring Algorithm](https://github.com/TheAlgorithms/Python/blob/master/other/scoring_algorithm.py)
* [Sdes](https://github.com/TheAlgorithms/Python/blob/master/other/sdes.py)
* [Tower Of Hanoi](https://github.com/TheAlgorithms/Python/blob/master/other/tower_of_hanoi.py)
## Physics
* [N Body Simulation](https://github.com/TheAlgorithms/Python/blob/master/physics/n_body_simulation.py)
* [Newtons Second Law Of Motion](https://github.com/TheAlgorithms/Python/blob/master/physics/newtons_second_law_of_motion.py)
## Project Euler
* Problem 001
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol3.py)
* [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol4.py)
* [Sol5](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol5.py)
* [Sol6](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol6.py)
* [Sol7](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol7.py)
* Problem 002
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol3.py)
* [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol4.py)
* [Sol5](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol5.py)
* Problem 003
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_003/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_003/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_003/sol3.py)
* Problem 004
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_004/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_004/sol2.py)
* Problem 005
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_005/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_005/sol2.py)
* Problem 006
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_006/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_006/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_006/sol3.py)
* [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_006/sol4.py)
* Problem 007
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_007/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_007/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_007/sol3.py)
* Problem 008
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_008/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_008/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_008/sol3.py)
* Problem 009
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_009/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_009/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_009/sol3.py)
* Problem 010
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_010/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_010/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_010/sol3.py)
* Problem 011
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_011/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_011/sol2.py)
* Problem 012
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_012/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_012/sol2.py)
* Problem 013
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_013/sol1.py)
* Problem 014
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_014/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_014/sol2.py)
* Problem 015
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_015/sol1.py)
* Problem 016
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_016/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_016/sol2.py)
* Problem 017
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_017/sol1.py)
* Problem 018
* [Solution](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_018/solution.py)
* Problem 019
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_019/sol1.py)
* Problem 020
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_020/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_020/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_020/sol3.py)
* [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_020/sol4.py)
* Problem 021
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_021/sol1.py)
* Problem 022
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_022/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_022/sol2.py)
* Problem 023
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_023/sol1.py)
* Problem 024
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_024/sol1.py)
* Problem 025
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_025/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_025/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_025/sol3.py)
* Problem 026
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_026/sol1.py)
* Problem 027
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_027/sol1.py)
* Problem 028
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_028/sol1.py)
* Problem 029
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_029/sol1.py)
* Problem 030
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_030/sol1.py)
* Problem 031
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_031/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_031/sol2.py)
* Problem 032
* [Sol32](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_032/sol32.py)
* Problem 033
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_033/sol1.py)
* Problem 034
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_034/sol1.py)
* Problem 035
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_035/sol1.py)
* Problem 036
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_036/sol1.py)
* Problem 037
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_037/sol1.py)
* Problem 038
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_038/sol1.py)
* Problem 039
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_039/sol1.py)
* Problem 040
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_040/sol1.py)
* Problem 041
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_041/sol1.py)
* Problem 042
* [Solution42](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_042/solution42.py)
* Problem 043
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_043/sol1.py)
* Problem 044
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_044/sol1.py)
* Problem 045
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_045/sol1.py)
* Problem 046
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_046/sol1.py)
* Problem 047
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_047/sol1.py)
* Problem 048
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_048/sol1.py)
* Problem 049
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_049/sol1.py)
* Problem 050
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_050/sol1.py)
* Problem 051
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_051/sol1.py)
* Problem 052
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_052/sol1.py)
* Problem 053
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_053/sol1.py)
* Problem 054
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_054/sol1.py)
* [Test Poker Hand](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_054/test_poker_hand.py)
* Problem 055
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_055/sol1.py)
* Problem 056
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_056/sol1.py)
* Problem 057
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_057/sol1.py)
* Problem 058
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_058/sol1.py)
* Problem 059
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_059/sol1.py)
* Problem 062
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_062/sol1.py)
* Problem 063
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_063/sol1.py)
* Problem 064
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_064/sol1.py)
* Problem 065
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_065/sol1.py)
* Problem 067
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_067/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_067/sol2.py)
* Problem 069
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_069/sol1.py)
* Problem 070
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_070/sol1.py)
* Problem 071
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_071/sol1.py)
* Problem 072
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_072/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_072/sol2.py)
* Problem 074
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_074/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_074/sol2.py)
* Problem 075
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_075/sol1.py)
* Problem 076
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_076/sol1.py)
* Problem 077
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_077/sol1.py)
* Problem 078
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_078/sol1.py)
* Problem 080
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_080/sol1.py)
* Problem 081
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_081/sol1.py)
* Problem 085
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_085/sol1.py)
* Problem 086
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_086/sol1.py)
* Problem 087
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_087/sol1.py)
* Problem 089
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_089/sol1.py)
* Problem 091
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_091/sol1.py)
* Problem 092
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_092/sol1.py)
* Problem 097
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_097/sol1.py)
* Problem 099
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_099/sol1.py)
* Problem 101
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_101/sol1.py)
* Problem 102
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_102/sol1.py)
* Problem 107
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_107/sol1.py)
* Problem 109
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_109/sol1.py)
* Problem 112
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_112/sol1.py)
* Problem 113
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_113/sol1.py)
* Problem 119
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_119/sol1.py)
* Problem 120
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_120/sol1.py)
* Problem 121
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_121/sol1.py)
* Problem 123
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_123/sol1.py)
* Problem 125
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_125/sol1.py)
* Problem 129
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_129/sol1.py)
* Problem 135
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_135/sol1.py)
* Problem 144
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_144/sol1.py)
* Problem 173
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_173/sol1.py)
* Problem 174
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_174/sol1.py)
* Problem 180
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_180/sol1.py)
* Problem 188
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_188/sol1.py)
* Problem 191
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_191/sol1.py)
* Problem 203
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_203/sol1.py)
* Problem 206
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_206/sol1.py)
* Problem 207
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_207/sol1.py)
* Problem 234
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_234/sol1.py)
* Problem 301
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_301/sol1.py)
* Problem 493
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_493/sol1.py)
* Problem 551
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_551/sol1.py)
* Problem 686
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_686/sol1.py)
## Quantum
* [Deutsch Jozsa](https://github.com/TheAlgorithms/Python/blob/master/quantum/deutsch_jozsa.py)
* [Half Adder](https://github.com/TheAlgorithms/Python/blob/master/quantum/half_adder.py)
* [Not Gate](https://github.com/TheAlgorithms/Python/blob/master/quantum/not_gate.py)
* [Quantum Entanglement](https://github.com/TheAlgorithms/Python/blob/master/quantum/quantum_entanglement.py)
* [Ripple Adder Classic](https://github.com/TheAlgorithms/Python/blob/master/quantum/ripple_adder_classic.py)
* [Single Qubit Measure](https://github.com/TheAlgorithms/Python/blob/master/quantum/single_qubit_measure.py)
## Scheduling
* [First Come First Served](https://github.com/TheAlgorithms/Python/blob/master/scheduling/first_come_first_served.py)
* [Round Robin](https://github.com/TheAlgorithms/Python/blob/master/scheduling/round_robin.py)
* [Shortest Job First](https://github.com/TheAlgorithms/Python/blob/master/scheduling/shortest_job_first.py)
## Searches
* [Binary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/binary_search.py)
* [Binary Tree Traversal](https://github.com/TheAlgorithms/Python/blob/master/searches/binary_tree_traversal.py)
* [Double Linear Search](https://github.com/TheAlgorithms/Python/blob/master/searches/double_linear_search.py)
* [Double Linear Search Recursion](https://github.com/TheAlgorithms/Python/blob/master/searches/double_linear_search_recursion.py)
* [Fibonacci Search](https://github.com/TheAlgorithms/Python/blob/master/searches/fibonacci_search.py)
* [Hill Climbing](https://github.com/TheAlgorithms/Python/blob/master/searches/hill_climbing.py)
* [Interpolation Search](https://github.com/TheAlgorithms/Python/blob/master/searches/interpolation_search.py)
* [Jump Search](https://github.com/TheAlgorithms/Python/blob/master/searches/jump_search.py)
* [Linear Search](https://github.com/TheAlgorithms/Python/blob/master/searches/linear_search.py)
* [Quick Select](https://github.com/TheAlgorithms/Python/blob/master/searches/quick_select.py)
* [Sentinel Linear Search](https://github.com/TheAlgorithms/Python/blob/master/searches/sentinel_linear_search.py)
* [Simple Binary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/simple_binary_search.py)
* [Simulated Annealing](https://github.com/TheAlgorithms/Python/blob/master/searches/simulated_annealing.py)
* [Tabu Search](https://github.com/TheAlgorithms/Python/blob/master/searches/tabu_search.py)
* [Ternary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/ternary_search.py)
## Sorts
* [Bead Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bead_sort.py)
* [Bitonic Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bitonic_sort.py)
* [Bogo Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bogo_sort.py)
* [Bubble Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bubble_sort.py)
* [Bucket Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bucket_sort.py)
* [Cocktail Shaker Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/cocktail_shaker_sort.py)
* [Comb Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/comb_sort.py)
* [Counting Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/counting_sort.py)
* [Cycle Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/cycle_sort.py)
* [Double Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/double_sort.py)
* [Dutch National Flag Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/dutch_national_flag_sort.py)
* [Exchange Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/exchange_sort.py)
* [External Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/external_sort.py)
* [Gnome Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/gnome_sort.py)
* [Heap Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/heap_sort.py)
* [Insertion Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/insertion_sort.py)
* [Intro Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/intro_sort.py)
* [Iterative Merge Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/iterative_merge_sort.py)
* [Merge Insertion Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/merge_insertion_sort.py)
* [Merge Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/merge_sort.py)
* [Msd Radix Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/msd_radix_sort.py)
* [Natural Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/natural_sort.py)
* [Odd Even Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_sort.py)
* [Odd Even Transposition Parallel](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_transposition_parallel.py)
* [Odd Even Transposition Single Threaded](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_transposition_single_threaded.py)
* [Pancake Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pancake_sort.py)
* [Patience Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/patience_sort.py)
* [Pigeon Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pigeon_sort.py)
* [Pigeonhole Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pigeonhole_sort.py)
* [Quick Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/quick_sort.py)
* [Quick Sort 3 Partition](https://github.com/TheAlgorithms/Python/blob/master/sorts/quick_sort_3_partition.py)
* [Radix Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/radix_sort.py)
* [Random Normal Distribution Quicksort](https://github.com/TheAlgorithms/Python/blob/master/sorts/random_normal_distribution_quicksort.py)
* [Random Pivot Quick Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/random_pivot_quick_sort.py)
* [Recursive Bubble Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/recursive_bubble_sort.py)
* [Recursive Insertion Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/recursive_insertion_sort.py)
* [Recursive Mergesort Array](https://github.com/TheAlgorithms/Python/blob/master/sorts/recursive_mergesort_array.py)
* [Recursive Quick Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/recursive_quick_sort.py)
* [Selection Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/selection_sort.py)
* [Shell Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/shell_sort.py)
* [Slowsort](https://github.com/TheAlgorithms/Python/blob/master/sorts/slowsort.py)
* [Stooge Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/stooge_sort.py)
* [Strand Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/strand_sort.py)
* [Tim Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/tim_sort.py)
* [Topological Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/topological_sort.py)
* [Tree Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/tree_sort.py)
* [Unknown Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/unknown_sort.py)
* [Wiggle Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/wiggle_sort.py)
## Strings
* [Aho Corasick](https://github.com/TheAlgorithms/Python/blob/master/strings/aho_corasick.py)
* [Alternative String Arrange](https://github.com/TheAlgorithms/Python/blob/master/strings/alternative_string_arrange.py)
* [Anagrams](https://github.com/TheAlgorithms/Python/blob/master/strings/anagrams.py)
* [Autocomplete Using Trie](https://github.com/TheAlgorithms/Python/blob/master/strings/autocomplete_using_trie.py)
* [Boyer Moore Search](https://github.com/TheAlgorithms/Python/blob/master/strings/boyer_moore_search.py)
* [Can String Be Rearranged As Palindrome](https://github.com/TheAlgorithms/Python/blob/master/strings/can_string_be_rearranged_as_palindrome.py)
* [Capitalize](https://github.com/TheAlgorithms/Python/blob/master/strings/capitalize.py)
* [Check Anagrams](https://github.com/TheAlgorithms/Python/blob/master/strings/check_anagrams.py)
* [Check Pangram](https://github.com/TheAlgorithms/Python/blob/master/strings/check_pangram.py)
* [Credit Card Validator](https://github.com/TheAlgorithms/Python/blob/master/strings/credit_card_validator.py)
* [Detecting English Programmatically](https://github.com/TheAlgorithms/Python/blob/master/strings/detecting_english_programmatically.py)
* [Frequency Finder](https://github.com/TheAlgorithms/Python/blob/master/strings/frequency_finder.py)
* [Indian Phone Validator](https://github.com/TheAlgorithms/Python/blob/master/strings/indian_phone_validator.py)
* [Is Contains Unique Chars](https://github.com/TheAlgorithms/Python/blob/master/strings/is_contains_unique_chars.py)
* [Is Palindrome](https://github.com/TheAlgorithms/Python/blob/master/strings/is_palindrome.py)
* [Jaro Winkler](https://github.com/TheAlgorithms/Python/blob/master/strings/jaro_winkler.py)
* [Join](https://github.com/TheAlgorithms/Python/blob/master/strings/join.py)
* [Knuth Morris Pratt](https://github.com/TheAlgorithms/Python/blob/master/strings/knuth_morris_pratt.py)
* [Levenshtein Distance](https://github.com/TheAlgorithms/Python/blob/master/strings/levenshtein_distance.py)
* [Lower](https://github.com/TheAlgorithms/Python/blob/master/strings/lower.py)
* [Manacher](https://github.com/TheAlgorithms/Python/blob/master/strings/manacher.py)
* [Min Cost String Conversion](https://github.com/TheAlgorithms/Python/blob/master/strings/min_cost_string_conversion.py)
* [Naive String Search](https://github.com/TheAlgorithms/Python/blob/master/strings/naive_string_search.py)
* [Palindrome](https://github.com/TheAlgorithms/Python/blob/master/strings/palindrome.py)
* [Prefix Function](https://github.com/TheAlgorithms/Python/blob/master/strings/prefix_function.py)
* [Rabin Karp](https://github.com/TheAlgorithms/Python/blob/master/strings/rabin_karp.py)
* [Remove Duplicate](https://github.com/TheAlgorithms/Python/blob/master/strings/remove_duplicate.py)
* [Reverse Letters](https://github.com/TheAlgorithms/Python/blob/master/strings/reverse_letters.py)
* [Reverse Long Words](https://github.com/TheAlgorithms/Python/blob/master/strings/reverse_long_words.py)
* [Reverse Words](https://github.com/TheAlgorithms/Python/blob/master/strings/reverse_words.py)
* [Split](https://github.com/TheAlgorithms/Python/blob/master/strings/split.py)
* [Upper](https://github.com/TheAlgorithms/Python/blob/master/strings/upper.py)
* [Wildcard Pattern Matching](https://github.com/TheAlgorithms/Python/blob/master/strings/wildcard_pattern_matching.py)
* [Word Occurrence](https://github.com/TheAlgorithms/Python/blob/master/strings/word_occurrence.py)
* [Word Patterns](https://github.com/TheAlgorithms/Python/blob/master/strings/word_patterns.py)
* [Z Function](https://github.com/TheAlgorithms/Python/blob/master/strings/z_function.py)
## Web Programming
* [Co2 Emission](https://github.com/TheAlgorithms/Python/blob/master/web_programming/co2_emission.py)
* [Covid Stats Via Xpath](https://github.com/TheAlgorithms/Python/blob/master/web_programming/covid_stats_via_xpath.py)
* [Crawl Google Results](https://github.com/TheAlgorithms/Python/blob/master/web_programming/crawl_google_results.py)
* [Crawl Google Scholar Citation](https://github.com/TheAlgorithms/Python/blob/master/web_programming/crawl_google_scholar_citation.py)
* [Currency Converter](https://github.com/TheAlgorithms/Python/blob/master/web_programming/currency_converter.py)
* [Current Stock Price](https://github.com/TheAlgorithms/Python/blob/master/web_programming/current_stock_price.py)
* [Current Weather](https://github.com/TheAlgorithms/Python/blob/master/web_programming/current_weather.py)
* [Daily Horoscope](https://github.com/TheAlgorithms/Python/blob/master/web_programming/daily_horoscope.py)
* [Download Images From Google Query](https://github.com/TheAlgorithms/Python/blob/master/web_programming/download_images_from_google_query.py)
* [Emails From Url](https://github.com/TheAlgorithms/Python/blob/master/web_programming/emails_from_url.py)
* [Fetch Bbc News](https://github.com/TheAlgorithms/Python/blob/master/web_programming/fetch_bbc_news.py)
* [Fetch Github Info](https://github.com/TheAlgorithms/Python/blob/master/web_programming/fetch_github_info.py)
* [Fetch Jobs](https://github.com/TheAlgorithms/Python/blob/master/web_programming/fetch_jobs.py)
* [Get Imdb Top 250 Movies Csv](https://github.com/TheAlgorithms/Python/blob/master/web_programming/get_imdb_top_250_movies_csv.py)
* [Get Imdbtop](https://github.com/TheAlgorithms/Python/blob/master/web_programming/get_imdbtop.py)
* [Get Top Hn Posts](https://github.com/TheAlgorithms/Python/blob/master/web_programming/get_top_hn_posts.py)
* [Get User Tweets](https://github.com/TheAlgorithms/Python/blob/master/web_programming/get_user_tweets.py)
* [Giphy](https://github.com/TheAlgorithms/Python/blob/master/web_programming/giphy.py)
* [Instagram Crawler](https://github.com/TheAlgorithms/Python/blob/master/web_programming/instagram_crawler.py)
* [Instagram Pic](https://github.com/TheAlgorithms/Python/blob/master/web_programming/instagram_pic.py)
* [Instagram Video](https://github.com/TheAlgorithms/Python/blob/master/web_programming/instagram_video.py)
* [Nasa Data](https://github.com/TheAlgorithms/Python/blob/master/web_programming/nasa_data.py)
* [Random Anime Character](https://github.com/TheAlgorithms/Python/blob/master/web_programming/random_anime_character.py)
* [Recaptcha Verification](https://github.com/TheAlgorithms/Python/blob/master/web_programming/recaptcha_verification.py)
* [Reddit](https://github.com/TheAlgorithms/Python/blob/master/web_programming/reddit.py)
* [Search Books By Isbn](https://github.com/TheAlgorithms/Python/blob/master/web_programming/search_books_by_isbn.py)
* [Slack Message](https://github.com/TheAlgorithms/Python/blob/master/web_programming/slack_message.py)
* [Test Fetch Github Info](https://github.com/TheAlgorithms/Python/blob/master/web_programming/test_fetch_github_info.py)
* [World Covid19 Stats](https://github.com/TheAlgorithms/Python/blob/master/web_programming/world_covid19_stats.py)
| 1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Borůvka's algorithm.
Determines the minimum spanning tree (MST) of a graph using the Borůvka's algorithm.
Borůvka's algorithm is a greedy algorithm for finding a minimum spanning tree in a
connected graph, or a minimum spanning forest if a graph that is not connected.
The time complexity of this algorithm is O(ELogV), where E represents the number
of edges, while V represents the number of nodes.
O(number_of_edges Log number_of_nodes)
The space complexity of this algorithm is O(V + E), since we have to keep a couple
of lists whose sizes are equal to the number of nodes, as well as keep all the
edges of a graph inside of the data structure itself.
Borůvka's algorithm gives us pretty much the same result as other MST Algorithms -
they all find the minimum spanning tree, and the time complexity is approximately
the same.
One advantage that Borůvka's algorithm has compared to the alternatives is that it
doesn't need to presort the edges or maintain a priority queue in order to find the
minimum spanning tree.
Even though that doesn't help its complexity, since it still passes the edges logE
times, it is a bit simpler to code.
Details: https://en.wikipedia.org/wiki/Bor%C5%AFvka%27s_algorithm
"""
from __future__ import annotations
class Graph:
def __init__(self, num_of_nodes: int) -> None:
"""
Arguments:
num_of_nodes - the number of nodes in the graph
Attributes:
m_num_of_nodes - the number of nodes in the graph.
m_edges - the list of edges.
m_component - the dictionary which stores the index of the component which
a node belongs to.
"""
self.m_num_of_nodes = num_of_nodes
self.m_edges: list[list[int]] = []
self.m_component: dict[int, int] = {}
def add_edge(self, u_node: int, v_node: int, weight: int) -> None:
"""Adds an edge in the format [first, second, edge weight] to graph."""
self.m_edges.append([u_node, v_node, weight])
def find_component(self, u_node: int) -> int:
"""Propagates a new component throughout a given component."""
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node])
def set_component(self, u_node: int) -> None:
"""Finds the component index of a given node"""
if self.m_component[u_node] != u_node:
for k in self.m_component:
self.m_component[k] = self.find_component(k)
def union(self, component_size: list, u_node: int, v_node: int) -> None:
"""Union finds the roots of components for two nodes, compares the components
in terms of size, and attaches the smaller one to the larger one to form
single component"""
if component_size[u_node] <= component_size[v_node]:
self.m_component[u_node] = v_node
component_size[v_node] += component_size[u_node]
self.set_component(u_node)
elif component_size[u_node] >= component_size[v_node]:
self.m_component[v_node] = self.find_component(u_node)
component_size[u_node] += component_size[v_node]
self.set_component(v_node)
def boruvka(self) -> None:
"""Performs Borůvka's algorithm to find MST."""
# Initialize additional lists required to algorithm.
component_size = []
mst_weight = 0
minimum_weight_edge: list[int] = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes):
self.m_component.update({node: node})
component_size.append(1)
num_of_components = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
u, v, w = edge
u_component = self.m_component[u]
v_component = self.m_component[v]
if u_component != v_component:
"""If the current minimum weight edge of component u doesn't
exist (is -1), or if it's greater than the edge we're
observing right now, we will assign the value of the edge
we're observing to it.
If the current minimum weight edge of component v doesn't
exist (is -1), or if it's greater than the edge we're
observing right now, we will assign the value of the edge
we're observing to it"""
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
minimum_weight_edge[component] = [u, v, w]
for edge in minimum_weight_edge:
if edge != -1:
u, v, w = edge
u_component = self.m_component[u]
v_component = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(component_size, u_component, v_component)
print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n")
num_of_components -= 1
minimum_weight_edge = [-1] * self.m_num_of_nodes
print(f"The total weight of the minimal spanning tree is: {mst_weight}")
def test_vector() -> None:
"""
>>> g = Graph(8)
>>> for u_v_w in ((0, 1, 10), (0, 2, 6), (0, 3, 5), (1, 3, 15), (2, 3, 4),
... (3, 4, 8), (4, 5, 10), (4, 6, 6), (4, 7, 5), (5, 7, 15), (6, 7, 4)):
... g.add_edge(*u_v_w)
>>> g.boruvka()
Added edge [0 - 3]
Added weight: 5
<BLANKLINE>
Added edge [0 - 1]
Added weight: 10
<BLANKLINE>
Added edge [2 - 3]
Added weight: 4
<BLANKLINE>
Added edge [4 - 7]
Added weight: 5
<BLANKLINE>
Added edge [4 - 5]
Added weight: 10
<BLANKLINE>
Added edge [6 - 7]
Added weight: 4
<BLANKLINE>
Added edge [3 - 4]
Added weight: 8
<BLANKLINE>
The total weight of the minimal spanning tree is: 46
"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| """Borůvka's algorithm.
Determines the minimum spanning tree (MST) of a graph using the Borůvka's algorithm.
Borůvka's algorithm is a greedy algorithm for finding a minimum spanning tree in a
connected graph, or a minimum spanning forest if a graph that is not connected.
The time complexity of this algorithm is O(ELogV), where E represents the number
of edges, while V represents the number of nodes.
O(number_of_edges Log number_of_nodes)
The space complexity of this algorithm is O(V + E), since we have to keep a couple
of lists whose sizes are equal to the number of nodes, as well as keep all the
edges of a graph inside of the data structure itself.
Borůvka's algorithm gives us pretty much the same result as other MST Algorithms -
they all find the minimum spanning tree, and the time complexity is approximately
the same.
One advantage that Borůvka's algorithm has compared to the alternatives is that it
doesn't need to presort the edges or maintain a priority queue in order to find the
minimum spanning tree.
Even though that doesn't help its complexity, since it still passes the edges logE
times, it is a bit simpler to code.
Details: https://en.wikipedia.org/wiki/Bor%C5%AFvka%27s_algorithm
"""
from __future__ import annotations
from typing import Any
class Graph:
def __init__(self, num_of_nodes: int) -> None:
"""
Arguments:
num_of_nodes - the number of nodes in the graph
Attributes:
m_num_of_nodes - the number of nodes in the graph.
m_edges - the list of edges.
m_component - the dictionary which stores the index of the component which
a node belongs to.
"""
self.m_num_of_nodes = num_of_nodes
self.m_edges: list[list[int]] = []
self.m_component: dict[int, int] = {}
def add_edge(self, u_node: int, v_node: int, weight: int) -> None:
"""Adds an edge in the format [first, second, edge weight] to graph."""
self.m_edges.append([u_node, v_node, weight])
def find_component(self, u_node: int) -> int:
"""Propagates a new component throughout a given component."""
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node])
def set_component(self, u_node: int) -> None:
"""Finds the component index of a given node"""
if self.m_component[u_node] != u_node:
for k in self.m_component:
self.m_component[k] = self.find_component(k)
def union(self, component_size: list[int], u_node: int, v_node: int) -> None:
"""Union finds the roots of components for two nodes, compares the components
in terms of size, and attaches the smaller one to the larger one to form
single component"""
if component_size[u_node] <= component_size[v_node]:
self.m_component[u_node] = v_node
component_size[v_node] += component_size[u_node]
self.set_component(u_node)
elif component_size[u_node] >= component_size[v_node]:
self.m_component[v_node] = self.find_component(u_node)
component_size[u_node] += component_size[v_node]
self.set_component(v_node)
def boruvka(self) -> None:
"""Performs Borůvka's algorithm to find MST."""
# Initialize additional lists required to algorithm.
component_size = []
mst_weight = 0
minimum_weight_edge: list[Any] = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes):
self.m_component.update({node: node})
component_size.append(1)
num_of_components = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
u, v, w = edge
u_component = self.m_component[u]
v_component = self.m_component[v]
if u_component != v_component:
"""If the current minimum weight edge of component u doesn't
exist (is -1), or if it's greater than the edge we're
observing right now, we will assign the value of the edge
we're observing to it.
If the current minimum weight edge of component v doesn't
exist (is -1), or if it's greater than the edge we're
observing right now, we will assign the value of the edge
we're observing to it"""
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
minimum_weight_edge[component] = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(edge, list):
u, v, w = edge
u_component = self.m_component[u]
v_component = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(component_size, u_component, v_component)
print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n")
num_of_components -= 1
minimum_weight_edge = [-1] * self.m_num_of_nodes
print(f"The total weight of the minimal spanning tree is: {mst_weight}")
def test_vector() -> None:
"""
>>> g = Graph(8)
>>> for u_v_w in ((0, 1, 10), (0, 2, 6), (0, 3, 5), (1, 3, 15), (2, 3, 4),
... (3, 4, 8), (4, 5, 10), (4, 6, 6), (4, 7, 5), (5, 7, 15), (6, 7, 4)):
... g.add_edge(*u_v_w)
>>> g.boruvka()
Added edge [0 - 3]
Added weight: 5
<BLANKLINE>
Added edge [0 - 1]
Added weight: 10
<BLANKLINE>
Added edge [2 - 3]
Added weight: 4
<BLANKLINE>
Added edge [4 - 7]
Added weight: 5
<BLANKLINE>
Added edge [4 - 5]
Added weight: 10
<BLANKLINE>
Added edge [6 - 7]
Added weight: 4
<BLANKLINE>
Added edge [3 - 4]
Added weight: 8
<BLANKLINE>
The total weight of the minimal spanning tree is: 46
"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| [mypy]
ignore_missing_imports = True
install_types = True
non_interactive = True
exclude = (graphs/boruvka.py|graphs/breadth_first_search.py|graphs/breadth_first_search_2.py|graphs/check_cycle.py|graphs/finding_bridges.py|graphs/greedy_min_vertex_cover.py|graphs/random_graph_generator.py|matrix_operation.py|other/least_recently_used.py|other/lfu_cache.py|other/lru_cache.py|searches/simulated_annealing.py|searches/ternary_search.py)
| [mypy]
ignore_missing_imports = True
install_types = True
non_interactive = True
exclude = (graphs/breadth_first_search.py|graphs/breadth_first_search_2.py|graphs/check_cycle.py|graphs/finding_bridges.py|graphs/greedy_min_vertex_cover.py|graphs/random_graph_generator.py|matrix_operation.py|other/least_recently_used.py|other/lfu_cache.py|other/lru_cache.py|searches/simulated_annealing.py|searches/ternary_search.py)
| 1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Coin sums
Problem 31: https://projecteuler.net/problem=31
In England the currency is made up of pound, £, and pence, p, and there are
eight coins in general circulation:
1p, 2p, 5p, 10p, 20p, 50p, £1 (100p) and £2 (200p).
It is possible to make £2 in the following way:
1×£1 + 1×50p + 2×20p + 1×5p + 1×2p + 3×1p
How many different ways can £2 be made using any number of coins?
"""
def one_pence() -> int:
return 1
def two_pence(x: int) -> int:
return 0 if x < 0 else two_pence(x - 2) + one_pence()
def five_pence(x: int) -> int:
return 0 if x < 0 else five_pence(x - 5) + two_pence(x)
def ten_pence(x: int) -> int:
return 0 if x < 0 else ten_pence(x - 10) + five_pence(x)
def twenty_pence(x: int) -> int:
return 0 if x < 0 else twenty_pence(x - 20) + ten_pence(x)
def fifty_pence(x: int) -> int:
return 0 if x < 0 else fifty_pence(x - 50) + twenty_pence(x)
def one_pound(x: int) -> int:
return 0 if x < 0 else one_pound(x - 100) + fifty_pence(x)
def two_pound(x: int) -> int:
return 0 if x < 0 else two_pound(x - 200) + one_pound(x)
def solution(n: int = 200) -> int:
"""Returns the number of different ways can n pence be made using any number of
coins?
>>> solution(500)
6295434
>>> solution(200)
73682
>>> solution(50)
451
>>> solution(10)
11
"""
return two_pound(n)
if __name__ == "__main__":
print(solution(int(input().strip())))
| """
Coin sums
Problem 31: https://projecteuler.net/problem=31
In England the currency is made up of pound, £, and pence, p, and there are
eight coins in general circulation:
1p, 2p, 5p, 10p, 20p, 50p, £1 (100p) and £2 (200p).
It is possible to make £2 in the following way:
1×£1 + 1×50p + 2×20p + 1×5p + 1×2p + 3×1p
How many different ways can £2 be made using any number of coins?
"""
def one_pence() -> int:
return 1
def two_pence(x: int) -> int:
return 0 if x < 0 else two_pence(x - 2) + one_pence()
def five_pence(x: int) -> int:
return 0 if x < 0 else five_pence(x - 5) + two_pence(x)
def ten_pence(x: int) -> int:
return 0 if x < 0 else ten_pence(x - 10) + five_pence(x)
def twenty_pence(x: int) -> int:
return 0 if x < 0 else twenty_pence(x - 20) + ten_pence(x)
def fifty_pence(x: int) -> int:
return 0 if x < 0 else fifty_pence(x - 50) + twenty_pence(x)
def one_pound(x: int) -> int:
return 0 if x < 0 else one_pound(x - 100) + fifty_pence(x)
def two_pound(x: int) -> int:
return 0 if x < 0 else two_pound(x - 200) + one_pound(x)
def solution(n: int = 200) -> int:
"""Returns the number of different ways can n pence be made using any number of
coins?
>>> solution(500)
6295434
>>> solution(200)
73682
>>> solution(50)
451
>>> solution(10)
11
"""
return two_pound(n)
if __name__ == "__main__":
print(solution(int(input().strip())))
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
In mathematics, the Lucas–Lehmer test (LLT) is a primality test for Mersenne
numbers. https://en.wikipedia.org/wiki/Lucas%E2%80%93Lehmer_primality_test
A Mersenne number is a number that is one less than a power of two.
That is M_p = 2^p - 1
https://en.wikipedia.org/wiki/Mersenne_prime
The Lucas–Lehmer test is the primality test used by the
Great Internet Mersenne Prime Search (GIMPS) to locate large primes.
"""
# Primality test 2^p - 1
# Return true if 2^p - 1 is prime
def lucas_lehmer_test(p: int) -> bool:
"""
>>> lucas_lehmer_test(p=7)
True
>>> lucas_lehmer_test(p=11)
False
# M_11 = 2^11 - 1 = 2047 = 23 * 89
"""
if p < 2:
raise ValueError("p should not be less than 2!")
elif p == 2:
return True
s = 4
M = (1 << p) - 1
for i in range(p - 2):
s = ((s * s) - 2) % M
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(11))
| """
In mathematics, the Lucas–Lehmer test (LLT) is a primality test for Mersenne
numbers. https://en.wikipedia.org/wiki/Lucas%E2%80%93Lehmer_primality_test
A Mersenne number is a number that is one less than a power of two.
That is M_p = 2^p - 1
https://en.wikipedia.org/wiki/Mersenne_prime
The Lucas–Lehmer test is the primality test used by the
Great Internet Mersenne Prime Search (GIMPS) to locate large primes.
"""
# Primality test 2^p - 1
# Return true if 2^p - 1 is prime
def lucas_lehmer_test(p: int) -> bool:
"""
>>> lucas_lehmer_test(p=7)
True
>>> lucas_lehmer_test(p=11)
False
# M_11 = 2^11 - 1 = 2047 = 23 * 89
"""
if p < 2:
raise ValueError("p should not be less than 2!")
elif p == 2:
return True
s = 4
M = (1 << p) - 1
for i in range(p - 2):
s = ((s * s) - 2) % M
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(11))
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Recursive Prorgam to create a Linked List from a sequence and
# print a string representation of it.
class Node:
def __init__(self, data=None):
self.data = data
self.next = None
def __repr__(self):
"""Returns a visual representation of the node and all its following nodes."""
string_rep = ""
temp = self
while temp:
string_rep += f"<{temp.data}> ---> "
temp = temp.next
string_rep += "<END>"
return string_rep
def make_linked_list(elements_list):
"""Creates a Linked List from the elements of the given sequence
(list/tuple) and returns the head of the Linked List."""
# if elements_list is empty
if not elements_list:
raise Exception("The Elements List is empty")
# Set first element as Head
head = Node(elements_list[0])
current = head
# Loop through elements from position 1
for data in elements_list[1:]:
current.next = Node(data)
current = current.next
return head
list_data = [1, 3, 5, 32, 44, 12, 43]
print(f"List: {list_data}")
print("Creating Linked List from List.")
linked_list = make_linked_list(list_data)
print("Linked List:")
print(linked_list)
| # Recursive Prorgam to create a Linked List from a sequence and
# print a string representation of it.
class Node:
def __init__(self, data=None):
self.data = data
self.next = None
def __repr__(self):
"""Returns a visual representation of the node and all its following nodes."""
string_rep = ""
temp = self
while temp:
string_rep += f"<{temp.data}> ---> "
temp = temp.next
string_rep += "<END>"
return string_rep
def make_linked_list(elements_list):
"""Creates a Linked List from the elements of the given sequence
(list/tuple) and returns the head of the Linked List."""
# if elements_list is empty
if not elements_list:
raise Exception("The Elements List is empty")
# Set first element as Head
head = Node(elements_list[0])
current = head
# Loop through elements from position 1
for data in elements_list[1:]:
current.next = Node(data)
current = current.next
return head
list_data = [1, 3, 5, 32, 44, 12, 43]
print(f"List: {list_data}")
print("Creating Linked List from List.")
linked_list = make_linked_list(list_data)
print("Linked List:")
print(linked_list)
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 70: https://projecteuler.net/problem=70
Euler's Totient function, φ(n) [sometimes called the phi function], is used to
determine the number of positive numbers less than or equal to n which are
relatively prime to n. For example, as 1, 2, 4, 5, 7, and 8, are all less than
nine and relatively prime to nine, φ(9)=6.
The number 1 is considered to be relatively prime to every positive number, so
φ(1)=1.
Interestingly, φ(87109)=79180, and it can be seen that 87109 is a permutation
of 79180.
Find the value of n, 1 < n < 10^7, for which φ(n) is a permutation of n and
the ratio n/φ(n) produces a minimum.
-----
This is essentially brute force. Calculate all totients up to 10^7 and
find the minimum ratio of n/φ(n) that way. To minimize the ratio, we want
to minimize n and maximize φ(n) as much as possible, so we can store the
minimum fraction's numerator and denominator and calculate new fractions
with each totient to compare against. To avoid dividing by zero, I opt to
use cross multiplication.
References:
Finding totients
https://en.wikipedia.org/wiki/Euler's_totient_function#Euler's_product_formula
"""
from __future__ import annotations
def get_totients(max_one: int) -> list[int]:
"""
Calculates a list of totients from 0 to max_one exclusive, using the
definition of Euler's product formula.
>>> get_totients(5)
[0, 1, 1, 2, 2]
>>> get_totients(10)
[0, 1, 1, 2, 2, 4, 2, 6, 4, 6]
"""
totients = [0] * max_one
for i in range(0, max_one):
totients[i] = i
for i in range(2, max_one):
if totients[i] == i:
for j in range(i, max_one, i):
totients[j] -= totients[j] // i
return totients
def has_same_digits(num1: int, num2: int) -> bool:
"""
Return True if num1 and num2 have the same frequency of every digit, False
otherwise.
>>> has_same_digits(123456789, 987654321)
True
>>> has_same_digits(123, 23)
False
>>> has_same_digits(1234566, 123456)
False
"""
return sorted(str(num1)) == sorted(str(num2))
def solution(max: int = 10000000) -> int:
"""
Finds the value of n from 1 to max such that n/φ(n) produces a minimum.
>>> solution(100)
21
>>> solution(10000)
4435
"""
min_numerator = 1 # i
min_denominator = 0 # φ(i)
totients = get_totients(max + 1)
for i in range(2, max + 1):
t = totients[i]
if i * min_denominator < min_numerator * t and has_same_digits(i, t):
min_numerator = i
min_denominator = t
return min_numerator
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 70: https://projecteuler.net/problem=70
Euler's Totient function, φ(n) [sometimes called the phi function], is used to
determine the number of positive numbers less than or equal to n which are
relatively prime to n. For example, as 1, 2, 4, 5, 7, and 8, are all less than
nine and relatively prime to nine, φ(9)=6.
The number 1 is considered to be relatively prime to every positive number, so
φ(1)=1.
Interestingly, φ(87109)=79180, and it can be seen that 87109 is a permutation
of 79180.
Find the value of n, 1 < n < 10^7, for which φ(n) is a permutation of n and
the ratio n/φ(n) produces a minimum.
-----
This is essentially brute force. Calculate all totients up to 10^7 and
find the minimum ratio of n/φ(n) that way. To minimize the ratio, we want
to minimize n and maximize φ(n) as much as possible, so we can store the
minimum fraction's numerator and denominator and calculate new fractions
with each totient to compare against. To avoid dividing by zero, I opt to
use cross multiplication.
References:
Finding totients
https://en.wikipedia.org/wiki/Euler's_totient_function#Euler's_product_formula
"""
from __future__ import annotations
def get_totients(max_one: int) -> list[int]:
"""
Calculates a list of totients from 0 to max_one exclusive, using the
definition of Euler's product formula.
>>> get_totients(5)
[0, 1, 1, 2, 2]
>>> get_totients(10)
[0, 1, 1, 2, 2, 4, 2, 6, 4, 6]
"""
totients = [0] * max_one
for i in range(0, max_one):
totients[i] = i
for i in range(2, max_one):
if totients[i] == i:
for j in range(i, max_one, i):
totients[j] -= totients[j] // i
return totients
def has_same_digits(num1: int, num2: int) -> bool:
"""
Return True if num1 and num2 have the same frequency of every digit, False
otherwise.
>>> has_same_digits(123456789, 987654321)
True
>>> has_same_digits(123, 23)
False
>>> has_same_digits(1234566, 123456)
False
"""
return sorted(str(num1)) == sorted(str(num2))
def solution(max: int = 10000000) -> int:
"""
Finds the value of n from 1 to max such that n/φ(n) produces a minimum.
>>> solution(100)
21
>>> solution(10000)
4435
"""
min_numerator = 1 # i
min_denominator = 0 # φ(i)
totients = get_totients(max + 1)
for i in range(2, max + 1):
t = totients[i]
if i * min_denominator < min_numerator * t and has_same_digits(i, t):
min_numerator = i
min_denominator = t
return min_numerator
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
from typing import Iterable, Union
import numpy as np
Vector = Union[Iterable[float], Iterable[int], np.ndarray]
VectorOut = Union[np.float64, int, float]
def euclidean_distance(vector_1: Vector, vector_2: Vector) -> VectorOut:
"""
Calculate the distance between the two endpoints of two vectors.
A vector is defined as a list, tuple, or numpy 1D array.
>>> euclidean_distance((0, 0), (2, 2))
2.8284271247461903
>>> euclidean_distance(np.array([0, 0, 0]), np.array([2, 2, 2]))
3.4641016151377544
>>> euclidean_distance(np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8]))
8.0
>>> euclidean_distance([1, 2, 3, 4], [5, 6, 7, 8])
8.0
"""
return np.sqrt(np.sum((np.asarray(vector_1) - np.asarray(vector_2)) ** 2))
def euclidean_distance_no_np(vector_1: Vector, vector_2: Vector) -> VectorOut:
"""
Calculate the distance between the two endpoints of two vectors without numpy.
A vector is defined as a list, tuple, or numpy 1D array.
>>> euclidean_distance_no_np((0, 0), (2, 2))
2.8284271247461903
>>> euclidean_distance_no_np([1, 2, 3, 4], [5, 6, 7, 8])
8.0
"""
return sum((v1 - v2) ** 2 for v1, v2 in zip(vector_1, vector_2)) ** (1 / 2)
if __name__ == "__main__":
def benchmark() -> None:
"""
Benchmarks
"""
from timeit import timeit
print("Without Numpy")
print(
timeit(
"euclidean_distance_no_np([1, 2, 3], [4, 5, 6])",
number=10000,
globals=globals(),
)
)
print("With Numpy")
print(
timeit(
"euclidean_distance([1, 2, 3], [4, 5, 6])",
number=10000,
globals=globals(),
)
)
benchmark()
| from __future__ import annotations
from typing import Iterable, Union
import numpy as np
Vector = Union[Iterable[float], Iterable[int], np.ndarray]
VectorOut = Union[np.float64, int, float]
def euclidean_distance(vector_1: Vector, vector_2: Vector) -> VectorOut:
"""
Calculate the distance between the two endpoints of two vectors.
A vector is defined as a list, tuple, or numpy 1D array.
>>> euclidean_distance((0, 0), (2, 2))
2.8284271247461903
>>> euclidean_distance(np.array([0, 0, 0]), np.array([2, 2, 2]))
3.4641016151377544
>>> euclidean_distance(np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8]))
8.0
>>> euclidean_distance([1, 2, 3, 4], [5, 6, 7, 8])
8.0
"""
return np.sqrt(np.sum((np.asarray(vector_1) - np.asarray(vector_2)) ** 2))
def euclidean_distance_no_np(vector_1: Vector, vector_2: Vector) -> VectorOut:
"""
Calculate the distance between the two endpoints of two vectors without numpy.
A vector is defined as a list, tuple, or numpy 1D array.
>>> euclidean_distance_no_np((0, 0), (2, 2))
2.8284271247461903
>>> euclidean_distance_no_np([1, 2, 3, 4], [5, 6, 7, 8])
8.0
"""
return sum((v1 - v2) ** 2 for v1, v2 in zip(vector_1, vector_2)) ** (1 / 2)
if __name__ == "__main__":
def benchmark() -> None:
"""
Benchmarks
"""
from timeit import timeit
print("Without Numpy")
print(
timeit(
"euclidean_distance_no_np([1, 2, 3], [4, 5, 6])",
number=10000,
globals=globals(),
)
)
print("With Numpy")
print(
timeit(
"euclidean_distance([1, 2, 3], [4, 5, 6])",
number=10000,
globals=globals(),
)
)
benchmark()
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Given a partially filled 9×9 2D array, the objective is to fill a 9×9
square grid with digits numbered 1 to 9, so that every row, column, and
and each of the nine 3×3 sub-grids contains all of the digits.
This can be solved using Backtracking and is similar to n-queens.
We check to see if a cell is safe or not and recursively call the
function on the next column to see if it returns True. if yes, we
have solved the puzzle. else, we backtrack and place another number
in that cell and repeat this process.
"""
from __future__ import annotations
Matrix = list[list[int]]
# assigning initial values to the grid
initial_grid: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
no_solution: Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def is_safe(grid: Matrix, row: int, column: int, n: int) -> bool:
"""
This function checks the grid to see if each row,
column, and the 3x3 subgrids contain the digit 'n'.
It returns False if it is not 'safe' (a duplicate digit
is found) else returns True if it is 'safe'
"""
for i in range(9):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3):
for j in range(3):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def find_empty_location(grid: Matrix) -> tuple[int, int] | None:
"""
This function finds an empty location so that we can assign a number
for that particular row and column.
"""
for i in range(9):
for j in range(9):
if grid[i][j] == 0:
return i, j
return None
def sudoku(grid: Matrix) -> Matrix | None:
"""
Takes a partially filled-in grid and attempts to assign values to
all unassigned locations in such a way to meet the requirements
for Sudoku solution (non-duplication across rows, columns, and boxes)
>>> sudoku(initial_grid) # doctest: +NORMALIZE_WHITESPACE
[[3, 1, 6, 5, 7, 8, 4, 9, 2],
[5, 2, 9, 1, 3, 4, 7, 6, 8],
[4, 8, 7, 6, 2, 9, 5, 3, 1],
[2, 6, 3, 4, 1, 5, 9, 8, 7],
[9, 7, 4, 8, 6, 3, 1, 2, 5],
[8, 5, 1, 7, 9, 2, 6, 4, 3],
[1, 3, 8, 9, 4, 7, 2, 5, 6],
[6, 9, 2, 3, 5, 1, 8, 7, 4],
[7, 4, 5, 2, 8, 6, 3, 1, 9]]
>>> sudoku(no_solution) is None
True
"""
if location := find_empty_location(grid):
row, column = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1, 10):
if is_safe(grid, row, column, digit):
grid[row][column] = digit
if sudoku(grid) is not None:
return grid
grid[row][column] = 0
return None
def print_solution(grid: Matrix) -> None:
"""
A function to print the solution in the form
of a 9x9 grid
"""
for row in grid:
for cell in row:
print(cell, end=" ")
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("\nExample grid:\n" + "=" * 20)
print_solution(example_grid)
print("\nExample grid solution:")
solution = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("Cannot find a solution.")
| """
Given a partially filled 9×9 2D array, the objective is to fill a 9×9
square grid with digits numbered 1 to 9, so that every row, column, and
and each of the nine 3×3 sub-grids contains all of the digits.
This can be solved using Backtracking and is similar to n-queens.
We check to see if a cell is safe or not and recursively call the
function on the next column to see if it returns True. if yes, we
have solved the puzzle. else, we backtrack and place another number
in that cell and repeat this process.
"""
from __future__ import annotations
Matrix = list[list[int]]
# assigning initial values to the grid
initial_grid: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
no_solution: Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def is_safe(grid: Matrix, row: int, column: int, n: int) -> bool:
"""
This function checks the grid to see if each row,
column, and the 3x3 subgrids contain the digit 'n'.
It returns False if it is not 'safe' (a duplicate digit
is found) else returns True if it is 'safe'
"""
for i in range(9):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3):
for j in range(3):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def find_empty_location(grid: Matrix) -> tuple[int, int] | None:
"""
This function finds an empty location so that we can assign a number
for that particular row and column.
"""
for i in range(9):
for j in range(9):
if grid[i][j] == 0:
return i, j
return None
def sudoku(grid: Matrix) -> Matrix | None:
"""
Takes a partially filled-in grid and attempts to assign values to
all unassigned locations in such a way to meet the requirements
for Sudoku solution (non-duplication across rows, columns, and boxes)
>>> sudoku(initial_grid) # doctest: +NORMALIZE_WHITESPACE
[[3, 1, 6, 5, 7, 8, 4, 9, 2],
[5, 2, 9, 1, 3, 4, 7, 6, 8],
[4, 8, 7, 6, 2, 9, 5, 3, 1],
[2, 6, 3, 4, 1, 5, 9, 8, 7],
[9, 7, 4, 8, 6, 3, 1, 2, 5],
[8, 5, 1, 7, 9, 2, 6, 4, 3],
[1, 3, 8, 9, 4, 7, 2, 5, 6],
[6, 9, 2, 3, 5, 1, 8, 7, 4],
[7, 4, 5, 2, 8, 6, 3, 1, 9]]
>>> sudoku(no_solution) is None
True
"""
if location := find_empty_location(grid):
row, column = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1, 10):
if is_safe(grid, row, column, digit):
grid[row][column] = digit
if sudoku(grid) is not None:
return grid
grid[row][column] = 0
return None
def print_solution(grid: Matrix) -> None:
"""
A function to print the solution in the form
of a 9x9 grid
"""
for row in grid:
for cell in row:
print(cell, end=" ")
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("\nExample grid:\n" + "=" * 20)
print_solution(example_grid)
print("\nExample grid solution:")
solution = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("Cannot find a solution.")
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Scraping jobs given job title and location from indeed website
"""
from __future__ import annotations
from typing import Generator
import requests
from bs4 import BeautifulSoup
url = "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def fetch_jobs(location: str = "mumbai") -> Generator[tuple[str, str], None, None]:
soup = BeautifulSoup(requests.get(url + location).content, "html.parser")
# This attribute finds out all the specifics listed in a job
for job in soup.find_all("div", attrs={"data-tn-component": "organicJob"}):
job_title = job.find("a", attrs={"data-tn-element": "jobTitle"}).text.strip()
company_name = job.find("span", {"class": "company"}).text.strip()
yield job_title, company_name
if __name__ == "__main__":
for i, job in enumerate(fetch_jobs("Bangalore"), 1):
print(f"Job {i:>2} is {job[0]} at {job[1]}")
| """
Scraping jobs given job title and location from indeed website
"""
from __future__ import annotations
from typing import Generator
import requests
from bs4 import BeautifulSoup
url = "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def fetch_jobs(location: str = "mumbai") -> Generator[tuple[str, str], None, None]:
soup = BeautifulSoup(requests.get(url + location).content, "html.parser")
# This attribute finds out all the specifics listed in a job
for job in soup.find_all("div", attrs={"data-tn-component": "organicJob"}):
job_title = job.find("a", attrs={"data-tn-element": "jobTitle"}).text.strip()
company_name = job.find("span", {"class": "company"}).text.strip()
yield job_title, company_name
if __name__ == "__main__":
for i, job in enumerate(fetch_jobs("Bangalore"), 1):
print(f"Job {i:>2} is {job[0]} at {job[1]}")
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint
class Node:
"""Binary Search Tree Node"""
def __init__(self, key, freq):
self.key = key
self.freq = freq
def __str__(self):
"""
>>> str(Node(1, 2))
'Node(key=1, freq=2)'
"""
return f"Node(key={self.key}, freq={self.freq})"
def print_binary_search_tree(root, key, i, j, parent, is_left):
"""
Recursive function to print a BST from a root table.
>>> key = [3, 8, 9, 10, 17, 21]
>>> root = [[0, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 3], [0, 0, 2, 3, 3, 3], \
[0, 0, 0, 3, 3, 3], [0, 0, 0, 0, 4, 5], [0, 0, 0, 0, 0, 5]]
>>> print_binary_search_tree(root, key, 0, 5, -1, False)
8 is the root of the binary search tree.
3 is the left child of key 8.
10 is the right child of key 8.
9 is the left child of key 10.
21 is the right child of key 10.
17 is the left child of key 21.
"""
if i > j or i < 0 or j > len(root) - 1:
return
node = root[i][j]
if parent == -1: # root does not have a parent
print(f"{key[node]} is the root of the binary search tree.")
elif is_left:
print(f"{key[node]} is the left child of key {parent}.")
else:
print(f"{key[node]} is the right child of key {parent}.")
print_binary_search_tree(root, key, i, node - 1, key[node], True)
print_binary_search_tree(root, key, node + 1, j, key[node], False)
def find_optimal_binary_search_tree(nodes):
"""
This function calculates and prints the optimal binary search tree.
The dynamic programming algorithm below runs in O(n^2) time.
Implemented from CLRS (Introduction to Algorithms) book.
https://en.wikipedia.org/wiki/Introduction_to_Algorithms
>>> find_optimal_binary_search_tree([Node(12, 8), Node(10, 34), Node(20, 50), \
Node(42, 3), Node(25, 40), Node(37, 30)])
Binary search tree nodes:
Node(key=10, freq=34)
Node(key=12, freq=8)
Node(key=20, freq=50)
Node(key=25, freq=40)
Node(key=37, freq=30)
Node(key=42, freq=3)
<BLANKLINE>
The cost of optimal BST for given tree nodes is 324.
20 is the root of the binary search tree.
10 is the left child of key 20.
12 is the right child of key 10.
25 is the right child of key 20.
37 is the right child of key 25.
42 is the right child of key 37.
"""
# Tree nodes must be sorted first, the code below sorts the keys in
# increasing order and rearrange its frequencies accordingly.
nodes.sort(key=lambda node: node.key)
n = len(nodes)
keys = [nodes[i].key for i in range(n)]
freqs = [nodes[i].freq for i in range(n)]
# This 2D array stores the overall tree cost (which's as minimized as possible);
# for a single key, cost is equal to frequency of the key.
dp = [[freqs[i] if i == j else 0 for j in range(n)] for i in range(n)]
# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
# array
sum = [[freqs[i] if i == j else 0 for j in range(n)] for i in range(n)]
# stores tree roots that will be used later for constructing binary search tree
root = [[i if i == j else 0 for j in range(n)] for i in range(n)]
for interval_length in range(2, n + 1):
for i in range(n - interval_length + 1):
j = i + interval_length - 1
dp[i][j] = sys.maxsize # set the value to "infinity"
sum[i][j] = sum[i][j - 1] + freqs[j]
# Apply Knuth's optimization
# Loop without optimization: for r in range(i, j + 1):
for r in range(root[i][j - 1], root[i + 1][j] + 1): # r is a temporal root
left = dp[i][r - 1] if r != i else 0 # optimal cost for left subtree
right = dp[r + 1][j] if r != j else 0 # optimal cost for right subtree
cost = left + sum[i][j] + right
if dp[i][j] > cost:
dp[i][j] = cost
root[i][j] = r
print("Binary search tree nodes:")
for node in nodes:
print(node)
print(f"\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.")
print_binary_search_tree(root, keys, 0, n - 1, -1, False)
def main():
# A sample binary search tree
nodes = [Node(i, randint(1, 50)) for i in range(10, 0, -1)]
find_optimal_binary_search_tree(nodes)
if __name__ == "__main__":
main()
| #!/usr/bin/env python3
# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint
class Node:
"""Binary Search Tree Node"""
def __init__(self, key, freq):
self.key = key
self.freq = freq
def __str__(self):
"""
>>> str(Node(1, 2))
'Node(key=1, freq=2)'
"""
return f"Node(key={self.key}, freq={self.freq})"
def print_binary_search_tree(root, key, i, j, parent, is_left):
"""
Recursive function to print a BST from a root table.
>>> key = [3, 8, 9, 10, 17, 21]
>>> root = [[0, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 3], [0, 0, 2, 3, 3, 3], \
[0, 0, 0, 3, 3, 3], [0, 0, 0, 0, 4, 5], [0, 0, 0, 0, 0, 5]]
>>> print_binary_search_tree(root, key, 0, 5, -1, False)
8 is the root of the binary search tree.
3 is the left child of key 8.
10 is the right child of key 8.
9 is the left child of key 10.
21 is the right child of key 10.
17 is the left child of key 21.
"""
if i > j or i < 0 or j > len(root) - 1:
return
node = root[i][j]
if parent == -1: # root does not have a parent
print(f"{key[node]} is the root of the binary search tree.")
elif is_left:
print(f"{key[node]} is the left child of key {parent}.")
else:
print(f"{key[node]} is the right child of key {parent}.")
print_binary_search_tree(root, key, i, node - 1, key[node], True)
print_binary_search_tree(root, key, node + 1, j, key[node], False)
def find_optimal_binary_search_tree(nodes):
"""
This function calculates and prints the optimal binary search tree.
The dynamic programming algorithm below runs in O(n^2) time.
Implemented from CLRS (Introduction to Algorithms) book.
https://en.wikipedia.org/wiki/Introduction_to_Algorithms
>>> find_optimal_binary_search_tree([Node(12, 8), Node(10, 34), Node(20, 50), \
Node(42, 3), Node(25, 40), Node(37, 30)])
Binary search tree nodes:
Node(key=10, freq=34)
Node(key=12, freq=8)
Node(key=20, freq=50)
Node(key=25, freq=40)
Node(key=37, freq=30)
Node(key=42, freq=3)
<BLANKLINE>
The cost of optimal BST for given tree nodes is 324.
20 is the root of the binary search tree.
10 is the left child of key 20.
12 is the right child of key 10.
25 is the right child of key 20.
37 is the right child of key 25.
42 is the right child of key 37.
"""
# Tree nodes must be sorted first, the code below sorts the keys in
# increasing order and rearrange its frequencies accordingly.
nodes.sort(key=lambda node: node.key)
n = len(nodes)
keys = [nodes[i].key for i in range(n)]
freqs = [nodes[i].freq for i in range(n)]
# This 2D array stores the overall tree cost (which's as minimized as possible);
# for a single key, cost is equal to frequency of the key.
dp = [[freqs[i] if i == j else 0 for j in range(n)] for i in range(n)]
# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
# array
sum = [[freqs[i] if i == j else 0 for j in range(n)] for i in range(n)]
# stores tree roots that will be used later for constructing binary search tree
root = [[i if i == j else 0 for j in range(n)] for i in range(n)]
for interval_length in range(2, n + 1):
for i in range(n - interval_length + 1):
j = i + interval_length - 1
dp[i][j] = sys.maxsize # set the value to "infinity"
sum[i][j] = sum[i][j - 1] + freqs[j]
# Apply Knuth's optimization
# Loop without optimization: for r in range(i, j + 1):
for r in range(root[i][j - 1], root[i + 1][j] + 1): # r is a temporal root
left = dp[i][r - 1] if r != i else 0 # optimal cost for left subtree
right = dp[r + 1][j] if r != j else 0 # optimal cost for right subtree
cost = left + sum[i][j] + right
if dp[i][j] > cost:
dp[i][j] = cost
root[i][j] = r
print("Binary search tree nodes:")
for node in nodes:
print(node)
print(f"\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.")
print_binary_search_tree(root, keys, 0, n - 1, -1, False)
def main():
# A sample binary search tree
nodes = [Node(i, randint(1, 50)) for i in range(10, 0, -1)]
find_optimal_binary_search_tree(nodes)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def search(list_data: list, key: int, left: int = 0, right: int = 0) -> int:
"""
Iterate through the array to find the index of key using recursion.
:param list_data: the list to be searched
:param key: the key to be searched
:param left: the index of first element
:param right: the index of last element
:return: the index of key value if found, -1 otherwise.
>>> search(list(range(0, 11)), 5)
5
>>> search([1, 2, 4, 5, 3], 4)
2
>>> search([1, 2, 4, 5, 3], 6)
-1
>>> search([5], 5)
0
>>> search([], 1)
-1
"""
right = right or len(list_data) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(list_data, key, left + 1, right - 1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| def search(list_data: list, key: int, left: int = 0, right: int = 0) -> int:
"""
Iterate through the array to find the index of key using recursion.
:param list_data: the list to be searched
:param key: the key to be searched
:param left: the index of first element
:param right: the index of last element
:return: the index of key value if found, -1 otherwise.
>>> search(list(range(0, 11)), 5)
5
>>> search([1, 2, 4, 5, 3], 4)
2
>>> search([1, 2, 4, 5, 3], 6)
-1
>>> search([5], 5)
0
>>> search([], 1)
-1
"""
right = right or len(list_data) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(list_data, key, left + 1, right - 1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 9: https://projecteuler.net/problem=9
Special Pythagorean triplet
A Pythagorean triplet is a set of three natural numbers, a < b < c, for which,
a^2 + b^2 = c^2
For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2.
There exists exactly one Pythagorean triplet for which a + b + c = 1000.
Find the product a*b*c.
References:
- https://en.wikipedia.org/wiki/Pythagorean_triple
"""
def solution() -> int:
"""
Returns the product of a,b,c which are Pythagorean Triplet that satisfies
the following:
1. a < b < c
2. a**2 + b**2 = c**2
3. a + b + c = 1000
>>> solution()
31875000
"""
for a in range(300):
for b in range(a + 1, 400):
for c in range(b + 1, 500):
if (a + b + c) == 1000:
if (a ** 2) + (b ** 2) == (c ** 2):
return a * b * c
return -1
def solution_fast() -> int:
"""
Returns the product of a,b,c which are Pythagorean Triplet that satisfies
the following:
1. a < b < c
2. a**2 + b**2 = c**2
3. a + b + c = 1000
>>> solution_fast()
31875000
"""
for a in range(300):
for b in range(400):
c = 1000 - a - b
if a < b < c and (a ** 2) + (b ** 2) == (c ** 2):
return a * b * c
return -1
def benchmark() -> None:
"""
Benchmark code comparing two different version function.
"""
import timeit
print(
timeit.timeit("solution()", setup="from __main__ import solution", number=1000)
)
print(
timeit.timeit(
"solution_fast()", setup="from __main__ import solution_fast", number=1000
)
)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 9: https://projecteuler.net/problem=9
Special Pythagorean triplet
A Pythagorean triplet is a set of three natural numbers, a < b < c, for which,
a^2 + b^2 = c^2
For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2.
There exists exactly one Pythagorean triplet for which a + b + c = 1000.
Find the product a*b*c.
References:
- https://en.wikipedia.org/wiki/Pythagorean_triple
"""
def solution() -> int:
"""
Returns the product of a,b,c which are Pythagorean Triplet that satisfies
the following:
1. a < b < c
2. a**2 + b**2 = c**2
3. a + b + c = 1000
>>> solution()
31875000
"""
for a in range(300):
for b in range(a + 1, 400):
for c in range(b + 1, 500):
if (a + b + c) == 1000:
if (a ** 2) + (b ** 2) == (c ** 2):
return a * b * c
return -1
def solution_fast() -> int:
"""
Returns the product of a,b,c which are Pythagorean Triplet that satisfies
the following:
1. a < b < c
2. a**2 + b**2 = c**2
3. a + b + c = 1000
>>> solution_fast()
31875000
"""
for a in range(300):
for b in range(400):
c = 1000 - a - b
if a < b < c and (a ** 2) + (b ** 2) == (c ** 2):
return a * b * c
return -1
def benchmark() -> None:
"""
Benchmark code comparing two different version function.
"""
import timeit
print(
timeit.timeit("solution()", setup="from __main__ import solution", number=1000)
)
print(
timeit.timeit(
"solution_fast()", setup="from __main__ import solution_fast", number=1000
)
)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
from random import random
class Node:
"""
Treap's node
Treap is a binary tree by value and heap by priority
"""
def __init__(self, value: int | None = None):
self.value = value
self.prior = random()
self.left: Node | None = None
self.right: Node | None = None
def __repr__(self) -> str:
from pprint import pformat
if self.left is None and self.right is None:
return f"'{self.value}: {self.prior:.5}'"
else:
return pformat(
{f"{self.value}: {self.prior:.5}": (self.left, self.right)}, indent=1
)
def __str__(self) -> str:
value = str(self.value) + " "
left = str(self.left or "")
right = str(self.right or "")
return value + left + right
def split(root: Node | None, value: int) -> tuple[Node | None, Node | None]:
"""
We split current tree into 2 trees with value:
Left tree contains all values less than split value.
Right tree contains all values greater or equal, than split value
"""
if root is None: # None tree is split into 2 Nones
return None, None
elif root.value is None:
return None, None
else:
if value < root.value:
"""
Right tree's root will be current node.
Now we split(with the same value) current node's left son
Left tree: left part of that split
Right tree's left son: right part of that split
"""
left, root.left = split(root.left, value)
return left, root
else:
"""
Just symmetric to previous case
"""
root.right, right = split(root.right, value)
return root, right
def merge(left: Node | None, right: Node | None) -> Node | None:
"""
We merge 2 trees into one.
Note: all left tree's values must be less than all right tree's
"""
if (not left) or (not right): # If one node is None, return the other
return left or right
elif left.prior < right.prior:
"""
Left will be root because it has more priority
Now we need to merge left's right son and right tree
"""
left.right = merge(left.right, right)
return left
else:
"""
Symmetric as well
"""
right.left = merge(left, right.left)
return right
def insert(root: Node | None, value: int) -> Node | None:
"""
Insert element
Split current tree with a value into left, right,
Insert new node into the middle
Merge left, node, right into root
"""
node = Node(value)
left, right = split(root, value)
return merge(merge(left, node), right)
def erase(root: Node | None, value: int) -> Node | None:
"""
Erase element
Split all nodes with values less into left,
Split all nodes with values greater into right.
Merge left, right
"""
left, right = split(root, value - 1)
_, right = split(right, value)
return merge(left, right)
def inorder(root: Node | None) -> None:
"""
Just recursive print of a tree
"""
if not root: # None
return
else:
inorder(root.left)
print(root.value, end=",")
inorder(root.right)
def interactTreap(root: Node | None, args: str) -> Node | None:
"""
Commands:
+ value to add value into treap
- value to erase all nodes with value
>>> root = interactTreap(None, "+1")
>>> inorder(root)
1,
>>> root = interactTreap(root, "+3 +5 +17 +19 +2 +16 +4 +0")
>>> inorder(root)
0,1,2,3,4,5,16,17,19,
>>> root = interactTreap(root, "+4 +4 +4")
>>> inorder(root)
0,1,2,3,4,4,4,4,5,16,17,19,
>>> root = interactTreap(root, "-0")
>>> inorder(root)
1,2,3,4,4,4,4,5,16,17,19,
>>> root = interactTreap(root, "-4")
>>> inorder(root)
1,2,3,5,16,17,19,
>>> root = interactTreap(root, "=0")
Unknown command
"""
for arg in args.split():
if arg[0] == "+":
root = insert(root, int(arg[1:]))
elif arg[0] == "-":
root = erase(root, int(arg[1:]))
else:
print("Unknown command")
return root
def main() -> None:
"""After each command, program prints treap"""
root = None
print(
"enter numbers to create a tree, + value to add value into treap, "
"- value to erase all nodes with value. 'q' to quit. "
)
args = input()
while args != "q":
root = interactTreap(root, args)
print(root)
args = input()
print("good by!")
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| from __future__ import annotations
from random import random
class Node:
"""
Treap's node
Treap is a binary tree by value and heap by priority
"""
def __init__(self, value: int | None = None):
self.value = value
self.prior = random()
self.left: Node | None = None
self.right: Node | None = None
def __repr__(self) -> str:
from pprint import pformat
if self.left is None and self.right is None:
return f"'{self.value}: {self.prior:.5}'"
else:
return pformat(
{f"{self.value}: {self.prior:.5}": (self.left, self.right)}, indent=1
)
def __str__(self) -> str:
value = str(self.value) + " "
left = str(self.left or "")
right = str(self.right or "")
return value + left + right
def split(root: Node | None, value: int) -> tuple[Node | None, Node | None]:
"""
We split current tree into 2 trees with value:
Left tree contains all values less than split value.
Right tree contains all values greater or equal, than split value
"""
if root is None: # None tree is split into 2 Nones
return None, None
elif root.value is None:
return None, None
else:
if value < root.value:
"""
Right tree's root will be current node.
Now we split(with the same value) current node's left son
Left tree: left part of that split
Right tree's left son: right part of that split
"""
left, root.left = split(root.left, value)
return left, root
else:
"""
Just symmetric to previous case
"""
root.right, right = split(root.right, value)
return root, right
def merge(left: Node | None, right: Node | None) -> Node | None:
"""
We merge 2 trees into one.
Note: all left tree's values must be less than all right tree's
"""
if (not left) or (not right): # If one node is None, return the other
return left or right
elif left.prior < right.prior:
"""
Left will be root because it has more priority
Now we need to merge left's right son and right tree
"""
left.right = merge(left.right, right)
return left
else:
"""
Symmetric as well
"""
right.left = merge(left, right.left)
return right
def insert(root: Node | None, value: int) -> Node | None:
"""
Insert element
Split current tree with a value into left, right,
Insert new node into the middle
Merge left, node, right into root
"""
node = Node(value)
left, right = split(root, value)
return merge(merge(left, node), right)
def erase(root: Node | None, value: int) -> Node | None:
"""
Erase element
Split all nodes with values less into left,
Split all nodes with values greater into right.
Merge left, right
"""
left, right = split(root, value - 1)
_, right = split(right, value)
return merge(left, right)
def inorder(root: Node | None) -> None:
"""
Just recursive print of a tree
"""
if not root: # None
return
else:
inorder(root.left)
print(root.value, end=",")
inorder(root.right)
def interactTreap(root: Node | None, args: str) -> Node | None:
"""
Commands:
+ value to add value into treap
- value to erase all nodes with value
>>> root = interactTreap(None, "+1")
>>> inorder(root)
1,
>>> root = interactTreap(root, "+3 +5 +17 +19 +2 +16 +4 +0")
>>> inorder(root)
0,1,2,3,4,5,16,17,19,
>>> root = interactTreap(root, "+4 +4 +4")
>>> inorder(root)
0,1,2,3,4,4,4,4,5,16,17,19,
>>> root = interactTreap(root, "-0")
>>> inorder(root)
1,2,3,4,4,4,4,5,16,17,19,
>>> root = interactTreap(root, "-4")
>>> inorder(root)
1,2,3,5,16,17,19,
>>> root = interactTreap(root, "=0")
Unknown command
"""
for arg in args.split():
if arg[0] == "+":
root = insert(root, int(arg[1:]))
elif arg[0] == "-":
root = erase(root, int(arg[1:]))
else:
print("Unknown command")
return root
def main() -> None:
"""After each command, program prints treap"""
root = None
print(
"enter numbers to create a tree, + value to add value into treap, "
"- value to erase all nodes with value. 'q' to quit. "
)
args = input()
while args != "q":
root = interactTreap(root, args)
print(root)
args = input()
print("good by!")
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from typing import Callable
import numpy as np
def explicit_euler(
ode_func: Callable, y0: float, x0: float, step_size: float, x_end: float
) -> np.ndarray:
"""Calculate numeric solution at each step to an ODE using Euler's Method
For reference to Euler's method refer to https://en.wikipedia.org/wiki/Euler_method.
Args:
ode_func (Callable): The ordinary differential equation
as a function of x and y.
y0 (float): The initial value for y.
x0 (float): The initial value for x.
step_size (float): The increment value for x.
x_end (float): The final value of x to be calculated.
Returns:
np.ndarray: Solution of y for every step in x.
>>> # the exact solution is math.exp(x)
>>> def f(x, y):
... return y
>>> y0 = 1
>>> y = explicit_euler(f, y0, 0.0, 0.01, 5)
>>> y[-1]
144.77277243257308
"""
N = int(np.ceil((x_end - x0) / step_size))
y = np.zeros((N + 1,))
y[0] = y0
x = x0
for k in range(N):
y[k + 1] = y[k] + step_size * ode_func(x, y[k])
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| from typing import Callable
import numpy as np
def explicit_euler(
ode_func: Callable, y0: float, x0: float, step_size: float, x_end: float
) -> np.ndarray:
"""Calculate numeric solution at each step to an ODE using Euler's Method
For reference to Euler's method refer to https://en.wikipedia.org/wiki/Euler_method.
Args:
ode_func (Callable): The ordinary differential equation
as a function of x and y.
y0 (float): The initial value for y.
x0 (float): The initial value for x.
step_size (float): The increment value for x.
x_end (float): The final value of x to be calculated.
Returns:
np.ndarray: Solution of y for every step in x.
>>> # the exact solution is math.exp(x)
>>> def f(x, y):
... return y
>>> y0 = 1
>>> y = explicit_euler(f, y0, 0.0, 0.01, 5)
>>> y[-1]
144.77277243257308
"""
N = int(np.ceil((x_end - x0) / step_size))
y = np.zeros((N + 1,))
y[0] = y0
x = x0
for k in range(N):
y[k + 1] = y[k] + step_size * ode_func(x, y[k])
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
from decimal import Decimal
def inverse_of_matrix(matrix: list[list[float]]) -> list[list[float]]:
"""
A matrix multiplied with its inverse gives the identity matrix.
This function finds the inverse of a 2x2 matrix.
If the determinant of a matrix is 0, its inverse does not exist.
Sources for fixing inaccurate float arithmetic:
https://stackoverflow.com/questions/6563058/how-do-i-use-accurate-float-arithmetic-in-python
https://docs.python.org/3/library/decimal.html
>>> inverse_of_matrix([[2, 5], [2, 0]])
[[0.0, 0.5], [0.2, -0.2]]
>>> inverse_of_matrix([[2.5, 5], [1, 2]])
Traceback (most recent call last):
...
ValueError: This matrix has no inverse.
>>> inverse_of_matrix([[12, -16], [-9, 0]])
[[0.0, -0.1111111111111111], [-0.0625, -0.08333333333333333]]
>>> inverse_of_matrix([[12, 3], [16, 8]])
[[0.16666666666666666, -0.0625], [-0.3333333333333333, 0.25]]
>>> inverse_of_matrix([[10, 5], [3, 2.5]])
[[0.25, -0.5], [-0.3, 1.0]]
"""
D = Decimal # An abbreviation to be conciseness
# Calculate the determinant of the matrix
determinant = D(matrix[0][0]) * D(matrix[1][1]) - D(matrix[1][0]) * D(matrix[0][1])
if determinant == 0:
raise ValueError("This matrix has no inverse.")
# Creates a copy of the matrix with swapped positions of the elements
swapped_matrix = [[0.0, 0.0], [0.0, 0.0]]
swapped_matrix[0][0], swapped_matrix[1][1] = matrix[1][1], matrix[0][0]
swapped_matrix[1][0], swapped_matrix[0][1] = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [[float(D(n) / determinant) or 0.0 for n in row] for row in swapped_matrix]
| from __future__ import annotations
from decimal import Decimal
def inverse_of_matrix(matrix: list[list[float]]) -> list[list[float]]:
"""
A matrix multiplied with its inverse gives the identity matrix.
This function finds the inverse of a 2x2 matrix.
If the determinant of a matrix is 0, its inverse does not exist.
Sources for fixing inaccurate float arithmetic:
https://stackoverflow.com/questions/6563058/how-do-i-use-accurate-float-arithmetic-in-python
https://docs.python.org/3/library/decimal.html
>>> inverse_of_matrix([[2, 5], [2, 0]])
[[0.0, 0.5], [0.2, -0.2]]
>>> inverse_of_matrix([[2.5, 5], [1, 2]])
Traceback (most recent call last):
...
ValueError: This matrix has no inverse.
>>> inverse_of_matrix([[12, -16], [-9, 0]])
[[0.0, -0.1111111111111111], [-0.0625, -0.08333333333333333]]
>>> inverse_of_matrix([[12, 3], [16, 8]])
[[0.16666666666666666, -0.0625], [-0.3333333333333333, 0.25]]
>>> inverse_of_matrix([[10, 5], [3, 2.5]])
[[0.25, -0.5], [-0.3, 1.0]]
"""
D = Decimal # An abbreviation to be conciseness
# Calculate the determinant of the matrix
determinant = D(matrix[0][0]) * D(matrix[1][1]) - D(matrix[1][0]) * D(matrix[0][1])
if determinant == 0:
raise ValueError("This matrix has no inverse.")
# Creates a copy of the matrix with swapped positions of the elements
swapped_matrix = [[0.0, 0.0], [0.0, 0.0]]
swapped_matrix[0][0], swapped_matrix[1][1] = matrix[1][1], matrix[0][0]
swapped_matrix[1][0], swapped_matrix[0][1] = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [[float(D(n) / determinant) or 0.0 for n in row] for row in swapped_matrix]
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Cellular Automata
* https://en.wikipedia.org/wiki/Cellular_automaton
* https://mathworld.wolfram.com/ElementaryCellularAutomaton.html
| # Cellular Automata
* https://en.wikipedia.org/wiki/Cellular_automaton
* https://mathworld.wolfram.com/ElementaryCellularAutomaton.html
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Combinatoric selections
Problem 47
The first two consecutive numbers to have two distinct prime factors are:
14 = 2 × 7
15 = 3 × 5
The first three consecutive numbers to have three distinct prime factors are:
644 = 2² × 7 × 23
645 = 3 × 5 × 43
646 = 2 × 17 × 19.
Find the first four consecutive integers to have four distinct prime factors each.
What is the first of these numbers?
"""
from functools import lru_cache
def unique_prime_factors(n: int) -> set:
"""
Find unique prime factors of an integer.
Tests include sorting because only the set really matters,
not the order in which it is produced.
>>> sorted(set(unique_prime_factors(14)))
[2, 7]
>>> sorted(set(unique_prime_factors(644)))
[2, 7, 23]
>>> sorted(set(unique_prime_factors(646)))
[2, 17, 19]
"""
i = 2
factors = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(i)
if n > 1:
factors.add(n)
return factors
@lru_cache
def upf_len(num: int) -> int:
"""
Memoize upf() length results for a given value.
>>> upf_len(14)
2
"""
return len(unique_prime_factors(num))
def equality(iterable: list) -> bool:
"""
Check equality of ALL elements in an interable.
>>> equality([1, 2, 3, 4])
False
>>> equality([2, 2, 2, 2])
True
>>> equality([1, 2, 3, 2, 1])
False
"""
return len(set(iterable)) in (0, 1)
def run(n: int) -> list:
"""
Runs core process to find problem solution.
>>> run(3)
[644, 645, 646]
"""
# Incrementor variable for our group list comprehension.
# This serves as the first number in each list of values
# to test.
base = 2
while True:
# Increment each value of a generated range
group = [base + i for i in range(n)]
# Run elements through out unique_prime_factors function
# Append our target number to the end.
checker = [upf_len(x) for x in group]
checker.append(n)
# If all numbers in the list are equal, return the group variable.
if equality(checker):
return group
# Increment our base variable by 1
base += 1
def solution(n: int = 4) -> int:
"""Return the first value of the first four consecutive integers to have four
distinct prime factors each.
>>> solution()
134043
"""
results = run(n)
return results[0] if len(results) else None
if __name__ == "__main__":
print(solution())
| """
Combinatoric selections
Problem 47
The first two consecutive numbers to have two distinct prime factors are:
14 = 2 × 7
15 = 3 × 5
The first three consecutive numbers to have three distinct prime factors are:
644 = 2² × 7 × 23
645 = 3 × 5 × 43
646 = 2 × 17 × 19.
Find the first four consecutive integers to have four distinct prime factors each.
What is the first of these numbers?
"""
from functools import lru_cache
def unique_prime_factors(n: int) -> set:
"""
Find unique prime factors of an integer.
Tests include sorting because only the set really matters,
not the order in which it is produced.
>>> sorted(set(unique_prime_factors(14)))
[2, 7]
>>> sorted(set(unique_prime_factors(644)))
[2, 7, 23]
>>> sorted(set(unique_prime_factors(646)))
[2, 17, 19]
"""
i = 2
factors = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(i)
if n > 1:
factors.add(n)
return factors
@lru_cache
def upf_len(num: int) -> int:
"""
Memoize upf() length results for a given value.
>>> upf_len(14)
2
"""
return len(unique_prime_factors(num))
def equality(iterable: list) -> bool:
"""
Check equality of ALL elements in an interable.
>>> equality([1, 2, 3, 4])
False
>>> equality([2, 2, 2, 2])
True
>>> equality([1, 2, 3, 2, 1])
False
"""
return len(set(iterable)) in (0, 1)
def run(n: int) -> list:
"""
Runs core process to find problem solution.
>>> run(3)
[644, 645, 646]
"""
# Incrementor variable for our group list comprehension.
# This serves as the first number in each list of values
# to test.
base = 2
while True:
# Increment each value of a generated range
group = [base + i for i in range(n)]
# Run elements through out unique_prime_factors function
# Append our target number to the end.
checker = [upf_len(x) for x in group]
checker.append(n)
# If all numbers in the list are equal, return the group variable.
if equality(checker):
return group
# Increment our base variable by 1
base += 1
def solution(n: int = 4) -> int:
"""Return the first value of the first four consecutive integers to have four
distinct prime factors each.
>>> solution()
134043
"""
results = run(n)
return results[0] if len(results) else None
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def points_to_polynomial(coordinates: list[list[int]]) -> str:
"""
coordinates is a two dimensional matrix: [[x, y], [x, y], ...]
number of points you want to use
>>> print(points_to_polynomial([]))
The program cannot work out a fitting polynomial.
>>> print(points_to_polynomial([[]]))
The program cannot work out a fitting polynomial.
>>> print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
f(x)=x^2*0.0+x^1*-0.0+x^0*0.0
>>> print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
f(x)=x^2*0.0+x^1*-0.0+x^0*1.0
>>> print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
f(x)=x^2*0.0+x^1*-0.0+x^0*3.0
>>> print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
f(x)=x^2*0.0+x^1*1.0+x^0*0.0
>>> print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
f(x)=x^2*1.0+x^1*-0.0+x^0*0.0
>>> print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
f(x)=x^2*1.0+x^1*-0.0+x^0*2.0
>>> print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
f(x)=x^2*-1.0+x^1*-0.0+x^0*-2.0
>>> print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))
f(x)=x^2*5.0+x^1*-18.0+x^0*18.0
"""
try:
check = 1
more_check = 0
d = coordinates[0][0]
for j in range(len(coordinates)):
if j == 0:
continue
if d == coordinates[j][0]:
more_check += 1
solved = "x=" + str(coordinates[j][0])
if more_check == len(coordinates) - 1:
check = 2
break
elif more_check > 0 and more_check != len(coordinates) - 1:
check = 3
else:
check = 1
if len(coordinates) == 1 and coordinates[0][0] == 0:
check = 2
solved = "x=0"
except Exception:
check = 3
x = len(coordinates)
if check == 1:
count_of_line = 0
matrix: list[list[float]] = []
# put the x and x to the power values in a matrix
while count_of_line < x:
count_in_line = 0
a = coordinates[count_of_line][0]
count_line: list[float] = []
while count_in_line < x:
count_line.append(a ** (x - (count_in_line + 1)))
count_in_line += 1
matrix.append(count_line)
count_of_line += 1
count_of_line = 0
# put the y values into a vector
vector: list[float] = []
while count_of_line < x:
vector.append(coordinates[count_of_line][1])
count_of_line += 1
count = 0
while count < x:
zahlen = 0
while zahlen < x:
if count == zahlen:
zahlen += 1
if zahlen == x:
break
bruch = matrix[zahlen][count] / matrix[count][count]
for counting_columns, item in enumerate(matrix[count]):
# manipulating all the values in the matrix
matrix[zahlen][counting_columns] -= item * bruch
# manipulating the values in the vector
vector[zahlen] -= vector[count] * bruch
zahlen += 1
count += 1
count = 0
# make solutions
solution: list[str] = []
while count < x:
solution.append(str(vector[count] / matrix[count][count]))
count += 1
count = 0
solved = "f(x)="
while count < x:
remove_e: list[str] = solution[count].split("E")
if len(remove_e) > 1:
solution[count] = remove_e[0] + "*10^" + remove_e[1]
solved += "x^" + str(x - (count + 1)) + "*" + str(solution[count])
if count + 1 != x:
solved += "+"
count += 1
return solved
elif check == 2:
return solved
else:
return "The program cannot work out a fitting polynomial."
if __name__ == "__main__":
print(points_to_polynomial([]))
print(points_to_polynomial([[]]))
print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))
| def points_to_polynomial(coordinates: list[list[int]]) -> str:
"""
coordinates is a two dimensional matrix: [[x, y], [x, y], ...]
number of points you want to use
>>> print(points_to_polynomial([]))
The program cannot work out a fitting polynomial.
>>> print(points_to_polynomial([[]]))
The program cannot work out a fitting polynomial.
>>> print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
f(x)=x^2*0.0+x^1*-0.0+x^0*0.0
>>> print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
f(x)=x^2*0.0+x^1*-0.0+x^0*1.0
>>> print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
f(x)=x^2*0.0+x^1*-0.0+x^0*3.0
>>> print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
f(x)=x^2*0.0+x^1*1.0+x^0*0.0
>>> print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
f(x)=x^2*1.0+x^1*-0.0+x^0*0.0
>>> print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
f(x)=x^2*1.0+x^1*-0.0+x^0*2.0
>>> print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
f(x)=x^2*-1.0+x^1*-0.0+x^0*-2.0
>>> print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))
f(x)=x^2*5.0+x^1*-18.0+x^0*18.0
"""
try:
check = 1
more_check = 0
d = coordinates[0][0]
for j in range(len(coordinates)):
if j == 0:
continue
if d == coordinates[j][0]:
more_check += 1
solved = "x=" + str(coordinates[j][0])
if more_check == len(coordinates) - 1:
check = 2
break
elif more_check > 0 and more_check != len(coordinates) - 1:
check = 3
else:
check = 1
if len(coordinates) == 1 and coordinates[0][0] == 0:
check = 2
solved = "x=0"
except Exception:
check = 3
x = len(coordinates)
if check == 1:
count_of_line = 0
matrix: list[list[float]] = []
# put the x and x to the power values in a matrix
while count_of_line < x:
count_in_line = 0
a = coordinates[count_of_line][0]
count_line: list[float] = []
while count_in_line < x:
count_line.append(a ** (x - (count_in_line + 1)))
count_in_line += 1
matrix.append(count_line)
count_of_line += 1
count_of_line = 0
# put the y values into a vector
vector: list[float] = []
while count_of_line < x:
vector.append(coordinates[count_of_line][1])
count_of_line += 1
count = 0
while count < x:
zahlen = 0
while zahlen < x:
if count == zahlen:
zahlen += 1
if zahlen == x:
break
bruch = matrix[zahlen][count] / matrix[count][count]
for counting_columns, item in enumerate(matrix[count]):
# manipulating all the values in the matrix
matrix[zahlen][counting_columns] -= item * bruch
# manipulating the values in the vector
vector[zahlen] -= vector[count] * bruch
zahlen += 1
count += 1
count = 0
# make solutions
solution: list[str] = []
while count < x:
solution.append(str(vector[count] / matrix[count][count]))
count += 1
count = 0
solved = "f(x)="
while count < x:
remove_e: list[str] = solution[count].split("E")
if len(remove_e) > 1:
solution[count] = remove_e[0] + "*10^" + remove_e[1]
solved += "x^" + str(x - (count + 1)) + "*" + str(solution[count])
if count + 1 != x:
solved += "+"
count += 1
return solved
elif check == 2:
return solved
else:
return "The program cannot work out a fitting polynomial."
if __name__ == "__main__":
print(points_to_polynomial([]))
print(points_to_polynomial([[]]))
print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Prim's (also known as Jarník's) algorithm is a greedy algorithm that finds a minimum
spanning tree for a weighted undirected graph. This means it finds a subset of the
edges that forms a tree that includes every vertex, where the total weight of all the
edges in the tree is minimized. The algorithm operates by building this tree one vertex
at a time, from an arbitrary starting vertex, at each step adding the cheapest possible
connection from the tree to another vertex.
"""
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
T = TypeVar("T")
def get_parent_position(position: int) -> int:
"""
heap helper function get the position of the parent of the current node
>>> get_parent_position(1)
0
>>> get_parent_position(2)
0
"""
return (position - 1) // 2
def get_child_left_position(position: int) -> int:
"""
heap helper function get the position of the left child of the current node
>>> get_child_left_position(0)
1
"""
return (2 * position) + 1
def get_child_right_position(position: int) -> int:
"""
heap helper function get the position of the right child of the current node
>>> get_child_right_position(0)
2
"""
return (2 * position) + 2
class MinPriorityQueue(Generic[T]):
"""
Minimum Priority Queue Class
Functions:
is_empty: function to check if the priority queue is empty
push: function to add an element with given priority to the queue
extract_min: function to remove and return the element with lowest weight (highest
priority)
update_key: function to update the weight of the given key
_bubble_up: helper function to place a node at the proper position (upward
movement)
_bubble_down: helper function to place a node at the proper position (downward
movement)
_swap_nodes: helper function to swap the nodes at the given positions
>>> queue = MinPriorityQueue()
>>> queue.push(1, 1000)
>>> queue.push(2, 100)
>>> queue.push(3, 4000)
>>> queue.push(4, 3000)
>>> print(queue.extract_min())
2
>>> queue.update_key(4, 50)
>>> print(queue.extract_min())
4
>>> print(queue.extract_min())
1
>>> print(queue.extract_min())
3
"""
def __init__(self) -> None:
self.heap: list[tuple[T, int]] = []
self.position_map: dict[T, int] = {}
self.elements: int = 0
def __len__(self) -> int:
return self.elements
def __repr__(self) -> str:
return str(self.heap)
def is_empty(self) -> bool:
# Check if the priority queue is empty
return self.elements == 0
def push(self, elem: T, weight: int) -> None:
# Add an element with given priority to the queue
self.heap.append((elem, weight))
self.position_map[elem] = self.elements
self.elements += 1
self._bubble_up(elem)
def extract_min(self) -> T:
# Remove and return the element with lowest weight (highest priority)
if self.elements > 1:
self._swap_nodes(0, self.elements - 1)
elem, _ = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
bubble_down_elem, _ = self.heap[0]
self._bubble_down(bubble_down_elem)
return elem
def update_key(self, elem: T, weight: int) -> None:
# Update the weight of the given key
position = self.position_map[elem]
self.heap[position] = (elem, weight)
if position > 0:
parent_position = get_parent_position(position)
_, parent_weight = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(elem)
else:
self._bubble_down(elem)
else:
self._bubble_down(elem)
def _bubble_up(self, elem: T) -> None:
# Place a node at the proper position (upward movement) [to be used internally
# only]
curr_pos = self.position_map[elem]
if curr_pos == 0:
return
parent_position = get_parent_position(curr_pos)
_, weight = self.heap[curr_pos]
_, parent_weight = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(parent_position, curr_pos)
return self._bubble_up(elem)
return
def _bubble_down(self, elem: T) -> None:
# Place a node at the proper position (downward movement) [to be used
# internally only]
curr_pos = self.position_map[elem]
_, weight = self.heap[curr_pos]
child_left_position = get_child_left_position(curr_pos)
child_right_position = get_child_right_position(curr_pos)
if child_left_position < self.elements and child_right_position < self.elements:
_, child_left_weight = self.heap[child_left_position]
_, child_right_weight = self.heap[child_right_position]
if child_right_weight < child_left_weight:
if child_right_weight < weight:
self._swap_nodes(child_right_position, curr_pos)
return self._bubble_down(elem)
if child_left_position < self.elements:
_, child_left_weight = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(child_left_position, curr_pos)
return self._bubble_down(elem)
else:
return
if child_right_position < self.elements:
_, child_right_weight = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(child_right_position, curr_pos)
return self._bubble_down(elem)
else:
return
def _swap_nodes(self, node1_pos: int, node2_pos: int) -> None:
# Swap the nodes at the given positions
node1_elem = self.heap[node1_pos][0]
node2_elem = self.heap[node2_pos][0]
self.heap[node1_pos], self.heap[node2_pos] = (
self.heap[node2_pos],
self.heap[node1_pos],
)
self.position_map[node1_elem] = node2_pos
self.position_map[node2_elem] = node1_pos
class GraphUndirectedWeighted(Generic[T]):
"""
Graph Undirected Weighted Class
Functions:
add_node: function to add a node in the graph
add_edge: function to add an edge between 2 nodes in the graph
"""
def __init__(self) -> None:
self.connections: dict[T, dict[T, int]] = {}
self.nodes: int = 0
def __repr__(self) -> str:
return str(self.connections)
def __len__(self) -> int:
return self.nodes
def add_node(self, node: T) -> None:
# Add a node in the graph if it is not in the graph
if node not in self.connections:
self.connections[node] = {}
self.nodes += 1
def add_edge(self, node1: T, node2: T, weight: int) -> None:
# Add an edge between 2 nodes in the graph
self.add_node(node1)
self.add_node(node2)
self.connections[node1][node2] = weight
self.connections[node2][node1] = weight
def prims_algo(
graph: GraphUndirectedWeighted[T],
) -> tuple[dict[T, int], dict[T, T | None]]:
"""
>>> graph = GraphUndirectedWeighted()
>>> graph.add_edge("a", "b", 3)
>>> graph.add_edge("b", "c", 10)
>>> graph.add_edge("c", "d", 5)
>>> graph.add_edge("a", "c", 15)
>>> graph.add_edge("b", "d", 100)
>>> dist, parent = prims_algo(graph)
>>> abs(dist["a"] - dist["b"])
3
>>> abs(dist["d"] - dist["b"])
15
>>> abs(dist["a"] - dist["c"])
13
"""
# prim's algorithm for minimum spanning tree
dist: dict[T, int] = {node: maxsize for node in graph.connections}
parent: dict[T, T | None] = {node: None for node in graph.connections}
priority_queue: MinPriorityQueue[T] = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(node, weight)
if priority_queue.is_empty():
return dist, parent
# initialization
node = priority_queue.extract_min()
dist[node] = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
dist[neighbour] = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(neighbour, dist[neighbour])
parent[neighbour] = node
# running prim's algorithm
while not priority_queue.is_empty():
node = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
dist[neighbour] = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(neighbour, dist[neighbour])
parent[neighbour] = node
return dist, parent
| """
Prim's (also known as Jarník's) algorithm is a greedy algorithm that finds a minimum
spanning tree for a weighted undirected graph. This means it finds a subset of the
edges that forms a tree that includes every vertex, where the total weight of all the
edges in the tree is minimized. The algorithm operates by building this tree one vertex
at a time, from an arbitrary starting vertex, at each step adding the cheapest possible
connection from the tree to another vertex.
"""
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
T = TypeVar("T")
def get_parent_position(position: int) -> int:
"""
heap helper function get the position of the parent of the current node
>>> get_parent_position(1)
0
>>> get_parent_position(2)
0
"""
return (position - 1) // 2
def get_child_left_position(position: int) -> int:
"""
heap helper function get the position of the left child of the current node
>>> get_child_left_position(0)
1
"""
return (2 * position) + 1
def get_child_right_position(position: int) -> int:
"""
heap helper function get the position of the right child of the current node
>>> get_child_right_position(0)
2
"""
return (2 * position) + 2
class MinPriorityQueue(Generic[T]):
"""
Minimum Priority Queue Class
Functions:
is_empty: function to check if the priority queue is empty
push: function to add an element with given priority to the queue
extract_min: function to remove and return the element with lowest weight (highest
priority)
update_key: function to update the weight of the given key
_bubble_up: helper function to place a node at the proper position (upward
movement)
_bubble_down: helper function to place a node at the proper position (downward
movement)
_swap_nodes: helper function to swap the nodes at the given positions
>>> queue = MinPriorityQueue()
>>> queue.push(1, 1000)
>>> queue.push(2, 100)
>>> queue.push(3, 4000)
>>> queue.push(4, 3000)
>>> print(queue.extract_min())
2
>>> queue.update_key(4, 50)
>>> print(queue.extract_min())
4
>>> print(queue.extract_min())
1
>>> print(queue.extract_min())
3
"""
def __init__(self) -> None:
self.heap: list[tuple[T, int]] = []
self.position_map: dict[T, int] = {}
self.elements: int = 0
def __len__(self) -> int:
return self.elements
def __repr__(self) -> str:
return str(self.heap)
def is_empty(self) -> bool:
# Check if the priority queue is empty
return self.elements == 0
def push(self, elem: T, weight: int) -> None:
# Add an element with given priority to the queue
self.heap.append((elem, weight))
self.position_map[elem] = self.elements
self.elements += 1
self._bubble_up(elem)
def extract_min(self) -> T:
# Remove and return the element with lowest weight (highest priority)
if self.elements > 1:
self._swap_nodes(0, self.elements - 1)
elem, _ = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
bubble_down_elem, _ = self.heap[0]
self._bubble_down(bubble_down_elem)
return elem
def update_key(self, elem: T, weight: int) -> None:
# Update the weight of the given key
position = self.position_map[elem]
self.heap[position] = (elem, weight)
if position > 0:
parent_position = get_parent_position(position)
_, parent_weight = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(elem)
else:
self._bubble_down(elem)
else:
self._bubble_down(elem)
def _bubble_up(self, elem: T) -> None:
# Place a node at the proper position (upward movement) [to be used internally
# only]
curr_pos = self.position_map[elem]
if curr_pos == 0:
return
parent_position = get_parent_position(curr_pos)
_, weight = self.heap[curr_pos]
_, parent_weight = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(parent_position, curr_pos)
return self._bubble_up(elem)
return
def _bubble_down(self, elem: T) -> None:
# Place a node at the proper position (downward movement) [to be used
# internally only]
curr_pos = self.position_map[elem]
_, weight = self.heap[curr_pos]
child_left_position = get_child_left_position(curr_pos)
child_right_position = get_child_right_position(curr_pos)
if child_left_position < self.elements and child_right_position < self.elements:
_, child_left_weight = self.heap[child_left_position]
_, child_right_weight = self.heap[child_right_position]
if child_right_weight < child_left_weight:
if child_right_weight < weight:
self._swap_nodes(child_right_position, curr_pos)
return self._bubble_down(elem)
if child_left_position < self.elements:
_, child_left_weight = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(child_left_position, curr_pos)
return self._bubble_down(elem)
else:
return
if child_right_position < self.elements:
_, child_right_weight = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(child_right_position, curr_pos)
return self._bubble_down(elem)
else:
return
def _swap_nodes(self, node1_pos: int, node2_pos: int) -> None:
# Swap the nodes at the given positions
node1_elem = self.heap[node1_pos][0]
node2_elem = self.heap[node2_pos][0]
self.heap[node1_pos], self.heap[node2_pos] = (
self.heap[node2_pos],
self.heap[node1_pos],
)
self.position_map[node1_elem] = node2_pos
self.position_map[node2_elem] = node1_pos
class GraphUndirectedWeighted(Generic[T]):
"""
Graph Undirected Weighted Class
Functions:
add_node: function to add a node in the graph
add_edge: function to add an edge between 2 nodes in the graph
"""
def __init__(self) -> None:
self.connections: dict[T, dict[T, int]] = {}
self.nodes: int = 0
def __repr__(self) -> str:
return str(self.connections)
def __len__(self) -> int:
return self.nodes
def add_node(self, node: T) -> None:
# Add a node in the graph if it is not in the graph
if node not in self.connections:
self.connections[node] = {}
self.nodes += 1
def add_edge(self, node1: T, node2: T, weight: int) -> None:
# Add an edge between 2 nodes in the graph
self.add_node(node1)
self.add_node(node2)
self.connections[node1][node2] = weight
self.connections[node2][node1] = weight
def prims_algo(
graph: GraphUndirectedWeighted[T],
) -> tuple[dict[T, int], dict[T, T | None]]:
"""
>>> graph = GraphUndirectedWeighted()
>>> graph.add_edge("a", "b", 3)
>>> graph.add_edge("b", "c", 10)
>>> graph.add_edge("c", "d", 5)
>>> graph.add_edge("a", "c", 15)
>>> graph.add_edge("b", "d", 100)
>>> dist, parent = prims_algo(graph)
>>> abs(dist["a"] - dist["b"])
3
>>> abs(dist["d"] - dist["b"])
15
>>> abs(dist["a"] - dist["c"])
13
"""
# prim's algorithm for minimum spanning tree
dist: dict[T, int] = {node: maxsize for node in graph.connections}
parent: dict[T, T | None] = {node: None for node in graph.connections}
priority_queue: MinPriorityQueue[T] = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(node, weight)
if priority_queue.is_empty():
return dist, parent
# initialization
node = priority_queue.extract_min()
dist[node] = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
dist[neighbour] = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(neighbour, dist[neighbour])
parent[neighbour] = node
# running prim's algorithm
while not priority_queue.is_empty():
node = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
dist[neighbour] = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(neighbour, dist[neighbour])
parent[neighbour] = node
return dist, parent
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Password generator allows you to generate a random password of length N."""
from random import choice, shuffle
from string import ascii_letters, digits, punctuation
def password_generator(length=8):
"""
>>> len(password_generator())
8
>>> len(password_generator(length=16))
16
>>> len(password_generator(257))
257
>>> len(password_generator(length=0))
0
>>> len(password_generator(-1))
0
"""
chars = ascii_letters + digits + punctuation
return "".join(choice(chars) for x in range(length))
# ALTERNATIVE METHODS
# ctbi= characters that must be in password
# i= how many letters or characters the password length will be
def alternative_password_generator(ctbi, i):
# Password generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i = i - len(ctbi)
quotient = int(i / 3)
remainder = i % 3
# chars = ctbi + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
chars = (
ctbi
+ random(ascii_letters, quotient + remainder)
+ random(digits, quotient)
+ random(punctuation, quotient)
)
chars = list(chars)
shuffle(chars)
return "".join(chars)
# random is a generalised function for letters, characters and numbers
def random(ctbi, i):
return "".join(choice(ctbi) for x in range(i))
def random_number(ctbi, i):
pass # Put your code here...
def random_letters(ctbi, i):
pass # Put your code here...
def random_characters(ctbi, i):
pass # Put your code here...
def main():
length = int(input("Please indicate the max length of your password: ").strip())
ctbi = input(
"Please indicate the characters that must be in your password: "
).strip()
print("Password generated:", password_generator(length))
print(
"Alternative Password generated:", alternative_password_generator(ctbi, length)
)
print("[If you are thinking of using this passsword, You better save it.]")
if __name__ == "__main__":
main()
| """Password generator allows you to generate a random password of length N."""
from random import choice, shuffle
from string import ascii_letters, digits, punctuation
def password_generator(length=8):
"""
>>> len(password_generator())
8
>>> len(password_generator(length=16))
16
>>> len(password_generator(257))
257
>>> len(password_generator(length=0))
0
>>> len(password_generator(-1))
0
"""
chars = ascii_letters + digits + punctuation
return "".join(choice(chars) for x in range(length))
# ALTERNATIVE METHODS
# ctbi= characters that must be in password
# i= how many letters or characters the password length will be
def alternative_password_generator(ctbi, i):
# Password generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i = i - len(ctbi)
quotient = int(i / 3)
remainder = i % 3
# chars = ctbi + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
chars = (
ctbi
+ random(ascii_letters, quotient + remainder)
+ random(digits, quotient)
+ random(punctuation, quotient)
)
chars = list(chars)
shuffle(chars)
return "".join(chars)
# random is a generalised function for letters, characters and numbers
def random(ctbi, i):
return "".join(choice(ctbi) for x in range(i))
def random_number(ctbi, i):
pass # Put your code here...
def random_letters(ctbi, i):
pass # Put your code here...
def random_characters(ctbi, i):
pass # Put your code here...
def main():
length = int(input("Please indicate the max length of your password: ").strip())
ctbi = input(
"Please indicate the characters that must be in your password: "
).strip()
print("Password generated:", password_generator(length))
print(
"Alternative Password generated:", alternative_password_generator(ctbi, length)
)
print("[If you are thinking of using this passsword, You better save it.]")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| ### Interest
* Compound Interest: "Compound interest is calculated by multiplying the initial principal amount by one plus the annual interest rate raised to the number of compound periods minus one." [Compound Interest](https://www.investopedia.com/)
* Simple Interest: "Simple interest paid or received over a certain period is a fixed percentage of the principal amount that was borrowed or lent. " [Simple Interest](https://www.investopedia.com/)
| ### Interest
* Compound Interest: "Compound interest is calculated by multiplying the initial principal amount by one plus the annual interest rate raised to the number of compound periods minus one." [Compound Interest](https://www.investopedia.com/)
* Simple Interest: "Simple interest paid or received over a certain period is a fixed percentage of the principal amount that was borrowed or lent. " [Simple Interest](https://www.investopedia.com/)
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Description
The Koch snowflake is a fractal curve and one of the earliest fractals to
have been described. The Koch snowflake can be built up iteratively, in a
sequence of stages. The first stage is an equilateral triangle, and each
successive stage is formed by adding outward bends to each side of the
previous stage, making smaller equilateral triangles.
This can be achieved through the following steps for each line:
1. divide the line segment into three segments of equal length.
2. draw an equilateral triangle that has the middle segment from step 1
as its base and points outward.
3. remove the line segment that is the base of the triangle from step 2.
(description adapted from https://en.wikipedia.org/wiki/Koch_snowflake )
(for a more detailed explanation and an implementation in the
Processing language, see https://natureofcode.com/book/chapter-8-fractals/
#84-the-koch-curve-and-the-arraylist-technique )
Requirements (pip):
- matplotlib
- numpy
"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
VECTOR_1 = numpy.array([0, 0])
VECTOR_2 = numpy.array([0.5, 0.8660254])
VECTOR_3 = numpy.array([1, 0])
INITIAL_VECTORS = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
# uncomment for simple Koch curve instead of Koch snowflake
# INITIAL_VECTORS = [VECTOR_1, VECTOR_3]
def iterate(initial_vectors: list[numpy.ndarray], steps: int) -> list[numpy.ndarray]:
"""
Go through the number of iterations determined by the argument "steps".
Be careful with high values (above 5) since the time to calculate increases
exponentially.
>>> iterate([numpy.array([0, 0]), numpy.array([1, 0])], 1)
[array([0, 0]), array([0.33333333, 0. ]), array([0.5 , \
0.28867513]), array([0.66666667, 0. ]), array([1, 0])]
"""
vectors = initial_vectors
for i in range(steps):
vectors = iteration_step(vectors)
return vectors
def iteration_step(vectors: list[numpy.ndarray]) -> list[numpy.ndarray]:
"""
Loops through each pair of adjacent vectors. Each line between two adjacent
vectors is divided into 4 segments by adding 3 additional vectors in-between
the original two vectors. The vector in the middle is constructed through a
60 degree rotation so it is bent outwards.
>>> iteration_step([numpy.array([0, 0]), numpy.array([1, 0])])
[array([0, 0]), array([0.33333333, 0. ]), array([0.5 , \
0.28867513]), array([0.66666667, 0. ]), array([1, 0])]
"""
new_vectors = []
for i, start_vector in enumerate(vectors[:-1]):
end_vector = vectors[i + 1]
new_vectors.append(start_vector)
difference_vector = end_vector - start_vector
new_vectors.append(start_vector + difference_vector / 3)
new_vectors.append(
start_vector + difference_vector / 3 + rotate(difference_vector / 3, 60)
)
new_vectors.append(start_vector + difference_vector * 2 / 3)
new_vectors.append(vectors[-1])
return new_vectors
def rotate(vector: numpy.ndarray, angle_in_degrees: float) -> numpy.ndarray:
"""
Standard rotation of a 2D vector with a rotation matrix
(see https://en.wikipedia.org/wiki/Rotation_matrix )
>>> rotate(numpy.array([1, 0]), 60)
array([0.5 , 0.8660254])
>>> rotate(numpy.array([1, 0]), 90)
array([6.123234e-17, 1.000000e+00])
"""
theta = numpy.radians(angle_in_degrees)
c, s = numpy.cos(theta), numpy.sin(theta)
rotation_matrix = numpy.array(((c, -s), (s, c)))
return numpy.dot(rotation_matrix, vector)
def plot(vectors: list[numpy.ndarray]) -> None:
"""
Utility function to plot the vectors using matplotlib.pyplot
No doctest was implemented since this function does not have a return value
"""
# avoid stretched display of graph
axes = plt.gca()
axes.set_aspect("equal")
# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
# y-coordinates as inputs, which are constructed from the vector-list using
# zip()
x_coordinates, y_coordinates = zip(*vectors)
plt.plot(x_coordinates, y_coordinates)
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
processed_vectors = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| """
Description
The Koch snowflake is a fractal curve and one of the earliest fractals to
have been described. The Koch snowflake can be built up iteratively, in a
sequence of stages. The first stage is an equilateral triangle, and each
successive stage is formed by adding outward bends to each side of the
previous stage, making smaller equilateral triangles.
This can be achieved through the following steps for each line:
1. divide the line segment into three segments of equal length.
2. draw an equilateral triangle that has the middle segment from step 1
as its base and points outward.
3. remove the line segment that is the base of the triangle from step 2.
(description adapted from https://en.wikipedia.org/wiki/Koch_snowflake )
(for a more detailed explanation and an implementation in the
Processing language, see https://natureofcode.com/book/chapter-8-fractals/
#84-the-koch-curve-and-the-arraylist-technique )
Requirements (pip):
- matplotlib
- numpy
"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
VECTOR_1 = numpy.array([0, 0])
VECTOR_2 = numpy.array([0.5, 0.8660254])
VECTOR_3 = numpy.array([1, 0])
INITIAL_VECTORS = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
# uncomment for simple Koch curve instead of Koch snowflake
# INITIAL_VECTORS = [VECTOR_1, VECTOR_3]
def iterate(initial_vectors: list[numpy.ndarray], steps: int) -> list[numpy.ndarray]:
"""
Go through the number of iterations determined by the argument "steps".
Be careful with high values (above 5) since the time to calculate increases
exponentially.
>>> iterate([numpy.array([0, 0]), numpy.array([1, 0])], 1)
[array([0, 0]), array([0.33333333, 0. ]), array([0.5 , \
0.28867513]), array([0.66666667, 0. ]), array([1, 0])]
"""
vectors = initial_vectors
for i in range(steps):
vectors = iteration_step(vectors)
return vectors
def iteration_step(vectors: list[numpy.ndarray]) -> list[numpy.ndarray]:
"""
Loops through each pair of adjacent vectors. Each line between two adjacent
vectors is divided into 4 segments by adding 3 additional vectors in-between
the original two vectors. The vector in the middle is constructed through a
60 degree rotation so it is bent outwards.
>>> iteration_step([numpy.array([0, 0]), numpy.array([1, 0])])
[array([0, 0]), array([0.33333333, 0. ]), array([0.5 , \
0.28867513]), array([0.66666667, 0. ]), array([1, 0])]
"""
new_vectors = []
for i, start_vector in enumerate(vectors[:-1]):
end_vector = vectors[i + 1]
new_vectors.append(start_vector)
difference_vector = end_vector - start_vector
new_vectors.append(start_vector + difference_vector / 3)
new_vectors.append(
start_vector + difference_vector / 3 + rotate(difference_vector / 3, 60)
)
new_vectors.append(start_vector + difference_vector * 2 / 3)
new_vectors.append(vectors[-1])
return new_vectors
def rotate(vector: numpy.ndarray, angle_in_degrees: float) -> numpy.ndarray:
"""
Standard rotation of a 2D vector with a rotation matrix
(see https://en.wikipedia.org/wiki/Rotation_matrix )
>>> rotate(numpy.array([1, 0]), 60)
array([0.5 , 0.8660254])
>>> rotate(numpy.array([1, 0]), 90)
array([6.123234e-17, 1.000000e+00])
"""
theta = numpy.radians(angle_in_degrees)
c, s = numpy.cos(theta), numpy.sin(theta)
rotation_matrix = numpy.array(((c, -s), (s, c)))
return numpy.dot(rotation_matrix, vector)
def plot(vectors: list[numpy.ndarray]) -> None:
"""
Utility function to plot the vectors using matplotlib.pyplot
No doctest was implemented since this function does not have a return value
"""
# avoid stretched display of graph
axes = plt.gca()
axes.set_aspect("equal")
# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
# y-coordinates as inputs, which are constructed from the vector-list using
# zip()
x_coordinates, y_coordinates = zip(*vectors)
plt.plot(x_coordinates, y_coordinates)
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
processed_vectors = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 493: https://projecteuler.net/problem=493
70 coloured balls are placed in an urn, 10 for each of the seven rainbow colours.
What is the expected number of distinct colours in 20 randomly picked balls?
Give your answer with nine digits after the decimal point (a.bcdefghij).
-----
This combinatorial problem can be solved by decomposing the problem into the
following steps:
1. Calculate the total number of possible picking cominations
[combinations := binom_coeff(70, 20)]
2. Calculate the number of combinations with one colour missing
[missing := binom_coeff(60, 20)]
3. Calculate the probability of one colour missing
[missing_prob := missing / combinations]
4. Calculate the probability of no colour missing
[no_missing_prob := 1 - missing_prob]
5. Calculate the expected number of distinct colours
[expected = 7 * no_missing_prob]
References:
- https://en.wikipedia.org/wiki/Binomial_coefficient
"""
import math
BALLS_PER_COLOUR = 10
NUM_COLOURS = 7
NUM_BALLS = BALLS_PER_COLOUR * NUM_COLOURS
def solution(num_picks: int = 20) -> str:
"""
Calculates the expected number of distinct colours
>>> solution(10)
'5.669644129'
>>> solution(30)
'6.985042712'
"""
total = math.comb(NUM_BALLS, num_picks)
missing_colour = math.comb(NUM_BALLS - BALLS_PER_COLOUR, num_picks)
result = NUM_COLOURS * (1 - missing_colour / total)
return f"{result:.9f}"
if __name__ == "__main__":
print(solution(20))
| """
Project Euler Problem 493: https://projecteuler.net/problem=493
70 coloured balls are placed in an urn, 10 for each of the seven rainbow colours.
What is the expected number of distinct colours in 20 randomly picked balls?
Give your answer with nine digits after the decimal point (a.bcdefghij).
-----
This combinatorial problem can be solved by decomposing the problem into the
following steps:
1. Calculate the total number of possible picking cominations
[combinations := binom_coeff(70, 20)]
2. Calculate the number of combinations with one colour missing
[missing := binom_coeff(60, 20)]
3. Calculate the probability of one colour missing
[missing_prob := missing / combinations]
4. Calculate the probability of no colour missing
[no_missing_prob := 1 - missing_prob]
5. Calculate the expected number of distinct colours
[expected = 7 * no_missing_prob]
References:
- https://en.wikipedia.org/wiki/Binomial_coefficient
"""
import math
BALLS_PER_COLOUR = 10
NUM_COLOURS = 7
NUM_BALLS = BALLS_PER_COLOUR * NUM_COLOURS
def solution(num_picks: int = 20) -> str:
"""
Calculates the expected number of distinct colours
>>> solution(10)
'5.669644129'
>>> solution(30)
'6.985042712'
"""
total = math.comb(NUM_BALLS, num_picks)
missing_colour = math.comb(NUM_BALLS - BALLS_PER_COLOUR, num_picks)
result = NUM_COLOURS * (1 - missing_colour / total)
return f"{result:.9f}"
if __name__ == "__main__":
print(solution(20))
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """ Multiple image resizing techniques """
import numpy as np
from cv2 import destroyAllWindows, imread, imshow, waitKey
class NearestNeighbour:
"""
Simplest and fastest version of image resizing.
Source: https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
"""
def __init__(self, img, dst_width: int, dst_height: int):
if dst_width < 0 or dst_height < 0:
raise ValueError("Destination width/height should be > 0")
self.img = img
self.src_w = img.shape[1]
self.src_h = img.shape[0]
self.dst_w = dst_width
self.dst_h = dst_height
self.ratio_x = self.src_w / self.dst_w
self.ratio_y = self.src_h / self.dst_h
self.output = self.output_img = (
np.ones((self.dst_h, self.dst_w, 3), np.uint8) * 255
)
def process(self):
for i in range(self.dst_h):
for j in range(self.dst_w):
self.output[i][j] = self.img[self.get_y(i)][self.get_x(j)]
def get_x(self, x: int) -> int:
"""
Get parent X coordinate for destination X
:param x: Destination X coordinate
:return: Parent X coordinate based on `x ratio`
>>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg",
... 1), 100, 100)
>>> nn.ratio_x = 0.5
>>> nn.get_x(4)
2
"""
return int(self.ratio_x * x)
def get_y(self, y: int) -> int:
"""
Get parent Y coordinate for destination Y
:param y: Destination X coordinate
:return: Parent X coordinate based on `y ratio`
>>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg",
... 1), 100, 100)
>>> nn.ratio_y = 0.5
>>> nn.get_y(4)
2
"""
return int(self.ratio_y * y)
if __name__ == "__main__":
dst_w, dst_h = 800, 600
im = imread("image_data/lena.jpg", 1)
n = NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
f"Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}", n.output
)
waitKey(0)
destroyAllWindows()
| """ Multiple image resizing techniques """
import numpy as np
from cv2 import destroyAllWindows, imread, imshow, waitKey
class NearestNeighbour:
"""
Simplest and fastest version of image resizing.
Source: https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
"""
def __init__(self, img, dst_width: int, dst_height: int):
if dst_width < 0 or dst_height < 0:
raise ValueError("Destination width/height should be > 0")
self.img = img
self.src_w = img.shape[1]
self.src_h = img.shape[0]
self.dst_w = dst_width
self.dst_h = dst_height
self.ratio_x = self.src_w / self.dst_w
self.ratio_y = self.src_h / self.dst_h
self.output = self.output_img = (
np.ones((self.dst_h, self.dst_w, 3), np.uint8) * 255
)
def process(self):
for i in range(self.dst_h):
for j in range(self.dst_w):
self.output[i][j] = self.img[self.get_y(i)][self.get_x(j)]
def get_x(self, x: int) -> int:
"""
Get parent X coordinate for destination X
:param x: Destination X coordinate
:return: Parent X coordinate based on `x ratio`
>>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg",
... 1), 100, 100)
>>> nn.ratio_x = 0.5
>>> nn.get_x(4)
2
"""
return int(self.ratio_x * x)
def get_y(self, y: int) -> int:
"""
Get parent Y coordinate for destination Y
:param y: Destination X coordinate
:return: Parent X coordinate based on `y ratio`
>>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg",
... 1), 100, 100)
>>> nn.ratio_y = 0.5
>>> nn.get_y(4)
2
"""
return int(self.ratio_y * y)
if __name__ == "__main__":
dst_w, dst_h = 800, 600
im = imread("image_data/lena.jpg", 1)
n = NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
f"Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}", n.output
)
waitKey(0)
destroyAllWindows()
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def double_sort(lst):
"""This sorting algorithm sorts an array using the principle of bubble sort,
but does it both from left to right and right to left.
Hence, it's called "Double sort"
:param collection: mutable ordered sequence of elements
:return: the same collection in ascending order
Examples:
>>> double_sort([-1 ,-2 ,-3 ,-4 ,-5 ,-6 ,-7])
[-7, -6, -5, -4, -3, -2, -1]
>>> double_sort([])
[]
>>> double_sort([-1 ,-2 ,-3 ,-4 ,-5 ,-6])
[-6, -5, -4, -3, -2, -1]
>>> double_sort([-3, 10, 16, -42, 29]) == sorted([-3, 10, 16, -42, 29])
True
"""
no_of_elements = len(lst)
for i in range(
0, int(((no_of_elements - 1) / 2) + 1)
): # we don't need to traverse to end of list as
for j in range(0, no_of_elements - 1):
if (
lst[j + 1] < lst[j]
): # applying bubble sort algorithm from left to right (or forwards)
temp = lst[j + 1]
lst[j + 1] = lst[j]
lst[j] = temp
if (
lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
): # applying bubble sort algorithm from right to left (or backwards)
temp = lst[no_of_elements - 1 - j]
lst[no_of_elements - 1 - j] = lst[no_of_elements - 2 - j]
lst[no_of_elements - 2 - j] = temp
return lst
if __name__ == "__main__":
print("enter the list to be sorted")
lst = [int(x) for x in input().split()] # inputing elements of the list in one line
sorted_lst = double_sort(lst)
print("the sorted list is")
print(sorted_lst)
| def double_sort(lst):
"""This sorting algorithm sorts an array using the principle of bubble sort,
but does it both from left to right and right to left.
Hence, it's called "Double sort"
:param collection: mutable ordered sequence of elements
:return: the same collection in ascending order
Examples:
>>> double_sort([-1 ,-2 ,-3 ,-4 ,-5 ,-6 ,-7])
[-7, -6, -5, -4, -3, -2, -1]
>>> double_sort([])
[]
>>> double_sort([-1 ,-2 ,-3 ,-4 ,-5 ,-6])
[-6, -5, -4, -3, -2, -1]
>>> double_sort([-3, 10, 16, -42, 29]) == sorted([-3, 10, 16, -42, 29])
True
"""
no_of_elements = len(lst)
for i in range(
0, int(((no_of_elements - 1) / 2) + 1)
): # we don't need to traverse to end of list as
for j in range(0, no_of_elements - 1):
if (
lst[j + 1] < lst[j]
): # applying bubble sort algorithm from left to right (or forwards)
temp = lst[j + 1]
lst[j + 1] = lst[j]
lst[j] = temp
if (
lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
): # applying bubble sort algorithm from right to left (or backwards)
temp = lst[no_of_elements - 1 - j]
lst[no_of_elements - 1 - j] = lst[no_of_elements - 2 - j]
lst[no_of_elements - 2 - j] = temp
return lst
if __name__ == "__main__":
print("enter the list to be sorted")
lst = [int(x) for x in input().split()] # inputing elements of the list in one line
sorted_lst = double_sort(lst)
print("the sorted list is")
print(sorted_lst)
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # https://www.tutorialspoint.com/python3/bitwise_operators_example.htm
def binary_xor(a: int, b: int) -> str:
"""
Take in 2 integers, convert them to binary,
return a binary number that is the
result of a binary xor operation on the integers provided.
>>> binary_xor(25, 32)
'0b111001'
>>> binary_xor(37, 50)
'0b010111'
>>> binary_xor(21, 30)
'0b01011'
>>> binary_xor(58, 73)
'0b1110011'
>>> binary_xor(0, 255)
'0b11111111'
>>> binary_xor(256, 256)
'0b000000000'
>>> binary_xor(0, -1)
Traceback (most recent call last):
...
ValueError: the value of both inputs must be positive
>>> binary_xor(0, 1.1)
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
>>> binary_xor("0", "1")
Traceback (most recent call last):
...
TypeError: '<' not supported between instances of 'str' and 'int'
"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive")
a_binary = str(bin(a))[2:] # remove the leading "0b"
b_binary = str(bin(b))[2:] # remove the leading "0b"
max_len = max(len(a_binary), len(b_binary))
return "0b" + "".join(
str(int(char_a != char_b))
for char_a, char_b in zip(a_binary.zfill(max_len), b_binary.zfill(max_len))
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| # https://www.tutorialspoint.com/python3/bitwise_operators_example.htm
def binary_xor(a: int, b: int) -> str:
"""
Take in 2 integers, convert them to binary,
return a binary number that is the
result of a binary xor operation on the integers provided.
>>> binary_xor(25, 32)
'0b111001'
>>> binary_xor(37, 50)
'0b010111'
>>> binary_xor(21, 30)
'0b01011'
>>> binary_xor(58, 73)
'0b1110011'
>>> binary_xor(0, 255)
'0b11111111'
>>> binary_xor(256, 256)
'0b000000000'
>>> binary_xor(0, -1)
Traceback (most recent call last):
...
ValueError: the value of both inputs must be positive
>>> binary_xor(0, 1.1)
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
>>> binary_xor("0", "1")
Traceback (most recent call last):
...
TypeError: '<' not supported between instances of 'str' and 'int'
"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive")
a_binary = str(bin(a))[2:] # remove the leading "0b"
b_binary = str(bin(b))[2:] # remove the leading "0b"
max_len = max(len(a_binary), len(b_binary))
return "0b" + "".join(
str(int(char_a != char_b))
for char_a, char_b in zip(a_binary.zfill(max_len), b_binary.zfill(max_len))
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """For more information about the Binomial Distribution -
https://en.wikipedia.org/wiki/Binomial_distribution"""
from math import factorial
def binomial_distribution(successes: int, trials: int, prob: float) -> float:
"""
Return probability of k successes out of n tries, with p probability for one
success
The function uses the factorial function in order to calculate the binomial
coefficient
>>> binomial_distribution(3, 5, 0.7)
0.30870000000000003
>>> binomial_distribution (2, 4, 0.5)
0.375
"""
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(successes, int) or not isinstance(trials, int):
raise ValueError("the function is defined for non-negative integers")
if not 0 < prob < 1:
raise ValueError("prob has to be in range of 1 - 0")
probability = (prob ** successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
coefficient = float(factorial(trials))
coefficient /= factorial(successes) * factorial(trials - successes)
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print("Probability of 2 successes out of 4 trails")
print("with probability of 0.75 is:", end=" ")
print(binomial_distribution(2, 4, 0.75))
| """For more information about the Binomial Distribution -
https://en.wikipedia.org/wiki/Binomial_distribution"""
from math import factorial
def binomial_distribution(successes: int, trials: int, prob: float) -> float:
"""
Return probability of k successes out of n tries, with p probability for one
success
The function uses the factorial function in order to calculate the binomial
coefficient
>>> binomial_distribution(3, 5, 0.7)
0.30870000000000003
>>> binomial_distribution (2, 4, 0.5)
0.375
"""
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(successes, int) or not isinstance(trials, int):
raise ValueError("the function is defined for non-negative integers")
if not 0 < prob < 1:
raise ValueError("prob has to be in range of 1 - 0")
probability = (prob ** successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
coefficient = float(factorial(trials))
coefficient /= factorial(successes) * factorial(trials - successes)
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print("Probability of 2 successes out of 4 trails")
print("with probability of 0.75 is:", end=" ")
print(binomial_distribution(2, 4, 0.75))
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/Best-first_search#Greedy_BFS
"""
from __future__ import annotations
Path = list[tuple[int, int]]
grid = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
delta = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right
class Node:
"""
>>> k = Node(0, 0, 4, 5, 0, None)
>>> k.calculate_heuristic()
9
>>> n = Node(1, 4, 3, 4, 2, None)
>>> n.calculate_heuristic()
2
>>> l = [k, n]
>>> n == l[0]
False
>>> l.sort()
>>> n == l[0]
True
"""
def __init__(
self,
pos_x: int,
pos_y: int,
goal_x: int,
goal_y: int,
g_cost: float,
parent: Node | None,
):
self.pos_x = pos_x
self.pos_y = pos_y
self.pos = (pos_y, pos_x)
self.goal_x = goal_x
self.goal_y = goal_y
self.g_cost = g_cost
self.parent = parent
self.f_cost = self.calculate_heuristic()
def calculate_heuristic(self) -> float:
"""
The heuristic here is the Manhattan Distance
Could elaborate to offer more than one choice
"""
dy = abs(self.pos_x - self.goal_x)
dx = abs(self.pos_y - self.goal_y)
return dx + dy
def __lt__(self, other) -> bool:
return self.f_cost < other.f_cost
class GreedyBestFirst:
"""
>>> gbf = GreedyBestFirst((0, 0), (len(grid) - 1, len(grid[0]) - 1))
>>> [x.pos for x in gbf.get_successors(gbf.start)]
[(1, 0), (0, 1)]
>>> (gbf.start.pos_y + delta[3][0], gbf.start.pos_x + delta[3][1])
(0, 1)
>>> (gbf.start.pos_y + delta[2][0], gbf.start.pos_x + delta[2][1])
(1, 0)
>>> gbf.retrace_path(gbf.start)
[(0, 0)]
>>> gbf.search() # doctest: +NORMALIZE_WHITESPACE
[(0, 0), (1, 0), (2, 0), (3, 0), (3, 1), (4, 1), (5, 1), (6, 1),
(6, 2), (6, 3), (5, 3), (5, 4), (5, 5), (6, 5), (6, 6)]
"""
def __init__(self, start: tuple[int, int], goal: tuple[int, int]):
self.start = Node(start[1], start[0], goal[1], goal[0], 0, None)
self.target = Node(goal[1], goal[0], goal[1], goal[0], 99999, None)
self.open_nodes = [self.start]
self.closed_nodes: list[Node] = []
self.reached = False
def search(self) -> Path | None:
"""
Search for the path,
if a path is not found, only the starting position is returned
"""
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
current_node = self.open_nodes.pop(0)
if current_node.pos == self.target.pos:
self.reached = True
return self.retrace_path(current_node)
self.closed_nodes.append(current_node)
successors = self.get_successors(current_node)
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(child_node)
else:
# retrieve the best current path
better_node = self.open_nodes.pop(self.open_nodes.index(child_node))
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(child_node)
else:
self.open_nodes.append(better_node)
if not self.reached:
return [self.start.pos]
return None
def get_successors(self, parent: Node) -> list[Node]:
"""
Returns a list of successors (both in the grid and free spaces)
"""
successors = []
for action in delta:
pos_x = parent.pos_x + action[1]
pos_y = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(grid) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
pos_x,
pos_y,
self.target.pos_y,
self.target.pos_x,
parent.g_cost + 1,
parent,
)
)
return successors
def retrace_path(self, node: Node | None) -> Path:
"""
Retrace the path from parents to parents until start node
"""
current_node = node
path = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x))
current_node = current_node.parent
path.reverse()
return path
if __name__ == "__main__":
init = (0, 0)
goal = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
print("------")
greedy_bf = GreedyBestFirst(init, goal)
path = greedy_bf.search()
if path:
for pos_x, pos_y in path:
grid[pos_x][pos_y] = 2
for elem in grid:
print(elem)
| """
https://en.wikipedia.org/wiki/Best-first_search#Greedy_BFS
"""
from __future__ import annotations
Path = list[tuple[int, int]]
grid = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
delta = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right
class Node:
"""
>>> k = Node(0, 0, 4, 5, 0, None)
>>> k.calculate_heuristic()
9
>>> n = Node(1, 4, 3, 4, 2, None)
>>> n.calculate_heuristic()
2
>>> l = [k, n]
>>> n == l[0]
False
>>> l.sort()
>>> n == l[0]
True
"""
def __init__(
self,
pos_x: int,
pos_y: int,
goal_x: int,
goal_y: int,
g_cost: float,
parent: Node | None,
):
self.pos_x = pos_x
self.pos_y = pos_y
self.pos = (pos_y, pos_x)
self.goal_x = goal_x
self.goal_y = goal_y
self.g_cost = g_cost
self.parent = parent
self.f_cost = self.calculate_heuristic()
def calculate_heuristic(self) -> float:
"""
The heuristic here is the Manhattan Distance
Could elaborate to offer more than one choice
"""
dy = abs(self.pos_x - self.goal_x)
dx = abs(self.pos_y - self.goal_y)
return dx + dy
def __lt__(self, other) -> bool:
return self.f_cost < other.f_cost
class GreedyBestFirst:
"""
>>> gbf = GreedyBestFirst((0, 0), (len(grid) - 1, len(grid[0]) - 1))
>>> [x.pos for x in gbf.get_successors(gbf.start)]
[(1, 0), (0, 1)]
>>> (gbf.start.pos_y + delta[3][0], gbf.start.pos_x + delta[3][1])
(0, 1)
>>> (gbf.start.pos_y + delta[2][0], gbf.start.pos_x + delta[2][1])
(1, 0)
>>> gbf.retrace_path(gbf.start)
[(0, 0)]
>>> gbf.search() # doctest: +NORMALIZE_WHITESPACE
[(0, 0), (1, 0), (2, 0), (3, 0), (3, 1), (4, 1), (5, 1), (6, 1),
(6, 2), (6, 3), (5, 3), (5, 4), (5, 5), (6, 5), (6, 6)]
"""
def __init__(self, start: tuple[int, int], goal: tuple[int, int]):
self.start = Node(start[1], start[0], goal[1], goal[0], 0, None)
self.target = Node(goal[1], goal[0], goal[1], goal[0], 99999, None)
self.open_nodes = [self.start]
self.closed_nodes: list[Node] = []
self.reached = False
def search(self) -> Path | None:
"""
Search for the path,
if a path is not found, only the starting position is returned
"""
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
current_node = self.open_nodes.pop(0)
if current_node.pos == self.target.pos:
self.reached = True
return self.retrace_path(current_node)
self.closed_nodes.append(current_node)
successors = self.get_successors(current_node)
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(child_node)
else:
# retrieve the best current path
better_node = self.open_nodes.pop(self.open_nodes.index(child_node))
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(child_node)
else:
self.open_nodes.append(better_node)
if not self.reached:
return [self.start.pos]
return None
def get_successors(self, parent: Node) -> list[Node]:
"""
Returns a list of successors (both in the grid and free spaces)
"""
successors = []
for action in delta:
pos_x = parent.pos_x + action[1]
pos_y = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(grid) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
pos_x,
pos_y,
self.target.pos_y,
self.target.pos_x,
parent.g_cost + 1,
parent,
)
)
return successors
def retrace_path(self, node: Node | None) -> Path:
"""
Retrace the path from parents to parents until start node
"""
current_node = node
path = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x))
current_node = current_node.parent
path.reverse()
return path
if __name__ == "__main__":
init = (0, 0)
goal = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
print("------")
greedy_bf = GreedyBestFirst(init, goal)
path = greedy_bf.search()
if path:
for pos_x, pos_y in path:
grid[pos_x][pos_y] = 2
for elem in grid:
print(elem)
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| alphabets = [chr(i) for i in range(32, 126)]
gear_one = [i for i in range(len(alphabets))]
gear_two = [i for i in range(len(alphabets))]
gear_three = [i for i in range(len(alphabets))]
reflector = [i for i in reversed(range(len(alphabets)))]
code = []
gear_one_pos = gear_two_pos = gear_three_pos = 0
def rotator():
global gear_one_pos
global gear_two_pos
global gear_three_pos
i = gear_one[0]
gear_one.append(i)
del gear_one[0]
gear_one_pos += 1
if gear_one_pos % int(len(alphabets)) == 0:
i = gear_two[0]
gear_two.append(i)
del gear_two[0]
gear_two_pos += 1
if gear_two_pos % int(len(alphabets)) == 0:
i = gear_three[0]
gear_three.append(i)
del gear_three[0]
gear_three_pos += 1
def engine(input_character):
target = alphabets.index(input_character)
target = gear_one[target]
target = gear_two[target]
target = gear_three[target]
target = reflector[target]
target = gear_three.index(target)
target = gear_two.index(target)
target = gear_one.index(target)
code.append(alphabets[target])
rotator()
if __name__ == "__main__":
decode = list(input("Type your message:\n"))
while True:
try:
token = int(input("Please set token:(must be only digits)\n"))
break
except Exception as error:
print(error)
for i in range(token):
rotator()
for j in decode:
engine(j)
print("\n" + "".join(code))
print(
f"\nYour Token is {token} please write it down.\nIf you want to decode "
f"this message again you should input same digits as token!"
)
| alphabets = [chr(i) for i in range(32, 126)]
gear_one = [i for i in range(len(alphabets))]
gear_two = [i for i in range(len(alphabets))]
gear_three = [i for i in range(len(alphabets))]
reflector = [i for i in reversed(range(len(alphabets)))]
code = []
gear_one_pos = gear_two_pos = gear_three_pos = 0
def rotator():
global gear_one_pos
global gear_two_pos
global gear_three_pos
i = gear_one[0]
gear_one.append(i)
del gear_one[0]
gear_one_pos += 1
if gear_one_pos % int(len(alphabets)) == 0:
i = gear_two[0]
gear_two.append(i)
del gear_two[0]
gear_two_pos += 1
if gear_two_pos % int(len(alphabets)) == 0:
i = gear_three[0]
gear_three.append(i)
del gear_three[0]
gear_three_pos += 1
def engine(input_character):
target = alphabets.index(input_character)
target = gear_one[target]
target = gear_two[target]
target = gear_three[target]
target = reflector[target]
target = gear_three.index(target)
target = gear_two.index(target)
target = gear_one.index(target)
code.append(alphabets[target])
rotator()
if __name__ == "__main__":
decode = list(input("Type your message:\n"))
while True:
try:
token = int(input("Please set token:(must be only digits)\n"))
break
except Exception as error:
print(error)
for i in range(token):
rotator()
for j in decode:
engine(j)
print("\n" + "".join(code))
print(
f"\nYour Token is {token} please write it down.\nIf you want to decode "
f"this message again you should input same digits as token!"
)
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
* Author: Manuel Di Lullo (https://github.com/manueldilullo)
* Description: Random graphs generator.
Uses graphs represented with an adjacency list.
URL: https://en.wikipedia.org/wiki/Random_graph
"""
import random
def random_graph(
vertices_number: int, probability: float, directed: bool = False
) -> dict:
"""
Generate a random graph
@input: vertices_number (number of vertices),
probability (probability that a generic edge (u,v) exists),
directed (if True: graph will be a directed graph,
otherwise it will be an undirected graph)
@examples:
>>> random.seed(1)
>>> random_graph(4, 0.5)
{0: [1], 1: [0, 2, 3], 2: [1, 3], 3: [1, 2]}
>>> random.seed(1)
>>> random_graph(4, 0.5, True)
{0: [1], 1: [2, 3], 2: [3], 3: []}
"""
graph = {i: [] for i in range(vertices_number)}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
return complete_graph(vertices_number)
# if probability is lower or equal than 0, then return a graph without edges
if probability <= 0:
return graph
# for each couple of nodes, add an edge from u to v
# if the number randomly generated is greater than probability probability
for i in range(vertices_number):
for j in range(i + 1, vertices_number):
if random.random() < probability:
graph[i].append(j)
if not directed:
# if the graph is undirected, add an edge in from j to i, either
graph[j].append(i)
return graph
def complete_graph(vertices_number: int) -> dict:
"""
Generate a complete graph with vertices_number vertices.
@input: vertices_number (number of vertices),
directed (False if the graph is undirected, True otherwise)
@example:
>>> print(complete_graph(3))
{0: [1, 2], 1: [0, 2], 2: [0, 1]}
"""
return {
i: [j for j in range(vertices_number) if i != j] for i in range(vertices_number)
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
* Author: Manuel Di Lullo (https://github.com/manueldilullo)
* Description: Random graphs generator.
Uses graphs represented with an adjacency list.
URL: https://en.wikipedia.org/wiki/Random_graph
"""
import random
def random_graph(
vertices_number: int, probability: float, directed: bool = False
) -> dict:
"""
Generate a random graph
@input: vertices_number (number of vertices),
probability (probability that a generic edge (u,v) exists),
directed (if True: graph will be a directed graph,
otherwise it will be an undirected graph)
@examples:
>>> random.seed(1)
>>> random_graph(4, 0.5)
{0: [1], 1: [0, 2, 3], 2: [1, 3], 3: [1, 2]}
>>> random.seed(1)
>>> random_graph(4, 0.5, True)
{0: [1], 1: [2, 3], 2: [3], 3: []}
"""
graph = {i: [] for i in range(vertices_number)}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
return complete_graph(vertices_number)
# if probability is lower or equal than 0, then return a graph without edges
if probability <= 0:
return graph
# for each couple of nodes, add an edge from u to v
# if the number randomly generated is greater than probability probability
for i in range(vertices_number):
for j in range(i + 1, vertices_number):
if random.random() < probability:
graph[i].append(j)
if not directed:
# if the graph is undirected, add an edge in from j to i, either
graph[j].append(i)
return graph
def complete_graph(vertices_number: int) -> dict:
"""
Generate a complete graph with vertices_number vertices.
@input: vertices_number (number of vertices),
directed (False if the graph is undirected, True otherwise)
@example:
>>> print(complete_graph(3))
{0: [1, 2], 1: [0, 2], 2: [0, 1]}
"""
return {
i: [j for j in range(vertices_number) if i != j] for i in range(vertices_number)
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Unnamed repository; edit this file 'description' to name the repository.
| Unnamed repository; edit this file 'description' to name the repository.
| -1 |
TheAlgorithms/Python | 5,794 | [mypy] Fix type annotations in `graphs/boruvka.py` | ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dylanbuchi | "2021-11-08T09:39:22Z" | "2021-11-08T13:47:10Z" | 2f6a7ae1fa44514f52f9a97f83d7bbb2b18e53f2 | ac4bdfd66dbbd4c7c92c73d894469aa4a5c3e5ab | [mypy] Fix type annotations in `graphs/boruvka.py`. ### Describe your change:
- Related issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def mode(input_list: list) -> list: # Defining function "mode."
"""This function returns the mode(Mode as in the measures of
central tendency) of the input data.
The input list may contain any Datastructure or any Datatype.
>>> input_list = [2, 3, 4, 5, 3, 4, 2, 5, 2, 2, 4, 2, 2, 2]
>>> mode(input_list)
[2]
>>> input_list = [3, 4, 5, 3, 4, 2, 5, 2, 2, 4, 4, 2, 2, 2]
>>> mode(input_list)
[2]
>>> input_list = [3, 4, 5, 3, 4, 2, 5, 2, 2, 4, 4, 4, 2, 2, 4, 2]
>>> mode(input_list)
[2, 4]
>>> input_list = ["x", "y", "y", "z"]
>>> mode(input_list)
['y']
>>> input_list = ["x", "x" , "y", "y", "z"]
>>> mode(input_list)
['x', 'y']
"""
result = list() # Empty list to store the counts of elements in input_list
for x in input_list:
result.append(input_list.count(x))
if not result:
return []
y = max(result) # Gets the maximum value in the result list.
# Gets values of modes
result = {input_list[i] for i, value in enumerate(result) if value == y}
return sorted(result)
if __name__ == "__main__":
import doctest
doctest.testmod()
| from typing import Any
def mode(input_list: list) -> list[Any]:
"""This function returns the mode(Mode as in the measures of
central tendency) of the input data.
The input list may contain any Datastructure or any Datatype.
>>> mode([2, 3, 4, 5, 3, 4, 2, 5, 2, 2, 4, 2, 2, 2])
[2]
>>> mode([3, 4, 5, 3, 4, 2, 5, 2, 2, 4, 4, 2, 2, 2])
[2]
>>> mode([3, 4, 5, 3, 4, 2, 5, 2, 2, 4, 4, 4, 2, 2, 4, 2])
[2, 4]
>>> mode(["x", "y", "y", "z"])
['y']
>>> mode(["x", "x" , "y", "y", "z"])
['x', 'y']
"""
if not input_list:
return []
result = [input_list.count(value) for value in input_list]
y = max(result) # Gets the maximum count in the input list.
# Gets values of modes
return sorted({input_list[i] for i, value in enumerate(result) if value == y})
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Gamma function is a very useful tool in math and physics.
It helps calculating complex integral in a convenient way.
for more info: https://en.wikipedia.org/wiki/Gamma_function
Python's Standard Library math.gamma() function overflows around gamma(171.624).
"""
from math import pi, sqrt
def gamma(num: float) -> float:
"""
Calculates the value of Gamma function of num
where num is either an integer (1, 2, 3..) or a half-integer (0.5, 1.5, 2.5 ...).
Implemented using recursion
Examples:
>>> from math import isclose, gamma as math_gamma
>>> gamma(0.5)
1.7724538509055159
>>> gamma(2)
1.0
>>> gamma(3.5)
3.3233509704478426
>>> gamma(171.5)
9.483367566824795e+307
>>> all(isclose(gamma(num), math_gamma(num)) for num in (0.5, 2, 3.5, 171.5))
True
>>> gamma(0)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(-1.1)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(-4)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(172)
Traceback (most recent call last):
...
OverflowError: math range error
>>> gamma(1.1)
Traceback (most recent call last):
...
NotImplementedError: num must be an integer or a half-integer
"""
if num <= 0:
raise ValueError("math domain error")
if num > 171.5:
raise OverflowError("math range error")
elif num - int(num) not in (0, 0.5):
raise NotImplementedError("num must be an integer or a half-integer")
elif num == 0.5:
return sqrt(pi)
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1)
def test_gamma() -> None:
"""
>>> test_gamma()
"""
assert gamma(0.5) == sqrt(pi)
assert gamma(1) == 1.0
assert gamma(2) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
num = 1
while num:
num = float(input("Gamma of: "))
print(f"gamma({num}) = {gamma(num)}")
print("\nEnter 0 to exit...")
| """
Gamma function is a very useful tool in math and physics.
It helps calculating complex integral in a convenient way.
for more info: https://en.wikipedia.org/wiki/Gamma_function
Python's Standard Library math.gamma() function overflows around gamma(171.624).
"""
from math import pi, sqrt
def gamma(num: float) -> float:
"""
Calculates the value of Gamma function of num
where num is either an integer (1, 2, 3..) or a half-integer (0.5, 1.5, 2.5 ...).
Implemented using recursion
Examples:
>>> from math import isclose, gamma as math_gamma
>>> gamma(0.5)
1.7724538509055159
>>> gamma(2)
1.0
>>> gamma(3.5)
3.3233509704478426
>>> gamma(171.5)
9.483367566824795e+307
>>> all(isclose(gamma(num), math_gamma(num)) for num in (0.5, 2, 3.5, 171.5))
True
>>> gamma(0)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(-1.1)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(-4)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(172)
Traceback (most recent call last):
...
OverflowError: math range error
>>> gamma(1.1)
Traceback (most recent call last):
...
NotImplementedError: num must be an integer or a half-integer
"""
if num <= 0:
raise ValueError("math domain error")
if num > 171.5:
raise OverflowError("math range error")
elif num - int(num) not in (0, 0.5):
raise NotImplementedError("num must be an integer or a half-integer")
elif num == 0.5:
return sqrt(pi)
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1)
def test_gamma() -> None:
"""
>>> test_gamma()
"""
assert gamma(0.5) == sqrt(pi)
assert gamma(1) == 1.0
assert gamma(2) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
num = 1.0
while num:
num = float(input("Gamma of: "))
print(f"gamma({num}) = {gamma(num)}")
print("\nEnter 0 to exit...")
| 1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Calculate the nth Proth number
Source:
https://handwiki.org/wiki/Proth_number
"""
import math
def proth(number: int) -> int:
"""
:param number: nth number to calculate in the sequence
:return: the nth number in Proth number
Note: indexing starts at 1 i.e. proth(1) gives the first Proth number of 3
>>> proth(6)
25
>>> proth(0)
Traceback (most recent call last):
...
ValueError: Input value of [number=0] must be > 0
>>> proth(-1)
Traceback (most recent call last):
...
ValueError: Input value of [number=-1] must be > 0
>>> proth(6.0)
Traceback (most recent call last):
...
TypeError: Input value of [number=6.0] must be an integer
"""
if not isinstance(number, int):
raise TypeError(f"Input value of [number={number}] must be an integer")
if number < 1:
raise ValueError(f"Input value of [number={number}] must be > 0")
elif number == 1:
return 3
elif number == 2:
return 5
else:
block_index = number // 3
"""
+1 for binary starting at 0 i.e. 2^0, 2^1, etc.
+1 to start the sequence at the 3rd Proth number
Hence, we have a +2 in the below statement
"""
block_index = math.log(block_index, 2) + 2
block_index = int(block_index)
proth_list = [3, 5]
proth_index = 2
increment = 3
for block in range(1, block_index):
for move in range(increment):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1])
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
for number in range(11):
value = 0
try:
value = proth(number)
except ValueError:
print(f"ValueError: there is no {number}th Proth number")
continue
print(f"The {number}th Proth number: {value}")
| """
Calculate the nth Proth number
Source:
https://handwiki.org/wiki/Proth_number
"""
import math
def proth(number: int) -> int:
"""
:param number: nth number to calculate in the sequence
:return: the nth number in Proth number
Note: indexing starts at 1 i.e. proth(1) gives the first Proth number of 3
>>> proth(6)
25
>>> proth(0)
Traceback (most recent call last):
...
ValueError: Input value of [number=0] must be > 0
>>> proth(-1)
Traceback (most recent call last):
...
ValueError: Input value of [number=-1] must be > 0
>>> proth(6.0)
Traceback (most recent call last):
...
TypeError: Input value of [number=6.0] must be an integer
"""
if not isinstance(number, int):
raise TypeError(f"Input value of [number={number}] must be an integer")
if number < 1:
raise ValueError(f"Input value of [number={number}] must be > 0")
elif number == 1:
return 3
elif number == 2:
return 5
else:
"""
+1 for binary starting at 0 i.e. 2^0, 2^1, etc.
+1 to start the sequence at the 3rd Proth number
Hence, we have a +2 in the below statement
"""
block_index = int(math.log(number // 3, 2)) + 2
proth_list = [3, 5]
proth_index = 2
increment = 3
for block in range(1, block_index):
for move in range(increment):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1])
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(11):
value = 0
try:
value = proth(number)
except ValueError:
print(f"ValueError: there is no {number}th Proth number")
continue
print(f"The {number}th Proth number: {value}")
| 1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is a pure Python implementation of the Geometric Series algorithm
https://en.wikipedia.org/wiki/Geometric_series
Run the doctests with the following command:
python3 -m doctest -v geometric_series.py
or
python -m doctest -v geometric_series.py
For manual testing run:
python3 geometric_series.py
"""
def geometric_series(nth_term: int, start_term_a: int, common_ratio_r: int) -> list:
"""Pure Python implementation of Geometric Series algorithm
:param nth_term: The last term (nth term of Geometric Series)
:param start_term_a : The first term of Geometric Series
:param common_ratio_r : The common ratio between all the terms
:return: The Geometric Series starting from first term a and multiple of common
ration with first term with increase in power till last term (nth term)
Examples:
>>> geometric_series(4, 2, 2)
[2, '4.0', '8.0', '16.0']
>>> geometric_series(4.0, 2.0, 2.0)
[2.0, '4.0', '8.0', '16.0']
>>> geometric_series(4.1, 2.1, 2.1)
[2.1, '4.41', '9.261000000000001', '19.448100000000004']
>>> geometric_series(4, 2, -2)
[2, '-4.0', '8.0', '-16.0']
>>> geometric_series(4, -2, 2)
[-2, '-4.0', '-8.0', '-16.0']
>>> geometric_series(-4, 2, 2)
[]
>>> geometric_series(0, 100, 500)
[]
>>> geometric_series(1, 1, 1)
[1]
>>> geometric_series(0, 0, 0)
[]
"""
if "" in (nth_term, start_term_a, common_ratio_r):
return ""
series = []
power = 1
multiple = common_ratio_r
for _ in range(int(nth_term)):
if series == []:
series.append(start_term_a)
else:
power += 1
series.append(str(float(start_term_a) * float(multiple)))
multiple = pow(float(common_ratio_r), power)
return series
if __name__ == "__main__":
nth_term = input("Enter the last number (n term) of the Geometric Series")
start_term_a = input("Enter the starting term (a) of the Geometric Series")
common_ratio_r = input(
"Enter the common ratio between two terms (r) of the Geometric Series"
)
print("Formula of Geometric Series => a + ar + ar^2 ... +ar^n")
print(geometric_series(nth_term, start_term_a, common_ratio_r))
| """
This is a pure Python implementation of the Geometric Series algorithm
https://en.wikipedia.org/wiki/Geometric_series
Run the doctests with the following command:
python3 -m doctest -v geometric_series.py
or
python -m doctest -v geometric_series.py
For manual testing run:
python3 geometric_series.py
"""
from __future__ import annotations
def geometric_series(
nth_term: float | int,
start_term_a: float | int,
common_ratio_r: float | int,
) -> list[float | int]:
"""
Pure Python implementation of Geometric Series algorithm
:param nth_term: The last term (nth term of Geometric Series)
:param start_term_a : The first term of Geometric Series
:param common_ratio_r : The common ratio between all the terms
:return: The Geometric Series starting from first term a and multiple of common
ration with first term with increase in power till last term (nth term)
Examples:
>>> geometric_series(4, 2, 2)
[2, 4.0, 8.0, 16.0]
>>> geometric_series(4.0, 2.0, 2.0)
[2.0, 4.0, 8.0, 16.0]
>>> geometric_series(4.1, 2.1, 2.1)
[2.1, 4.41, 9.261000000000001, 19.448100000000004]
>>> geometric_series(4, 2, -2)
[2, -4.0, 8.0, -16.0]
>>> geometric_series(4, -2, 2)
[-2, -4.0, -8.0, -16.0]
>>> geometric_series(-4, 2, 2)
[]
>>> geometric_series(0, 100, 500)
[]
>>> geometric_series(1, 1, 1)
[1]
>>> geometric_series(0, 0, 0)
[]
"""
if not all((nth_term, start_term_a, common_ratio_r)):
return []
series: list[float | int] = []
power = 1
multiple = common_ratio_r
for _ in range(int(nth_term)):
if series == []:
series.append(start_term_a)
else:
power += 1
series.append(float(start_term_a * multiple))
multiple = pow(float(common_ratio_r), power)
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
nth_term = float(input("Enter the last number (n term) of the Geometric Series"))
start_term_a = float(input("Enter the starting term (a) of the Geometric Series"))
common_ratio_r = float(
input("Enter the common ratio between two terms (r) of the Geometric Series")
)
print("Formula of Geometric Series => a + ar + ar^2 ... +ar^n")
print(geometric_series(nth_term, start_term_a, common_ratio_r))
| 1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is a pure Python implementation of the P-Series algorithm
https://en.wikipedia.org/wiki/Harmonic_series_(mathematics)#P-series
For doctests run following command:
python -m doctest -v p_series.py
or
python3 -m doctest -v p_series.py
For manual testing run:
python3 p_series.py
"""
def p_series(nth_term: int, power: int) -> list:
"""Pure Python implementation of P-Series algorithm
:return: The P-Series starting from 1 to last (nth) term
Examples:
>>> p_series(5, 2)
[1, '1/4', '1/9', '1/16', '1/25']
>>> p_series(-5, 2)
[]
>>> p_series(5, -2)
[1, '1/0.25', '1/0.1111111111111111', '1/0.0625', '1/0.04']
>>> p_series("", 1000)
''
>>> p_series(0, 0)
[]
>>> p_series(1, 1)
[1]
"""
if nth_term == "":
return nth_term
nth_term = int(nth_term)
power = int(power)
series = []
for temp in range(int(nth_term)):
series.append(f"1/{pow(temp + 1, int(power))}" if series else 1)
return series
if __name__ == "__main__":
nth_term = input("Enter the last number (nth term) of the P-Series")
power = input("Enter the power for P-Series")
print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
print(p_series(nth_term, power))
| """
This is a pure Python implementation of the P-Series algorithm
https://en.wikipedia.org/wiki/Harmonic_series_(mathematics)#P-series
For doctests run following command:
python -m doctest -v p_series.py
or
python3 -m doctest -v p_series.py
For manual testing run:
python3 p_series.py
"""
from __future__ import annotations
def p_series(nth_term: int | float | str, power: int | float | str) -> list[str]:
"""
Pure Python implementation of P-Series algorithm
:return: The P-Series starting from 1 to last (nth) term
Examples:
>>> p_series(5, 2)
['1', '1 / 4', '1 / 9', '1 / 16', '1 / 25']
>>> p_series(-5, 2)
[]
>>> p_series(5, -2)
['1', '1 / 0.25', '1 / 0.1111111111111111', '1 / 0.0625', '1 / 0.04']
>>> p_series("", 1000)
['']
>>> p_series(0, 0)
[]
>>> p_series(1, 1)
['1']
"""
if nth_term == "":
return [""]
nth_term = int(nth_term)
power = int(power)
series: list[str] = []
for temp in range(int(nth_term)):
series.append(f"1 / {pow(temp + 1, int(power))}" if series else "1")
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
nth_term = int(input("Enter the last number (nth term) of the P-Series"))
power = int(input("Enter the power for P-Series"))
print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
print(p_series(nth_term, power))
| 1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| [mypy]
ignore_missing_imports = True
install_types = True
non_interactive = True
exclude = (data_structures/stacks/next_greater_element.py|graphs/boruvka.py|graphs/breadth_first_search.py|graphs/breadth_first_search_2.py|graphs/check_cycle.py|graphs/finding_bridges.py|graphs/greedy_min_vertex_cover.py|graphs/random_graph_generator.py|maths/average_mode.py|maths/gamma_recursive.py|maths/proth_number.py|maths/series/geometric_series.py|maths/series/p_series.py|matrix_operation.py|other/least_recently_used.py|other/lfu_cache.py|other/lru_cache.py|searches/simulated_annealing.py|searches/ternary_search.py)
| [mypy]
ignore_missing_imports = True
install_types = True
non_interactive = True
exclude = (graphs/boruvka.py|graphs/breadth_first_search.py|graphs/breadth_first_search_2.py|graphs/check_cycle.py|graphs/finding_bridges.py|graphs/greedy_min_vertex_cover.py|graphs/random_graph_generator.py|matrix_operation.py|other/least_recently_used.py|other/lfu_cache.py|other/lru_cache.py|searches/simulated_annealing.py|searches/ternary_search.py)
| 1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Simple multithreaded algorithm to show how the 4 phases of a genetic algorithm works
(Evaluation, Selection, Crossover and Mutation)
https://en.wikipedia.org/wiki/Genetic_algorithm
Author: D4rkia
"""
from __future__ import annotations
import random
# Maximum size of the population. bigger could be faster but is more memory expensive
N_POPULATION = 200
# Number of elements selected in every generation for evolution the selection takes
# place from the best to the worst of that generation must be smaller than N_POPULATION
N_SELECTED = 50
# Probability that an element of a generation can mutate changing one of its genes this
# guarantees that all genes will be used during evolution
MUTATION_PROBABILITY = 0.4
# just a seed to improve randomness required by the algorithm
random.seed(random.randint(0, 1000))
def basic(target: str, genes: list[str], debug: bool = True) -> tuple[int, int, str]:
"""
Verify that the target contains no genes besides the ones inside genes variable.
>>> from string import ascii_lowercase
>>> basic("doctest", ascii_lowercase, debug=False)[2]
'doctest'
>>> genes = list(ascii_lowercase)
>>> genes.remove("e")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e'] is not in genes list, evolution cannot converge
>>> genes.remove("s")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e', 's'] is not in genes list, evolution cannot converge
>>> genes.remove("t")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e', 's', 't'] is not in genes list, evolution cannot converge
"""
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
raise ValueError(f"{N_POPULATION} must be bigger than {N_SELECTED}")
# Verify that the target contains no genes besides the ones inside genes variable.
not_in_genes_list = sorted({c for c in target if c not in genes})
if not_in_genes_list:
raise ValueError(
f"{not_in_genes_list} is not in genes list, evolution cannot converge"
)
# Generate random starting population
population = []
for _ in range(N_POPULATION):
population.append("".join([random.choice(genes) for i in range(len(target))]))
# Just some logs to know what the algorithms is doing
generation, total_population = 0, 0
# This loop will end when we will find a perfect match for our target
while True:
generation += 1
total_population += len(population)
# Random population created now it's time to evaluate
def evaluate(item: str, main_target: str = target) -> tuple[str, float]:
"""
Evaluate how similar the item is with the target by just
counting each char in the right position
>>> evaluate("Helxo Worlx", Hello World)
["Helxo Worlx", 9]
"""
score = len(
[g for position, g in enumerate(item) if g == main_target[position]]
)
return (item, float(score))
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this will probably be slower
# we just need to call evaluate for every item inside population
population_score = [evaluate(item) for item in population]
# Check if there is a matching evolution
population_score = sorted(population_score, key=lambda x: x[1], reverse=True)
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the Best result every 10 generation
# just to know that the algorithm is working
if debug and generation % 10 == 0:
print(
f"\nGeneration: {generation}"
f"\nTotal Population:{total_population}"
f"\nBest score: {population_score[0][1]}"
f"\nBest string: {population_score[0][0]}"
)
# Flush the old population keeping some of the best evolutions
# Keeping this avoid regression of evolution
population_best = population[: int(N_POPULATION / 3)]
population.clear()
population.extend(population_best)
# Normalize population score from 0 to 1
population_score = [
(item, score / len(target)) for item, score in population_score
]
# Select, Crossover and Mutate a new population
def select(parent_1: tuple[str, float]) -> list[str]:
"""Select the second parent and generate new population"""
pop = []
# Generate more child proportionally to the fitness score
child_n = int(parent_1[1] * 100) + 1
child_n = 10 if child_n >= 10 else child_n
for _ in range(child_n):
parent_2 = population_score[random.randint(0, N_SELECTED)][0]
child_1, child_2 = crossover(parent_1[0], parent_2)
# Append new string to the population list
pop.append(mutate(child_1))
pop.append(mutate(child_2))
return pop
def crossover(parent_1: str, parent_2: str) -> tuple[str, str]:
"""Slice and combine two string in a random point"""
random_slice = random.randint(0, len(parent_1) - 1)
child_1 = parent_1[:random_slice] + parent_2[random_slice:]
child_2 = parent_2[:random_slice] + parent_1[random_slice:]
return (child_1, child_2)
def mutate(child: str) -> str:
"""Mutate a random gene of a child with another one from the list"""
child_list = list(child)
if random.uniform(0, 1) < MUTATION_PROBABILITY:
child_list[random.randint(0, len(child)) - 1] = random.choice(genes)
return "".join(child_list)
# This is Selection
for i in range(N_SELECTED):
population.extend(select(population_score[int(i)]))
# Check if the population has already reached the maximum value and if so,
# break the cycle. if this check is disabled the algorithm will take
# forever to compute large strings but will also calculate small string in
# a lot fewer generations
if len(population) > N_POPULATION:
break
if __name__ == "__main__":
target_str = (
"This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
)
genes_list = list(
" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
"nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
)
print(
"\nGeneration: %s\nTotal Population: %s\nTarget: %s"
% basic(target_str, genes_list)
)
| """
Simple multithreaded algorithm to show how the 4 phases of a genetic algorithm works
(Evaluation, Selection, Crossover and Mutation)
https://en.wikipedia.org/wiki/Genetic_algorithm
Author: D4rkia
"""
from __future__ import annotations
import random
# Maximum size of the population. bigger could be faster but is more memory expensive
N_POPULATION = 200
# Number of elements selected in every generation for evolution the selection takes
# place from the best to the worst of that generation must be smaller than N_POPULATION
N_SELECTED = 50
# Probability that an element of a generation can mutate changing one of its genes this
# guarantees that all genes will be used during evolution
MUTATION_PROBABILITY = 0.4
# just a seed to improve randomness required by the algorithm
random.seed(random.randint(0, 1000))
def basic(target: str, genes: list[str], debug: bool = True) -> tuple[int, int, str]:
"""
Verify that the target contains no genes besides the ones inside genes variable.
>>> from string import ascii_lowercase
>>> basic("doctest", ascii_lowercase, debug=False)[2]
'doctest'
>>> genes = list(ascii_lowercase)
>>> genes.remove("e")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e'] is not in genes list, evolution cannot converge
>>> genes.remove("s")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e', 's'] is not in genes list, evolution cannot converge
>>> genes.remove("t")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e', 's', 't'] is not in genes list, evolution cannot converge
"""
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
raise ValueError(f"{N_POPULATION} must be bigger than {N_SELECTED}")
# Verify that the target contains no genes besides the ones inside genes variable.
not_in_genes_list = sorted({c for c in target if c not in genes})
if not_in_genes_list:
raise ValueError(
f"{not_in_genes_list} is not in genes list, evolution cannot converge"
)
# Generate random starting population
population = []
for _ in range(N_POPULATION):
population.append("".join([random.choice(genes) for i in range(len(target))]))
# Just some logs to know what the algorithms is doing
generation, total_population = 0, 0
# This loop will end when we will find a perfect match for our target
while True:
generation += 1
total_population += len(population)
# Random population created now it's time to evaluate
def evaluate(item: str, main_target: str = target) -> tuple[str, float]:
"""
Evaluate how similar the item is with the target by just
counting each char in the right position
>>> evaluate("Helxo Worlx", Hello World)
["Helxo Worlx", 9]
"""
score = len(
[g for position, g in enumerate(item) if g == main_target[position]]
)
return (item, float(score))
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this will probably be slower
# we just need to call evaluate for every item inside population
population_score = [evaluate(item) for item in population]
# Check if there is a matching evolution
population_score = sorted(population_score, key=lambda x: x[1], reverse=True)
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the Best result every 10 generation
# just to know that the algorithm is working
if debug and generation % 10 == 0:
print(
f"\nGeneration: {generation}"
f"\nTotal Population:{total_population}"
f"\nBest score: {population_score[0][1]}"
f"\nBest string: {population_score[0][0]}"
)
# Flush the old population keeping some of the best evolutions
# Keeping this avoid regression of evolution
population_best = population[: int(N_POPULATION / 3)]
population.clear()
population.extend(population_best)
# Normalize population score from 0 to 1
population_score = [
(item, score / len(target)) for item, score in population_score
]
# Select, Crossover and Mutate a new population
def select(parent_1: tuple[str, float]) -> list[str]:
"""Select the second parent and generate new population"""
pop = []
# Generate more child proportionally to the fitness score
child_n = int(parent_1[1] * 100) + 1
child_n = 10 if child_n >= 10 else child_n
for _ in range(child_n):
parent_2 = population_score[random.randint(0, N_SELECTED)][0]
child_1, child_2 = crossover(parent_1[0], parent_2)
# Append new string to the population list
pop.append(mutate(child_1))
pop.append(mutate(child_2))
return pop
def crossover(parent_1: str, parent_2: str) -> tuple[str, str]:
"""Slice and combine two string in a random point"""
random_slice = random.randint(0, len(parent_1) - 1)
child_1 = parent_1[:random_slice] + parent_2[random_slice:]
child_2 = parent_2[:random_slice] + parent_1[random_slice:]
return (child_1, child_2)
def mutate(child: str) -> str:
"""Mutate a random gene of a child with another one from the list"""
child_list = list(child)
if random.uniform(0, 1) < MUTATION_PROBABILITY:
child_list[random.randint(0, len(child)) - 1] = random.choice(genes)
return "".join(child_list)
# This is Selection
for i in range(N_SELECTED):
population.extend(select(population_score[int(i)]))
# Check if the population has already reached the maximum value and if so,
# break the cycle. if this check is disabled the algorithm will take
# forever to compute large strings but will also calculate small string in
# a lot fewer generations
if len(population) > N_POPULATION:
break
if __name__ == "__main__":
target_str = (
"This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
)
genes_list = list(
" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
"nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
)
print(
"\nGeneration: %s\nTotal Population: %s\nTarget: %s"
% basic(target_str, genes_list)
)
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #
| #
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is a pure Python implementation of the heap sort algorithm.
For doctests run following command:
python -m doctest -v heap_sort.py
or
python3 -m doctest -v heap_sort.py
For manual testing run:
python heap_sort.py
"""
def heapify(unsorted, index, heap_size):
largest = index
left_index = 2 * index + 1
right_index = 2 * index + 2
if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
largest = left_index
if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
largest = right_index
if largest != index:
unsorted[largest], unsorted[index] = unsorted[index], unsorted[largest]
heapify(unsorted, largest, heap_size)
def heap_sort(unsorted):
"""
Pure implementation of the heap sort algorithm in Python
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> heap_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> heap_sort([])
[]
>>> heap_sort([-2, -5, -45])
[-45, -5, -2]
"""
n = len(unsorted)
for i in range(n // 2 - 1, -1, -1):
heapify(unsorted, i, n)
for i in range(n - 1, 0, -1):
unsorted[0], unsorted[i] = unsorted[i], unsorted[0]
heapify(unsorted, 0, i)
return unsorted
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(heap_sort(unsorted))
| """
This is a pure Python implementation of the heap sort algorithm.
For doctests run following command:
python -m doctest -v heap_sort.py
or
python3 -m doctest -v heap_sort.py
For manual testing run:
python heap_sort.py
"""
def heapify(unsorted, index, heap_size):
largest = index
left_index = 2 * index + 1
right_index = 2 * index + 2
if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
largest = left_index
if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
largest = right_index
if largest != index:
unsorted[largest], unsorted[index] = unsorted[index], unsorted[largest]
heapify(unsorted, largest, heap_size)
def heap_sort(unsorted):
"""
Pure implementation of the heap sort algorithm in Python
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> heap_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> heap_sort([])
[]
>>> heap_sort([-2, -5, -45])
[-45, -5, -2]
"""
n = len(unsorted)
for i in range(n // 2 - 1, -1, -1):
heapify(unsorted, i, n)
for i in range(n - 1, 0, -1):
unsorted[0], unsorted[i] = unsorted[i], unsorted[0]
heapify(unsorted, 0, i)
return unsorted
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(heap_sort(unsorted))
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem 16: https://projecteuler.net/problem=16
2^15 = 32768 and the sum of its digits is 3 + 2 + 7 + 6 + 8 = 26.
What is the sum of the digits of the number 2^1000?
"""
def solution(power: int = 1000) -> int:
"""Returns the sum of the digits of the number 2^power.
>>> solution(1000)
1366
>>> solution(50)
76
>>> solution(20)
31
>>> solution(15)
26
"""
n = 2 ** power
r = 0
while n:
r, n = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| """
Problem 16: https://projecteuler.net/problem=16
2^15 = 32768 and the sum of its digits is 3 + 2 + 7 + 6 + 8 = 26.
What is the sum of the digits of the number 2^1000?
"""
def solution(power: int = 1000) -> int:
"""Returns the sum of the digits of the number 2^power.
>>> solution(1000)
1366
>>> solution(50)
76
>>> solution(20)
31
>>> solution(15)
26
"""
n = 2 ** power
r = 0
while n:
r, n = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Wavelet tree is a data-structure designed to efficiently answer various range queries
for arrays. Wavelets trees are different from other binary trees in the sense that
the nodes are split based on the actual values of the elements and not on indices,
such as the with segment trees or fenwick trees. You can read more about them here:
1. https://users.dcc.uchile.cl/~jperez/papers/ioiconf16.pdf
2. https://www.youtube.com/watch?v=4aSv9PcecDw&t=811s
3. https://www.youtube.com/watch?v=CybAgVF-MMc&t=1178s
"""
from __future__ import annotations
test_array = [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
class Node:
def __init__(self, length: int) -> None:
self.minn: int = -1
self.maxx: int = -1
self.map_left: list[int] = [-1] * length
self.left: Node | None = None
self.right: Node | None = None
def __repr__(self) -> str:
"""
>>> node = Node(length=27)
>>> repr(node)
'min_value: -1, max_value: -1'
>>> repr(node) == str(node)
True
"""
return f"min_value: {self.minn}, max_value: {self.maxx}"
def build_tree(arr: list[int]) -> Node | None:
"""
Builds the tree for arr and returns the root
of the constructed tree
>>> build_tree(test_array)
min_value: 0, max_value: 9
"""
root = Node(len(arr))
root.minn, root.maxx = min(arr), max(arr)
# Leaf node case where the node contains only one unique value
if root.minn == root.maxx:
return root
"""
Take the mean of min and max element of arr as the pivot and
partition arr into left_arr and right_arr with all elements <= pivot in the
left_arr and the rest in right_arr, maintaining the order of the elements,
then recursively build trees for left_arr and right_arr
"""
pivot = (root.minn + root.maxx) // 2
left_arr: list[int] = []
right_arr: list[int] = []
for index, num in enumerate(arr):
if num <= pivot:
left_arr.append(num)
else:
right_arr.append(num)
root.map_left[index] = len(left_arr)
root.left = build_tree(left_arr)
root.right = build_tree(right_arr)
return root
def rank_till_index(node: Node | None, num: int, index: int) -> int:
"""
Returns the number of occurrences of num in interval [0, index] in the list
>>> root = build_tree(test_array)
>>> rank_till_index(root, 6, 6)
1
>>> rank_till_index(root, 2, 0)
1
>>> rank_till_index(root, 1, 10)
2
>>> rank_till_index(root, 17, 7)
0
>>> rank_till_index(root, 0, 9)
1
"""
if index < 0 or node is None:
return 0
# Leaf node cases
if node.minn == node.maxx:
return index + 1 if node.minn == num else 0
pivot = (node.minn + node.maxx) // 2
if num <= pivot:
# go the left subtree and map index to the left subtree
return rank_till_index(node.left, num, node.map_left[index] - 1)
else:
# go to the right subtree and map index to the right subtree
return rank_till_index(node.right, num, index - node.map_left[index])
def rank(node: Node | None, num: int, start: int, end: int) -> int:
"""
Returns the number of occurrences of num in interval [start, end] in the list
>>> root = build_tree(test_array)
>>> rank(root, 6, 3, 13)
2
>>> rank(root, 2, 0, 19)
4
>>> rank(root, 9, 2 ,2)
0
>>> rank(root, 0, 5, 10)
2
"""
if start > end:
return 0
rank_till_end = rank_till_index(node, num, end)
rank_before_start = rank_till_index(node, num, start - 1)
return rank_till_end - rank_before_start
def quantile(node: Node | None, index: int, start: int, end: int) -> int:
"""
Returns the index'th smallest element in interval [start, end] in the list
index is 0-indexed
>>> root = build_tree(test_array)
>>> quantile(root, 2, 2, 5)
5
>>> quantile(root, 5, 2, 13)
4
>>> quantile(root, 0, 6, 6)
8
>>> quantile(root, 4, 2, 5)
-1
"""
if index > (end - start) or start > end or node is None:
return -1
# Leaf node case
if node.minn == node.maxx:
return node.minn
# Number of elements in the left subtree in interval [start, end]
num_elements_in_left_tree = node.map_left[end] - (
node.map_left[start - 1] if start else 0
)
if num_elements_in_left_tree > index:
return quantile(
node.left,
index,
(node.map_left[start - 1] if start else 0),
node.map_left[end] - 1,
)
else:
return quantile(
node.right,
index - num_elements_in_left_tree,
start - (node.map_left[start - 1] if start else 0),
end - node.map_left[end],
)
def range_counting(
node: Node | None, start: int, end: int, start_num: int, end_num: int
) -> int:
"""
Returns the number of elements in range [start_num, end_num]
in interval [start, end] in the list
>>> root = build_tree(test_array)
>>> range_counting(root, 1, 10, 3, 7)
3
>>> range_counting(root, 2, 2, 1, 4)
1
>>> range_counting(root, 0, 19, 0, 100)
20
>>> range_counting(root, 1, 0, 1, 100)
0
>>> range_counting(root, 0, 17, 100, 1)
0
"""
if (
start > end
or node is None
or start_num > end_num
or node.minn > end_num
or node.maxx < start_num
):
return 0
if start_num <= node.minn and node.maxx <= end_num:
return end - start + 1
left = range_counting(
node.left,
(node.map_left[start - 1] if start else 0),
node.map_left[end] - 1,
start_num,
end_num,
)
right = range_counting(
node.right,
start - (node.map_left[start - 1] if start else 0),
end - node.map_left[end],
start_num,
end_num,
)
return left + right
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Wavelet tree is a data-structure designed to efficiently answer various range queries
for arrays. Wavelets trees are different from other binary trees in the sense that
the nodes are split based on the actual values of the elements and not on indices,
such as the with segment trees or fenwick trees. You can read more about them here:
1. https://users.dcc.uchile.cl/~jperez/papers/ioiconf16.pdf
2. https://www.youtube.com/watch?v=4aSv9PcecDw&t=811s
3. https://www.youtube.com/watch?v=CybAgVF-MMc&t=1178s
"""
from __future__ import annotations
test_array = [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
class Node:
def __init__(self, length: int) -> None:
self.minn: int = -1
self.maxx: int = -1
self.map_left: list[int] = [-1] * length
self.left: Node | None = None
self.right: Node | None = None
def __repr__(self) -> str:
"""
>>> node = Node(length=27)
>>> repr(node)
'min_value: -1, max_value: -1'
>>> repr(node) == str(node)
True
"""
return f"min_value: {self.minn}, max_value: {self.maxx}"
def build_tree(arr: list[int]) -> Node | None:
"""
Builds the tree for arr and returns the root
of the constructed tree
>>> build_tree(test_array)
min_value: 0, max_value: 9
"""
root = Node(len(arr))
root.minn, root.maxx = min(arr), max(arr)
# Leaf node case where the node contains only one unique value
if root.minn == root.maxx:
return root
"""
Take the mean of min and max element of arr as the pivot and
partition arr into left_arr and right_arr with all elements <= pivot in the
left_arr and the rest in right_arr, maintaining the order of the elements,
then recursively build trees for left_arr and right_arr
"""
pivot = (root.minn + root.maxx) // 2
left_arr: list[int] = []
right_arr: list[int] = []
for index, num in enumerate(arr):
if num <= pivot:
left_arr.append(num)
else:
right_arr.append(num)
root.map_left[index] = len(left_arr)
root.left = build_tree(left_arr)
root.right = build_tree(right_arr)
return root
def rank_till_index(node: Node | None, num: int, index: int) -> int:
"""
Returns the number of occurrences of num in interval [0, index] in the list
>>> root = build_tree(test_array)
>>> rank_till_index(root, 6, 6)
1
>>> rank_till_index(root, 2, 0)
1
>>> rank_till_index(root, 1, 10)
2
>>> rank_till_index(root, 17, 7)
0
>>> rank_till_index(root, 0, 9)
1
"""
if index < 0 or node is None:
return 0
# Leaf node cases
if node.minn == node.maxx:
return index + 1 if node.minn == num else 0
pivot = (node.minn + node.maxx) // 2
if num <= pivot:
# go the left subtree and map index to the left subtree
return rank_till_index(node.left, num, node.map_left[index] - 1)
else:
# go to the right subtree and map index to the right subtree
return rank_till_index(node.right, num, index - node.map_left[index])
def rank(node: Node | None, num: int, start: int, end: int) -> int:
"""
Returns the number of occurrences of num in interval [start, end] in the list
>>> root = build_tree(test_array)
>>> rank(root, 6, 3, 13)
2
>>> rank(root, 2, 0, 19)
4
>>> rank(root, 9, 2 ,2)
0
>>> rank(root, 0, 5, 10)
2
"""
if start > end:
return 0
rank_till_end = rank_till_index(node, num, end)
rank_before_start = rank_till_index(node, num, start - 1)
return rank_till_end - rank_before_start
def quantile(node: Node | None, index: int, start: int, end: int) -> int:
"""
Returns the index'th smallest element in interval [start, end] in the list
index is 0-indexed
>>> root = build_tree(test_array)
>>> quantile(root, 2, 2, 5)
5
>>> quantile(root, 5, 2, 13)
4
>>> quantile(root, 0, 6, 6)
8
>>> quantile(root, 4, 2, 5)
-1
"""
if index > (end - start) or start > end or node is None:
return -1
# Leaf node case
if node.minn == node.maxx:
return node.minn
# Number of elements in the left subtree in interval [start, end]
num_elements_in_left_tree = node.map_left[end] - (
node.map_left[start - 1] if start else 0
)
if num_elements_in_left_tree > index:
return quantile(
node.left,
index,
(node.map_left[start - 1] if start else 0),
node.map_left[end] - 1,
)
else:
return quantile(
node.right,
index - num_elements_in_left_tree,
start - (node.map_left[start - 1] if start else 0),
end - node.map_left[end],
)
def range_counting(
node: Node | None, start: int, end: int, start_num: int, end_num: int
) -> int:
"""
Returns the number of elements in range [start_num, end_num]
in interval [start, end] in the list
>>> root = build_tree(test_array)
>>> range_counting(root, 1, 10, 3, 7)
3
>>> range_counting(root, 2, 2, 1, 4)
1
>>> range_counting(root, 0, 19, 0, 100)
20
>>> range_counting(root, 1, 0, 1, 100)
0
>>> range_counting(root, 0, 17, 100, 1)
0
"""
if (
start > end
or node is None
or start_num > end_num
or node.minn > end_num
or node.maxx < start_num
):
return 0
if start_num <= node.minn and node.maxx <= end_num:
return end - start + 1
left = range_counting(
node.left,
(node.map_left[start - 1] if start else 0),
node.map_left[end] - 1,
start_num,
end_num,
)
right = range_counting(
node.right,
start - (node.map_left[start - 1] if start else 0),
end - node.map_left[end],
start_num,
end_num,
)
return left + right
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Algorithm for calculating the most cost-efficient sequence for converting one string
into another.
The only allowed operations are
--- Cost to copy a character is copy_cost
--- Cost to replace a character is replace_cost
--- Cost to delete a character is delete_cost
--- Cost to insert a character is insert_cost
"""
def compute_transform_tables(
source_string: str,
destination_string: str,
copy_cost: int,
replace_cost: int,
delete_cost: int,
insert_cost: int,
) -> tuple[list[list[int]], list[list[str]]]:
source_seq = list(source_string)
destination_seq = list(destination_string)
len_source_seq = len(source_seq)
len_destination_seq = len(destination_seq)
costs = [
[0 for _ in range(len_destination_seq + 1)] for _ in range(len_source_seq + 1)
]
ops = [
["0" for _ in range(len_destination_seq + 1)] for _ in range(len_source_seq + 1)
]
for i in range(1, len_source_seq + 1):
costs[i][0] = i * delete_cost
ops[i][0] = "D%c" % source_seq[i - 1]
for i in range(1, len_destination_seq + 1):
costs[0][i] = i * insert_cost
ops[0][i] = "I%c" % destination_seq[i - 1]
for i in range(1, len_source_seq + 1):
for j in range(1, len_destination_seq + 1):
if source_seq[i - 1] == destination_seq[j - 1]:
costs[i][j] = costs[i - 1][j - 1] + copy_cost
ops[i][j] = "C%c" % source_seq[i - 1]
else:
costs[i][j] = costs[i - 1][j - 1] + replace_cost
ops[i][j] = "R%c" % source_seq[i - 1] + str(destination_seq[j - 1])
if costs[i - 1][j] + delete_cost < costs[i][j]:
costs[i][j] = costs[i - 1][j] + delete_cost
ops[i][j] = "D%c" % source_seq[i - 1]
if costs[i][j - 1] + insert_cost < costs[i][j]:
costs[i][j] = costs[i][j - 1] + insert_cost
ops[i][j] = "I%c" % destination_seq[j - 1]
return costs, ops
def assemble_transformation(ops: list[list[str]], i: int, j: int) -> list[str]:
if i == 0 and j == 0:
return []
else:
if ops[i][j][0] == "C" or ops[i][j][0] == "R":
seq = assemble_transformation(ops, i - 1, j - 1)
seq.append(ops[i][j])
return seq
elif ops[i][j][0] == "D":
seq = assemble_transformation(ops, i - 1, j)
seq.append(ops[i][j])
return seq
else:
seq = assemble_transformation(ops, i, j - 1)
seq.append(ops[i][j])
return seq
if __name__ == "__main__":
_, operations = compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)
m = len(operations)
n = len(operations[0])
sequence = assemble_transformation(operations, m - 1, n - 1)
string = list("Python")
i = 0
cost = 0
with open("min_cost.txt", "w") as file:
for op in sequence:
print("".join(string))
if op[0] == "C":
file.write("%-16s" % "Copy %c" % op[1])
file.write("\t\t\t" + "".join(string))
file.write("\r\n")
cost -= 1
elif op[0] == "R":
string[i] = op[2]
file.write("%-16s" % ("Replace %c" % op[1] + " with " + str(op[2])))
file.write("\t\t" + "".join(string))
file.write("\r\n")
cost += 1
elif op[0] == "D":
string.pop(i)
file.write("%-16s" % "Delete %c" % op[1])
file.write("\t\t\t" + "".join(string))
file.write("\r\n")
cost += 2
else:
string.insert(i, op[1])
file.write("%-16s" % "Insert %c" % op[1])
file.write("\t\t\t" + "".join(string))
file.write("\r\n")
cost += 2
i += 1
print("".join(string))
print("Cost: ", cost)
file.write("\r\nMinimum cost: " + str(cost))
| """
Algorithm for calculating the most cost-efficient sequence for converting one string
into another.
The only allowed operations are
--- Cost to copy a character is copy_cost
--- Cost to replace a character is replace_cost
--- Cost to delete a character is delete_cost
--- Cost to insert a character is insert_cost
"""
def compute_transform_tables(
source_string: str,
destination_string: str,
copy_cost: int,
replace_cost: int,
delete_cost: int,
insert_cost: int,
) -> tuple[list[list[int]], list[list[str]]]:
source_seq = list(source_string)
destination_seq = list(destination_string)
len_source_seq = len(source_seq)
len_destination_seq = len(destination_seq)
costs = [
[0 for _ in range(len_destination_seq + 1)] for _ in range(len_source_seq + 1)
]
ops = [
["0" for _ in range(len_destination_seq + 1)] for _ in range(len_source_seq + 1)
]
for i in range(1, len_source_seq + 1):
costs[i][0] = i * delete_cost
ops[i][0] = "D%c" % source_seq[i - 1]
for i in range(1, len_destination_seq + 1):
costs[0][i] = i * insert_cost
ops[0][i] = "I%c" % destination_seq[i - 1]
for i in range(1, len_source_seq + 1):
for j in range(1, len_destination_seq + 1):
if source_seq[i - 1] == destination_seq[j - 1]:
costs[i][j] = costs[i - 1][j - 1] + copy_cost
ops[i][j] = "C%c" % source_seq[i - 1]
else:
costs[i][j] = costs[i - 1][j - 1] + replace_cost
ops[i][j] = "R%c" % source_seq[i - 1] + str(destination_seq[j - 1])
if costs[i - 1][j] + delete_cost < costs[i][j]:
costs[i][j] = costs[i - 1][j] + delete_cost
ops[i][j] = "D%c" % source_seq[i - 1]
if costs[i][j - 1] + insert_cost < costs[i][j]:
costs[i][j] = costs[i][j - 1] + insert_cost
ops[i][j] = "I%c" % destination_seq[j - 1]
return costs, ops
def assemble_transformation(ops: list[list[str]], i: int, j: int) -> list[str]:
if i == 0 and j == 0:
return []
else:
if ops[i][j][0] == "C" or ops[i][j][0] == "R":
seq = assemble_transformation(ops, i - 1, j - 1)
seq.append(ops[i][j])
return seq
elif ops[i][j][0] == "D":
seq = assemble_transformation(ops, i - 1, j)
seq.append(ops[i][j])
return seq
else:
seq = assemble_transformation(ops, i, j - 1)
seq.append(ops[i][j])
return seq
if __name__ == "__main__":
_, operations = compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)
m = len(operations)
n = len(operations[0])
sequence = assemble_transformation(operations, m - 1, n - 1)
string = list("Python")
i = 0
cost = 0
with open("min_cost.txt", "w") as file:
for op in sequence:
print("".join(string))
if op[0] == "C":
file.write("%-16s" % "Copy %c" % op[1])
file.write("\t\t\t" + "".join(string))
file.write("\r\n")
cost -= 1
elif op[0] == "R":
string[i] = op[2]
file.write("%-16s" % ("Replace %c" % op[1] + " with " + str(op[2])))
file.write("\t\t" + "".join(string))
file.write("\r\n")
cost += 1
elif op[0] == "D":
string.pop(i)
file.write("%-16s" % "Delete %c" % op[1])
file.write("\t\t\t" + "".join(string))
file.write("\r\n")
cost += 2
else:
string.insert(i, op[1])
file.write("%-16s" % "Insert %c" % op[1])
file.write("\t\t\t" + "".join(string))
file.write("\r\n")
cost += 2
i += 1
print("".join(string))
print("Cost: ", cost)
file.write("\r\nMinimum cost: " + str(cost))
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import random
class Point:
def __init__(self, x: float, y: float) -> None:
self.x = x
self.y = y
def is_in_unit_circle(self) -> bool:
"""
True, if the point lies in the unit circle
False, otherwise
"""
return (self.x ** 2 + self.y ** 2) <= 1
@classmethod
def random_unit_square(cls):
"""
Generates a point randomly drawn from the unit square [0, 1) x [0, 1).
"""
return cls(x=random.random(), y=random.random())
def estimate_pi(number_of_simulations: int) -> float:
"""
Generates an estimate of the mathematical constant PI.
See https://en.wikipedia.org/wiki/Monte_Carlo_method#Overview
The estimate is generated by Monte Carlo simulations. Let U be uniformly drawn from
the unit square [0, 1) x [0, 1). The probability that U lies in the unit circle is:
P[U in unit circle] = 1/4 PI
and therefore
PI = 4 * P[U in unit circle]
We can get an estimate of the probability P[U in unit circle].
See https://en.wikipedia.org/wiki/Empirical_probability by:
1. Draw a point uniformly from the unit square.
2. Repeat the first step n times and count the number of points in the unit
circle, which is called m.
3. An estimate of P[U in unit circle] is m/n
"""
if number_of_simulations < 1:
raise ValueError("At least one simulation is necessary to estimate PI.")
number_in_unit_circle = 0
for simulation_index in range(number_of_simulations):
random_point = Point.random_unit_square()
if random_point.is_in_unit_circle():
number_in_unit_circle += 1
return 4 * number_in_unit_circle / number_of_simulations
if __name__ == "__main__":
# import doctest
# doctest.testmod()
from math import pi
prompt = "Please enter the desired number of Monte Carlo simulations: "
my_pi = estimate_pi(int(input(prompt).strip()))
print(f"An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}")
| import random
class Point:
def __init__(self, x: float, y: float) -> None:
self.x = x
self.y = y
def is_in_unit_circle(self) -> bool:
"""
True, if the point lies in the unit circle
False, otherwise
"""
return (self.x ** 2 + self.y ** 2) <= 1
@classmethod
def random_unit_square(cls):
"""
Generates a point randomly drawn from the unit square [0, 1) x [0, 1).
"""
return cls(x=random.random(), y=random.random())
def estimate_pi(number_of_simulations: int) -> float:
"""
Generates an estimate of the mathematical constant PI.
See https://en.wikipedia.org/wiki/Monte_Carlo_method#Overview
The estimate is generated by Monte Carlo simulations. Let U be uniformly drawn from
the unit square [0, 1) x [0, 1). The probability that U lies in the unit circle is:
P[U in unit circle] = 1/4 PI
and therefore
PI = 4 * P[U in unit circle]
We can get an estimate of the probability P[U in unit circle].
See https://en.wikipedia.org/wiki/Empirical_probability by:
1. Draw a point uniformly from the unit square.
2. Repeat the first step n times and count the number of points in the unit
circle, which is called m.
3. An estimate of P[U in unit circle] is m/n
"""
if number_of_simulations < 1:
raise ValueError("At least one simulation is necessary to estimate PI.")
number_in_unit_circle = 0
for simulation_index in range(number_of_simulations):
random_point = Point.random_unit_square()
if random_point.is_in_unit_circle():
number_in_unit_circle += 1
return 4 * number_in_unit_circle / number_of_simulations
if __name__ == "__main__":
# import doctest
# doctest.testmod()
from math import pi
prompt = "Please enter the desired number of Monte Carlo simulations: "
my_pi = estimate_pi(int(input(prompt).strip()))
print(f"An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}")
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/python
""" Author: OMKAR PATHAK """
from __future__ import annotations
from queue import Queue
class Graph:
def __init__(self) -> None:
self.vertices: dict[int, list[int]] = {}
def print_graph(self) -> None:
"""
prints adjacency list representation of graaph
>>> g = Graph()
>>> g.print_graph()
>>> g.add_edge(0, 1)
>>> g.print_graph()
0 : 1
"""
for i in self.vertices:
print(i, " : ", " -> ".join([str(j) for j in self.vertices[i]]))
def add_edge(self, from_vertex: int, to_vertex: int) -> None:
"""
adding the edge between two vertices
>>> g = Graph()
>>> g.print_graph()
>>> g.add_edge(0, 1)
>>> g.print_graph()
0 : 1
"""
if from_vertex in self.vertices:
self.vertices[from_vertex].append(to_vertex)
else:
self.vertices[from_vertex] = [to_vertex]
def bfs(self, start_vertex: int) -> set[int]:
"""
>>> g = Graph()
>>> g.add_edge(0, 1)
>>> g.add_edge(0, 1)
>>> g.add_edge(0, 2)
>>> g.add_edge(1, 2)
>>> g.add_edge(2, 0)
>>> g.add_edge(2, 3)
>>> g.add_edge(3, 3)
>>> sorted(g.bfs(2))
[0, 1, 2, 3]
"""
# initialize set for storing already visited vertices
visited = set()
# create a first in first out queue to store all the vertices for BFS
queue = Queue()
# mark the source node as visited and enqueue it
visited.add(start_vertex)
queue.put(start_vertex)
while not queue.empty():
vertex = queue.get()
# loop through all adjacent vertex and enqueue it if not yet visited
for adjacent_vertex in self.vertices[vertex]:
if adjacent_vertex not in visited:
queue.put(adjacent_vertex)
visited.add(adjacent_vertex)
return visited
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
g = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
# 0 : 1 -> 2
# 1 : 2
# 2 : 0 -> 3
# 3 : 3
assert sorted(g.bfs(2)) == [0, 1, 2, 3]
| #!/usr/bin/python
""" Author: OMKAR PATHAK """
from __future__ import annotations
from queue import Queue
class Graph:
def __init__(self) -> None:
self.vertices: dict[int, list[int]] = {}
def print_graph(self) -> None:
"""
prints adjacency list representation of graaph
>>> g = Graph()
>>> g.print_graph()
>>> g.add_edge(0, 1)
>>> g.print_graph()
0 : 1
"""
for i in self.vertices:
print(i, " : ", " -> ".join([str(j) for j in self.vertices[i]]))
def add_edge(self, from_vertex: int, to_vertex: int) -> None:
"""
adding the edge between two vertices
>>> g = Graph()
>>> g.print_graph()
>>> g.add_edge(0, 1)
>>> g.print_graph()
0 : 1
"""
if from_vertex in self.vertices:
self.vertices[from_vertex].append(to_vertex)
else:
self.vertices[from_vertex] = [to_vertex]
def bfs(self, start_vertex: int) -> set[int]:
"""
>>> g = Graph()
>>> g.add_edge(0, 1)
>>> g.add_edge(0, 1)
>>> g.add_edge(0, 2)
>>> g.add_edge(1, 2)
>>> g.add_edge(2, 0)
>>> g.add_edge(2, 3)
>>> g.add_edge(3, 3)
>>> sorted(g.bfs(2))
[0, 1, 2, 3]
"""
# initialize set for storing already visited vertices
visited = set()
# create a first in first out queue to store all the vertices for BFS
queue = Queue()
# mark the source node as visited and enqueue it
visited.add(start_vertex)
queue.put(start_vertex)
while not queue.empty():
vertex = queue.get()
# loop through all adjacent vertex and enqueue it if not yet visited
for adjacent_vertex in self.vertices[vertex]:
if adjacent_vertex not in visited:
queue.put(adjacent_vertex)
visited.add(adjacent_vertex)
return visited
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
g = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
# 0 : 1 -> 2
# 1 : 2
# 2 : 0 -> 3
# 3 : 3
assert sorted(g.bfs(2)) == [0, 1, 2, 3]
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Checks if a system of forces is in static equilibrium.
"""
from __future__ import annotations
from numpy import array, cos, cross, ndarray, radians, sin
def polar_force(
magnitude: float, angle: float, radian_mode: bool = False
) -> list[float]:
"""
Resolves force along rectangular components.
(force, angle) => (force_x, force_y)
>>> polar_force(10, 45)
[7.0710678118654755, 7.071067811865475]
>>> polar_force(10, 3.14, radian_mode=True)
[-9.999987317275394, 0.01592652916486828]
"""
if radian_mode:
return [magnitude * cos(angle), magnitude * sin(angle)]
return [magnitude * cos(radians(angle)), magnitude * sin(radians(angle))]
def in_static_equilibrium(
forces: ndarray, location: ndarray, eps: float = 10 ** -1
) -> bool:
"""
Check if a system is in equilibrium.
It takes two numpy.array objects.
forces ==> [
[force1_x, force1_y],
[force2_x, force2_y],
....]
location ==> [
[x1, y1],
[x2, y2],
....]
>>> force = array([[1, 1], [-1, 2]])
>>> location = array([[1, 0], [10, 0]])
>>> in_static_equilibrium(force, location)
False
"""
# summation of moments is zero
moments: ndarray = cross(location, forces)
sum_moments: float = sum(moments)
return abs(sum_moments) < eps
if __name__ == "__main__":
# Test to check if it works
forces = array(
[polar_force(718.4, 180 - 30), polar_force(879.54, 45), polar_force(100, -90)]
)
location = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem 1 in image_data/2D_problems.jpg
forces = array(
[
polar_force(30 * 9.81, 15),
polar_force(215, 180 - 45),
polar_force(264, 90 - 30),
]
)
location = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem in image_data/2D_problems_1.jpg
forces = array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])
location = array([[0, 0], [6, 0], [10, 0], [12, 0]])
assert in_static_equilibrium(forces, location)
import doctest
doctest.testmod()
| """
Checks if a system of forces is in static equilibrium.
"""
from __future__ import annotations
from numpy import array, cos, cross, ndarray, radians, sin
def polar_force(
magnitude: float, angle: float, radian_mode: bool = False
) -> list[float]:
"""
Resolves force along rectangular components.
(force, angle) => (force_x, force_y)
>>> polar_force(10, 45)
[7.0710678118654755, 7.071067811865475]
>>> polar_force(10, 3.14, radian_mode=True)
[-9.999987317275394, 0.01592652916486828]
"""
if radian_mode:
return [magnitude * cos(angle), magnitude * sin(angle)]
return [magnitude * cos(radians(angle)), magnitude * sin(radians(angle))]
def in_static_equilibrium(
forces: ndarray, location: ndarray, eps: float = 10 ** -1
) -> bool:
"""
Check if a system is in equilibrium.
It takes two numpy.array objects.
forces ==> [
[force1_x, force1_y],
[force2_x, force2_y],
....]
location ==> [
[x1, y1],
[x2, y2],
....]
>>> force = array([[1, 1], [-1, 2]])
>>> location = array([[1, 0], [10, 0]])
>>> in_static_equilibrium(force, location)
False
"""
# summation of moments is zero
moments: ndarray = cross(location, forces)
sum_moments: float = sum(moments)
return abs(sum_moments) < eps
if __name__ == "__main__":
# Test to check if it works
forces = array(
[polar_force(718.4, 180 - 30), polar_force(879.54, 45), polar_force(100, -90)]
)
location = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem 1 in image_data/2D_problems.jpg
forces = array(
[
polar_force(30 * 9.81, 15),
polar_force(215, 180 - 45),
polar_force(264, 90 - 30),
]
)
location = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem in image_data/2D_problems_1.jpg
forces = array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])
location = array([[0, 0], [6, 0], [10, 0], [12, 0]])
assert in_static_equilibrium(forces, location)
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| B64_CHARSET = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def base64_encode(data: bytes) -> bytes:
"""Encodes data according to RFC4648.
The data is first transformed to binary and appended with binary digits so that its
length becomes a multiple of 6, then each 6 binary digits will match a character in
the B64_CHARSET string. The number of appended binary digits would later determine
how many "=" signs should be added, the padding.
For every 2 binary digits added, a "=" sign is added in the output.
We can add any binary digits to make it a multiple of 6, for instance, consider the
following example:
"AA" -> 0010100100101001 -> 001010 010010 1001
As can be seen above, 2 more binary digits should be added, so there's 4
possibilities here: 00, 01, 10 or 11.
That being said, Base64 encoding can be used in Steganography to hide data in these
appended digits.
>>> from base64 import b64encode
>>> a = b"This pull request is part of Hacktoberfest20!"
>>> b = b"https://tools.ietf.org/html/rfc4648"
>>> c = b"A"
>>> base64_encode(a) == b64encode(a)
True
>>> base64_encode(b) == b64encode(b)
True
>>> base64_encode(c) == b64encode(c)
True
>>> base64_encode("abc")
Traceback (most recent call last):
...
TypeError: a bytes-like object is required, not 'str'
"""
# Make sure the supplied data is a bytes-like object
if not isinstance(data, bytes):
raise TypeError(
f"a bytes-like object is required, not '{data.__class__.__name__}'"
)
binary_stream = "".join(bin(byte)[2:].zfill(8) for byte in data)
padding_needed = len(binary_stream) % 6 != 0
if padding_needed:
# The padding that will be added later
padding = b"=" * ((6 - len(binary_stream) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(binary_stream) % 6)
else:
padding = b""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6], 2)]
for index in range(0, len(binary_stream), 6)
).encode()
+ padding
)
def base64_decode(encoded_data: str) -> bytes:
"""Decodes data according to RFC4648.
This does the reverse operation of base64_encode.
We first transform the encoded data back to a binary stream, take off the
previously appended binary digits according to the padding, at this point we
would have a binary stream whose length is multiple of 8, the last step is
to convert every 8 bits to a byte.
>>> from base64 import b64decode
>>> a = "VGhpcyBwdWxsIHJlcXVlc3QgaXMgcGFydCBvZiBIYWNrdG9iZXJmZXN0MjAh"
>>> b = "aHR0cHM6Ly90b29scy5pZXRmLm9yZy9odG1sL3JmYzQ2NDg="
>>> c = "QQ=="
>>> base64_decode(a) == b64decode(a)
True
>>> base64_decode(b) == b64decode(b)
True
>>> base64_decode(c) == b64decode(c)
True
>>> base64_decode("abc")
Traceback (most recent call last):
...
AssertionError: Incorrect padding
"""
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(encoded_data, bytes) and not isinstance(encoded_data, str):
raise TypeError(
"argument should be a bytes-like object or ASCII string, not "
f"'{encoded_data.__class__.__name__}'"
)
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(encoded_data, bytes):
try:
encoded_data = encoded_data.decode("utf-8")
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters")
padding = encoded_data.count("=")
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding]
), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data
), "Invalid base64 character(s) found."
# Check the padding
assert len(encoded_data) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
encoded_data = encoded_data[:-padding]
binary_stream = "".join(
bin(B64_CHARSET.index(char))[2:].zfill(6) for char in encoded_data
)[: -padding * 2]
else:
binary_stream = "".join(
bin(B64_CHARSET.index(char))[2:].zfill(6) for char in encoded_data
)
data = [
int(binary_stream[index : index + 8], 2)
for index in range(0, len(binary_stream), 8)
]
return bytes(data)
if __name__ == "__main__":
import doctest
doctest.testmod()
| B64_CHARSET = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def base64_encode(data: bytes) -> bytes:
"""Encodes data according to RFC4648.
The data is first transformed to binary and appended with binary digits so that its
length becomes a multiple of 6, then each 6 binary digits will match a character in
the B64_CHARSET string. The number of appended binary digits would later determine
how many "=" signs should be added, the padding.
For every 2 binary digits added, a "=" sign is added in the output.
We can add any binary digits to make it a multiple of 6, for instance, consider the
following example:
"AA" -> 0010100100101001 -> 001010 010010 1001
As can be seen above, 2 more binary digits should be added, so there's 4
possibilities here: 00, 01, 10 or 11.
That being said, Base64 encoding can be used in Steganography to hide data in these
appended digits.
>>> from base64 import b64encode
>>> a = b"This pull request is part of Hacktoberfest20!"
>>> b = b"https://tools.ietf.org/html/rfc4648"
>>> c = b"A"
>>> base64_encode(a) == b64encode(a)
True
>>> base64_encode(b) == b64encode(b)
True
>>> base64_encode(c) == b64encode(c)
True
>>> base64_encode("abc")
Traceback (most recent call last):
...
TypeError: a bytes-like object is required, not 'str'
"""
# Make sure the supplied data is a bytes-like object
if not isinstance(data, bytes):
raise TypeError(
f"a bytes-like object is required, not '{data.__class__.__name__}'"
)
binary_stream = "".join(bin(byte)[2:].zfill(8) for byte in data)
padding_needed = len(binary_stream) % 6 != 0
if padding_needed:
# The padding that will be added later
padding = b"=" * ((6 - len(binary_stream) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(binary_stream) % 6)
else:
padding = b""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6], 2)]
for index in range(0, len(binary_stream), 6)
).encode()
+ padding
)
def base64_decode(encoded_data: str) -> bytes:
"""Decodes data according to RFC4648.
This does the reverse operation of base64_encode.
We first transform the encoded data back to a binary stream, take off the
previously appended binary digits according to the padding, at this point we
would have a binary stream whose length is multiple of 8, the last step is
to convert every 8 bits to a byte.
>>> from base64 import b64decode
>>> a = "VGhpcyBwdWxsIHJlcXVlc3QgaXMgcGFydCBvZiBIYWNrdG9iZXJmZXN0MjAh"
>>> b = "aHR0cHM6Ly90b29scy5pZXRmLm9yZy9odG1sL3JmYzQ2NDg="
>>> c = "QQ=="
>>> base64_decode(a) == b64decode(a)
True
>>> base64_decode(b) == b64decode(b)
True
>>> base64_decode(c) == b64decode(c)
True
>>> base64_decode("abc")
Traceback (most recent call last):
...
AssertionError: Incorrect padding
"""
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(encoded_data, bytes) and not isinstance(encoded_data, str):
raise TypeError(
"argument should be a bytes-like object or ASCII string, not "
f"'{encoded_data.__class__.__name__}'"
)
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(encoded_data, bytes):
try:
encoded_data = encoded_data.decode("utf-8")
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters")
padding = encoded_data.count("=")
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding]
), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data
), "Invalid base64 character(s) found."
# Check the padding
assert len(encoded_data) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
encoded_data = encoded_data[:-padding]
binary_stream = "".join(
bin(B64_CHARSET.index(char))[2:].zfill(6) for char in encoded_data
)[: -padding * 2]
else:
binary_stream = "".join(
bin(B64_CHARSET.index(char))[2:].zfill(6) for char in encoded_data
)
data = [
int(binary_stream[index : index + 8], 2)
for index in range(0, len(binary_stream), 8)
]
return bytes(data)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Random Forest Regressor Example
from sklearn.datasets import load_boston
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
def main():
"""
Random Forest Regressor Example using sklearn function.
Boston house price dataset is used to demonstrate the algorithm.
"""
# Load Boston house price dataset
boston = load_boston()
print(boston.keys())
# Split dataset into train and test data
X = boston["data"] # features
Y = boston["target"]
x_train, x_test, y_train, y_test = train_test_split(
X, Y, test_size=0.3, random_state=1
)
# Random Forest Regressor
rand_for = RandomForestRegressor(random_state=42, n_estimators=300)
rand_for.fit(x_train, y_train)
# Predict target for test data
predictions = rand_for.predict(x_test)
predictions = predictions.reshape(len(predictions), 1)
# Error printing
print(f"Mean Absolute Error:\t {mean_absolute_error(y_test, predictions)}")
print(f"Mean Square Error :\t {mean_squared_error(y_test, predictions)}")
if __name__ == "__main__":
main()
| # Random Forest Regressor Example
from sklearn.datasets import load_boston
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
def main():
"""
Random Forest Regressor Example using sklearn function.
Boston house price dataset is used to demonstrate the algorithm.
"""
# Load Boston house price dataset
boston = load_boston()
print(boston.keys())
# Split dataset into train and test data
X = boston["data"] # features
Y = boston["target"]
x_train, x_test, y_train, y_test = train_test_split(
X, Y, test_size=0.3, random_state=1
)
# Random Forest Regressor
rand_for = RandomForestRegressor(random_state=42, n_estimators=300)
rand_for.fit(x_train, y_train)
# Predict target for test data
predictions = rand_for.predict(x_test)
predictions = predictions.reshape(len(predictions), 1)
# Error printing
print(f"Mean Absolute Error:\t {mean_absolute_error(y_test, predictions)}")
print(f"Mean Square Error :\t {mean_squared_error(y_test, predictions)}")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Resources:
- https://en.wikipedia.org/wiki/Conjugate_gradient_method
- https://en.wikipedia.org/wiki/Definite_symmetric_matrix
"""
from typing import Any
import numpy as np
def _is_matrix_spd(matrix: np.ndarray) -> bool:
"""
Returns True if input matrix is symmetric positive definite.
Returns False otherwise.
For a matrix to be SPD, all eigenvalues must be positive.
>>> import numpy as np
>>> matrix = np.array([
... [4.12401784, -5.01453636, -0.63865857],
... [-5.01453636, 12.33347422, -3.40493586],
... [-0.63865857, -3.40493586, 5.78591885]])
>>> _is_matrix_spd(matrix)
True
>>> matrix = np.array([
... [0.34634879, 1.96165514, 2.18277744],
... [0.74074469, -1.19648894, -1.34223498],
... [-0.7687067 , 0.06018373, -1.16315631]])
>>> _is_matrix_spd(matrix)
False
"""
# Ensure matrix is square.
assert np.shape(matrix)[0] == np.shape(matrix)[1]
# If matrix not symmetric, exit right away.
if np.allclose(matrix, matrix.T) is False:
return False
# Get eigenvalues and eignevectors for a symmetric matrix.
eigen_values, _ = np.linalg.eigh(matrix)
# Check sign of all eigenvalues.
# np.all returns a value of type np.bool_
return bool(np.all(eigen_values > 0))
def _create_spd_matrix(dimension: int) -> Any:
"""
Returns a symmetric positive definite matrix given a dimension.
Input:
dimension gives the square matrix dimension.
Output:
spd_matrix is an diminesion x dimensions symmetric positive definite (SPD) matrix.
>>> import numpy as np
>>> dimension = 3
>>> spd_matrix = _create_spd_matrix(dimension)
>>> _is_matrix_spd(spd_matrix)
True
"""
random_matrix = np.random.randn(dimension, dimension)
spd_matrix = np.dot(random_matrix, random_matrix.T)
assert _is_matrix_spd(spd_matrix)
return spd_matrix
def conjugate_gradient(
spd_matrix: np.ndarray,
load_vector: np.ndarray,
max_iterations: int = 1000,
tol: float = 1e-8,
) -> Any:
"""
Returns solution to the linear system np.dot(spd_matrix, x) = b.
Input:
spd_matrix is an NxN Symmetric Positive Definite (SPD) matrix.
load_vector is an Nx1 vector.
Output:
x is an Nx1 vector that is the solution vector.
>>> import numpy as np
>>> spd_matrix = np.array([
... [8.73256573, -5.02034289, -2.68709226],
... [-5.02034289, 3.78188322, 0.91980451],
... [-2.68709226, 0.91980451, 1.94746467]])
>>> b = np.array([
... [-5.80872761],
... [ 3.23807431],
... [ 1.95381422]])
>>> conjugate_gradient(spd_matrix, b)
array([[-0.63114139],
[-0.01561498],
[ 0.13979294]])
"""
# Ensure proper dimensionality.
assert np.shape(spd_matrix)[0] == np.shape(spd_matrix)[1]
assert np.shape(load_vector)[0] == np.shape(spd_matrix)[0]
assert _is_matrix_spd(spd_matrix)
# Initialize solution guess, residual, search direction.
x0 = np.zeros((np.shape(load_vector)[0], 1))
r0 = np.copy(load_vector)
p0 = np.copy(r0)
# Set initial errors in solution guess and residual.
error_residual = 1e9
error_x_solution = 1e9
error = 1e9
# Set iteration counter to threshold number of iterations.
iterations = 0
while error > tol:
# Save this value so we only calculate the matrix-vector product once.
w = np.dot(spd_matrix, p0)
# The main algorithm.
# Update search direction magnitude.
alpha = np.dot(r0.T, r0) / np.dot(p0.T, w)
# Update solution guess.
x = x0 + alpha * p0
# Calculate new residual.
r = r0 - alpha * w
# Calculate new Krylov subspace scale.
beta = np.dot(r.T, r) / np.dot(r0.T, r0)
# Calculate new A conjuage search direction.
p = r + beta * p0
# Calculate errors.
error_residual = np.linalg.norm(r - r0)
error_x_solution = np.linalg.norm(x - x0)
error = np.maximum(error_residual, error_x_solution)
# Update variables.
x0 = np.copy(x)
r0 = np.copy(r)
p0 = np.copy(p)
# Update number of iterations.
iterations += 1
if iterations > max_iterations:
break
return x
def test_conjugate_gradient() -> None:
"""
>>> test_conjugate_gradient() # self running tests
"""
# Create linear system with SPD matrix and known solution x_true.
dimension = 3
spd_matrix = _create_spd_matrix(dimension)
x_true = np.random.randn(dimension, 1)
b = np.dot(spd_matrix, x_true)
# Numpy solution.
x_numpy = np.linalg.solve(spd_matrix, b)
# Our implementation.
x_conjugate_gradient = conjugate_gradient(spd_matrix, b)
# Ensure both solutions are close to x_true (and therefore one another).
assert np.linalg.norm(x_numpy - x_true) <= 1e-6
assert np.linalg.norm(x_conjugate_gradient - x_true) <= 1e-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_conjugate_gradient()
| """
Resources:
- https://en.wikipedia.org/wiki/Conjugate_gradient_method
- https://en.wikipedia.org/wiki/Definite_symmetric_matrix
"""
from typing import Any
import numpy as np
def _is_matrix_spd(matrix: np.ndarray) -> bool:
"""
Returns True if input matrix is symmetric positive definite.
Returns False otherwise.
For a matrix to be SPD, all eigenvalues must be positive.
>>> import numpy as np
>>> matrix = np.array([
... [4.12401784, -5.01453636, -0.63865857],
... [-5.01453636, 12.33347422, -3.40493586],
... [-0.63865857, -3.40493586, 5.78591885]])
>>> _is_matrix_spd(matrix)
True
>>> matrix = np.array([
... [0.34634879, 1.96165514, 2.18277744],
... [0.74074469, -1.19648894, -1.34223498],
... [-0.7687067 , 0.06018373, -1.16315631]])
>>> _is_matrix_spd(matrix)
False
"""
# Ensure matrix is square.
assert np.shape(matrix)[0] == np.shape(matrix)[1]
# If matrix not symmetric, exit right away.
if np.allclose(matrix, matrix.T) is False:
return False
# Get eigenvalues and eignevectors for a symmetric matrix.
eigen_values, _ = np.linalg.eigh(matrix)
# Check sign of all eigenvalues.
# np.all returns a value of type np.bool_
return bool(np.all(eigen_values > 0))
def _create_spd_matrix(dimension: int) -> Any:
"""
Returns a symmetric positive definite matrix given a dimension.
Input:
dimension gives the square matrix dimension.
Output:
spd_matrix is an diminesion x dimensions symmetric positive definite (SPD) matrix.
>>> import numpy as np
>>> dimension = 3
>>> spd_matrix = _create_spd_matrix(dimension)
>>> _is_matrix_spd(spd_matrix)
True
"""
random_matrix = np.random.randn(dimension, dimension)
spd_matrix = np.dot(random_matrix, random_matrix.T)
assert _is_matrix_spd(spd_matrix)
return spd_matrix
def conjugate_gradient(
spd_matrix: np.ndarray,
load_vector: np.ndarray,
max_iterations: int = 1000,
tol: float = 1e-8,
) -> Any:
"""
Returns solution to the linear system np.dot(spd_matrix, x) = b.
Input:
spd_matrix is an NxN Symmetric Positive Definite (SPD) matrix.
load_vector is an Nx1 vector.
Output:
x is an Nx1 vector that is the solution vector.
>>> import numpy as np
>>> spd_matrix = np.array([
... [8.73256573, -5.02034289, -2.68709226],
... [-5.02034289, 3.78188322, 0.91980451],
... [-2.68709226, 0.91980451, 1.94746467]])
>>> b = np.array([
... [-5.80872761],
... [ 3.23807431],
... [ 1.95381422]])
>>> conjugate_gradient(spd_matrix, b)
array([[-0.63114139],
[-0.01561498],
[ 0.13979294]])
"""
# Ensure proper dimensionality.
assert np.shape(spd_matrix)[0] == np.shape(spd_matrix)[1]
assert np.shape(load_vector)[0] == np.shape(spd_matrix)[0]
assert _is_matrix_spd(spd_matrix)
# Initialize solution guess, residual, search direction.
x0 = np.zeros((np.shape(load_vector)[0], 1))
r0 = np.copy(load_vector)
p0 = np.copy(r0)
# Set initial errors in solution guess and residual.
error_residual = 1e9
error_x_solution = 1e9
error = 1e9
# Set iteration counter to threshold number of iterations.
iterations = 0
while error > tol:
# Save this value so we only calculate the matrix-vector product once.
w = np.dot(spd_matrix, p0)
# The main algorithm.
# Update search direction magnitude.
alpha = np.dot(r0.T, r0) / np.dot(p0.T, w)
# Update solution guess.
x = x0 + alpha * p0
# Calculate new residual.
r = r0 - alpha * w
# Calculate new Krylov subspace scale.
beta = np.dot(r.T, r) / np.dot(r0.T, r0)
# Calculate new A conjuage search direction.
p = r + beta * p0
# Calculate errors.
error_residual = np.linalg.norm(r - r0)
error_x_solution = np.linalg.norm(x - x0)
error = np.maximum(error_residual, error_x_solution)
# Update variables.
x0 = np.copy(x)
r0 = np.copy(r)
p0 = np.copy(p)
# Update number of iterations.
iterations += 1
if iterations > max_iterations:
break
return x
def test_conjugate_gradient() -> None:
"""
>>> test_conjugate_gradient() # self running tests
"""
# Create linear system with SPD matrix and known solution x_true.
dimension = 3
spd_matrix = _create_spd_matrix(dimension)
x_true = np.random.randn(dimension, 1)
b = np.dot(spd_matrix, x_true)
# Numpy solution.
x_numpy = np.linalg.solve(spd_matrix, b)
# Our implementation.
x_conjugate_gradient = conjugate_gradient(spd_matrix, b)
# Ensure both solutions are close to x_true (and therefore one another).
assert np.linalg.norm(x_numpy - x_true) <= 1e-6
assert np.linalg.norm(x_conjugate_gradient - x_true) <= 1e-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_conjugate_gradient()
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
A pure Python implementation of the quick sort algorithm
For doctests run following command:
python3 -m doctest -v quick_sort.py
For manual testing run:
python3 quick_sort.py
"""
from __future__ import annotations
def quick_sort(collection: list) -> list:
"""A pure Python implementation of quick sort algorithm
:param collection: a mutable collection of comparable items
:return: the same collection ordered by ascending
Examples:
>>> quick_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> quick_sort([])
[]
>>> quick_sort([-2, 5, 0, -45])
[-45, -2, 0, 5]
"""
if len(collection) < 2:
return collection
pivot = collection.pop() # Use the last element as the first pivot
greater: list[int] = [] # All elements greater than pivot
lesser: list[int] = [] # All elements less than or equal to pivot
for element in collection:
(greater if element > pivot else lesser).append(element)
return quick_sort(lesser) + [pivot] + quick_sort(greater)
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(quick_sort(unsorted))
| """
A pure Python implementation of the quick sort algorithm
For doctests run following command:
python3 -m doctest -v quick_sort.py
For manual testing run:
python3 quick_sort.py
"""
from __future__ import annotations
def quick_sort(collection: list) -> list:
"""A pure Python implementation of quick sort algorithm
:param collection: a mutable collection of comparable items
:return: the same collection ordered by ascending
Examples:
>>> quick_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> quick_sort([])
[]
>>> quick_sort([-2, 5, 0, -45])
[-45, -2, 0, 5]
"""
if len(collection) < 2:
return collection
pivot = collection.pop() # Use the last element as the first pivot
greater: list[int] = [] # All elements greater than pivot
lesser: list[int] = [] # All elements less than or equal to pivot
for element in collection:
(greater if element > pivot else lesser).append(element)
return quick_sort(lesser) + [pivot] + quick_sort(greater)
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(quick_sort(unsorted))
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import os
import random
import sys
from . import cryptomath_module as cryptoMath
from . import rabin_miller as rabinMiller
def main() -> None:
print("Making key files...")
makeKeyFiles("rsa", 1024)
print("Key files generation successful.")
def generateKey(keySize: int) -> tuple[tuple[int, int], tuple[int, int]]:
print("Generating prime p...")
p = rabinMiller.generateLargePrime(keySize)
print("Generating prime q...")
q = rabinMiller.generateLargePrime(keySize)
n = p * q
print("Generating e that is relatively prime to (p - 1) * (q - 1)...")
while True:
e = random.randrange(2 ** (keySize - 1), 2 ** (keySize))
if cryptoMath.gcd(e, (p - 1) * (q - 1)) == 1:
break
print("Calculating d that is mod inverse of e...")
d = cryptoMath.find_mod_inverse(e, (p - 1) * (q - 1))
publicKey = (n, e)
privateKey = (n, d)
return (publicKey, privateKey)
def makeKeyFiles(name: str, keySize: int) -> None:
if os.path.exists("%s_pubkey.txt" % (name)) or os.path.exists(
"%s_privkey.txt" % (name)
):
print("\nWARNING:")
print(
'"%s_pubkey.txt" or "%s_privkey.txt" already exists. \n'
"Use a different name or delete these files and re-run this program."
% (name, name)
)
sys.exit()
publicKey, privateKey = generateKey(keySize)
print("\nWriting public key to file %s_pubkey.txt..." % name)
with open("%s_pubkey.txt" % name, "w") as out_file:
out_file.write(f"{keySize},{publicKey[0]},{publicKey[1]}")
print("Writing private key to file %s_privkey.txt..." % name)
with open("%s_privkey.txt" % name, "w") as out_file:
out_file.write(f"{keySize},{privateKey[0]},{privateKey[1]}")
if __name__ == "__main__":
main()
| import os
import random
import sys
from . import cryptomath_module as cryptoMath
from . import rabin_miller as rabinMiller
def main() -> None:
print("Making key files...")
makeKeyFiles("rsa", 1024)
print("Key files generation successful.")
def generateKey(keySize: int) -> tuple[tuple[int, int], tuple[int, int]]:
print("Generating prime p...")
p = rabinMiller.generateLargePrime(keySize)
print("Generating prime q...")
q = rabinMiller.generateLargePrime(keySize)
n = p * q
print("Generating e that is relatively prime to (p - 1) * (q - 1)...")
while True:
e = random.randrange(2 ** (keySize - 1), 2 ** (keySize))
if cryptoMath.gcd(e, (p - 1) * (q - 1)) == 1:
break
print("Calculating d that is mod inverse of e...")
d = cryptoMath.find_mod_inverse(e, (p - 1) * (q - 1))
publicKey = (n, e)
privateKey = (n, d)
return (publicKey, privateKey)
def makeKeyFiles(name: str, keySize: int) -> None:
if os.path.exists("%s_pubkey.txt" % (name)) or os.path.exists(
"%s_privkey.txt" % (name)
):
print("\nWARNING:")
print(
'"%s_pubkey.txt" or "%s_privkey.txt" already exists. \n'
"Use a different name or delete these files and re-run this program."
% (name, name)
)
sys.exit()
publicKey, privateKey = generateKey(keySize)
print("\nWriting public key to file %s_pubkey.txt..." % name)
with open("%s_pubkey.txt" % name, "w") as out_file:
out_file.write(f"{keySize},{publicKey[0]},{publicKey[1]}")
print("Writing private key to file %s_privkey.txt..." % name)
with open("%s_privkey.txt" % name, "w") as out_file:
out_file.write(f"{keySize},{privateKey[0]},{privateKey[1]}")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
wiki: https://en.wikipedia.org/wiki/Pangram
"""
def check_pangram(
input_str: str = "The quick brown fox jumps over the lazy dog",
) -> bool:
"""
A Pangram String contains all the alphabets at least once.
>>> check_pangram("The quick brown fox jumps over the lazy dog")
True
>>> check_pangram("Waltz, bad nymph, for quick jigs vex.")
True
>>> check_pangram("Jived fox nymph grabs quick waltz.")
True
>>> check_pangram("My name is Unknown")
False
>>> check_pangram("The quick brown fox jumps over the la_y dog")
False
>>> check_pangram()
True
"""
frequency = set()
input_str = input_str.replace(
" ", ""
) # Replacing all the Whitespaces in our sentence
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower())
return True if len(frequency) == 26 else False
def check_pangram_faster(
input_str: str = "The quick brown fox jumps over the lazy dog",
) -> bool:
"""
>>> check_pangram_faster("The quick brown fox jumps over the lazy dog")
True
>>> check_pangram_faster("Waltz, bad nymph, for quick jigs vex.")
True
>>> check_pangram_faster("Jived fox nymph grabs quick waltz.")
True
>>> check_pangram_faster("The quick brown fox jumps over the la_y dog")
False
>>> check_pangram_faster()
True
"""
flag = [False] * 26
for char in input_str:
if char.islower():
flag[ord(char) - 97] = True
elif char.isupper():
flag[ord(char) - 65] = True
return all(flag)
def benchmark() -> None:
"""
Benchmark code comparing different version.
"""
from timeit import timeit
setup = "from __main__ import check_pangram, check_pangram_faster"
print(timeit("check_pangram()", setup=setup))
print(timeit("check_pangram_faster()", setup=setup))
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| """
wiki: https://en.wikipedia.org/wiki/Pangram
"""
def check_pangram(
input_str: str = "The quick brown fox jumps over the lazy dog",
) -> bool:
"""
A Pangram String contains all the alphabets at least once.
>>> check_pangram("The quick brown fox jumps over the lazy dog")
True
>>> check_pangram("Waltz, bad nymph, for quick jigs vex.")
True
>>> check_pangram("Jived fox nymph grabs quick waltz.")
True
>>> check_pangram("My name is Unknown")
False
>>> check_pangram("The quick brown fox jumps over the la_y dog")
False
>>> check_pangram()
True
"""
frequency = set()
input_str = input_str.replace(
" ", ""
) # Replacing all the Whitespaces in our sentence
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower())
return True if len(frequency) == 26 else False
def check_pangram_faster(
input_str: str = "The quick brown fox jumps over the lazy dog",
) -> bool:
"""
>>> check_pangram_faster("The quick brown fox jumps over the lazy dog")
True
>>> check_pangram_faster("Waltz, bad nymph, for quick jigs vex.")
True
>>> check_pangram_faster("Jived fox nymph grabs quick waltz.")
True
>>> check_pangram_faster("The quick brown fox jumps over the la_y dog")
False
>>> check_pangram_faster()
True
"""
flag = [False] * 26
for char in input_str:
if char.islower():
flag[ord(char) - 97] = True
elif char.isupper():
flag[ord(char) - 65] = True
return all(flag)
def benchmark() -> None:
"""
Benchmark code comparing different version.
"""
from timeit import timeit
setup = "from __main__ import check_pangram, check_pangram_faster"
print(timeit("check_pangram()", setup=setup))
print(timeit("check_pangram_faster()", setup=setup))
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from PIL import Image
"""
Mean thresholding algorithm for image processing
https://en.wikipedia.org/wiki/Thresholding_(image_processing)
"""
def mean_threshold(image: Image) -> Image:
"""
image: is a grayscale PIL image object
"""
height, width = image.size
mean = 0
pixels = image.load()
for i in range(width):
for j in range(height):
pixel = pixels[j, i]
mean += pixel
mean //= width * height
for j in range(width):
for i in range(height):
pixels[i, j] = 255 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
image = mean_threshold(Image.open("path_to_image").convert("L"))
image.save("output_image_path")
| from PIL import Image
"""
Mean thresholding algorithm for image processing
https://en.wikipedia.org/wiki/Thresholding_(image_processing)
"""
def mean_threshold(image: Image) -> Image:
"""
image: is a grayscale PIL image object
"""
height, width = image.size
mean = 0
pixels = image.load()
for i in range(width):
for j in range(height):
pixel = pixels[j, i]
mean += pixel
mean //= width * height
for j in range(width):
for i in range(height):
pixels[i, j] = 255 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
image = mean_threshold(Image.open("path_to_image").convert("L"))
image.save("output_image_path")
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem 45: https://projecteuler.net/problem=45
Triangle, pentagonal, and hexagonal numbers are generated by the following formulae:
Triangle T(n) = (n * (n + 1)) / 2 1, 3, 6, 10, 15, ...
Pentagonal P(n) = (n * (3 * n − 1)) / 2 1, 5, 12, 22, 35, ...
Hexagonal H(n) = n * (2 * n − 1) 1, 6, 15, 28, 45, ...
It can be verified that T(285) = P(165) = H(143) = 40755.
Find the next triangle number that is also pentagonal and hexagonal.
All trinagle numbers are hexagonal numbers.
T(2n-1) = n * (2 * n - 1) = H(n)
So we shall check only for hexagonal numbers which are also pentagonal.
"""
def hexagonal_num(n: int) -> int:
"""
Returns nth hexagonal number
>>> hexagonal_num(143)
40755
>>> hexagonal_num(21)
861
>>> hexagonal_num(10)
190
"""
return n * (2 * n - 1)
def is_pentagonal(n: int) -> bool:
"""
Returns True if n is pentagonal, False otherwise.
>>> is_pentagonal(330)
True
>>> is_pentagonal(7683)
False
>>> is_pentagonal(2380)
True
"""
root = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def solution(start: int = 144) -> int:
"""
Returns the next number which is triangular, pentagonal and hexagonal.
>>> solution(144)
1533776805
"""
n = start
num = hexagonal_num(n)
while not is_pentagonal(num):
n += 1
num = hexagonal_num(n)
return num
if __name__ == "__main__":
print(f"{solution()} = ")
| """
Problem 45: https://projecteuler.net/problem=45
Triangle, pentagonal, and hexagonal numbers are generated by the following formulae:
Triangle T(n) = (n * (n + 1)) / 2 1, 3, 6, 10, 15, ...
Pentagonal P(n) = (n * (3 * n − 1)) / 2 1, 5, 12, 22, 35, ...
Hexagonal H(n) = n * (2 * n − 1) 1, 6, 15, 28, 45, ...
It can be verified that T(285) = P(165) = H(143) = 40755.
Find the next triangle number that is also pentagonal and hexagonal.
All trinagle numbers are hexagonal numbers.
T(2n-1) = n * (2 * n - 1) = H(n)
So we shall check only for hexagonal numbers which are also pentagonal.
"""
def hexagonal_num(n: int) -> int:
"""
Returns nth hexagonal number
>>> hexagonal_num(143)
40755
>>> hexagonal_num(21)
861
>>> hexagonal_num(10)
190
"""
return n * (2 * n - 1)
def is_pentagonal(n: int) -> bool:
"""
Returns True if n is pentagonal, False otherwise.
>>> is_pentagonal(330)
True
>>> is_pentagonal(7683)
False
>>> is_pentagonal(2380)
True
"""
root = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def solution(start: int = 144) -> int:
"""
Returns the next number which is triangular, pentagonal and hexagonal.
>>> solution(144)
1533776805
"""
n = start
num = hexagonal_num(n)
while not is_pentagonal(num):
n += 1
num = hexagonal_num(n)
return num
if __name__ == "__main__":
print(f"{solution()} = ")
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is pure Python implementation of interpolation search algorithm
"""
def interpolation_search(sorted_collection, item):
"""Pure implementation of interpolation search algorithm in Python
Be careful collection must be ascending sorted, otherwise result will be
unpredictable
:param sorted_collection: some ascending sorted collection with comparable items
:param item: item value to search
:return: index of found item or None if item is not found
"""
left = 0
right = len(sorted_collection) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
point = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(sorted_collection):
return None
current_item = sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
right = left
left = point
elif point > right:
left = right
right = point
else:
if item < current_item:
right = point - 1
else:
left = point + 1
return None
def interpolation_search_by_recursion(sorted_collection, item, left, right):
"""Pure implementation of interpolation search algorithm in Python by recursion
Be careful collection must be ascending sorted, otherwise result will be
unpredictable
First recursion should be started with left=0 and right=(len(sorted_collection)-1)
:param sorted_collection: some ascending sorted collection with comparable items
:param item: item value to search
:return: index of found item or None if item is not found
"""
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
point = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(sorted_collection):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(sorted_collection, item, point, left)
elif point > right:
return interpolation_search_by_recursion(sorted_collection, item, right, left)
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
sorted_collection, item, left, point - 1
)
else:
return interpolation_search_by_recursion(
sorted_collection, item, point + 1, right
)
def __assert_sorted(collection):
"""Check if collection is ascending sorted, if not - raises :py:class:`ValueError`
:param collection: collection
:return: True if collection is ascending sorted
:raise: :py:class:`ValueError` if collection is not ascending sorted
Examples:
>>> __assert_sorted([0, 1, 2, 4])
True
>>> __assert_sorted([10, -1, 5])
Traceback (most recent call last):
...
ValueError: Collection must be ascending sorted
"""
if collection != sorted(collection):
raise ValueError("Collection must be ascending sorted")
return True
if __name__ == "__main__":
import sys
"""
user_input = input('Enter numbers separated by comma:\n').strip()
collection = [int(item) for item in user_input.split(',')]
try:
__assert_sorted(collection)
except ValueError:
sys.exit('Sequence must be ascending sorted to apply interpolation search')
target_input = input('Enter a single number to be found in the list:\n')
target = int(target_input)
"""
debug = 0
if debug == 1:
collection = [10, 30, 40, 45, 50, 66, 77, 93]
try:
__assert_sorted(collection)
except ValueError:
sys.exit("Sequence must be ascending sorted to apply interpolation search")
target = 67
result = interpolation_search(collection, target)
if result is not None:
print(f"{target} found at positions: {result}")
else:
print("Not found")
| """
This is pure Python implementation of interpolation search algorithm
"""
def interpolation_search(sorted_collection, item):
"""Pure implementation of interpolation search algorithm in Python
Be careful collection must be ascending sorted, otherwise result will be
unpredictable
:param sorted_collection: some ascending sorted collection with comparable items
:param item: item value to search
:return: index of found item or None if item is not found
"""
left = 0
right = len(sorted_collection) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
point = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(sorted_collection):
return None
current_item = sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
right = left
left = point
elif point > right:
left = right
right = point
else:
if item < current_item:
right = point - 1
else:
left = point + 1
return None
def interpolation_search_by_recursion(sorted_collection, item, left, right):
"""Pure implementation of interpolation search algorithm in Python by recursion
Be careful collection must be ascending sorted, otherwise result will be
unpredictable
First recursion should be started with left=0 and right=(len(sorted_collection)-1)
:param sorted_collection: some ascending sorted collection with comparable items
:param item: item value to search
:return: index of found item or None if item is not found
"""
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
point = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(sorted_collection):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(sorted_collection, item, point, left)
elif point > right:
return interpolation_search_by_recursion(sorted_collection, item, right, left)
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
sorted_collection, item, left, point - 1
)
else:
return interpolation_search_by_recursion(
sorted_collection, item, point + 1, right
)
def __assert_sorted(collection):
"""Check if collection is ascending sorted, if not - raises :py:class:`ValueError`
:param collection: collection
:return: True if collection is ascending sorted
:raise: :py:class:`ValueError` if collection is not ascending sorted
Examples:
>>> __assert_sorted([0, 1, 2, 4])
True
>>> __assert_sorted([10, -1, 5])
Traceback (most recent call last):
...
ValueError: Collection must be ascending sorted
"""
if collection != sorted(collection):
raise ValueError("Collection must be ascending sorted")
return True
if __name__ == "__main__":
import sys
"""
user_input = input('Enter numbers separated by comma:\n').strip()
collection = [int(item) for item in user_input.split(',')]
try:
__assert_sorted(collection)
except ValueError:
sys.exit('Sequence must be ascending sorted to apply interpolation search')
target_input = input('Enter a single number to be found in the list:\n')
target = int(target_input)
"""
debug = 0
if debug == 1:
collection = [10, 30, 40, 45, 50, 66, 77, 93]
try:
__assert_sorted(collection)
except ValueError:
sys.exit("Sequence must be ascending sorted to apply interpolation search")
target = 67
result = interpolation_search(collection, target)
if result is not None:
print(f"{target} found at positions: {result}")
else:
print("Not found")
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from typing import Optional
class Node:
def __init__(self, data: int) -> None:
self.data = data
self.next = None
class LinkedList:
def __init__(self):
self.head = None
def push(self, new_data: int) -> int:
new_node = Node(new_data)
new_node.next = self.head
self.head = new_node
return self.head.data
def middle_element(self) -> Optional[int]:
"""
>>> link = LinkedList()
>>> link.middle_element()
No element found.
>>> link.push(5)
5
>>> link.push(6)
6
>>> link.push(8)
8
>>> link.push(8)
8
>>> link.push(10)
10
>>> link.push(12)
12
>>> link.push(17)
17
>>> link.push(7)
7
>>> link.push(3)
3
>>> link.push(20)
20
>>> link.push(-20)
-20
>>> link.middle_element()
12
>>>
"""
slow_pointer = self.head
fast_pointer = self.head
if self.head:
while fast_pointer and fast_pointer.next:
fast_pointer = fast_pointer.next.next
slow_pointer = slow_pointer.next
return slow_pointer.data
else:
print("No element found.")
return None
if __name__ == "__main__":
link = LinkedList()
for i in range(int(input().strip())):
data = int(input().strip())
link.push(data)
print(link.middle_element())
| from typing import Optional
class Node:
def __init__(self, data: int) -> None:
self.data = data
self.next = None
class LinkedList:
def __init__(self):
self.head = None
def push(self, new_data: int) -> int:
new_node = Node(new_data)
new_node.next = self.head
self.head = new_node
return self.head.data
def middle_element(self) -> Optional[int]:
"""
>>> link = LinkedList()
>>> link.middle_element()
No element found.
>>> link.push(5)
5
>>> link.push(6)
6
>>> link.push(8)
8
>>> link.push(8)
8
>>> link.push(10)
10
>>> link.push(12)
12
>>> link.push(17)
17
>>> link.push(7)
7
>>> link.push(3)
3
>>> link.push(20)
20
>>> link.push(-20)
-20
>>> link.middle_element()
12
>>>
"""
slow_pointer = self.head
fast_pointer = self.head
if self.head:
while fast_pointer and fast_pointer.next:
fast_pointer = fast_pointer.next.next
slow_pointer = slow_pointer.next
return slow_pointer.data
else:
print("No element found.")
return None
if __name__ == "__main__":
link = LinkedList()
for i in range(int(input().strip())):
data = int(input().strip())
link.push(data)
print(link.middle_element())
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #
| #
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def is_contains_unique_chars(input_str: str) -> bool:
"""
Check if all characters in the string is unique or not.
>>> is_contains_unique_chars("I_love.py")
True
>>> is_contains_unique_chars("I don't love Python")
False
Time complexity: O(n)
Space compexity: O(1) 19320 bytes as we are having 144697 characters in unicode
"""
# Each bit will represent each unicode character
# For example 65th bit representing 'A'
# https://stackoverflow.com/a/12811293
bitmap = 0
for ch in input_str:
ch_unicode = ord(ch)
ch_bit_index_on = pow(2, ch_unicode)
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| def is_contains_unique_chars(input_str: str) -> bool:
"""
Check if all characters in the string is unique or not.
>>> is_contains_unique_chars("I_love.py")
True
>>> is_contains_unique_chars("I don't love Python")
False
Time complexity: O(n)
Space compexity: O(1) 19320 bytes as we are having 144697 characters in unicode
"""
# Each bit will represent each unicode character
# For example 65th bit representing 'A'
# https://stackoverflow.com/a/12811293
bitmap = 0
for ch in input_str:
ch_unicode = ord(ch)
ch_bit_index_on = pow(2, ch_unicode)
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Adler-32 is a checksum algorithm which was invented by Mark Adler in 1995.
Compared to a cyclic redundancy check of the same length, it trades reliability for
speed (preferring the latter).
Adler-32 is more reliable than Fletcher-16, and slightly less reliable than
Fletcher-32.[2]
source: https://en.wikipedia.org/wiki/Adler-32
"""
def adler32(plain_text: str) -> int:
"""
Function implements adler-32 hash.
Iterates and evaluates a new value for each character
>>> adler32('Algorithms')
363791387
>>> adler32('go adler em all')
708642122
"""
MOD_ADLER = 65521
a = 1
b = 0
for plain_chr in plain_text:
a = (a + ord(plain_chr)) % MOD_ADLER
b = (b + a) % MOD_ADLER
return (b << 16) | a
| """
Adler-32 is a checksum algorithm which was invented by Mark Adler in 1995.
Compared to a cyclic redundancy check of the same length, it trades reliability for
speed (preferring the latter).
Adler-32 is more reliable than Fletcher-16, and slightly less reliable than
Fletcher-32.[2]
source: https://en.wikipedia.org/wiki/Adler-32
"""
def adler32(plain_text: str) -> int:
"""
Function implements adler-32 hash.
Iterates and evaluates a new value for each character
>>> adler32('Algorithms')
363791387
>>> adler32('go adler em all')
708642122
"""
MOD_ADLER = 65521
a = 1
b = 0
for plain_chr in plain_text:
a = (a + ord(plain_chr)) % MOD_ADLER
b = (b + a) % MOD_ADLER
return (b << 16) | a
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Recursive Prorgam to create a Linked List from a sequence and
# print a string representation of it.
class Node:
def __init__(self, data=None):
self.data = data
self.next = None
def __repr__(self):
"""Returns a visual representation of the node and all its following nodes."""
string_rep = ""
temp = self
while temp:
string_rep += f"<{temp.data}> ---> "
temp = temp.next
string_rep += "<END>"
return string_rep
def make_linked_list(elements_list):
"""Creates a Linked List from the elements of the given sequence
(list/tuple) and returns the head of the Linked List."""
# if elements_list is empty
if not elements_list:
raise Exception("The Elements List is empty")
# Set first element as Head
head = Node(elements_list[0])
current = head
# Loop through elements from position 1
for data in elements_list[1:]:
current.next = Node(data)
current = current.next
return head
list_data = [1, 3, 5, 32, 44, 12, 43]
print(f"List: {list_data}")
print("Creating Linked List from List.")
linked_list = make_linked_list(list_data)
print("Linked List:")
print(linked_list)
| # Recursive Prorgam to create a Linked List from a sequence and
# print a string representation of it.
class Node:
def __init__(self, data=None):
self.data = data
self.next = None
def __repr__(self):
"""Returns a visual representation of the node and all its following nodes."""
string_rep = ""
temp = self
while temp:
string_rep += f"<{temp.data}> ---> "
temp = temp.next
string_rep += "<END>"
return string_rep
def make_linked_list(elements_list):
"""Creates a Linked List from the elements of the given sequence
(list/tuple) and returns the head of the Linked List."""
# if elements_list is empty
if not elements_list:
raise Exception("The Elements List is empty")
# Set first element as Head
head = Node(elements_list[0])
current = head
# Loop through elements from position 1
for data in elements_list[1:]:
current.next = Node(data)
current = current.next
return head
list_data = [1, 3, 5, 32, 44, 12, 43]
print(f"List: {list_data}")
print("Creating Linked List from List.")
linked_list = make_linked_list(list_data)
print("Linked List:")
print(linked_list)
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def moveTower(height, fromPole, toPole, withPole):
"""
>>> moveTower(3, 'A', 'B', 'C')
moving disk from A to B
moving disk from A to C
moving disk from B to C
moving disk from A to B
moving disk from C to A
moving disk from C to B
moving disk from A to B
"""
if height >= 1:
moveTower(height - 1, fromPole, withPole, toPole)
moveDisk(fromPole, toPole)
moveTower(height - 1, withPole, toPole, fromPole)
def moveDisk(fp, tp):
print("moving disk from", fp, "to", tp)
def main():
height = int(input("Height of hanoi: ").strip())
moveTower(height, "A", "B", "C")
if __name__ == "__main__":
main()
| def moveTower(height, fromPole, toPole, withPole):
"""
>>> moveTower(3, 'A', 'B', 'C')
moving disk from A to B
moving disk from A to C
moving disk from B to C
moving disk from A to B
moving disk from C to A
moving disk from C to B
moving disk from A to B
"""
if height >= 1:
moveTower(height - 1, fromPole, withPole, toPole)
moveDisk(fromPole, toPole)
moveTower(height - 1, withPole, toPole, fromPole)
def moveDisk(fp, tp):
print("moving disk from", fp, "to", tp)
def main():
height = int(input("Height of hanoi: ").strip())
moveTower(height, "A", "B", "C")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Author : Mehdi ALAOUI
This is a pure Python implementation of Dynamic Programming solution to the longest
increasing subsequence of a given sequence.
The problem is :
Given an array, to find the longest and increasing sub-array in that given array and
return it.
Example: [10, 22, 9, 33, 21, 50, 41, 60, 80] as input will return
[10, 22, 33, 41, 60, 80] as output
"""
from __future__ import annotations
def longest_subsequence(array: list[int]) -> list[int]: # This function is recursive
"""
Some examples
>>> longest_subsequence([10, 22, 9, 33, 21, 50, 41, 60, 80])
[10, 22, 33, 41, 60, 80]
>>> longest_subsequence([4, 8, 7, 5, 1, 12, 2, 3, 9])
[1, 2, 3, 9]
>>> longest_subsequence([9, 8, 7, 6, 5, 7])
[8]
>>> longest_subsequence([1, 1, 1])
[1, 1, 1]
>>> longest_subsequence([])
[]
"""
array_length = len(array)
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
pivot = array[0]
isFound = False
i = 1
longest_subseq: list[int] = []
while not isFound and i < array_length:
if array[i] < pivot:
isFound = True
temp_array = [element for element in array[i:] if element >= array[i]]
temp_array = longest_subsequence(temp_array)
if len(temp_array) > len(longest_subseq):
longest_subseq = temp_array
else:
i += 1
temp_array = [element for element in array[1:] if element >= pivot]
temp_array = [pivot] + longest_subsequence(temp_array)
if len(temp_array) > len(longest_subseq):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Author : Mehdi ALAOUI
This is a pure Python implementation of Dynamic Programming solution to the longest
increasing subsequence of a given sequence.
The problem is :
Given an array, to find the longest and increasing sub-array in that given array and
return it.
Example: [10, 22, 9, 33, 21, 50, 41, 60, 80] as input will return
[10, 22, 33, 41, 60, 80] as output
"""
from __future__ import annotations
def longest_subsequence(array: list[int]) -> list[int]: # This function is recursive
"""
Some examples
>>> longest_subsequence([10, 22, 9, 33, 21, 50, 41, 60, 80])
[10, 22, 33, 41, 60, 80]
>>> longest_subsequence([4, 8, 7, 5, 1, 12, 2, 3, 9])
[1, 2, 3, 9]
>>> longest_subsequence([9, 8, 7, 6, 5, 7])
[8]
>>> longest_subsequence([1, 1, 1])
[1, 1, 1]
>>> longest_subsequence([])
[]
"""
array_length = len(array)
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
pivot = array[0]
isFound = False
i = 1
longest_subseq: list[int] = []
while not isFound and i < array_length:
if array[i] < pivot:
isFound = True
temp_array = [element for element in array[i:] if element >= array[i]]
temp_array = longest_subsequence(temp_array)
if len(temp_array) > len(longest_subseq):
longest_subseq = temp_array
else:
i += 1
temp_array = [element for element in array[1:] if element >= pivot]
temp_array = [pivot] + longest_subsequence(temp_array)
if len(temp_array) > len(longest_subseq):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def reverse_words(input_str: str) -> str:
"""
Reverses words in a given string
>>> reverse_words("I love Python")
'Python love I'
>>> reverse_words("I Love Python")
'Python Love I'
"""
return " ".join(input_str.split()[::-1])
if __name__ == "__main__":
import doctest
doctest.testmod()
| def reverse_words(input_str: str) -> str:
"""
Reverses words in a given string
>>> reverse_words("I love Python")
'Python love I'
>>> reverse_words("I Love Python")
'Python Love I'
"""
return " ".join(input_str.split()[::-1])
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Newton's Method."""
# Newton's Method - https://en.wikipedia.org/wiki/Newton%27s_method
from typing import Callable
RealFunc = Callable[[float], float] # type alias for a real -> real function
# function is the f(x) and derivative is the f'(x)
def newton(
function: RealFunc,
derivative: RealFunc,
starting_int: int,
) -> float:
"""
>>> newton(lambda x: x ** 3 - 2 * x - 5, lambda x: 3 * x ** 2 - 2, 3)
2.0945514815423474
>>> newton(lambda x: x ** 3 - 1, lambda x: 3 * x ** 2, -2)
1.0
>>> newton(lambda x: x ** 3 - 1, lambda x: 3 * x ** 2, -4)
1.0000000000000102
>>> import math
>>> newton(math.sin, math.cos, 1)
0.0
>>> newton(math.sin, math.cos, 2)
3.141592653589793
>>> newton(math.cos, lambda x: -math.sin(x), 2)
1.5707963267948966
>>> newton(math.cos, lambda x: -math.sin(x), 0)
Traceback (most recent call last):
...
ZeroDivisionError: Could not find root
"""
prev_guess = float(starting_int)
while True:
try:
next_guess = prev_guess - function(prev_guess) / derivative(prev_guess)
except ZeroDivisionError:
raise ZeroDivisionError("Could not find root") from None
if abs(prev_guess - next_guess) < 10 ** -5:
return next_guess
prev_guess = next_guess
def f(x: float) -> float:
return (x ** 3) - (2 * x) - 5
def f1(x: float) -> float:
return 3 * (x ** 2) - 2
if __name__ == "__main__":
print(newton(f, f1, 3))
| """Newton's Method."""
# Newton's Method - https://en.wikipedia.org/wiki/Newton%27s_method
from typing import Callable
RealFunc = Callable[[float], float] # type alias for a real -> real function
# function is the f(x) and derivative is the f'(x)
def newton(
function: RealFunc,
derivative: RealFunc,
starting_int: int,
) -> float:
"""
>>> newton(lambda x: x ** 3 - 2 * x - 5, lambda x: 3 * x ** 2 - 2, 3)
2.0945514815423474
>>> newton(lambda x: x ** 3 - 1, lambda x: 3 * x ** 2, -2)
1.0
>>> newton(lambda x: x ** 3 - 1, lambda x: 3 * x ** 2, -4)
1.0000000000000102
>>> import math
>>> newton(math.sin, math.cos, 1)
0.0
>>> newton(math.sin, math.cos, 2)
3.141592653589793
>>> newton(math.cos, lambda x: -math.sin(x), 2)
1.5707963267948966
>>> newton(math.cos, lambda x: -math.sin(x), 0)
Traceback (most recent call last):
...
ZeroDivisionError: Could not find root
"""
prev_guess = float(starting_int)
while True:
try:
next_guess = prev_guess - function(prev_guess) / derivative(prev_guess)
except ZeroDivisionError:
raise ZeroDivisionError("Could not find root") from None
if abs(prev_guess - next_guess) < 10 ** -5:
return next_guess
prev_guess = next_guess
def f(x: float) -> float:
return (x ** 3) - (2 * x) - 5
def f1(x: float) -> float:
return 3 * (x ** 2) - 2
if __name__ == "__main__":
print(newton(f, f1, 3))
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem Description:
Given a binary tree, return its mirror.
"""
def binary_tree_mirror_dict(binary_tree_mirror_dictionary: dict, root: int):
if not root or root not in binary_tree_mirror_dictionary:
return
left_child, right_child = binary_tree_mirror_dictionary[root][:2]
binary_tree_mirror_dictionary[root] = [right_child, left_child]
binary_tree_mirror_dict(binary_tree_mirror_dictionary, left_child)
binary_tree_mirror_dict(binary_tree_mirror_dictionary, right_child)
def binary_tree_mirror(binary_tree: dict, root: int = 1) -> dict:
"""
>>> binary_tree_mirror({ 1: [2,3], 2: [4,5], 3: [6,7], 7: [8,9]}, 1)
{1: [3, 2], 2: [5, 4], 3: [7, 6], 7: [9, 8]}
>>> binary_tree_mirror({ 1: [2,3], 2: [4,5], 3: [6,7], 4: [10,11]}, 1)
{1: [3, 2], 2: [5, 4], 3: [7, 6], 4: [11, 10]}
>>> binary_tree_mirror({ 1: [2,3], 2: [4,5], 3: [6,7], 4: [10,11]}, 5)
Traceback (most recent call last):
...
ValueError: root 5 is not present in the binary_tree
>>> binary_tree_mirror({}, 5)
Traceback (most recent call last):
...
ValueError: binary tree cannot be empty
"""
if not binary_tree:
raise ValueError("binary tree cannot be empty")
if root not in binary_tree:
raise ValueError(f"root {root} is not present in the binary_tree")
binary_tree_mirror_dictionary = dict(binary_tree)
binary_tree_mirror_dict(binary_tree_mirror_dictionary, root)
return binary_tree_mirror_dictionary
if __name__ == "__main__":
binary_tree = {1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
print(f"Binary tree: {binary_tree}")
binary_tree_mirror_dictionary = binary_tree_mirror(binary_tree, 5)
print(f"Binary tree mirror: {binary_tree_mirror_dictionary}")
| """
Problem Description:
Given a binary tree, return its mirror.
"""
def binary_tree_mirror_dict(binary_tree_mirror_dictionary: dict, root: int):
if not root or root not in binary_tree_mirror_dictionary:
return
left_child, right_child = binary_tree_mirror_dictionary[root][:2]
binary_tree_mirror_dictionary[root] = [right_child, left_child]
binary_tree_mirror_dict(binary_tree_mirror_dictionary, left_child)
binary_tree_mirror_dict(binary_tree_mirror_dictionary, right_child)
def binary_tree_mirror(binary_tree: dict, root: int = 1) -> dict:
"""
>>> binary_tree_mirror({ 1: [2,3], 2: [4,5], 3: [6,7], 7: [8,9]}, 1)
{1: [3, 2], 2: [5, 4], 3: [7, 6], 7: [9, 8]}
>>> binary_tree_mirror({ 1: [2,3], 2: [4,5], 3: [6,7], 4: [10,11]}, 1)
{1: [3, 2], 2: [5, 4], 3: [7, 6], 4: [11, 10]}
>>> binary_tree_mirror({ 1: [2,3], 2: [4,5], 3: [6,7], 4: [10,11]}, 5)
Traceback (most recent call last):
...
ValueError: root 5 is not present in the binary_tree
>>> binary_tree_mirror({}, 5)
Traceback (most recent call last):
...
ValueError: binary tree cannot be empty
"""
if not binary_tree:
raise ValueError("binary tree cannot be empty")
if root not in binary_tree:
raise ValueError(f"root {root} is not present in the binary_tree")
binary_tree_mirror_dictionary = dict(binary_tree)
binary_tree_mirror_dict(binary_tree_mirror_dictionary, root)
return binary_tree_mirror_dictionary
if __name__ == "__main__":
binary_tree = {1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
print(f"Binary tree: {binary_tree}")
binary_tree_mirror_dictionary = binary_tree_mirror(binary_tree, 5)
print(f"Binary tree mirror: {binary_tree_mirror_dictionary}")
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Introspective Sort is hybrid sort (Quick Sort + Heap Sort + Insertion Sort)
if the size of the list is under 16, use insertion sort
https://en.wikipedia.org/wiki/Introsort
"""
import math
def insertion_sort(array: list, start: int = 0, end: int = 0) -> list:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> insertion_sort(array, 0, len(array))
[1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
"""
end = end or len(array)
for i in range(start, end):
temp_index = i
temp_index_value = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
array[temp_index] = array[temp_index - 1]
temp_index -= 1
array[temp_index] = temp_index_value
return array
def heapify(array: list, index: int, heap_size: int) -> None: # Max Heap
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> heapify(array, len(array) // 2 ,len(array))
"""
largest = index
left_index = 2 * index + 1 # Left Node
right_index = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
largest = left_index
if right_index < heap_size and array[largest] < array[right_index]:
largest = right_index
if largest != index:
array[index], array[largest] = array[largest], array[index]
heapify(array, largest, heap_size)
def heap_sort(array: list) -> list:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> heap_sort(array)
[1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
"""
n = len(array)
for i in range(n // 2, -1, -1):
heapify(array, i, n)
for i in range(n - 1, 0, -1):
array[i], array[0] = array[0], array[i]
heapify(array, 0, i)
return array
def median_of_3(
array: list, first_index: int, middle_index: int, last_index: int
) -> int:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> median_of_3(array, 0, 0 + ((len(array) - 0) // 2) + 1, len(array) - 1)
12
"""
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def partition(array: list, low: int, high: int, pivot: int) -> int:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> partition(array, 0, len(array), 12)
8
"""
i = low
j = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
array[i], array[j] = array[j], array[i]
i += 1
def sort(array: list) -> list:
"""
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> sort([4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12])
[1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
>>> sort([-1, -5, -3, -13, -44])
[-44, -13, -5, -3, -1]
>>> sort([])
[]
>>> sort([5])
[5]
>>> sort([-3, 0, -7, 6, 23, -34])
[-34, -7, -3, 0, 6, 23]
>>> sort([1.7, 1.0, 3.3, 2.1, 0.3 ])
[0.3, 1.0, 1.7, 2.1, 3.3]
>>> sort(['d', 'a', 'b', 'e', 'c'])
['a', 'b', 'c', 'd', 'e']
"""
if len(array) == 0:
return array
max_depth = 2 * math.ceil(math.log2(len(array)))
size_threshold = 16
return intro_sort(array, 0, len(array), size_threshold, max_depth)
def intro_sort(
array: list, start: int, end: int, size_threshold: int, max_depth: int
) -> list:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> max_depth = 2 * math.ceil(math.log2(len(array)))
>>> intro_sort(array, 0, len(array), 16, max_depth)
[1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
"""
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(array)
max_depth -= 1
pivot = median_of_3(array, start, start + ((end - start) // 2) + 1, end - 1)
p = partition(array, start, end, pivot)
intro_sort(array, p, end, size_threshold, max_depth)
end = p
return insertion_sort(array, start, end)
if __name__ == "__main__":
import doctest
doctest.testmod()
user_input = input("Enter numbers separated by a comma : ").strip()
unsorted = [float(item) for item in user_input.split(",")]
print(sort(unsorted))
| """
Introspective Sort is hybrid sort (Quick Sort + Heap Sort + Insertion Sort)
if the size of the list is under 16, use insertion sort
https://en.wikipedia.org/wiki/Introsort
"""
import math
def insertion_sort(array: list, start: int = 0, end: int = 0) -> list:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> insertion_sort(array, 0, len(array))
[1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
"""
end = end or len(array)
for i in range(start, end):
temp_index = i
temp_index_value = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
array[temp_index] = array[temp_index - 1]
temp_index -= 1
array[temp_index] = temp_index_value
return array
def heapify(array: list, index: int, heap_size: int) -> None: # Max Heap
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> heapify(array, len(array) // 2 ,len(array))
"""
largest = index
left_index = 2 * index + 1 # Left Node
right_index = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
largest = left_index
if right_index < heap_size and array[largest] < array[right_index]:
largest = right_index
if largest != index:
array[index], array[largest] = array[largest], array[index]
heapify(array, largest, heap_size)
def heap_sort(array: list) -> list:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> heap_sort(array)
[1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
"""
n = len(array)
for i in range(n // 2, -1, -1):
heapify(array, i, n)
for i in range(n - 1, 0, -1):
array[i], array[0] = array[0], array[i]
heapify(array, 0, i)
return array
def median_of_3(
array: list, first_index: int, middle_index: int, last_index: int
) -> int:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> median_of_3(array, 0, 0 + ((len(array) - 0) // 2) + 1, len(array) - 1)
12
"""
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def partition(array: list, low: int, high: int, pivot: int) -> int:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> partition(array, 0, len(array), 12)
8
"""
i = low
j = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
array[i], array[j] = array[j], array[i]
i += 1
def sort(array: list) -> list:
"""
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> sort([4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12])
[1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
>>> sort([-1, -5, -3, -13, -44])
[-44, -13, -5, -3, -1]
>>> sort([])
[]
>>> sort([5])
[5]
>>> sort([-3, 0, -7, 6, 23, -34])
[-34, -7, -3, 0, 6, 23]
>>> sort([1.7, 1.0, 3.3, 2.1, 0.3 ])
[0.3, 1.0, 1.7, 2.1, 3.3]
>>> sort(['d', 'a', 'b', 'e', 'c'])
['a', 'b', 'c', 'd', 'e']
"""
if len(array) == 0:
return array
max_depth = 2 * math.ceil(math.log2(len(array)))
size_threshold = 16
return intro_sort(array, 0, len(array), size_threshold, max_depth)
def intro_sort(
array: list, start: int, end: int, size_threshold: int, max_depth: int
) -> list:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> max_depth = 2 * math.ceil(math.log2(len(array)))
>>> intro_sort(array, 0, len(array), 16, max_depth)
[1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
"""
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(array)
max_depth -= 1
pivot = median_of_3(array, start, start + ((end - start) // 2) + 1, end - 1)
p = partition(array, start, end, pivot)
intro_sort(array, p, end, size_threshold, max_depth)
end = p
return insertion_sort(array, start, end)
if __name__ == "__main__":
import doctest
doctest.testmod()
user_input = input("Enter numbers separated by a comma : ").strip()
unsorted = [float(item) for item in user_input.split(",")]
print(sort(unsorted))
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def reverse_letters(input_str: str) -> str:
"""
Reverses letters in a given string without adjusting the position of the words
>>> reverse_letters('The cat in the hat')
'ehT tac ni eht tah'
>>> reverse_letters('The quick brown fox jumped over the lazy dog.')
'ehT kciuq nworb xof depmuj revo eht yzal .god'
>>> reverse_letters('Is this true?')
'sI siht ?eurt'
>>> reverse_letters("I love Python")
'I evol nohtyP'
"""
return " ".join([word[::-1] for word in input_str.split()])
if __name__ == "__main__":
import doctest
doctest.testmod()
| def reverse_letters(input_str: str) -> str:
"""
Reverses letters in a given string without adjusting the position of the words
>>> reverse_letters('The cat in the hat')
'ehT tac ni eht tah'
>>> reverse_letters('The quick brown fox jumped over the lazy dog.')
'ehT kciuq nworb xof depmuj revo eht yzal .god'
>>> reverse_letters('Is this true?')
'sI siht ?eurt'
>>> reverse_letters("I love Python")
'I evol nohtyP'
"""
return " ".join([word[::-1] for word in input_str.split()])
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
"""
Python program to translate to and from Morse code.
https://en.wikipedia.org/wiki/Morse_code
"""
# fmt: off
MORSE_CODE_DICT = {
"A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
"H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
"O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
"V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
"2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
"8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", '"': ".-..-.",
"?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
"(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
REVERSE_DICT = {value: key for key, value in MORSE_CODE_DICT.items()}
def encrypt(message: str) -> str:
"""
>>> encrypt("Sos!")
'... --- ... -.-.--'
>>> encrypt("SOS!") == encrypt("sos!")
True
"""
return " ".join(MORSE_CODE_DICT[char] for char in message.upper())
def decrypt(message: str) -> str:
"""
>>> decrypt('... --- ... -.-.--')
'SOS!'
"""
return "".join(REVERSE_DICT[char] for char in message.split())
def main() -> None:
"""
>>> s = "".join(MORSE_CODE_DICT)
>>> decrypt(encrypt(s)) == s
True
"""
message = "Morse code here!"
print(message)
message = encrypt(message)
print(message)
message = decrypt(message)
print(message)
if __name__ == "__main__":
main()
| #!/usr/bin/env python3
"""
Python program to translate to and from Morse code.
https://en.wikipedia.org/wiki/Morse_code
"""
# fmt: off
MORSE_CODE_DICT = {
"A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
"H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
"O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
"V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
"2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
"8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", '"': ".-..-.",
"?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
"(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
REVERSE_DICT = {value: key for key, value in MORSE_CODE_DICT.items()}
def encrypt(message: str) -> str:
"""
>>> encrypt("Sos!")
'... --- ... -.-.--'
>>> encrypt("SOS!") == encrypt("sos!")
True
"""
return " ".join(MORSE_CODE_DICT[char] for char in message.upper())
def decrypt(message: str) -> str:
"""
>>> decrypt('... --- ... -.-.--')
'SOS!'
"""
return "".join(REVERSE_DICT[char] for char in message.split())
def main() -> None:
"""
>>> s = "".join(MORSE_CODE_DICT)
>>> decrypt(encrypt(s)) == s
True
"""
message = "Morse code here!"
print(message)
message = encrypt(message)
print(message)
message = decrypt(message)
print(message)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Setup for pytest
[pytest]
markers =
mat_ops: mark a test as utilizing matrix operations.
addopts = --durations=10
| # Setup for pytest
[pytest]
markers =
mat_ops: mark a test as utilizing matrix operations.
addopts = --durations=10
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Simulate the evolution of a highway with only one road that is a loop.
The highway is divided in cells, each cell can have at most one car in it.
The highway is a loop so when a car comes to one end, it will come out on the other.
Each car is represented by its speed (from 0 to 5).
Some information about speed:
-1 means that the cell on the highway is empty
0 to 5 are the speed of the cars with 0 being the lowest and 5 the highest
highway: list[int] Where every position and speed of every car will be stored
probability The probability that a driver will slow down
initial_speed The speed of the cars a the start
frequency How many cells there are between two cars at the start
max_speed The maximum speed a car can go to
number_of_cells How many cell are there in the highway
number_of_update How many times will the position be updated
More information here: https://en.wikipedia.org/wiki/Nagel%E2%80%93Schreckenberg_model
Examples for doctest:
>>> simulate(construct_highway(6, 3, 0), 2, 0, 2)
[[0, -1, -1, 0, -1, -1], [-1, 1, -1, -1, 1, -1], [-1, -1, 1, -1, -1, 1]]
>>> simulate(construct_highway(5, 2, -2), 3, 0, 2)
[[0, -1, 0, -1, 0], [0, -1, 0, -1, -1], [0, -1, -1, 1, -1], [-1, 1, -1, 0, -1]]
"""
from random import randint, random
def construct_highway(
number_of_cells: int,
frequency: int,
initial_speed: int,
random_frequency: bool = False,
random_speed: bool = False,
max_speed: int = 5,
) -> list:
"""
Build the highway following the parameters given
>>> construct_highway(10, 2, 6)
[[6, -1, 6, -1, 6, -1, 6, -1, 6, -1]]
>>> construct_highway(10, 10, 2)
[[2, -1, -1, -1, -1, -1, -1, -1, -1, -1]]
"""
highway = [[-1] * number_of_cells] # Create a highway without any car
i = 0
if initial_speed < 0:
initial_speed = 0
while i < number_of_cells:
highway[0][i] = (
randint(0, max_speed) if random_speed else initial_speed
) # Place the cars
i += (
randint(1, max_speed * 2) if random_frequency else frequency
) # Arbitrary number, may need tuning
return highway
def get_distance(highway_now: list, car_index: int) -> int:
"""
Get the distance between a car (at index car_index) and the next car
>>> get_distance([6, -1, 6, -1, 6], 2)
1
>>> get_distance([2, -1, -1, -1, 3, 1, 0, 1, 3, 2], 0)
3
>>> get_distance([-1, -1, -1, -1, 2, -1, -1, -1, 3], -1)
4
"""
distance = 0
cells = highway_now[car_index + 1 :]
for cell in range(len(cells)): # May need a better name for this
if cells[cell] != -1: # If the cell is not empty then
return distance # we have the distance we wanted
distance += 1
# Here if the car is near the end of the highway
return distance + get_distance(highway_now, -1)
def update(highway_now: list, probability: float, max_speed: int) -> list:
"""
Update the speed of the cars
>>> update([-1, -1, -1, -1, -1, 2, -1, -1, -1, -1, 3], 0.0, 5)
[-1, -1, -1, -1, -1, 3, -1, -1, -1, -1, 4]
>>> update([-1, -1, 2, -1, -1, -1, -1, 3], 0.0, 5)
[-1, -1, 3, -1, -1, -1, -1, 1]
"""
number_of_cells = len(highway_now)
# Beforce calculations, the highway is empty
next_highway = [-1] * number_of_cells
for car_index in range(number_of_cells):
if highway_now[car_index] != -1:
# Add 1 to the current speed of the car and cap the speed
next_highway[car_index] = min(highway_now[car_index] + 1, max_speed)
# Number of empty cell before the next car
dn = get_distance(highway_now, car_index) - 1
# We can't have the car causing an accident
next_highway[car_index] = min(next_highway[car_index], dn)
if random() < probability:
# Randomly, a driver will slow down
next_highway[car_index] = max(next_highway[car_index] - 1, 0)
return next_highway
def simulate(
highway: list, number_of_update: int, probability: float, max_speed: int
) -> list:
"""
The main function, it will simulate the evolution of the highway
>>> simulate([[-1, 2, -1, -1, -1, 3]], 2, 0.0, 3)
[[-1, 2, -1, -1, -1, 3], [-1, -1, -1, 2, -1, 0], [1, -1, -1, 0, -1, -1]]
>>> simulate([[-1, 2, -1, 3]], 4, 0.0, 3)
[[-1, 2, -1, 3], [-1, 0, -1, 0], [-1, 0, -1, 0], [-1, 0, -1, 0], [-1, 0, -1, 0]]
"""
number_of_cells = len(highway[0])
for i in range(number_of_update):
next_speeds_calculated = update(highway[i], probability, max_speed)
real_next_speeds = [-1] * number_of_cells
for car_index in range(number_of_cells):
speed = next_speeds_calculated[car_index]
if speed != -1:
# Change the position based on the speed (with % to create the loop)
index = (car_index + speed) % number_of_cells
# Commit the change of position
real_next_speeds[index] = speed
highway.append(real_next_speeds)
return highway
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Simulate the evolution of a highway with only one road that is a loop.
The highway is divided in cells, each cell can have at most one car in it.
The highway is a loop so when a car comes to one end, it will come out on the other.
Each car is represented by its speed (from 0 to 5).
Some information about speed:
-1 means that the cell on the highway is empty
0 to 5 are the speed of the cars with 0 being the lowest and 5 the highest
highway: list[int] Where every position and speed of every car will be stored
probability The probability that a driver will slow down
initial_speed The speed of the cars a the start
frequency How many cells there are between two cars at the start
max_speed The maximum speed a car can go to
number_of_cells How many cell are there in the highway
number_of_update How many times will the position be updated
More information here: https://en.wikipedia.org/wiki/Nagel%E2%80%93Schreckenberg_model
Examples for doctest:
>>> simulate(construct_highway(6, 3, 0), 2, 0, 2)
[[0, -1, -1, 0, -1, -1], [-1, 1, -1, -1, 1, -1], [-1, -1, 1, -1, -1, 1]]
>>> simulate(construct_highway(5, 2, -2), 3, 0, 2)
[[0, -1, 0, -1, 0], [0, -1, 0, -1, -1], [0, -1, -1, 1, -1], [-1, 1, -1, 0, -1]]
"""
from random import randint, random
def construct_highway(
number_of_cells: int,
frequency: int,
initial_speed: int,
random_frequency: bool = False,
random_speed: bool = False,
max_speed: int = 5,
) -> list:
"""
Build the highway following the parameters given
>>> construct_highway(10, 2, 6)
[[6, -1, 6, -1, 6, -1, 6, -1, 6, -1]]
>>> construct_highway(10, 10, 2)
[[2, -1, -1, -1, -1, -1, -1, -1, -1, -1]]
"""
highway = [[-1] * number_of_cells] # Create a highway without any car
i = 0
if initial_speed < 0:
initial_speed = 0
while i < number_of_cells:
highway[0][i] = (
randint(0, max_speed) if random_speed else initial_speed
) # Place the cars
i += (
randint(1, max_speed * 2) if random_frequency else frequency
) # Arbitrary number, may need tuning
return highway
def get_distance(highway_now: list, car_index: int) -> int:
"""
Get the distance between a car (at index car_index) and the next car
>>> get_distance([6, -1, 6, -1, 6], 2)
1
>>> get_distance([2, -1, -1, -1, 3, 1, 0, 1, 3, 2], 0)
3
>>> get_distance([-1, -1, -1, -1, 2, -1, -1, -1, 3], -1)
4
"""
distance = 0
cells = highway_now[car_index + 1 :]
for cell in range(len(cells)): # May need a better name for this
if cells[cell] != -1: # If the cell is not empty then
return distance # we have the distance we wanted
distance += 1
# Here if the car is near the end of the highway
return distance + get_distance(highway_now, -1)
def update(highway_now: list, probability: float, max_speed: int) -> list:
"""
Update the speed of the cars
>>> update([-1, -1, -1, -1, -1, 2, -1, -1, -1, -1, 3], 0.0, 5)
[-1, -1, -1, -1, -1, 3, -1, -1, -1, -1, 4]
>>> update([-1, -1, 2, -1, -1, -1, -1, 3], 0.0, 5)
[-1, -1, 3, -1, -1, -1, -1, 1]
"""
number_of_cells = len(highway_now)
# Beforce calculations, the highway is empty
next_highway = [-1] * number_of_cells
for car_index in range(number_of_cells):
if highway_now[car_index] != -1:
# Add 1 to the current speed of the car and cap the speed
next_highway[car_index] = min(highway_now[car_index] + 1, max_speed)
# Number of empty cell before the next car
dn = get_distance(highway_now, car_index) - 1
# We can't have the car causing an accident
next_highway[car_index] = min(next_highway[car_index], dn)
if random() < probability:
# Randomly, a driver will slow down
next_highway[car_index] = max(next_highway[car_index] - 1, 0)
return next_highway
def simulate(
highway: list, number_of_update: int, probability: float, max_speed: int
) -> list:
"""
The main function, it will simulate the evolution of the highway
>>> simulate([[-1, 2, -1, -1, -1, 3]], 2, 0.0, 3)
[[-1, 2, -1, -1, -1, 3], [-1, -1, -1, 2, -1, 0], [1, -1, -1, 0, -1, -1]]
>>> simulate([[-1, 2, -1, 3]], 4, 0.0, 3)
[[-1, 2, -1, 3], [-1, 0, -1, 0], [-1, 0, -1, 0], [-1, 0, -1, 0], [-1, 0, -1, 0]]
"""
number_of_cells = len(highway[0])
for i in range(number_of_update):
next_speeds_calculated = update(highway[i], probability, max_speed)
real_next_speeds = [-1] * number_of_cells
for car_index in range(number_of_cells):
speed = next_speeds_calculated[car_index]
if speed != -1:
# Change the position based on the speed (with % to create the loop)
index = (car_index + speed) % number_of_cells
# Commit the change of position
real_next_speeds[index] = speed
highway.append(real_next_speeds)
return highway
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Python program for Bitonic Sort.
Note that this program works only when size of input is a power of 2.
"""
from __future__ import annotations
def comp_and_swap(array: list[int], index1: int, index2: int, direction: int) -> None:
"""Compare the value at given index1 and index2 of the array and swap them as per
the given direction.
The parameter direction indicates the sorting direction, ASCENDING(1) or
DESCENDING(0); if (a[i] > a[j]) agrees with the direction, then a[i] and a[j] are
interchanged.
>>> arr = [12, 42, -21, 1]
>>> comp_and_swap(arr, 1, 2, 1)
>>> print(arr)
[12, -21, 42, 1]
>>> comp_and_swap(arr, 1, 2, 0)
>>> print(arr)
[12, 42, -21, 1]
>>> comp_and_swap(arr, 0, 3, 1)
>>> print(arr)
[1, 42, -21, 12]
>>> comp_and_swap(arr, 0, 3, 0)
>>> print(arr)
[12, 42, -21, 1]
"""
if (direction == 1 and array[index1] > array[index2]) or (
direction == 0 and array[index1] < array[index2]
):
array[index1], array[index2] = array[index2], array[index1]
def bitonic_merge(array: list[int], low: int, length: int, direction: int) -> None:
"""
It recursively sorts a bitonic sequence in ascending order, if direction = 1, and in
descending if direction = 0.
The sequence to be sorted starts at index position low, the parameter length is the
number of elements to be sorted.
>>> arr = [12, 42, -21, 1]
>>> bitonic_merge(arr, 0, 4, 1)
>>> print(arr)
[-21, 1, 12, 42]
>>> bitonic_merge(arr, 0, 4, 0)
>>> print(arr)
[42, 12, 1, -21]
"""
if length > 1:
middle = int(length / 2)
for i in range(low, low + middle):
comp_and_swap(array, i, i + middle, direction)
bitonic_merge(array, low, middle, direction)
bitonic_merge(array, low + middle, middle, direction)
def bitonic_sort(array: list[int], low: int, length: int, direction: int) -> None:
"""
This function first produces a bitonic sequence by recursively sorting its two
halves in opposite sorting orders, and then calls bitonic_merge to make them in the
same order.
>>> arr = [12, 34, 92, -23, 0, -121, -167, 145]
>>> bitonic_sort(arr, 0, 8, 1)
>>> arr
[-167, -121, -23, 0, 12, 34, 92, 145]
>>> bitonic_sort(arr, 0, 8, 0)
>>> arr
[145, 92, 34, 12, 0, -23, -121, -167]
"""
if length > 1:
middle = int(length / 2)
bitonic_sort(array, low, middle, 1)
bitonic_sort(array, low + middle, middle, 0)
bitonic_merge(array, low, length, direction)
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item.strip()) for item in user_input.split(",")]
bitonic_sort(unsorted, 0, len(unsorted), 1)
print("\nSorted array in ascending order is: ", end="")
print(*unsorted, sep=", ")
bitonic_merge(unsorted, 0, len(unsorted), 0)
print("Sorted array in descending order is: ", end="")
print(*unsorted, sep=", ")
| """
Python program for Bitonic Sort.
Note that this program works only when size of input is a power of 2.
"""
from __future__ import annotations
def comp_and_swap(array: list[int], index1: int, index2: int, direction: int) -> None:
"""Compare the value at given index1 and index2 of the array and swap them as per
the given direction.
The parameter direction indicates the sorting direction, ASCENDING(1) or
DESCENDING(0); if (a[i] > a[j]) agrees with the direction, then a[i] and a[j] are
interchanged.
>>> arr = [12, 42, -21, 1]
>>> comp_and_swap(arr, 1, 2, 1)
>>> print(arr)
[12, -21, 42, 1]
>>> comp_and_swap(arr, 1, 2, 0)
>>> print(arr)
[12, 42, -21, 1]
>>> comp_and_swap(arr, 0, 3, 1)
>>> print(arr)
[1, 42, -21, 12]
>>> comp_and_swap(arr, 0, 3, 0)
>>> print(arr)
[12, 42, -21, 1]
"""
if (direction == 1 and array[index1] > array[index2]) or (
direction == 0 and array[index1] < array[index2]
):
array[index1], array[index2] = array[index2], array[index1]
def bitonic_merge(array: list[int], low: int, length: int, direction: int) -> None:
"""
It recursively sorts a bitonic sequence in ascending order, if direction = 1, and in
descending if direction = 0.
The sequence to be sorted starts at index position low, the parameter length is the
number of elements to be sorted.
>>> arr = [12, 42, -21, 1]
>>> bitonic_merge(arr, 0, 4, 1)
>>> print(arr)
[-21, 1, 12, 42]
>>> bitonic_merge(arr, 0, 4, 0)
>>> print(arr)
[42, 12, 1, -21]
"""
if length > 1:
middle = int(length / 2)
for i in range(low, low + middle):
comp_and_swap(array, i, i + middle, direction)
bitonic_merge(array, low, middle, direction)
bitonic_merge(array, low + middle, middle, direction)
def bitonic_sort(array: list[int], low: int, length: int, direction: int) -> None:
"""
This function first produces a bitonic sequence by recursively sorting its two
halves in opposite sorting orders, and then calls bitonic_merge to make them in the
same order.
>>> arr = [12, 34, 92, -23, 0, -121, -167, 145]
>>> bitonic_sort(arr, 0, 8, 1)
>>> arr
[-167, -121, -23, 0, 12, 34, 92, 145]
>>> bitonic_sort(arr, 0, 8, 0)
>>> arr
[145, 92, 34, 12, 0, -23, -121, -167]
"""
if length > 1:
middle = int(length / 2)
bitonic_sort(array, low, middle, 1)
bitonic_sort(array, low + middle, middle, 0)
bitonic_merge(array, low, length, direction)
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item.strip()) for item in user_input.split(",")]
bitonic_sort(unsorted, 0, len(unsorted), 1)
print("\nSorted array in ascending order is: ", end="")
print(*unsorted, sep=", ")
bitonic_merge(unsorted, 0, len(unsorted), 0)
print("Sorted array in descending order is: ", end="")
print(*unsorted, sep=", ")
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from bisect import bisect
from itertools import accumulate
def fracKnapsack(vl, wt, W, n):
"""
>>> fracKnapsack([60, 100, 120], [10, 20, 30], 50, 3)
240.0
"""
r = list(sorted(zip(vl, wt), key=lambda x: x[0] / x[1], reverse=True))
vl, wt = [i[0] for i in r], [i[1] for i in r]
acc = list(accumulate(wt))
k = bisect(acc, W)
return (
0
if k == 0
else sum(vl[:k]) + (W - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k])
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| from bisect import bisect
from itertools import accumulate
def fracKnapsack(vl, wt, W, n):
"""
>>> fracKnapsack([60, 100, 120], [10, 20, 30], 50, 3)
240.0
"""
r = list(sorted(zip(vl, wt), key=lambda x: x[0] / x[1], reverse=True))
vl, wt = [i[0] for i in r], [i[1] for i in r]
acc = list(accumulate(wt))
k = bisect(acc, W)
return (
0
if k == 0
else sum(vl[:k]) + (W - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k])
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Is IP v4 address valid?
A valid IP address must be four octets in the form of A.B.C.D,
where A,B,C and D are numbers from 0-254
for example: 192.168.23.1, 172.254.254.254 are valid IP address
192.168.255.0, 255.192.3.121 are invalid IP address
"""
def is_ip_v4_address_valid(ip_v4_address: str) -> bool:
"""
print "Valid IP address" If IP is valid.
or
print "Invalid IP address" If IP is invalid.
>>> is_ip_v4_address_valid("192.168.0.23")
True
>>> is_ip_v4_address_valid("192.255.15.8")
False
>>> is_ip_v4_address_valid("172.100.0.8")
True
>>> is_ip_v4_address_valid("254.255.0.255")
False
>>> is_ip_v4_address_valid("1.2.33333333.4")
False
>>> is_ip_v4_address_valid("1.2.-3.4")
False
>>> is_ip_v4_address_valid("1.2.3")
False
>>> is_ip_v4_address_valid("1.2.3.4.5")
False
>>> is_ip_v4_address_valid("1.2.A.4")
False
>>> is_ip_v4_address_valid("0.0.0.0")
True
>>> is_ip_v4_address_valid("1.2.3.")
False
"""
octets = [int(i) for i in ip_v4_address.split(".") if i.isdigit()]
return len(octets) == 4 and all(0 <= int(octet) <= 254 for octet in octets)
if __name__ == "__main__":
ip = input().strip()
valid_or_invalid = "valid" if is_ip_v4_address_valid(ip) else "invalid"
print(f"{ip} is a {valid_or_invalid} IP v4 address.")
| """
Is IP v4 address valid?
A valid IP address must be four octets in the form of A.B.C.D,
where A,B,C and D are numbers from 0-254
for example: 192.168.23.1, 172.254.254.254 are valid IP address
192.168.255.0, 255.192.3.121 are invalid IP address
"""
def is_ip_v4_address_valid(ip_v4_address: str) -> bool:
"""
print "Valid IP address" If IP is valid.
or
print "Invalid IP address" If IP is invalid.
>>> is_ip_v4_address_valid("192.168.0.23")
True
>>> is_ip_v4_address_valid("192.255.15.8")
False
>>> is_ip_v4_address_valid("172.100.0.8")
True
>>> is_ip_v4_address_valid("254.255.0.255")
False
>>> is_ip_v4_address_valid("1.2.33333333.4")
False
>>> is_ip_v4_address_valid("1.2.-3.4")
False
>>> is_ip_v4_address_valid("1.2.3")
False
>>> is_ip_v4_address_valid("1.2.3.4.5")
False
>>> is_ip_v4_address_valid("1.2.A.4")
False
>>> is_ip_v4_address_valid("0.0.0.0")
True
>>> is_ip_v4_address_valid("1.2.3.")
False
"""
octets = [int(i) for i in ip_v4_address.split(".") if i.isdigit()]
return len(octets) == 4 and all(0 <= int(octet) <= 254 for octet in octets)
if __name__ == "__main__":
ip = input().strip()
valid_or_invalid = "valid" if is_ip_v4_address_valid(ip) else "invalid"
print(f"{ip} is a {valid_or_invalid} IP v4 address.")
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
from __future__ import annotations
import json
import requests
from bs4 import BeautifulSoup
from fake_useragent import UserAgent
headers = {"UserAgent": UserAgent().random}
def extract_user_profile(script) -> dict:
"""
May raise json.decoder.JSONDecodeError
"""
data = script.contents[0]
info = json.loads(data[data.find('{"config"') : -1])
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class InstagramUser:
"""
Class Instagram crawl instagram user information
Usage: (doctest failing on GitHub Actions)
# >>> instagram_user = InstagramUser("github")
# >>> instagram_user.is_verified
True
# >>> instagram_user.biography
'Built for developers.'
"""
def __init__(self, username):
self.url = f"https://www.instagram.com/{username}/"
self.user_data = self.get_json()
def get_json(self) -> dict:
"""
Return a dict of user information
"""
html = requests.get(self.url, headers=headers).text
scripts = BeautifulSoup(html, "html.parser").find_all("script")
try:
return extract_user_profile(scripts[4])
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3])
def __repr__(self) -> str:
return f"{self.__class__.__name__}('{self.username}')"
def __str__(self) -> str:
return f"{self.fullname} ({self.username}) is {self.biography}"
@property
def username(self) -> str:
return self.user_data["username"]
@property
def fullname(self) -> str:
return self.user_data["full_name"]
@property
def biography(self) -> str:
return self.user_data["biography"]
@property
def email(self) -> str:
return self.user_data["business_email"]
@property
def website(self) -> str:
return self.user_data["external_url"]
@property
def number_of_followers(self) -> int:
return self.user_data["edge_followed_by"]["count"]
@property
def number_of_followings(self) -> int:
return self.user_data["edge_follow"]["count"]
@property
def number_of_posts(self) -> int:
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def profile_picture_url(self) -> str:
return self.user_data["profile_pic_url_hd"]
@property
def is_verified(self) -> bool:
return self.user_data["is_verified"]
@property
def is_private(self) -> bool:
return self.user_data["is_private"]
def test_instagram_user(username: str = "github") -> None:
"""
A self running doctest
>>> test_instagram_user()
"""
import os
if os.environ.get("CI"):
return None # test failing on GitHub Actions
instagram_user = InstagramUser(username)
assert instagram_user.user_data
assert isinstance(instagram_user.user_data, dict)
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 150
assert instagram_user.number_of_followers > 120000
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "[email protected]"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith("https://instagram.")
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
instagram_user = InstagramUser("github")
print(instagram_user)
print(f"{instagram_user.number_of_posts = }")
print(f"{instagram_user.number_of_followers = }")
print(f"{instagram_user.number_of_followings = }")
print(f"{instagram_user.email = }")
print(f"{instagram_user.website = }")
print(f"{instagram_user.profile_picture_url = }")
print(f"{instagram_user.is_verified = }")
print(f"{instagram_user.is_private = }")
| #!/usr/bin/env python3
from __future__ import annotations
import json
import requests
from bs4 import BeautifulSoup
from fake_useragent import UserAgent
headers = {"UserAgent": UserAgent().random}
def extract_user_profile(script) -> dict:
"""
May raise json.decoder.JSONDecodeError
"""
data = script.contents[0]
info = json.loads(data[data.find('{"config"') : -1])
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class InstagramUser:
"""
Class Instagram crawl instagram user information
Usage: (doctest failing on GitHub Actions)
# >>> instagram_user = InstagramUser("github")
# >>> instagram_user.is_verified
True
# >>> instagram_user.biography
'Built for developers.'
"""
def __init__(self, username):
self.url = f"https://www.instagram.com/{username}/"
self.user_data = self.get_json()
def get_json(self) -> dict:
"""
Return a dict of user information
"""
html = requests.get(self.url, headers=headers).text
scripts = BeautifulSoup(html, "html.parser").find_all("script")
try:
return extract_user_profile(scripts[4])
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3])
def __repr__(self) -> str:
return f"{self.__class__.__name__}('{self.username}')"
def __str__(self) -> str:
return f"{self.fullname} ({self.username}) is {self.biography}"
@property
def username(self) -> str:
return self.user_data["username"]
@property
def fullname(self) -> str:
return self.user_data["full_name"]
@property
def biography(self) -> str:
return self.user_data["biography"]
@property
def email(self) -> str:
return self.user_data["business_email"]
@property
def website(self) -> str:
return self.user_data["external_url"]
@property
def number_of_followers(self) -> int:
return self.user_data["edge_followed_by"]["count"]
@property
def number_of_followings(self) -> int:
return self.user_data["edge_follow"]["count"]
@property
def number_of_posts(self) -> int:
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def profile_picture_url(self) -> str:
return self.user_data["profile_pic_url_hd"]
@property
def is_verified(self) -> bool:
return self.user_data["is_verified"]
@property
def is_private(self) -> bool:
return self.user_data["is_private"]
def test_instagram_user(username: str = "github") -> None:
"""
A self running doctest
>>> test_instagram_user()
"""
import os
if os.environ.get("CI"):
return None # test failing on GitHub Actions
instagram_user = InstagramUser(username)
assert instagram_user.user_data
assert isinstance(instagram_user.user_data, dict)
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 150
assert instagram_user.number_of_followers > 120000
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "[email protected]"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith("https://instagram.")
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
instagram_user = InstagramUser("github")
print(instagram_user)
print(f"{instagram_user.number_of_posts = }")
print(f"{instagram_user.number_of_followers = }")
print(f"{instagram_user.number_of_followings = }")
print(f"{instagram_user.email = }")
print(f"{instagram_user.website = }")
print(f"{instagram_user.profile_picture_url = }")
print(f"{instagram_user.is_verified = }")
print(f"{instagram_user.is_private = }")
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Description :
Newton's second law of motion pertains to the behavior of objects for which
all existing forces are not balanced.
The second law states that the acceleration of an object is dependent upon two variables
- the net force acting upon the object and the mass of the object.
The acceleration of an object depends directly
upon the net force acting upon the object,
and inversely upon the mass of the object.
As the force acting upon an object is increased,
the acceleration of the object is increased.
As the mass of an object is increased, the acceleration of the object is decreased.
Source: https://www.physicsclassroom.com/class/newtlaws/Lesson-3/Newton-s-Second-Law
Formulation: Fnet = m • a
Diagrammatic Explanation:
Forces are unbalanced
|
|
|
V
There is acceleration
/\
/ \
/ \
/ \
/ \
/ \
/ \
__________________ ____ ________________
|The acceleration | |The acceleration |
|depends directly | |depends inversely |
|on the net Force | |upon the object's |
|_________________| |mass_______________|
Units:
1 Newton = 1 kg X meters / (seconds^2)
How to use?
Inputs:
___________________________________________________
|Name | Units | Type |
|-------------|-------------------------|-----------|
|mass | (in kgs) | float |
|-------------|-------------------------|-----------|
|acceleration | (in meters/(seconds^2)) | float |
|_____________|_________________________|___________|
Output:
___________________________________________________
|Name | Units | Type |
|-------------|-------------------------|-----------|
|force | (in Newtons) | float |
|_____________|_________________________|___________|
"""
def newtons_second_law_of_motion(mass: float, acceleration: float) -> float:
"""
>>> newtons_second_law_of_motion(10, 10)
100
>>> newtons_second_law_of_motion(2.0, 1)
2.0
"""
force = float()
try:
force = mass * acceleration
except Exception:
return -0.0
return force
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo
mass = 12.5
acceleration = 10
force = newtons_second_law_of_motion(mass, acceleration)
print("The force is ", force, "N")
| """
Description :
Newton's second law of motion pertains to the behavior of objects for which
all existing forces are not balanced.
The second law states that the acceleration of an object is dependent upon two variables
- the net force acting upon the object and the mass of the object.
The acceleration of an object depends directly
upon the net force acting upon the object,
and inversely upon the mass of the object.
As the force acting upon an object is increased,
the acceleration of the object is increased.
As the mass of an object is increased, the acceleration of the object is decreased.
Source: https://www.physicsclassroom.com/class/newtlaws/Lesson-3/Newton-s-Second-Law
Formulation: Fnet = m • a
Diagrammatic Explanation:
Forces are unbalanced
|
|
|
V
There is acceleration
/\
/ \
/ \
/ \
/ \
/ \
/ \
__________________ ____ ________________
|The acceleration | |The acceleration |
|depends directly | |depends inversely |
|on the net Force | |upon the object's |
|_________________| |mass_______________|
Units:
1 Newton = 1 kg X meters / (seconds^2)
How to use?
Inputs:
___________________________________________________
|Name | Units | Type |
|-------------|-------------------------|-----------|
|mass | (in kgs) | float |
|-------------|-------------------------|-----------|
|acceleration | (in meters/(seconds^2)) | float |
|_____________|_________________________|___________|
Output:
___________________________________________________
|Name | Units | Type |
|-------------|-------------------------|-----------|
|force | (in Newtons) | float |
|_____________|_________________________|___________|
"""
def newtons_second_law_of_motion(mass: float, acceleration: float) -> float:
"""
>>> newtons_second_law_of_motion(10, 10)
100
>>> newtons_second_law_of_motion(2.0, 1)
2.0
"""
force = float()
try:
force = mass * acceleration
except Exception:
return -0.0
return force
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo
mass = 12.5
acceleration = 10
force = newtons_second_law_of_motion(mass, acceleration)
print("The force is ", force, "N")
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
References: wikipedia:square free number
python/black : True
flake8 : True
"""
from __future__ import annotations
def is_square_free(factors: list[int]) -> bool:
"""
# doctest: +NORMALIZE_WHITESPACE
This functions takes a list of prime factors as input.
returns True if the factors are square free.
>>> is_square_free([1, 1, 2, 3, 4])
False
These are wrong but should return some value
it simply checks for repition in the numbers.
>>> is_square_free([1, 3, 4, 'sd', 0.0])
True
>>> is_square_free([1, 0.5, 2, 0.0])
True
>>> is_square_free([1, 2, 2, 5])
False
>>> is_square_free('asd')
True
>>> is_square_free(24)
Traceback (most recent call last):
...
TypeError: 'int' object is not iterable
"""
return len(set(factors)) == len(factors)
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
References: wikipedia:square free number
python/black : True
flake8 : True
"""
from __future__ import annotations
def is_square_free(factors: list[int]) -> bool:
"""
# doctest: +NORMALIZE_WHITESPACE
This functions takes a list of prime factors as input.
returns True if the factors are square free.
>>> is_square_free([1, 1, 2, 3, 4])
False
These are wrong but should return some value
it simply checks for repition in the numbers.
>>> is_square_free([1, 3, 4, 'sd', 0.0])
True
>>> is_square_free([1, 0.5, 2, 0.0])
True
>>> is_square_free([1, 2, 2, 5])
False
>>> is_square_free('asd')
True
>>> is_square_free(24)
Traceback (most recent call last):
...
TypeError: 'int' object is not iterable
"""
return len(set(factors)) == len(factors)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
An Armstrong number is equal to the sum of its own digits each raised to the
power of the number of digits.
For example, 370 is an Armstrong number because 3*3*3 + 7*7*7 + 0*0*0 = 370.
Armstrong numbers are also called Narcissistic numbers and Pluperfect numbers.
On-Line Encyclopedia of Integer Sequences entry: https://oeis.org/A005188
"""
PASSING = (1, 153, 370, 371, 1634, 24678051, 115132219018763992565095597973971522401)
FAILING: tuple = (-153, -1, 0, 1.2, 200, "A", [], {}, None)
def armstrong_number(n: int) -> bool:
"""
Return True if n is an Armstrong number or False if it is not.
>>> all(armstrong_number(n) for n in PASSING)
True
>>> any(armstrong_number(n) for n in FAILING)
False
"""
if not isinstance(n, int) or n < 1:
return False
# Initialization of sum and number of digits.
sum = 0
number_of_digits = 0
temp = n
# Calculation of digits of the number
while temp > 0:
number_of_digits += 1
temp //= 10
# Dividing number into separate digits and find Armstrong number
temp = n
while temp > 0:
rem = temp % 10
sum += rem ** number_of_digits
temp //= 10
return n == sum
def pluperfect_number(n: int) -> bool:
"""Return True if n is a pluperfect number or False if it is not
>>> all(armstrong_number(n) for n in PASSING)
True
>>> any(armstrong_number(n) for n in FAILING)
False
"""
if not isinstance(n, int) or n < 1:
return False
# Init a "histogram" of the digits
digit_histogram = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
digit_total = 0
sum = 0
temp = n
while temp > 0:
temp, rem = divmod(temp, 10)
digit_histogram[rem] += 1
digit_total += 1
for (cnt, i) in zip(digit_histogram, range(len(digit_histogram))):
sum += cnt * i ** digit_total
return n == sum
def narcissistic_number(n: int) -> bool:
"""Return True if n is a narcissistic number or False if it is not.
>>> all(armstrong_number(n) for n in PASSING)
True
>>> any(armstrong_number(n) for n in FAILING)
False
"""
if not isinstance(n, int) or n < 1:
return False
expo = len(str(n)) # the power that all digits will be raised to
# check if sum of each digit multiplied expo times is equal to number
return n == sum(int(i) ** expo for i in str(n))
def main():
"""
Request that user input an integer and tell them if it is Armstrong number.
"""
num = int(input("Enter an integer to see if it is an Armstrong number: ").strip())
print(f"{num} is {'' if armstrong_number(num) else 'not '}an Armstrong number.")
print(f"{num} is {'' if narcissistic_number(num) else 'not '}an Armstrong number.")
print(f"{num} is {'' if pluperfect_number(num) else 'not '}an Armstrong number.")
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| """
An Armstrong number is equal to the sum of its own digits each raised to the
power of the number of digits.
For example, 370 is an Armstrong number because 3*3*3 + 7*7*7 + 0*0*0 = 370.
Armstrong numbers are also called Narcissistic numbers and Pluperfect numbers.
On-Line Encyclopedia of Integer Sequences entry: https://oeis.org/A005188
"""
PASSING = (1, 153, 370, 371, 1634, 24678051, 115132219018763992565095597973971522401)
FAILING: tuple = (-153, -1, 0, 1.2, 200, "A", [], {}, None)
def armstrong_number(n: int) -> bool:
"""
Return True if n is an Armstrong number or False if it is not.
>>> all(armstrong_number(n) for n in PASSING)
True
>>> any(armstrong_number(n) for n in FAILING)
False
"""
if not isinstance(n, int) or n < 1:
return False
# Initialization of sum and number of digits.
sum = 0
number_of_digits = 0
temp = n
# Calculation of digits of the number
while temp > 0:
number_of_digits += 1
temp //= 10
# Dividing number into separate digits and find Armstrong number
temp = n
while temp > 0:
rem = temp % 10
sum += rem ** number_of_digits
temp //= 10
return n == sum
def pluperfect_number(n: int) -> bool:
"""Return True if n is a pluperfect number or False if it is not
>>> all(armstrong_number(n) for n in PASSING)
True
>>> any(armstrong_number(n) for n in FAILING)
False
"""
if not isinstance(n, int) or n < 1:
return False
# Init a "histogram" of the digits
digit_histogram = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
digit_total = 0
sum = 0
temp = n
while temp > 0:
temp, rem = divmod(temp, 10)
digit_histogram[rem] += 1
digit_total += 1
for (cnt, i) in zip(digit_histogram, range(len(digit_histogram))):
sum += cnt * i ** digit_total
return n == sum
def narcissistic_number(n: int) -> bool:
"""Return True if n is a narcissistic number or False if it is not.
>>> all(armstrong_number(n) for n in PASSING)
True
>>> any(armstrong_number(n) for n in FAILING)
False
"""
if not isinstance(n, int) or n < 1:
return False
expo = len(str(n)) # the power that all digits will be raised to
# check if sum of each digit multiplied expo times is equal to number
return n == sum(int(i) ** expo for i in str(n))
def main():
"""
Request that user input an integer and tell them if it is Armstrong number.
"""
num = int(input("Enter an integer to see if it is an Armstrong number: ").strip())
print(f"{num} is {'' if armstrong_number(num) else 'not '}an Armstrong number.")
print(f"{num} is {'' if narcissistic_number(num) else 'not '}an Armstrong number.")
print(f"{num} is {'' if pluperfect_number(num) else 'not '}an Armstrong number.")
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 10: https://projecteuler.net/problem=10
Summation of primes
The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17.
Find the sum of all the primes below two million.
References:
- https://en.wikipedia.org/wiki/Prime_number
"""
from math import sqrt
def is_prime(n: int) -> bool:
"""
Returns boolean representing primality of given number num.
>>> is_prime(2)
True
>>> is_prime(3)
True
>>> is_prime(27)
False
>>> is_prime(2999)
True
"""
if 1 < n < 4:
return True
elif n < 2 or not n % 2:
return False
return not any(not n % i for i in range(3, int(sqrt(n) + 1), 2))
def solution(n: int = 2000000) -> int:
"""
Returns the sum of all the primes below n.
>>> solution(1000)
76127
>>> solution(5000)
1548136
>>> solution(10000)
5736396
>>> solution(7)
10
"""
return sum(num for num in range(3, n, 2) if is_prime(num)) + 2 if n > 2 else 0
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 10: https://projecteuler.net/problem=10
Summation of primes
The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17.
Find the sum of all the primes below two million.
References:
- https://en.wikipedia.org/wiki/Prime_number
"""
from math import sqrt
def is_prime(n: int) -> bool:
"""
Returns boolean representing primality of given number num.
>>> is_prime(2)
True
>>> is_prime(3)
True
>>> is_prime(27)
False
>>> is_prime(2999)
True
"""
if 1 < n < 4:
return True
elif n < 2 or not n % 2:
return False
return not any(not n % i for i in range(3, int(sqrt(n) + 1), 2))
def solution(n: int = 2000000) -> int:
"""
Returns the sum of all the primes below n.
>>> solution(1000)
76127
>>> solution(5000)
1548136
>>> solution(10000)
5736396
>>> solution(7)
10
"""
return sum(num for num in range(3, n, 2) if is_prime(num)) + 2 if n > 2 else 0
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 5,782 | [mypy] Fix type annotations for maths directory | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| Rohanrbharadwaj | "2021-11-06T13:38:09Z" | "2021-11-07T15:13:59Z" | db5aa1d18890439e4108fa416679dbab5859f30c | a98465230f21e6ece76332eeca1558613788c387 | [mypy] Fix type annotations for maths directory. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Get book and author data from https://openlibrary.org
ISBN: https://en.wikipedia.org/wiki/International_Standard_Book_Number
"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def get_openlibrary_data(olid: str = "isbn/0140328726") -> dict:
"""
Given an 'isbn/0140328726', return book data from Open Library as a Python dict.
Given an '/authors/OL34184A', return authors data as a Python dict.
This code must work for olids with or without a leading slash ('/').
# Comment out doctests if they take too long or have results that may change
# >>> get_openlibrary_data(olid='isbn/0140328726') # doctest: +ELLIPSIS
{'publishers': ['Puffin'], 'number_of_pages': 96, 'isbn_10': ['0140328726'], ...
# >>> get_openlibrary_data(olid='/authors/OL7353617A') # doctest: +ELLIPSIS
{'name': 'Adrian Brisku', 'created': {'type': '/type/datetime', ...
>>> pass # Placate https://github.com/apps/algorithms-keeper
"""
new_olid = olid.strip().strip("/") # Remove leading/trailing whitespace & slashes
if new_olid.count("/") != 1:
raise ValueError(f"{olid} is not a valid Open Library olid")
return requests.get(f"https://openlibrary.org/{new_olid}.json").json()
def summarize_book(ol_book_data: dict) -> dict:
"""
Given Open Library book data, return a summary as a Python dict.
>>> pass # Placate https://github.com/apps/algorithms-keeper
"""
desired_keys = {
"title": "Title",
"publish_date": "Publish date",
"authors": "Authors",
"number_of_pages": "Number of pages:",
"first_sentence": "First sentence",
"isbn_10": "ISBN (10)",
"isbn_13": "ISBN (13)",
}
data = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
data["Authors"] = [
get_openlibrary_data(author["key"])["name"] for author in data["Authors"]
]
data["First sentence"] = data["First sentence"]["value"]
for key, value in data.items():
if isinstance(value, list):
data[key] = ", ".join(value)
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
isbn = input("\nEnter the ISBN code to search (or 'quit' to stop): ").strip()
if isbn.lower() in ("", "q", "quit", "exit", "stop"):
break
if len(isbn) not in (10, 13) or not isbn.isdigit():
print(f"Sorry, {isbn} is not a valid ISBN. Please, input a valid ISBN.")
continue
print(f"\nSearching Open Library for ISBN: {isbn}...\n")
try:
book_summary = summarize_book(get_openlibrary_data(f"isbn/{isbn}"))
print("\n".join(f"{key}: {value}" for key, value in book_summary.items()))
except JSONDecodeError: # Workaround for requests.exceptions.RequestException:
print(f"Sorry, there are no results for ISBN: {isbn}.")
| """
Get book and author data from https://openlibrary.org
ISBN: https://en.wikipedia.org/wiki/International_Standard_Book_Number
"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def get_openlibrary_data(olid: str = "isbn/0140328726") -> dict:
"""
Given an 'isbn/0140328726', return book data from Open Library as a Python dict.
Given an '/authors/OL34184A', return authors data as a Python dict.
This code must work for olids with or without a leading slash ('/').
# Comment out doctests if they take too long or have results that may change
# >>> get_openlibrary_data(olid='isbn/0140328726') # doctest: +ELLIPSIS
{'publishers': ['Puffin'], 'number_of_pages': 96, 'isbn_10': ['0140328726'], ...
# >>> get_openlibrary_data(olid='/authors/OL7353617A') # doctest: +ELLIPSIS
{'name': 'Adrian Brisku', 'created': {'type': '/type/datetime', ...
>>> pass # Placate https://github.com/apps/algorithms-keeper
"""
new_olid = olid.strip().strip("/") # Remove leading/trailing whitespace & slashes
if new_olid.count("/") != 1:
raise ValueError(f"{olid} is not a valid Open Library olid")
return requests.get(f"https://openlibrary.org/{new_olid}.json").json()
def summarize_book(ol_book_data: dict) -> dict:
"""
Given Open Library book data, return a summary as a Python dict.
>>> pass # Placate https://github.com/apps/algorithms-keeper
"""
desired_keys = {
"title": "Title",
"publish_date": "Publish date",
"authors": "Authors",
"number_of_pages": "Number of pages:",
"first_sentence": "First sentence",
"isbn_10": "ISBN (10)",
"isbn_13": "ISBN (13)",
}
data = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
data["Authors"] = [
get_openlibrary_data(author["key"])["name"] for author in data["Authors"]
]
data["First sentence"] = data["First sentence"]["value"]
for key, value in data.items():
if isinstance(value, list):
data[key] = ", ".join(value)
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
isbn = input("\nEnter the ISBN code to search (or 'quit' to stop): ").strip()
if isbn.lower() in ("", "q", "quit", "exit", "stop"):
break
if len(isbn) not in (10, 13) or not isbn.isdigit():
print(f"Sorry, {isbn} is not a valid ISBN. Please, input a valid ISBN.")
continue
print(f"\nSearching Open Library for ISBN: {isbn}...\n")
try:
book_summary = summarize_book(get_openlibrary_data(f"isbn/{isbn}"))
print("\n".join(f"{key}: {value}" for key, value in book_summary.items()))
except JSONDecodeError: # Workaround for requests.exceptions.RequestException:
print(f"Sorry, there are no results for ISBN: {isbn}.")
| -1 |
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