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TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def 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()
| 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()
| -1 |
TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem 33: https://projecteuler.net/problem=33
The fraction 49/98 is a curious fraction, as an inexperienced
mathematician in attempting to simplify it may incorrectly believe
that 49/98 = 4/8, which is correct, is obtained by cancelling the 9s.
We shall consider fractions like, 30/50 = 3/5, to be trivial examples.
There are exactly four non-trivial examples of this type of fraction,
less than one in value, and containing two digits in the numerator
and denominator.
If the product of these four fractions is given in its lowest common
terms, find the value of the denominator.
"""
from fractions import Fraction
from typing import List
def is_digit_cancelling(num: int, den: int) -> bool:
if num != den:
if num % 10 == den // 10:
if (num // 10) / (den % 10) == num / den:
return True
return False
def fraction_list(digit_len: int) -> List[str]:
"""
>>> fraction_list(2)
['16/64', '19/95', '26/65', '49/98']
>>> fraction_list(3)
['16/64', '19/95', '26/65', '49/98']
>>> fraction_list(4)
['16/64', '19/95', '26/65', '49/98']
>>> fraction_list(0)
[]
>>> fraction_list(5)
['16/64', '19/95', '26/65', '49/98']
"""
solutions = []
den = 11
last_digit = int("1" + "0" * digit_len)
for num in range(den, last_digit):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(num, den):
solutions.append(f"{num}/{den}")
den += 1
num += 1
den = 10
return solutions
def solution(n: int = 2) -> int:
"""
Return the solution to the problem
"""
result = 1.0
for fraction in fraction_list(n):
frac = Fraction(fraction)
result *= frac.denominator / frac.numerator
return int(result)
if __name__ == "__main__":
print(solution())
| """
Problem 33: https://projecteuler.net/problem=33
The fraction 49/98 is a curious fraction, as an inexperienced
mathematician in attempting to simplify it may incorrectly believe
that 49/98 = 4/8, which is correct, is obtained by cancelling the 9s.
We shall consider fractions like, 30/50 = 3/5, to be trivial examples.
There are exactly four non-trivial examples of this type of fraction,
less than one in value, and containing two digits in the numerator
and denominator.
If the product of these four fractions is given in its lowest common
terms, find the value of the denominator.
"""
from fractions import Fraction
from typing import List
def is_digit_cancelling(num: int, den: int) -> bool:
if num != den:
if num % 10 == den // 10:
if (num // 10) / (den % 10) == num / den:
return True
return False
def fraction_list(digit_len: int) -> List[str]:
"""
>>> fraction_list(2)
['16/64', '19/95', '26/65', '49/98']
>>> fraction_list(3)
['16/64', '19/95', '26/65', '49/98']
>>> fraction_list(4)
['16/64', '19/95', '26/65', '49/98']
>>> fraction_list(0)
[]
>>> fraction_list(5)
['16/64', '19/95', '26/65', '49/98']
"""
solutions = []
den = 11
last_digit = int("1" + "0" * digit_len)
for num in range(den, last_digit):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(num, den):
solutions.append(f"{num}/{den}")
den += 1
num += 1
den = 10
return solutions
def solution(n: int = 2) -> int:
"""
Return the solution to the problem
"""
result = 1.0
for fraction in fraction_list(n):
frac = Fraction(fraction)
result *= frac.denominator / frac.numerator
return int(result)
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 135: https://projecteuler.net/problem=135
Given the positive integers, x, y, and z,
are consecutive terms of an arithmetic progression,
the least value of the positive integer, n,
for which the equation,
x2 − y2 − z2 = n, has exactly two solutions is n = 27:
342 − 272 − 202 = 122 − 92 − 62 = 27
It turns out that n = 1155 is the least value
which has exactly ten solutions.
How many values of n less than one million
have exactly ten distinct solutions?
Taking x,y,z of the form a+d,a,a-d respectively,
the given equation reduces to a*(4d-a)=n.
Calculating no of solutions for every n till 1 million by fixing a
,and n must be multiple of a.
Total no of steps=n*(1/1+1/2+1/3+1/4..+1/n)
,so roughly O(nlogn) time complexity.
"""
def solution(limit: int = 1000000) -> int:
"""
returns the values of n less than or equal to the limit
have exactly ten distinct solutions.
>>> solution(100)
0
>>> solution(10000)
45
>>> solution(50050)
292
"""
limit = limit + 1
frequency = [0] * limit
for first_term in range(1, limit):
for n in range(first_term, limit, first_term):
common_difference = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
count = sum(1 for x in frequency[1:limit] if x == 10)
return count
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 135: https://projecteuler.net/problem=135
Given the positive integers, x, y, and z,
are consecutive terms of an arithmetic progression,
the least value of the positive integer, n,
for which the equation,
x2 − y2 − z2 = n, has exactly two solutions is n = 27:
342 − 272 − 202 = 122 − 92 − 62 = 27
It turns out that n = 1155 is the least value
which has exactly ten solutions.
How many values of n less than one million
have exactly ten distinct solutions?
Taking x,y,z of the form a+d,a,a-d respectively,
the given equation reduces to a*(4d-a)=n.
Calculating no of solutions for every n till 1 million by fixing a
,and n must be multiple of a.
Total no of steps=n*(1/1+1/2+1/3+1/4..+1/n)
,so roughly O(nlogn) time complexity.
"""
def solution(limit: int = 1000000) -> int:
"""
returns the values of n less than or equal to the limit
have exactly ten distinct solutions.
>>> solution(100)
0
>>> solution(10000)
45
>>> solution(50050)
292
"""
limit = limit + 1
frequency = [0] * limit
for first_term in range(1, limit):
for n in range(first_term, limit, first_term):
common_difference = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
count = sum(1 for x in frequency[1:limit] if x == 10)
return count
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 91: https://projecteuler.net/problem=91
The points P (x1, y1) and Q (x2, y2) are plotted at integer coordinates and
are joined to the origin, O(0,0), to form ΔOPQ.

There are exactly fourteen triangles containing a right angle that can be formed
when each coordinate lies between 0 and 2 inclusive; that is,
0 ≤ x1, y1, x2, y2 ≤ 2.

Given that 0 ≤ x1, y1, x2, y2 ≤ 50, how many right triangles can be formed?
"""
from itertools import combinations, product
def is_right(x1: int, y1: int, x2: int, y2: int) -> bool:
"""
Check if the triangle described by P(x1,y1), Q(x2,y2) and O(0,0) is right-angled.
Note: this doesn't check if P and Q are equal, but that's handled by the use of
itertools.combinations in the solution function.
>>> is_right(0, 1, 2, 0)
True
>>> is_right(1, 0, 2, 2)
False
"""
if x1 == y1 == 0 or x2 == y2 == 0:
return False
a_square = x1 * x1 + y1 * y1
b_square = x2 * x2 + y2 * y2
c_square = (x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2)
return (
a_square + b_square == c_square
or a_square + c_square == b_square
or b_square + c_square == a_square
)
def solution(limit: int = 50) -> int:
"""
Return the number of right triangles OPQ that can be formed by two points P, Q
which have both x- and y- coordinates between 0 and limit inclusive.
>>> solution(2)
14
>>> solution(10)
448
"""
return sum(
1
for pt1, pt2 in combinations(product(range(limit + 1), repeat=2), 2)
if is_right(*pt1, *pt2)
)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 91: https://projecteuler.net/problem=91
The points P (x1, y1) and Q (x2, y2) are plotted at integer coordinates and
are joined to the origin, O(0,0), to form ΔOPQ.

There are exactly fourteen triangles containing a right angle that can be formed
when each coordinate lies between 0 and 2 inclusive; that is,
0 ≤ x1, y1, x2, y2 ≤ 2.

Given that 0 ≤ x1, y1, x2, y2 ≤ 50, how many right triangles can be formed?
"""
from itertools import combinations, product
def is_right(x1: int, y1: int, x2: int, y2: int) -> bool:
"""
Check if the triangle described by P(x1,y1), Q(x2,y2) and O(0,0) is right-angled.
Note: this doesn't check if P and Q are equal, but that's handled by the use of
itertools.combinations in the solution function.
>>> is_right(0, 1, 2, 0)
True
>>> is_right(1, 0, 2, 2)
False
"""
if x1 == y1 == 0 or x2 == y2 == 0:
return False
a_square = x1 * x1 + y1 * y1
b_square = x2 * x2 + y2 * y2
c_square = (x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2)
return (
a_square + b_square == c_square
or a_square + c_square == b_square
or b_square + c_square == a_square
)
def solution(limit: int = 50) -> int:
"""
Return the number of right triangles OPQ that can be formed by two points P, Q
which have both x- and y- coordinates between 0 and limit inclusive.
>>> solution(2)
14
>>> solution(10)
448
"""
return sum(
1
for pt1, pt2 in combinations(product(range(limit + 1), repeat=2), 2)
if is_right(*pt1, *pt2)
)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Bead sort only works for sequences of nonegative integers.
https://en.wikipedia.org/wiki/Bead_sort
"""
def bead_sort(sequence: list) -> list:
"""
>>> bead_sort([6, 11, 12, 4, 1, 5])
[1, 4, 5, 6, 11, 12]
>>> bead_sort([9, 8, 7, 6, 5, 4 ,3, 2, 1])
[1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> bead_sort([5, 0, 4, 3])
[0, 3, 4, 5]
>>> bead_sort([8, 2, 1])
[1, 2, 8]
>>> bead_sort([1, .9, 0.0, 0, -1, -.9])
Traceback (most recent call last):
...
TypeError: Sequence must be list of nonnegative integers
>>> bead_sort("Hello world")
Traceback (most recent call last):
...
TypeError: Sequence must be list of nonnegative integers
"""
if any(not isinstance(x, int) or x < 0 for x in sequence):
raise TypeError("Sequence must be list of nonnegative integers")
for _ in range(len(sequence)):
for i, (rod_upper, rod_lower) in enumerate(zip(sequence, sequence[1:])):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| """
Bead sort only works for sequences of nonegative integers.
https://en.wikipedia.org/wiki/Bead_sort
"""
def bead_sort(sequence: list) -> list:
"""
>>> bead_sort([6, 11, 12, 4, 1, 5])
[1, 4, 5, 6, 11, 12]
>>> bead_sort([9, 8, 7, 6, 5, 4 ,3, 2, 1])
[1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> bead_sort([5, 0, 4, 3])
[0, 3, 4, 5]
>>> bead_sort([8, 2, 1])
[1, 2, 8]
>>> bead_sort([1, .9, 0.0, 0, -1, -.9])
Traceback (most recent call last):
...
TypeError: Sequence must be list of nonnegative integers
>>> bead_sort("Hello world")
Traceback (most recent call last):
...
TypeError: Sequence must be list of nonnegative integers
"""
if any(not isinstance(x, int) or x < 0 for x in sequence):
raise TypeError("Sequence must be list of nonnegative integers")
for _ in range(len(sequence)):
for i, (rod_upper, rod_lower) in enumerate(zip(sequence, sequence[1:])):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| -1 |
TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is to show simple COVID19 info fetching from worldometers site using lxml
* The main motivation to use lxml in place of bs4 is that it is faster and therefore
more convenient to use in Python web projects (e.g. Django or Flask-based)
"""
from collections import namedtuple
import requests
from lxml import html # type: ignore
covid_data = namedtuple("covid_data", "cases deaths recovered")
def covid_stats(url: str = "https://www.worldometers.info/coronavirus/") -> covid_data:
xpath_str = '//div[@class = "maincounter-number"]/span/text()'
return covid_data(*html.fromstring(requests.get(url).content).xpath(xpath_str))
fmt = """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""
print(fmt.format(*covid_stats()))
| """
This is to show simple COVID19 info fetching from worldometers site using lxml
* The main motivation to use lxml in place of bs4 is that it is faster and therefore
more convenient to use in Python web projects (e.g. Django or Flask-based)
"""
from collections import namedtuple
import requests
from lxml import html # type: ignore
covid_data = namedtuple("covid_data", "cases deaths recovered")
def covid_stats(url: str = "https://www.worldometers.info/coronavirus/") -> covid_data:
xpath_str = '//div[@class = "maincounter-number"]/span/text()'
return covid_data(*html.fromstring(requests.get(url).content).xpath(xpath_str))
fmt = """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""
print(fmt.format(*covid_stats()))
| -1 |
TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Implementing Secant method in Python
Author: dimgrichr
"""
from math import exp
def f(x: float) -> float:
"""
>>> f(5)
39.98652410600183
"""
return 8 * x - 2 * exp(-x)
def secant_method(lower_bound: float, upper_bound: float, repeats: int) -> float:
"""
>>> secant_method(1, 3, 2)
0.2139409276214589
"""
x0 = lower_bound
x1 = upper_bound
for i in range(0, repeats):
x0, x1 = x1, x1 - (f(x1) * (x1 - x0)) / (f(x1) - f(x0))
return x1
if __name__ == "__main__":
print(f"Example: {secant_method(1, 3, 2)}")
| """
Implementing Secant method in Python
Author: dimgrichr
"""
from math import exp
def f(x: float) -> float:
"""
>>> f(5)
39.98652410600183
"""
return 8 * x - 2 * exp(-x)
def secant_method(lower_bound: float, upper_bound: float, repeats: int) -> float:
"""
>>> secant_method(1, 3, 2)
0.2139409276214589
"""
x0 = lower_bound
x1 = upper_bound
for i in range(0, repeats):
x0, x1 = x1, x1 - (f(x1) * (x1 - x0)) / (f(x1) - f(x0))
return x1
if __name__ == "__main__":
print(f"Example: {secant_method(1, 3, 2)}")
| -1 |
TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
A pure Python implementation of the insertion sort algorithm
This algorithm sorts a collection by comparing adjacent elements.
When it finds that order is not respected, it moves the element compared
backward until the order is correct. It then goes back directly to the
element's initial position resuming forward comparison.
For doctests run following command:
python3 -m doctest -v insertion_sort.py
For manual testing run:
python3 insertion_sort.py
"""
def insertion_sort(collection: list) -> list:
"""A pure Python implementation of the insertion sort algorithm
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> insertion_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> insertion_sort([]) == sorted([])
True
>>> insertion_sort([-2, -5, -45]) == sorted([-2, -5, -45])
True
>>> insertion_sort(['d', 'a', 'b', 'e', 'c']) == sorted(['d', 'a', 'b', 'e', 'c'])
True
>>> import random
>>> collection = random.sample(range(-50, 50), 100)
>>> insertion_sort(collection) == sorted(collection)
True
>>> import string
>>> collection = random.choices(string.ascii_letters + string.digits, k=100)
>>> insertion_sort(collection) == sorted(collection)
True
"""
for insert_index, insert_value in enumerate(collection[1:]):
temp_index = insert_index
while insert_index >= 0 and insert_value < collection[insert_index]:
collection[insert_index + 1] = collection[insert_index]
insert_index -= 1
if insert_index != temp_index:
collection[insert_index + 1] = insert_value
return collection
if __name__ == "__main__":
from doctest import testmod
testmod()
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(f"{insertion_sort(unsorted) = }")
| """
A pure Python implementation of the insertion sort algorithm
This algorithm sorts a collection by comparing adjacent elements.
When it finds that order is not respected, it moves the element compared
backward until the order is correct. It then goes back directly to the
element's initial position resuming forward comparison.
For doctests run following command:
python3 -m doctest -v insertion_sort.py
For manual testing run:
python3 insertion_sort.py
"""
def insertion_sort(collection: list) -> list:
"""A pure Python implementation of the insertion sort algorithm
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> insertion_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> insertion_sort([]) == sorted([])
True
>>> insertion_sort([-2, -5, -45]) == sorted([-2, -5, -45])
True
>>> insertion_sort(['d', 'a', 'b', 'e', 'c']) == sorted(['d', 'a', 'b', 'e', 'c'])
True
>>> import random
>>> collection = random.sample(range(-50, 50), 100)
>>> insertion_sort(collection) == sorted(collection)
True
>>> import string
>>> collection = random.choices(string.ascii_letters + string.digits, k=100)
>>> insertion_sort(collection) == sorted(collection)
True
"""
for insert_index, insert_value in enumerate(collection[1:]):
temp_index = insert_index
while insert_index >= 0 and insert_value < collection[insert_index]:
collection[insert_index + 1] = collection[insert_index]
insert_index -= 1
if insert_index != temp_index:
collection[insert_index + 1] = insert_value
return collection
if __name__ == "__main__":
from doctest import testmod
testmod()
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(f"{insertion_sort(unsorted) = }")
| -1 |
TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 4: https://projecteuler.net/problem=4
Largest palindrome product
A palindromic number reads the same both ways. The largest palindrome made
from the product of two 2-digit numbers is 9009 = 91 × 99.
Find the largest palindrome made from the product of two 3-digit numbers.
References:
- https://en.wikipedia.org/wiki/Palindromic_number
"""
def solution(n: int = 998001) -> int:
"""
Returns the largest palindrome made from the product of two 3-digit
numbers which is less than n.
>>> solution(20000)
19591
>>> solution(30000)
29992
>>> solution(40000)
39893
"""
answer = 0
for i in range(999, 99, -1): # 3 digit numbers range from 999 down to 100
for j in range(999, 99, -1):
product_string = str(i * j)
if product_string == product_string[::-1] and i * j < n:
answer = max(answer, i * j)
return answer
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 4: https://projecteuler.net/problem=4
Largest palindrome product
A palindromic number reads the same both ways. The largest palindrome made
from the product of two 2-digit numbers is 9009 = 91 × 99.
Find the largest palindrome made from the product of two 3-digit numbers.
References:
- https://en.wikipedia.org/wiki/Palindromic_number
"""
def solution(n: int = 998001) -> int:
"""
Returns the largest palindrome made from the product of two 3-digit
numbers which is less than n.
>>> solution(20000)
19591
>>> solution(30000)
29992
>>> solution(40000)
39893
"""
answer = 0
for i in range(999, 99, -1): # 3 digit numbers range from 999 down to 100
for j in range(999, 99, -1):
product_string = str(i * j)
if product_string == product_string[::-1] and i * j < n:
answer = max(answer, i * j)
return answer
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import math
def perfect_square(num: int) -> bool:
"""
Check if a number is perfect square number or not
:param num: the number to be checked
:return: True if number is square number, otherwise False
>>> perfect_square(9)
True
>>> perfect_square(16)
True
>>> perfect_square(1)
True
>>> perfect_square(0)
True
>>> perfect_square(10)
False
"""
return math.sqrt(num) * math.sqrt(num) == num
def perfect_square_binary_search(n: int) -> bool:
"""
Check if a number is perfect square using binary search.
Time complexity : O(Log(n))
Space complexity: O(1)
>>> perfect_square_binary_search(9)
True
>>> perfect_square_binary_search(16)
True
>>> perfect_square_binary_search(1)
True
>>> perfect_square_binary_search(0)
True
>>> perfect_square_binary_search(10)
False
>>> perfect_square_binary_search(-1)
False
>>> perfect_square_binary_search(1.1)
False
>>> perfect_square_binary_search("a")
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'str'
>>> perfect_square_binary_search(None)
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'NoneType'
>>> perfect_square_binary_search([])
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'list'
"""
left = 0
right = n
while left <= right:
mid = (left + right) // 2
if mid ** 2 == n:
return True
elif mid ** 2 > n:
right = mid - 1
else:
left = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| import math
def perfect_square(num: int) -> bool:
"""
Check if a number is perfect square number or not
:param num: the number to be checked
:return: True if number is square number, otherwise False
>>> perfect_square(9)
True
>>> perfect_square(16)
True
>>> perfect_square(1)
True
>>> perfect_square(0)
True
>>> perfect_square(10)
False
"""
return math.sqrt(num) * math.sqrt(num) == num
def perfect_square_binary_search(n: int) -> bool:
"""
Check if a number is perfect square using binary search.
Time complexity : O(Log(n))
Space complexity: O(1)
>>> perfect_square_binary_search(9)
True
>>> perfect_square_binary_search(16)
True
>>> perfect_square_binary_search(1)
True
>>> perfect_square_binary_search(0)
True
>>> perfect_square_binary_search(10)
False
>>> perfect_square_binary_search(-1)
False
>>> perfect_square_binary_search(1.1)
False
>>> perfect_square_binary_search("a")
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'str'
>>> perfect_square_binary_search(None)
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'NoneType'
>>> perfect_square_binary_search([])
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'list'
"""
left = 0
right = n
while left <= right:
mid = (left + right) // 2
if mid ** 2 == n:
return True
elif mid ** 2 > n:
right = mid - 1
else:
left = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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**2 + b**2 = c**2
2. a + b + c = 1000
# The code below has been commented due to slow execution affecting Travis.
# >>> solution()
# 31875000
"""
return [
a * b * (1000 - a - b)
for a in range(1, 999)
for b in range(a, 999)
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
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**2 + b**2 = c**2
2. a + b + c = 1000
# The code below has been commented due to slow execution affecting Travis.
# >>> solution()
# 31875000
"""
return [
a * b * (1000 - a - b)
for a in range(1, 999)
for b in range(a, 999)
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Author: João Gustavo A. Amorim & Gabriel Kunz
# Author email: [email protected] and [email protected]
# Coding date: apr 2019
# Black: True
"""
* This code implement the Hamming code:
https://en.wikipedia.org/wiki/Hamming_code - In telecommunication,
Hamming codes are a family of linear error-correcting codes. Hamming
codes can detect up to two-bit errors or correct one-bit errors
without detection of uncorrected errors. By contrast, the simple
parity code cannot correct errors, and can detect only an odd number
of bits in error. Hamming codes are perfect codes, that is, they
achieve the highest possible rate for codes with their block length
and minimum distance of three.
* the implemented code consists of:
* a function responsible for encoding the message (emitterConverter)
* return the encoded message
* a function responsible for decoding the message (receptorConverter)
* return the decoded message and a ack of data integrity
* how to use:
to be used you must declare how many parity bits (sizePari)
you want to include in the message.
it is desired (for test purposes) to select a bit to be set
as an error. This serves to check whether the code is working correctly.
Lastly, the variable of the message/word that must be desired to be
encoded (text).
* how this work:
declaration of variables (sizePari, be, text)
converts the message/word (text) to binary using the
text_to_bits function
encodes the message using the rules of hamming encoding
decodes the message using the rules of hamming encoding
print the original message, the encoded message and the
decoded message
forces an error in the coded text variable
decodes the message that was forced the error
print the original message, the encoded message, the bit changed
message and the decoded message
"""
# Imports
import numpy as np
# Functions of binary conversion--------------------------------------
def text_to_bits(text, encoding="utf-8", errors="surrogatepass"):
"""
>>> text_to_bits("msg")
'011011010111001101100111'
"""
bits = bin(int.from_bytes(text.encode(encoding, errors), "big"))[2:]
return bits.zfill(8 * ((len(bits) + 7) // 8))
def text_from_bits(bits, encoding="utf-8", errors="surrogatepass"):
"""
>>> text_from_bits('011011010111001101100111')
'msg'
"""
n = int(bits, 2)
return n.to_bytes((n.bit_length() + 7) // 8, "big").decode(encoding, errors) or "\0"
# Functions of hamming code-------------------------------------------
def emitterConverter(sizePar, data):
"""
:param sizePar: how many parity bits the message must have
:param data: information bits
:return: message to be transmitted by unreliable medium
- bits of information merged with parity bits
>>> emitterConverter(4, "101010111111")
['1', '1', '1', '1', '0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1']
"""
if sizePar + len(data) <= 2 ** sizePar - (len(data) - 1):
print("ERROR - size of parity don't match with size of data")
exit(0)
dataOut = []
parity = []
binPos = [bin(x)[2:] for x in range(1, sizePar + len(data) + 1)]
# sorted information data for the size of the output data
dataOrd = []
# data position template + parity
dataOutGab = []
# parity bit counter
qtdBP = 0
# counter position of data bits
contData = 0
for x in range(1, sizePar + len(data) + 1):
# Performs a template of bit positions - who should be given,
# and who should be parity
if qtdBP < sizePar:
if (np.log(x) / np.log(2)).is_integer():
dataOutGab.append("P")
qtdBP = qtdBP + 1
else:
dataOutGab.append("D")
else:
dataOutGab.append("D")
# Sorts the data to the new output size
if dataOutGab[-1] == "D":
dataOrd.append(data[contData])
contData += 1
else:
dataOrd.append(None)
# Calculates parity
qtdBP = 0 # parity bit counter
for bp in range(1, sizePar + 1):
# Bit counter one for a given parity
contBO = 0
# counter to control the loop reading
contLoop = 0
for x in dataOrd:
if x is not None:
try:
aux = (binPos[contLoop])[-1 * (bp)]
except IndexError:
aux = "0"
if aux == "1":
if x == "1":
contBO += 1
contLoop += 1
parity.append(contBO % 2)
qtdBP += 1
# Mount the message
ContBP = 0 # parity bit counter
for x in range(0, sizePar + len(data)):
if dataOrd[x] is None:
dataOut.append(str(parity[ContBP]))
ContBP += 1
else:
dataOut.append(dataOrd[x])
return dataOut
def receptorConverter(sizePar, data):
"""
>>> receptorConverter(4, "1111010010111111")
(['1', '0', '1', '0', '1', '0', '1', '1', '1', '1', '1', '1'], True)
"""
# data position template + parity
dataOutGab = []
# Parity bit counter
qtdBP = 0
# Counter p data bit reading
contData = 0
# list of parity received
parityReceived = []
dataOutput = []
for x in range(1, len(data) + 1):
# Performs a template of bit positions - who should be given,
# and who should be parity
if qtdBP < sizePar and (np.log(x) / np.log(2)).is_integer():
dataOutGab.append("P")
qtdBP = qtdBP + 1
else:
dataOutGab.append("D")
# Sorts the data to the new output size
if dataOutGab[-1] == "D":
dataOutput.append(data[contData])
else:
parityReceived.append(data[contData])
contData += 1
# -----------calculates the parity with the data
dataOut = []
parity = []
binPos = [bin(x)[2:] for x in range(1, sizePar + len(dataOutput) + 1)]
# sorted information data for the size of the output data
dataOrd = []
# Data position feedback + parity
dataOutGab = []
# Parity bit counter
qtdBP = 0
# Counter p data bit reading
contData = 0
for x in range(1, sizePar + len(dataOutput) + 1):
# Performs a template position of bits - who should be given,
# and who should be parity
if qtdBP < sizePar and (np.log(x) / np.log(2)).is_integer():
dataOutGab.append("P")
qtdBP = qtdBP + 1
else:
dataOutGab.append("D")
# Sorts the data to the new output size
if dataOutGab[-1] == "D":
dataOrd.append(dataOutput[contData])
contData += 1
else:
dataOrd.append(None)
# Calculates parity
qtdBP = 0 # parity bit counter
for bp in range(1, sizePar + 1):
# Bit counter one for a certain parity
contBO = 0
# Counter to control loop reading
contLoop = 0
for x in dataOrd:
if x is not None:
try:
aux = (binPos[contLoop])[-1 * (bp)]
except IndexError:
aux = "0"
if aux == "1" and x == "1":
contBO += 1
contLoop += 1
parity.append(str(contBO % 2))
qtdBP += 1
# Mount the message
ContBP = 0 # Parity bit counter
for x in range(0, sizePar + len(dataOutput)):
if dataOrd[x] is None:
dataOut.append(str(parity[ContBP]))
ContBP += 1
else:
dataOut.append(dataOrd[x])
ack = parityReceived == parity
return dataOutput, ack
# ---------------------------------------------------------------------
"""
# Example how to use
# number of parity bits
sizePari = 4
# location of the bit that will be forced an error
be = 2
# Message/word to be encoded and decoded with hamming
# text = input("Enter the word to be read: ")
text = "Message01"
# Convert the message to binary
binaryText = text_to_bits(text)
# Prints the binary of the string
print("Text input in binary is '" + binaryText + "'")
# total transmitted bits
totalBits = len(binaryText) + sizePari
print("Size of data is " + str(totalBits))
print("\n --Message exchange--")
print("Data to send ------------> " + binaryText)
dataOut = emitterConverter(sizePari, binaryText)
print("Data converted ----------> " + "".join(dataOut))
dataReceiv, ack = receptorConverter(sizePari, dataOut)
print(
"Data receive ------------> "
+ "".join(dataReceiv)
+ "\t\t -- Data integrity: "
+ str(ack)
)
print("\n --Force error--")
print("Data to send ------------> " + binaryText)
dataOut = emitterConverter(sizePari, binaryText)
print("Data converted ----------> " + "".join(dataOut))
# forces error
dataOut[-be] = "1" * (dataOut[-be] == "0") + "0" * (dataOut[-be] == "1")
print("Data after transmission -> " + "".join(dataOut))
dataReceiv, ack = receptorConverter(sizePari, dataOut)
print(
"Data receive ------------> "
+ "".join(dataReceiv)
+ "\t\t -- Data integrity: "
+ str(ack)
)
"""
| # Author: João Gustavo A. Amorim & Gabriel Kunz
# Author email: [email protected] and [email protected]
# Coding date: apr 2019
# Black: True
"""
* This code implement the Hamming code:
https://en.wikipedia.org/wiki/Hamming_code - In telecommunication,
Hamming codes are a family of linear error-correcting codes. Hamming
codes can detect up to two-bit errors or correct one-bit errors
without detection of uncorrected errors. By contrast, the simple
parity code cannot correct errors, and can detect only an odd number
of bits in error. Hamming codes are perfect codes, that is, they
achieve the highest possible rate for codes with their block length
and minimum distance of three.
* the implemented code consists of:
* a function responsible for encoding the message (emitterConverter)
* return the encoded message
* a function responsible for decoding the message (receptorConverter)
* return the decoded message and a ack of data integrity
* how to use:
to be used you must declare how many parity bits (sizePari)
you want to include in the message.
it is desired (for test purposes) to select a bit to be set
as an error. This serves to check whether the code is working correctly.
Lastly, the variable of the message/word that must be desired to be
encoded (text).
* how this work:
declaration of variables (sizePari, be, text)
converts the message/word (text) to binary using the
text_to_bits function
encodes the message using the rules of hamming encoding
decodes the message using the rules of hamming encoding
print the original message, the encoded message and the
decoded message
forces an error in the coded text variable
decodes the message that was forced the error
print the original message, the encoded message, the bit changed
message and the decoded message
"""
# Imports
import numpy as np
# Functions of binary conversion--------------------------------------
def text_to_bits(text, encoding="utf-8", errors="surrogatepass"):
"""
>>> text_to_bits("msg")
'011011010111001101100111'
"""
bits = bin(int.from_bytes(text.encode(encoding, errors), "big"))[2:]
return bits.zfill(8 * ((len(bits) + 7) // 8))
def text_from_bits(bits, encoding="utf-8", errors="surrogatepass"):
"""
>>> text_from_bits('011011010111001101100111')
'msg'
"""
n = int(bits, 2)
return n.to_bytes((n.bit_length() + 7) // 8, "big").decode(encoding, errors) or "\0"
# Functions of hamming code-------------------------------------------
def emitterConverter(sizePar, data):
"""
:param sizePar: how many parity bits the message must have
:param data: information bits
:return: message to be transmitted by unreliable medium
- bits of information merged with parity bits
>>> emitterConverter(4, "101010111111")
['1', '1', '1', '1', '0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1']
"""
if sizePar + len(data) <= 2 ** sizePar - (len(data) - 1):
print("ERROR - size of parity don't match with size of data")
exit(0)
dataOut = []
parity = []
binPos = [bin(x)[2:] for x in range(1, sizePar + len(data) + 1)]
# sorted information data for the size of the output data
dataOrd = []
# data position template + parity
dataOutGab = []
# parity bit counter
qtdBP = 0
# counter position of data bits
contData = 0
for x in range(1, sizePar + len(data) + 1):
# Performs a template of bit positions - who should be given,
# and who should be parity
if qtdBP < sizePar:
if (np.log(x) / np.log(2)).is_integer():
dataOutGab.append("P")
qtdBP = qtdBP + 1
else:
dataOutGab.append("D")
else:
dataOutGab.append("D")
# Sorts the data to the new output size
if dataOutGab[-1] == "D":
dataOrd.append(data[contData])
contData += 1
else:
dataOrd.append(None)
# Calculates parity
qtdBP = 0 # parity bit counter
for bp in range(1, sizePar + 1):
# Bit counter one for a given parity
contBO = 0
# counter to control the loop reading
contLoop = 0
for x in dataOrd:
if x is not None:
try:
aux = (binPos[contLoop])[-1 * (bp)]
except IndexError:
aux = "0"
if aux == "1":
if x == "1":
contBO += 1
contLoop += 1
parity.append(contBO % 2)
qtdBP += 1
# Mount the message
ContBP = 0 # parity bit counter
for x in range(0, sizePar + len(data)):
if dataOrd[x] is None:
dataOut.append(str(parity[ContBP]))
ContBP += 1
else:
dataOut.append(dataOrd[x])
return dataOut
def receptorConverter(sizePar, data):
"""
>>> receptorConverter(4, "1111010010111111")
(['1', '0', '1', '0', '1', '0', '1', '1', '1', '1', '1', '1'], True)
"""
# data position template + parity
dataOutGab = []
# Parity bit counter
qtdBP = 0
# Counter p data bit reading
contData = 0
# list of parity received
parityReceived = []
dataOutput = []
for x in range(1, len(data) + 1):
# Performs a template of bit positions - who should be given,
# and who should be parity
if qtdBP < sizePar and (np.log(x) / np.log(2)).is_integer():
dataOutGab.append("P")
qtdBP = qtdBP + 1
else:
dataOutGab.append("D")
# Sorts the data to the new output size
if dataOutGab[-1] == "D":
dataOutput.append(data[contData])
else:
parityReceived.append(data[contData])
contData += 1
# -----------calculates the parity with the data
dataOut = []
parity = []
binPos = [bin(x)[2:] for x in range(1, sizePar + len(dataOutput) + 1)]
# sorted information data for the size of the output data
dataOrd = []
# Data position feedback + parity
dataOutGab = []
# Parity bit counter
qtdBP = 0
# Counter p data bit reading
contData = 0
for x in range(1, sizePar + len(dataOutput) + 1):
# Performs a template position of bits - who should be given,
# and who should be parity
if qtdBP < sizePar and (np.log(x) / np.log(2)).is_integer():
dataOutGab.append("P")
qtdBP = qtdBP + 1
else:
dataOutGab.append("D")
# Sorts the data to the new output size
if dataOutGab[-1] == "D":
dataOrd.append(dataOutput[contData])
contData += 1
else:
dataOrd.append(None)
# Calculates parity
qtdBP = 0 # parity bit counter
for bp in range(1, sizePar + 1):
# Bit counter one for a certain parity
contBO = 0
# Counter to control loop reading
contLoop = 0
for x in dataOrd:
if x is not None:
try:
aux = (binPos[contLoop])[-1 * (bp)]
except IndexError:
aux = "0"
if aux == "1" and x == "1":
contBO += 1
contLoop += 1
parity.append(str(contBO % 2))
qtdBP += 1
# Mount the message
ContBP = 0 # Parity bit counter
for x in range(0, sizePar + len(dataOutput)):
if dataOrd[x] is None:
dataOut.append(str(parity[ContBP]))
ContBP += 1
else:
dataOut.append(dataOrd[x])
ack = parityReceived == parity
return dataOutput, ack
# ---------------------------------------------------------------------
"""
# Example how to use
# number of parity bits
sizePari = 4
# location of the bit that will be forced an error
be = 2
# Message/word to be encoded and decoded with hamming
# text = input("Enter the word to be read: ")
text = "Message01"
# Convert the message to binary
binaryText = text_to_bits(text)
# Prints the binary of the string
print("Text input in binary is '" + binaryText + "'")
# total transmitted bits
totalBits = len(binaryText) + sizePari
print("Size of data is " + str(totalBits))
print("\n --Message exchange--")
print("Data to send ------------> " + binaryText)
dataOut = emitterConverter(sizePari, binaryText)
print("Data converted ----------> " + "".join(dataOut))
dataReceiv, ack = receptorConverter(sizePari, dataOut)
print(
"Data receive ------------> "
+ "".join(dataReceiv)
+ "\t\t -- Data integrity: "
+ str(ack)
)
print("\n --Force error--")
print("Data to send ------------> " + binaryText)
dataOut = emitterConverter(sizePari, binaryText)
print("Data converted ----------> " + "".join(dataOut))
# forces error
dataOut[-be] = "1" * (dataOut[-be] == "0") + "0" * (dataOut[-be] == "1")
print("Data after transmission -> " + "".join(dataOut))
dataReceiv, ack = receptorConverter(sizePari, dataOut)
print(
"Data receive ------------> "
+ "".join(dataReceiv)
+ "\t\t -- Data integrity: "
+ str(ack)
)
"""
| -1 |
TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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 | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 35
https://projecteuler.net/problem=35
Problem Statement:
The number 197 is called a circular prime because all rotations of the digits:
197, 971, and 719, are themselves prime.
There are thirteen such primes below 100: 2, 3, 5, 7, 11, 13, 17, 31, 37, 71, 73,
79, and 97.
How many circular primes are there below one million?
To solve this problem in an efficient manner, we will first mark all the primes
below 1 million using the Seive of Eratosthenes. Then, out of all these primes,
we will rule out the numbers which contain an even digit. After this we will
generate each circular combination of the number and check if all are prime.
"""
from __future__ import annotations
seive = [True] * 1000001
i = 2
while i * i <= 1000000:
if seive[i]:
for j in range(i * i, 1000001, i):
seive[j] = False
i += 1
def is_prime(n: int) -> bool:
"""
For 2 <= n <= 1000000, return True if n is prime.
>>> is_prime(87)
False
>>> is_prime(23)
True
>>> is_prime(25363)
False
"""
return seive[n]
def contains_an_even_digit(n: int) -> bool:
"""
Return True if n contains an even digit.
>>> contains_an_even_digit(0)
True
>>> contains_an_even_digit(975317933)
False
>>> contains_an_even_digit(-245679)
True
"""
return any(digit in "02468" for digit in str(n))
def find_circular_primes(limit: int = 1000000) -> list[int]:
"""
Return circular primes below limit.
>>> len(find_circular_primes(100))
13
>>> len(find_circular_primes(1000000))
55
"""
result = [2] # result already includes the number 2.
for num in range(3, limit + 1, 2):
if is_prime(num) and not contains_an_even_digit(num):
str_num = str(num)
list_nums = [int(str_num[j:] + str_num[:j]) for j in range(len(str_num))]
if all(is_prime(i) for i in list_nums):
result.append(num)
return result
def solution() -> int:
"""
>>> solution()
55
"""
return len(find_circular_primes())
if __name__ == "__main__":
print(f"{len(find_circular_primes()) = }")
| """
Project Euler Problem 35
https://projecteuler.net/problem=35
Problem Statement:
The number 197 is called a circular prime because all rotations of the digits:
197, 971, and 719, are themselves prime.
There are thirteen such primes below 100: 2, 3, 5, 7, 11, 13, 17, 31, 37, 71, 73,
79, and 97.
How many circular primes are there below one million?
To solve this problem in an efficient manner, we will first mark all the primes
below 1 million using the Seive of Eratosthenes. Then, out of all these primes,
we will rule out the numbers which contain an even digit. After this we will
generate each circular combination of the number and check if all are prime.
"""
from __future__ import annotations
seive = [True] * 1000001
i = 2
while i * i <= 1000000:
if seive[i]:
for j in range(i * i, 1000001, i):
seive[j] = False
i += 1
def is_prime(n: int) -> bool:
"""
For 2 <= n <= 1000000, return True if n is prime.
>>> is_prime(87)
False
>>> is_prime(23)
True
>>> is_prime(25363)
False
"""
return seive[n]
def contains_an_even_digit(n: int) -> bool:
"""
Return True if n contains an even digit.
>>> contains_an_even_digit(0)
True
>>> contains_an_even_digit(975317933)
False
>>> contains_an_even_digit(-245679)
True
"""
return any(digit in "02468" for digit in str(n))
def find_circular_primes(limit: int = 1000000) -> list[int]:
"""
Return circular primes below limit.
>>> len(find_circular_primes(100))
13
>>> len(find_circular_primes(1000000))
55
"""
result = [2] # result already includes the number 2.
for num in range(3, limit + 1, 2):
if is_prime(num) and not contains_an_even_digit(num):
str_num = str(num)
list_nums = [int(str_num[j:] + str_num[:j]) for j in range(len(str_num))]
if all(is_prime(i) for i in list_nums):
result.append(num)
return result
def solution() -> int:
"""
>>> solution()
55
"""
return len(find_circular_primes())
if __name__ == "__main__":
print(f"{len(find_circular_primes()) = }")
| -1 |
TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -340,495,-153,-910,835,-947
-175,41,-421,-714,574,-645
| -340,495,-153,-910,835,-947
-175,41,-421,-714,574,-645
| -1 |
TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from typing import Any
class Node:
def __init__(self, data: Any):
self.data = data
self.next = None
class CircularLinkedList:
def __init__(self):
self.head = None
self.tail = None
def __iter__(self):
node = self.head
while self.head:
yield node.data
node = node.next
if node == self.head:
break
def __len__(self) -> int:
return len(tuple(iter(self)))
def __repr__(self):
return "->".join(str(item) for item in iter(self))
def insert_tail(self, data: Any) -> None:
self.insert_nth(len(self), data)
def insert_head(self, data: Any) -> None:
self.insert_nth(0, data)
def insert_nth(self, index: int, data: Any) -> None:
if index < 0 or index > len(self):
raise IndexError("list index out of range.")
new_node = Node(data)
if self.head is None:
new_node.next = new_node # first node points itself
self.tail = self.head = new_node
elif index == 0: # insert at head
new_node.next = self.head
self.head = self.tail.next = new_node
else:
temp = self.head
for _ in range(index - 1):
temp = temp.next
new_node.next = temp.next
temp.next = new_node
if index == len(self) - 1: # insert at tail
self.tail = new_node
def delete_front(self):
return self.delete_nth(0)
def delete_tail(self) -> None:
return self.delete_nth(len(self) - 1)
def delete_nth(self, index: int = 0):
if not 0 <= index < len(self):
raise IndexError("list index out of range.")
delete_node = self.head
if self.head == self.tail: # just one node
self.head = self.tail = None
elif index == 0: # delete head node
self.tail.next = self.tail.next.next
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
if index == len(self) - 1: # delete at tail
self.tail = temp
return delete_node.data
def is_empty(self):
return len(self) == 0
def test_circular_linked_list() -> None:
"""
>>> test_circular_linked_list()
"""
circular_linked_list = CircularLinkedList()
assert len(circular_linked_list) == 0
assert circular_linked_list.is_empty() is True
assert str(circular_linked_list) == ""
try:
circular_linked_list.delete_front()
assert False # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
assert False # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1)
assert False
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0)
assert False
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5):
assert len(circular_linked_list) == i
circular_linked_list.insert_nth(i, i + 1)
assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 6))
circular_linked_list.insert_tail(6)
assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 7))
circular_linked_list.insert_head(0)
assert str(circular_linked_list) == "->".join(str(i) for i in range(0, 7))
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 6))
assert circular_linked_list.delete_nth(2) == 3
circular_linked_list.insert_nth(2, 3)
assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 6))
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod()
| from typing import Any
class Node:
def __init__(self, data: Any):
self.data = data
self.next = None
class CircularLinkedList:
def __init__(self):
self.head = None
self.tail = None
def __iter__(self):
node = self.head
while self.head:
yield node.data
node = node.next
if node == self.head:
break
def __len__(self) -> int:
return len(tuple(iter(self)))
def __repr__(self):
return "->".join(str(item) for item in iter(self))
def insert_tail(self, data: Any) -> None:
self.insert_nth(len(self), data)
def insert_head(self, data: Any) -> None:
self.insert_nth(0, data)
def insert_nth(self, index: int, data: Any) -> None:
if index < 0 or index > len(self):
raise IndexError("list index out of range.")
new_node = Node(data)
if self.head is None:
new_node.next = new_node # first node points itself
self.tail = self.head = new_node
elif index == 0: # insert at head
new_node.next = self.head
self.head = self.tail.next = new_node
else:
temp = self.head
for _ in range(index - 1):
temp = temp.next
new_node.next = temp.next
temp.next = new_node
if index == len(self) - 1: # insert at tail
self.tail = new_node
def delete_front(self):
return self.delete_nth(0)
def delete_tail(self) -> None:
return self.delete_nth(len(self) - 1)
def delete_nth(self, index: int = 0):
if not 0 <= index < len(self):
raise IndexError("list index out of range.")
delete_node = self.head
if self.head == self.tail: # just one node
self.head = self.tail = None
elif index == 0: # delete head node
self.tail.next = self.tail.next.next
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
if index == len(self) - 1: # delete at tail
self.tail = temp
return delete_node.data
def is_empty(self):
return len(self) == 0
def test_circular_linked_list() -> None:
"""
>>> test_circular_linked_list()
"""
circular_linked_list = CircularLinkedList()
assert len(circular_linked_list) == 0
assert circular_linked_list.is_empty() is True
assert str(circular_linked_list) == ""
try:
circular_linked_list.delete_front()
assert False # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
assert False # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1)
assert False
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0)
assert False
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5):
assert len(circular_linked_list) == i
circular_linked_list.insert_nth(i, i + 1)
assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 6))
circular_linked_list.insert_tail(6)
assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 7))
circular_linked_list.insert_head(0)
assert str(circular_linked_list) == "->".join(str(i) for i in range(0, 7))
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 6))
assert circular_linked_list.delete_nth(2) == 3
circular_linked_list.insert_nth(2, 3)
assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 6))
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,673 | [fixed] unused variable, standalone running, import doctest module | ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] 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: #{$ISSUE_NO}`.
| slowy07 | "2021-08-26T02:42:09Z" | "2021-08-28T18:07:10Z" | 46e56fa6f2e473d1300846b2b96e56f498872400 | 8e5c3536c728dd7451ca301dc2d5bfb3f68b0e1a | [fixed] unused variable, standalone running, import doctest module. ### **Describe your change:**
fixed unused variable, and using ``if __name__ == "__main__"``, and imported doctest module for following style code
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | from collections import deque
from typing import Dict, List, Union
class Automaton:
def __init__(self, keywords: List[str]):
self.adlist = list()
self.adlist.append(
{"value": "", "next_states": [], "fail_state": 0, "output": []}
)
for keyword in keywords:
self.add_keyword(keyword)
self.set_fail_transitions()
def find_next_state(self, current_state: int, char: str) -> Union[int, None]:
for state in self.adlist[current_state]["next_states"]:
if char == self.adlist[state]["value"]:
return state
return None
def add_keyword(self, keyword: str) -> None:
current_state = 0
for character in keyword:
if self.find_next_state(current_state, character):
current_state = self.find_next_state(current_state, character)
else:
self.adlist.append(
{
"value": character,
"next_states": [],
"fail_state": 0,
"output": [],
}
)
self.adlist[current_state]["next_states"].append(len(self.adlist) - 1)
current_state = len(self.adlist) - 1
self.adlist[current_state]["output"].append(keyword)
def set_fail_transitions(self) -> None:
q = deque()
for node in self.adlist[0]["next_states"]:
q.append(node)
self.adlist[node]["fail_state"] = 0
while q:
r = q.popleft()
for child in self.adlist[r]["next_states"]:
q.append(child)
state = self.adlist[r]["fail_state"]
while (
self.find_next_state(state, self.adlist[child]["value"]) is None
and state != 0
):
state = self.adlist[state]["fail_state"]
self.adlist[child]["fail_state"] = self.find_next_state(
state, self.adlist[child]["value"]
)
if self.adlist[child]["fail_state"] is None:
self.adlist[child]["fail_state"] = 0
self.adlist[child]["output"] = (
self.adlist[child]["output"]
+ self.adlist[self.adlist[child]["fail_state"]]["output"]
)
def search_in(self, string: str) -> Dict[str, List[int]]:
"""
>>> A = Automaton(["what", "hat", "ver", "er"])
>>> A.search_in("whatever, err ... , wherever")
{'what': [0], 'hat': [1], 'ver': [5, 25], 'er': [6, 10, 22, 26]}
"""
result = dict() # returns a dict with keywords and list of its occurrences
current_state = 0
for i in range(len(string)):
while (
self.find_next_state(current_state, string[i]) is None
and current_state != 0
):
current_state = self.adlist[current_state]["fail_state"]
current_state = self.find_next_state(current_state, string[i])
if current_state is None:
current_state = 0
else:
for key in self.adlist[current_state]["output"]:
if not (key in result):
result[key] = []
result[key].append(i - len(key) + 1)
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| from collections import deque
from typing import Dict, List, Union
class Automaton:
def __init__(self, keywords: list[str]):
self.adlist: list[dict] = list()
self.adlist.append(
{"value": "", "next_states": [], "fail_state": 0, "output": []}
)
for keyword in keywords:
self.add_keyword(keyword)
self.set_fail_transitions()
def find_next_state(self, current_state: int, char: str) -> Union[int, None]:
for state in self.adlist[current_state]["next_states"]:
if char == self.adlist[state]["value"]:
return state
return None
def add_keyword(self, keyword: str) -> None:
current_state = 0
for character in keyword:
next_state = self.find_next_state(current_state, character)
if next_state is None:
self.adlist.append(
{
"value": character,
"next_states": [],
"fail_state": 0,
"output": [],
}
)
self.adlist[current_state]["next_states"].append(len(self.adlist) - 1)
current_state = len(self.adlist) - 1
else:
current_state = next_state
self.adlist[current_state]["output"].append(keyword)
def set_fail_transitions(self) -> None:
q: deque = deque()
for node in self.adlist[0]["next_states"]:
q.append(node)
self.adlist[node]["fail_state"] = 0
while q:
r = q.popleft()
for child in self.adlist[r]["next_states"]:
q.append(child)
state = self.adlist[r]["fail_state"]
while (
self.find_next_state(state, self.adlist[child]["value"]) is None
and state != 0
):
state = self.adlist[state]["fail_state"]
self.adlist[child]["fail_state"] = self.find_next_state(
state, self.adlist[child]["value"]
)
if self.adlist[child]["fail_state"] is None:
self.adlist[child]["fail_state"] = 0
self.adlist[child]["output"] = (
self.adlist[child]["output"]
+ self.adlist[self.adlist[child]["fail_state"]]["output"]
)
def search_in(self, string: str) -> Dict[str, List[int]]:
"""
>>> A = Automaton(["what", "hat", "ver", "er"])
>>> A.search_in("whatever, err ... , wherever")
{'what': [0], 'hat': [1], 'ver': [5, 25], 'er': [6, 10, 22, 26]}
"""
result: dict = (
dict()
) # returns a dict with keywords and list of its occurrences
current_state = 0
for i in range(len(string)):
while (
self.find_next_state(current_state, string[i]) is None
and current_state != 0
):
current_state = self.adlist[current_state]["fail_state"]
next_state = self.find_next_state(current_state, string[i])
if next_state is None:
current_state = 0
else:
current_state = next_state
for key in self.adlist[current_state]["output"]:
if not (key in result):
result[key] = []
result[key].append(i - len(key) + 1)
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | def palindromic_string(input_string: str) -> str:
"""
>>> palindromic_string('abbbaba')
'abbba'
>>> palindromic_string('ababa')
'ababa'
Manacher’s algorithm which finds Longest palindromic Substring in linear time.
1. first this convert input_string("xyx") into new_string("x|y|x") where odd
positions are actual input characters.
2. for each character in new_string it find corresponding length and store the
length and l,r to store previously calculated info.(please look the explanation
for details)
3. return corresponding output_string by removing all "|"
"""
max_length = 0
# if input_string is "aba" than new_input_string become "a|b|a"
new_input_string = ""
output_string = ""
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(input_string) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
l, r = 0, 0
# length[i] shows the length of palindromic substring with center i
length = [1 for i in range(len(new_input_string))]
# for each character in new_string find corresponding palindromic string
for i in range(len(new_input_string)):
k = 1 if i > r else min(length[l + r - i] // 2, r - i + 1)
while (
i - k >= 0
and i + k < len(new_input_string)
and new_input_string[k + i] == new_input_string[i - k]
):
k += 1
length[i] = 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if i + k - 1 > r:
l = i - k + 1 # noqa: E741
r = i + k - 1
# update max_length and start position
if max_length < length[i]:
max_length = length[i]
start = i
# create that string
s = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
"""
...a0...a1...a2.....a3......a4...a5...a6....
consider the string for which we are calculating the longest palindromic substring is
shown above where ... are some characters in between and right now we are calculating
the length of palindromic substring with center at a5 with following conditions :
i) we have stored the length of palindromic substring which has center at a3 (starts at
l ends at r) and it is the furthest ending till now, and it has ending after a6
ii) a2 and a4 are equally distant from a3 so char(a2) == char(a4)
iii) a0 and a6 are equally distant from a3 so char(a0) == char(a6)
iv) a1 is corresponding equal character of a5 in palindrome with center a3 (remember
that in below derivation of a4==a6)
now for a5 we will calculate the length of palindromic substring with center as a5 but
can we use previously calculated information in some way?
Yes, look the above string we know that a5 is inside the palindrome with center a3 and
previously we have have calculated that
a0==a2 (palindrome of center a1)
a2==a4 (palindrome of center a3)
a0==a6 (palindrome of center a3)
so a4==a6
so we can say that palindrome at center a5 is at least as long as palindrome at center
a1 but this only holds if a0 and a6 are inside the limits of palindrome centered at a3
so finally ..
len_of_palindrome__at(a5) = min(len_of_palindrome_at(a1), r-a5)
where a3 lies from l to r and we have to keep updating that
and if the a5 lies outside of l,r boundary we calculate length of palindrome with
bruteforce and update l,r.
it gives the linear time complexity just like z-function
"""
| def palindromic_string(input_string: str) -> str:
"""
>>> palindromic_string('abbbaba')
'abbba'
>>> palindromic_string('ababa')
'ababa'
Manacher’s algorithm which finds Longest palindromic Substring in linear time.
1. first this convert input_string("xyx") into new_string("x|y|x") where odd
positions are actual input characters.
2. for each character in new_string it find corresponding length and store the
length and l,r to store previously calculated info.(please look the explanation
for details)
3. return corresponding output_string by removing all "|"
"""
max_length = 0
# if input_string is "aba" than new_input_string become "a|b|a"
new_input_string = ""
output_string = ""
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(input_string) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
l, r = 0, 0
# length[i] shows the length of palindromic substring with center i
length = [1 for i in range(len(new_input_string))]
# for each character in new_string find corresponding palindromic string
start = 0
for j in range(len(new_input_string)):
k = 1 if j > r else min(length[l + r - j] // 2, r - j + 1)
while (
j - k >= 0
and j + k < len(new_input_string)
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
length[j] = 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
l = j - k + 1 # noqa: E741
r = j + k - 1
# update max_length and start position
if max_length < length[j]:
max_length = length[j]
start = j
# create that string
s = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
"""
...a0...a1...a2.....a3......a4...a5...a6....
consider the string for which we are calculating the longest palindromic substring is
shown above where ... are some characters in between and right now we are calculating
the length of palindromic substring with center at a5 with following conditions :
i) we have stored the length of palindromic substring which has center at a3 (starts at
l ends at r) and it is the furthest ending till now, and it has ending after a6
ii) a2 and a4 are equally distant from a3 so char(a2) == char(a4)
iii) a0 and a6 are equally distant from a3 so char(a0) == char(a6)
iv) a1 is corresponding equal character of a5 in palindrome with center a3 (remember
that in below derivation of a4==a6)
now for a5 we will calculate the length of palindromic substring with center as a5 but
can we use previously calculated information in some way?
Yes, look the above string we know that a5 is inside the palindrome with center a3 and
previously we have have calculated that
a0==a2 (palindrome of center a1)
a2==a4 (palindrome of center a3)
a0==a6 (palindrome of center a3)
so a4==a6
so we can say that palindrome at center a5 is at least as long as palindrome at center
a1 but this only holds if a0 and a6 are inside the limits of palindrome centered at a3
so finally ..
len_of_palindrome__at(a5) = min(len_of_palindrome_at(a1), r-a5)
where a3 lies from l to r and we have to keep updating that
and if the a5 lies outside of l,r boundary we calculate length of palindrome with
bruteforce and update l,r.
it gives the linear time complexity just like z-function
"""
| 1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | from typing import List, Tuple
"""
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[int], 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[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 | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
Problem 33: https://projecteuler.net/problem=33
The fraction 49/98 is a curious fraction, as an inexperienced
mathematician in attempting to simplify it may incorrectly believe
that 49/98 = 4/8, which is correct, is obtained by cancelling the 9s.
We shall consider fractions like, 30/50 = 3/5, to be trivial examples.
There are exactly four non-trivial examples of this type of fraction,
less than one in value, and containing two digits in the numerator
and denominator.
If the product of these four fractions is given in its lowest common
terms, find the value of the denominator.
"""
from fractions import Fraction
from typing import List
def is_digit_cancelling(num: int, den: int) -> bool:
if num != den:
if num % 10 == den // 10:
if (num // 10) / (den % 10) == num / den:
return True
return False
def fraction_list(digit_len: int) -> List[str]:
"""
>>> fraction_list(2)
['16/64', '19/95', '26/65', '49/98']
>>> fraction_list(3)
['16/64', '19/95', '26/65', '49/98']
>>> fraction_list(4)
['16/64', '19/95', '26/65', '49/98']
>>> fraction_list(0)
[]
>>> fraction_list(5)
['16/64', '19/95', '26/65', '49/98']
"""
solutions = []
den = 11
last_digit = int("1" + "0" * digit_len)
for num in range(den, last_digit):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(num, den):
solutions.append(f"{num}/{den}")
den += 1
num += 1
den = 10
return solutions
def solution(n: int = 2) -> int:
"""
Return the solution to the problem
"""
result = 1.0
for fraction in fraction_list(n):
frac = Fraction(fraction)
result *= frac.denominator / frac.numerator
return int(result)
if __name__ == "__main__":
print(solution())
| """
Problem 33: https://projecteuler.net/problem=33
The fraction 49/98 is a curious fraction, as an inexperienced
mathematician in attempting to simplify it may incorrectly believe
that 49/98 = 4/8, which is correct, is obtained by cancelling the 9s.
We shall consider fractions like, 30/50 = 3/5, to be trivial examples.
There are exactly four non-trivial examples of this type of fraction,
less than one in value, and containing two digits in the numerator
and denominator.
If the product of these four fractions is given in its lowest common
terms, find the value of the denominator.
"""
from fractions import Fraction
from typing import List
def is_digit_cancelling(num: int, den: int) -> bool:
if num != den:
if num % 10 == den // 10:
if (num // 10) / (den % 10) == num / den:
return True
return False
def fraction_list(digit_len: int) -> List[str]:
"""
>>> fraction_list(2)
['16/64', '19/95', '26/65', '49/98']
>>> fraction_list(3)
['16/64', '19/95', '26/65', '49/98']
>>> fraction_list(4)
['16/64', '19/95', '26/65', '49/98']
>>> fraction_list(0)
[]
>>> fraction_list(5)
['16/64', '19/95', '26/65', '49/98']
"""
solutions = []
den = 11
last_digit = int("1" + "0" * digit_len)
for num in range(den, last_digit):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(num, den):
solutions.append(f"{num}/{den}")
den += 1
num += 1
den = 10
return solutions
def solution(n: int = 2) -> int:
"""
Return the solution to the problem
"""
result = 1.0
for fraction in fraction_list(n):
frac = Fraction(fraction)
result *= frac.denominator / frac.numerator
return int(result)
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
A Trie/Prefix Tree is a kind of search tree used to provide quick lookup
of words/patterns in a set of words. A basic Trie however has O(n^2) space complexity
making it impractical in practice. It however provides O(max(search_string, length of
longest word)) lookup time making it an optimal approach when space is not an issue.
"""
class TrieNode:
def __init__(self):
self.nodes = dict() # Mapping from char to TrieNode
self.is_leaf = False
def insert_many(self, words: [str]):
"""
Inserts a list of words into the Trie
:param words: list of string words
:return: None
"""
for word in words:
self.insert(word)
def insert(self, word: str):
"""
Inserts a word into the Trie
:param word: word to be inserted
:return: None
"""
curr = self
for char in word:
if char not in curr.nodes:
curr.nodes[char] = TrieNode()
curr = curr.nodes[char]
curr.is_leaf = True
def find(self, word: str) -> bool:
"""
Tries to find word in a Trie
:param word: word to look for
:return: Returns True if word is found, False otherwise
"""
curr = self
for char in word:
if char not in curr.nodes:
return False
curr = curr.nodes[char]
return curr.is_leaf
def delete(self, word: str):
"""
Deletes a word in a Trie
:param word: word to delete
:return: None
"""
def _delete(curr: TrieNode, word: str, index: int):
if index == len(word):
# If word does not exist
if not curr.is_leaf:
return False
curr.is_leaf = False
return len(curr.nodes) == 0
char = word[index]
char_node = curr.nodes.get(char)
# If char not in current trie node
if not char_node:
return False
# Flag to check if node can be deleted
delete_curr = _delete(char_node, word, index + 1)
if delete_curr:
del curr.nodes[char]
return len(curr.nodes) == 0
return delete_curr
_delete(self, word, 0)
def print_words(node: TrieNode, word: str):
"""
Prints all the words in a Trie
:param node: root node of Trie
:param word: Word variable should be empty at start
:return: None
"""
if node.is_leaf:
print(word, end=" ")
for key, value in node.nodes.items():
print_words(value, word + key)
def test_trie():
words = "banana bananas bandana band apple all beast".split()
root = TrieNode()
root.insert_many(words)
# print_words(root, "")
assert all(root.find(word) for word in words)
assert root.find("banana")
assert not root.find("bandanas")
assert not root.find("apps")
assert root.find("apple")
assert root.find("all")
root.delete("all")
assert not root.find("all")
root.delete("banana")
assert not root.find("banana")
assert root.find("bananas")
return True
def print_results(msg: str, passes: bool) -> None:
print(str(msg), "works!" if passes else "doesn't work :(")
def pytests():
assert test_trie()
def main():
"""
>>> pytests()
"""
print_results("Testing trie functionality", test_trie())
if __name__ == "__main__":
main()
| """
A Trie/Prefix Tree is a kind of search tree used to provide quick lookup
of words/patterns in a set of words. A basic Trie however has O(n^2) space complexity
making it impractical in practice. It however provides O(max(search_string, length of
longest word)) lookup time making it an optimal approach when space is not an issue.
"""
class TrieNode:
def __init__(self):
self.nodes = dict() # Mapping from char to TrieNode
self.is_leaf = False
def insert_many(self, words: [str]):
"""
Inserts a list of words into the Trie
:param words: list of string words
:return: None
"""
for word in words:
self.insert(word)
def insert(self, word: str):
"""
Inserts a word into the Trie
:param word: word to be inserted
:return: None
"""
curr = self
for char in word:
if char not in curr.nodes:
curr.nodes[char] = TrieNode()
curr = curr.nodes[char]
curr.is_leaf = True
def find(self, word: str) -> bool:
"""
Tries to find word in a Trie
:param word: word to look for
:return: Returns True if word is found, False otherwise
"""
curr = self
for char in word:
if char not in curr.nodes:
return False
curr = curr.nodes[char]
return curr.is_leaf
def delete(self, word: str):
"""
Deletes a word in a Trie
:param word: word to delete
:return: None
"""
def _delete(curr: TrieNode, word: str, index: int):
if index == len(word):
# If word does not exist
if not curr.is_leaf:
return False
curr.is_leaf = False
return len(curr.nodes) == 0
char = word[index]
char_node = curr.nodes.get(char)
# If char not in current trie node
if not char_node:
return False
# Flag to check if node can be deleted
delete_curr = _delete(char_node, word, index + 1)
if delete_curr:
del curr.nodes[char]
return len(curr.nodes) == 0
return delete_curr
_delete(self, word, 0)
def print_words(node: TrieNode, word: str):
"""
Prints all the words in a Trie
:param node: root node of Trie
:param word: Word variable should be empty at start
:return: None
"""
if node.is_leaf:
print(word, end=" ")
for key, value in node.nodes.items():
print_words(value, word + key)
def test_trie():
words = "banana bananas bandana band apple all beast".split()
root = TrieNode()
root.insert_many(words)
# print_words(root, "")
assert all(root.find(word) for word in words)
assert root.find("banana")
assert not root.find("bandanas")
assert not root.find("apps")
assert root.find("apple")
assert root.find("all")
root.delete("all")
assert not root.find("all")
root.delete("banana")
assert not root.find("banana")
assert root.find("bananas")
return True
def print_results(msg: str, passes: bool) -> None:
print(str(msg), "works!" if passes else "doesn't work :(")
def pytests():
assert test_trie()
def main():
"""
>>> pytests()
"""
print_results("Testing trie functionality", test_trie())
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | -1 |
||
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | #!/usr/bin/env python3
import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType
import pytest
import requests
PROJECT_EULER_DIR_PATH = pathlib.Path.cwd().joinpath("project_euler")
PROJECT_EULER_ANSWERS_PATH = pathlib.Path.cwd().joinpath(
"scripts", "project_euler_answers.json"
)
with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
PROBLEM_ANSWERS: dict[str, str] = json.load(file_handle)
def convert_path_to_module(file_path: pathlib.Path) -> ModuleType:
"""Converts a file path to a Python module"""
spec = importlib.util.spec_from_file_location(file_path.name, str(file_path))
module = importlib.util.module_from_spec(spec) # type: ignore
spec.loader.exec_module(module) # type: ignore
return module
def all_solution_file_paths() -> list[pathlib.Path]:
"""Collects all the solution file path in the Project Euler directory"""
solution_file_paths = []
for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
if problem_dir_path.is_file() or problem_dir_path.name.startswith("_"):
continue
for file_path in problem_dir_path.iterdir():
if file_path.suffix != ".py" or file_path.name.startswith(("_", "test")):
continue
solution_file_paths.append(file_path)
return solution_file_paths
def get_files_url() -> str:
"""Return the pull request number which triggered this action."""
with open(os.environ["GITHUB_EVENT_PATH"]) as file:
event = json.load(file)
return event["pull_request"]["url"] + "/files"
def added_solution_file_path() -> list[pathlib.Path]:
"""Collects only the solution file path which got added in the current
pull request.
This will only be triggered if the script is ran from GitHub Actions.
"""
solution_file_paths = []
headers = {
"Accept": "application/vnd.github.v3+json",
"Authorization": "token " + os.environ["GITHUB_TOKEN"],
}
files = requests.get(get_files_url(), headers=headers).json()
for file in files:
filepath = pathlib.Path.cwd().joinpath(file["filename"])
if (
filepath.suffix != ".py"
or filepath.name.startswith(("_", "test"))
or not filepath.name.startswith("sol")
):
continue
solution_file_paths.append(filepath)
return solution_file_paths
def collect_solution_file_paths() -> list[pathlib.Path]:
if os.environ.get("CI") and os.environ.get("GITHUB_EVENT_NAME") == "pull_request":
# Return only if there are any, otherwise default to all solutions
if filepaths := added_solution_file_path():
return filepaths
return all_solution_file_paths()
@pytest.mark.parametrize(
"solution_path",
collect_solution_file_paths(),
ids=lambda path: f"{path.parent.name}/{path.name}",
)
def test_project_euler(solution_path: pathlib.Path) -> None:
"""Testing for all Project Euler solutions"""
# problem_[extract this part] and pad it with zeroes for width 3
problem_number: str = solution_path.parent.name[8:].zfill(3)
expected: str = PROBLEM_ANSWERS[problem_number]
solution_module = convert_path_to_module(solution_path)
answer = str(solution_module.solution()) # type: ignore
answer = hashlib.sha256(answer.encode()).hexdigest()
assert (
answer == expected
), f"Expected solution to {problem_number} to have hash {expected}, got {answer}"
| #!/usr/bin/env python3
import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType
import pytest
import requests
PROJECT_EULER_DIR_PATH = pathlib.Path.cwd().joinpath("project_euler")
PROJECT_EULER_ANSWERS_PATH = pathlib.Path.cwd().joinpath(
"scripts", "project_euler_answers.json"
)
with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
PROBLEM_ANSWERS: dict[str, str] = json.load(file_handle)
def convert_path_to_module(file_path: pathlib.Path) -> ModuleType:
"""Converts a file path to a Python module"""
spec = importlib.util.spec_from_file_location(file_path.name, str(file_path))
module = importlib.util.module_from_spec(spec) # type: ignore
spec.loader.exec_module(module) # type: ignore
return module
def all_solution_file_paths() -> list[pathlib.Path]:
"""Collects all the solution file path in the Project Euler directory"""
solution_file_paths = []
for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
if problem_dir_path.is_file() or problem_dir_path.name.startswith("_"):
continue
for file_path in problem_dir_path.iterdir():
if file_path.suffix != ".py" or file_path.name.startswith(("_", "test")):
continue
solution_file_paths.append(file_path)
return solution_file_paths
def get_files_url() -> str:
"""Return the pull request number which triggered this action."""
with open(os.environ["GITHUB_EVENT_PATH"]) as file:
event = json.load(file)
return event["pull_request"]["url"] + "/files"
def added_solution_file_path() -> list[pathlib.Path]:
"""Collects only the solution file path which got added in the current
pull request.
This will only be triggered if the script is ran from GitHub Actions.
"""
solution_file_paths = []
headers = {
"Accept": "application/vnd.github.v3+json",
"Authorization": "token " + os.environ["GITHUB_TOKEN"],
}
files = requests.get(get_files_url(), headers=headers).json()
for file in files:
filepath = pathlib.Path.cwd().joinpath(file["filename"])
if (
filepath.suffix != ".py"
or filepath.name.startswith(("_", "test"))
or not filepath.name.startswith("sol")
):
continue
solution_file_paths.append(filepath)
return solution_file_paths
def collect_solution_file_paths() -> list[pathlib.Path]:
if os.environ.get("CI") and os.environ.get("GITHUB_EVENT_NAME") == "pull_request":
# Return only if there are any, otherwise default to all solutions
if filepaths := added_solution_file_path():
return filepaths
return all_solution_file_paths()
@pytest.mark.parametrize(
"solution_path",
collect_solution_file_paths(),
ids=lambda path: f"{path.parent.name}/{path.name}",
)
def test_project_euler(solution_path: pathlib.Path) -> None:
"""Testing for all Project Euler solutions"""
# problem_[extract this part] and pad it with zeroes for width 3
problem_number: str = solution_path.parent.name[8:].zfill(3)
expected: str = PROBLEM_ANSWERS[problem_number]
solution_module = convert_path_to_module(solution_path)
answer = str(solution_module.solution()) # type: ignore
answer = hashlib.sha256(answer.encode()).hexdigest()
assert (
answer == expected
), f"Expected solution to {problem_number} to have hash {expected}, got {answer}"
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | -1 |
||
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | import math
def perfect_square(num: int) -> bool:
"""
Check if a number is perfect square number or not
:param num: the number to be checked
:return: True if number is square number, otherwise False
>>> perfect_square(9)
True
>>> perfect_square(16)
True
>>> perfect_square(1)
True
>>> perfect_square(0)
True
>>> perfect_square(10)
False
"""
return math.sqrt(num) * math.sqrt(num) == num
def perfect_square_binary_search(n: int) -> bool:
"""
Check if a number is perfect square using binary search.
Time complexity : O(Log(n))
Space complexity: O(1)
>>> perfect_square_binary_search(9)
True
>>> perfect_square_binary_search(16)
True
>>> perfect_square_binary_search(1)
True
>>> perfect_square_binary_search(0)
True
>>> perfect_square_binary_search(10)
False
>>> perfect_square_binary_search(-1)
False
>>> perfect_square_binary_search(1.1)
False
>>> perfect_square_binary_search("a")
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'str'
>>> perfect_square_binary_search(None)
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'NoneType'
>>> perfect_square_binary_search([])
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'list'
"""
left = 0
right = n
while left <= right:
mid = (left + right) // 2
if mid ** 2 == n:
return True
elif mid ** 2 > n:
right = mid - 1
else:
left = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| import math
def perfect_square(num: int) -> bool:
"""
Check if a number is perfect square number or not
:param num: the number to be checked
:return: True if number is square number, otherwise False
>>> perfect_square(9)
True
>>> perfect_square(16)
True
>>> perfect_square(1)
True
>>> perfect_square(0)
True
>>> perfect_square(10)
False
"""
return math.sqrt(num) * math.sqrt(num) == num
def perfect_square_binary_search(n: int) -> bool:
"""
Check if a number is perfect square using binary search.
Time complexity : O(Log(n))
Space complexity: O(1)
>>> perfect_square_binary_search(9)
True
>>> perfect_square_binary_search(16)
True
>>> perfect_square_binary_search(1)
True
>>> perfect_square_binary_search(0)
True
>>> perfect_square_binary_search(10)
False
>>> perfect_square_binary_search(-1)
False
>>> perfect_square_binary_search(1.1)
False
>>> perfect_square_binary_search("a")
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'str'
>>> perfect_square_binary_search(None)
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'NoneType'
>>> perfect_square_binary_search([])
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'list'
"""
left = 0
right = n
while left <= right:
mid = (left + right) // 2
if mid ** 2 == n:
return True
elif mid ** 2 > n:
right = mid - 1
else:
left = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | -1 |
||
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
What is the greatest product of four adjacent numbers (horizontally,
vertically, or diagonally) in this 20x20 array?
08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08
49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00
81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65
52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91
22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80
24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50
32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70
67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21
24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72
21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95
78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92
16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57
86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58
19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40
04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66
88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69
04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36
20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16
20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54
01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48
"""
import os
def solution():
"""Returns the greatest product of four adjacent numbers (horizontally,
vertically, or diagonally).
>>> solution()
70600674
"""
with open(os.path.dirname(__file__) + "/grid.txt") as f:
l = [] # noqa: E741
for i in range(20):
l.append([int(x) for x in f.readline().split()])
maximum = 0
# right
for i in range(20):
for j in range(17):
temp = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
if temp > maximum:
maximum = temp
# down
for i in range(17):
for j in range(20):
temp = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
if temp > maximum:
maximum = temp
# diagonal 1
for i in range(17):
for j in range(17):
temp = l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
if temp > maximum:
maximum = temp
# diagonal 2
for i in range(17):
for j in range(3, 20):
temp = l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
if temp > maximum:
maximum = temp
return maximum
if __name__ == "__main__":
print(solution())
| """
What is the greatest product of four adjacent numbers (horizontally,
vertically, or diagonally) in this 20x20 array?
08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08
49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00
81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65
52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91
22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80
24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50
32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70
67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21
24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72
21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95
78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92
16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57
86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58
19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40
04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66
88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69
04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36
20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16
20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54
01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48
"""
import os
def solution():
"""Returns the greatest product of four adjacent numbers (horizontally,
vertically, or diagonally).
>>> solution()
70600674
"""
with open(os.path.dirname(__file__) + "/grid.txt") as f:
l = [] # noqa: E741
for i in range(20):
l.append([int(x) for x in f.readline().split()])
maximum = 0
# right
for i in range(20):
for j in range(17):
temp = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
if temp > maximum:
maximum = temp
# down
for i in range(17):
for j in range(20):
temp = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
if temp > maximum:
maximum = temp
# diagonal 1
for i in range(17):
for j in range(17):
temp = l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
if temp > maximum:
maximum = temp
# diagonal 2
for i in range(17):
for j in range(3, 20):
temp = l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
if temp > maximum:
maximum = temp
return maximum
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | # Print all subset combinations of n element in given set of r element.
def combination_util(arr, n, r, index, data, i):
"""
Current combination is ready to be printed, print it
arr[] ---> Input Array
data[] ---> Temporary array to store current combination
start & end ---> Staring and Ending indexes in arr[]
index ---> Current index in data[]
r ---> Size of a combination to be printed
"""
if index == r:
for j in range(r):
print(data[j], end=" ")
print(" ")
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
data[index] = arr[i]
combination_util(arr, n, r, index + 1, data, i + 1)
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(arr, n, r, index, data, i + 1)
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def print_combination(arr, n, r):
# A temporary array to store all combination one by one
data = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(arr, n, r, 0, data, 0)
# Driver function to check for above function
arr = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| # Print all subset combinations of n element in given set of r element.
def combination_util(arr, n, r, index, data, i):
"""
Current combination is ready to be printed, print it
arr[] ---> Input Array
data[] ---> Temporary array to store current combination
start & end ---> Staring and Ending indexes in arr[]
index ---> Current index in data[]
r ---> Size of a combination to be printed
"""
if index == r:
for j in range(r):
print(data[j], end=" ")
print(" ")
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
data[index] = arr[i]
combination_util(arr, n, r, index + 1, data, i + 1)
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(arr, n, r, index, data, i + 1)
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def print_combination(arr, n, r):
# A temporary array to store all combination one by one
data = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(arr, n, r, 0, data, 0)
# Driver function to check for above function
arr = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
Isolate the Decimal part of a Number
https://stackoverflow.com/questions/3886402/how-to-get-numbers-after-decimal-point
"""
def decimal_isolate(number, digitAmount):
"""
Isolates the decimal part of a number.
If digitAmount > 0 round to that decimal place, else print the entire decimal.
>>> decimal_isolate(1.53, 0)
0.53
>>> decimal_isolate(35.345, 1)
0.3
>>> decimal_isolate(35.345, 2)
0.34
>>> decimal_isolate(35.345, 3)
0.345
>>> decimal_isolate(-14.789, 3)
-0.789
>>> decimal_isolate(0, 2)
0
>>> decimal_isolate(-14.123, 1)
-0.1
>>> decimal_isolate(-14.123, 2)
-0.12
>>> decimal_isolate(-14.123, 3)
-0.123
"""
if digitAmount > 0:
return round(number - int(number), digitAmount)
return number - int(number)
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| """
Isolate the Decimal part of a Number
https://stackoverflow.com/questions/3886402/how-to-get-numbers-after-decimal-point
"""
def decimal_isolate(number, digitAmount):
"""
Isolates the decimal part of a number.
If digitAmount > 0 round to that decimal place, else print the entire decimal.
>>> decimal_isolate(1.53, 0)
0.53
>>> decimal_isolate(35.345, 1)
0.3
>>> decimal_isolate(35.345, 2)
0.34
>>> decimal_isolate(35.345, 3)
0.345
>>> decimal_isolate(-14.789, 3)
-0.789
>>> decimal_isolate(0, 2)
0
>>> decimal_isolate(-14.123, 1)
-0.1
>>> decimal_isolate(-14.123, 2)
-0.12
>>> decimal_isolate(-14.123, 3)
-0.123
"""
if digitAmount > 0:
return round(number - int(number), digitAmount)
return number - int(number)
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
Project Euler Problem 35
https://projecteuler.net/problem=35
Problem Statement:
The number 197 is called a circular prime because all rotations of the digits:
197, 971, and 719, are themselves prime.
There are thirteen such primes below 100: 2, 3, 5, 7, 11, 13, 17, 31, 37, 71, 73,
79, and 97.
How many circular primes are there below one million?
To solve this problem in an efficient manner, we will first mark all the primes
below 1 million using the Seive of Eratosthenes. Then, out of all these primes,
we will rule out the numbers which contain an even digit. After this we will
generate each circular combination of the number and check if all are prime.
"""
from __future__ import annotations
seive = [True] * 1000001
i = 2
while i * i <= 1000000:
if seive[i]:
for j in range(i * i, 1000001, i):
seive[j] = False
i += 1
def is_prime(n: int) -> bool:
"""
For 2 <= n <= 1000000, return True if n is prime.
>>> is_prime(87)
False
>>> is_prime(23)
True
>>> is_prime(25363)
False
"""
return seive[n]
def contains_an_even_digit(n: int) -> bool:
"""
Return True if n contains an even digit.
>>> contains_an_even_digit(0)
True
>>> contains_an_even_digit(975317933)
False
>>> contains_an_even_digit(-245679)
True
"""
return any(digit in "02468" for digit in str(n))
def find_circular_primes(limit: int = 1000000) -> list[int]:
"""
Return circular primes below limit.
>>> len(find_circular_primes(100))
13
>>> len(find_circular_primes(1000000))
55
"""
result = [2] # result already includes the number 2.
for num in range(3, limit + 1, 2):
if is_prime(num) and not contains_an_even_digit(num):
str_num = str(num)
list_nums = [int(str_num[j:] + str_num[:j]) for j in range(len(str_num))]
if all(is_prime(i) for i in list_nums):
result.append(num)
return result
def solution() -> int:
"""
>>> solution()
55
"""
return len(find_circular_primes())
if __name__ == "__main__":
print(f"{len(find_circular_primes()) = }")
| """
Project Euler Problem 35
https://projecteuler.net/problem=35
Problem Statement:
The number 197 is called a circular prime because all rotations of the digits:
197, 971, and 719, are themselves prime.
There are thirteen such primes below 100: 2, 3, 5, 7, 11, 13, 17, 31, 37, 71, 73,
79, and 97.
How many circular primes are there below one million?
To solve this problem in an efficient manner, we will first mark all the primes
below 1 million using the Seive of Eratosthenes. Then, out of all these primes,
we will rule out the numbers which contain an even digit. After this we will
generate each circular combination of the number and check if all are prime.
"""
from __future__ import annotations
seive = [True] * 1000001
i = 2
while i * i <= 1000000:
if seive[i]:
for j in range(i * i, 1000001, i):
seive[j] = False
i += 1
def is_prime(n: int) -> bool:
"""
For 2 <= n <= 1000000, return True if n is prime.
>>> is_prime(87)
False
>>> is_prime(23)
True
>>> is_prime(25363)
False
"""
return seive[n]
def contains_an_even_digit(n: int) -> bool:
"""
Return True if n contains an even digit.
>>> contains_an_even_digit(0)
True
>>> contains_an_even_digit(975317933)
False
>>> contains_an_even_digit(-245679)
True
"""
return any(digit in "02468" for digit in str(n))
def find_circular_primes(limit: int = 1000000) -> list[int]:
"""
Return circular primes below limit.
>>> len(find_circular_primes(100))
13
>>> len(find_circular_primes(1000000))
55
"""
result = [2] # result already includes the number 2.
for num in range(3, limit + 1, 2):
if is_prime(num) and not contains_an_even_digit(num):
str_num = str(num)
list_nums = [int(str_num[j:] + str_num[:j]) for j in range(len(str_num))]
if all(is_prime(i) for i in list_nums):
result.append(num)
return result
def solution() -> int:
"""
>>> solution()
55
"""
return len(find_circular_primes())
if __name__ == "__main__":
print(f"{len(find_circular_primes()) = }")
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
Project Euler Problem 89: https://projecteuler.net/problem=89
For a number written in Roman numerals to be considered valid there are basic rules
which must be followed. Even though the rules allow some numbers to be expressed in
more than one way there is always a "best" way of writing a particular number.
For example, it would appear that there are at least six ways of writing the number
sixteen:
IIIIIIIIIIIIIIII
VIIIIIIIIIII
VVIIIIII
XIIIIII
VVVI
XVI
However, according to the rules only XIIIIII and XVI are valid, and the last example
is considered to be the most efficient, as it uses the least number of numerals.
The 11K text file, roman.txt (right click and 'Save Link/Target As...'), contains one
thousand numbers written in valid, but not necessarily minimal, Roman numerals; see
About... Roman Numerals for the definitive rules for this problem.
Find the number of characters saved by writing each of these in their minimal form.
Note: You can assume that all the Roman numerals in the file contain no more than four
consecutive identical units.
"""
import os
SYMBOLS = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def parse_roman_numerals(numerals: str) -> int:
"""
Converts a string of roman numerals to an integer.
e.g.
>>> parse_roman_numerals("LXXXIX")
89
>>> parse_roman_numerals("IIII")
4
"""
total_value = 0
index = 0
while index < len(numerals) - 1:
current_value = SYMBOLS[numerals[index]]
next_value = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def generate_roman_numerals(num: int) -> str:
"""
Generates a string of roman numerals for a given integer.
e.g.
>>> generate_roman_numerals(89)
'LXXXIX'
>>> generate_roman_numerals(4)
'IV'
"""
numerals = ""
m_count = num // 1000
numerals += m_count * "M"
num %= 1000
c_count = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
x_count = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def solution(roman_numerals_filename: str = "/p089_roman.txt") -> int:
"""
Calculates and returns the answer to project euler problem 89.
>>> solution("/numeralcleanup_test.txt")
16
"""
savings = 0
file1 = open(os.path.dirname(__file__) + roman_numerals_filename, "r")
lines = file1.readlines()
for line in lines:
original = line.strip()
num = parse_roman_numerals(original)
shortened = generate_roman_numerals(num)
savings += len(original) - len(shortened)
return savings
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 89: https://projecteuler.net/problem=89
For a number written in Roman numerals to be considered valid there are basic rules
which must be followed. Even though the rules allow some numbers to be expressed in
more than one way there is always a "best" way of writing a particular number.
For example, it would appear that there are at least six ways of writing the number
sixteen:
IIIIIIIIIIIIIIII
VIIIIIIIIIII
VVIIIIII
XIIIIII
VVVI
XVI
However, according to the rules only XIIIIII and XVI are valid, and the last example
is considered to be the most efficient, as it uses the least number of numerals.
The 11K text file, roman.txt (right click and 'Save Link/Target As...'), contains one
thousand numbers written in valid, but not necessarily minimal, Roman numerals; see
About... Roman Numerals for the definitive rules for this problem.
Find the number of characters saved by writing each of these in their minimal form.
Note: You can assume that all the Roman numerals in the file contain no more than four
consecutive identical units.
"""
import os
SYMBOLS = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def parse_roman_numerals(numerals: str) -> int:
"""
Converts a string of roman numerals to an integer.
e.g.
>>> parse_roman_numerals("LXXXIX")
89
>>> parse_roman_numerals("IIII")
4
"""
total_value = 0
index = 0
while index < len(numerals) - 1:
current_value = SYMBOLS[numerals[index]]
next_value = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def generate_roman_numerals(num: int) -> str:
"""
Generates a string of roman numerals for a given integer.
e.g.
>>> generate_roman_numerals(89)
'LXXXIX'
>>> generate_roman_numerals(4)
'IV'
"""
numerals = ""
m_count = num // 1000
numerals += m_count * "M"
num %= 1000
c_count = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
x_count = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def solution(roman_numerals_filename: str = "/p089_roman.txt") -> int:
"""
Calculates and returns the answer to project euler problem 89.
>>> solution("/numeralcleanup_test.txt")
16
"""
savings = 0
file1 = open(os.path.dirname(__file__) + roman_numerals_filename, "r")
lines = file1.readlines()
for line in lines:
original = line.strip()
num = parse_roman_numerals(original)
shortened = generate_roman_numerals(num)
savings += len(original) - len(shortened)
return savings
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
Problem 31: https://projecteuler.net/problem=31
Coin sums
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?
Hint:
> There are 100 pence in a pound (£1 = 100p)
> There are coins(in pence) are available: 1, 2, 5, 10, 20, 50, 100 and 200.
> how many different ways you can combine these values to create 200 pence.
Example:
to make 6p there are 5 ways
1,1,1,1,1,1
1,1,1,1,2
1,1,2,2
2,2,2
1,5
to make 5p there are 4 ways
1,1,1,1,1
1,1,1,2
1,2,2
5
"""
def solution(pence: int = 200) -> int:
"""Returns the number of different ways to make X pence using any number of coins.
The solution is based on dynamic programming paradigm in a bottom-up fashion.
>>> solution(500)
6295434
>>> solution(200)
73682
>>> solution(50)
451
>>> solution(10)
11
"""
coins = [1, 2, 5, 10, 20, 50, 100, 200]
number_of_ways = [0] * (pence + 1)
number_of_ways[0] = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(coin, pence + 1, 1):
number_of_ways[i] += number_of_ways[i - coin]
return number_of_ways[pence]
if __name__ == "__main__":
assert solution(200) == 73682
| """
Problem 31: https://projecteuler.net/problem=31
Coin sums
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?
Hint:
> There are 100 pence in a pound (£1 = 100p)
> There are coins(in pence) are available: 1, 2, 5, 10, 20, 50, 100 and 200.
> how many different ways you can combine these values to create 200 pence.
Example:
to make 6p there are 5 ways
1,1,1,1,1,1
1,1,1,1,2
1,1,2,2
2,2,2
1,5
to make 5p there are 4 ways
1,1,1,1,1
1,1,1,2
1,2,2
5
"""
def solution(pence: int = 200) -> int:
"""Returns the number of different ways to make X pence using any number of coins.
The solution is based on dynamic programming paradigm in a bottom-up fashion.
>>> solution(500)
6295434
>>> solution(200)
73682
>>> solution(50)
451
>>> solution(10)
11
"""
coins = [1, 2, 5, 10, 20, 50, 100, 200]
number_of_ways = [0] * (pence + 1)
number_of_ways[0] = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(coin, pence + 1, 1):
number_of_ways[i] += number_of_ways[i - coin]
return number_of_ways[pence]
if __name__ == "__main__":
assert solution(200) == 73682
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """Convert a Decimal Number to a Binary Number."""
def decimal_to_binary(num: int) -> str:
"""
Convert an Integer Decimal Number to a Binary Number as str.
>>> decimal_to_binary(0)
'0b0'
>>> decimal_to_binary(2)
'0b10'
>>> decimal_to_binary(7)
'0b111'
>>> decimal_to_binary(35)
'0b100011'
>>> # negatives work too
>>> decimal_to_binary(-2)
'-0b10'
>>> # other floats will error
>>> decimal_to_binary(16.16) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
>>> # strings will error as well
>>> decimal_to_binary('0xfffff') # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: 'str' object cannot be interpreted as an integer
"""
if isinstance(num, float):
raise TypeError("'float' object cannot be interpreted as an integer")
if isinstance(num, str):
raise TypeError("'str' object cannot be interpreted as an integer")
if num == 0:
return "0b0"
negative = False
if num < 0:
negative = True
num = -num
binary: list[int] = []
while num > 0:
binary.insert(0, num % 2)
num >>= 1
if negative:
return "-0b" + "".join(str(e) for e in binary)
return "0b" + "".join(str(e) for e in binary)
if __name__ == "__main__":
import doctest
doctest.testmod()
| """Convert a Decimal Number to a Binary Number."""
def decimal_to_binary(num: int) -> str:
"""
Convert an Integer Decimal Number to a Binary Number as str.
>>> decimal_to_binary(0)
'0b0'
>>> decimal_to_binary(2)
'0b10'
>>> decimal_to_binary(7)
'0b111'
>>> decimal_to_binary(35)
'0b100011'
>>> # negatives work too
>>> decimal_to_binary(-2)
'-0b10'
>>> # other floats will error
>>> decimal_to_binary(16.16) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
>>> # strings will error as well
>>> decimal_to_binary('0xfffff') # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: 'str' object cannot be interpreted as an integer
"""
if isinstance(num, float):
raise TypeError("'float' object cannot be interpreted as an integer")
if isinstance(num, str):
raise TypeError("'str' object cannot be interpreted as an integer")
if num == 0:
return "0b0"
negative = False
if num < 0:
negative = True
num = -num
binary: list[int] = []
while num > 0:
binary.insert(0, num % 2)
num >>= 1
if negative:
return "-0b" + "".join(str(e) for e in binary)
return "0b" + "".join(str(e) for e in binary)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | #!/usr/bin/env python3
"""
module to operations with prime numbers
"""
def check_prime(number):
"""
it's not the best solution
"""
special_non_primes = [0, 1, 2]
if number in special_non_primes[:2]:
return 2
elif number == special_non_primes[-1]:
return 3
return all([number % i for i in range(2, number)])
def next_prime(value, factor=1, **kwargs):
value = factor * value
first_value_val = value
while not check_prime(value):
value += 1 if not ("desc" in kwargs.keys() and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1, **kwargs)
return value
| #!/usr/bin/env python3
"""
module to operations with prime numbers
"""
def check_prime(number):
"""
it's not the best solution
"""
special_non_primes = [0, 1, 2]
if number in special_non_primes[:2]:
return 2
elif number == special_non_primes[-1]:
return 3
return all([number % i for i in range(2, number)])
def next_prime(value, factor=1, **kwargs):
value = factor * value
first_value_val = value
while not check_prime(value):
value += 1 if not ("desc" in kwargs.keys() and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1, **kwargs)
return value
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | # Fibonacci Sequence Using Recursion
def recur_fibo(n: int) -> int:
"""
>>> [recur_fibo(i) for i in range(12)]
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
"""
return n if n <= 1 else recur_fibo(n - 1) + recur_fibo(n - 2)
def main() -> None:
limit = int(input("How many terms to include in fibonacci series: "))
if limit > 0:
print(f"The first {limit} terms of the fibonacci series are as follows:")
print([recur_fibo(n) for n in range(limit)])
else:
print("Please enter a positive integer: ")
if __name__ == "__main__":
main()
| # Fibonacci Sequence Using Recursion
def recur_fibo(n: int) -> int:
"""
>>> [recur_fibo(i) for i in range(12)]
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
"""
return n if n <= 1 else recur_fibo(n - 1) + recur_fibo(n - 2)
def main() -> None:
limit = int(input("How many terms to include in fibonacci series: "))
if limit > 0:
print(f"The first {limit} terms of the fibonacci series are as follows:")
print([recur_fibo(n) for n in range(limit)])
else:
print("Please enter a positive integer: ")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | from sklearn import svm
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
# different functions implementing different types of SVM's
def NuSVC(train_x, train_y):
svc_NuSVC = svm.NuSVC()
svc_NuSVC.fit(train_x, train_y)
return svc_NuSVC
def Linearsvc(train_x, train_y):
svc_linear = svm.LinearSVC(tol=10e-2)
svc_linear.fit(train_x, train_y)
return svc_linear
def SVC(train_x, train_y):
# svm.SVC(C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True,
# probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False,
# max_iter=1000, random_state=None)
# various parameters like "kernel","gamma","C" can effectively tuned for a given
# machine learning model.
SVC = svm.SVC(gamma="auto")
SVC.fit(train_x, train_y)
return SVC
def test(X_new):
"""
3 test cases to be passed
an array containing the sepal length (cm), sepal width (cm), petal length (cm),
petal width (cm) based on which the target name will be predicted
>>> test([1,2,1,4])
'virginica'
>>> test([5, 2, 4, 1])
'versicolor'
>>> test([6,3,4,1])
'versicolor'
"""
iris = load_iris()
# splitting the dataset to test and train
train_x, test_x, train_y, test_y = train_test_split(
iris["data"], iris["target"], random_state=4
)
# any of the 3 types of SVM can be used
# current_model=SVC(train_x, train_y)
# current_model=NuSVC(train_x, train_y)
current_model = Linearsvc(train_x, train_y)
prediction = current_model.predict([X_new])
return iris["target_names"][prediction][0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| from sklearn import svm
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
# different functions implementing different types of SVM's
def NuSVC(train_x, train_y):
svc_NuSVC = svm.NuSVC()
svc_NuSVC.fit(train_x, train_y)
return svc_NuSVC
def Linearsvc(train_x, train_y):
svc_linear = svm.LinearSVC(tol=10e-2)
svc_linear.fit(train_x, train_y)
return svc_linear
def SVC(train_x, train_y):
# svm.SVC(C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True,
# probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False,
# max_iter=1000, random_state=None)
# various parameters like "kernel","gamma","C" can effectively tuned for a given
# machine learning model.
SVC = svm.SVC(gamma="auto")
SVC.fit(train_x, train_y)
return SVC
def test(X_new):
"""
3 test cases to be passed
an array containing the sepal length (cm), sepal width (cm), petal length (cm),
petal width (cm) based on which the target name will be predicted
>>> test([1,2,1,4])
'virginica'
>>> test([5, 2, 4, 1])
'versicolor'
>>> test([6,3,4,1])
'versicolor'
"""
iris = load_iris()
# splitting the dataset to test and train
train_x, test_x, train_y, test_y = train_test_split(
iris["data"], iris["target"], random_state=4
)
# any of the 3 types of SVM can be used
# current_model=SVC(train_x, train_y)
# current_model=NuSVC(train_x, train_y)
current_model = Linearsvc(train_x, train_y)
prediction = current_model.predict([X_new])
return iris["target_names"][prediction][0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | -1 |
||
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | def find_min(nums):
"""
Find Minimum Number in a List
:param nums: contains elements
:return: min number in list
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_min(nums) == min(nums)
True
True
True
True
"""
min_num = nums[0]
for num in nums:
if min_num > num:
min_num = num
return min_num
def main():
assert find_min([0, 1, 2, 3, 4, 5, -3, 24, -56]) == -56
if __name__ == "__main__":
main()
| def find_min(nums):
"""
Find Minimum Number in a List
:param nums: contains elements
:return: min number in list
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_min(nums) == min(nums)
True
True
True
True
"""
min_num = nums[0]
for num in nums:
if min_num > num:
min_num = num
return min_num
def main():
assert find_min([0, 1, 2, 3, 4, 5, -3, 24, -56]) == -56
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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 i decided to call it "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 i decided to call it "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 | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | -1 |
||
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | from __future__ import annotations
from typing import Generic, TypeVar
T = TypeVar("T")
class DisjointSetTreeNode(Generic[T]):
# Disjoint Set Node to store the parent and rank
def __init__(self, data: T) -> None:
self.data = data
self.parent = self
self.rank = 0
class DisjointSetTree(Generic[T]):
# Disjoint Set DataStructure
def __init__(self) -> None:
# map from node name to the node object
self.map: dict[T, DisjointSetTreeNode[T]] = {}
def make_set(self, data: T) -> None:
# create a new set with x as its member
self.map[data] = DisjointSetTreeNode(data)
def find_set(self, data: T) -> DisjointSetTreeNode[T]:
# find the set x belongs to (with path-compression)
elem_ref = self.map[data]
if elem_ref != elem_ref.parent:
elem_ref.parent = self.find_set(elem_ref.parent.data)
return elem_ref.parent
def link(
self, node1: DisjointSetTreeNode[T], node2: DisjointSetTreeNode[T]
) -> None:
# helper function for union operation
if node1.rank > node2.rank:
node2.parent = node1
else:
node1.parent = node2
if node1.rank == node2.rank:
node2.rank += 1
def union(self, data1: T, data2: T) -> None:
# merge 2 disjoint sets
self.link(self.find_set(data1), self.find_set(data2))
class GraphUndirectedWeighted(Generic[T]):
def __init__(self) -> None:
# connections: map from the node to the neighbouring nodes (with weights)
self.connections: dict[T, dict[T, int]] = {}
def add_node(self, node: T) -> None:
# add a node ONLY if its not present in the graph
if node not in self.connections:
self.connections[node] = {}
def add_edge(self, node1: T, node2: T, weight: int) -> None:
# add an edge with the given weight
self.add_node(node1)
self.add_node(node2)
self.connections[node1][node2] = weight
self.connections[node2][node1] = weight
def kruskal(self) -> GraphUndirectedWeighted[T]:
# Kruskal's Algorithm to generate a Minimum Spanning Tree (MST) of a graph
"""
Details: https://en.wikipedia.org/wiki/Kruskal%27s_algorithm
Example:
>>> g1 = GraphUndirectedWeighted[int]()
>>> g1.add_edge(1, 2, 1)
>>> g1.add_edge(2, 3, 2)
>>> g1.add_edge(3, 4, 1)
>>> g1.add_edge(3, 5, 100) # Removed in MST
>>> g1.add_edge(4, 5, 5)
>>> assert 5 in g1.connections[3]
>>> mst = g1.kruskal()
>>> assert 5 not in mst.connections[3]
>>> g2 = GraphUndirectedWeighted[str]()
>>> g2.add_edge('A', 'B', 1)
>>> g2.add_edge('B', 'C', 2)
>>> g2.add_edge('C', 'D', 1)
>>> g2.add_edge('C', 'E', 100) # Removed in MST
>>> g2.add_edge('D', 'E', 5)
>>> assert 'E' in g2.connections["C"]
>>> mst = g2.kruskal()
>>> assert 'E' not in mst.connections['C']
"""
# getting the edges in ascending order of weights
edges = []
seen = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start))
edges.append((start, end, self.connections[start][end]))
edges.sort(key=lambda x: x[2])
# creating the disjoint set
disjoint_set = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(node)
# MST generation
num_edges = 0
index = 0
graph = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections) - 1:
u, v, w = edges[index]
index += 1
parent_u = disjoint_set.find_set(u)
parent_v = disjoint_set.find_set(v)
if parent_u != parent_v:
num_edges += 1
graph.add_edge(u, v, w)
disjoint_set.union(u, v)
return graph
| from __future__ import annotations
from typing import Generic, TypeVar
T = TypeVar("T")
class DisjointSetTreeNode(Generic[T]):
# Disjoint Set Node to store the parent and rank
def __init__(self, data: T) -> None:
self.data = data
self.parent = self
self.rank = 0
class DisjointSetTree(Generic[T]):
# Disjoint Set DataStructure
def __init__(self) -> None:
# map from node name to the node object
self.map: dict[T, DisjointSetTreeNode[T]] = {}
def make_set(self, data: T) -> None:
# create a new set with x as its member
self.map[data] = DisjointSetTreeNode(data)
def find_set(self, data: T) -> DisjointSetTreeNode[T]:
# find the set x belongs to (with path-compression)
elem_ref = self.map[data]
if elem_ref != elem_ref.parent:
elem_ref.parent = self.find_set(elem_ref.parent.data)
return elem_ref.parent
def link(
self, node1: DisjointSetTreeNode[T], node2: DisjointSetTreeNode[T]
) -> None:
# helper function for union operation
if node1.rank > node2.rank:
node2.parent = node1
else:
node1.parent = node2
if node1.rank == node2.rank:
node2.rank += 1
def union(self, data1: T, data2: T) -> None:
# merge 2 disjoint sets
self.link(self.find_set(data1), self.find_set(data2))
class GraphUndirectedWeighted(Generic[T]):
def __init__(self) -> None:
# connections: map from the node to the neighbouring nodes (with weights)
self.connections: dict[T, dict[T, int]] = {}
def add_node(self, node: T) -> None:
# add a node ONLY if its not present in the graph
if node not in self.connections:
self.connections[node] = {}
def add_edge(self, node1: T, node2: T, weight: int) -> None:
# add an edge with the given weight
self.add_node(node1)
self.add_node(node2)
self.connections[node1][node2] = weight
self.connections[node2][node1] = weight
def kruskal(self) -> GraphUndirectedWeighted[T]:
# Kruskal's Algorithm to generate a Minimum Spanning Tree (MST) of a graph
"""
Details: https://en.wikipedia.org/wiki/Kruskal%27s_algorithm
Example:
>>> g1 = GraphUndirectedWeighted[int]()
>>> g1.add_edge(1, 2, 1)
>>> g1.add_edge(2, 3, 2)
>>> g1.add_edge(3, 4, 1)
>>> g1.add_edge(3, 5, 100) # Removed in MST
>>> g1.add_edge(4, 5, 5)
>>> assert 5 in g1.connections[3]
>>> mst = g1.kruskal()
>>> assert 5 not in mst.connections[3]
>>> g2 = GraphUndirectedWeighted[str]()
>>> g2.add_edge('A', 'B', 1)
>>> g2.add_edge('B', 'C', 2)
>>> g2.add_edge('C', 'D', 1)
>>> g2.add_edge('C', 'E', 100) # Removed in MST
>>> g2.add_edge('D', 'E', 5)
>>> assert 'E' in g2.connections["C"]
>>> mst = g2.kruskal()
>>> assert 'E' not in mst.connections['C']
"""
# getting the edges in ascending order of weights
edges = []
seen = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start))
edges.append((start, end, self.connections[start][end]))
edges.sort(key=lambda x: x[2])
# creating the disjoint set
disjoint_set = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(node)
# MST generation
num_edges = 0
index = 0
graph = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections) - 1:
u, v, w = edges[index]
index += 1
parent_u = disjoint_set.find_set(u)
parent_v = disjoint_set.find_set(v)
if parent_u != parent_v:
num_edges += 1
graph.add_edge(u, v, w)
disjoint_set.union(u, v)
return graph
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | from collections import deque
from .hash_table import HashTable
class HashTableWithLinkedList(HashTable):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def _set_value(self, key, data):
self.values[key] = deque([]) if self.values[key] is None else self.values[key]
self.values[key].appendleft(data)
self._keys[key] = self.values[key]
def balanced_factor(self):
return (
sum([self.charge_factor - len(slot) for slot in self.values])
/ self.size_table
* self.charge_factor
)
def _collision_resolution(self, key, data=None):
if not (
len(self.values[key]) == self.charge_factor and self.values.count(None) == 0
):
return key
return super()._collision_resolution(key, data)
| from collections import deque
from .hash_table import HashTable
class HashTableWithLinkedList(HashTable):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def _set_value(self, key, data):
self.values[key] = deque([]) if self.values[key] is None else self.values[key]
self.values[key].appendleft(data)
self._keys[key] = self.values[key]
def balanced_factor(self):
return (
sum([self.charge_factor - len(slot) for slot in self.values])
/ self.size_table
* self.charge_factor
)
def _collision_resolution(self, key, data=None):
if not (
len(self.values[key]) == self.charge_factor and self.values.count(None) == 0
):
return key
return super()._collision_resolution(key, data)
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
Reference: https://en.wikipedia.org/wiki/Gaussian_function
"""
from numpy import exp, pi, sqrt
def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int:
"""
>>> gaussian(1)
0.24197072451914337
>>> gaussian(24)
3.342714441794458e-126
>>> gaussian(1, 4, 2)
0.06475879783294587
>>> gaussian(1, 5, 3)
0.05467002489199788
Supports NumPy Arrays
Use numpy.meshgrid with this to generate gaussian blur on images.
>>> import numpy as np
>>> x = np.arange(15)
>>> gaussian(x)
array([3.98942280e-01, 2.41970725e-01, 5.39909665e-02, 4.43184841e-03,
1.33830226e-04, 1.48671951e-06, 6.07588285e-09, 9.13472041e-12,
5.05227108e-15, 1.02797736e-18, 7.69459863e-23, 2.11881925e-27,
2.14638374e-32, 7.99882776e-38, 1.09660656e-43])
>>> gaussian(15)
5.530709549844416e-50
>>> gaussian([1,2, 'string'])
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for -: 'list' and 'float'
>>> gaussian('hello world')
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for -: 'str' and 'float'
>>> gaussian(10**234) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
OverflowError: (34, 'Result too large')
>>> gaussian(10**-326)
0.3989422804014327
>>> gaussian(2523, mu=234234, sigma=3425)
0.0
"""
return 1 / sqrt(2 * pi * sigma ** 2) * exp(-((x - mu) ** 2) / (2 * sigma ** 2))
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Reference: https://en.wikipedia.org/wiki/Gaussian_function
"""
from numpy import exp, pi, sqrt
def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int:
"""
>>> gaussian(1)
0.24197072451914337
>>> gaussian(24)
3.342714441794458e-126
>>> gaussian(1, 4, 2)
0.06475879783294587
>>> gaussian(1, 5, 3)
0.05467002489199788
Supports NumPy Arrays
Use numpy.meshgrid with this to generate gaussian blur on images.
>>> import numpy as np
>>> x = np.arange(15)
>>> gaussian(x)
array([3.98942280e-01, 2.41970725e-01, 5.39909665e-02, 4.43184841e-03,
1.33830226e-04, 1.48671951e-06, 6.07588285e-09, 9.13472041e-12,
5.05227108e-15, 1.02797736e-18, 7.69459863e-23, 2.11881925e-27,
2.14638374e-32, 7.99882776e-38, 1.09660656e-43])
>>> gaussian(15)
5.530709549844416e-50
>>> gaussian([1,2, 'string'])
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for -: 'list' and 'float'
>>> gaussian('hello world')
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for -: 'str' and 'float'
>>> gaussian(10**234) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
OverflowError: (34, 'Result too large')
>>> gaussian(10**-326)
0.3989422804014327
>>> gaussian(2523, mu=234234, sigma=3425)
0.0
"""
return 1 / sqrt(2 * pi * sigma ** 2) * exp(-((x - mu) ** 2) / (2 * sigma ** 2))
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | def aliquot_sum(input_num: int) -> int:
"""
Finds the aliquot sum of an input integer, where the
aliquot sum of a number n is defined as the sum of all
natural numbers less than n that divide n evenly. For
example, the aliquot sum of 15 is 1 + 3 + 5 = 9. This is
a simple O(n) implementation.
@param input_num: a positive integer whose aliquot sum is to be found
@return: the aliquot sum of input_num, if input_num is positive.
Otherwise, raise a ValueError
Wikipedia Explanation: https://en.wikipedia.org/wiki/Aliquot_sum
>>> aliquot_sum(15)
9
>>> aliquot_sum(6)
6
>>> aliquot_sum(-1)
Traceback (most recent call last):
...
ValueError: Input must be positive
>>> aliquot_sum(0)
Traceback (most recent call last):
...
ValueError: Input must be positive
>>> aliquot_sum(1.6)
Traceback (most recent call last):
...
ValueError: Input must be an integer
>>> aliquot_sum(12)
16
>>> aliquot_sum(1)
0
>>> aliquot_sum(19)
1
"""
if not isinstance(input_num, int):
raise ValueError("Input must be an integer")
if input_num <= 0:
raise ValueError("Input must be positive")
return sum(
divisor for divisor in range(1, input_num // 2 + 1) if input_num % divisor == 0
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| def aliquot_sum(input_num: int) -> int:
"""
Finds the aliquot sum of an input integer, where the
aliquot sum of a number n is defined as the sum of all
natural numbers less than n that divide n evenly. For
example, the aliquot sum of 15 is 1 + 3 + 5 = 9. This is
a simple O(n) implementation.
@param input_num: a positive integer whose aliquot sum is to be found
@return: the aliquot sum of input_num, if input_num is positive.
Otherwise, raise a ValueError
Wikipedia Explanation: https://en.wikipedia.org/wiki/Aliquot_sum
>>> aliquot_sum(15)
9
>>> aliquot_sum(6)
6
>>> aliquot_sum(-1)
Traceback (most recent call last):
...
ValueError: Input must be positive
>>> aliquot_sum(0)
Traceback (most recent call last):
...
ValueError: Input must be positive
>>> aliquot_sum(1.6)
Traceback (most recent call last):
...
ValueError: Input must be an integer
>>> aliquot_sum(12)
16
>>> aliquot_sum(1)
0
>>> aliquot_sum(19)
1
"""
if not isinstance(input_num, int):
raise ValueError("Input must be an integer")
if input_num <= 0:
raise ValueError("Input must be positive")
return sum(
divisor for divisor in range(1, input_num // 2 + 1) if input_num % divisor == 0
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | https://docs.python.org/3/reference/expressions.html#binary-bitwise-operations
https://docs.python.org/3/reference/expressions.html#unary-arithmetic-and-bitwise-operations
https://docs.python.org/3/library/stdtypes.html#bitwise-operations-on-integer-types
https://wiki.python.org/moin/BitManipulation
https://wiki.python.org/moin/BitwiseOperators
https://www.tutorialspoint.com/python3/bitwise_operators_example.htm
| https://docs.python.org/3/reference/expressions.html#binary-bitwise-operations
https://docs.python.org/3/reference/expressions.html#unary-arithmetic-and-bitwise-operations
https://docs.python.org/3/library/stdtypes.html#bitwise-operations-on-integer-types
https://wiki.python.org/moin/BitManipulation
https://wiki.python.org/moin/BitwiseOperators
https://www.tutorialspoint.com/python3/bitwise_operators_example.htm
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | import sys
"""
Dynamic Programming
Implementation of Matrix Chain Multiplication
Time Complexity: O(n^3)
Space Complexity: O(n^2)
"""
def MatrixChainOrder(array):
N = len(array)
Matrix = [[0 for x in range(N)] for x in range(N)]
Sol = [[0 for x in range(N)] for x in range(N)]
for ChainLength in range(2, N):
for a in range(1, N - ChainLength + 1):
b = a + ChainLength - 1
Matrix[a][b] = sys.maxsize
for c in range(a, b):
cost = (
Matrix[a][c] + Matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
)
if cost < Matrix[a][b]:
Matrix[a][b] = cost
Sol[a][b] = c
return Matrix, Sol
# Print order of matrix with Ai as Matrix
def PrintOptimalSolution(OptimalSolution, i, j):
if i == j:
print("A" + str(i), end=" ")
else:
print("(", end=" ")
PrintOptimalSolution(OptimalSolution, i, OptimalSolution[i][j])
PrintOptimalSolution(OptimalSolution, OptimalSolution[i][j] + 1, j)
print(")", end=" ")
def main():
array = [30, 35, 15, 5, 10, 20, 25]
n = len(array)
# Size of matrix created from above array will be
# 30*35 35*15 15*5 5*10 10*20 20*25
Matrix, OptimalSolution = MatrixChainOrder(array)
print("No. of Operation required: " + str(Matrix[1][n - 1]))
PrintOptimalSolution(OptimalSolution, 1, n - 1)
if __name__ == "__main__":
main()
| import sys
"""
Dynamic Programming
Implementation of Matrix Chain Multiplication
Time Complexity: O(n^3)
Space Complexity: O(n^2)
"""
def MatrixChainOrder(array):
N = len(array)
Matrix = [[0 for x in range(N)] for x in range(N)]
Sol = [[0 for x in range(N)] for x in range(N)]
for ChainLength in range(2, N):
for a in range(1, N - ChainLength + 1):
b = a + ChainLength - 1
Matrix[a][b] = sys.maxsize
for c in range(a, b):
cost = (
Matrix[a][c] + Matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
)
if cost < Matrix[a][b]:
Matrix[a][b] = cost
Sol[a][b] = c
return Matrix, Sol
# Print order of matrix with Ai as Matrix
def PrintOptimalSolution(OptimalSolution, i, j):
if i == j:
print("A" + str(i), end=" ")
else:
print("(", end=" ")
PrintOptimalSolution(OptimalSolution, i, OptimalSolution[i][j])
PrintOptimalSolution(OptimalSolution, OptimalSolution[i][j] + 1, j)
print(")", end=" ")
def main():
array = [30, 35, 15, 5, 10, 20, 25]
n = len(array)
# Size of matrix created from above array will be
# 30*35 35*15 15*5 5*10 10*20 20*25
Matrix, OptimalSolution = MatrixChainOrder(array)
print("No. of Operation required: " + str(Matrix[1][n - 1]))
PrintOptimalSolution(OptimalSolution, 1, n - 1)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | -1 |
||
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
Peak signal-to-noise ratio - PSNR
https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
Source:
https://tutorials.techonical.com/how-to-calculate-psnr-value-of-two-images-using-python
"""
import math
import os
import cv2
import numpy as np
def psnr(original, contrast):
mse = np.mean((original - contrast) ** 2)
if mse == 0:
return 100
PIXEL_MAX = 255.0
PSNR = 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
return PSNR
def main():
dir_path = os.path.dirname(os.path.realpath(__file__))
# Loading images (original image and compressed image)
original = cv2.imread(os.path.join(dir_path, "image_data/original_image.png"))
contrast = cv2.imread(os.path.join(dir_path, "image_data/compressed_image.png"), 1)
original2 = cv2.imread(os.path.join(dir_path, "image_data/PSNR-example-base.png"))
contrast2 = cv2.imread(
os.path.join(dir_path, "image_data/PSNR-example-comp-10.jpg"), 1
)
# Value expected: 29.73dB
print("-- First Test --")
print(f"PSNR value is {psnr(original, contrast)} dB")
# # Value expected: 31.53dB (Wikipedia Example)
print("\n-- Second Test --")
print(f"PSNR value is {psnr(original2, contrast2)} dB")
if __name__ == "__main__":
main()
| """
Peak signal-to-noise ratio - PSNR
https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
Source:
https://tutorials.techonical.com/how-to-calculate-psnr-value-of-two-images-using-python
"""
import math
import os
import cv2
import numpy as np
def psnr(original, contrast):
mse = np.mean((original - contrast) ** 2)
if mse == 0:
return 100
PIXEL_MAX = 255.0
PSNR = 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
return PSNR
def main():
dir_path = os.path.dirname(os.path.realpath(__file__))
# Loading images (original image and compressed image)
original = cv2.imread(os.path.join(dir_path, "image_data/original_image.png"))
contrast = cv2.imread(os.path.join(dir_path, "image_data/compressed_image.png"), 1)
original2 = cv2.imread(os.path.join(dir_path, "image_data/PSNR-example-base.png"))
contrast2 = cv2.imread(
os.path.join(dir_path, "image_data/PSNR-example-comp-10.jpg"), 1
)
# Value expected: 29.73dB
print("-- First Test --")
print(f"PSNR value is {psnr(original, contrast)} dB")
# # Value expected: 31.53dB (Wikipedia Example)
print("\n-- Second Test --")
print(f"PSNR value is {psnr(original2, contrast2)} dB")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,641 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-20T13:50:02Z" | "2021-08-25T11:35:36Z" | 78a5d3a5587ef649c2b4d2286cfb949886095467 | 5e7eed610ce81fa96e033f4d2a1781ed8637cb41 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | 9caf4784aada17dc75348f77cc8c356df503c0f3 https://github.com/TheAlgorithms/Python
| 9caf4784aada17dc75348f77cc8c356df503c0f3 https://github.com/TheAlgorithms/Python
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | # Created by susmith98
from collections import Counter
from timeit import timeit
# Problem Description:
# Check if characters of the given string can be rearranged to form a palindrome.
# Counter is faster for long strings and non-Counter is faster for short strings.
def can_string_be_rearranged_as_palindrome_counter(
input_str: str = "",
) -> bool:
"""
A Palindrome is a String that reads the same forward as it does backwards.
Examples of Palindromes mom, dad, malayalam
>>> can_string_be_rearranged_as_palindrome_counter("Momo")
True
>>> can_string_be_rearranged_as_palindrome_counter("Mother")
False
>>> can_string_be_rearranged_as_palindrome_counter("Father")
False
>>> can_string_be_rearranged_as_palindrome_counter("A man a plan a canal Panama")
True
"""
return sum(c % 2 for c in Counter(input_str.replace(" ", "").lower()).values()) < 2
def can_string_be_rearranged_as_palindrome(input_str: str = "") -> bool:
"""
A Palindrome is a String that reads the same forward as it does backwards.
Examples of Palindromes mom, dad, malayalam
>>> can_string_be_rearranged_as_palindrome("Momo")
True
>>> can_string_be_rearranged_as_palindrome("Mother")
False
>>> can_string_be_rearranged_as_palindrome("Father")
False
>>> can_string_be_rearranged_as_palindrome_counter("A man a plan a canal Panama")
True
"""
if len(input_str) == 0:
return True
lower_case_input_str = input_str.replace(" ", "").lower()
# character_freq_dict: Stores the frequency of every character in the input string
character_freq_dict = {}
for character in lower_case_input_str:
character_freq_dict[character] = character_freq_dict.get(character, 0) + 1
"""
Above line of code is equivalent to:
1) Getting the frequency of current character till previous index
>>> character_freq = character_freq_dict.get(character, 0)
2) Incrementing the frequency of current character by 1
>>> character_freq = character_freq + 1
3) Updating the frequency of current character
>>> character_freq_dict[character] = character_freq
"""
"""
OBSERVATIONS:
Even length palindrome
-> Every character appears even no.of times.
Odd length palindrome
-> Every character appears even no.of times except for one character.
LOGIC:
Step 1: We'll count number of characters that appear odd number of times i.e oddChar
Step 2:If we find more than 1 character that appears odd number of times,
It is not possible to rearrange as a palindrome
"""
oddChar = 0
for character_count in character_freq_dict.values():
if character_count % 2:
oddChar += 1
if oddChar > 1:
return False
return True
def benchmark(input_str: str = "") -> None:
"""
Benchmark code for comparing above 2 functions
"""
print("\nFor string = ", input_str, ":")
print(
"> can_string_be_rearranged_as_palindrome_counter()",
"\tans =",
can_string_be_rearranged_as_palindrome_counter(input_str),
"\ttime =",
timeit(
"z.can_string_be_rearranged_as_palindrome_counter(z.check_str)",
setup="import __main__ as z",
),
"seconds",
)
print(
"> can_string_be_rearranged_as_palindrome()",
"\tans =",
can_string_be_rearranged_as_palindrome(input_str),
"\ttime =",
timeit(
"z.can_string_be_rearranged_as_palindrome(z.check_str)",
setup="import __main__ as z",
),
"seconds",
)
if __name__ == "__main__":
check_str = input(
"Enter string to determine if it can be rearranged as a palindrome or not: "
).strip()
benchmark(check_str)
status = can_string_be_rearranged_as_palindrome_counter(check_str)
print(f"{check_str} can {'' if status else 'not '}be rearranged as a palindrome")
| # Created by susmith98
from collections import Counter
from timeit import timeit
# Problem Description:
# Check if characters of the given string can be rearranged to form a palindrome.
# Counter is faster for long strings and non-Counter is faster for short strings.
def can_string_be_rearranged_as_palindrome_counter(
input_str: str = "",
) -> bool:
"""
A Palindrome is a String that reads the same forward as it does backwards.
Examples of Palindromes mom, dad, malayalam
>>> can_string_be_rearranged_as_palindrome_counter("Momo")
True
>>> can_string_be_rearranged_as_palindrome_counter("Mother")
False
>>> can_string_be_rearranged_as_palindrome_counter("Father")
False
>>> can_string_be_rearranged_as_palindrome_counter("A man a plan a canal Panama")
True
"""
return sum(c % 2 for c in Counter(input_str.replace(" ", "").lower()).values()) < 2
def can_string_be_rearranged_as_palindrome(input_str: str = "") -> bool:
"""
A Palindrome is a String that reads the same forward as it does backwards.
Examples of Palindromes mom, dad, malayalam
>>> can_string_be_rearranged_as_palindrome("Momo")
True
>>> can_string_be_rearranged_as_palindrome("Mother")
False
>>> can_string_be_rearranged_as_palindrome("Father")
False
>>> can_string_be_rearranged_as_palindrome_counter("A man a plan a canal Panama")
True
"""
if len(input_str) == 0:
return True
lower_case_input_str = input_str.replace(" ", "").lower()
# character_freq_dict: Stores the frequency of every character in the input string
character_freq_dict: dict[str, int] = {}
for character in lower_case_input_str:
character_freq_dict[character] = character_freq_dict.get(character, 0) + 1
"""
Above line of code is equivalent to:
1) Getting the frequency of current character till previous index
>>> character_freq = character_freq_dict.get(character, 0)
2) Incrementing the frequency of current character by 1
>>> character_freq = character_freq + 1
3) Updating the frequency of current character
>>> character_freq_dict[character] = character_freq
"""
"""
OBSERVATIONS:
Even length palindrome
-> Every character appears even no.of times.
Odd length palindrome
-> Every character appears even no.of times except for one character.
LOGIC:
Step 1: We'll count number of characters that appear odd number of times i.e oddChar
Step 2:If we find more than 1 character that appears odd number of times,
It is not possible to rearrange as a palindrome
"""
oddChar = 0
for character_count in character_freq_dict.values():
if character_count % 2:
oddChar += 1
if oddChar > 1:
return False
return True
def benchmark(input_str: str = "") -> None:
"""
Benchmark code for comparing above 2 functions
"""
print("\nFor string = ", input_str, ":")
print(
"> can_string_be_rearranged_as_palindrome_counter()",
"\tans =",
can_string_be_rearranged_as_palindrome_counter(input_str),
"\ttime =",
timeit(
"z.can_string_be_rearranged_as_palindrome_counter(z.check_str)",
setup="import __main__ as z",
),
"seconds",
)
print(
"> can_string_be_rearranged_as_palindrome()",
"\tans =",
can_string_be_rearranged_as_palindrome(input_str),
"\ttime =",
timeit(
"z.can_string_be_rearranged_as_palindrome(z.check_str)",
setup="import __main__ as z",
),
"seconds",
)
if __name__ == "__main__":
check_str = input(
"Enter string to determine if it can be rearranged as a palindrome or not: "
).strip()
benchmark(check_str)
status = can_string_be_rearranged_as_palindrome_counter(check_str)
print(f"{check_str} can {'' if status else 'not '}be rearranged as a palindrome")
| 1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
This is a Python implementation of the levenshtein distance.
Levenshtein distance is a string metric for measuring the
difference between two sequences.
For doctests run following command:
python -m doctest -v levenshtein-distance.py
or
python3 -m doctest -v levenshtein-distance.py
For manual testing run:
python levenshtein-distance.py
"""
def levenshtein_distance(first_word: str, second_word: str) -> int:
"""Implementation of the levenshtein distance in Python.
:param first_word: the first word to measure the difference.
:param second_word: the second word to measure the difference.
:return: the levenshtein distance between the two words.
Examples:
>>> levenshtein_distance("planet", "planetary")
3
>>> levenshtein_distance("", "test")
4
>>> levenshtein_distance("book", "back")
2
>>> levenshtein_distance("book", "book")
0
>>> levenshtein_distance("test", "")
4
>>> levenshtein_distance("", "")
0
>>> levenshtein_distance("orchestration", "container")
10
"""
# The longer word should come first
if len(first_word) < len(second_word):
return levenshtein_distance(second_word, first_word)
if len(second_word) == 0:
return len(first_word)
previous_row = range(len(second_word) + 1)
for i, c1 in enumerate(first_word):
current_row = [i + 1]
for j, c2 in enumerate(second_word):
# Calculate insertions, deletions and substitutions
insertions = previous_row[j + 1] + 1
deletions = current_row[j] + 1
substitutions = previous_row[j] + (c1 != c2)
# Get the minimum to append to the current row
current_row.append(min(insertions, deletions, substitutions))
# Store the previous row
previous_row = current_row
# Returns the last element (distance)
return previous_row[-1]
if __name__ == "__main__":
first_word = input("Enter the first word:\n").strip()
second_word = input("Enter the second word:\n").strip()
result = levenshtein_distance(first_word, second_word)
print(f"Levenshtein distance between {first_word} and {second_word} is {result}")
| """
This is a Python implementation of the levenshtein distance.
Levenshtein distance is a string metric for measuring the
difference between two sequences.
For doctests run following command:
python -m doctest -v levenshtein-distance.py
or
python3 -m doctest -v levenshtein-distance.py
For manual testing run:
python levenshtein-distance.py
"""
def levenshtein_distance(first_word: str, second_word: str) -> int:
"""Implementation of the levenshtein distance in Python.
:param first_word: the first word to measure the difference.
:param second_word: the second word to measure the difference.
:return: the levenshtein distance between the two words.
Examples:
>>> levenshtein_distance("planet", "planetary")
3
>>> levenshtein_distance("", "test")
4
>>> levenshtein_distance("book", "back")
2
>>> levenshtein_distance("book", "book")
0
>>> levenshtein_distance("test", "")
4
>>> levenshtein_distance("", "")
0
>>> levenshtein_distance("orchestration", "container")
10
"""
# The longer word should come first
if len(first_word) < len(second_word):
return levenshtein_distance(second_word, first_word)
if len(second_word) == 0:
return len(first_word)
previous_row = list(range(len(second_word) + 1))
for i, c1 in enumerate(first_word):
current_row = [i + 1]
for j, c2 in enumerate(second_word):
# Calculate insertions, deletions and substitutions
insertions = previous_row[j + 1] + 1
deletions = current_row[j] + 1
substitutions = previous_row[j] + (c1 != c2)
# Get the minimum to append to the current row
current_row.append(min(insertions, deletions, substitutions))
# Store the previous row
previous_row = current_row
# Returns the last element (distance)
return previous_row[-1]
if __name__ == "__main__":
first_word = input("Enter the first word:\n").strip()
second_word = input("Enter the second word:\n").strip()
result = levenshtein_distance(first_word, second_word)
print(f"Levenshtein distance between {first_word} and {second_word} is {result}")
| 1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | # Created by sarathkaul on 17/11/19
# Modified by Arkadip Bhattacharya(@darkmatter18) on 20/04/2020
from collections import defaultdict
def word_occurence(sentence: str) -> dict:
"""
>>> from collections import Counter
>>> SENTENCE = "a b A b c b d b d e f e g e h e i e j e 0"
>>> occurence_dict = word_occurence(SENTENCE)
>>> all(occurence_dict[word] == count for word, count
... in Counter(SENTENCE.split()).items())
True
>>> dict(word_occurence("Two spaces"))
{'Two': 1, 'spaces': 1}
"""
occurrence = defaultdict(int)
# Creating a dictionary containing count of each word
for word in sentence.split():
occurrence[word] += 1
return occurrence
if __name__ == "__main__":
for word, count in word_occurence("INPUT STRING").items():
print(f"{word}: {count}")
| # Created by sarathkaul on 17/11/19
# Modified by Arkadip Bhattacharya(@darkmatter18) on 20/04/2020
from collections import defaultdict
def word_occurence(sentence: str) -> dict:
"""
>>> from collections import Counter
>>> SENTENCE = "a b A b c b d b d e f e g e h e i e j e 0"
>>> occurence_dict = word_occurence(SENTENCE)
>>> all(occurence_dict[word] == count for word, count
... in Counter(SENTENCE.split()).items())
True
>>> dict(word_occurence("Two spaces"))
{'Two': 1, 'spaces': 1}
"""
occurrence: dict = defaultdict(int)
# Creating a dictionary containing count of each word
for word in sentence.split():
occurrence[word] += 1
return occurrence
if __name__ == "__main__":
for word, count in word_occurence("INPUT STRING").items():
print(f"{word}: {count}")
| 1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | def get_word_pattern(word: str) -> str:
"""
>>> get_word_pattern("pattern")
'0.1.2.2.3.4.5'
>>> get_word_pattern("word pattern")
'0.1.2.3.4.5.6.7.7.8.2.9'
>>> get_word_pattern("get word pattern")
'0.1.2.3.4.5.6.7.3.8.9.2.2.1.6.10'
"""
word = word.upper()
next_num = 0
letter_nums = {}
word_pattern = []
for letter in word:
if letter not in letter_nums:
letter_nums[letter] = str(next_num)
next_num += 1
word_pattern.append(letter_nums[letter])
return ".".join(word_pattern)
if __name__ == "__main__":
import pprint
import time
start_time = time.time()
with open("dictionary.txt") as in_file:
wordList = in_file.read().splitlines()
all_patterns = {}
for word in wordList:
pattern = get_word_pattern(word)
if pattern in all_patterns:
all_patterns[pattern].append(word)
else:
all_patterns[pattern] = [word]
with open("word_patterns.txt", "w") as out_file:
out_file.write(pprint.pformat(all_patterns))
totalTime = round(time.time() - start_time, 2)
print(f"Done! {len(all_patterns):,} word patterns found in {totalTime} seconds.")
# Done! 9,581 word patterns found in 0.58 seconds.
| def get_word_pattern(word: str) -> str:
"""
>>> get_word_pattern("pattern")
'0.1.2.2.3.4.5'
>>> get_word_pattern("word pattern")
'0.1.2.3.4.5.6.7.7.8.2.9'
>>> get_word_pattern("get word pattern")
'0.1.2.3.4.5.6.7.3.8.9.2.2.1.6.10'
"""
word = word.upper()
next_num = 0
letter_nums = {}
word_pattern = []
for letter in word:
if letter not in letter_nums:
letter_nums[letter] = str(next_num)
next_num += 1
word_pattern.append(letter_nums[letter])
return ".".join(word_pattern)
if __name__ == "__main__":
import pprint
import time
start_time = time.time()
with open("dictionary.txt") as in_file:
wordList = in_file.read().splitlines()
all_patterns: dict = {}
for word in wordList:
pattern = get_word_pattern(word)
if pattern in all_patterns:
all_patterns[pattern].append(word)
else:
all_patterns[pattern] = [word]
with open("word_patterns.txt", "w") as out_file:
out_file.write(pprint.pformat(all_patterns))
totalTime = round(time.time() - start_time, 2)
print(f"Done! {len(all_patterns):,} word patterns found in {totalTime} seconds.")
# Done! 9,581 word patterns found in 0.58 seconds.
| 1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | -1 |
||
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
ARITHMETIC MEAN : https://en.wikipedia.org/wiki/Arithmetic_mean
"""
def is_arithmetic_series(series: list) -> bool:
"""
checking whether the input series is arithmetic series or not
>>> is_arithmetic_series([2, 4, 6])
True
>>> is_arithmetic_series([3, 6, 12, 24])
False
>>> is_arithmetic_series([1, 2, 3])
True
"""
if len(series) == 1:
return True
common_diff = series[1] - series[0]
for index in range(len(series) - 1):
if series[index + 1] - series[index] != common_diff:
return False
return True
def arithmetic_mean(series: list) -> float:
"""
return the arithmetic mean of series
>>> arithmetic_mean([2, 4, 6])
4.0
>>> arithmetic_mean([3, 6, 9, 12])
7.5
>>> arithmetic_mean(4)
Traceback (most recent call last):
...
ValueError: Input series is not valid, valid series - [2, 4, 6]
>>> arithmetic_mean([4, 8, 1])
Traceback (most recent call last):
...
ValueError: Input list is not an arithmetic series
>>> arithmetic_mean([1, 2, 3])
2.0
>>> arithmetic_mean([])
Traceback (most recent call last):
...
ValueError: Input list must be a non empty list
"""
if not isinstance(series, list):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]")
if len(series) == 0:
raise ValueError("Input list must be a non empty list")
if not is_arithmetic_series(series):
raise ValueError("Input list is not an arithmetic series")
answer = 0
for val in series:
answer += val
return answer / len(series)
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
ARITHMETIC MEAN : https://en.wikipedia.org/wiki/Arithmetic_mean
"""
def is_arithmetic_series(series: list) -> bool:
"""
checking whether the input series is arithmetic series or not
>>> is_arithmetic_series([2, 4, 6])
True
>>> is_arithmetic_series([3, 6, 12, 24])
False
>>> is_arithmetic_series([1, 2, 3])
True
"""
if len(series) == 1:
return True
common_diff = series[1] - series[0]
for index in range(len(series) - 1):
if series[index + 1] - series[index] != common_diff:
return False
return True
def arithmetic_mean(series: list) -> float:
"""
return the arithmetic mean of series
>>> arithmetic_mean([2, 4, 6])
4.0
>>> arithmetic_mean([3, 6, 9, 12])
7.5
>>> arithmetic_mean(4)
Traceback (most recent call last):
...
ValueError: Input series is not valid, valid series - [2, 4, 6]
>>> arithmetic_mean([4, 8, 1])
Traceback (most recent call last):
...
ValueError: Input list is not an arithmetic series
>>> arithmetic_mean([1, 2, 3])
2.0
>>> arithmetic_mean([])
Traceback (most recent call last):
...
ValueError: Input list must be a non empty list
"""
if not isinstance(series, list):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]")
if len(series) == 0:
raise ValueError("Input list must be a non empty list")
if not is_arithmetic_series(series):
raise ValueError("Input list is not an arithmetic series")
answer = 0
for val in series:
answer += val
return answer / len(series)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | -1 |
||
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | 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 | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | from __future__ import annotations
def stable_matching(
donor_pref: list[list[int]], recipient_pref: list[list[int]]
) -> list[int]:
"""
Finds the stable match in any bipartite graph, i.e a pairing where no 2 objects
prefer each other over their partner. The function accepts the preferences of
oegan donors and recipients (where both are assigned numbers from 0 to n-1) and
returns a list where the index position corresponds to the donor and value at the
index is the organ recipient.
To better understand the algorithm, see also:
https://github.com/akashvshroff/Gale_Shapley_Stable_Matching (README).
https://www.youtube.com/watch?v=Qcv1IqHWAzg&t=13s (Numberphile YouTube).
>>> donor_pref = [[0, 1, 3, 2], [0, 2, 3, 1], [1, 0, 2, 3], [0, 3, 1, 2]]
>>> recipient_pref = [[3, 1, 2, 0], [3, 1, 0, 2], [0, 3, 1, 2], [1, 0, 3, 2]]
>>> print(stable_matching(donor_pref, recipient_pref))
[1, 2, 3, 0]
"""
assert len(donor_pref) == len(recipient_pref)
n = len(donor_pref)
unmatched_donors = list(range(n))
donor_record = [-1] * n # who the donor has donated to
rec_record = [-1] * n # who the recipient has received from
num_donations = [0] * n
while unmatched_donors:
donor = unmatched_donors[0]
donor_preference = donor_pref[donor]
recipient = donor_preference[num_donations[donor]]
num_donations[donor] += 1
rec_preference = recipient_pref[recipient]
prev_donor = rec_record[recipient]
if prev_donor != -1:
if rec_preference.index(prev_donor) > rec_preference.index(donor):
rec_record[recipient] = donor
donor_record[donor] = recipient
unmatched_donors.append(prev_donor)
unmatched_donors.remove(donor)
else:
rec_record[recipient] = donor
donor_record[donor] = recipient
unmatched_donors.remove(donor)
return donor_record
| from __future__ import annotations
def stable_matching(
donor_pref: list[list[int]], recipient_pref: list[list[int]]
) -> list[int]:
"""
Finds the stable match in any bipartite graph, i.e a pairing where no 2 objects
prefer each other over their partner. The function accepts the preferences of
oegan donors and recipients (where both are assigned numbers from 0 to n-1) and
returns a list where the index position corresponds to the donor and value at the
index is the organ recipient.
To better understand the algorithm, see also:
https://github.com/akashvshroff/Gale_Shapley_Stable_Matching (README).
https://www.youtube.com/watch?v=Qcv1IqHWAzg&t=13s (Numberphile YouTube).
>>> donor_pref = [[0, 1, 3, 2], [0, 2, 3, 1], [1, 0, 2, 3], [0, 3, 1, 2]]
>>> recipient_pref = [[3, 1, 2, 0], [3, 1, 0, 2], [0, 3, 1, 2], [1, 0, 3, 2]]
>>> print(stable_matching(donor_pref, recipient_pref))
[1, 2, 3, 0]
"""
assert len(donor_pref) == len(recipient_pref)
n = len(donor_pref)
unmatched_donors = list(range(n))
donor_record = [-1] * n # who the donor has donated to
rec_record = [-1] * n # who the recipient has received from
num_donations = [0] * n
while unmatched_donors:
donor = unmatched_donors[0]
donor_preference = donor_pref[donor]
recipient = donor_preference[num_donations[donor]]
num_donations[donor] += 1
rec_preference = recipient_pref[recipient]
prev_donor = rec_record[recipient]
if prev_donor != -1:
if rec_preference.index(prev_donor) > rec_preference.index(donor):
rec_record[recipient] = donor
donor_record[donor] = recipient
unmatched_donors.append(prev_donor)
unmatched_donors.remove(donor)
else:
rec_record[recipient] = donor
donor_record[donor] = recipient
unmatched_donors.remove(donor)
return donor_record
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
Gaussian elimination method for solving a system of linear equations.
Gaussian elimination - https://en.wikipedia.org/wiki/Gaussian_elimination
"""
import numpy as np
def retroactive_resolution(coefficients: np.matrix, vector: np.ndarray) -> np.ndarray:
"""
This function performs a retroactive linear system resolution
for triangular matrix
Examples:
2x1 + 2x2 - 1x3 = 5 2x1 + 2x2 = -1
0x1 - 2x2 - 1x3 = -7 0x1 - 2x2 = -1
0x1 + 0x2 + 5x3 = 15
>>> gaussian_elimination([[2, 2, -1], [0, -2, -1], [0, 0, 5]], [[5], [-7], [15]])
array([[2.],
[2.],
[3.]])
>>> gaussian_elimination([[2, 2], [0, -2]], [[-1], [-1]])
array([[-1. ],
[ 0.5]])
"""
rows, columns = np.shape(coefficients)
x = np.zeros((rows, 1), dtype=float)
for row in reversed(range(rows)):
sum = 0
for col in range(row + 1, columns):
sum += coefficients[row, col] * x[col]
x[row, 0] = (vector[row] - sum) / coefficients[row, row]
return x
def gaussian_elimination(coefficients: np.matrix, vector: np.ndarray) -> np.ndarray:
"""
This function performs Gaussian elimination method
Examples:
1x1 - 4x2 - 2x3 = -2 1x1 + 2x2 = 5
5x1 + 2x2 - 2x3 = -3 5x1 + 2x2 = 5
1x1 - 1x2 + 0x3 = 4
>>> gaussian_elimination([[1, -4, -2], [5, 2, -2], [1, -1, 0]], [[-2], [-3], [4]])
array([[ 2.3 ],
[-1.7 ],
[ 5.55]])
>>> gaussian_elimination([[1, 2], [5, 2]], [[5], [5]])
array([[0. ],
[2.5]])
"""
# coefficients must to be a square matrix so we need to check first
rows, columns = np.shape(coefficients)
if rows != columns:
return np.array((), dtype=float)
# augmented matrix
augmented_mat = np.concatenate((coefficients, vector), axis=1)
augmented_mat = augmented_mat.astype("float64")
# scale the matrix leaving it triangular
for row in range(rows - 1):
pivot = augmented_mat[row, row]
for col in range(row + 1, columns):
factor = augmented_mat[col, row] / pivot
augmented_mat[col, :] -= factor * augmented_mat[row, :]
x = retroactive_resolution(
augmented_mat[:, 0:columns], augmented_mat[:, columns : columns + 1]
)
return x
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Gaussian elimination method for solving a system of linear equations.
Gaussian elimination - https://en.wikipedia.org/wiki/Gaussian_elimination
"""
import numpy as np
def retroactive_resolution(coefficients: np.matrix, vector: np.ndarray) -> np.ndarray:
"""
This function performs a retroactive linear system resolution
for triangular matrix
Examples:
2x1 + 2x2 - 1x3 = 5 2x1 + 2x2 = -1
0x1 - 2x2 - 1x3 = -7 0x1 - 2x2 = -1
0x1 + 0x2 + 5x3 = 15
>>> gaussian_elimination([[2, 2, -1], [0, -2, -1], [0, 0, 5]], [[5], [-7], [15]])
array([[2.],
[2.],
[3.]])
>>> gaussian_elimination([[2, 2], [0, -2]], [[-1], [-1]])
array([[-1. ],
[ 0.5]])
"""
rows, columns = np.shape(coefficients)
x = np.zeros((rows, 1), dtype=float)
for row in reversed(range(rows)):
sum = 0
for col in range(row + 1, columns):
sum += coefficients[row, col] * x[col]
x[row, 0] = (vector[row] - sum) / coefficients[row, row]
return x
def gaussian_elimination(coefficients: np.matrix, vector: np.ndarray) -> np.ndarray:
"""
This function performs Gaussian elimination method
Examples:
1x1 - 4x2 - 2x3 = -2 1x1 + 2x2 = 5
5x1 + 2x2 - 2x3 = -3 5x1 + 2x2 = 5
1x1 - 1x2 + 0x3 = 4
>>> gaussian_elimination([[1, -4, -2], [5, 2, -2], [1, -1, 0]], [[-2], [-3], [4]])
array([[ 2.3 ],
[-1.7 ],
[ 5.55]])
>>> gaussian_elimination([[1, 2], [5, 2]], [[5], [5]])
array([[0. ],
[2.5]])
"""
# coefficients must to be a square matrix so we need to check first
rows, columns = np.shape(coefficients)
if rows != columns:
return np.array((), dtype=float)
# augmented matrix
augmented_mat = np.concatenate((coefficients, vector), axis=1)
augmented_mat = augmented_mat.astype("float64")
# scale the matrix leaving it triangular
for row in range(rows - 1):
pivot = augmented_mat[row, row]
for col in range(row + 1, columns):
factor = augmented_mat[col, row] / pivot
augmented_mat[col, :] -= factor * augmented_mat[row, :]
x = retroactive_resolution(
augmented_mat[:, 0:columns], augmented_mat[:, columns : columns + 1]
)
return x
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
Project Euler Problem 6: https://projecteuler.net/problem=6
Sum square difference
The sum of the squares of the first ten natural numbers is,
1^2 + 2^2 + ... + 10^2 = 385
The square of the sum of the first ten natural numbers is,
(1 + 2 + ... + 10)^2 = 55^2 = 3025
Hence the difference between the sum of the squares of the first ten
natural numbers and the square of the sum is 3025 - 385 = 2640.
Find the difference between the sum of the squares of the first one
hundred natural numbers and the square of the sum.
"""
def solution(n: int = 100) -> int:
"""
Returns the difference between the sum of the squares of the first n
natural numbers and the square of the sum.
>>> solution(10)
2640
>>> solution(15)
13160
>>> solution(20)
41230
>>> solution(50)
1582700
"""
sum_of_squares = n * (n + 1) * (2 * n + 1) / 6
square_of_sum = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 6: https://projecteuler.net/problem=6
Sum square difference
The sum of the squares of the first ten natural numbers is,
1^2 + 2^2 + ... + 10^2 = 385
The square of the sum of the first ten natural numbers is,
(1 + 2 + ... + 10)^2 = 55^2 = 3025
Hence the difference between the sum of the squares of the first ten
natural numbers and the square of the sum is 3025 - 385 = 2640.
Find the difference between the sum of the squares of the first one
hundred natural numbers and the square of the sum.
"""
def solution(n: int = 100) -> int:
"""
Returns the difference between the sum of the squares of the first n
natural numbers and the square of the sum.
>>> solution(10)
2640
>>> solution(15)
13160
>>> solution(20)
41230
>>> solution(50)
1582700
"""
sum_of_squares = n * (n + 1) * (2 * n + 1) / 6
square_of_sum = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | -1 |
||
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
Based on "Skip Lists: A Probabilistic Alternative to Balanced Trees" by William Pugh
https://epaperpress.com/sortsearch/download/skiplist.pdf
"""
from __future__ import annotations
from random import random
from typing import Generic, Optional, TypeVar
KT = TypeVar("KT")
VT = TypeVar("VT")
class Node(Generic[KT, VT]):
def __init__(self, key: KT, value: VT):
self.key = key
self.value = value
self.forward: list[Node[KT, VT]] = []
def __repr__(self) -> str:
"""
:return: Visual representation of Node
>>> node = Node("Key", 2)
>>> repr(node)
'Node(Key: 2)'
"""
return f"Node({self.key}: {self.value})"
@property
def level(self) -> int:
"""
:return: Number of forward references
>>> node = Node("Key", 2)
>>> node.level
0
>>> node.forward.append(Node("Key2", 4))
>>> node.level
1
>>> node.forward.append(Node("Key3", 6))
>>> node.level
2
"""
return len(self.forward)
class SkipList(Generic[KT, VT]):
def __init__(self, p: float = 0.5, max_level: int = 16):
self.head = Node("root", None)
self.level = 0
self.p = p
self.max_level = max_level
def __str__(self) -> str:
"""
:return: Visual representation of SkipList
>>> skip_list = SkipList()
>>> print(skip_list)
SkipList(level=0)
>>> skip_list.insert("Key1", "Value")
>>> print(skip_list) # doctest: +ELLIPSIS
SkipList(level=...
[root]--...
[Key1]--Key1...
None *...
>>> skip_list.insert("Key2", "OtherValue")
>>> print(skip_list) # doctest: +ELLIPSIS
SkipList(level=...
[root]--...
[Key1]--Key1...
[Key2]--Key2...
None *...
"""
items = list(self)
if len(items) == 0:
return f"SkipList(level={self.level})"
label_size = max((len(str(item)) for item in items), default=4)
label_size = max(label_size, 4) + 4
node = self.head
lines = []
forwards = node.forward.copy()
lines.append(f"[{node.key}]".ljust(label_size, "-") + "* " * len(forwards))
lines.append(" " * label_size + "| " * len(forwards))
while len(node.forward) != 0:
node = node.forward[0]
lines.append(
f"[{node.key}]".ljust(label_size, "-")
+ " ".join(str(n.key) if n.key == node.key else "|" for n in forwards)
)
lines.append(" " * label_size + "| " * len(forwards))
forwards[: node.level] = node.forward
lines.append("None".ljust(label_size) + "* " * len(forwards))
return f"SkipList(level={self.level})\n" + "\n".join(lines)
def __iter__(self):
node = self.head
while len(node.forward) != 0:
yield node.forward[0].key
node = node.forward[0]
def random_level(self) -> int:
"""
:return: Random level from [1, self.max_level] interval.
Higher values are less likely.
"""
level = 1
while random() < self.p and level < self.max_level:
level += 1
return level
def _locate_node(self, key) -> tuple[Optional[Node[KT, VT]], list[Node[KT, VT]]]:
"""
:param key: Searched key,
:return: Tuple with searched node (or None if given key is not present)
and list of nodes that refer (if key is present) of should refer to
given node.
"""
# Nodes with refer or should refer to output node
update_vector = []
node = self.head
for i in reversed(range(self.level)):
# i < node.level - When node level is lesser than `i` decrement `i`.
# node.forward[i].key < key - Jumping to node with key value higher
# or equal to searched key would result
# in skipping searched key.
while i < node.level and node.forward[i].key < key:
node = node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(node)
update_vector.reverse() # Note that we were inserting values in reverse order.
# len(node.forward) != 0 - If current node doesn't contain any further
# references then searched key is not present.
# node.forward[0].key == key - Next node key should be equal to search key
# if key is present.
if len(node.forward) != 0 and node.forward[0].key == key:
return node.forward[0], update_vector
else:
return None, update_vector
def delete(self, key: KT):
"""
:param key: Key to remove from list.
>>> skip_list = SkipList()
>>> skip_list.insert(2, "Two")
>>> skip_list.insert(1, "One")
>>> skip_list.insert(3, "Three")
>>> list(skip_list)
[1, 2, 3]
>>> skip_list.delete(2)
>>> list(skip_list)
[1, 3]
"""
node, update_vector = self._locate_node(key)
if node is not None:
for i, update_node in enumerate(update_vector):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
update_node.forward[i] = node.forward[i]
else:
update_node.forward = update_node.forward[:i]
def insert(self, key: KT, value: VT):
"""
:param key: Key to insert.
:param value: Value associated with given key.
>>> skip_list = SkipList()
>>> skip_list.insert(2, "Two")
>>> skip_list.find(2)
'Two'
>>> list(skip_list)
[2]
"""
node, update_vector = self._locate_node(key)
if node is not None:
node.value = value
else:
level = self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for i in range(self.level - 1, level):
update_vector.append(self.head)
self.level = level
new_node = Node(key, value)
for i, update_node in enumerate(update_vector[:level]):
# Change references to pass through new node.
if update_node.level > i:
new_node.forward.append(update_node.forward[i])
if update_node.level < i + 1:
update_node.forward.append(new_node)
else:
update_node.forward[i] = new_node
def find(self, key: VT) -> Optional[VT]:
"""
:param key: Search key.
:return: Value associated with given key or None if given key is not present.
>>> skip_list = SkipList()
>>> skip_list.find(2)
>>> skip_list.insert(2, "Two")
>>> skip_list.find(2)
'Two'
>>> skip_list.insert(2, "Three")
>>> skip_list.find(2)
'Three'
"""
node, _ = self._locate_node(key)
if node is not None:
return node.value
return None
def test_insert():
skip_list = SkipList()
skip_list.insert("Key1", 3)
skip_list.insert("Key2", 12)
skip_list.insert("Key3", 41)
skip_list.insert("Key4", -19)
node = skip_list.head
all_values = {}
while node.level != 0:
node = node.forward[0]
all_values[node.key] = node.value
assert len(all_values) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 12
assert all_values["Key3"] == 41
assert all_values["Key4"] == -19
def test_insert_overrides_existing_value():
skip_list = SkipList()
skip_list.insert("Key1", 10)
skip_list.insert("Key1", 12)
skip_list.insert("Key5", 7)
skip_list.insert("Key7", 10)
skip_list.insert("Key10", 5)
skip_list.insert("Key7", 7)
skip_list.insert("Key5", 5)
skip_list.insert("Key10", 10)
node = skip_list.head
all_values = {}
while node.level != 0:
node = node.forward[0]
all_values[node.key] = node.value
if len(all_values) != 4:
print()
assert len(all_values) == 4
assert all_values["Key1"] == 12
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 10
def test_searching_empty_list_returns_none():
skip_list = SkipList()
assert skip_list.find("Some key") is None
def test_search():
skip_list = SkipList()
skip_list.insert("Key2", 20)
assert skip_list.find("Key2") == 20
skip_list.insert("Some Key", 10)
skip_list.insert("Key2", 8)
skip_list.insert("V", 13)
assert skip_list.find("Y") is None
assert skip_list.find("Key2") == 8
assert skip_list.find("Some Key") == 10
assert skip_list.find("V") == 13
def test_deleting_item_from_empty_list_do_nothing():
skip_list = SkipList()
skip_list.delete("Some key")
assert len(skip_list.head.forward) == 0
def test_deleted_items_are_not_founded_by_find_method():
skip_list = SkipList()
skip_list.insert("Key1", 12)
skip_list.insert("V", 13)
skip_list.insert("X", 14)
skip_list.insert("Key2", 15)
skip_list.delete("V")
skip_list.delete("Key2")
assert skip_list.find("V") is None
assert skip_list.find("Key2") is None
def test_delete_removes_only_given_key():
skip_list = SkipList()
skip_list.insert("Key1", 12)
skip_list.insert("V", 13)
skip_list.insert("X", 14)
skip_list.insert("Key2", 15)
skip_list.delete("V")
assert skip_list.find("V") is None
assert skip_list.find("X") == 14
assert skip_list.find("Key1") == 12
assert skip_list.find("Key2") == 15
skip_list.delete("X")
assert skip_list.find("V") is None
assert skip_list.find("X") is None
assert skip_list.find("Key1") == 12
assert skip_list.find("Key2") == 15
skip_list.delete("Key1")
assert skip_list.find("V") is None
assert skip_list.find("X") is None
assert skip_list.find("Key1") is None
assert skip_list.find("Key2") == 15
skip_list.delete("Key2")
assert skip_list.find("V") is None
assert skip_list.find("X") is None
assert skip_list.find("Key1") is None
assert skip_list.find("Key2") is None
def test_delete_doesnt_leave_dead_nodes():
skip_list = SkipList()
skip_list.insert("Key1", 12)
skip_list.insert("V", 13)
skip_list.insert("X", 142)
skip_list.insert("Key2", 15)
skip_list.delete("X")
def traverse_keys(node):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(forward_node)
assert len(set(traverse_keys(skip_list.head))) == 4
def test_iter_always_yields_sorted_values():
def is_sorted(lst):
for item, next_item in zip(lst, lst[1:]):
if next_item < item:
return False
return True
skip_list = SkipList()
for i in range(10):
skip_list.insert(i, i)
assert is_sorted(list(skip_list))
skip_list.delete(5)
skip_list.delete(8)
skip_list.delete(2)
assert is_sorted(list(skip_list))
skip_list.insert(-12, -12)
skip_list.insert(77, 77)
assert is_sorted(list(skip_list))
def pytests():
for i in range(100):
# Repeat test 100 times due to the probabilistic nature of skip list
# random values == random bugs
test_insert()
test_insert_overrides_existing_value()
test_searching_empty_list_returns_none()
test_search()
test_deleting_item_from_empty_list_do_nothing()
test_deleted_items_are_not_founded_by_find_method()
test_delete_removes_only_given_key()
test_delete_doesnt_leave_dead_nodes()
test_iter_always_yields_sorted_values()
def main():
"""
>>> pytests()
"""
skip_list = SkipList()
skip_list.insert(2, "2")
skip_list.insert(4, "4")
skip_list.insert(6, "4")
skip_list.insert(4, "5")
skip_list.insert(8, "4")
skip_list.insert(9, "4")
skip_list.delete(4)
print(skip_list)
if __name__ == "__main__":
main()
| """
Based on "Skip Lists: A Probabilistic Alternative to Balanced Trees" by William Pugh
https://epaperpress.com/sortsearch/download/skiplist.pdf
"""
from __future__ import annotations
from random import random
from typing import Generic, Optional, TypeVar
KT = TypeVar("KT")
VT = TypeVar("VT")
class Node(Generic[KT, VT]):
def __init__(self, key: KT, value: VT):
self.key = key
self.value = value
self.forward: list[Node[KT, VT]] = []
def __repr__(self) -> str:
"""
:return: Visual representation of Node
>>> node = Node("Key", 2)
>>> repr(node)
'Node(Key: 2)'
"""
return f"Node({self.key}: {self.value})"
@property
def level(self) -> int:
"""
:return: Number of forward references
>>> node = Node("Key", 2)
>>> node.level
0
>>> node.forward.append(Node("Key2", 4))
>>> node.level
1
>>> node.forward.append(Node("Key3", 6))
>>> node.level
2
"""
return len(self.forward)
class SkipList(Generic[KT, VT]):
def __init__(self, p: float = 0.5, max_level: int = 16):
self.head = Node("root", None)
self.level = 0
self.p = p
self.max_level = max_level
def __str__(self) -> str:
"""
:return: Visual representation of SkipList
>>> skip_list = SkipList()
>>> print(skip_list)
SkipList(level=0)
>>> skip_list.insert("Key1", "Value")
>>> print(skip_list) # doctest: +ELLIPSIS
SkipList(level=...
[root]--...
[Key1]--Key1...
None *...
>>> skip_list.insert("Key2", "OtherValue")
>>> print(skip_list) # doctest: +ELLIPSIS
SkipList(level=...
[root]--...
[Key1]--Key1...
[Key2]--Key2...
None *...
"""
items = list(self)
if len(items) == 0:
return f"SkipList(level={self.level})"
label_size = max((len(str(item)) for item in items), default=4)
label_size = max(label_size, 4) + 4
node = self.head
lines = []
forwards = node.forward.copy()
lines.append(f"[{node.key}]".ljust(label_size, "-") + "* " * len(forwards))
lines.append(" " * label_size + "| " * len(forwards))
while len(node.forward) != 0:
node = node.forward[0]
lines.append(
f"[{node.key}]".ljust(label_size, "-")
+ " ".join(str(n.key) if n.key == node.key else "|" for n in forwards)
)
lines.append(" " * label_size + "| " * len(forwards))
forwards[: node.level] = node.forward
lines.append("None".ljust(label_size) + "* " * len(forwards))
return f"SkipList(level={self.level})\n" + "\n".join(lines)
def __iter__(self):
node = self.head
while len(node.forward) != 0:
yield node.forward[0].key
node = node.forward[0]
def random_level(self) -> int:
"""
:return: Random level from [1, self.max_level] interval.
Higher values are less likely.
"""
level = 1
while random() < self.p and level < self.max_level:
level += 1
return level
def _locate_node(self, key) -> tuple[Optional[Node[KT, VT]], list[Node[KT, VT]]]:
"""
:param key: Searched key,
:return: Tuple with searched node (or None if given key is not present)
and list of nodes that refer (if key is present) of should refer to
given node.
"""
# Nodes with refer or should refer to output node
update_vector = []
node = self.head
for i in reversed(range(self.level)):
# i < node.level - When node level is lesser than `i` decrement `i`.
# node.forward[i].key < key - Jumping to node with key value higher
# or equal to searched key would result
# in skipping searched key.
while i < node.level and node.forward[i].key < key:
node = node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(node)
update_vector.reverse() # Note that we were inserting values in reverse order.
# len(node.forward) != 0 - If current node doesn't contain any further
# references then searched key is not present.
# node.forward[0].key == key - Next node key should be equal to search key
# if key is present.
if len(node.forward) != 0 and node.forward[0].key == key:
return node.forward[0], update_vector
else:
return None, update_vector
def delete(self, key: KT):
"""
:param key: Key to remove from list.
>>> skip_list = SkipList()
>>> skip_list.insert(2, "Two")
>>> skip_list.insert(1, "One")
>>> skip_list.insert(3, "Three")
>>> list(skip_list)
[1, 2, 3]
>>> skip_list.delete(2)
>>> list(skip_list)
[1, 3]
"""
node, update_vector = self._locate_node(key)
if node is not None:
for i, update_node in enumerate(update_vector):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
update_node.forward[i] = node.forward[i]
else:
update_node.forward = update_node.forward[:i]
def insert(self, key: KT, value: VT):
"""
:param key: Key to insert.
:param value: Value associated with given key.
>>> skip_list = SkipList()
>>> skip_list.insert(2, "Two")
>>> skip_list.find(2)
'Two'
>>> list(skip_list)
[2]
"""
node, update_vector = self._locate_node(key)
if node is not None:
node.value = value
else:
level = self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for i in range(self.level - 1, level):
update_vector.append(self.head)
self.level = level
new_node = Node(key, value)
for i, update_node in enumerate(update_vector[:level]):
# Change references to pass through new node.
if update_node.level > i:
new_node.forward.append(update_node.forward[i])
if update_node.level < i + 1:
update_node.forward.append(new_node)
else:
update_node.forward[i] = new_node
def find(self, key: VT) -> Optional[VT]:
"""
:param key: Search key.
:return: Value associated with given key or None if given key is not present.
>>> skip_list = SkipList()
>>> skip_list.find(2)
>>> skip_list.insert(2, "Two")
>>> skip_list.find(2)
'Two'
>>> skip_list.insert(2, "Three")
>>> skip_list.find(2)
'Three'
"""
node, _ = self._locate_node(key)
if node is not None:
return node.value
return None
def test_insert():
skip_list = SkipList()
skip_list.insert("Key1", 3)
skip_list.insert("Key2", 12)
skip_list.insert("Key3", 41)
skip_list.insert("Key4", -19)
node = skip_list.head
all_values = {}
while node.level != 0:
node = node.forward[0]
all_values[node.key] = node.value
assert len(all_values) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 12
assert all_values["Key3"] == 41
assert all_values["Key4"] == -19
def test_insert_overrides_existing_value():
skip_list = SkipList()
skip_list.insert("Key1", 10)
skip_list.insert("Key1", 12)
skip_list.insert("Key5", 7)
skip_list.insert("Key7", 10)
skip_list.insert("Key10", 5)
skip_list.insert("Key7", 7)
skip_list.insert("Key5", 5)
skip_list.insert("Key10", 10)
node = skip_list.head
all_values = {}
while node.level != 0:
node = node.forward[0]
all_values[node.key] = node.value
if len(all_values) != 4:
print()
assert len(all_values) == 4
assert all_values["Key1"] == 12
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 10
def test_searching_empty_list_returns_none():
skip_list = SkipList()
assert skip_list.find("Some key") is None
def test_search():
skip_list = SkipList()
skip_list.insert("Key2", 20)
assert skip_list.find("Key2") == 20
skip_list.insert("Some Key", 10)
skip_list.insert("Key2", 8)
skip_list.insert("V", 13)
assert skip_list.find("Y") is None
assert skip_list.find("Key2") == 8
assert skip_list.find("Some Key") == 10
assert skip_list.find("V") == 13
def test_deleting_item_from_empty_list_do_nothing():
skip_list = SkipList()
skip_list.delete("Some key")
assert len(skip_list.head.forward) == 0
def test_deleted_items_are_not_founded_by_find_method():
skip_list = SkipList()
skip_list.insert("Key1", 12)
skip_list.insert("V", 13)
skip_list.insert("X", 14)
skip_list.insert("Key2", 15)
skip_list.delete("V")
skip_list.delete("Key2")
assert skip_list.find("V") is None
assert skip_list.find("Key2") is None
def test_delete_removes_only_given_key():
skip_list = SkipList()
skip_list.insert("Key1", 12)
skip_list.insert("V", 13)
skip_list.insert("X", 14)
skip_list.insert("Key2", 15)
skip_list.delete("V")
assert skip_list.find("V") is None
assert skip_list.find("X") == 14
assert skip_list.find("Key1") == 12
assert skip_list.find("Key2") == 15
skip_list.delete("X")
assert skip_list.find("V") is None
assert skip_list.find("X") is None
assert skip_list.find("Key1") == 12
assert skip_list.find("Key2") == 15
skip_list.delete("Key1")
assert skip_list.find("V") is None
assert skip_list.find("X") is None
assert skip_list.find("Key1") is None
assert skip_list.find("Key2") == 15
skip_list.delete("Key2")
assert skip_list.find("V") is None
assert skip_list.find("X") is None
assert skip_list.find("Key1") is None
assert skip_list.find("Key2") is None
def test_delete_doesnt_leave_dead_nodes():
skip_list = SkipList()
skip_list.insert("Key1", 12)
skip_list.insert("V", 13)
skip_list.insert("X", 142)
skip_list.insert("Key2", 15)
skip_list.delete("X")
def traverse_keys(node):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(forward_node)
assert len(set(traverse_keys(skip_list.head))) == 4
def test_iter_always_yields_sorted_values():
def is_sorted(lst):
for item, next_item in zip(lst, lst[1:]):
if next_item < item:
return False
return True
skip_list = SkipList()
for i in range(10):
skip_list.insert(i, i)
assert is_sorted(list(skip_list))
skip_list.delete(5)
skip_list.delete(8)
skip_list.delete(2)
assert is_sorted(list(skip_list))
skip_list.insert(-12, -12)
skip_list.insert(77, 77)
assert is_sorted(list(skip_list))
def pytests():
for i in range(100):
# Repeat test 100 times due to the probabilistic nature of skip list
# random values == random bugs
test_insert()
test_insert_overrides_existing_value()
test_searching_empty_list_returns_none()
test_search()
test_deleting_item_from_empty_list_do_nothing()
test_deleted_items_are_not_founded_by_find_method()
test_delete_removes_only_given_key()
test_delete_doesnt_leave_dead_nodes()
test_iter_always_yields_sorted_values()
def main():
"""
>>> pytests()
"""
skip_list = SkipList()
skip_list.insert(2, "2")
skip_list.insert(4, "4")
skip_list.insert(6, "4")
skip_list.insert(4, "5")
skip_list.insert(8, "4")
skip_list.insert(9, "4")
skip_list.delete(4)
print(skip_list)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | -1 |
||
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | -1 |
||
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | # Python code to demonstrate working of
# extend(), extendleft(), rotate(), reverse()
# importing "collections" for deque operations
import collections
# initializing deque
de = collections.deque([1, 2, 3])
# using extend() to add numbers to right end
# adds 4,5,6 to right end
de.extend([4, 5, 6])
# printing modified deque
print("The deque after extending deque at end is : ")
print(de)
# using extendleft() to add numbers to left end
# adds 7,8,9 to right end
de.extendleft([7, 8, 9])
# printing modified deque
print("The deque after extending deque at beginning is : ")
print(de)
# using rotate() to rotate the deque
# rotates by 3 to left
de.rotate(-3)
# printing modified deque
print("The deque after rotating deque is : ")
print(de)
# using reverse() to reverse the deque
de.reverse()
# printing modified deque
print("The deque after reversing deque is : ")
print(de)
# get right-end value and eliminate
startValue = de.pop()
print("The deque after popping value at end is : ")
print(de)
# get left-end value and eliminate
endValue = de.popleft()
print("The deque after popping value at start is : ")
print(de)
# eliminate element searched by value
de.remove(5)
print("The deque after eliminating element searched by value : ")
print(de)
| # Python code to demonstrate working of
# extend(), extendleft(), rotate(), reverse()
# importing "collections" for deque operations
import collections
# initializing deque
de = collections.deque([1, 2, 3])
# using extend() to add numbers to right end
# adds 4,5,6 to right end
de.extend([4, 5, 6])
# printing modified deque
print("The deque after extending deque at end is : ")
print(de)
# using extendleft() to add numbers to left end
# adds 7,8,9 to right end
de.extendleft([7, 8, 9])
# printing modified deque
print("The deque after extending deque at beginning is : ")
print(de)
# using rotate() to rotate the deque
# rotates by 3 to left
de.rotate(-3)
# printing modified deque
print("The deque after rotating deque is : ")
print(de)
# using reverse() to reverse the deque
de.reverse()
# printing modified deque
print("The deque after reversing deque is : ")
print(de)
# get right-end value and eliminate
startValue = de.pop()
print("The deque after popping value at end is : ")
print(de)
# get left-end value and eliminate
endValue = de.popleft()
print("The deque after popping value at start is : ")
print(de)
# eliminate element searched by value
de.remove(5)
print("The deque after eliminating element searched by value : ")
print(de)
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | # Fibonacci Sequence Using Recursion
def recur_fibo(n: int) -> int:
"""
>>> [recur_fibo(i) for i in range(12)]
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
"""
return n if n <= 1 else recur_fibo(n - 1) + recur_fibo(n - 2)
def main() -> None:
limit = int(input("How many terms to include in fibonacci series: "))
if limit > 0:
print(f"The first {limit} terms of the fibonacci series are as follows:")
print([recur_fibo(n) for n in range(limit)])
else:
print("Please enter a positive integer: ")
if __name__ == "__main__":
main()
| # Fibonacci Sequence Using Recursion
def recur_fibo(n: int) -> int:
"""
>>> [recur_fibo(i) for i in range(12)]
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
"""
return n if n <= 1 else recur_fibo(n - 1) + recur_fibo(n - 2)
def main() -> None:
limit = int(input("How many terms to include in fibonacci series: "))
if limit > 0:
print(f"The first {limit} terms of the fibonacci series are as follows:")
print([recur_fibo(n) for n in range(limit)])
else:
print("Please enter a positive integer: ")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
Problem 13: https://projecteuler.net/problem=13
Problem Statement:
Work out the first ten digits of the sum of the following one-hundred 50-digit
numbers.
"""
import os
def solution():
"""
Returns the first ten digits of the sum of the array elements
from the file num.txt
>>> solution()
'5537376230'
"""
file_path = os.path.join(os.path.dirname(__file__), "num.txt")
with open(file_path) as file_hand:
return str(sum([int(line) for line in file_hand]))[:10]
if __name__ == "__main__":
print(solution())
| """
Problem 13: https://projecteuler.net/problem=13
Problem Statement:
Work out the first ten digits of the sum of the following one-hundred 50-digit
numbers.
"""
import os
def solution():
"""
Returns the first ten digits of the sum of the array elements
from the file num.txt
>>> solution()
'5537376230'
"""
file_path = os.path.join(os.path.dirname(__file__), "num.txt")
with open(file_path) as file_hand:
return str(sum([int(line) for line in file_hand]))[:10]
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | import re
def indian_phone_validator(phone: str) -> bool:
"""
Determine whether the string is a valid phone number or not
:param phone:
:return: Boolean
>>> indian_phone_validator("+91123456789")
False
>>> indian_phone_validator("+919876543210")
True
>>> indian_phone_validator("01234567896")
False
>>> indian_phone_validator("919876543218")
True
>>> indian_phone_validator("+91-1234567899")
False
"""
pat = re.compile(r"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$")
match = re.search(pat, phone)
if match:
return match.string == phone
return False
if __name__ == "__main__":
print(indian_phone_validator("+918827897895"))
| import re
def indian_phone_validator(phone: str) -> bool:
"""
Determine whether the string is a valid phone number or not
:param phone:
:return: Boolean
>>> indian_phone_validator("+91123456789")
False
>>> indian_phone_validator("+919876543210")
True
>>> indian_phone_validator("01234567896")
False
>>> indian_phone_validator("919876543218")
True
>>> indian_phone_validator("+91-1234567899")
False
"""
pat = re.compile(r"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$")
match = re.search(pat, phone)
if match:
return match.string == phone
return False
if __name__ == "__main__":
print(indian_phone_validator("+918827897895"))
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
Problem 34: https://projecteuler.net/problem=34
145 is a curious number, as 1! + 4! + 5! = 1 + 24 + 120 = 145.
Find the sum of all numbers which are equal to the sum of the factorial of their digits.
Note: As 1! = 1 and 2! = 2 are not sums they are not included.
"""
from math import factorial
def sum_of_digit_factorial(n: int) -> int:
"""
Returns the sum of the digits in n
>>> sum_of_digit_factorial(15)
121
>>> sum_of_digit_factorial(0)
1
"""
return sum(factorial(int(char)) for char in str(n))
def solution() -> int:
"""
Returns the sum of all numbers whose
sum of the factorials of all digits
add up to the number itself.
>>> solution()
40730
"""
limit = 7 * factorial(9) + 1
return sum(i for i in range(3, limit) if sum_of_digit_factorial(i) == i)
if __name__ == "__main__":
print(f"{solution()} = ")
| """
Problem 34: https://projecteuler.net/problem=34
145 is a curious number, as 1! + 4! + 5! = 1 + 24 + 120 = 145.
Find the sum of all numbers which are equal to the sum of the factorial of their digits.
Note: As 1! = 1 and 2! = 2 are not sums they are not included.
"""
from math import factorial
def sum_of_digit_factorial(n: int) -> int:
"""
Returns the sum of the digits in n
>>> sum_of_digit_factorial(15)
121
>>> sum_of_digit_factorial(0)
1
"""
return sum(factorial(int(char)) for char in str(n))
def solution() -> int:
"""
Returns the sum of all numbers whose
sum of the factorials of all digits
add up to the number itself.
>>> solution()
40730
"""
limit = 7 * factorial(9) + 1
return sum(i for i in range(3, limit) if sum_of_digit_factorial(i) == i)
if __name__ == "__main__":
print(f"{solution()} = ")
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | -1 |
||
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
Checking valid Ip Address.
A valid IP address must be 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 check_valid_ip(ip: str) -> bool:
"""
print "Valid IP address" If IP is valid.
or
print "Invalid IP address" If IP is Invalid.
>>> check_valid_ip("192.168.0.23")
True
>>> check_valid_ip("192.255.15.8")
False
>>> check_valid_ip("172.100.0.8")
True
>>> check_valid_ip("254.255.0.255")
False
"""
ip1 = ip.replace(".", " ")
list1 = [int(i) for i in ip1.split() if i.isdigit()]
count = 0
for i in list1:
if i > 254:
count += 1
break
if count:
return False
return True
if __name__ == "__main__":
ip = input()
output = check_valid_ip(ip)
if output is True:
print(f"{ip} is a Valid IP address")
else:
print(f"{ip} is an Invalid IP address")
| """
Checking valid Ip Address.
A valid IP address must be 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 check_valid_ip(ip: str) -> bool:
"""
print "Valid IP address" If IP is valid.
or
print "Invalid IP address" If IP is Invalid.
>>> check_valid_ip("192.168.0.23")
True
>>> check_valid_ip("192.255.15.8")
False
>>> check_valid_ip("172.100.0.8")
True
>>> check_valid_ip("254.255.0.255")
False
"""
ip1 = ip.replace(".", " ")
list1 = [int(i) for i in ip1.split() if i.isdigit()]
count = 0
for i in list1:
if i > 254:
count += 1
break
if count:
return False
return True
if __name__ == "__main__":
ip = input()
output = check_valid_ip(ip)
if output is True:
print(f"{ip} is a Valid IP address")
else:
print(f"{ip} is an Invalid IP address")
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
https://en.wikipedia.org/wiki/Floor_and_ceiling_functions
"""
def ceil(x) -> int:
"""
Return the ceiling of x as an Integral.
:param x: the number
:return: the smallest integer >= x.
>>> import math
>>> all(ceil(n) == math.ceil(n) for n
... in (1, -1, 0, -0, 1.1, -1.1, 1.0, -1.0, 1_000_000_000))
True
"""
return int(x) if x - int(x) <= 0 else int(x) + 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
https://en.wikipedia.org/wiki/Floor_and_ceiling_functions
"""
def ceil(x) -> int:
"""
Return the ceiling of x as an Integral.
:param x: the number
:return: the smallest integer >= x.
>>> import math
>>> all(ceil(n) == math.ceil(n) for n
... in (1, -1, 0, -0, 1.1, -1.1, 1.0, -1.0, 1_000_000_000))
True
"""
return int(x) if x - int(x) <= 0 else int(x) + 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | #!/usr/bin/env python3
from __future__ import annotations
import random
from typing import Generic, Iterable, List, Optional, TypeVar
T = TypeVar("T")
class RandomizedHeapNode(Generic[T]):
"""
One node of the randomized heap. Contains the value and references to
two children.
"""
def __init__(self, value: T) -> None:
self._value: T = value
self.left: Optional[RandomizedHeapNode[T]] = None
self.right: Optional[RandomizedHeapNode[T]] = None
@property
def value(self) -> T:
"""Return the value of the node."""
return self._value
@staticmethod
def merge(
root1: Optional[RandomizedHeapNode[T]], root2: Optional[RandomizedHeapNode[T]]
) -> Optional[RandomizedHeapNode[T]]:
"""Merge 2 nodes together."""
if not root1:
return root2
if not root2:
return root1
if root1.value > root2.value:
root1, root2 = root2, root1
if random.choice([True, False]):
root1.left, root1.right = root1.right, root1.left
root1.left = RandomizedHeapNode.merge(root1.left, root2)
return root1
class RandomizedHeap(Generic[T]):
"""
A data structure that allows inserting a new value and to pop the smallest
values. Both operations take O(logN) time where N is the size of the
structure.
Wiki: https://en.wikipedia.org/wiki/Randomized_meldable_heap
>>> RandomizedHeap([2, 3, 1, 5, 1, 7]).to_sorted_list()
[1, 1, 2, 3, 5, 7]
>>> rh = RandomizedHeap()
>>> rh.pop()
Traceback (most recent call last):
...
IndexError: Can't get top element for the empty heap.
>>> rh.insert(1)
>>> rh.insert(-1)
>>> rh.insert(0)
>>> rh.to_sorted_list()
[-1, 0, 1]
"""
def __init__(self, data: Optional[Iterable[T]] = ()) -> None:
"""
>>> rh = RandomizedHeap([3, 1, 3, 7])
>>> rh.to_sorted_list()
[1, 3, 3, 7]
"""
self._root: Optional[RandomizedHeapNode[T]] = None
for item in data:
self.insert(item)
def insert(self, value: T) -> None:
"""
Insert the value into the heap.
>>> rh = RandomizedHeap()
>>> rh.insert(3)
>>> rh.insert(1)
>>> rh.insert(3)
>>> rh.insert(7)
>>> rh.to_sorted_list()
[1, 3, 3, 7]
"""
self._root = RandomizedHeapNode.merge(self._root, RandomizedHeapNode(value))
def pop(self) -> T:
"""
Pop the smallest value from the heap and return it.
>>> rh = RandomizedHeap([3, 1, 3, 7])
>>> rh.pop()
1
>>> rh.pop()
3
>>> rh.pop()
3
>>> rh.pop()
7
>>> rh.pop()
Traceback (most recent call last):
...
IndexError: Can't get top element for the empty heap.
"""
result = self.top()
self._root = RandomizedHeapNode.merge(self._root.left, self._root.right)
return result
def top(self) -> T:
"""
Return the smallest value from the heap.
>>> rh = RandomizedHeap()
>>> rh.insert(3)
>>> rh.top()
3
>>> rh.insert(1)
>>> rh.top()
1
>>> rh.insert(3)
>>> rh.top()
1
>>> rh.insert(7)
>>> rh.top()
1
"""
if not self._root:
raise IndexError("Can't get top element for the empty heap.")
return self._root.value
def clear(self):
"""
Clear the heap.
>>> rh = RandomizedHeap([3, 1, 3, 7])
>>> rh.clear()
>>> rh.pop()
Traceback (most recent call last):
...
IndexError: Can't get top element for the empty heap.
"""
self._root = None
def to_sorted_list(self) -> List[T]:
"""
Returns sorted list containing all the values in the heap.
>>> rh = RandomizedHeap([3, 1, 3, 7])
>>> rh.to_sorted_list()
[1, 3, 3, 7]
"""
result = []
while self:
result.append(self.pop())
return result
def __bool__(self) -> bool:
"""
Check if the heap is not empty.
>>> rh = RandomizedHeap()
>>> bool(rh)
False
>>> rh.insert(1)
>>> bool(rh)
True
>>> rh.clear()
>>> bool(rh)
False
"""
return self._root is not None
if __name__ == "__main__":
import doctest
doctest.testmod()
| #!/usr/bin/env python3
from __future__ import annotations
import random
from typing import Generic, Iterable, List, Optional, TypeVar
T = TypeVar("T")
class RandomizedHeapNode(Generic[T]):
"""
One node of the randomized heap. Contains the value and references to
two children.
"""
def __init__(self, value: T) -> None:
self._value: T = value
self.left: Optional[RandomizedHeapNode[T]] = None
self.right: Optional[RandomizedHeapNode[T]] = None
@property
def value(self) -> T:
"""Return the value of the node."""
return self._value
@staticmethod
def merge(
root1: Optional[RandomizedHeapNode[T]], root2: Optional[RandomizedHeapNode[T]]
) -> Optional[RandomizedHeapNode[T]]:
"""Merge 2 nodes together."""
if not root1:
return root2
if not root2:
return root1
if root1.value > root2.value:
root1, root2 = root2, root1
if random.choice([True, False]):
root1.left, root1.right = root1.right, root1.left
root1.left = RandomizedHeapNode.merge(root1.left, root2)
return root1
class RandomizedHeap(Generic[T]):
"""
A data structure that allows inserting a new value and to pop the smallest
values. Both operations take O(logN) time where N is the size of the
structure.
Wiki: https://en.wikipedia.org/wiki/Randomized_meldable_heap
>>> RandomizedHeap([2, 3, 1, 5, 1, 7]).to_sorted_list()
[1, 1, 2, 3, 5, 7]
>>> rh = RandomizedHeap()
>>> rh.pop()
Traceback (most recent call last):
...
IndexError: Can't get top element for the empty heap.
>>> rh.insert(1)
>>> rh.insert(-1)
>>> rh.insert(0)
>>> rh.to_sorted_list()
[-1, 0, 1]
"""
def __init__(self, data: Optional[Iterable[T]] = ()) -> None:
"""
>>> rh = RandomizedHeap([3, 1, 3, 7])
>>> rh.to_sorted_list()
[1, 3, 3, 7]
"""
self._root: Optional[RandomizedHeapNode[T]] = None
for item in data:
self.insert(item)
def insert(self, value: T) -> None:
"""
Insert the value into the heap.
>>> rh = RandomizedHeap()
>>> rh.insert(3)
>>> rh.insert(1)
>>> rh.insert(3)
>>> rh.insert(7)
>>> rh.to_sorted_list()
[1, 3, 3, 7]
"""
self._root = RandomizedHeapNode.merge(self._root, RandomizedHeapNode(value))
def pop(self) -> T:
"""
Pop the smallest value from the heap and return it.
>>> rh = RandomizedHeap([3, 1, 3, 7])
>>> rh.pop()
1
>>> rh.pop()
3
>>> rh.pop()
3
>>> rh.pop()
7
>>> rh.pop()
Traceback (most recent call last):
...
IndexError: Can't get top element for the empty heap.
"""
result = self.top()
self._root = RandomizedHeapNode.merge(self._root.left, self._root.right)
return result
def top(self) -> T:
"""
Return the smallest value from the heap.
>>> rh = RandomizedHeap()
>>> rh.insert(3)
>>> rh.top()
3
>>> rh.insert(1)
>>> rh.top()
1
>>> rh.insert(3)
>>> rh.top()
1
>>> rh.insert(7)
>>> rh.top()
1
"""
if not self._root:
raise IndexError("Can't get top element for the empty heap.")
return self._root.value
def clear(self):
"""
Clear the heap.
>>> rh = RandomizedHeap([3, 1, 3, 7])
>>> rh.clear()
>>> rh.pop()
Traceback (most recent call last):
...
IndexError: Can't get top element for the empty heap.
"""
self._root = None
def to_sorted_list(self) -> List[T]:
"""
Returns sorted list containing all the values in the heap.
>>> rh = RandomizedHeap([3, 1, 3, 7])
>>> rh.to_sorted_list()
[1, 3, 3, 7]
"""
result = []
while self:
result.append(self.pop())
return result
def __bool__(self) -> bool:
"""
Check if the heap is not empty.
>>> rh = RandomizedHeap()
>>> bool(rh)
False
>>> rh.insert(1)
>>> bool(rh)
True
>>> rh.clear()
>>> bool(rh)
False
"""
return self._root is not None
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | -1 |
||
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | -1 |
||
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | -1 |
||
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
render 3d points for 2d surfaces.
"""
from __future__ import annotations
import math
__version__ = "2020.9.26"
__author__ = "xcodz-dot, cclaus, dhruvmanila"
def convert_to_2d(
x: float, y: float, z: float, scale: float, distance: float
) -> tuple[float, float]:
"""
Converts 3d point to a 2d drawable point
>>> convert_to_2d(1.0, 2.0, 3.0, 10.0, 10.0)
(7.6923076923076925, 15.384615384615385)
>>> convert_to_2d(1, 2, 3, 10, 10)
(7.6923076923076925, 15.384615384615385)
>>> convert_to_2d("1", 2, 3, 10, 10) # '1' is str
Traceback (most recent call last):
...
TypeError: Input values must either be float or int: ['1', 2, 3, 10, 10]
"""
if not all(isinstance(val, (float, int)) for val in locals().values()):
raise TypeError(
"Input values must either be float or int: " f"{list(locals().values())}"
)
projected_x = ((x * distance) / (z + distance)) * scale
projected_y = ((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def rotate(
x: float, y: float, z: float, axis: str, angle: float
) -> tuple[float, float, float]:
"""
rotate a point around a certain axis with a certain angle
angle can be any integer between 1, 360 and axis can be any one of
'x', 'y', 'z'
>>> rotate(1.0, 2.0, 3.0, 'y', 90.0)
(3.130524675073759, 2.0, 0.4470070007889556)
>>> rotate(1, 2, 3, "z", 180)
(0.999736015495891, -2.0001319704760485, 3)
>>> rotate('1', 2, 3, "z", 90.0) # '1' is str
Traceback (most recent call last):
...
TypeError: Input values except axis must either be float or int: ['1', 2, 3, 90.0]
>>> rotate(1, 2, 3, "n", 90) # 'n' is not a valid axis
Traceback (most recent call last):
...
ValueError: not a valid axis, choose one of 'x', 'y', 'z'
>>> rotate(1, 2, 3, "x", -90)
(1, -2.5049096187183877, -2.5933429780983657)
>>> rotate(1, 2, 3, "x", 450) # 450 wrap around to 90
(1, 3.5776792428178217, -0.44744970165427644)
"""
if not isinstance(axis, str):
raise TypeError("Axis must be a str")
input_variables = locals()
del input_variables["axis"]
if not all(isinstance(val, (float, int)) for val in input_variables.values()):
raise TypeError(
"Input values except axis must either be float or int: "
f"{list(input_variables.values())}"
)
angle = (angle % 360) / 450 * 180 / math.pi
if axis == "z":
new_x = x * math.cos(angle) - y * math.sin(angle)
new_y = y * math.cos(angle) + x * math.sin(angle)
new_z = z
elif axis == "x":
new_y = y * math.cos(angle) - z * math.sin(angle)
new_z = z * math.cos(angle) + y * math.sin(angle)
new_x = x
elif axis == "y":
new_x = x * math.cos(angle) - z * math.sin(angle)
new_z = z * math.cos(angle) + x * math.sin(angle)
new_y = y
else:
raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'")
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{convert_to_2d(1.0, 2.0, 3.0, 10.0, 10.0) = }")
print(f"{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }")
| """
render 3d points for 2d surfaces.
"""
from __future__ import annotations
import math
__version__ = "2020.9.26"
__author__ = "xcodz-dot, cclaus, dhruvmanila"
def convert_to_2d(
x: float, y: float, z: float, scale: float, distance: float
) -> tuple[float, float]:
"""
Converts 3d point to a 2d drawable point
>>> convert_to_2d(1.0, 2.0, 3.0, 10.0, 10.0)
(7.6923076923076925, 15.384615384615385)
>>> convert_to_2d(1, 2, 3, 10, 10)
(7.6923076923076925, 15.384615384615385)
>>> convert_to_2d("1", 2, 3, 10, 10) # '1' is str
Traceback (most recent call last):
...
TypeError: Input values must either be float or int: ['1', 2, 3, 10, 10]
"""
if not all(isinstance(val, (float, int)) for val in locals().values()):
raise TypeError(
"Input values must either be float or int: " f"{list(locals().values())}"
)
projected_x = ((x * distance) / (z + distance)) * scale
projected_y = ((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def rotate(
x: float, y: float, z: float, axis: str, angle: float
) -> tuple[float, float, float]:
"""
rotate a point around a certain axis with a certain angle
angle can be any integer between 1, 360 and axis can be any one of
'x', 'y', 'z'
>>> rotate(1.0, 2.0, 3.0, 'y', 90.0)
(3.130524675073759, 2.0, 0.4470070007889556)
>>> rotate(1, 2, 3, "z", 180)
(0.999736015495891, -2.0001319704760485, 3)
>>> rotate('1', 2, 3, "z", 90.0) # '1' is str
Traceback (most recent call last):
...
TypeError: Input values except axis must either be float or int: ['1', 2, 3, 90.0]
>>> rotate(1, 2, 3, "n", 90) # 'n' is not a valid axis
Traceback (most recent call last):
...
ValueError: not a valid axis, choose one of 'x', 'y', 'z'
>>> rotate(1, 2, 3, "x", -90)
(1, -2.5049096187183877, -2.5933429780983657)
>>> rotate(1, 2, 3, "x", 450) # 450 wrap around to 90
(1, 3.5776792428178217, -0.44744970165427644)
"""
if not isinstance(axis, str):
raise TypeError("Axis must be a str")
input_variables = locals()
del input_variables["axis"]
if not all(isinstance(val, (float, int)) for val in input_variables.values()):
raise TypeError(
"Input values except axis must either be float or int: "
f"{list(input_variables.values())}"
)
angle = (angle % 360) / 450 * 180 / math.pi
if axis == "z":
new_x = x * math.cos(angle) - y * math.sin(angle)
new_y = y * math.cos(angle) + x * math.sin(angle)
new_z = z
elif axis == "x":
new_y = y * math.cos(angle) - z * math.sin(angle)
new_z = z * math.cos(angle) + y * math.sin(angle)
new_x = x
elif axis == "y":
new_x = x * math.cos(angle) - z * math.sin(angle)
new_z = z * math.cos(angle) + x * math.sin(angle)
new_y = y
else:
raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'")
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{convert_to_2d(1.0, 2.0, 3.0, 10.0, 10.0) = }")
print(f"{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }")
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
Project Euler Problem 3: https://projecteuler.net/problem=3
Largest prime factor
The prime factors of 13195 are 5, 7, 13 and 29.
What is the largest prime factor of the number 600851475143?
References:
- https://en.wikipedia.org/wiki/Prime_number#Unique_factorization
"""
import math
def isprime(num: int) -> bool:
"""
Returns boolean representing primality of given number num.
>>> isprime(2)
True
>>> isprime(3)
True
>>> isprime(27)
False
>>> isprime(2999)
True
>>> isprime(0)
Traceback (most recent call last):
...
ValueError: Parameter num must be greater than or equal to two.
>>> isprime(1)
Traceback (most recent call last):
...
ValueError: Parameter num must be greater than or equal to two.
"""
if num <= 1:
raise ValueError("Parameter num must be greater than or equal to two.")
if num == 2:
return True
elif num % 2 == 0:
return False
for i in range(3, int(math.sqrt(num)) + 1, 2):
if num % i == 0:
return False
return True
def solution(n: int = 600851475143) -> int:
"""
Returns the largest prime factor of a given number n.
>>> solution(13195)
29
>>> solution(10)
5
>>> solution(17)
17
>>> solution(3.4)
3
>>> solution(0)
Traceback (most recent call last):
...
ValueError: Parameter n must be greater than or equal to one.
>>> solution(-17)
Traceback (most recent call last):
...
ValueError: Parameter n must be greater than or equal to one.
>>> solution([])
Traceback (most recent call last):
...
TypeError: Parameter n must be int or castable to int.
>>> solution("asd")
Traceback (most recent call last):
...
TypeError: Parameter n must be int or castable to int.
"""
try:
n = int(n)
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int.")
if n <= 0:
raise ValueError("Parameter n must be greater than or equal to one.")
max_number = 0
if isprime(n):
return n
while n % 2 == 0:
n //= 2
if isprime(n):
return n
for i in range(3, int(math.sqrt(n)) + 1, 2):
if n % i == 0:
if isprime(n / i):
max_number = n / i
break
elif isprime(i):
max_number = i
return max_number
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 3: https://projecteuler.net/problem=3
Largest prime factor
The prime factors of 13195 are 5, 7, 13 and 29.
What is the largest prime factor of the number 600851475143?
References:
- https://en.wikipedia.org/wiki/Prime_number#Unique_factorization
"""
import math
def isprime(num: int) -> bool:
"""
Returns boolean representing primality of given number num.
>>> isprime(2)
True
>>> isprime(3)
True
>>> isprime(27)
False
>>> isprime(2999)
True
>>> isprime(0)
Traceback (most recent call last):
...
ValueError: Parameter num must be greater than or equal to two.
>>> isprime(1)
Traceback (most recent call last):
...
ValueError: Parameter num must be greater than or equal to two.
"""
if num <= 1:
raise ValueError("Parameter num must be greater than or equal to two.")
if num == 2:
return True
elif num % 2 == 0:
return False
for i in range(3, int(math.sqrt(num)) + 1, 2):
if num % i == 0:
return False
return True
def solution(n: int = 600851475143) -> int:
"""
Returns the largest prime factor of a given number n.
>>> solution(13195)
29
>>> solution(10)
5
>>> solution(17)
17
>>> solution(3.4)
3
>>> solution(0)
Traceback (most recent call last):
...
ValueError: Parameter n must be greater than or equal to one.
>>> solution(-17)
Traceback (most recent call last):
...
ValueError: Parameter n must be greater than or equal to one.
>>> solution([])
Traceback (most recent call last):
...
TypeError: Parameter n must be int or castable to int.
>>> solution("asd")
Traceback (most recent call last):
...
TypeError: Parameter n must be int or castable to int.
"""
try:
n = int(n)
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int.")
if n <= 0:
raise ValueError("Parameter n must be greater than or equal to one.")
max_number = 0
if isprime(n):
return n
while n % 2 == 0:
n //= 2
if isprime(n):
return n
for i in range(3, int(math.sqrt(n)) + 1, 2):
if n % i == 0:
if isprime(n / i):
max_number = n / i
break
elif isprime(i):
max_number = i
return max_number
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
Graph Coloring also called "m coloring problem"
consists of coloring given graph with at most m colors
such that no adjacent vertices are assigned same color
Wikipedia: https://en.wikipedia.org/wiki/Graph_coloring
"""
from typing import List
def valid_coloring(
neighbours: List[int], colored_vertices: List[int], color: int
) -> bool:
"""
For each neighbour check if coloring constraint is satisfied
If any of the neighbours fail the constraint return False
If all neighbours validate constraint return True
>>> neighbours = [0,1,0,1,0]
>>> colored_vertices = [0, 2, 1, 2, 0]
>>> color = 1
>>> valid_coloring(neighbours, colored_vertices, color)
True
>>> color = 2
>>> valid_coloring(neighbours, colored_vertices, color)
False
"""
# Does any neighbour not satisfy the constraints
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(neighbours)
)
def util_color(
graph: List[List[int]], max_colors: int, colored_vertices: List[int], index: int
) -> bool:
"""
Pseudo-Code
Base Case:
1. Check if coloring is complete
1.1 If complete return True (meaning that we successfully colored graph)
Recursive Step:
2. Itterates over each color:
Check if current coloring is valid:
2.1. Color given vertex
2.2. Do recursive call check if this coloring leads to solving problem
2.4. if current coloring leads to solution return
2.5. Uncolor given vertex
>>> graph = [[0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1],
... [0, 1, 0, 1, 0],
... [0, 1, 1, 0, 0],
... [0, 1, 0, 0, 0]]
>>> max_colors = 3
>>> colored_vertices = [0, 1, 0, 0, 0]
>>> index = 3
>>> util_color(graph, max_colors, colored_vertices, index)
True
>>> max_colors = 2
>>> util_color(graph, max_colors, colored_vertices, index)
False
"""
# Base Case
if index == len(graph):
return True
# Recursive Step
for i in range(max_colors):
if valid_coloring(graph[index], colored_vertices, i):
# Color current vertex
colored_vertices[index] = i
# Validate coloring
if util_color(graph, max_colors, colored_vertices, index + 1):
return True
# Backtrack
colored_vertices[index] = -1
return False
def color(graph: List[List[int]], max_colors: int) -> List[int]:
"""
Wrapper function to call subroutine called util_color
which will either return True or False.
If True is returned colored_vertices list is filled with correct colorings
>>> graph = [[0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1],
... [0, 1, 0, 1, 0],
... [0, 1, 1, 0, 0],
... [0, 1, 0, 0, 0]]
>>> max_colors = 3
>>> color(graph, max_colors)
[0, 1, 0, 2, 0]
>>> max_colors = 2
>>> color(graph, max_colors)
[]
"""
colored_vertices = [-1] * len(graph)
if util_color(graph, max_colors, colored_vertices, 0):
return colored_vertices
return []
| """
Graph Coloring also called "m coloring problem"
consists of coloring given graph with at most m colors
such that no adjacent vertices are assigned same color
Wikipedia: https://en.wikipedia.org/wiki/Graph_coloring
"""
from typing import List
def valid_coloring(
neighbours: List[int], colored_vertices: List[int], color: int
) -> bool:
"""
For each neighbour check if coloring constraint is satisfied
If any of the neighbours fail the constraint return False
If all neighbours validate constraint return True
>>> neighbours = [0,1,0,1,0]
>>> colored_vertices = [0, 2, 1, 2, 0]
>>> color = 1
>>> valid_coloring(neighbours, colored_vertices, color)
True
>>> color = 2
>>> valid_coloring(neighbours, colored_vertices, color)
False
"""
# Does any neighbour not satisfy the constraints
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(neighbours)
)
def util_color(
graph: List[List[int]], max_colors: int, colored_vertices: List[int], index: int
) -> bool:
"""
Pseudo-Code
Base Case:
1. Check if coloring is complete
1.1 If complete return True (meaning that we successfully colored graph)
Recursive Step:
2. Itterates over each color:
Check if current coloring is valid:
2.1. Color given vertex
2.2. Do recursive call check if this coloring leads to solving problem
2.4. if current coloring leads to solution return
2.5. Uncolor given vertex
>>> graph = [[0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1],
... [0, 1, 0, 1, 0],
... [0, 1, 1, 0, 0],
... [0, 1, 0, 0, 0]]
>>> max_colors = 3
>>> colored_vertices = [0, 1, 0, 0, 0]
>>> index = 3
>>> util_color(graph, max_colors, colored_vertices, index)
True
>>> max_colors = 2
>>> util_color(graph, max_colors, colored_vertices, index)
False
"""
# Base Case
if index == len(graph):
return True
# Recursive Step
for i in range(max_colors):
if valid_coloring(graph[index], colored_vertices, i):
# Color current vertex
colored_vertices[index] = i
# Validate coloring
if util_color(graph, max_colors, colored_vertices, index + 1):
return True
# Backtrack
colored_vertices[index] = -1
return False
def color(graph: List[List[int]], max_colors: int) -> List[int]:
"""
Wrapper function to call subroutine called util_color
which will either return True or False.
If True is returned colored_vertices list is filled with correct colorings
>>> graph = [[0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1],
... [0, 1, 0, 1, 0],
... [0, 1, 1, 0, 0],
... [0, 1, 0, 0, 0]]
>>> max_colors = 3
>>> color(graph, max_colors)
[0, 1, 0, 2, 0]
>>> max_colors = 2
>>> color(graph, max_colors)
[]
"""
colored_vertices = [-1] * len(graph)
if util_color(graph, max_colors, colored_vertices, 0):
return colored_vertices
return []
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | -1 |
||
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | JFIF C
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TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | -1 |
||
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
One of the several implementations of Lempel–Ziv–Welch decompression algorithm
https://en.wikipedia.org/wiki/Lempel%E2%80%93Ziv%E2%80%93Welch
"""
import math
import sys
def read_file_binary(file_path: str) -> str:
"""
Reads given file as bytes and returns them as a long string
"""
result = ""
try:
with open(file_path, "rb") as binary_file:
data = binary_file.read()
for dat in data:
curr_byte = f"{dat:08b}"
result += curr_byte
return result
except OSError:
print("File not accessible")
sys.exit()
def decompress_data(data_bits: str) -> str:
"""
Decompresses given data_bits using Lempel–Ziv–Welch compression algorithm
and returns the result as a string
"""
lexicon = {"0": "0", "1": "1"}
result, curr_string = "", ""
index = len(lexicon)
for i in range(len(data_bits)):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
last_match_id = lexicon[curr_string]
result += last_match_id
lexicon[curr_string] = last_match_id + "0"
if math.log2(index).is_integer():
newLex = {}
for curr_key in list(lexicon):
newLex["0" + curr_key] = lexicon.pop(curr_key)
lexicon = newLex
lexicon[bin(index)[2:]] = last_match_id + "1"
index += 1
curr_string = ""
return result
def write_file_binary(file_path: str, to_write: str) -> None:
"""
Writes given to_write string (should only consist of 0's and 1's) as bytes in the
file
"""
byte_length = 8
try:
with open(file_path, "wb") as opened_file:
result_byte_array = [
to_write[i : i + byte_length]
for i in range(0, len(to_write), byte_length)
]
if len(result_byte_array[-1]) % byte_length == 0:
result_byte_array.append("10000000")
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1]) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(elem, 2).to_bytes(1, byteorder="big"))
except OSError:
print("File not accessible")
sys.exit()
def remove_prefix(data_bits: str) -> str:
"""
Removes size prefix, that compressed file should have
Returns the result
"""
counter = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
data_bits = data_bits[counter:]
data_bits = data_bits[counter + 1 :]
return data_bits
def compress(source_path: str, destination_path: str) -> None:
"""
Reads source file, decompresses it and writes the result in destination file
"""
data_bits = read_file_binary(source_path)
data_bits = remove_prefix(data_bits)
decompressed = decompress_data(data_bits)
write_file_binary(destination_path, decompressed)
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| """
One of the several implementations of Lempel–Ziv–Welch decompression algorithm
https://en.wikipedia.org/wiki/Lempel%E2%80%93Ziv%E2%80%93Welch
"""
import math
import sys
def read_file_binary(file_path: str) -> str:
"""
Reads given file as bytes and returns them as a long string
"""
result = ""
try:
with open(file_path, "rb") as binary_file:
data = binary_file.read()
for dat in data:
curr_byte = f"{dat:08b}"
result += curr_byte
return result
except OSError:
print("File not accessible")
sys.exit()
def decompress_data(data_bits: str) -> str:
"""
Decompresses given data_bits using Lempel–Ziv–Welch compression algorithm
and returns the result as a string
"""
lexicon = {"0": "0", "1": "1"}
result, curr_string = "", ""
index = len(lexicon)
for i in range(len(data_bits)):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
last_match_id = lexicon[curr_string]
result += last_match_id
lexicon[curr_string] = last_match_id + "0"
if math.log2(index).is_integer():
newLex = {}
for curr_key in list(lexicon):
newLex["0" + curr_key] = lexicon.pop(curr_key)
lexicon = newLex
lexicon[bin(index)[2:]] = last_match_id + "1"
index += 1
curr_string = ""
return result
def write_file_binary(file_path: str, to_write: str) -> None:
"""
Writes given to_write string (should only consist of 0's and 1's) as bytes in the
file
"""
byte_length = 8
try:
with open(file_path, "wb") as opened_file:
result_byte_array = [
to_write[i : i + byte_length]
for i in range(0, len(to_write), byte_length)
]
if len(result_byte_array[-1]) % byte_length == 0:
result_byte_array.append("10000000")
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1]) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(elem, 2).to_bytes(1, byteorder="big"))
except OSError:
print("File not accessible")
sys.exit()
def remove_prefix(data_bits: str) -> str:
"""
Removes size prefix, that compressed file should have
Returns the result
"""
counter = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
data_bits = data_bits[counter:]
data_bits = data_bits[counter + 1 :]
return data_bits
def compress(source_path: str, destination_path: str) -> None:
"""
Reads source file, decompresses it and writes the result in destination file
"""
data_bits = read_file_binary(source_path)
data_bits = remove_prefix(data_bits)
decompressed = decompress_data(data_bits)
write_file_binary(destination_path, decompressed)
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | #!/usr/bin/env python3
"""
This program calculates the nth Fibonacci number in O(log(n)).
It's possible to calculate F(1_000_000) in less than a second.
"""
from __future__ import annotations
import sys
def fibonacci(n: int) -> int:
"""
return F(n)
>>> [fibonacci(i) for i in range(13)]
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144]
"""
if n < 0:
raise ValueError("Negative arguments are not supported")
return _fib(n)[0]
# returns (F(n), F(n-1))
def _fib(n: int) -> tuple[int, int]:
if n == 0: # (F(0), F(1))
return (0, 1)
# F(2n) = F(n)[2F(n+1) − F(n)]
# F(2n+1) = F(n+1)^2+F(n)^2
a, b = _fib(n // 2)
c = a * (b * 2 - a)
d = a * a + b * b
return (d, c + d) if n % 2 else (c, d)
if __name__ == "__main__":
n = int(sys.argv[1])
print(f"fibonacci({n}) is {fibonacci(n)}")
| #!/usr/bin/env python3
"""
This program calculates the nth Fibonacci number in O(log(n)).
It's possible to calculate F(1_000_000) in less than a second.
"""
from __future__ import annotations
import sys
def fibonacci(n: int) -> int:
"""
return F(n)
>>> [fibonacci(i) for i in range(13)]
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144]
"""
if n < 0:
raise ValueError("Negative arguments are not supported")
return _fib(n)[0]
# returns (F(n), F(n-1))
def _fib(n: int) -> tuple[int, int]:
if n == 0: # (F(0), F(1))
return (0, 1)
# F(2n) = F(n)[2F(n+1) − F(n)]
# F(2n+1) = F(n+1)^2+F(n)^2
a, b = _fib(n // 2)
c = a * (b * 2 - a)
d = a * a + b * b
return (d, c + d) if n % 2 else (c, d)
if __name__ == "__main__":
n = int(sys.argv[1])
print(f"fibonacci({n}) is {fibonacci(n)}")
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
Project Euler Problem 7: https://projecteuler.net/problem=7
10001st prime
By listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we
can see that the 6th prime is 13.
What is the 10001st prime number?
References:
- https://en.wikipedia.org/wiki/Prime_number
"""
def isprime(number: int) -> bool:
"""
Determines whether the given number is prime or not
>>> isprime(2)
True
>>> isprime(15)
False
>>> isprime(29)
True
"""
for i in range(2, int(number ** 0.5) + 1):
if number % i == 0:
return False
return True
def solution(nth: int = 10001) -> int:
"""
Returns the n-th prime number.
>>> solution(6)
13
>>> solution(1)
2
>>> solution(3)
5
>>> solution(20)
71
>>> solution(50)
229
>>> solution(100)
541
>>> solution(3.4)
5
>>> solution(0)
Traceback (most recent call last):
...
ValueError: Parameter nth must be greater than or equal to one.
>>> solution(-17)
Traceback (most recent call last):
...
ValueError: Parameter nth must be greater than or equal to one.
>>> solution([])
Traceback (most recent call last):
...
TypeError: Parameter nth must be int or castable to int.
>>> solution("asd")
Traceback (most recent call last):
...
TypeError: Parameter nth must be int or castable to int.
"""
try:
nth = int(nth)
except (TypeError, ValueError):
raise TypeError("Parameter nth must be int or castable to int.") from None
if nth <= 0:
raise ValueError("Parameter nth must be greater than or equal to one.")
primes = []
num = 2
while len(primes) < nth:
if isprime(num):
primes.append(num)
num += 1
else:
num += 1
return primes[len(primes) - 1]
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 7: https://projecteuler.net/problem=7
10001st prime
By listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we
can see that the 6th prime is 13.
What is the 10001st prime number?
References:
- https://en.wikipedia.org/wiki/Prime_number
"""
def isprime(number: int) -> bool:
"""
Determines whether the given number is prime or not
>>> isprime(2)
True
>>> isprime(15)
False
>>> isprime(29)
True
"""
for i in range(2, int(number ** 0.5) + 1):
if number % i == 0:
return False
return True
def solution(nth: int = 10001) -> int:
"""
Returns the n-th prime number.
>>> solution(6)
13
>>> solution(1)
2
>>> solution(3)
5
>>> solution(20)
71
>>> solution(50)
229
>>> solution(100)
541
>>> solution(3.4)
5
>>> solution(0)
Traceback (most recent call last):
...
ValueError: Parameter nth must be greater than or equal to one.
>>> solution(-17)
Traceback (most recent call last):
...
ValueError: Parameter nth must be greater than or equal to one.
>>> solution([])
Traceback (most recent call last):
...
TypeError: Parameter nth must be int or castable to int.
>>> solution("asd")
Traceback (most recent call last):
...
TypeError: Parameter nth must be int or castable to int.
"""
try:
nth = int(nth)
except (TypeError, ValueError):
raise TypeError("Parameter nth must be int or castable to int.") from None
if nth <= 0:
raise ValueError("Parameter nth must be greater than or equal to one.")
primes = []
num = 2
while len(primes) < nth:
if isprime(num):
primes.append(num)
num += 1
else:
num += 1
return primes[len(primes) - 1]
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket")
@patch("builtins.open")
def test_send_file_running_as_expected(file, sock):
# ===== initialization =====
conn = Mock()
sock.return_value.accept.return_value = conn, Mock()
f = iter([1, None])
file.return_value.__enter__.return_value.read.side_effect = lambda _: next(f)
# ===== invoke =====
send_file(filename="mytext.txt", testing=True)
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket")
@patch("builtins.open")
def test_send_file_running_as_expected(file, sock):
# ===== initialization =====
conn = Mock()
sock.return_value.accept.return_value = conn, Mock()
f = iter([1, None])
file.return_value.__enter__.return_value.read.side_effect = lambda _: next(f)
# ===== invoke =====
send_file(filename="mytext.txt", testing=True)
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | import string
from math import log10
"""
tf-idf Wikipedia: https://en.wikipedia.org/wiki/Tf%E2%80%93idf
tf-idf and other word frequency algorithms are often used
as a weighting factor in information retrieval and text
mining. 83% of text-based recommender systems use
tf-idf for term weighting. In Layman's terms, tf-idf
is a statistic intended to reflect how important a word
is to a document in a corpus (a collection of documents)
Here I've implemented several word frequency algorithms
that are commonly used in information retrieval: Term Frequency,
Document Frequency, and TF-IDF (Term-Frequency*Inverse-Document-Frequency)
are included.
Term Frequency is a statistical function that
returns a number representing how frequently
an expression occurs in a document. This
indicates how significant a particular term is in
a given document.
Document Frequency is a statistical function that returns
an integer representing the number of documents in a
corpus that a term occurs in (where the max number returned
would be the number of documents in the corpus).
Inverse Document Frequency is mathematically written as
log10(N/df), where N is the number of documents in your
corpus and df is the Document Frequency. If df is 0, a
ZeroDivisionError will be thrown.
Term-Frequency*Inverse-Document-Frequency is a measure
of the originality of a term. It is mathematically written
as tf*log10(N/df). It compares the number of times
a term appears in a document with the number of documents
the term appears in. If df is 0, a ZeroDivisionError will be thrown.
"""
def term_frequency(term: str, document: str) -> int:
"""
Return the number of times a term occurs within
a given document.
@params: term, the term to search a document for, and document,
the document to search within
@returns: an integer representing the number of times a term is
found within the document
@examples:
>>> term_frequency("to", "To be, or not to be")
2
"""
# strip all punctuation and newlines and replace it with ''
document_without_punctuation = document.translate(
str.maketrans("", "", string.punctuation)
).replace("\n", "")
tokenize_document = document_without_punctuation.split(" ") # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()])
def document_frequency(term: str, corpus: str) -> tuple[int, int]:
"""
Calculate the number of documents in a corpus that contain a
given term
@params : term, the term to search each document for, and corpus, a collection of
documents. Each document should be separated by a newline.
@returns : the number of documents in the corpus that contain the term you are
searching for and the number of documents in the corpus
@examples :
>>> document_frequency("first", "This is the first document in the corpus.\\nThIs\
is the second document in the corpus.\\nTHIS is \
the third document in the corpus.")
(1, 3)
"""
corpus_without_punctuation = corpus.lower().translate(
str.maketrans("", "", string.punctuation)
) # strip all punctuation and replace it with ''
docs = corpus_without_punctuation.split("\n")
term = term.lower()
return (len([doc for doc in docs if term in doc]), len(docs))
def inverse_document_frequency(df: int, N: int, smoothing=False) -> float:
"""
Return an integer denoting the importance
of a word. This measure of importance is
calculated by log10(N/df), where N is the
number of documents and df is
the Document Frequency.
@params : df, the Document Frequency, N,
the number of documents in the corpus and
smoothing, if True return the idf-smooth
@returns : log10(N/df) or 1+log10(N/1+df)
@examples :
>>> inverse_document_frequency(3, 0)
Traceback (most recent call last):
...
ValueError: log10(0) is undefined.
>>> inverse_document_frequency(1, 3)
0.477
>>> inverse_document_frequency(0, 3)
Traceback (most recent call last):
...
ZeroDivisionError: df must be > 0
>>> inverse_document_frequency(0, 3,True)
1.477
"""
if smoothing:
if N == 0:
raise ValueError("log10(0) is undefined.")
return round(1 + log10(N / (1 + df)), 3)
if df == 0:
raise ZeroDivisionError("df must be > 0")
elif N == 0:
raise ValueError("log10(0) is undefined.")
return round(log10(N / df), 3)
def tf_idf(tf: int, idf: int) -> float:
"""
Combine the term frequency
and inverse document frequency functions to
calculate the originality of a term. This
'originality' is calculated by multiplying
the term frequency and the inverse document
frequency : tf-idf = TF * IDF
@params : tf, the term frequency, and idf, the inverse document
frequency
@examples :
>>> tf_idf(2, 0.477)
0.954
"""
return round(tf * idf, 3)
| import string
from math import log10
"""
tf-idf Wikipedia: https://en.wikipedia.org/wiki/Tf%E2%80%93idf
tf-idf and other word frequency algorithms are often used
as a weighting factor in information retrieval and text
mining. 83% of text-based recommender systems use
tf-idf for term weighting. In Layman's terms, tf-idf
is a statistic intended to reflect how important a word
is to a document in a corpus (a collection of documents)
Here I've implemented several word frequency algorithms
that are commonly used in information retrieval: Term Frequency,
Document Frequency, and TF-IDF (Term-Frequency*Inverse-Document-Frequency)
are included.
Term Frequency is a statistical function that
returns a number representing how frequently
an expression occurs in a document. This
indicates how significant a particular term is in
a given document.
Document Frequency is a statistical function that returns
an integer representing the number of documents in a
corpus that a term occurs in (where the max number returned
would be the number of documents in the corpus).
Inverse Document Frequency is mathematically written as
log10(N/df), where N is the number of documents in your
corpus and df is the Document Frequency. If df is 0, a
ZeroDivisionError will be thrown.
Term-Frequency*Inverse-Document-Frequency is a measure
of the originality of a term. It is mathematically written
as tf*log10(N/df). It compares the number of times
a term appears in a document with the number of documents
the term appears in. If df is 0, a ZeroDivisionError will be thrown.
"""
def term_frequency(term: str, document: str) -> int:
"""
Return the number of times a term occurs within
a given document.
@params: term, the term to search a document for, and document,
the document to search within
@returns: an integer representing the number of times a term is
found within the document
@examples:
>>> term_frequency("to", "To be, or not to be")
2
"""
# strip all punctuation and newlines and replace it with ''
document_without_punctuation = document.translate(
str.maketrans("", "", string.punctuation)
).replace("\n", "")
tokenize_document = document_without_punctuation.split(" ") # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()])
def document_frequency(term: str, corpus: str) -> tuple[int, int]:
"""
Calculate the number of documents in a corpus that contain a
given term
@params : term, the term to search each document for, and corpus, a collection of
documents. Each document should be separated by a newline.
@returns : the number of documents in the corpus that contain the term you are
searching for and the number of documents in the corpus
@examples :
>>> document_frequency("first", "This is the first document in the corpus.\\nThIs\
is the second document in the corpus.\\nTHIS is \
the third document in the corpus.")
(1, 3)
"""
corpus_without_punctuation = corpus.lower().translate(
str.maketrans("", "", string.punctuation)
) # strip all punctuation and replace it with ''
docs = corpus_without_punctuation.split("\n")
term = term.lower()
return (len([doc for doc in docs if term in doc]), len(docs))
def inverse_document_frequency(df: int, N: int, smoothing=False) -> float:
"""
Return an integer denoting the importance
of a word. This measure of importance is
calculated by log10(N/df), where N is the
number of documents and df is
the Document Frequency.
@params : df, the Document Frequency, N,
the number of documents in the corpus and
smoothing, if True return the idf-smooth
@returns : log10(N/df) or 1+log10(N/1+df)
@examples :
>>> inverse_document_frequency(3, 0)
Traceback (most recent call last):
...
ValueError: log10(0) is undefined.
>>> inverse_document_frequency(1, 3)
0.477
>>> inverse_document_frequency(0, 3)
Traceback (most recent call last):
...
ZeroDivisionError: df must be > 0
>>> inverse_document_frequency(0, 3,True)
1.477
"""
if smoothing:
if N == 0:
raise ValueError("log10(0) is undefined.")
return round(1 + log10(N / (1 + df)), 3)
if df == 0:
raise ZeroDivisionError("df must be > 0")
elif N == 0:
raise ValueError("log10(0) is undefined.")
return round(log10(N / df), 3)
def tf_idf(tf: int, idf: int) -> float:
"""
Combine the term frequency
and inverse document frequency functions to
calculate the originality of a term. This
'originality' is calculated by multiplying
the term frequency and the inverse document
frequency : tf-idf = TF * IDF
@params : tf, the term frequency, and idf, the inverse document
frequency
@examples :
>>> tf_idf(2, 0.477)
0.954
"""
return round(tf * idf, 3)
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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 | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
Project Euler Problem 65: https://projecteuler.net/problem=65
The square root of 2 can be written as an infinite continued fraction.
sqrt(2) = 1 + 1 / (2 + 1 / (2 + 1 / (2 + 1 / (2 + ...))))
The infinite continued fraction can be written, sqrt(2) = [1;(2)], (2)
indicates that 2 repeats ad infinitum. In a similar way, sqrt(23) =
[4;(1,3,1,8)].
It turns out that the sequence of partial values of continued
fractions for square roots provide the best rational approximations.
Let us consider the convergents for sqrt(2).
1 + 1 / 2 = 3/2
1 + 1 / (2 + 1 / 2) = 7/5
1 + 1 / (2 + 1 / (2 + 1 / 2)) = 17/12
1 + 1 / (2 + 1 / (2 + 1 / (2 + 1 / 2))) = 41/29
Hence the sequence of the first ten convergents for sqrt(2) are:
1, 3/2, 7/5, 17/12, 41/29, 99/70, 239/169, 577/408, 1393/985, 3363/2378, ...
What is most surprising is that the important mathematical constant,
e = [2;1,2,1,1,4,1,1,6,1,...,1,2k,1,...].
The first ten terms in the sequence of convergents for e are:
2, 3, 8/3, 11/4, 19/7, 87/32, 106/39, 193/71, 1264/465, 1457/536, ...
The sum of digits in the numerator of the 10th convergent is
1 + 4 + 5 + 7 = 17.
Find the sum of the digits in the numerator of the 100th convergent
of the continued fraction for e.
-----
The solution mostly comes down to finding an equation that will generate
the numerator of the continued fraction. For the i-th numerator, the
pattern is:
n_i = m_i * n_(i-1) + n_(i-2)
for m_i = the i-th index of the continued fraction representation of e,
n_0 = 1, and n_1 = 2 as the first 2 numbers of the representation.
For example:
n_9 = 6 * 193 + 106 = 1264
1 + 2 + 6 + 4 = 13
n_10 = 1 * 193 + 1264 = 1457
1 + 4 + 5 + 7 = 17
"""
def sum_digits(num: int) -> int:
"""
Returns the sum of every digit in num.
>>> sum_digits(1)
1
>>> sum_digits(12345)
15
>>> sum_digits(999001)
28
"""
digit_sum = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def solution(max: int = 100) -> int:
"""
Returns the sum of the digits in the numerator of the max-th convergent of
the continued fraction for e.
>>> solution(9)
13
>>> solution(10)
17
>>> solution(50)
91
"""
pre_numerator = 1
cur_numerator = 2
for i in range(2, max + 1):
temp = pre_numerator
e_cont = 2 * i // 3 if i % 3 == 0 else 1
pre_numerator = cur_numerator
cur_numerator = e_cont * pre_numerator + temp
return sum_digits(cur_numerator)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 65: https://projecteuler.net/problem=65
The square root of 2 can be written as an infinite continued fraction.
sqrt(2) = 1 + 1 / (2 + 1 / (2 + 1 / (2 + 1 / (2 + ...))))
The infinite continued fraction can be written, sqrt(2) = [1;(2)], (2)
indicates that 2 repeats ad infinitum. In a similar way, sqrt(23) =
[4;(1,3,1,8)].
It turns out that the sequence of partial values of continued
fractions for square roots provide the best rational approximations.
Let us consider the convergents for sqrt(2).
1 + 1 / 2 = 3/2
1 + 1 / (2 + 1 / 2) = 7/5
1 + 1 / (2 + 1 / (2 + 1 / 2)) = 17/12
1 + 1 / (2 + 1 / (2 + 1 / (2 + 1 / 2))) = 41/29
Hence the sequence of the first ten convergents for sqrt(2) are:
1, 3/2, 7/5, 17/12, 41/29, 99/70, 239/169, 577/408, 1393/985, 3363/2378, ...
What is most surprising is that the important mathematical constant,
e = [2;1,2,1,1,4,1,1,6,1,...,1,2k,1,...].
The first ten terms in the sequence of convergents for e are:
2, 3, 8/3, 11/4, 19/7, 87/32, 106/39, 193/71, 1264/465, 1457/536, ...
The sum of digits in the numerator of the 10th convergent is
1 + 4 + 5 + 7 = 17.
Find the sum of the digits in the numerator of the 100th convergent
of the continued fraction for e.
-----
The solution mostly comes down to finding an equation that will generate
the numerator of the continued fraction. For the i-th numerator, the
pattern is:
n_i = m_i * n_(i-1) + n_(i-2)
for m_i = the i-th index of the continued fraction representation of e,
n_0 = 1, and n_1 = 2 as the first 2 numbers of the representation.
For example:
n_9 = 6 * 193 + 106 = 1264
1 + 2 + 6 + 4 = 13
n_10 = 1 * 193 + 1264 = 1457
1 + 4 + 5 + 7 = 17
"""
def sum_digits(num: int) -> int:
"""
Returns the sum of every digit in num.
>>> sum_digits(1)
1
>>> sum_digits(12345)
15
>>> sum_digits(999001)
28
"""
digit_sum = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def solution(max: int = 100) -> int:
"""
Returns the sum of the digits in the numerator of the max-th convergent of
the continued fraction for e.
>>> solution(9)
13
>>> solution(10)
17
>>> solution(50)
91
"""
pre_numerator = 1
cur_numerator = 2
for i in range(2, max + 1):
temp = pre_numerator
e_cont = 2 * i // 3 if i % 3 == 0 else 1
pre_numerator = cur_numerator
cur_numerator = e_cont * pre_numerator + temp
return sum_digits(cur_numerator)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
Output:
Enter a Postfix Equation (space separated) = 5 6 9 * +
Symbol | Action | Stack
-----------------------------------
5 | push(5) | 5
6 | push(6) | 5,6
9 | push(9) | 5,6,9
| pop(9) | 5,6
| pop(6) | 5
* | push(6*9) | 5,54
| pop(54) | 5
| pop(5) |
+ | push(5+54) | 59
Result = 59
"""
import operator as op
def Solve(Postfix):
Stack = []
Div = lambda x, y: int(x / y) # noqa: E731 integer division operation
Opr = {
"^": op.pow,
"*": op.mul,
"/": Div,
"+": op.add,
"-": op.sub,
} # operators & their respective operation
# print table header
print("Symbol".center(8), "Action".center(12), "Stack", sep=" | ")
print("-" * (30 + len(Postfix)))
for x in Postfix:
if x.isdigit(): # if x in digit
Stack.append(x) # append x to stack
# output in tabular format
print(x.rjust(8), ("push(" + x + ")").ljust(12), ",".join(Stack), sep=" | ")
else:
B = Stack.pop() # pop stack
# output in tabular format
print("".rjust(8), ("pop(" + B + ")").ljust(12), ",".join(Stack), sep=" | ")
A = Stack.pop() # pop stack
# output in tabular format
print("".rjust(8), ("pop(" + A + ")").ljust(12), ",".join(Stack), sep=" | ")
Stack.append(
str(Opr[x](int(A), int(B)))
) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8),
("push(" + A + x + B + ")").ljust(12),
",".join(Stack),
sep=" | ",
)
return int(Stack[0])
if __name__ == "__main__":
Postfix = input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
print("\n\tResult = ", Solve(Postfix))
| """
Output:
Enter a Postfix Equation (space separated) = 5 6 9 * +
Symbol | Action | Stack
-----------------------------------
5 | push(5) | 5
6 | push(6) | 5,6
9 | push(9) | 5,6,9
| pop(9) | 5,6
| pop(6) | 5
* | push(6*9) | 5,54
| pop(54) | 5
| pop(5) |
+ | push(5+54) | 59
Result = 59
"""
import operator as op
def Solve(Postfix):
Stack = []
Div = lambda x, y: int(x / y) # noqa: E731 integer division operation
Opr = {
"^": op.pow,
"*": op.mul,
"/": Div,
"+": op.add,
"-": op.sub,
} # operators & their respective operation
# print table header
print("Symbol".center(8), "Action".center(12), "Stack", sep=" | ")
print("-" * (30 + len(Postfix)))
for x in Postfix:
if x.isdigit(): # if x in digit
Stack.append(x) # append x to stack
# output in tabular format
print(x.rjust(8), ("push(" + x + ")").ljust(12), ",".join(Stack), sep=" | ")
else:
B = Stack.pop() # pop stack
# output in tabular format
print("".rjust(8), ("pop(" + B + ")").ljust(12), ",".join(Stack), sep=" | ")
A = Stack.pop() # pop stack
# output in tabular format
print("".rjust(8), ("pop(" + A + ")").ljust(12), ",".join(Stack), sep=" | ")
Stack.append(
str(Opr[x](int(A), int(B)))
) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8),
("push(" + A + x + B + ")").ljust(12),
",".join(Stack),
sep=" | ",
)
return int(Stack[0])
if __name__ == "__main__":
Postfix = input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
print("\n\tResult = ", Solve(Postfix))
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
The first known prime found to exceed one million digits was discovered in 1999,
and is a Mersenne prime of the form 2**6972593 − 1; it contains exactly 2,098,960
digits. Subsequently other Mersenne primes, of the form 2**p − 1, have been found
which contain more digits.
However, in 2004 there was found a massive non-Mersenne prime which contains
2,357,207 digits: (28433 * (2 ** 7830457 + 1)).
Find the last ten digits of this prime number.
"""
def solution(n: int = 10) -> str:
"""
Returns the last n digits of NUMBER.
>>> solution()
'8739992577'
>>> solution(8)
'39992577'
>>> solution(1)
'7'
>>> solution(-1)
Traceback (most recent call last):
...
ValueError: Invalid input
>>> solution(8.3)
Traceback (most recent call last):
...
ValueError: Invalid input
>>> solution("a")
Traceback (most recent call last):
...
ValueError: Invalid input
"""
if not isinstance(n, int) or n < 0:
raise ValueError("Invalid input")
MODULUS = 10 ** n
NUMBER = 28433 * (pow(2, 7830457, MODULUS)) + 1
return str(NUMBER % MODULUS)
if __name__ == "__main__":
from doctest import testmod
testmod()
print(f"{solution(10) = }")
| """
The first known prime found to exceed one million digits was discovered in 1999,
and is a Mersenne prime of the form 2**6972593 − 1; it contains exactly 2,098,960
digits. Subsequently other Mersenne primes, of the form 2**p − 1, have been found
which contain more digits.
However, in 2004 there was found a massive non-Mersenne prime which contains
2,357,207 digits: (28433 * (2 ** 7830457 + 1)).
Find the last ten digits of this prime number.
"""
def solution(n: int = 10) -> str:
"""
Returns the last n digits of NUMBER.
>>> solution()
'8739992577'
>>> solution(8)
'39992577'
>>> solution(1)
'7'
>>> solution(-1)
Traceback (most recent call last):
...
ValueError: Invalid input
>>> solution(8.3)
Traceback (most recent call last):
...
ValueError: Invalid input
>>> solution("a")
Traceback (most recent call last):
...
ValueError: Invalid input
"""
if not isinstance(n, int) or n < 0:
raise ValueError("Invalid input")
MODULUS = 10 ** n
NUMBER = 28433 * (pow(2, 7830457, MODULUS)) + 1
return str(NUMBER % MODULUS)
if __name__ == "__main__":
from doctest import testmod
testmod()
print(f"{solution(10) = }")
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | # https://en.wikipedia.org/wiki/Hill_climbing
import math
class SearchProblem:
"""
An interface to define search problems.
The interface will be illustrated using the example of mathematical function.
"""
def __init__(self, x: int, y: int, step_size: int, function_to_optimize):
"""
The constructor of the search problem.
x: the x coordinate of the current search state.
y: the y coordinate of the current search state.
step_size: size of the step to take when looking for neighbors.
function_to_optimize: a function to optimize having the signature f(x, y).
"""
self.x = x
self.y = y
self.step_size = step_size
self.function = function_to_optimize
def score(self) -> int:
"""
Returns the output of the function called with current x and y coordinates.
>>> def test_function(x, y):
... return x + y
>>> SearchProblem(0, 0, 1, test_function).score() # 0 + 0 = 0
0
>>> SearchProblem(5, 7, 1, test_function).score() # 5 + 7 = 12
12
"""
return self.function(self.x, self.y)
def get_neighbors(self):
"""
Returns a list of coordinates of neighbors adjacent to the current coordinates.
Neighbors:
| 0 | 1 | 2 |
| 3 | _ | 4 |
| 5 | 6 | 7 |
"""
step_size = self.step_size
return [
SearchProblem(x, y, step_size, self.function)
for x, y in (
(self.x - step_size, self.y - step_size),
(self.x - step_size, self.y),
(self.x - step_size, self.y + step_size),
(self.x, self.y - step_size),
(self.x, self.y + step_size),
(self.x + step_size, self.y - step_size),
(self.x + step_size, self.y),
(self.x + step_size, self.y + step_size),
)
]
def __hash__(self):
"""
hash the string representation of the current search state.
"""
return hash(str(self))
def __eq__(self, obj):
"""
Check if the 2 objects are equal.
"""
if isinstance(obj, SearchProblem):
return hash(str(self)) == hash(str(obj))
return False
def __str__(self):
"""
string representation of the current search state.
>>> str(SearchProblem(0, 0, 1, None))
'x: 0 y: 0'
>>> str(SearchProblem(2, 5, 1, None))
'x: 2 y: 5'
"""
return f"x: {self.x} y: {self.y}"
def hill_climbing(
search_prob,
find_max: bool = True,
max_x: float = math.inf,
min_x: float = -math.inf,
max_y: float = math.inf,
min_y: float = -math.inf,
visualization: bool = False,
max_iter: int = 10000,
) -> SearchProblem:
"""
Implementation of the hill climbling algorithm.
We start with a given state, find all its neighbors,
move towards the neighbor which provides the maximum (or minimum) change.
We keep doing this until we are at a state where we do not have any
neighbors which can improve the solution.
Args:
search_prob: The search state at the start.
find_max: If True, the algorithm should find the maximum else the minimum.
max_x, min_x, max_y, min_y: the maximum and minimum bounds of x and y.
visualization: If True, a matplotlib graph is displayed.
max_iter: number of times to run the iteration.
Returns a search state having the maximum (or minimum) score.
"""
current_state = search_prob
scores = [] # list to store the current score at each iteration
iterations = 0
solution_found = False
visited = set()
while not solution_found and iterations < max_iter:
visited.add(current_state)
iterations += 1
current_score = current_state.score()
scores.append(current_score)
neighbors = current_state.get_neighbors()
max_change = -math.inf
min_change = math.inf
next_state = None # to hold the next best neighbor
for neighbor in neighbors:
if neighbor in visited:
continue # do not want to visit the same state again
if (
neighbor.x > max_x
or neighbor.x < min_x
or neighbor.y > max_y
or neighbor.y < min_y
):
continue # neighbor outside our bounds
change = neighbor.score() - current_score
if find_max: # finding max
# going to direction with greatest ascent
if change > max_change and change > 0:
max_change = change
next_state = neighbor
else: # finding min
# to direction with greatest descent
if change < min_change and change < 0:
min_change = change
next_state = neighbor
if next_state is not None:
# we found at least one neighbor which improved the current state
current_state = next_state
else:
# since we have no neighbor that improves the solution we stop the search
solution_found = True
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(iterations), scores)
plt.xlabel("Iterations")
plt.ylabel("Function values")
plt.show()
return current_state
if __name__ == "__main__":
import doctest
doctest.testmod()
def test_f1(x, y):
return (x ** 2) + (y ** 2)
# starting the problem with initial coordinates (3, 4)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(prob, find_max=False)
print(
"The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{local_min.score()}"
)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
def test_f2(x, y):
return (3 * x ** 2) - (6 * y)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(prob, find_max=True)
print(
"The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{local_min.score()}"
)
| # https://en.wikipedia.org/wiki/Hill_climbing
import math
class SearchProblem:
"""
An interface to define search problems.
The interface will be illustrated using the example of mathematical function.
"""
def __init__(self, x: int, y: int, step_size: int, function_to_optimize):
"""
The constructor of the search problem.
x: the x coordinate of the current search state.
y: the y coordinate of the current search state.
step_size: size of the step to take when looking for neighbors.
function_to_optimize: a function to optimize having the signature f(x, y).
"""
self.x = x
self.y = y
self.step_size = step_size
self.function = function_to_optimize
def score(self) -> int:
"""
Returns the output of the function called with current x and y coordinates.
>>> def test_function(x, y):
... return x + y
>>> SearchProblem(0, 0, 1, test_function).score() # 0 + 0 = 0
0
>>> SearchProblem(5, 7, 1, test_function).score() # 5 + 7 = 12
12
"""
return self.function(self.x, self.y)
def get_neighbors(self):
"""
Returns a list of coordinates of neighbors adjacent to the current coordinates.
Neighbors:
| 0 | 1 | 2 |
| 3 | _ | 4 |
| 5 | 6 | 7 |
"""
step_size = self.step_size
return [
SearchProblem(x, y, step_size, self.function)
for x, y in (
(self.x - step_size, self.y - step_size),
(self.x - step_size, self.y),
(self.x - step_size, self.y + step_size),
(self.x, self.y - step_size),
(self.x, self.y + step_size),
(self.x + step_size, self.y - step_size),
(self.x + step_size, self.y),
(self.x + step_size, self.y + step_size),
)
]
def __hash__(self):
"""
hash the string representation of the current search state.
"""
return hash(str(self))
def __eq__(self, obj):
"""
Check if the 2 objects are equal.
"""
if isinstance(obj, SearchProblem):
return hash(str(self)) == hash(str(obj))
return False
def __str__(self):
"""
string representation of the current search state.
>>> str(SearchProblem(0, 0, 1, None))
'x: 0 y: 0'
>>> str(SearchProblem(2, 5, 1, None))
'x: 2 y: 5'
"""
return f"x: {self.x} y: {self.y}"
def hill_climbing(
search_prob,
find_max: bool = True,
max_x: float = math.inf,
min_x: float = -math.inf,
max_y: float = math.inf,
min_y: float = -math.inf,
visualization: bool = False,
max_iter: int = 10000,
) -> SearchProblem:
"""
Implementation of the hill climbling algorithm.
We start with a given state, find all its neighbors,
move towards the neighbor which provides the maximum (or minimum) change.
We keep doing this until we are at a state where we do not have any
neighbors which can improve the solution.
Args:
search_prob: The search state at the start.
find_max: If True, the algorithm should find the maximum else the minimum.
max_x, min_x, max_y, min_y: the maximum and minimum bounds of x and y.
visualization: If True, a matplotlib graph is displayed.
max_iter: number of times to run the iteration.
Returns a search state having the maximum (or minimum) score.
"""
current_state = search_prob
scores = [] # list to store the current score at each iteration
iterations = 0
solution_found = False
visited = set()
while not solution_found and iterations < max_iter:
visited.add(current_state)
iterations += 1
current_score = current_state.score()
scores.append(current_score)
neighbors = current_state.get_neighbors()
max_change = -math.inf
min_change = math.inf
next_state = None # to hold the next best neighbor
for neighbor in neighbors:
if neighbor in visited:
continue # do not want to visit the same state again
if (
neighbor.x > max_x
or neighbor.x < min_x
or neighbor.y > max_y
or neighbor.y < min_y
):
continue # neighbor outside our bounds
change = neighbor.score() - current_score
if find_max: # finding max
# going to direction with greatest ascent
if change > max_change and change > 0:
max_change = change
next_state = neighbor
else: # finding min
# to direction with greatest descent
if change < min_change and change < 0:
min_change = change
next_state = neighbor
if next_state is not None:
# we found at least one neighbor which improved the current state
current_state = next_state
else:
# since we have no neighbor that improves the solution we stop the search
solution_found = True
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(iterations), scores)
plt.xlabel("Iterations")
plt.ylabel("Function values")
plt.show()
return current_state
if __name__ == "__main__":
import doctest
doctest.testmod()
def test_f1(x, y):
return (x ** 2) + (y ** 2)
# starting the problem with initial coordinates (3, 4)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(prob, find_max=False)
print(
"The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{local_min.score()}"
)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
def test_f2(x, y):
return (3 * x ** 2) - (6 * y)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(prob, find_max=True)
print(
"The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{local_min.score()}"
)
| -1 |
TheAlgorithms/Python | 4,637 | [mypy] Fix type annotations for strings | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`. | imp2002 | "2021-08-19T11:09:41Z" | "2021-08-19T12:08:20Z" | 9cb5760e895179f8aaa97dd577442189064c724d | 20a4fdf38465c2731100c3fbd1aac847cd0b9322 | [mypy] Fix type annotations for strings. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`. | """
By starting at the top of the triangle below and moving to adjacent numbers on
the row below, the maximum total from top to bottom is 23.
3
7 4
2 4 6
8 5 9 3
That is, 3 + 7 + 4 + 9 = 23.
Find the maximum total from top to bottom of the triangle below:
75
95 64
17 47 82
18 35 87 10
20 04 82 47 65
19 01 23 75 03 34
88 02 77 73 07 63 67
99 65 04 28 06 16 70 92
41 41 26 56 83 40 80 70 33
41 48 72 33 47 32 37 16 94 29
53 71 44 65 25 43 91 52 97 51 14
70 11 33 28 77 73 17 78 39 68 17 57
91 71 52 38 17 14 91 43 58 50 27 29 48
63 66 04 68 89 53 67 30 73 16 69 87 40 31
04 62 98 27 23 09 70 98 73 93 38 53 60 04 23
"""
import os
def solution():
"""
Finds the maximum total in a triangle as described by the problem statement
above.
>>> solution()
1074
"""
script_dir = os.path.dirname(os.path.realpath(__file__))
triangle = os.path.join(script_dir, "triangle.txt")
with open(triangle) as f:
triangle = f.readlines()
a = [[int(y) for y in x.rstrip("\r\n").split(" ")] for x in triangle]
for i in range(1, len(a)):
for j in range(len(a[i])):
if j != len(a[i - 1]):
number1 = a[i - 1][j]
else:
number1 = 0
if j > 0:
number2 = a[i - 1][j - 1]
else:
number2 = 0
a[i][j] += max(number1, number2)
return max(a[-1])
if __name__ == "__main__":
print(solution())
| """
By starting at the top of the triangle below and moving to adjacent numbers on
the row below, the maximum total from top to bottom is 23.
3
7 4
2 4 6
8 5 9 3
That is, 3 + 7 + 4 + 9 = 23.
Find the maximum total from top to bottom of the triangle below:
75
95 64
17 47 82
18 35 87 10
20 04 82 47 65
19 01 23 75 03 34
88 02 77 73 07 63 67
99 65 04 28 06 16 70 92
41 41 26 56 83 40 80 70 33
41 48 72 33 47 32 37 16 94 29
53 71 44 65 25 43 91 52 97 51 14
70 11 33 28 77 73 17 78 39 68 17 57
91 71 52 38 17 14 91 43 58 50 27 29 48
63 66 04 68 89 53 67 30 73 16 69 87 40 31
04 62 98 27 23 09 70 98 73 93 38 53 60 04 23
"""
import os
def solution():
"""
Finds the maximum total in a triangle as described by the problem statement
above.
>>> solution()
1074
"""
script_dir = os.path.dirname(os.path.realpath(__file__))
triangle = os.path.join(script_dir, "triangle.txt")
with open(triangle) as f:
triangle = f.readlines()
a = [[int(y) for y in x.rstrip("\r\n").split(" ")] for x in triangle]
for i in range(1, len(a)):
for j in range(len(a[i])):
if j != len(a[i - 1]):
number1 = a[i - 1][j]
else:
number1 = 0
if j > 0:
number2 = a[i - 1][j - 1]
else:
number2 = 0
a[i][j] += max(number1, number2)
return max(a[-1])
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 4,617 | [mypy] Fix type annotations for maths | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`.
| imp2002 | "2021-08-16T14:48:38Z" | "2021-08-18T10:45:07Z" | 4545270ace03411ec861361329345a36195b881d | af0810fca133dde19b39fc7735572b6989ea269b | [mypy] Fix type annotations for maths. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
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 = (-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 | 4,617 | [mypy] Fix type annotations for maths | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`.
| imp2002 | "2021-08-16T14:48:38Z" | "2021-08-18T10:45:07Z" | 4545270ace03411ec861361329345a36195b881d | af0810fca133dde19b39fc7735572b6989ea269b | [mypy] Fix type annotations for maths. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from typing import Union
def median(nums: Union[int, float]) -> Union[int, float]:
"""
Find median of a list of numbers.
Wiki: https://en.wikipedia.org/wiki/Median
>>> median([0])
0
>>> median([4,1,3,2])
2.5
>>> median([2, 70, 6, 50, 20, 8, 4])
8
Args:
nums: List of nums
Returns:
Median.
"""
sorted_list = sorted(nums)
length = len(sorted_list)
mid_index = length >> 1
return (
(sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
if length % 2 == 0
else sorted_list[mid_index]
)
def main():
import doctest
doctest.testmod()
if __name__ == "__main__":
main()
| from typing import Union
def median(nums: list) -> Union[int, float]:
"""
Find median of a list of numbers.
Wiki: https://en.wikipedia.org/wiki/Median
>>> median([0])
0
>>> median([4, 1, 3, 2])
2.5
>>> median([2, 70, 6, 50, 20, 8, 4])
8
Args:
nums: List of nums
Returns:
Median.
"""
sorted_list = sorted(nums)
length = len(sorted_list)
mid_index = length >> 1
return (
(sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
if length % 2 == 0
else sorted_list[mid_index]
)
def main():
import doctest
doctest.testmod()
if __name__ == "__main__":
main()
| 1 |
TheAlgorithms/Python | 4,617 | [mypy] Fix type annotations for maths | Related issue #4052
### **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.
* [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: #{$ISSUE_NO}`.
| imp2002 | "2021-08-16T14:48:38Z" | "2021-08-18T10:45:07Z" | 4545270ace03411ec861361329345a36195b881d | af0810fca133dde19b39fc7735572b6989ea269b | [mypy] Fix type annotations for maths. Related issue #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is a pure Python implementation of the Harmonic Series algorithm
https://en.wikipedia.org/wiki/Harmonic_series_(mathematics)
For doctests run following command:
python -m doctest -v harmonic_series.py
or
python3 -m doctest -v harmonic_series.py
For manual testing run:
python3 harmonic_series.py
"""
def harmonic_series(n_term: str) -> list:
"""Pure Python implementation of Harmonic Series algorithm
:param n_term: The last (nth) term of Harmonic Series
:return: The Harmonic Series starting from 1 to last (nth) term
Examples:
>>> harmonic_series(5)
['1', '1/2', '1/3', '1/4', '1/5']
>>> harmonic_series(5.0)
['1', '1/2', '1/3', '1/4', '1/5']
>>> harmonic_series(5.1)
['1', '1/2', '1/3', '1/4', '1/5']
>>> harmonic_series(-5)
[]
>>> harmonic_series(0)
[]
>>> harmonic_series(1)
['1']
"""
if n_term == "":
return n_term
series = []
for temp in range(int(n_term)):
series.append(f"1/{temp + 1}" if series else "1")
return series
if __name__ == "__main__":
nth_term = input("Enter the last number (nth term) of the Harmonic Series")
print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
print(harmonic_series(nth_term))
| """
This is a pure Python implementation of the Harmonic Series algorithm
https://en.wikipedia.org/wiki/Harmonic_series_(mathematics)
For doctests run following command:
python -m doctest -v harmonic_series.py
or
python3 -m doctest -v harmonic_series.py
For manual testing run:
python3 harmonic_series.py
"""
def harmonic_series(n_term: str) -> list:
"""Pure Python implementation of Harmonic Series algorithm
:param n_term: The last (nth) term of Harmonic Series
:return: The Harmonic Series starting from 1 to last (nth) term
Examples:
>>> harmonic_series(5)
['1', '1/2', '1/3', '1/4', '1/5']
>>> harmonic_series(5.0)
['1', '1/2', '1/3', '1/4', '1/5']
>>> harmonic_series(5.1)
['1', '1/2', '1/3', '1/4', '1/5']
>>> harmonic_series(-5)
[]
>>> harmonic_series(0)
[]
>>> harmonic_series(1)
['1']
"""
if n_term == "":
return []
series: list = []
for temp in range(int(n_term)):
series.append(f"1/{temp + 1}" if series else "1")
return series
if __name__ == "__main__":
nth_term = input("Enter the last number (nth term) of the Harmonic Series")
print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
print(harmonic_series(nth_term))
| 1 |
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