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TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
from .hash_table import HashTable
class QuadraticProbing(HashTable):
"""
Basic Hash Table example with open addressing using Quadratic Probing
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def _collision_resolution(self, key, data=None):
i = 1
new_key = self.hash_function(key + i * i)
while self.values[new_key] is not None and self.values[new_key] != key:
i += 1
new_key = (
self.hash_function(key + i * i)
if not self.balanced_factor() >= self.lim_charge
else None
)
if new_key is None:
break
return new_key
| #!/usr/bin/env python3
from .hash_table import HashTable
class QuadraticProbing(HashTable):
"""
Basic Hash Table example with open addressing using Quadratic Probing
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def _collision_resolution(self, key, data=None):
i = 1
new_key = self.hash_function(key + i * i)
while self.values[new_key] is not None and self.values[new_key] != key:
i += 1
new_key = (
self.hash_function(key + i * i)
if not self.balanced_factor() >= self.lim_charge
else None
)
if new_key is None:
break
return new_key
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/local/bin/python3
"""
Problem Description: Given two binary tree, return the merged tree.
The rule for merging is that if two nodes overlap, then put the value sum of
both nodes to the new value of the merged node. Otherwise, the NOT null node
will be used as the node of new tree.
"""
from __future__ import annotations
class Node:
"""
A binary node has value variable and pointers to its left and right node.
"""
def __init__(self, value: int = 0) -> None:
self.value = value
self.left: Node | None = None
self.right: Node | None = None
def merge_two_binary_trees(tree1: Node | None, tree2: Node | None) -> Node | None:
"""
Returns root node of the merged tree.
>>> tree1 = Node(5)
>>> tree1.left = Node(6)
>>> tree1.right = Node(7)
>>> tree1.left.left = Node(2)
>>> tree2 = Node(4)
>>> tree2.left = Node(5)
>>> tree2.right = Node(8)
>>> tree2.left.right = Node(1)
>>> tree2.right.right = Node(4)
>>> merged_tree = merge_two_binary_trees(tree1, tree2)
>>> print_preorder(merged_tree)
9
11
2
1
15
4
"""
if tree1 is None:
return tree2
if tree2 is None:
return tree1
tree1.value = tree1.value + tree2.value
tree1.left = merge_two_binary_trees(tree1.left, tree2.left)
tree1.right = merge_two_binary_trees(tree1.right, tree2.right)
return tree1
def print_preorder(root: Node | None) -> None:
"""
Print pre-order traversal of the tree.
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> print_preorder(root)
1
2
3
>>> print_preorder(root.right)
3
"""
if root:
print(root.value)
print_preorder(root.left)
print_preorder(root.right)
if __name__ == "__main__":
tree1 = Node(1)
tree1.left = Node(2)
tree1.right = Node(3)
tree1.left.left = Node(4)
tree2 = Node(2)
tree2.left = Node(4)
tree2.right = Node(6)
tree2.left.right = Node(9)
tree2.right.right = Node(5)
print("Tree1 is: ")
print_preorder(tree1)
print("Tree2 is: ")
print_preorder(tree2)
merged_tree = merge_two_binary_trees(tree1, tree2)
print("Merged Tree is: ")
print_preorder(merged_tree)
| #!/usr/local/bin/python3
"""
Problem Description: Given two binary tree, return the merged tree.
The rule for merging is that if two nodes overlap, then put the value sum of
both nodes to the new value of the merged node. Otherwise, the NOT null node
will be used as the node of new tree.
"""
from __future__ import annotations
class Node:
"""
A binary node has value variable and pointers to its left and right node.
"""
def __init__(self, value: int = 0) -> None:
self.value = value
self.left: Node | None = None
self.right: Node | None = None
def merge_two_binary_trees(tree1: Node | None, tree2: Node | None) -> Node | None:
"""
Returns root node of the merged tree.
>>> tree1 = Node(5)
>>> tree1.left = Node(6)
>>> tree1.right = Node(7)
>>> tree1.left.left = Node(2)
>>> tree2 = Node(4)
>>> tree2.left = Node(5)
>>> tree2.right = Node(8)
>>> tree2.left.right = Node(1)
>>> tree2.right.right = Node(4)
>>> merged_tree = merge_two_binary_trees(tree1, tree2)
>>> print_preorder(merged_tree)
9
11
2
1
15
4
"""
if tree1 is None:
return tree2
if tree2 is None:
return tree1
tree1.value = tree1.value + tree2.value
tree1.left = merge_two_binary_trees(tree1.left, tree2.left)
tree1.right = merge_two_binary_trees(tree1.right, tree2.right)
return tree1
def print_preorder(root: Node | None) -> None:
"""
Print pre-order traversal of the tree.
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> print_preorder(root)
1
2
3
>>> print_preorder(root.right)
3
"""
if root:
print(root.value)
print_preorder(root.left)
print_preorder(root.right)
if __name__ == "__main__":
tree1 = Node(1)
tree1.left = Node(2)
tree1.right = Node(3)
tree1.left.left = Node(4)
tree2 = Node(2)
tree2.left = Node(4)
tree2.right = Node(6)
tree2.left.right = Node(9)
tree2.right.right = Node(5)
print("Tree1 is: ")
print_preorder(tree1)
print("Tree2 is: ")
print_preorder(tree2)
merged_tree = merge_two_binary_trees(tree1, tree2)
print("Merged Tree is: ")
print_preorder(merged_tree)
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is pure Python implementation of sentinel linear search algorithm
For doctests run following command:
python -m doctest -v sentinel_linear_search.py
or
python3 -m doctest -v sentinel_linear_search.py
For manual testing run:
python sentinel_linear_search.py
"""
def sentinel_linear_search(sequence, target):
"""Pure implementation of sentinel linear search algorithm in Python
:param sequence: some sequence with comparable items
:param target: item value to search
:return: index of found item or None if item is not found
Examples:
>>> sentinel_linear_search([0, 5, 7, 10, 15], 0)
0
>>> sentinel_linear_search([0, 5, 7, 10, 15], 15)
4
>>> sentinel_linear_search([0, 5, 7, 10, 15], 5)
1
>>> sentinel_linear_search([0, 5, 7, 10, 15], 6)
"""
sequence.append(target)
index = 0
while sequence[index] != target:
index += 1
sequence.pop()
if index == len(sequence):
return None
return index
if __name__ == "__main__":
user_input = input("Enter numbers separated by comma:\n").strip()
sequence = [int(item) for item in user_input.split(",")]
target_input = input("Enter a single number to be found in the list:\n")
target = int(target_input)
result = sentinel_linear_search(sequence, target)
if result is not None:
print(f"{target} found at positions: {result}")
else:
print("Not found")
| """
This is pure Python implementation of sentinel linear search algorithm
For doctests run following command:
python -m doctest -v sentinel_linear_search.py
or
python3 -m doctest -v sentinel_linear_search.py
For manual testing run:
python sentinel_linear_search.py
"""
def sentinel_linear_search(sequence, target):
"""Pure implementation of sentinel linear search algorithm in Python
:param sequence: some sequence with comparable items
:param target: item value to search
:return: index of found item or None if item is not found
Examples:
>>> sentinel_linear_search([0, 5, 7, 10, 15], 0)
0
>>> sentinel_linear_search([0, 5, 7, 10, 15], 15)
4
>>> sentinel_linear_search([0, 5, 7, 10, 15], 5)
1
>>> sentinel_linear_search([0, 5, 7, 10, 15], 6)
"""
sequence.append(target)
index = 0
while sequence[index] != target:
index += 1
sequence.pop()
if index == len(sequence):
return None
return index
if __name__ == "__main__":
user_input = input("Enter numbers separated by comma:\n").strip()
sequence = [int(item) for item in user_input.split(",")]
target_input = input("Enter a single number to be found in the list:\n")
target = int(target_input)
result = sentinel_linear_search(sequence, target)
if result is not None:
print(f"{target} found at positions: {result}")
else:
print("Not found")
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #############################
# Author: Aravind Kashyap
# File: lis.py
# comments: This programme outputs the Longest Strictly Increasing Subsequence in
# O(NLogN) Where N is the Number of elements in the list
#############################
from __future__ import annotations
def ceil_index(v, l, r, key): # noqa: E741
while r - l > 1:
m = (l + r) // 2
if v[m] >= key:
r = m
else:
l = m # noqa: E741
return r
def longest_increasing_subsequence_length(v: list[int]) -> int:
"""
>>> longest_increasing_subsequence_length([2, 5, 3, 7, 11, 8, 10, 13, 6])
6
>>> longest_increasing_subsequence_length([])
0
>>> longest_increasing_subsequence_length([0, 8, 4, 12, 2, 10, 6, 14, 1, 9, 5, 13,
... 3, 11, 7, 15])
6
>>> longest_increasing_subsequence_length([5, 4, 3, 2, 1])
1
"""
if len(v) == 0:
return 0
tail = [0] * len(v)
length = 1
tail[0] = v[0]
for i in range(1, len(v)):
if v[i] < tail[0]:
tail[0] = v[i]
elif v[i] > tail[length - 1]:
tail[length] = v[i]
length += 1
else:
tail[ceil_index(tail, -1, length - 1, v[i])] = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| #############################
# Author: Aravind Kashyap
# File: lis.py
# comments: This programme outputs the Longest Strictly Increasing Subsequence in
# O(NLogN) Where N is the Number of elements in the list
#############################
from __future__ import annotations
def ceil_index(v, l, r, key): # noqa: E741
while r - l > 1:
m = (l + r) // 2
if v[m] >= key:
r = m
else:
l = m # noqa: E741
return r
def longest_increasing_subsequence_length(v: list[int]) -> int:
"""
>>> longest_increasing_subsequence_length([2, 5, 3, 7, 11, 8, 10, 13, 6])
6
>>> longest_increasing_subsequence_length([])
0
>>> longest_increasing_subsequence_length([0, 8, 4, 12, 2, 10, 6, 14, 1, 9, 5, 13,
... 3, 11, 7, 15])
6
>>> longest_increasing_subsequence_length([5, 4, 3, 2, 1])
1
"""
if len(v) == 0:
return 0
tail = [0] * len(v)
length = 1
tail[0] = v[0]
for i in range(1, len(v)):
if v[i] < tail[0]:
tail[0] = v[i]
elif v[i] > tail[length - 1]:
tail[length] = v[i]
length += 1
else:
tail[ceil_index(tail, -1, length - 1, v[i])] = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """For more information about the Binomial Distribution -
https://en.wikipedia.org/wiki/Binomial_distribution"""
from math import factorial
def binomial_distribution(successes: int, trials: int, prob: float) -> float:
"""
Return probability of k successes out of n tries, with p probability for one
success
The function uses the factorial function in order to calculate the binomial
coefficient
>>> binomial_distribution(3, 5, 0.7)
0.30870000000000003
>>> binomial_distribution (2, 4, 0.5)
0.375
"""
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(successes, int) or not isinstance(trials, int):
raise ValueError("the function is defined for non-negative integers")
if not 0 < prob < 1:
raise ValueError("prob has to be in range of 1 - 0")
probability = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
coefficient = float(factorial(trials))
coefficient /= factorial(successes) * factorial(trials - successes)
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print("Probability of 2 successes out of 4 trails")
print("with probability of 0.75 is:", end=" ")
print(binomial_distribution(2, 4, 0.75))
| """For more information about the Binomial Distribution -
https://en.wikipedia.org/wiki/Binomial_distribution"""
from math import factorial
def binomial_distribution(successes: int, trials: int, prob: float) -> float:
"""
Return probability of k successes out of n tries, with p probability for one
success
The function uses the factorial function in order to calculate the binomial
coefficient
>>> binomial_distribution(3, 5, 0.7)
0.30870000000000003
>>> binomial_distribution (2, 4, 0.5)
0.375
"""
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(successes, int) or not isinstance(trials, int):
raise ValueError("the function is defined for non-negative integers")
if not 0 < prob < 1:
raise ValueError("prob has to be in range of 1 - 0")
probability = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
coefficient = float(factorial(trials))
coefficient /= factorial(successes) * factorial(trials - successes)
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print("Probability of 2 successes out of 4 trails")
print("with probability of 0.75 is:", end=" ")
print(binomial_distribution(2, 4, 0.75))
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
dataset = pd.read_csv(
"https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
"position_salaries.csv"
)
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
poly_reg = PolynomialFeatures(degree=4)
X_poly = poly_reg.fit_transform(X)
pol_reg = LinearRegression()
pol_reg.fit(X_poly, y)
# Visualizing the Polymonial Regression results
def viz_polymonial():
plt.scatter(X, y, color="red")
plt.plot(X, pol_reg.predict(poly_reg.fit_transform(X)), color="blue")
plt.title("Truth or Bluff (Linear Regression)")
plt.xlabel("Position level")
plt.ylabel("Salary")
plt.show()
return
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
dataset = pd.read_csv(
"https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
"position_salaries.csv"
)
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
poly_reg = PolynomialFeatures(degree=4)
X_poly = poly_reg.fit_transform(X)
pol_reg = LinearRegression()
pol_reg.fit(X_poly, y)
# Visualizing the Polymonial Regression results
def viz_polymonial():
plt.scatter(X, y, color="red")
plt.plot(X, pol_reg.predict(poly_reg.fit_transform(X)), color="blue")
plt.title("Truth or Bluff (Linear Regression)")
plt.xlabel("Position level")
plt.ylabel("Salary")
plt.show()
return
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def hamming_distance(string1: str, string2: str) -> int:
"""Calculate the Hamming distance between two equal length strings
In information theory, the Hamming distance between two strings of equal
length is the number of positions at which the corresponding symbols are
different. https://en.wikipedia.org/wiki/Hamming_distance
Args:
string1 (str): Sequence 1
string2 (str): Sequence 2
Returns:
int: Hamming distance
>>> hamming_distance("python", "python")
0
>>> hamming_distance("karolin", "kathrin")
3
>>> hamming_distance("00000", "11111")
5
>>> hamming_distance("karolin", "kath")
Traceback (most recent call last):
...
ValueError: String lengths must match!
"""
if len(string1) != len(string2):
raise ValueError("String lengths must match!")
count = 0
for char1, char2 in zip(string1, string2):
if char1 != char2:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| def hamming_distance(string1: str, string2: str) -> int:
"""Calculate the Hamming distance between two equal length strings
In information theory, the Hamming distance between two strings of equal
length is the number of positions at which the corresponding symbols are
different. https://en.wikipedia.org/wiki/Hamming_distance
Args:
string1 (str): Sequence 1
string2 (str): Sequence 2
Returns:
int: Hamming distance
>>> hamming_distance("python", "python")
0
>>> hamming_distance("karolin", "kathrin")
3
>>> hamming_distance("00000", "11111")
5
>>> hamming_distance("karolin", "kath")
Traceback (most recent call last):
...
ValueError: String lengths must match!
"""
if len(string1) != len(string2):
raise ValueError("String lengths must match!")
count = 0
for char1, char2 in zip(string1, string2):
if char1 != char2:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Convert a positive Decimal Number to Any Other Representation"""
def decimal_to_any(num: int, base: int) -> str:
"""
Convert a positive integer to another base as str.
>>> decimal_to_any(0, 2)
'0'
>>> decimal_to_any(5, 4)
'11'
>>> decimal_to_any(20, 3)
'202'
>>> decimal_to_any(58, 16)
'3A'
>>> decimal_to_any(243, 17)
'E5'
>>> decimal_to_any(34923, 36)
'QY3'
>>> decimal_to_any(10, 11)
'A'
>>> decimal_to_any(16, 16)
'10'
>>> decimal_to_any(36, 36)
'10'
>>> # negatives will error
>>> decimal_to_any(-45, 8) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: parameter must be positive int
>>> # floats will error
>>> decimal_to_any(34.4, 6) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: int() can't convert non-string with explicit base
>>> # a float base will error
>>> decimal_to_any(5, 2.5) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
>>> # a str base will error
>>> decimal_to_any(10, '16') # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: 'str' object cannot be interpreted as an integer
>>> # a base less than 2 will error
>>> decimal_to_any(7, 0) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: base must be >= 2
>>> # a base greater than 36 will error
>>> decimal_to_any(34, 37) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: base must be <= 36
"""
if isinstance(num, float):
raise TypeError("int() can't convert non-string with explicit base")
if num < 0:
raise ValueError("parameter must be positive int")
if isinstance(base, str):
raise TypeError("'str' object cannot be interpreted as an integer")
if isinstance(base, float):
raise TypeError("'float' object cannot be interpreted as an integer")
if base in (0, 1):
raise ValueError("base must be >= 2")
if base > 36:
raise ValueError("base must be <= 36")
# fmt: off
ALPHABET_VALUES = {'10': 'A', '11': 'B', '12': 'C', '13': 'D', '14': 'E', '15': 'F', # noqa: N806, E501
'16': 'G', '17': 'H', '18': 'I', '19': 'J', '20': 'K', '21': 'L',
'22': 'M', '23': 'N', '24': 'O', '25': 'P', '26': 'Q', '27': 'R',
'28': 'S', '29': 'T', '30': 'U', '31': 'V', '32': 'W', '33': 'X',
'34': 'Y', '35': 'Z'}
# fmt: on
new_value = ""
mod = 0
div = 0
while div != 1:
div, mod = divmod(num, base)
if base >= 11 and 9 < mod < 36:
actual_value = ALPHABET_VALUES[str(mod)]
mod = actual_value
new_value += str(mod)
div = num // base
num = div
if div == 0:
return str(new_value[::-1])
elif div == 1:
new_value += str(div)
return str(new_value[::-1])
return new_value[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for base in range(2, 37):
for num in range(1000):
assert int(decimal_to_any(num, base), base) == num, (
num,
base,
decimal_to_any(num, base),
int(decimal_to_any(num, base), base),
)
| """Convert a positive Decimal Number to Any Other Representation"""
def decimal_to_any(num: int, base: int) -> str:
"""
Convert a positive integer to another base as str.
>>> decimal_to_any(0, 2)
'0'
>>> decimal_to_any(5, 4)
'11'
>>> decimal_to_any(20, 3)
'202'
>>> decimal_to_any(58, 16)
'3A'
>>> decimal_to_any(243, 17)
'E5'
>>> decimal_to_any(34923, 36)
'QY3'
>>> decimal_to_any(10, 11)
'A'
>>> decimal_to_any(16, 16)
'10'
>>> decimal_to_any(36, 36)
'10'
>>> # negatives will error
>>> decimal_to_any(-45, 8) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: parameter must be positive int
>>> # floats will error
>>> decimal_to_any(34.4, 6) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: int() can't convert non-string with explicit base
>>> # a float base will error
>>> decimal_to_any(5, 2.5) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
>>> # a str base will error
>>> decimal_to_any(10, '16') # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: 'str' object cannot be interpreted as an integer
>>> # a base less than 2 will error
>>> decimal_to_any(7, 0) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: base must be >= 2
>>> # a base greater than 36 will error
>>> decimal_to_any(34, 37) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: base must be <= 36
"""
if isinstance(num, float):
raise TypeError("int() can't convert non-string with explicit base")
if num < 0:
raise ValueError("parameter must be positive int")
if isinstance(base, str):
raise TypeError("'str' object cannot be interpreted as an integer")
if isinstance(base, float):
raise TypeError("'float' object cannot be interpreted as an integer")
if base in (0, 1):
raise ValueError("base must be >= 2")
if base > 36:
raise ValueError("base must be <= 36")
# fmt: off
ALPHABET_VALUES = {'10': 'A', '11': 'B', '12': 'C', '13': 'D', '14': 'E', '15': 'F', # noqa: N806, E501
'16': 'G', '17': 'H', '18': 'I', '19': 'J', '20': 'K', '21': 'L',
'22': 'M', '23': 'N', '24': 'O', '25': 'P', '26': 'Q', '27': 'R',
'28': 'S', '29': 'T', '30': 'U', '31': 'V', '32': 'W', '33': 'X',
'34': 'Y', '35': 'Z'}
# fmt: on
new_value = ""
mod = 0
div = 0
while div != 1:
div, mod = divmod(num, base)
if base >= 11 and 9 < mod < 36:
actual_value = ALPHABET_VALUES[str(mod)]
mod = actual_value
new_value += str(mod)
div = num // base
num = div
if div == 0:
return str(new_value[::-1])
elif div == 1:
new_value += str(div)
return str(new_value[::-1])
return new_value[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for base in range(2, 37):
for num in range(1000):
assert int(decimal_to_any(num, base), base) == num, (
num,
base,
decimal_to_any(num, base),
int(decimal_to_any(num, base), base),
)
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Random Forest Classifier Example
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import plot_confusion_matrix
from sklearn.model_selection import train_test_split
def main():
"""
Random Forest Classifier Example using sklearn function.
Iris type dataset is used to demonstrate algorithm.
"""
# Load Iris dataset
iris = load_iris()
# Split dataset into train and test data
x = iris["data"] # features
y = iris["target"]
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.3, random_state=1
)
# Random Forest Classifier
rand_for = RandomForestClassifier(random_state=42, n_estimators=100)
rand_for.fit(x_train, y_train)
# Display Confusion Matrix of Classifier
plot_confusion_matrix(
rand_for,
x_test,
y_test,
display_labels=iris["target_names"],
cmap="Blues",
normalize="true",
)
plt.title("Normalized Confusion Matrix - IRIS Dataset")
plt.show()
if __name__ == "__main__":
main()
| # Random Forest Classifier Example
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import plot_confusion_matrix
from sklearn.model_selection import train_test_split
def main():
"""
Random Forest Classifier Example using sklearn function.
Iris type dataset is used to demonstrate algorithm.
"""
# Load Iris dataset
iris = load_iris()
# Split dataset into train and test data
x = iris["data"] # features
y = iris["target"]
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.3, random_state=1
)
# Random Forest Classifier
rand_for = RandomForestClassifier(random_state=42, n_estimators=100)
rand_for.fit(x_train, y_train)
# Display Confusion Matrix of Classifier
plot_confusion_matrix(
rand_for,
x_test,
y_test,
display_labels=iris["target_names"],
cmap="Blues",
normalize="true",
)
plt.title("Normalized Confusion Matrix - IRIS Dataset")
plt.show()
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def perfect_cube(n: int) -> bool:
"""
Check if a number is a perfect cube or not.
>>> perfect_cube(27)
True
>>> perfect_cube(4)
False
"""
val = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| def perfect_cube(n: int) -> bool:
"""
Check if a number is a perfect cube or not.
>>> perfect_cube(27)
True
>>> perfect_cube(4)
False
"""
val = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
min_primitive_root = 3
# I have written my code naively same as definition of primitive root
# however every time I run this program, memory exceeded...
# so I used 4.80 Algorithm in
# Handbook of Applied Cryptography(CRC Press, ISBN : 0-8493-8523-7, October 1996)
# and it seems to run nicely!
def primitive_root(p_val: int) -> int:
print("Generating primitive root of p")
while True:
g = random.randrange(3, p_val)
if pow(g, 2, p_val) == 1:
continue
if pow(g, p_val, p_val) == 1:
continue
return g
def generate_key(key_size: int) -> tuple[tuple[int, int, int, int], tuple[int, int]]:
print("Generating prime p...")
p = rabin_miller.generate_large_prime(key_size) # select large prime number.
e_1 = primitive_root(p) # one primitive root on modulo p.
d = random.randrange(3, p) # private_key -> have to be greater than 2 for safety.
e_2 = cryptomath.find_mod_inverse(pow(e_1, d, p), p)
public_key = (key_size, e_1, e_2, p)
private_key = (key_size, d)
return public_key, private_key
def make_key_files(name: str, key_size: int) -> None:
if os.path.exists(f"{name}_pubkey.txt") or os.path.exists(f"{name}_privkey.txt"):
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()
public_key, private_key = generate_key(key_size)
print(f"\nWriting public key to file {name}_pubkey.txt...")
with open(f"{name}_pubkey.txt", "w") as fo:
fo.write(
"%d,%d,%d,%d" % (public_key[0], public_key[1], public_key[2], public_key[3])
)
print(f"Writing private key to file {name}_privkey.txt...")
with open(f"{name}_privkey.txt", "w") as fo:
fo.write("%d,%d" % (private_key[0], private_key[1]))
def main() -> None:
print("Making key files...")
make_key_files("elgamal", 2048)
print("Key files generation successful")
if __name__ == "__main__":
main()
| import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
min_primitive_root = 3
# I have written my code naively same as definition of primitive root
# however every time I run this program, memory exceeded...
# so I used 4.80 Algorithm in
# Handbook of Applied Cryptography(CRC Press, ISBN : 0-8493-8523-7, October 1996)
# and it seems to run nicely!
def primitive_root(p_val: int) -> int:
print("Generating primitive root of p")
while True:
g = random.randrange(3, p_val)
if pow(g, 2, p_val) == 1:
continue
if pow(g, p_val, p_val) == 1:
continue
return g
def generate_key(key_size: int) -> tuple[tuple[int, int, int, int], tuple[int, int]]:
print("Generating prime p...")
p = rabin_miller.generate_large_prime(key_size) # select large prime number.
e_1 = primitive_root(p) # one primitive root on modulo p.
d = random.randrange(3, p) # private_key -> have to be greater than 2 for safety.
e_2 = cryptomath.find_mod_inverse(pow(e_1, d, p), p)
public_key = (key_size, e_1, e_2, p)
private_key = (key_size, d)
return public_key, private_key
def make_key_files(name: str, key_size: int) -> None:
if os.path.exists(f"{name}_pubkey.txt") or os.path.exists(f"{name}_privkey.txt"):
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()
public_key, private_key = generate_key(key_size)
print(f"\nWriting public key to file {name}_pubkey.txt...")
with open(f"{name}_pubkey.txt", "w") as fo:
fo.write(
"%d,%d,%d,%d" % (public_key[0], public_key[1], public_key[2], public_key[3])
)
print(f"Writing private key to file {name}_privkey.txt...")
with open(f"{name}_privkey.txt", "w") as fo:
fo.write("%d,%d" % (private_key[0], private_key[1]))
def main() -> None:
print("Making key files...")
make_key_files("elgamal", 2048)
print("Key files generation successful")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Author: https://github.com/bhushan-borole
"""
"""
The input graph for the algorithm is:
A B C
A 0 1 1
B 0 0 1
C 1 0 0
"""
graph = [[0, 1, 1], [0, 0, 1], [1, 0, 0]]
class Node:
def __init__(self, name):
self.name = name
self.inbound = []
self.outbound = []
def add_inbound(self, node):
self.inbound.append(node)
def add_outbound(self, node):
self.outbound.append(node)
def __repr__(self):
return f"Node {self.name}: Inbound: {self.inbound} ; Outbound: {self.outbound}"
def page_rank(nodes, limit=3, d=0.85):
ranks = {}
for node in nodes:
ranks[node.name] = 1
outbounds = {}
for node in nodes:
outbounds[node.name] = len(node.outbound)
for i in range(limit):
print(f"======= Iteration {i + 1} =======")
for _, node in enumerate(nodes):
ranks[node.name] = (1 - d) + d * sum(
ranks[ib] / outbounds[ib] for ib in node.inbound
)
print(ranks)
def main():
names = list(input("Enter Names of the Nodes: ").split())
nodes = [Node(name) for name in names]
for ri, row in enumerate(graph):
for ci, col in enumerate(row):
if col == 1:
nodes[ci].add_inbound(names[ri])
nodes[ri].add_outbound(names[ci])
print("======= Nodes =======")
for node in nodes:
print(node)
page_rank(nodes)
if __name__ == "__main__":
main()
| """
Author: https://github.com/bhushan-borole
"""
"""
The input graph for the algorithm is:
A B C
A 0 1 1
B 0 0 1
C 1 0 0
"""
graph = [[0, 1, 1], [0, 0, 1], [1, 0, 0]]
class Node:
def __init__(self, name):
self.name = name
self.inbound = []
self.outbound = []
def add_inbound(self, node):
self.inbound.append(node)
def add_outbound(self, node):
self.outbound.append(node)
def __repr__(self):
return f"Node {self.name}: Inbound: {self.inbound} ; Outbound: {self.outbound}"
def page_rank(nodes, limit=3, d=0.85):
ranks = {}
for node in nodes:
ranks[node.name] = 1
outbounds = {}
for node in nodes:
outbounds[node.name] = len(node.outbound)
for i in range(limit):
print(f"======= Iteration {i + 1} =======")
for _, node in enumerate(nodes):
ranks[node.name] = (1 - d) + d * sum(
ranks[ib] / outbounds[ib] for ib in node.inbound
)
print(ranks)
def main():
names = list(input("Enter Names of the Nodes: ").split())
nodes = [Node(name) for name in names]
for ri, row in enumerate(graph):
for ci, col in enumerate(row):
if col == 1:
nodes[ci].add_inbound(names[ri])
nodes[ri].add_outbound(names[ci])
print("======= Nodes =======")
for node in nodes:
print(node)
page_rank(nodes)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """ https://en.wikipedia.org/wiki/Rail_fence_cipher """
def encrypt(input_string: str, key: int) -> str:
"""
Shuffles the character of a string by placing each of them
in a grid (the height is dependent on the key) in a zigzag
formation and reading it left to right.
>>> encrypt("Hello World", 4)
'HWe olordll'
>>> encrypt("This is a message", 0)
Traceback (most recent call last):
...
ValueError: Height of grid can't be 0 or negative
>>> encrypt(b"This is a byte string", 5)
Traceback (most recent call last):
...
TypeError: sequence item 0: expected str instance, int found
"""
temp_grid: list[list[str]] = [[] for _ in range(key)]
lowest = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative")
if key == 1 or len(input_string) <= key:
return input_string
for position, character in enumerate(input_string):
num = position % (lowest * 2) # puts it in bounds
num = min(num, lowest * 2 - num) # creates zigzag pattern
temp_grid[num].append(character)
grid = ["".join(row) for row in temp_grid]
output_string = "".join(grid)
return output_string
def decrypt(input_string: str, key: int) -> str:
"""
Generates a template based on the key and fills it in with
the characters of the input string and then reading it in
a zigzag formation.
>>> decrypt("HWe olordll", 4)
'Hello World'
>>> decrypt("This is a message", -10)
Traceback (most recent call last):
...
ValueError: Height of grid can't be 0 or negative
>>> decrypt("My key is very big", 100)
'My key is very big'
"""
grid = []
lowest = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative")
if key == 1:
return input_string
temp_grid: list[list[str]] = [[] for _ in range(key)] # generates template
for position in range(len(input_string)):
num = position % (lowest * 2) # puts it in bounds
num = min(num, lowest * 2 - num) # creates zigzag pattern
temp_grid[num].append("*")
counter = 0
for row in temp_grid: # fills in the characters
splice = input_string[counter : counter + len(row)]
grid.append([character for character in splice])
counter += len(row)
output_string = "" # reads as zigzag
for position in range(len(input_string)):
num = position % (lowest * 2) # puts it in bounds
num = min(num, lowest * 2 - num) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0)
return output_string
def bruteforce(input_string: str) -> dict[int, str]:
"""Uses decrypt function by guessing every key
>>> bruteforce("HWe olordll")[4]
'Hello World'
"""
results = {}
for key_guess in range(1, len(input_string)): # tries every key
results[key_guess] = decrypt(input_string, key_guess)
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| """ https://en.wikipedia.org/wiki/Rail_fence_cipher """
def encrypt(input_string: str, key: int) -> str:
"""
Shuffles the character of a string by placing each of them
in a grid (the height is dependent on the key) in a zigzag
formation and reading it left to right.
>>> encrypt("Hello World", 4)
'HWe olordll'
>>> encrypt("This is a message", 0)
Traceback (most recent call last):
...
ValueError: Height of grid can't be 0 or negative
>>> encrypt(b"This is a byte string", 5)
Traceback (most recent call last):
...
TypeError: sequence item 0: expected str instance, int found
"""
temp_grid: list[list[str]] = [[] for _ in range(key)]
lowest = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative")
if key == 1 or len(input_string) <= key:
return input_string
for position, character in enumerate(input_string):
num = position % (lowest * 2) # puts it in bounds
num = min(num, lowest * 2 - num) # creates zigzag pattern
temp_grid[num].append(character)
grid = ["".join(row) for row in temp_grid]
output_string = "".join(grid)
return output_string
def decrypt(input_string: str, key: int) -> str:
"""
Generates a template based on the key and fills it in with
the characters of the input string and then reading it in
a zigzag formation.
>>> decrypt("HWe olordll", 4)
'Hello World'
>>> decrypt("This is a message", -10)
Traceback (most recent call last):
...
ValueError: Height of grid can't be 0 or negative
>>> decrypt("My key is very big", 100)
'My key is very big'
"""
grid = []
lowest = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative")
if key == 1:
return input_string
temp_grid: list[list[str]] = [[] for _ in range(key)] # generates template
for position in range(len(input_string)):
num = position % (lowest * 2) # puts it in bounds
num = min(num, lowest * 2 - num) # creates zigzag pattern
temp_grid[num].append("*")
counter = 0
for row in temp_grid: # fills in the characters
splice = input_string[counter : counter + len(row)]
grid.append([character for character in splice])
counter += len(row)
output_string = "" # reads as zigzag
for position in range(len(input_string)):
num = position % (lowest * 2) # puts it in bounds
num = min(num, lowest * 2 - num) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0)
return output_string
def bruteforce(input_string: str) -> dict[int, str]:
"""Uses decrypt function by guessing every key
>>> bruteforce("HWe olordll")[4]
'Hello World'
"""
results = {}
for key_guess in range(1, len(input_string)): # tries every key
results[key_guess] = decrypt(input_string, key_guess)
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This function implements the shell sort algorithm
which is slightly faster than its pure implementation.
This shell sort is implemented using a gap, which
shrinks by a certain factor each iteration. In this
implementation, the gap is initially set to the
length of the collection. The gap is then reduced by
a certain factor (1.3) each iteration.
For each iteration, the algorithm compares elements
that are a certain number of positions apart
(determined by the gap). If the element at the higher
position is greater than the element at the lower
position, the two elements are swapped. The process
is repeated until the gap is equal to 1.
The reason this is more efficient is that it reduces
the number of comparisons that need to be made. By
using a smaller gap, the list is sorted more quickly.
"""
def shell_sort(collection: list) -> list:
"""Implementation of shell sort algorithm in Python
:param collection: Some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
>>> shell_sort([3, 2, 1])
[1, 2, 3]
>>> shell_sort([])
[]
>>> shell_sort([1])
[1]
"""
# Choose an initial gap value
gap = len(collection)
# Set the gap value to be decreased by a factor of 1.3
# after each iteration
shrink = 1.3
# Continue sorting until the gap is 1
while gap > 1:
# Decrease the gap value
gap = int(gap / shrink)
# Sort the elements using insertion sort
for i in range(gap, len(collection)):
temp = collection[i]
j = i
while j >= gap and collection[j - gap] > temp:
collection[j] = collection[j - gap]
j -= gap
collection[j] = temp
return collection
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
This function implements the shell sort algorithm
which is slightly faster than its pure implementation.
This shell sort is implemented using a gap, which
shrinks by a certain factor each iteration. In this
implementation, the gap is initially set to the
length of the collection. The gap is then reduced by
a certain factor (1.3) each iteration.
For each iteration, the algorithm compares elements
that are a certain number of positions apart
(determined by the gap). If the element at the higher
position is greater than the element at the lower
position, the two elements are swapped. The process
is repeated until the gap is equal to 1.
The reason this is more efficient is that it reduces
the number of comparisons that need to be made. By
using a smaller gap, the list is sorted more quickly.
"""
def shell_sort(collection: list) -> list:
"""Implementation of shell sort algorithm in Python
:param collection: Some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
>>> shell_sort([3, 2, 1])
[1, 2, 3]
>>> shell_sort([])
[]
>>> shell_sort([1])
[1]
"""
# Choose an initial gap value
gap = len(collection)
# Set the gap value to be decreased by a factor of 1.3
# after each iteration
shrink = 1.3
# Continue sorting until the gap is 1
while gap > 1:
# Decrease the gap value
gap = int(gap / shrink)
# Sort the elements using insertion sort
for i in range(gap, len(collection)):
temp = collection[i]
j = i
while j >= gap and collection[j - gap] > temp:
collection[j] = collection[j - gap]
j -= gap
collection[j] = temp
return collection
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #
| #
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Prime Check."""
import math
import unittest
def is_prime(number: int) -> bool:
"""Checks to see if a number is a prime in O(sqrt(n)).
A number is prime if it has exactly two factors: 1 and itself.
>>> is_prime(0)
False
>>> is_prime(1)
False
>>> is_prime(2)
True
>>> is_prime(3)
True
>>> is_prime(27)
False
>>> is_prime(87)
False
>>> is_prime(563)
True
>>> is_prime(2999)
True
>>> is_prime(67483)
False
"""
# precondition
assert isinstance(number, int) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5, int(math.sqrt(number) + 1), 6):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class Test(unittest.TestCase):
def test_primes(self):
self.assertTrue(is_prime(2))
self.assertTrue(is_prime(3))
self.assertTrue(is_prime(5))
self.assertTrue(is_prime(7))
self.assertTrue(is_prime(11))
self.assertTrue(is_prime(13))
self.assertTrue(is_prime(17))
self.assertTrue(is_prime(19))
self.assertTrue(is_prime(23))
self.assertTrue(is_prime(29))
def test_not_primes(self):
with self.assertRaises(AssertionError):
is_prime(-19)
self.assertFalse(
is_prime(0),
"Zero doesn't have any positive factors, primes must have exactly two.",
)
self.assertFalse(
is_prime(1),
"One only has 1 positive factor, primes must have exactly two.",
)
self.assertFalse(is_prime(2 * 2))
self.assertFalse(is_prime(2 * 3))
self.assertFalse(is_prime(3 * 3))
self.assertFalse(is_prime(3 * 5))
self.assertFalse(is_prime(3 * 5 * 7))
if __name__ == "__main__":
unittest.main()
| """Prime Check."""
import math
import unittest
def is_prime(number: int) -> bool:
"""Checks to see if a number is a prime in O(sqrt(n)).
A number is prime if it has exactly two factors: 1 and itself.
>>> is_prime(0)
False
>>> is_prime(1)
False
>>> is_prime(2)
True
>>> is_prime(3)
True
>>> is_prime(27)
False
>>> is_prime(87)
False
>>> is_prime(563)
True
>>> is_prime(2999)
True
>>> is_prime(67483)
False
"""
# precondition
assert isinstance(number, int) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5, int(math.sqrt(number) + 1), 6):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class Test(unittest.TestCase):
def test_primes(self):
self.assertTrue(is_prime(2))
self.assertTrue(is_prime(3))
self.assertTrue(is_prime(5))
self.assertTrue(is_prime(7))
self.assertTrue(is_prime(11))
self.assertTrue(is_prime(13))
self.assertTrue(is_prime(17))
self.assertTrue(is_prime(19))
self.assertTrue(is_prime(23))
self.assertTrue(is_prime(29))
def test_not_primes(self):
with self.assertRaises(AssertionError):
is_prime(-19)
self.assertFalse(
is_prime(0),
"Zero doesn't have any positive factors, primes must have exactly two.",
)
self.assertFalse(
is_prime(1),
"One only has 1 positive factor, primes must have exactly two.",
)
self.assertFalse(is_prime(2 * 2))
self.assertFalse(is_prime(2 * 3))
self.assertFalse(is_prime(3 * 3))
self.assertFalse(is_prime(3 * 5))
self.assertFalse(is_prime(3 * 5 * 7))
if __name__ == "__main__":
unittest.main()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Approximates the area under the curve using the trapezoidal rule
"""
from __future__ import annotations
from collections.abc import Callable
def trapezoidal_area(
fnc: Callable[[int | float], int | float],
x_start: int | float,
x_end: int | float,
steps: int = 100,
) -> float:
"""
Treats curve as a collection of linear lines and sums the area of the
trapezium shape they form
:param fnc: a function which defines a curve
:param x_start: left end point to indicate the start of line segment
:param x_end: right end point to indicate end of line segment
:param steps: an accuracy gauge; more steps increases the accuracy
:return: a float representing the length of the curve
>>> def f(x):
... return 5
>>> '%.3f' % trapezoidal_area(f, 12.0, 14.0, 1000)
'10.000'
>>> def f(x):
... return 9*x**2
>>> '%.4f' % trapezoidal_area(f, -4.0, 0, 10000)
'192.0000'
>>> '%.4f' % trapezoidal_area(f, -4.0, 4.0, 10000)
'384.0000'
"""
x1 = x_start
fx1 = fnc(x_start)
area = 0.0
for _ in range(steps):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
x2 = (x_end - x_start) / steps + x1
fx2 = fnc(x2)
area += abs(fx2 + fx1) * (x2 - x1) / 2
# Increment step
x1 = x2
fx1 = fx2
return area
if __name__ == "__main__":
def f(x):
return x**3
print("f(x) = x^3")
print("The area between the curve, x = -10, x = 10 and the x axis is:")
i = 10
while i <= 100000:
area = trapezoidal_area(f, -5, 5, i)
print(f"with {i} steps: {area}")
i *= 10
| """
Approximates the area under the curve using the trapezoidal rule
"""
from __future__ import annotations
from collections.abc import Callable
def trapezoidal_area(
fnc: Callable[[int | float], int | float],
x_start: int | float,
x_end: int | float,
steps: int = 100,
) -> float:
"""
Treats curve as a collection of linear lines and sums the area of the
trapezium shape they form
:param fnc: a function which defines a curve
:param x_start: left end point to indicate the start of line segment
:param x_end: right end point to indicate end of line segment
:param steps: an accuracy gauge; more steps increases the accuracy
:return: a float representing the length of the curve
>>> def f(x):
... return 5
>>> '%.3f' % trapezoidal_area(f, 12.0, 14.0, 1000)
'10.000'
>>> def f(x):
... return 9*x**2
>>> '%.4f' % trapezoidal_area(f, -4.0, 0, 10000)
'192.0000'
>>> '%.4f' % trapezoidal_area(f, -4.0, 4.0, 10000)
'384.0000'
"""
x1 = x_start
fx1 = fnc(x_start)
area = 0.0
for _ in range(steps):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
x2 = (x_end - x_start) / steps + x1
fx2 = fnc(x2)
area += abs(fx2 + fx1) * (x2 - x1) / 2
# Increment step
x1 = x2
fx1 = fx2
return area
if __name__ == "__main__":
def f(x):
return x**3
print("f(x) = x^3")
print("The area between the curve, x = -10, x = 10 and the x axis is:")
i = 10
while i <= 100000:
area = trapezoidal_area(f, -5, 5, i)
print(f"with {i} steps: {area}")
i *= 10
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| r"""
Problem:
The n queens problem is: placing N queens on a N * N chess board such that no queen
can attack any other queens placed on that chess board. This means that one queen
cannot have any other queen on its horizontal, vertical and diagonal lines.
Solution:
To solve this problem we will use simple math. First we know the queen can move in all
the possible ways, we can simplify it in this: vertical, horizontal, diagonal left and
diagonal right.
We can visualize it like this:
left diagonal = \
right diagonal = /
On a chessboard vertical movement could be the rows and horizontal movement could be
the columns.
In programming we can use an array, and in this array each index could be the rows and
each value in the array could be the column. For example:
. Q . . We have this chessboard with one queen in each column and each queen
. . . Q can't attack to each other.
Q . . . The array for this example would look like this: [1, 3, 0, 2]
. . Q .
So if we use an array and we verify that each value in the array is different to each
other we know that at least the queens can't attack each other in horizontal and
vertical.
At this point we have it halfway completed and we will treat the chessboard as a
Cartesian plane. Hereinafter we are going to remember basic math, so in the school we
learned this formula:
Slope of a line:
y2 - y1
m = ----------
x2 - x1
This formula allow us to get the slope. For the angles 45º (right diagonal) and 135º
(left diagonal) this formula gives us m = 1, and m = -1 respectively.
See::
https://www.enotes.com/homework-help/write-equation-line-that-hits-origin-45-degree-1474860
Then we have this other formula:
Slope intercept:
y = mx + b
b is where the line crosses the Y axis (to get more information see:
https://www.mathsisfun.com/y_intercept.html), if we change the formula to solve for b
we would have:
y - mx = b
And since we already have the m values for the angles 45º and 135º, this formula would
look like this:
45º: y - (1)x = b
45º: y - x = b
135º: y - (-1)x = b
135º: y + x = b
y = row
x = column
Applying these two formulas we can check if a queen in some position is being attacked
for another one or vice versa.
"""
from __future__ import annotations
def depth_first_search(
possible_board: list[int],
diagonal_right_collisions: list[int],
diagonal_left_collisions: list[int],
boards: list[list[str]],
n: int,
) -> None:
"""
>>> boards = []
>>> depth_first_search([], [], [], boards, 4)
>>> for board in boards:
... print(board)
['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
['. . Q . ', 'Q . . . ', '. . . Q ', '. Q . . ']
"""
# Get next row in the current board (possible_board) to fill it with a queen
row = len(possible_board)
# If row is equal to the size of the board it means there are a queen in each row in
# the current board (possible_board)
if row == n:
# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
boards.append([". " * i + "Q " + ". " * (n - 1 - i) for i in possible_board])
return
# We iterate each column in the row to find all possible results in each row
for col in range(n):
# We apply that we learned previously. First we check that in the current board
# (possible_board) there are not other same value because if there is it means
# that there are a collision in vertical. Then we apply the two formulas we
# learned before:
#
# 45º: y - x = b or 45: row - col = b
# 135º: y + x = b or row + col = b.
#
# And we verify if the results of this two formulas not exist in their variables
# respectively. (diagonal_right_collisions, diagonal_left_collisions)
#
# If any or these are True it means there is a collision so we continue to the
# next value in the for loop.
if (
col in possible_board
or row - col in diagonal_right_collisions
or row + col in diagonal_left_collisions
):
continue
# If it is False we call dfs function again and we update the inputs
depth_first_search(
possible_board + [col],
diagonal_right_collisions + [row - col],
diagonal_left_collisions + [row + col],
boards,
n,
)
def n_queens_solution(n: int) -> None:
boards: list[list[str]] = []
depth_first_search([], [], [], boards, n)
# Print all the boards
for board in boards:
for column in board:
print(column)
print("")
print(len(boards), "solutions were found.")
if __name__ == "__main__":
import doctest
doctest.testmod()
n_queens_solution(4)
| r"""
Problem:
The n queens problem is: placing N queens on a N * N chess board such that no queen
can attack any other queens placed on that chess board. This means that one queen
cannot have any other queen on its horizontal, vertical and diagonal lines.
Solution:
To solve this problem we will use simple math. First we know the queen can move in all
the possible ways, we can simplify it in this: vertical, horizontal, diagonal left and
diagonal right.
We can visualize it like this:
left diagonal = \
right diagonal = /
On a chessboard vertical movement could be the rows and horizontal movement could be
the columns.
In programming we can use an array, and in this array each index could be the rows and
each value in the array could be the column. For example:
. Q . . We have this chessboard with one queen in each column and each queen
. . . Q can't attack to each other.
Q . . . The array for this example would look like this: [1, 3, 0, 2]
. . Q .
So if we use an array and we verify that each value in the array is different to each
other we know that at least the queens can't attack each other in horizontal and
vertical.
At this point we have it halfway completed and we will treat the chessboard as a
Cartesian plane. Hereinafter we are going to remember basic math, so in the school we
learned this formula:
Slope of a line:
y2 - y1
m = ----------
x2 - x1
This formula allow us to get the slope. For the angles 45º (right diagonal) and 135º
(left diagonal) this formula gives us m = 1, and m = -1 respectively.
See::
https://www.enotes.com/homework-help/write-equation-line-that-hits-origin-45-degree-1474860
Then we have this other formula:
Slope intercept:
y = mx + b
b is where the line crosses the Y axis (to get more information see:
https://www.mathsisfun.com/y_intercept.html), if we change the formula to solve for b
we would have:
y - mx = b
And since we already have the m values for the angles 45º and 135º, this formula would
look like this:
45º: y - (1)x = b
45º: y - x = b
135º: y - (-1)x = b
135º: y + x = b
y = row
x = column
Applying these two formulas we can check if a queen in some position is being attacked
for another one or vice versa.
"""
from __future__ import annotations
def depth_first_search(
possible_board: list[int],
diagonal_right_collisions: list[int],
diagonal_left_collisions: list[int],
boards: list[list[str]],
n: int,
) -> None:
"""
>>> boards = []
>>> depth_first_search([], [], [], boards, 4)
>>> for board in boards:
... print(board)
['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
['. . Q . ', 'Q . . . ', '. . . Q ', '. Q . . ']
"""
# Get next row in the current board (possible_board) to fill it with a queen
row = len(possible_board)
# If row is equal to the size of the board it means there are a queen in each row in
# the current board (possible_board)
if row == n:
# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
boards.append([". " * i + "Q " + ". " * (n - 1 - i) for i in possible_board])
return
# We iterate each column in the row to find all possible results in each row
for col in range(n):
# We apply that we learned previously. First we check that in the current board
# (possible_board) there are not other same value because if there is it means
# that there are a collision in vertical. Then we apply the two formulas we
# learned before:
#
# 45º: y - x = b or 45: row - col = b
# 135º: y + x = b or row + col = b.
#
# And we verify if the results of this two formulas not exist in their variables
# respectively. (diagonal_right_collisions, diagonal_left_collisions)
#
# If any or these are True it means there is a collision so we continue to the
# next value in the for loop.
if (
col in possible_board
or row - col in diagonal_right_collisions
or row + col in diagonal_left_collisions
):
continue
# If it is False we call dfs function again and we update the inputs
depth_first_search(
possible_board + [col],
diagonal_right_collisions + [row - col],
diagonal_left_collisions + [row + col],
boards,
n,
)
def n_queens_solution(n: int) -> None:
boards: list[list[str]] = []
depth_first_search([], [], [], boards, n)
# Print all the boards
for board in boards:
for column in board:
print(column)
print("")
print(len(boards), "solutions were found.")
if __name__ == "__main__":
import doctest
doctest.testmod()
n_queens_solution(4)
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is an implementation of Pigeon Hole Sort.
For doctests run following command:
python3 -m doctest -v pigeon_sort.py
or
python -m doctest -v pigeon_sort.py
For manual testing run:
python pigeon_sort.py
"""
from __future__ import annotations
def pigeon_sort(array: list[int]) -> list[int]:
"""
Implementation of pigeon hole sort algorithm
:param array: Collection of comparable items
:return: Collection sorted in ascending order
>>> pigeon_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> pigeon_sort([])
[]
>>> pigeon_sort([-2, -5, -45])
[-45, -5, -2]
"""
if len(array) == 0:
return array
_min, _max = min(array), max(array)
# Compute the variables
holes_range = _max - _min + 1
holes, holes_repeat = [0] * holes_range, [0] * holes_range
# Make the sorting.
for i in array:
index = i - _min
holes[index] = i
holes_repeat[index] += 1
# Makes the array back by replacing the numbers.
index = 0
for i in range(holes_range):
while holes_repeat[i] > 0:
array[index] = holes[i]
index += 1
holes_repeat[i] -= 1
# Returns the sorted array.
return array
if __name__ == "__main__":
import doctest
doctest.testmod()
user_input = input("Enter numbers separated by comma:\n")
unsorted = [int(x) for x in user_input.split(",")]
print(pigeon_sort(unsorted))
| """
This is an implementation of Pigeon Hole Sort.
For doctests run following command:
python3 -m doctest -v pigeon_sort.py
or
python -m doctest -v pigeon_sort.py
For manual testing run:
python pigeon_sort.py
"""
from __future__ import annotations
def pigeon_sort(array: list[int]) -> list[int]:
"""
Implementation of pigeon hole sort algorithm
:param array: Collection of comparable items
:return: Collection sorted in ascending order
>>> pigeon_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> pigeon_sort([])
[]
>>> pigeon_sort([-2, -5, -45])
[-45, -5, -2]
"""
if len(array) == 0:
return array
_min, _max = min(array), max(array)
# Compute the variables
holes_range = _max - _min + 1
holes, holes_repeat = [0] * holes_range, [0] * holes_range
# Make the sorting.
for i in array:
index = i - _min
holes[index] = i
holes_repeat[index] += 1
# Makes the array back by replacing the numbers.
index = 0
for i in range(holes_range):
while holes_repeat[i] > 0:
array[index] = holes[i]
index += 1
holes_repeat[i] -= 1
# Returns the sorted array.
return array
if __name__ == "__main__":
import doctest
doctest.testmod()
user_input = input("Enter numbers separated by comma:\n")
unsorted = [int(x) for x in user_input.split(",")]
print(pigeon_sort(unsorted))
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
def merge(left_half: list, right_half: list) -> list:
"""Helper function for mergesort.
>>> left_half = [-2]
>>> right_half = [-1]
>>> merge(left_half, right_half)
[-2, -1]
>>> left_half = [1,2,3]
>>> right_half = [4,5,6]
>>> merge(left_half, right_half)
[1, 2, 3, 4, 5, 6]
>>> left_half = [-2]
>>> right_half = [-1]
>>> merge(left_half, right_half)
[-2, -1]
>>> left_half = [12, 15]
>>> right_half = [13, 14]
>>> merge(left_half, right_half)
[12, 13, 14, 15]
>>> left_half = []
>>> right_half = []
>>> merge(left_half, right_half)
[]
"""
sorted_array = [None] * (len(right_half) + len(left_half))
pointer1 = 0 # pointer to current index for left Half
pointer2 = 0 # pointer to current index for the right Half
index = 0 # pointer to current index for the sorted array Half
while pointer1 < len(left_half) and pointer2 < len(right_half):
if left_half[pointer1] < right_half[pointer2]:
sorted_array[index] = left_half[pointer1]
pointer1 += 1
index += 1
else:
sorted_array[index] = right_half[pointer2]
pointer2 += 1
index += 1
while pointer1 < len(left_half):
sorted_array[index] = left_half[pointer1]
pointer1 += 1
index += 1
while pointer2 < len(right_half):
sorted_array[index] = right_half[pointer2]
pointer2 += 1
index += 1
return sorted_array
def merge_sort(array: list) -> list:
"""Returns a list of sorted array elements using merge sort.
>>> from random import shuffle
>>> array = [-2, 3, -10, 11, 99, 100000, 100, -200]
>>> shuffle(array)
>>> merge_sort(array)
[-200, -10, -2, 3, 11, 99, 100, 100000]
>>> shuffle(array)
>>> merge_sort(array)
[-200, -10, -2, 3, 11, 99, 100, 100000]
>>> array = [-200]
>>> merge_sort(array)
[-200]
>>> array = [-2, 3, -10, 11, 99, 100000, 100, -200]
>>> shuffle(array)
>>> sorted(array) == merge_sort(array)
True
>>> array = [-2]
>>> merge_sort(array)
[-2]
>>> array = []
>>> merge_sort(array)
[]
>>> array = [10000000, 1, -1111111111, 101111111112, 9000002]
>>> sorted(array) == merge_sort(array)
True
"""
if len(array) <= 1:
return array
# the actual formula to calculate the middle element = left + (right - left) // 2
# this avoids integer overflow in case of large N
middle = 0 + (len(array) - 0) // 2
# Split the array into halves till the array length becomes equal to One
# merge the arrays of single length returned by mergeSort function and
# pass them into the merge arrays function which merges the array
left_half = array[:middle]
right_half = array[middle:]
return merge(merge_sort(left_half), merge_sort(right_half))
if __name__ == "__main__":
import doctest
doctest.testmod()
| from __future__ import annotations
def merge(left_half: list, right_half: list) -> list:
"""Helper function for mergesort.
>>> left_half = [-2]
>>> right_half = [-1]
>>> merge(left_half, right_half)
[-2, -1]
>>> left_half = [1,2,3]
>>> right_half = [4,5,6]
>>> merge(left_half, right_half)
[1, 2, 3, 4, 5, 6]
>>> left_half = [-2]
>>> right_half = [-1]
>>> merge(left_half, right_half)
[-2, -1]
>>> left_half = [12, 15]
>>> right_half = [13, 14]
>>> merge(left_half, right_half)
[12, 13, 14, 15]
>>> left_half = []
>>> right_half = []
>>> merge(left_half, right_half)
[]
"""
sorted_array = [None] * (len(right_half) + len(left_half))
pointer1 = 0 # pointer to current index for left Half
pointer2 = 0 # pointer to current index for the right Half
index = 0 # pointer to current index for the sorted array Half
while pointer1 < len(left_half) and pointer2 < len(right_half):
if left_half[pointer1] < right_half[pointer2]:
sorted_array[index] = left_half[pointer1]
pointer1 += 1
index += 1
else:
sorted_array[index] = right_half[pointer2]
pointer2 += 1
index += 1
while pointer1 < len(left_half):
sorted_array[index] = left_half[pointer1]
pointer1 += 1
index += 1
while pointer2 < len(right_half):
sorted_array[index] = right_half[pointer2]
pointer2 += 1
index += 1
return sorted_array
def merge_sort(array: list) -> list:
"""Returns a list of sorted array elements using merge sort.
>>> from random import shuffle
>>> array = [-2, 3, -10, 11, 99, 100000, 100, -200]
>>> shuffle(array)
>>> merge_sort(array)
[-200, -10, -2, 3, 11, 99, 100, 100000]
>>> shuffle(array)
>>> merge_sort(array)
[-200, -10, -2, 3, 11, 99, 100, 100000]
>>> array = [-200]
>>> merge_sort(array)
[-200]
>>> array = [-2, 3, -10, 11, 99, 100000, 100, -200]
>>> shuffle(array)
>>> sorted(array) == merge_sort(array)
True
>>> array = [-2]
>>> merge_sort(array)
[-2]
>>> array = []
>>> merge_sort(array)
[]
>>> array = [10000000, 1, -1111111111, 101111111112, 9000002]
>>> sorted(array) == merge_sort(array)
True
"""
if len(array) <= 1:
return array
# the actual formula to calculate the middle element = left + (right - left) // 2
# this avoids integer overflow in case of large N
middle = 0 + (len(array) - 0) // 2
# Split the array into halves till the array length becomes equal to One
# merge the arrays of single length returned by mergeSort function and
# pass them into the merge arrays function which merges the array
left_half = array[:middle]
right_half = array[middle:]
return merge(merge_sort(left_half), merge_sort(right_half))
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Conway's Game of Life implemented in Python.
https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life
"""
from __future__ import annotations
from PIL import Image
# Define glider example
GLIDER = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
]
# Define blinker example
BLINKER = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def new_generation(cells: list[list[int]]) -> list[list[int]]:
"""
Generates the next generation for a given state of Conway's Game of Life.
>>> new_generation(BLINKER)
[[0, 0, 0], [1, 1, 1], [0, 0, 0]]
"""
next_generation = []
for i in range(len(cells)):
next_generation_row = []
for j in range(len(cells[i])):
# Get the number of live neighbours
neighbour_count = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i]) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i]) - 1:
neighbour_count += cells[i][j + 1]
if i < len(cells) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(cells) - 1:
neighbour_count += cells[i + 1][j]
if i < len(cells) - 1 and j < len(cells[i]) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
alive = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1)
else:
next_generation_row.append(0)
next_generation.append(next_generation_row)
return next_generation
def generate_images(cells: list[list[int]], frames: int) -> list[Image.Image]:
"""
Generates a list of images of subsequent Game of Life states.
"""
images = []
for _ in range(frames):
# Create output image
img = Image.new("RGB", (len(cells[0]), len(cells)))
pixels = img.load()
# Save cells to image
for x in range(len(cells)):
for y in range(len(cells[0])):
colour = 255 - cells[y][x] * 255
pixels[x, y] = (colour, colour, colour)
# Save image
images.append(img)
cells = new_generation(cells)
return images
if __name__ == "__main__":
images = generate_images(GLIDER, 16)
images[0].save("out.gif", save_all=True, append_images=images[1:])
| """
Conway's Game of Life implemented in Python.
https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life
"""
from __future__ import annotations
from PIL import Image
# Define glider example
GLIDER = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
]
# Define blinker example
BLINKER = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def new_generation(cells: list[list[int]]) -> list[list[int]]:
"""
Generates the next generation for a given state of Conway's Game of Life.
>>> new_generation(BLINKER)
[[0, 0, 0], [1, 1, 1], [0, 0, 0]]
"""
next_generation = []
for i in range(len(cells)):
next_generation_row = []
for j in range(len(cells[i])):
# Get the number of live neighbours
neighbour_count = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i]) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i]) - 1:
neighbour_count += cells[i][j + 1]
if i < len(cells) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(cells) - 1:
neighbour_count += cells[i + 1][j]
if i < len(cells) - 1 and j < len(cells[i]) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
alive = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1)
else:
next_generation_row.append(0)
next_generation.append(next_generation_row)
return next_generation
def generate_images(cells: list[list[int]], frames: int) -> list[Image.Image]:
"""
Generates a list of images of subsequent Game of Life states.
"""
images = []
for _ in range(frames):
# Create output image
img = Image.new("RGB", (len(cells[0]), len(cells)))
pixels = img.load()
# Save cells to image
for x in range(len(cells)):
for y in range(len(cells[0])):
colour = 255 - cells[y][x] * 255
pixels[x, y] = (colour, colour, colour)
# Save image
images.append(img)
cells = new_generation(cells)
return images
if __name__ == "__main__":
images = generate_images(GLIDER, 16)
images[0].save("out.gif", save_all=True, append_images=images[1:])
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/Weird_number
Fun fact: The set of weird numbers has positive asymptotic density.
"""
from math import sqrt
def factors(number: int) -> list[int]:
"""
>>> factors(12)
[1, 2, 3, 4, 6]
>>> factors(1)
[1]
>>> factors(100)
[1, 2, 4, 5, 10, 20, 25, 50]
# >>> factors(-12)
# [1, 2, 3, 4, 6]
"""
values = [1]
for i in range(2, int(sqrt(number)) + 1, 1):
if number % i == 0:
values.append(i)
if int(number // i) != i:
values.append(int(number // i))
return sorted(values)
def abundant(n: int) -> bool:
"""
>>> abundant(0)
True
>>> abundant(1)
False
>>> abundant(12)
True
>>> abundant(13)
False
>>> abundant(20)
True
# >>> abundant(-12)
# True
"""
return sum(factors(n)) > n
def semi_perfect(number: int) -> bool:
"""
>>> semi_perfect(0)
True
>>> semi_perfect(1)
True
>>> semi_perfect(12)
True
>>> semi_perfect(13)
False
# >>> semi_perfect(-12)
# True
"""
values = factors(number)
r = len(values)
subset = [[0 for i in range(number + 1)] for j in range(r + 1)]
for i in range(r + 1):
subset[i][0] = True
for i in range(1, number + 1):
subset[0][i] = False
for i in range(1, r + 1):
for j in range(1, number + 1):
if j < values[i - 1]:
subset[i][j] = subset[i - 1][j]
else:
subset[i][j] = subset[i - 1][j] or subset[i - 1][j - values[i - 1]]
return subset[r][number] != 0
def weird(number: int) -> bool:
"""
>>> weird(0)
False
>>> weird(70)
True
>>> weird(77)
False
"""
return abundant(number) and not semi_perfect(number)
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
for number in (69, 70, 71):
print(f"{number} is {'' if weird(number) else 'not '}weird.")
| """
https://en.wikipedia.org/wiki/Weird_number
Fun fact: The set of weird numbers has positive asymptotic density.
"""
from math import sqrt
def factors(number: int) -> list[int]:
"""
>>> factors(12)
[1, 2, 3, 4, 6]
>>> factors(1)
[1]
>>> factors(100)
[1, 2, 4, 5, 10, 20, 25, 50]
# >>> factors(-12)
# [1, 2, 3, 4, 6]
"""
values = [1]
for i in range(2, int(sqrt(number)) + 1, 1):
if number % i == 0:
values.append(i)
if int(number // i) != i:
values.append(int(number // i))
return sorted(values)
def abundant(n: int) -> bool:
"""
>>> abundant(0)
True
>>> abundant(1)
False
>>> abundant(12)
True
>>> abundant(13)
False
>>> abundant(20)
True
# >>> abundant(-12)
# True
"""
return sum(factors(n)) > n
def semi_perfect(number: int) -> bool:
"""
>>> semi_perfect(0)
True
>>> semi_perfect(1)
True
>>> semi_perfect(12)
True
>>> semi_perfect(13)
False
# >>> semi_perfect(-12)
# True
"""
values = factors(number)
r = len(values)
subset = [[0 for i in range(number + 1)] for j in range(r + 1)]
for i in range(r + 1):
subset[i][0] = True
for i in range(1, number + 1):
subset[0][i] = False
for i in range(1, r + 1):
for j in range(1, number + 1):
if j < values[i - 1]:
subset[i][j] = subset[i - 1][j]
else:
subset[i][j] = subset[i - 1][j] or subset[i - 1][j - values[i - 1]]
return subset[r][number] != 0
def weird(number: int) -> bool:
"""
>>> weird(0)
False
>>> weird(70)
True
>>> weird(77)
False
"""
return abundant(number) and not semi_perfect(number)
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
for number in (69, 70, 71):
print(f"{number} is {'' if weird(number) else 'not '}weird.")
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Given an array-like data structure A[1..n], how many pairs
(i, j) for all 1 <= i < j <= n such that A[i] > A[j]? These pairs are
called inversions. Counting the number of such inversions in an array-like
object is the important. Among other things, counting inversions can help
us determine how close a given array is to being sorted.
In this implementation, I provide two algorithms, a divide-and-conquer
algorithm which runs in nlogn and the brute-force n^2 algorithm.
"""
def count_inversions_bf(arr):
"""
Counts the number of inversions using a naive brute-force algorithm
Parameters
----------
arr: arr: array-like, the list containing the items for which the number
of inversions is desired. The elements of `arr` must be comparable.
Returns
-------
num_inversions: The total number of inversions in `arr`
Examples
---------
>>> count_inversions_bf([1, 4, 2, 4, 1])
4
>>> count_inversions_bf([1, 1, 2, 4, 4])
0
>>> count_inversions_bf([])
0
"""
num_inversions = 0
n = len(arr)
for i in range(n - 1):
for j in range(i + 1, n):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def count_inversions_recursive(arr):
"""
Counts the number of inversions using a divide-and-conquer algorithm
Parameters
-----------
arr: array-like, the list containing the items for which the number
of inversions is desired. The elements of `arr` must be comparable.
Returns
-------
C: a sorted copy of `arr`.
num_inversions: int, the total number of inversions in 'arr'
Examples
--------
>>> count_inversions_recursive([1, 4, 2, 4, 1])
([1, 1, 2, 4, 4], 4)
>>> count_inversions_recursive([1, 1, 2, 4, 4])
([1, 1, 2, 4, 4], 0)
>>> count_inversions_recursive([])
([], 0)
"""
if len(arr) <= 1:
return arr, 0
mid = len(arr) // 2
p = arr[0:mid]
q = arr[mid:]
a, inversion_p = count_inversions_recursive(p)
b, inversions_q = count_inversions_recursive(q)
c, cross_inversions = _count_cross_inversions(a, b)
num_inversions = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def _count_cross_inversions(p, q):
"""
Counts the inversions across two sorted arrays.
And combine the two arrays into one sorted array
For all 1<= i<=len(P) and for all 1 <= j <= len(Q),
if P[i] > Q[j], then (i, j) is a cross inversion
Parameters
----------
P: array-like, sorted in non-decreasing order
Q: array-like, sorted in non-decreasing order
Returns
------
R: array-like, a sorted array of the elements of `P` and `Q`
num_inversion: int, the number of inversions across `P` and `Q`
Examples
--------
>>> _count_cross_inversions([1, 2, 3], [0, 2, 5])
([0, 1, 2, 2, 3, 5], 4)
>>> _count_cross_inversions([1, 2, 3], [3, 4, 5])
([1, 2, 3, 3, 4, 5], 0)
"""
r = []
i = j = num_inversion = 0
while i < len(p) and j < len(q):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(p) - i
r.append(q[j])
j += 1
else:
r.append(p[i])
i += 1
if i < len(p):
r.extend(p[i:])
else:
r.extend(q[j:])
return r, num_inversion
def main():
arr_1 = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
num_inversions_bf = count_inversions_bf(arr_1)
_, num_inversions_recursive = count_inversions_recursive(arr_1)
assert num_inversions_bf == num_inversions_recursive == 8
print("number of inversions = ", num_inversions_bf)
# testing an array with zero inversion (a sorted arr_1)
arr_1.sort()
num_inversions_bf = count_inversions_bf(arr_1)
_, num_inversions_recursive = count_inversions_recursive(arr_1)
assert num_inversions_bf == num_inversions_recursive == 0
print("number of inversions = ", num_inversions_bf)
# an empty list should also have zero inversions
arr_1 = []
num_inversions_bf = count_inversions_bf(arr_1)
_, num_inversions_recursive = count_inversions_recursive(arr_1)
assert num_inversions_bf == num_inversions_recursive == 0
print("number of inversions = ", num_inversions_bf)
if __name__ == "__main__":
main()
| """
Given an array-like data structure A[1..n], how many pairs
(i, j) for all 1 <= i < j <= n such that A[i] > A[j]? These pairs are
called inversions. Counting the number of such inversions in an array-like
object is the important. Among other things, counting inversions can help
us determine how close a given array is to being sorted.
In this implementation, I provide two algorithms, a divide-and-conquer
algorithm which runs in nlogn and the brute-force n^2 algorithm.
"""
def count_inversions_bf(arr):
"""
Counts the number of inversions using a naive brute-force algorithm
Parameters
----------
arr: arr: array-like, the list containing the items for which the number
of inversions is desired. The elements of `arr` must be comparable.
Returns
-------
num_inversions: The total number of inversions in `arr`
Examples
---------
>>> count_inversions_bf([1, 4, 2, 4, 1])
4
>>> count_inversions_bf([1, 1, 2, 4, 4])
0
>>> count_inversions_bf([])
0
"""
num_inversions = 0
n = len(arr)
for i in range(n - 1):
for j in range(i + 1, n):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def count_inversions_recursive(arr):
"""
Counts the number of inversions using a divide-and-conquer algorithm
Parameters
-----------
arr: array-like, the list containing the items for which the number
of inversions is desired. The elements of `arr` must be comparable.
Returns
-------
C: a sorted copy of `arr`.
num_inversions: int, the total number of inversions in 'arr'
Examples
--------
>>> count_inversions_recursive([1, 4, 2, 4, 1])
([1, 1, 2, 4, 4], 4)
>>> count_inversions_recursive([1, 1, 2, 4, 4])
([1, 1, 2, 4, 4], 0)
>>> count_inversions_recursive([])
([], 0)
"""
if len(arr) <= 1:
return arr, 0
mid = len(arr) // 2
p = arr[0:mid]
q = arr[mid:]
a, inversion_p = count_inversions_recursive(p)
b, inversions_q = count_inversions_recursive(q)
c, cross_inversions = _count_cross_inversions(a, b)
num_inversions = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def _count_cross_inversions(p, q):
"""
Counts the inversions across two sorted arrays.
And combine the two arrays into one sorted array
For all 1<= i<=len(P) and for all 1 <= j <= len(Q),
if P[i] > Q[j], then (i, j) is a cross inversion
Parameters
----------
P: array-like, sorted in non-decreasing order
Q: array-like, sorted in non-decreasing order
Returns
------
R: array-like, a sorted array of the elements of `P` and `Q`
num_inversion: int, the number of inversions across `P` and `Q`
Examples
--------
>>> _count_cross_inversions([1, 2, 3], [0, 2, 5])
([0, 1, 2, 2, 3, 5], 4)
>>> _count_cross_inversions([1, 2, 3], [3, 4, 5])
([1, 2, 3, 3, 4, 5], 0)
"""
r = []
i = j = num_inversion = 0
while i < len(p) and j < len(q):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(p) - i
r.append(q[j])
j += 1
else:
r.append(p[i])
i += 1
if i < len(p):
r.extend(p[i:])
else:
r.extend(q[j:])
return r, num_inversion
def main():
arr_1 = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
num_inversions_bf = count_inversions_bf(arr_1)
_, num_inversions_recursive = count_inversions_recursive(arr_1)
assert num_inversions_bf == num_inversions_recursive == 8
print("number of inversions = ", num_inversions_bf)
# testing an array with zero inversion (a sorted arr_1)
arr_1.sort()
num_inversions_bf = count_inversions_bf(arr_1)
_, num_inversions_recursive = count_inversions_recursive(arr_1)
assert num_inversions_bf == num_inversions_recursive == 0
print("number of inversions = ", num_inversions_bf)
# an empty list should also have zero inversions
arr_1 = []
num_inversions_bf = count_inversions_bf(arr_1)
_, num_inversions_recursive = count_inversions_recursive(arr_1)
assert num_inversions_bf == num_inversions_recursive == 0
print("number of inversions = ", num_inversions_bf)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def alternative_string_arrange(first_str: str, second_str: str) -> str:
"""
Return the alternative arrangements of the two strings.
:param first_str:
:param second_str:
:return: String
>>> alternative_string_arrange("ABCD", "XY")
'AXBYCD'
>>> alternative_string_arrange("XY", "ABCD")
'XAYBCD'
>>> alternative_string_arrange("AB", "XYZ")
'AXBYZ'
>>> alternative_string_arrange("ABC", "")
'ABC'
"""
first_str_length: int = len(first_str)
second_str_length: int = len(second_str)
abs_length: int = (
first_str_length if first_str_length > second_str_length else second_str_length
)
output_list: list = []
for char_count in range(abs_length):
if char_count < first_str_length:
output_list.append(first_str[char_count])
if char_count < second_str_length:
output_list.append(second_str[char_count])
return "".join(output_list)
if __name__ == "__main__":
print(alternative_string_arrange("AB", "XYZ"), end=" ")
| def alternative_string_arrange(first_str: str, second_str: str) -> str:
"""
Return the alternative arrangements of the two strings.
:param first_str:
:param second_str:
:return: String
>>> alternative_string_arrange("ABCD", "XY")
'AXBYCD'
>>> alternative_string_arrange("XY", "ABCD")
'XAYBCD'
>>> alternative_string_arrange("AB", "XYZ")
'AXBYZ'
>>> alternative_string_arrange("ABC", "")
'ABC'
"""
first_str_length: int = len(first_str)
second_str_length: int = len(second_str)
abs_length: int = (
first_str_length if first_str_length > second_str_length else second_str_length
)
output_list: list = []
for char_count in range(abs_length):
if char_count < first_str_length:
output_list.append(first_str[char_count])
if char_count < second_str_length:
output_list.append(second_str[char_count])
return "".join(output_list)
if __name__ == "__main__":
print(alternative_string_arrange("AB", "XYZ"), end=" ")
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/Computus#Gauss'_Easter_algorithm
"""
import math
from datetime import datetime, timedelta
def gauss_easter(year: int) -> datetime:
"""
Calculation Gregorian easter date for given year
>>> gauss_easter(2007)
datetime.datetime(2007, 4, 8, 0, 0)
>>> gauss_easter(2008)
datetime.datetime(2008, 3, 23, 0, 0)
>>> gauss_easter(2020)
datetime.datetime(2020, 4, 12, 0, 0)
>>> gauss_easter(2021)
datetime.datetime(2021, 4, 4, 0, 0)
"""
metonic_cycle = year % 19
julian_leap_year = year % 4
non_leap_year = year % 7
leap_day_inhibits = math.floor(year / 100)
lunar_orbit_correction = math.floor((13 + 8 * leap_day_inhibits) / 25)
leap_day_reinstall_number = leap_day_inhibits / 4
secular_moon_shift = (
15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
) % 30
century_starting_point = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7
# days to be added to March 21
days_to_add = (19 * metonic_cycle + secular_moon_shift) % 30
# PHM -> Paschal Full Moon
days_from_phm_to_sunday = (
2 * julian_leap_year
+ 4 * non_leap_year
+ 6 * days_to_add
+ century_starting_point
) % 7
if days_to_add == 29 and days_from_phm_to_sunday == 6:
return datetime(year, 4, 19)
elif days_to_add == 28 and days_from_phm_to_sunday == 6:
return datetime(year, 4, 18)
else:
return datetime(year, 3, 22) + timedelta(
days=int(days_to_add + days_from_phm_to_sunday)
)
if __name__ == "__main__":
for year in (1994, 2000, 2010, 2021, 2023):
tense = "will be" if year > datetime.now().year else "was"
print(f"Easter in {year} {tense} {gauss_easter(year)}")
| """
https://en.wikipedia.org/wiki/Computus#Gauss'_Easter_algorithm
"""
import math
from datetime import datetime, timedelta
def gauss_easter(year: int) -> datetime:
"""
Calculation Gregorian easter date for given year
>>> gauss_easter(2007)
datetime.datetime(2007, 4, 8, 0, 0)
>>> gauss_easter(2008)
datetime.datetime(2008, 3, 23, 0, 0)
>>> gauss_easter(2020)
datetime.datetime(2020, 4, 12, 0, 0)
>>> gauss_easter(2021)
datetime.datetime(2021, 4, 4, 0, 0)
"""
metonic_cycle = year % 19
julian_leap_year = year % 4
non_leap_year = year % 7
leap_day_inhibits = math.floor(year / 100)
lunar_orbit_correction = math.floor((13 + 8 * leap_day_inhibits) / 25)
leap_day_reinstall_number = leap_day_inhibits / 4
secular_moon_shift = (
15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
) % 30
century_starting_point = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7
# days to be added to March 21
days_to_add = (19 * metonic_cycle + secular_moon_shift) % 30
# PHM -> Paschal Full Moon
days_from_phm_to_sunday = (
2 * julian_leap_year
+ 4 * non_leap_year
+ 6 * days_to_add
+ century_starting_point
) % 7
if days_to_add == 29 and days_from_phm_to_sunday == 6:
return datetime(year, 4, 19)
elif days_to_add == 28 and days_from_phm_to_sunday == 6:
return datetime(year, 4, 18)
else:
return datetime(year, 3, 22) + timedelta(
days=int(days_to_add + days_from_phm_to_sunday)
)
if __name__ == "__main__":
for year in (1994, 2000, 2010, 2021, 2023):
tense = "will be" if year > datetime.now().year else "was"
print(f"Easter in {year} {tense} {gauss_easter(year)}")
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
filepaths = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
upper_files = [file for file in filepaths if file != file.lower()]
if upper_files:
print(f"{len(upper_files)} files contain uppercase characters:")
print("\n".join(upper_files) + "\n")
space_files = [file for file in filepaths if " " in file]
if space_files:
print(f"{len(space_files)} files contain space characters:")
print("\n".join(space_files) + "\n")
hyphen_files = [file for file in filepaths if "-" in file]
if hyphen_files:
print(f"{len(hyphen_files)} files contain hyphen characters:")
print("\n".join(hyphen_files) + "\n")
nodir_files = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(f"{len(nodir_files)} files are not in a directory:")
print("\n".join(nodir_files) + "\n")
bad_files = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| #!/usr/bin/env python3
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
filepaths = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
upper_files = [file for file in filepaths if file != file.lower()]
if upper_files:
print(f"{len(upper_files)} files contain uppercase characters:")
print("\n".join(upper_files) + "\n")
space_files = [file for file in filepaths if " " in file]
if space_files:
print(f"{len(space_files)} files contain space characters:")
print("\n".join(space_files) + "\n")
hyphen_files = [file for file in filepaths if "-" in file]
if hyphen_files:
print(f"{len(hyphen_files)} files contain hyphen characters:")
print("\n".join(hyphen_files) + "\n")
nodir_files = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(f"{len(nodir_files)} files are not in a directory:")
print("\n".join(nodir_files) + "\n")
bad_files = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem 14: https://projecteuler.net/problem=14
Collatz conjecture: start with any positive integer n. Next term obtained from
the previous term as follows:
If the previous term is even, the next term is one half the previous term.
If the previous term is odd, the next term is 3 times the previous term plus 1.
The conjecture states the sequence will always reach 1 regardless of starting
n.
Problem Statement:
The following iterative sequence is defined for the set of positive integers:
n → n/2 (n is even)
n → 3n + 1 (n is odd)
Using the rule above and starting with 13, we generate the following sequence:
13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1
It can be seen that this sequence (starting at 13 and finishing at 1) contains
10 terms. Although it has not been proved yet (Collatz Problem), it is thought
that all starting numbers finish at 1.
Which starting number, under one million, produces the longest chain?
"""
from __future__ import annotations
COLLATZ_SEQUENCE_LENGTHS = {1: 1}
def collatz_sequence_length(n: int) -> int:
"""Returns the Collatz sequence length for n."""
if n in COLLATZ_SEQUENCE_LENGTHS:
return COLLATZ_SEQUENCE_LENGTHS[n]
if n % 2 == 0:
next_n = n // 2
else:
next_n = 3 * n + 1
sequence_length = collatz_sequence_length(next_n) + 1
COLLATZ_SEQUENCE_LENGTHS[n] = sequence_length
return sequence_length
def solution(n: int = 1000000) -> int:
"""Returns the number under n that generates the longest Collatz sequence.
>>> solution(1000000)
837799
>>> solution(200)
171
>>> solution(5000)
3711
>>> solution(15000)
13255
"""
result = max((collatz_sequence_length(i), i) for i in range(1, n))
return result[1]
if __name__ == "__main__":
print(solution(int(input().strip())))
| """
Problem 14: https://projecteuler.net/problem=14
Collatz conjecture: start with any positive integer n. Next term obtained from
the previous term as follows:
If the previous term is even, the next term is one half the previous term.
If the previous term is odd, the next term is 3 times the previous term plus 1.
The conjecture states the sequence will always reach 1 regardless of starting
n.
Problem Statement:
The following iterative sequence is defined for the set of positive integers:
n → n/2 (n is even)
n → 3n + 1 (n is odd)
Using the rule above and starting with 13, we generate the following sequence:
13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1
It can be seen that this sequence (starting at 13 and finishing at 1) contains
10 terms. Although it has not been proved yet (Collatz Problem), it is thought
that all starting numbers finish at 1.
Which starting number, under one million, produces the longest chain?
"""
from __future__ import annotations
COLLATZ_SEQUENCE_LENGTHS = {1: 1}
def collatz_sequence_length(n: int) -> int:
"""Returns the Collatz sequence length for n."""
if n in COLLATZ_SEQUENCE_LENGTHS:
return COLLATZ_SEQUENCE_LENGTHS[n]
if n % 2 == 0:
next_n = n // 2
else:
next_n = 3 * n + 1
sequence_length = collatz_sequence_length(next_n) + 1
COLLATZ_SEQUENCE_LENGTHS[n] = sequence_length
return sequence_length
def solution(n: int = 1000000) -> int:
"""Returns the number under n that generates the longest Collatz sequence.
>>> solution(1000000)
837799
>>> solution(200)
171
>>> solution(5000)
3711
>>> solution(15000)
13255
"""
result = max((collatz_sequence_length(i), i) for i in range(1, n))
return result[1]
if __name__ == "__main__":
print(solution(int(input().strip())))
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
== Carmichael Numbers ==
A number n is said to be a Carmichael number if it
satisfies the following modular arithmetic condition:
power(b, n-1) MOD n = 1,
for all b ranging from 1 to n such that b and
n are relatively prime, i.e, gcd(b, n) = 1
Examples of Carmichael Numbers: 561, 1105, ...
https://en.wikipedia.org/wiki/Carmichael_number
"""
def gcd(a: int, b: int) -> int:
if a < b:
return gcd(b, a)
if a % b == 0:
return b
return gcd(b, a % b)
def power(x: int, y: int, mod: int) -> int:
if y == 0:
return 1
temp = power(x, y // 2, mod) % mod
temp = (temp * temp) % mod
if y % 2 == 1:
temp = (temp * x) % mod
return temp
def is_carmichael_number(n: int) -> bool:
b = 2
while b < n:
if gcd(b, n) == 1 and power(b, n - 1, n) != 1:
return False
b += 1
return True
if __name__ == "__main__":
number = int(input("Enter number: ").strip())
if is_carmichael_number(number):
print(f"{number} is a Carmichael Number.")
else:
print(f"{number} is not a Carmichael Number.")
| """
== Carmichael Numbers ==
A number n is said to be a Carmichael number if it
satisfies the following modular arithmetic condition:
power(b, n-1) MOD n = 1,
for all b ranging from 1 to n such that b and
n are relatively prime, i.e, gcd(b, n) = 1
Examples of Carmichael Numbers: 561, 1105, ...
https://en.wikipedia.org/wiki/Carmichael_number
"""
def gcd(a: int, b: int) -> int:
if a < b:
return gcd(b, a)
if a % b == 0:
return b
return gcd(b, a % b)
def power(x: int, y: int, mod: int) -> int:
if y == 0:
return 1
temp = power(x, y // 2, mod) % mod
temp = (temp * temp) % mod
if y % 2 == 1:
temp = (temp * x) % mod
return temp
def is_carmichael_number(n: int) -> bool:
b = 2
while b < n:
if gcd(b, n) == 1 and power(b, n - 1, n) != 1:
return False
b += 1
return True
if __name__ == "__main__":
number = int(input("Enter number: ").strip())
if is_carmichael_number(number):
print(f"{number} is a Carmichael Number.")
else:
print(f"{number} is not a Carmichael Number.")
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| class Graph:
def __init__(self, vertex):
self.vertex = vertex
self.graph = [[0] * vertex for i in range(vertex)]
def add_edge(self, u, v):
self.graph[u - 1][v - 1] = 1
self.graph[v - 1][u - 1] = 1
def show(self):
for i in self.graph:
for j in i:
print(j, end=" ")
print(" ")
g = Graph(100)
g.add_edge(1, 4)
g.add_edge(4, 2)
g.add_edge(4, 5)
g.add_edge(2, 5)
g.add_edge(5, 3)
g.show()
| class Graph:
def __init__(self, vertex):
self.vertex = vertex
self.graph = [[0] * vertex for i in range(vertex)]
def add_edge(self, u, v):
self.graph[u - 1][v - 1] = 1
self.graph[v - 1][u - 1] = 1
def show(self):
for i in self.graph:
for j in i:
print(j, end=" ")
print(" ")
g = Graph(100)
g.add_edge(1, 4)
g.add_edge(4, 2)
g.add_edge(4, 5)
g.add_edge(2, 5)
g.add_edge(5, 3)
g.show()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem 46: https://projecteuler.net/problem=46
It was proposed by Christian Goldbach that every odd composite number can be
written as the sum of a prime and twice a square.
9 = 7 + 2 × 12
15 = 7 + 2 × 22
21 = 3 + 2 × 32
25 = 7 + 2 × 32
27 = 19 + 2 × 22
33 = 31 + 2 × 12
It turns out that the conjecture was false.
What is the smallest odd composite that cannot be written as the sum of a
prime and twice a square?
"""
from __future__ import annotations
import math
def is_prime(number: int) -> bool:
"""Checks to see if a number is a prime in O(sqrt(n)).
A number is prime if it has exactly two factors: 1 and itself.
>>> is_prime(0)
False
>>> is_prime(1)
False
>>> is_prime(2)
True
>>> is_prime(3)
True
>>> is_prime(27)
False
>>> is_prime(87)
False
>>> is_prime(563)
True
>>> is_prime(2999)
True
>>> is_prime(67483)
False
"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5, int(math.sqrt(number) + 1), 6):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
odd_composites = [num for num in range(3, 100001, 2) if not is_prime(num)]
def compute_nums(n: int) -> list[int]:
"""
Returns a list of first n odd composite numbers which do
not follow the conjecture.
>>> compute_nums(1)
[5777]
>>> compute_nums(2)
[5777, 5993]
>>> compute_nums(0)
Traceback (most recent call last):
...
ValueError: n must be >= 0
>>> compute_nums("a")
Traceback (most recent call last):
...
ValueError: n must be an integer
>>> compute_nums(1.1)
Traceback (most recent call last):
...
ValueError: n must be an integer
"""
if not isinstance(n, int):
raise ValueError("n must be an integer")
if n <= 0:
raise ValueError("n must be >= 0")
list_nums = []
for num in range(len(odd_composites)):
i = 0
while 2 * i * i <= odd_composites[num]:
rem = odd_composites[num] - 2 * i * i
if is_prime(rem):
break
i += 1
else:
list_nums.append(odd_composites[num])
if len(list_nums) == n:
return list_nums
return []
def solution() -> int:
"""Return the solution to the problem"""
return compute_nums(1)[0]
if __name__ == "__main__":
print(f"{solution() = }")
| """
Problem 46: https://projecteuler.net/problem=46
It was proposed by Christian Goldbach that every odd composite number can be
written as the sum of a prime and twice a square.
9 = 7 + 2 × 12
15 = 7 + 2 × 22
21 = 3 + 2 × 32
25 = 7 + 2 × 32
27 = 19 + 2 × 22
33 = 31 + 2 × 12
It turns out that the conjecture was false.
What is the smallest odd composite that cannot be written as the sum of a
prime and twice a square?
"""
from __future__ import annotations
import math
def is_prime(number: int) -> bool:
"""Checks to see if a number is a prime in O(sqrt(n)).
A number is prime if it has exactly two factors: 1 and itself.
>>> is_prime(0)
False
>>> is_prime(1)
False
>>> is_prime(2)
True
>>> is_prime(3)
True
>>> is_prime(27)
False
>>> is_prime(87)
False
>>> is_prime(563)
True
>>> is_prime(2999)
True
>>> is_prime(67483)
False
"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5, int(math.sqrt(number) + 1), 6):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
odd_composites = [num for num in range(3, 100001, 2) if not is_prime(num)]
def compute_nums(n: int) -> list[int]:
"""
Returns a list of first n odd composite numbers which do
not follow the conjecture.
>>> compute_nums(1)
[5777]
>>> compute_nums(2)
[5777, 5993]
>>> compute_nums(0)
Traceback (most recent call last):
...
ValueError: n must be >= 0
>>> compute_nums("a")
Traceback (most recent call last):
...
ValueError: n must be an integer
>>> compute_nums(1.1)
Traceback (most recent call last):
...
ValueError: n must be an integer
"""
if not isinstance(n, int):
raise ValueError("n must be an integer")
if n <= 0:
raise ValueError("n must be >= 0")
list_nums = []
for num in range(len(odd_composites)):
i = 0
while 2 * i * i <= odd_composites[num]:
rem = odd_composites[num] - 2 * i * i
if is_prime(rem):
break
i += 1
else:
list_nums.append(odd_composites[num])
if len(list_nums) == n:
return list_nums
return []
def solution() -> int:
"""Return the solution to the problem"""
return compute_nums(1)[0]
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import glob
import os
import random
from string import ascii_lowercase, digits
import cv2
"""
Flip image and bounding box for computer vision task
https://paperswithcode.com/method/randomhorizontalflip
"""
# Params
LABEL_DIR = ""
IMAGE_DIR = ""
OUTPUT_DIR = ""
FLIP_TYPE = 1 # (0 is vertical, 1 is horizontal)
def main() -> None:
"""
Get images list and annotations list from input dir.
Update new images and annotations.
Save images and annotations in output dir.
>>> pass # A doctest is not possible for this function.
"""
img_paths, annos = get_dataset(LABEL_DIR, IMAGE_DIR)
print("Processing...")
new_images, new_annos, paths = update_image_and_anno(img_paths, annos, FLIP_TYPE)
for index, image in enumerate(new_images):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
letter_code = random_chars(32)
file_name = paths[index].split(os.sep)[-1].rsplit(".", 1)[0]
file_root = f"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
cv2.imwrite(f"/{file_root}.jpg", image, [cv2.IMWRITE_JPEG_QUALITY, 85])
print(f"Success {index+1}/{len(new_images)} with {file_name}")
annos_list = []
for anno in new_annos[index]:
obj = f"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
annos_list.append(obj)
with open(f"/{file_root}.txt", "w") as outfile:
outfile.write("\n".join(line for line in annos_list))
def get_dataset(label_dir: str, img_dir: str) -> tuple[list, list]:
"""
- label_dir <type: str>: Path to label include annotation of images
- img_dir <type: str>: Path to folder contain images
Return <type: list>: List of images path and labels
>>> pass # A doctest is not possible for this function.
"""
img_paths = []
labels = []
for label_file in glob.glob(os.path.join(label_dir, "*.txt")):
label_name = label_file.split(os.sep)[-1].rsplit(".", 1)[0]
with open(label_file) as in_file:
obj_lists = in_file.readlines()
img_path = os.path.join(img_dir, f"{label_name}.jpg")
boxes = []
for obj_list in obj_lists:
obj = obj_list.rstrip("\n").split(" ")
boxes.append(
[
int(obj[0]),
float(obj[1]),
float(obj[2]),
float(obj[3]),
float(obj[4]),
]
)
if not boxes:
continue
img_paths.append(img_path)
labels.append(boxes)
return img_paths, labels
def update_image_and_anno(
img_list: list, anno_list: list, flip_type: int = 1
) -> tuple[list, list, list]:
"""
- img_list <type: list>: list of all images
- anno_list <type: list>: list of all annotations of specific image
- flip_type <type: int>: 0 is vertical, 1 is horizontal
Return:
- new_imgs_list <type: narray>: image after resize
- new_annos_lists <type: list>: list of new annotation after scale
- path_list <type: list>: list the name of image file
>>> pass # A doctest is not possible for this function.
"""
new_annos_lists = []
path_list = []
new_imgs_list = []
for idx in range(len(img_list)):
new_annos = []
path = img_list[idx]
path_list.append(path)
img_annos = anno_list[idx]
img = cv2.imread(path)
if flip_type == 1:
new_img = cv2.flip(img, flip_type)
for bbox in img_annos:
x_center_new = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]])
elif flip_type == 0:
new_img = cv2.flip(img, flip_type)
for bbox in img_annos:
y_center_new = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]])
new_annos_lists.append(new_annos)
new_imgs_list.append(new_img)
return new_imgs_list, new_annos_lists, path_list
def random_chars(number_char: int = 32) -> str:
"""
Automatic generate random 32 characters.
Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
>>> len(random_chars(32))
32
"""
assert number_char > 1, "The number of character should greater than 1"
letter_code = ascii_lowercase + digits
return "".join(random.choice(letter_code) for _ in range(number_char))
if __name__ == "__main__":
main()
print("DONE ✅")
| import glob
import os
import random
from string import ascii_lowercase, digits
import cv2
"""
Flip image and bounding box for computer vision task
https://paperswithcode.com/method/randomhorizontalflip
"""
# Params
LABEL_DIR = ""
IMAGE_DIR = ""
OUTPUT_DIR = ""
FLIP_TYPE = 1 # (0 is vertical, 1 is horizontal)
def main() -> None:
"""
Get images list and annotations list from input dir.
Update new images and annotations.
Save images and annotations in output dir.
>>> pass # A doctest is not possible for this function.
"""
img_paths, annos = get_dataset(LABEL_DIR, IMAGE_DIR)
print("Processing...")
new_images, new_annos, paths = update_image_and_anno(img_paths, annos, FLIP_TYPE)
for index, image in enumerate(new_images):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
letter_code = random_chars(32)
file_name = paths[index].split(os.sep)[-1].rsplit(".", 1)[0]
file_root = f"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
cv2.imwrite(f"/{file_root}.jpg", image, [cv2.IMWRITE_JPEG_QUALITY, 85])
print(f"Success {index+1}/{len(new_images)} with {file_name}")
annos_list = []
for anno in new_annos[index]:
obj = f"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
annos_list.append(obj)
with open(f"/{file_root}.txt", "w") as outfile:
outfile.write("\n".join(line for line in annos_list))
def get_dataset(label_dir: str, img_dir: str) -> tuple[list, list]:
"""
- label_dir <type: str>: Path to label include annotation of images
- img_dir <type: str>: Path to folder contain images
Return <type: list>: List of images path and labels
>>> pass # A doctest is not possible for this function.
"""
img_paths = []
labels = []
for label_file in glob.glob(os.path.join(label_dir, "*.txt")):
label_name = label_file.split(os.sep)[-1].rsplit(".", 1)[0]
with open(label_file) as in_file:
obj_lists = in_file.readlines()
img_path = os.path.join(img_dir, f"{label_name}.jpg")
boxes = []
for obj_list in obj_lists:
obj = obj_list.rstrip("\n").split(" ")
boxes.append(
[
int(obj[0]),
float(obj[1]),
float(obj[2]),
float(obj[3]),
float(obj[4]),
]
)
if not boxes:
continue
img_paths.append(img_path)
labels.append(boxes)
return img_paths, labels
def update_image_and_anno(
img_list: list, anno_list: list, flip_type: int = 1
) -> tuple[list, list, list]:
"""
- img_list <type: list>: list of all images
- anno_list <type: list>: list of all annotations of specific image
- flip_type <type: int>: 0 is vertical, 1 is horizontal
Return:
- new_imgs_list <type: narray>: image after resize
- new_annos_lists <type: list>: list of new annotation after scale
- path_list <type: list>: list the name of image file
>>> pass # A doctest is not possible for this function.
"""
new_annos_lists = []
path_list = []
new_imgs_list = []
for idx in range(len(img_list)):
new_annos = []
path = img_list[idx]
path_list.append(path)
img_annos = anno_list[idx]
img = cv2.imread(path)
if flip_type == 1:
new_img = cv2.flip(img, flip_type)
for bbox in img_annos:
x_center_new = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]])
elif flip_type == 0:
new_img = cv2.flip(img, flip_type)
for bbox in img_annos:
y_center_new = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]])
new_annos_lists.append(new_annos)
new_imgs_list.append(new_img)
return new_imgs_list, new_annos_lists, path_list
def random_chars(number_char: int = 32) -> str:
"""
Automatic generate random 32 characters.
Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
>>> len(random_chars(32))
32
"""
assert number_char > 1, "The number of character should greater than 1"
letter_code = ascii_lowercase + digits
return "".join(random.choice(letter_code) for _ in range(number_char))
if __name__ == "__main__":
main()
print("DONE ✅")
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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 | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
def print_distance(distance: list[float], src):
print(f"Vertex\tShortest Distance from vertex {src}")
for i, d in enumerate(distance):
print(f"{i}\t\t{d}")
def check_negative_cycle(
graph: list[dict[str, int]], distance: list[float], edge_count: int
):
for j in range(edge_count):
u, v, w = (graph[j][k] for k in ["src", "dst", "weight"])
if distance[u] != float("inf") and distance[u] + w < distance[v]:
return True
return False
def bellman_ford(
graph: list[dict[str, int]], vertex_count: int, edge_count: int, src: int
) -> list[float]:
"""
Returns shortest paths from a vertex src to all
other vertices.
>>> edges = [(2, 1, -10), (3, 2, 3), (0, 3, 5), (0, 1, 4)]
>>> g = [{"src": s, "dst": d, "weight": w} for s, d, w in edges]
>>> bellman_ford(g, 4, 4, 0)
[0.0, -2.0, 8.0, 5.0]
>>> g = [{"src": s, "dst": d, "weight": w} for s, d, w in edges + [(1, 3, 5)]]
>>> bellman_ford(g, 4, 5, 0)
Traceback (most recent call last):
...
Exception: Negative cycle found
"""
distance = [float("inf")] * vertex_count
distance[src] = 0.0
for _ in range(vertex_count - 1):
for j in range(edge_count):
u, v, w = (graph[j][k] for k in ["src", "dst", "weight"])
if distance[u] != float("inf") and distance[u] + w < distance[v]:
distance[v] = distance[u] + w
negative_cycle_exists = check_negative_cycle(graph, distance, edge_count)
if negative_cycle_exists:
raise Exception("Negative cycle found")
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
V = int(input("Enter number of vertices: ").strip())
E = int(input("Enter number of edges: ").strip())
graph: list[dict[str, int]] = [dict() for j in range(E)]
for i in range(E):
print("Edge ", i + 1)
src, dest, weight = (
int(x)
for x in input("Enter source, destination, weight: ").strip().split(" ")
)
graph[i] = {"src": src, "dst": dest, "weight": weight}
source = int(input("\nEnter shortest path source:").strip())
shortest_distance = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| from __future__ import annotations
def print_distance(distance: list[float], src):
print(f"Vertex\tShortest Distance from vertex {src}")
for i, d in enumerate(distance):
print(f"{i}\t\t{d}")
def check_negative_cycle(
graph: list[dict[str, int]], distance: list[float], edge_count: int
):
for j in range(edge_count):
u, v, w = (graph[j][k] for k in ["src", "dst", "weight"])
if distance[u] != float("inf") and distance[u] + w < distance[v]:
return True
return False
def bellman_ford(
graph: list[dict[str, int]], vertex_count: int, edge_count: int, src: int
) -> list[float]:
"""
Returns shortest paths from a vertex src to all
other vertices.
>>> edges = [(2, 1, -10), (3, 2, 3), (0, 3, 5), (0, 1, 4)]
>>> g = [{"src": s, "dst": d, "weight": w} for s, d, w in edges]
>>> bellman_ford(g, 4, 4, 0)
[0.0, -2.0, 8.0, 5.0]
>>> g = [{"src": s, "dst": d, "weight": w} for s, d, w in edges + [(1, 3, 5)]]
>>> bellman_ford(g, 4, 5, 0)
Traceback (most recent call last):
...
Exception: Negative cycle found
"""
distance = [float("inf")] * vertex_count
distance[src] = 0.0
for _ in range(vertex_count - 1):
for j in range(edge_count):
u, v, w = (graph[j][k] for k in ["src", "dst", "weight"])
if distance[u] != float("inf") and distance[u] + w < distance[v]:
distance[v] = distance[u] + w
negative_cycle_exists = check_negative_cycle(graph, distance, edge_count)
if negative_cycle_exists:
raise Exception("Negative cycle found")
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
V = int(input("Enter number of vertices: ").strip())
E = int(input("Enter number of edges: ").strip())
graph: list[dict[str, int]] = [dict() for j in range(E)]
for i in range(E):
print("Edge ", i + 1)
src, dest, weight = (
int(x)
for x in input("Enter source, destination, weight: ").strip().split(" ")
)
graph[i] = {"src": src, "dst": dest, "weight": weight}
source = int(input("\nEnter shortest path source:").strip())
shortest_distance = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import math
class SegmentTree:
def __init__(self, a):
self.N = len(a)
self.st = [0] * (
4 * self.N
) # approximate the overall size of segment tree with array N
self.build(1, 0, self.N - 1)
def left(self, idx):
return idx * 2
def right(self, idx):
return idx * 2 + 1
def build(self, idx, l, r): # noqa: E741
if l == r: # noqa: E741
self.st[idx] = A[l]
else:
mid = (l + r) // 2
self.build(self.left(idx), l, mid)
self.build(self.right(idx), mid + 1, r)
self.st[idx] = max(self.st[self.left(idx)], self.st[self.right(idx)])
def update(self, a, b, val):
return self.update_recursive(1, 0, self.N - 1, a - 1, b - 1, val)
def update_recursive(self, idx, l, r, a, b, val): # noqa: E741
"""
update(1, 1, N, a, b, v) for update val v to [a,b]
"""
if r < a or l > b:
return True
if l == r: # noqa: E741
self.st[idx] = val
return True
mid = (l + r) // 2
self.update_recursive(self.left(idx), l, mid, a, b, val)
self.update_recursive(self.right(idx), mid + 1, r, a, b, val)
self.st[idx] = max(self.st[self.left(idx)], self.st[self.right(idx)])
return True
def query(self, a, b):
return self.query_recursive(1, 0, self.N - 1, a - 1, b - 1)
def query_recursive(self, idx, l, r, a, b): # noqa: E741
"""
query(1, 1, N, a, b) for query max of [a,b]
"""
if r < a or l > b:
return -math.inf
if l >= a and r <= b: # noqa: E741
return self.st[idx]
mid = (l + r) // 2
q1 = self.query_recursive(self.left(idx), l, mid, a, b)
q2 = self.query_recursive(self.right(idx), mid + 1, r, a, b)
return max(q1, q2)
def show_data(self):
show_list = []
for i in range(1, N + 1):
show_list += [self.query(i, i)]
print(show_list)
if __name__ == "__main__":
A = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
N = 15
segt = SegmentTree(A)
print(segt.query(4, 6))
print(segt.query(7, 11))
print(segt.query(7, 12))
segt.update(1, 3, 111)
print(segt.query(1, 15))
segt.update(7, 8, 235)
segt.show_data()
| import math
class SegmentTree:
def __init__(self, a):
self.N = len(a)
self.st = [0] * (
4 * self.N
) # approximate the overall size of segment tree with array N
self.build(1, 0, self.N - 1)
def left(self, idx):
return idx * 2
def right(self, idx):
return idx * 2 + 1
def build(self, idx, l, r): # noqa: E741
if l == r: # noqa: E741
self.st[idx] = A[l]
else:
mid = (l + r) // 2
self.build(self.left(idx), l, mid)
self.build(self.right(idx), mid + 1, r)
self.st[idx] = max(self.st[self.left(idx)], self.st[self.right(idx)])
def update(self, a, b, val):
return self.update_recursive(1, 0, self.N - 1, a - 1, b - 1, val)
def update_recursive(self, idx, l, r, a, b, val): # noqa: E741
"""
update(1, 1, N, a, b, v) for update val v to [a,b]
"""
if r < a or l > b:
return True
if l == r: # noqa: E741
self.st[idx] = val
return True
mid = (l + r) // 2
self.update_recursive(self.left(idx), l, mid, a, b, val)
self.update_recursive(self.right(idx), mid + 1, r, a, b, val)
self.st[idx] = max(self.st[self.left(idx)], self.st[self.right(idx)])
return True
def query(self, a, b):
return self.query_recursive(1, 0, self.N - 1, a - 1, b - 1)
def query_recursive(self, idx, l, r, a, b): # noqa: E741
"""
query(1, 1, N, a, b) for query max of [a,b]
"""
if r < a or l > b:
return -math.inf
if l >= a and r <= b: # noqa: E741
return self.st[idx]
mid = (l + r) // 2
q1 = self.query_recursive(self.left(idx), l, mid, a, b)
q2 = self.query_recursive(self.right(idx), mid + 1, r, a, b)
return max(q1, q2)
def show_data(self):
show_list = []
for i in range(1, N + 1):
show_list += [self.query(i, i)]
print(show_list)
if __name__ == "__main__":
A = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
N = 15
segt = SegmentTree(A)
print(segt.query(4, 6))
print(segt.query(7, 11))
print(segt.query(7, 12))
segt.update(1, 3, 111)
print(segt.query(1, 15))
segt.update(7, 8, 235)
segt.show_data()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
The Fibonacci sequence is defined by the recurrence relation:
Fn = Fn−1 + Fn−2, where F1 = 1 and F2 = 1.
Hence the first 12 terms will be:
F1 = 1
F2 = 1
F3 = 2
F4 = 3
F5 = 5
F6 = 8
F7 = 13
F8 = 21
F9 = 34
F10 = 55
F11 = 89
F12 = 144
The 12th term, F12, is the first term to contain three digits.
What is the index of the first term in the Fibonacci sequence to contain 1000
digits?
"""
from collections.abc import Generator
def fibonacci_generator() -> Generator[int, None, None]:
"""
A generator that produces numbers in the Fibonacci sequence
>>> generator = fibonacci_generator()
>>> next(generator)
1
>>> next(generator)
2
>>> next(generator)
3
>>> next(generator)
5
>>> next(generator)
8
"""
a, b = 0, 1
while True:
a, b = b, a + b
yield b
def solution(n: int = 1000) -> int:
"""Returns the index of the first term in the Fibonacci sequence to contain
n digits.
>>> solution(1000)
4782
>>> solution(100)
476
>>> solution(50)
237
>>> solution(3)
12
"""
answer = 1
gen = fibonacci_generator()
while len(str(next(gen))) < n:
answer += 1
return answer + 1
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| """
The Fibonacci sequence is defined by the recurrence relation:
Fn = Fn−1 + Fn−2, where F1 = 1 and F2 = 1.
Hence the first 12 terms will be:
F1 = 1
F2 = 1
F3 = 2
F4 = 3
F5 = 5
F6 = 8
F7 = 13
F8 = 21
F9 = 34
F10 = 55
F11 = 89
F12 = 144
The 12th term, F12, is the first term to contain three digits.
What is the index of the first term in the Fibonacci sequence to contain 1000
digits?
"""
from collections.abc import Generator
def fibonacci_generator() -> Generator[int, None, None]:
"""
A generator that produces numbers in the Fibonacci sequence
>>> generator = fibonacci_generator()
>>> next(generator)
1
>>> next(generator)
2
>>> next(generator)
3
>>> next(generator)
5
>>> next(generator)
8
"""
a, b = 0, 1
while True:
a, b = b, a + b
yield b
def solution(n: int = 1000) -> int:
"""Returns the index of the first term in the Fibonacci sequence to contain
n digits.
>>> solution(1000)
4782
>>> solution(100)
476
>>> solution(50)
237
>>> solution(3)
12
"""
answer = 1
gen = fibonacci_generator()
while len(str(next(gen))) < n:
answer += 1
return answer + 1
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/Combination
"""
from math import factorial
def combinations(n: int, k: int) -> int:
"""
Returns the number of different combinations of k length which can
be made from n values, where n >= k.
Examples:
>>> combinations(10,5)
252
>>> combinations(6,3)
20
>>> combinations(20,5)
15504
>>> combinations(52, 5)
2598960
>>> combinations(0, 0)
1
>>> combinations(-4, -5)
...
Traceback (most recent call last):
ValueError: Please enter positive integers for n and k where n >= k
"""
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n >= k")
return int(factorial(n) / ((factorial(k)) * (factorial(n - k))))
if __name__ == "__main__":
print(
"\nThe number of five-card hands possible from a standard",
f"fifty-two card deck is: {combinations(52, 5)}",
)
print(
"\nIf a class of 40 students must be arranged into groups of",
f"4 for group projects, there are {combinations(40, 4)} ways",
"to arrange them.\n",
)
print(
"If 10 teams are competing in a Formula One race, there",
f"are {combinations(10, 3)} ways that first, second and",
"third place can be awarded.\n",
)
| """
https://en.wikipedia.org/wiki/Combination
"""
from math import factorial
def combinations(n: int, k: int) -> int:
"""
Returns the number of different combinations of k length which can
be made from n values, where n >= k.
Examples:
>>> combinations(10,5)
252
>>> combinations(6,3)
20
>>> combinations(20,5)
15504
>>> combinations(52, 5)
2598960
>>> combinations(0, 0)
1
>>> combinations(-4, -5)
...
Traceback (most recent call last):
ValueError: Please enter positive integers for n and k where n >= k
"""
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n >= k")
return int(factorial(n) / ((factorial(k)) * (factorial(n - k))))
if __name__ == "__main__":
print(
"\nThe number of five-card hands possible from a standard",
f"fifty-two card deck is: {combinations(52, 5)}",
)
print(
"\nIf a class of 40 students must be arranged into groups of",
f"4 for group projects, there are {combinations(40, 4)} ways",
"to arrange them.\n",
)
print(
"If 10 teams are competing in a Formula One race, there",
f"are {combinations(10, 3)} ways that first, second and",
"third place can be awarded.\n",
)
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Implements a disjoint set using Lists and some added heuristics for efficiency
Union by Rank Heuristic and Path Compression
"""
class DisjointSet:
def __init__(self, set_counts: list) -> None:
"""
Initialize with a list of the number of items in each set
and with rank = 1 for each set
"""
self.set_counts = set_counts
self.max_set = max(set_counts)
num_sets = len(set_counts)
self.ranks = [1] * num_sets
self.parents = list(range(num_sets))
def merge(self, src: int, dst: int) -> bool:
"""
Merge two sets together using Union by rank heuristic
Return True if successful
Merge two disjoint sets
>>> A = DisjointSet([1, 1, 1])
>>> A.merge(1, 2)
True
>>> A.merge(0, 2)
True
>>> A.merge(0, 1)
False
"""
src_parent = self.get_parent(src)
dst_parent = self.get_parent(dst)
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
self.set_counts[src_parent] = 0
self.parents[src_parent] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
joined_set_size = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
self.set_counts[dst_parent] = 0
self.parents[dst_parent] = src_parent
joined_set_size = self.set_counts[src_parent]
self.max_set = max(self.max_set, joined_set_size)
return True
def get_parent(self, disj_set: int) -> int:
"""
Find the Parent of a given set
>>> A = DisjointSet([1, 1, 1])
>>> A.merge(1, 2)
True
>>> A.get_parent(0)
0
>>> A.get_parent(1)
2
"""
if self.parents[disj_set] == disj_set:
return disj_set
self.parents[disj_set] = self.get_parent(self.parents[disj_set])
return self.parents[disj_set]
| """
Implements a disjoint set using Lists and some added heuristics for efficiency
Union by Rank Heuristic and Path Compression
"""
class DisjointSet:
def __init__(self, set_counts: list) -> None:
"""
Initialize with a list of the number of items in each set
and with rank = 1 for each set
"""
self.set_counts = set_counts
self.max_set = max(set_counts)
num_sets = len(set_counts)
self.ranks = [1] * num_sets
self.parents = list(range(num_sets))
def merge(self, src: int, dst: int) -> bool:
"""
Merge two sets together using Union by rank heuristic
Return True if successful
Merge two disjoint sets
>>> A = DisjointSet([1, 1, 1])
>>> A.merge(1, 2)
True
>>> A.merge(0, 2)
True
>>> A.merge(0, 1)
False
"""
src_parent = self.get_parent(src)
dst_parent = self.get_parent(dst)
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
self.set_counts[src_parent] = 0
self.parents[src_parent] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
joined_set_size = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
self.set_counts[dst_parent] = 0
self.parents[dst_parent] = src_parent
joined_set_size = self.set_counts[src_parent]
self.max_set = max(self.max_set, joined_set_size)
return True
def get_parent(self, disj_set: int) -> int:
"""
Find the Parent of a given set
>>> A = DisjointSet([1, 1, 1])
>>> A.merge(1, 2)
True
>>> A.get_parent(0)
0
>>> A.get_parent(1)
2
"""
if self.parents[disj_set] == disj_set:
return disj_set
self.parents[disj_set] = self.get_parent(self.parents[disj_set])
return self.parents[disj_set]
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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 | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
from collections.abc import Iterable
from typing import Union
import numpy as np
Vector = Union[Iterable[float], Iterable[int], np.ndarray]
VectorOut = Union[np.float64, int, float]
def euclidean_distance(vector_1: Vector, vector_2: Vector) -> VectorOut:
"""
Calculate the distance between the two endpoints of two vectors.
A vector is defined as a list, tuple, or numpy 1D array.
>>> euclidean_distance((0, 0), (2, 2))
2.8284271247461903
>>> euclidean_distance(np.array([0, 0, 0]), np.array([2, 2, 2]))
3.4641016151377544
>>> euclidean_distance(np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8]))
8.0
>>> euclidean_distance([1, 2, 3, 4], [5, 6, 7, 8])
8.0
"""
return np.sqrt(np.sum((np.asarray(vector_1) - np.asarray(vector_2)) ** 2))
def euclidean_distance_no_np(vector_1: Vector, vector_2: Vector) -> VectorOut:
"""
Calculate the distance between the two endpoints of two vectors without numpy.
A vector is defined as a list, tuple, or numpy 1D array.
>>> euclidean_distance_no_np((0, 0), (2, 2))
2.8284271247461903
>>> euclidean_distance_no_np([1, 2, 3, 4], [5, 6, 7, 8])
8.0
"""
return sum((v1 - v2) ** 2 for v1, v2 in zip(vector_1, vector_2)) ** (1 / 2)
if __name__ == "__main__":
def benchmark() -> None:
"""
Benchmarks
"""
from timeit import timeit
print("Without Numpy")
print(
timeit(
"euclidean_distance_no_np([1, 2, 3], [4, 5, 6])",
number=10000,
globals=globals(),
)
)
print("With Numpy")
print(
timeit(
"euclidean_distance([1, 2, 3], [4, 5, 6])",
number=10000,
globals=globals(),
)
)
benchmark()
| from __future__ import annotations
from collections.abc import Iterable
from typing import Union
import numpy as np
Vector = Union[Iterable[float], Iterable[int], np.ndarray]
VectorOut = Union[np.float64, int, float]
def euclidean_distance(vector_1: Vector, vector_2: Vector) -> VectorOut:
"""
Calculate the distance between the two endpoints of two vectors.
A vector is defined as a list, tuple, or numpy 1D array.
>>> euclidean_distance((0, 0), (2, 2))
2.8284271247461903
>>> euclidean_distance(np.array([0, 0, 0]), np.array([2, 2, 2]))
3.4641016151377544
>>> euclidean_distance(np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8]))
8.0
>>> euclidean_distance([1, 2, 3, 4], [5, 6, 7, 8])
8.0
"""
return np.sqrt(np.sum((np.asarray(vector_1) - np.asarray(vector_2)) ** 2))
def euclidean_distance_no_np(vector_1: Vector, vector_2: Vector) -> VectorOut:
"""
Calculate the distance between the two endpoints of two vectors without numpy.
A vector is defined as a list, tuple, or numpy 1D array.
>>> euclidean_distance_no_np((0, 0), (2, 2))
2.8284271247461903
>>> euclidean_distance_no_np([1, 2, 3, 4], [5, 6, 7, 8])
8.0
"""
return sum((v1 - v2) ** 2 for v1, v2 in zip(vector_1, vector_2)) ** (1 / 2)
if __name__ == "__main__":
def benchmark() -> None:
"""
Benchmarks
"""
from timeit import timeit
print("Without Numpy")
print(
timeit(
"euclidean_distance_no_np([1, 2, 3], [4, 5, 6])",
number=10000,
globals=globals(),
)
)
print("With Numpy")
print(
timeit(
"euclidean_distance([1, 2, 3], [4, 5, 6])",
number=10000,
globals=globals(),
)
)
benchmark()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/Doubly_linked_list
"""
class Node:
def __init__(self, data):
self.data = data
self.previous = None
self.next = None
def __str__(self):
return f"{self.data}"
class DoublyLinkedList:
def __init__(self):
self.head = None
self.tail = None
def __iter__(self):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.insert_at_head('b')
>>> linked_list.insert_at_head('a')
>>> linked_list.insert_at_tail('c')
>>> tuple(linked_list)
('a', 'b', 'c')
"""
node = self.head
while node:
yield node.data
node = node.next
def __str__(self):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.insert_at_tail('a')
>>> linked_list.insert_at_tail('b')
>>> linked_list.insert_at_tail('c')
>>> str(linked_list)
'a->b->c'
"""
return "->".join([str(item) for item in self])
def __len__(self):
"""
>>> linked_list = DoublyLinkedList()
>>> for i in range(0, 5):
... linked_list.insert_at_nth(i, i + 1)
>>> len(linked_list) == 5
True
"""
return len(tuple(iter(self)))
def insert_at_head(self, data):
self.insert_at_nth(0, data)
def insert_at_tail(self, data):
self.insert_at_nth(len(self), data)
def insert_at_nth(self, index: int, data):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.insert_at_nth(-1, 666)
Traceback (most recent call last):
....
IndexError: list index out of range
>>> linked_list.insert_at_nth(1, 666)
Traceback (most recent call last):
....
IndexError: list index out of range
>>> linked_list.insert_at_nth(0, 2)
>>> linked_list.insert_at_nth(0, 1)
>>> linked_list.insert_at_nth(2, 4)
>>> linked_list.insert_at_nth(2, 3)
>>> str(linked_list)
'1->2->3->4'
>>> linked_list.insert_at_nth(5, 5)
Traceback (most recent call last):
....
IndexError: list index out of range
"""
if not 0 <= index <= len(self):
raise IndexError("list index out of range")
new_node = Node(data)
if self.head is None:
self.head = self.tail = new_node
elif index == 0:
self.head.previous = new_node
new_node.next = self.head
self.head = new_node
elif index == len(self):
self.tail.next = new_node
new_node.previous = self.tail
self.tail = new_node
else:
temp = self.head
for _ in range(0, index):
temp = temp.next
temp.previous.next = new_node
new_node.previous = temp.previous
new_node.next = temp
temp.previous = new_node
def delete_head(self):
return self.delete_at_nth(0)
def delete_tail(self):
return self.delete_at_nth(len(self) - 1)
def delete_at_nth(self, index: int):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.delete_at_nth(0)
Traceback (most recent call last):
....
IndexError: list index out of range
>>> for i in range(0, 5):
... linked_list.insert_at_nth(i, i + 1)
>>> linked_list.delete_at_nth(0) == 1
True
>>> linked_list.delete_at_nth(3) == 5
True
>>> linked_list.delete_at_nth(1) == 3
True
>>> str(linked_list)
'2->4'
>>> linked_list.delete_at_nth(2)
Traceback (most recent call last):
....
IndexError: list index out of range
"""
if not 0 <= index <= len(self) - 1:
raise IndexError("list index out of range")
delete_node = self.head # default first node
if len(self) == 1:
self.head = self.tail = None
elif index == 0:
self.head = self.head.next
self.head.previous = None
elif index == len(self) - 1:
delete_node = self.tail
self.tail = self.tail.previous
self.tail.next = None
else:
temp = self.head
for _ in range(0, index):
temp = temp.next
delete_node = temp
temp.next.previous = temp.previous
temp.previous.next = temp.next
return delete_node.data
def delete(self, data) -> str:
current = self.head
while current.data != data: # Find the position to delete
if current.next:
current = current.next
else: # We have reached the end an no value matches
return "No data matching given value"
if current == self.head:
self.delete_head()
elif current == self.tail:
self.delete_tail()
else: # Before: 1 <--> 2(current) <--> 3
current.previous.next = current.next # 1 --> 3
current.next.previous = current.previous # 1 <--> 3
return data
def is_empty(self):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.is_empty()
True
>>> linked_list.insert_at_tail(1)
>>> linked_list.is_empty()
False
"""
return len(self) == 0
def test_doubly_linked_list() -> None:
"""
>>> test_doubly_linked_list()
"""
linked_list = DoublyLinkedList()
assert linked_list.is_empty() is True
assert str(linked_list) == ""
try:
linked_list.delete_head()
raise AssertionError() # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError() # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10):
assert len(linked_list) == i
linked_list.insert_at_nth(i, i + 1)
assert str(linked_list) == "->".join(str(i) for i in range(1, 11))
linked_list.insert_at_head(0)
linked_list.insert_at_tail(11)
assert str(linked_list) == "->".join(str(i) for i in range(0, 12))
assert linked_list.delete_head() == 0
assert linked_list.delete_at_nth(9) == 10
assert linked_list.delete_tail() == 11
assert len(linked_list) == 9
assert str(linked_list) == "->".join(str(i) for i in range(1, 10))
if __name__ == "__main__":
from doctest import testmod
testmod()
| """
https://en.wikipedia.org/wiki/Doubly_linked_list
"""
class Node:
def __init__(self, data):
self.data = data
self.previous = None
self.next = None
def __str__(self):
return f"{self.data}"
class DoublyLinkedList:
def __init__(self):
self.head = None
self.tail = None
def __iter__(self):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.insert_at_head('b')
>>> linked_list.insert_at_head('a')
>>> linked_list.insert_at_tail('c')
>>> tuple(linked_list)
('a', 'b', 'c')
"""
node = self.head
while node:
yield node.data
node = node.next
def __str__(self):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.insert_at_tail('a')
>>> linked_list.insert_at_tail('b')
>>> linked_list.insert_at_tail('c')
>>> str(linked_list)
'a->b->c'
"""
return "->".join([str(item) for item in self])
def __len__(self):
"""
>>> linked_list = DoublyLinkedList()
>>> for i in range(0, 5):
... linked_list.insert_at_nth(i, i + 1)
>>> len(linked_list) == 5
True
"""
return len(tuple(iter(self)))
def insert_at_head(self, data):
self.insert_at_nth(0, data)
def insert_at_tail(self, data):
self.insert_at_nth(len(self), data)
def insert_at_nth(self, index: int, data):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.insert_at_nth(-1, 666)
Traceback (most recent call last):
....
IndexError: list index out of range
>>> linked_list.insert_at_nth(1, 666)
Traceback (most recent call last):
....
IndexError: list index out of range
>>> linked_list.insert_at_nth(0, 2)
>>> linked_list.insert_at_nth(0, 1)
>>> linked_list.insert_at_nth(2, 4)
>>> linked_list.insert_at_nth(2, 3)
>>> str(linked_list)
'1->2->3->4'
>>> linked_list.insert_at_nth(5, 5)
Traceback (most recent call last):
....
IndexError: list index out of range
"""
if not 0 <= index <= len(self):
raise IndexError("list index out of range")
new_node = Node(data)
if self.head is None:
self.head = self.tail = new_node
elif index == 0:
self.head.previous = new_node
new_node.next = self.head
self.head = new_node
elif index == len(self):
self.tail.next = new_node
new_node.previous = self.tail
self.tail = new_node
else:
temp = self.head
for _ in range(0, index):
temp = temp.next
temp.previous.next = new_node
new_node.previous = temp.previous
new_node.next = temp
temp.previous = new_node
def delete_head(self):
return self.delete_at_nth(0)
def delete_tail(self):
return self.delete_at_nth(len(self) - 1)
def delete_at_nth(self, index: int):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.delete_at_nth(0)
Traceback (most recent call last):
....
IndexError: list index out of range
>>> for i in range(0, 5):
... linked_list.insert_at_nth(i, i + 1)
>>> linked_list.delete_at_nth(0) == 1
True
>>> linked_list.delete_at_nth(3) == 5
True
>>> linked_list.delete_at_nth(1) == 3
True
>>> str(linked_list)
'2->4'
>>> linked_list.delete_at_nth(2)
Traceback (most recent call last):
....
IndexError: list index out of range
"""
if not 0 <= index <= len(self) - 1:
raise IndexError("list index out of range")
delete_node = self.head # default first node
if len(self) == 1:
self.head = self.tail = None
elif index == 0:
self.head = self.head.next
self.head.previous = None
elif index == len(self) - 1:
delete_node = self.tail
self.tail = self.tail.previous
self.tail.next = None
else:
temp = self.head
for _ in range(0, index):
temp = temp.next
delete_node = temp
temp.next.previous = temp.previous
temp.previous.next = temp.next
return delete_node.data
def delete(self, data) -> str:
current = self.head
while current.data != data: # Find the position to delete
if current.next:
current = current.next
else: # We have reached the end an no value matches
return "No data matching given value"
if current == self.head:
self.delete_head()
elif current == self.tail:
self.delete_tail()
else: # Before: 1 <--> 2(current) <--> 3
current.previous.next = current.next # 1 --> 3
current.next.previous = current.previous # 1 <--> 3
return data
def is_empty(self):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.is_empty()
True
>>> linked_list.insert_at_tail(1)
>>> linked_list.is_empty()
False
"""
return len(self) == 0
def test_doubly_linked_list() -> None:
"""
>>> test_doubly_linked_list()
"""
linked_list = DoublyLinkedList()
assert linked_list.is_empty() is True
assert str(linked_list) == ""
try:
linked_list.delete_head()
raise AssertionError() # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError() # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10):
assert len(linked_list) == i
linked_list.insert_at_nth(i, i + 1)
assert str(linked_list) == "->".join(str(i) for i in range(1, 11))
linked_list.insert_at_head(0)
linked_list.insert_at_tail(11)
assert str(linked_list) == "->".join(str(i) for i in range(0, 12))
assert linked_list.delete_head() == 0
assert linked_list.delete_at_nth(9) == 10
assert linked_list.delete_tail() == 11
assert len(linked_list) == 9
assert str(linked_list) == "->".join(str(i) for i in range(1, 10))
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is a type of divide and conquer algorithm which divides the search space into
3 parts and finds the target value based on the property of the array or list
(usually monotonic property).
Time Complexity : O(log3 N)
Space Complexity : O(1)
"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
precision = 10
# This is the linear search that will occur after the search space has become smaller.
def lin_search(left: int, right: int, array: list[int], target: int) -> int:
"""Perform linear search in list. Returns -1 if element is not found.
Parameters
----------
left : int
left index bound.
right : int
right index bound.
array : List[int]
List of elements to be searched on
target : int
Element that is searched
Returns
-------
int
index of element that is looked for.
Examples
--------
>>> lin_search(0, 4, [4, 5, 6, 7], 7)
3
>>> lin_search(0, 3, [4, 5, 6, 7], 7)
-1
>>> lin_search(0, 2, [-18, 2], -18)
0
>>> lin_search(0, 1, [5], 5)
0
>>> lin_search(0, 3, ['a', 'c', 'd'], 'c')
1
>>> lin_search(0, 3, [.1, .4 , -.1], .1)
0
>>> lin_search(0, 3, [.1, .4 , -.1], -.1)
2
"""
for i in range(left, right):
if array[i] == target:
return i
return -1
def ite_ternary_search(array: list[int], target: int) -> int:
"""Iterative method of the ternary search algorithm.
>>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
>>> ite_ternary_search(test_list, 3)
-1
>>> ite_ternary_search(test_list, 13)
4
>>> ite_ternary_search([4, 5, 6, 7], 4)
0
>>> ite_ternary_search([4, 5, 6, 7], -10)
-1
>>> ite_ternary_search([-18, 2], -18)
0
>>> ite_ternary_search([5], 5)
0
>>> ite_ternary_search(['a', 'c', 'd'], 'c')
1
>>> ite_ternary_search(['a', 'c', 'd'], 'f')
-1
>>> ite_ternary_search([], 1)
-1
>>> ite_ternary_search([.1, .4 , -.1], .1)
0
"""
left = 0
right = len(array)
while left <= right:
if right - left < precision:
return lin_search(left, right, array, target)
one_third = (left + right) // 3 + 1
two_third = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
right = one_third - 1
elif array[two_third] < target:
left = two_third + 1
else:
left = one_third + 1
right = two_third - 1
else:
return -1
def rec_ternary_search(left: int, right: int, array: list[int], target: int) -> int:
"""Recursive method of the ternary search algorithm.
>>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
>>> rec_ternary_search(0, len(test_list), test_list, 3)
-1
>>> rec_ternary_search(4, len(test_list), test_list, 42)
8
>>> rec_ternary_search(0, 2, [4, 5, 6, 7], 4)
0
>>> rec_ternary_search(0, 3, [4, 5, 6, 7], -10)
-1
>>> rec_ternary_search(0, 1, [-18, 2], -18)
0
>>> rec_ternary_search(0, 1, [5], 5)
0
>>> rec_ternary_search(0, 2, ['a', 'c', 'd'], 'c')
1
>>> rec_ternary_search(0, 2, ['a', 'c', 'd'], 'f')
-1
>>> rec_ternary_search(0, 0, [], 1)
-1
>>> rec_ternary_search(0, 3, [.1, .4 , -.1], .1)
0
"""
if left < right:
if right - left < precision:
return lin_search(left, right, array, target)
one_third = (left + right) // 3 + 1
two_third = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(left, one_third - 1, array, target)
elif array[two_third] < target:
return rec_ternary_search(two_third + 1, right, array, target)
else:
return rec_ternary_search(one_third + 1, two_third - 1, array, target)
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
user_input = input("Enter numbers separated by comma:\n").strip()
collection = [int(item.strip()) for item in user_input.split(",")]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
target = int(input("Enter the number to be found in the list:\n").strip())
result1 = ite_ternary_search(collection, target)
result2 = rec_ternary_search(0, len(collection) - 1, collection, target)
if result2 != -1:
print(f"Iterative search: {target} found at positions: {result1}")
print(f"Recursive search: {target} found at positions: {result2}")
else:
print("Not found")
| """
This is a type of divide and conquer algorithm which divides the search space into
3 parts and finds the target value based on the property of the array or list
(usually monotonic property).
Time Complexity : O(log3 N)
Space Complexity : O(1)
"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
precision = 10
# This is the linear search that will occur after the search space has become smaller.
def lin_search(left: int, right: int, array: list[int], target: int) -> int:
"""Perform linear search in list. Returns -1 if element is not found.
Parameters
----------
left : int
left index bound.
right : int
right index bound.
array : List[int]
List of elements to be searched on
target : int
Element that is searched
Returns
-------
int
index of element that is looked for.
Examples
--------
>>> lin_search(0, 4, [4, 5, 6, 7], 7)
3
>>> lin_search(0, 3, [4, 5, 6, 7], 7)
-1
>>> lin_search(0, 2, [-18, 2], -18)
0
>>> lin_search(0, 1, [5], 5)
0
>>> lin_search(0, 3, ['a', 'c', 'd'], 'c')
1
>>> lin_search(0, 3, [.1, .4 , -.1], .1)
0
>>> lin_search(0, 3, [.1, .4 , -.1], -.1)
2
"""
for i in range(left, right):
if array[i] == target:
return i
return -1
def ite_ternary_search(array: list[int], target: int) -> int:
"""Iterative method of the ternary search algorithm.
>>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
>>> ite_ternary_search(test_list, 3)
-1
>>> ite_ternary_search(test_list, 13)
4
>>> ite_ternary_search([4, 5, 6, 7], 4)
0
>>> ite_ternary_search([4, 5, 6, 7], -10)
-1
>>> ite_ternary_search([-18, 2], -18)
0
>>> ite_ternary_search([5], 5)
0
>>> ite_ternary_search(['a', 'c', 'd'], 'c')
1
>>> ite_ternary_search(['a', 'c', 'd'], 'f')
-1
>>> ite_ternary_search([], 1)
-1
>>> ite_ternary_search([.1, .4 , -.1], .1)
0
"""
left = 0
right = len(array)
while left <= right:
if right - left < precision:
return lin_search(left, right, array, target)
one_third = (left + right) // 3 + 1
two_third = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
right = one_third - 1
elif array[two_third] < target:
left = two_third + 1
else:
left = one_third + 1
right = two_third - 1
else:
return -1
def rec_ternary_search(left: int, right: int, array: list[int], target: int) -> int:
"""Recursive method of the ternary search algorithm.
>>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
>>> rec_ternary_search(0, len(test_list), test_list, 3)
-1
>>> rec_ternary_search(4, len(test_list), test_list, 42)
8
>>> rec_ternary_search(0, 2, [4, 5, 6, 7], 4)
0
>>> rec_ternary_search(0, 3, [4, 5, 6, 7], -10)
-1
>>> rec_ternary_search(0, 1, [-18, 2], -18)
0
>>> rec_ternary_search(0, 1, [5], 5)
0
>>> rec_ternary_search(0, 2, ['a', 'c', 'd'], 'c')
1
>>> rec_ternary_search(0, 2, ['a', 'c', 'd'], 'f')
-1
>>> rec_ternary_search(0, 0, [], 1)
-1
>>> rec_ternary_search(0, 3, [.1, .4 , -.1], .1)
0
"""
if left < right:
if right - left < precision:
return lin_search(left, right, array, target)
one_third = (left + right) // 3 + 1
two_third = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(left, one_third - 1, array, target)
elif array[two_third] < target:
return rec_ternary_search(two_third + 1, right, array, target)
else:
return rec_ternary_search(one_third + 1, two_third - 1, array, target)
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
user_input = input("Enter numbers separated by comma:\n").strip()
collection = [int(item.strip()) for item in user_input.split(",")]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
target = int(input("Enter the number to be found in the list:\n").strip())
result1 = ite_ternary_search(collection, target)
result2 = rec_ternary_search(0, len(collection) - 1, collection, target)
if result2 != -1:
print(f"Iterative search: {target} found at positions: {result1}")
print(f"Recursive search: {target} found at positions: {result2}")
else:
print("Not found")
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from collections.abc import Sequence
def evaluate_poly(poly: Sequence[float], x: float) -> float:
"""Evaluate a polynomial f(x) at specified point x and return the value.
Arguments:
poly -- the coefficients of a polynomial as an iterable in order of
ascending degree
x -- the point at which to evaluate the polynomial
>>> evaluate_poly((0.0, 0.0, 5.0, 9.3, 7.0), 10.0)
79800.0
"""
return sum(c * (x**i) for i, c in enumerate(poly))
def horner(poly: Sequence[float], x: float) -> float:
"""Evaluate a polynomial at specified point using Horner's method.
In terms of computational complexity, Horner's method is an efficient method
of evaluating a polynomial. It avoids the use of expensive exponentiation,
and instead uses only multiplication and addition to evaluate the polynomial
in O(n), where n is the degree of the polynomial.
https://en.wikipedia.org/wiki/Horner's_method
Arguments:
poly -- the coefficients of a polynomial as an iterable in order of
ascending degree
x -- the point at which to evaluate the polynomial
>>> horner((0.0, 0.0, 5.0, 9.3, 7.0), 10.0)
79800.0
"""
result = 0.0
for coeff in reversed(poly):
result = result * x + coeff
return result
if __name__ == "__main__":
"""
Example:
>>> poly = (0.0, 0.0, 5.0, 9.3, 7.0) # f(x) = 7.0x^4 + 9.3x^3 + 5.0x^2
>>> x = -13.0
>>> # f(-13) = 7.0(-13)^4 + 9.3(-13)^3 + 5.0(-13)^2 = 180339.9
>>> print(evaluate_poly(poly, x))
180339.9
"""
poly = (0.0, 0.0, 5.0, 9.3, 7.0)
x = 10.0
print(evaluate_poly(poly, x))
print(horner(poly, x))
| from collections.abc import Sequence
def evaluate_poly(poly: Sequence[float], x: float) -> float:
"""Evaluate a polynomial f(x) at specified point x and return the value.
Arguments:
poly -- the coefficients of a polynomial as an iterable in order of
ascending degree
x -- the point at which to evaluate the polynomial
>>> evaluate_poly((0.0, 0.0, 5.0, 9.3, 7.0), 10.0)
79800.0
"""
return sum(c * (x**i) for i, c in enumerate(poly))
def horner(poly: Sequence[float], x: float) -> float:
"""Evaluate a polynomial at specified point using Horner's method.
In terms of computational complexity, Horner's method is an efficient method
of evaluating a polynomial. It avoids the use of expensive exponentiation,
and instead uses only multiplication and addition to evaluate the polynomial
in O(n), where n is the degree of the polynomial.
https://en.wikipedia.org/wiki/Horner's_method
Arguments:
poly -- the coefficients of a polynomial as an iterable in order of
ascending degree
x -- the point at which to evaluate the polynomial
>>> horner((0.0, 0.0, 5.0, 9.3, 7.0), 10.0)
79800.0
"""
result = 0.0
for coeff in reversed(poly):
result = result * x + coeff
return result
if __name__ == "__main__":
"""
Example:
>>> poly = (0.0, 0.0, 5.0, 9.3, 7.0) # f(x) = 7.0x^4 + 9.3x^3 + 5.0x^2
>>> x = -13.0
>>> # f(-13) = 7.0(-13)^4 + 9.3(-13)^3 + 5.0(-13)^2 = 180339.9
>>> print(evaluate_poly(poly, x))
180339.9
"""
poly = (0.0, 0.0, 5.0, 9.3, 7.0)
x = 10.0
print(evaluate_poly(poly, x))
print(horner(poly, x))
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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 __future__ import annotations
from fractions import Fraction
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 __future__ import annotations
from fractions import Fraction
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 | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def remove_duplicates(sentence: str) -> str:
"""
Remove duplicates from sentence
>>> remove_duplicates("Python is great and Java is also great")
'Java Python also and great is'
>>> remove_duplicates("Python is great and Java is also great")
'Java Python also and great is'
"""
return " ".join(sorted(set(sentence.split())))
if __name__ == "__main__":
import doctest
doctest.testmod()
| def remove_duplicates(sentence: str) -> str:
"""
Remove duplicates from sentence
>>> remove_duplicates("Python is great and Java is also great")
'Java Python also and great is'
>>> remove_duplicates("Python is great and Java is also great")
'Java Python also and great is'
"""
return " ".join(sorted(set(sentence.split())))
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # This is a comment.
# Each line is a file pattern followed by one or more owners.
# More details are here: https://help.github.com/articles/about-codeowners/
# The '*' pattern is global owners.
# Order is important. The last matching pattern has the most precedence.
/.* @cclauss @dhruvmanila
# /arithmetic_analysis/
# /backtracking/
# /bit_manipulation/
# /blockchain/
# /boolean_algebra/
# /cellular_automata/
# /ciphers/ @cclauss # TODO: Uncomment this line after Hacktoberfest
# /compression/
# /computer_vision/
# /conversions/ @cclauss # TODO: Uncomment this line after Hacktoberfest
# /data_structures/ @cclauss # TODO: Uncomment this line after Hacktoberfest
# /digital_image_processing/
# /divide_and_conquer/
/dynamic_programming/ @Kush1101
# /file_transfer/
# /fuzzy_logic/
# /genetic_algorithm/
# /geodesy/
# /graphics/
# /graphs/
# /greedy_method/
# /hashes/
# /images/
# /linear_algebra/
# /machine_learning/
/maths/ @Kush1101
# /matrix/
# /networking_flow/
# /neural_network/
# /other/ @cclauss # TODO: Uncomment this line after Hacktoberfest
/project_euler/ @dhruvmanila @Kush1101
# /quantum/
# /scheduling/
# /scripts/
# /searches/
# /sorts/
# /strings/ @cclauss # TODO: Uncomment this line after Hacktoberfest
# /traversals/
/web_programming/ @cclauss
| # This is a comment.
# Each line is a file pattern followed by one or more owners.
# More details are here: https://help.github.com/articles/about-codeowners/
# The '*' pattern is global owners.
# Order is important. The last matching pattern has the most precedence.
/.* @cclauss @dhruvmanila
# /arithmetic_analysis/
# /backtracking/
# /bit_manipulation/
# /blockchain/
# /boolean_algebra/
# /cellular_automata/
# /ciphers/ @cclauss # TODO: Uncomment this line after Hacktoberfest
# /compression/
# /computer_vision/
# /conversions/ @cclauss # TODO: Uncomment this line after Hacktoberfest
# /data_structures/ @cclauss # TODO: Uncomment this line after Hacktoberfest
# /digital_image_processing/
# /divide_and_conquer/
/dynamic_programming/ @Kush1101
# /file_transfer/
# /fuzzy_logic/
# /genetic_algorithm/
# /geodesy/
# /graphics/
# /graphs/
# /greedy_method/
# /hashes/
# /images/
# /linear_algebra/
# /machine_learning/
/maths/ @Kush1101
# /matrix/
# /networking_flow/
# /neural_network/
# /other/ @cclauss # TODO: Uncomment this line after Hacktoberfest
/project_euler/ @dhruvmanila @Kush1101
# /quantum/
# /scheduling/
# /scripts/
# /searches/
# /sorts/
# /strings/ @cclauss # TODO: Uncomment this line after Hacktoberfest
# /traversals/
/web_programming/ @cclauss
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Created on Thu Oct 5 16:44:23 2017
@author: Christian Bender
This Python library contains some useful functions to deal with
prime numbers and whole numbers.
Overview:
is_prime(number)
sieve_er(N)
get_prime_numbers(N)
prime_factorization(number)
greatest_prime_factor(number)
smallest_prime_factor(number)
get_prime(n)
get_primes_between(pNumber1, pNumber2)
----
is_even(number)
is_odd(number)
gcd(number1, number2) // greatest common divisor
kg_v(number1, number2) // least common multiple
get_divisors(number) // all divisors of 'number' inclusive 1, number
is_perfect_number(number)
NEW-FUNCTIONS
simplify_fraction(numerator, denominator)
factorial (n) // n!
fib (n) // calculate the n-th fibonacci term.
-----
goldbach(number) // Goldbach's assumption
"""
from math import sqrt
def is_prime(number: int) -> bool:
"""
input: positive integer 'number'
returns true if 'number' is prime otherwise false.
"""
# precondition
assert isinstance(number, int) and (
number >= 0
), "'number' must been an int and positive"
status = True
# 0 and 1 are none primes.
if number <= 1:
status = False
for divisor in range(2, int(round(sqrt(number))) + 1):
# if 'number' divisible by 'divisor' then sets 'status'
# of false and break up the loop.
if number % divisor == 0:
status = False
break
# precondition
assert isinstance(status, bool), "'status' must been from type bool"
return status
# ------------------------------------------
def sieve_er(n):
"""
input: positive integer 'N' > 2
returns a list of prime numbers from 2 up to N.
This function implements the algorithm called
sieve of erathostenes.
"""
# precondition
assert isinstance(n, int) and (n > 2), "'N' must been an int and > 2"
# beginList: contains all natural numbers from 2 up to N
begin_list = [x for x in range(2, n + 1)]
ans = [] # this list will be returns.
# actual sieve of erathostenes
for i in range(len(begin_list)):
for j in range(i + 1, len(begin_list)):
if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
begin_list[j] = 0
# filters actual prime numbers.
ans = [x for x in begin_list if x != 0]
# precondition
assert isinstance(ans, list), "'ans' must been from type list"
return ans
# --------------------------------
def get_prime_numbers(n):
"""
input: positive integer 'N' > 2
returns a list of prime numbers from 2 up to N (inclusive)
This function is more efficient as function 'sieveEr(...)'
"""
# precondition
assert isinstance(n, int) and (n > 2), "'N' must been an int and > 2"
ans = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2, n + 1):
if is_prime(number):
ans.append(number)
# precondition
assert isinstance(ans, list), "'ans' must been from type list"
return ans
# -----------------------------------------
def prime_factorization(number):
"""
input: positive integer 'number'
returns a list of the prime number factors of 'number'
"""
# precondition
assert isinstance(number, int) and number >= 0, "'number' must been an int and >= 0"
ans = [] # this list will be returns of the function.
# potential prime number factors.
factor = 2
quotient = number
if number == 0 or number == 1:
ans.append(number)
# if 'number' not prime then builds the prime factorization of 'number'
elif not is_prime(number):
while quotient != 1:
if is_prime(factor) and (quotient % factor == 0):
ans.append(factor)
quotient /= factor
else:
factor += 1
else:
ans.append(number)
# precondition
assert isinstance(ans, list), "'ans' must been from type list"
return ans
# -----------------------------------------
def greatest_prime_factor(number):
"""
input: positive integer 'number' >= 0
returns the greatest prime number factor of 'number'
"""
# precondition
assert isinstance(number, int) and (
number >= 0
), "'number' bust been an int and >= 0"
ans = 0
# prime factorization of 'number'
prime_factors = prime_factorization(number)
ans = max(prime_factors)
# precondition
assert isinstance(ans, int), "'ans' must been from type int"
return ans
# ----------------------------------------------
def smallest_prime_factor(number):
"""
input: integer 'number' >= 0
returns the smallest prime number factor of 'number'
"""
# precondition
assert isinstance(number, int) and (
number >= 0
), "'number' bust been an int and >= 0"
ans = 0
# prime factorization of 'number'
prime_factors = prime_factorization(number)
ans = min(prime_factors)
# precondition
assert isinstance(ans, int), "'ans' must been from type int"
return ans
# ----------------------
def is_even(number):
"""
input: integer 'number'
returns true if 'number' is even, otherwise false.
"""
# precondition
assert isinstance(number, int), "'number' must been an int"
assert isinstance(number % 2 == 0, bool), "compare bust been from type bool"
return number % 2 == 0
# ------------------------
def is_odd(number):
"""
input: integer 'number'
returns true if 'number' is odd, otherwise false.
"""
# precondition
assert isinstance(number, int), "'number' must been an int"
assert isinstance(number % 2 != 0, bool), "compare bust been from type bool"
return number % 2 != 0
# ------------------------
def goldbach(number):
"""
Goldbach's assumption
input: a even positive integer 'number' > 2
returns a list of two prime numbers whose sum is equal to 'number'
"""
# precondition
assert (
isinstance(number, int) and (number > 2) and is_even(number)
), "'number' must been an int, even and > 2"
ans = [] # this list will returned
# creates a list of prime numbers between 2 up to 'number'
prime_numbers = get_prime_numbers(number)
len_pn = len(prime_numbers)
# run variable for while-loops.
i = 0
j = None
# exit variable. for break up the loops
loop = True
while i < len_pn and loop:
j = i + 1
while j < len_pn and loop:
if prime_numbers[i] + prime_numbers[j] == number:
loop = False
ans.append(prime_numbers[i])
ans.append(prime_numbers[j])
j += 1
i += 1
# precondition
assert (
isinstance(ans, list)
and (len(ans) == 2)
and (ans[0] + ans[1] == number)
and is_prime(ans[0])
and is_prime(ans[1])
), "'ans' must contains two primes. And sum of elements must been eq 'number'"
return ans
# ----------------------------------------------
def gcd(number1, number2):
"""
Greatest common divisor
input: two positive integer 'number1' and 'number2'
returns the greatest common divisor of 'number1' and 'number2'
"""
# precondition
assert (
isinstance(number1, int)
and isinstance(number2, int)
and (number1 >= 0)
and (number2 >= 0)
), "'number1' and 'number2' must been positive integer."
rest = 0
while number2 != 0:
rest = number1 % number2
number1 = number2
number2 = rest
# precondition
assert isinstance(number1, int) and (
number1 >= 0
), "'number' must been from type int and positive"
return number1
# ----------------------------------------------------
def kg_v(number1, number2):
"""
Least common multiple
input: two positive integer 'number1' and 'number2'
returns the least common multiple of 'number1' and 'number2'
"""
# precondition
assert (
isinstance(number1, int)
and isinstance(number2, int)
and (number1 >= 1)
and (number2 >= 1)
), "'number1' and 'number2' must been positive integer."
ans = 1 # actual answer that will be return.
# for kgV (x,1)
if number1 > 1 and number2 > 1:
# builds the prime factorization of 'number1' and 'number2'
prime_fac_1 = prime_factorization(number1)
prime_fac_2 = prime_factorization(number2)
elif number1 == 1 or number2 == 1:
prime_fac_1 = []
prime_fac_2 = []
ans = max(number1, number2)
count1 = 0
count2 = 0
done = [] # captured numbers int both 'primeFac1' and 'primeFac2'
# iterates through primeFac1
for n in prime_fac_1:
if n not in done:
if n in prime_fac_2:
count1 = prime_fac_1.count(n)
count2 = prime_fac_2.count(n)
for _ in range(max(count1, count2)):
ans *= n
else:
count1 = prime_fac_1.count(n)
for _ in range(count1):
ans *= n
done.append(n)
# iterates through primeFac2
for n in prime_fac_2:
if n not in done:
count2 = prime_fac_2.count(n)
for _ in range(count2):
ans *= n
done.append(n)
# precondition
assert isinstance(ans, int) and (
ans >= 0
), "'ans' must been from type int and positive"
return ans
# ----------------------------------
def get_prime(n):
"""
Gets the n-th prime number.
input: positive integer 'n' >= 0
returns the n-th prime number, beginning at index 0
"""
# precondition
assert isinstance(n, int) and (n >= 0), "'number' must been a positive int"
index = 0
ans = 2 # this variable holds the answer
while index < n:
index += 1
ans += 1 # counts to the next number
# if ans not prime then
# runs to the next prime number.
while not is_prime(ans):
ans += 1
# precondition
assert isinstance(ans, int) and is_prime(
ans
), "'ans' must been a prime number and from type int"
return ans
# ---------------------------------------------------
def get_primes_between(p_number_1, p_number_2):
"""
input: prime numbers 'pNumber1' and 'pNumber2'
pNumber1 < pNumber2
returns a list of all prime numbers between 'pNumber1' (exclusive)
and 'pNumber2' (exclusive)
"""
# precondition
assert (
is_prime(p_number_1) and is_prime(p_number_2) and (p_number_1 < p_number_2)
), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"
number = p_number_1 + 1 # jump to the next number
ans = [] # this list will be returns.
# if number is not prime then
# fetch the next prime number.
while not is_prime(number):
number += 1
while number < p_number_2:
ans.append(number)
number += 1
# fetch the next prime number.
while not is_prime(number):
number += 1
# precondition
assert (
isinstance(ans, list)
and ans[0] != p_number_1
and ans[len(ans) - 1] != p_number_2
), "'ans' must been a list without the arguments"
# 'ans' contains not 'pNumber1' and 'pNumber2' !
return ans
# ----------------------------------------------------
def get_divisors(n):
"""
input: positive integer 'n' >= 1
returns all divisors of n (inclusive 1 and 'n')
"""
# precondition
assert isinstance(n, int) and (n >= 1), "'n' must been int and >= 1"
ans = [] # will be returned.
for divisor in range(1, n + 1):
if n % divisor == 0:
ans.append(divisor)
# precondition
assert ans[0] == 1 and ans[len(ans) - 1] == n, "Error in function getDivisiors(...)"
return ans
# ----------------------------------------------------
def is_perfect_number(number):
"""
input: positive integer 'number' > 1
returns true if 'number' is a perfect number otherwise false.
"""
# precondition
assert isinstance(number, int) and (
number > 1
), "'number' must been an int and >= 1"
divisors = get_divisors(number)
# precondition
assert (
isinstance(divisors, list)
and (divisors[0] == 1)
and (divisors[len(divisors) - 1] == number)
), "Error in help-function getDivisiors(...)"
# summed all divisors up to 'number' (exclusive), hence [:-1]
return sum(divisors[:-1]) == number
# ------------------------------------------------------------
def simplify_fraction(numerator, denominator):
"""
input: two integer 'numerator' and 'denominator'
assumes: 'denominator' != 0
returns: a tuple with simplify numerator and denominator.
"""
# precondition
assert (
isinstance(numerator, int)
and isinstance(denominator, int)
and (denominator != 0)
), "The arguments must been from type int and 'denominator' != 0"
# build the greatest common divisor of numerator and denominator.
gcd_of_fraction = gcd(abs(numerator), abs(denominator))
# precondition
assert (
isinstance(gcd_of_fraction, int)
and (numerator % gcd_of_fraction == 0)
and (denominator % gcd_of_fraction == 0)
), "Error in function gcd(...,...)"
return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
# -----------------------------------------------------------------
def factorial(n):
"""
input: positive integer 'n'
returns the factorial of 'n' (n!)
"""
# precondition
assert isinstance(n, int) and (n >= 0), "'n' must been a int and >= 0"
ans = 1 # this will be return.
for factor in range(1, n + 1):
ans *= factor
return ans
# -------------------------------------------------------------------
def fib(n):
"""
input: positive integer 'n'
returns the n-th fibonacci term , indexing by 0
"""
# precondition
assert isinstance(n, int) and (n >= 0), "'n' must been an int and >= 0"
tmp = 0
fib1 = 1
ans = 1 # this will be return
for _ in range(n - 1):
tmp = ans
ans += fib1
fib1 = tmp
return ans
| """
Created on Thu Oct 5 16:44:23 2017
@author: Christian Bender
This Python library contains some useful functions to deal with
prime numbers and whole numbers.
Overview:
is_prime(number)
sieve_er(N)
get_prime_numbers(N)
prime_factorization(number)
greatest_prime_factor(number)
smallest_prime_factor(number)
get_prime(n)
get_primes_between(pNumber1, pNumber2)
----
is_even(number)
is_odd(number)
gcd(number1, number2) // greatest common divisor
kg_v(number1, number2) // least common multiple
get_divisors(number) // all divisors of 'number' inclusive 1, number
is_perfect_number(number)
NEW-FUNCTIONS
simplify_fraction(numerator, denominator)
factorial (n) // n!
fib (n) // calculate the n-th fibonacci term.
-----
goldbach(number) // Goldbach's assumption
"""
from math import sqrt
def is_prime(number: int) -> bool:
"""
input: positive integer 'number'
returns true if 'number' is prime otherwise false.
"""
# precondition
assert isinstance(number, int) and (
number >= 0
), "'number' must been an int and positive"
status = True
# 0 and 1 are none primes.
if number <= 1:
status = False
for divisor in range(2, int(round(sqrt(number))) + 1):
# if 'number' divisible by 'divisor' then sets 'status'
# of false and break up the loop.
if number % divisor == 0:
status = False
break
# precondition
assert isinstance(status, bool), "'status' must been from type bool"
return status
# ------------------------------------------
def sieve_er(n):
"""
input: positive integer 'N' > 2
returns a list of prime numbers from 2 up to N.
This function implements the algorithm called
sieve of erathostenes.
"""
# precondition
assert isinstance(n, int) and (n > 2), "'N' must been an int and > 2"
# beginList: contains all natural numbers from 2 up to N
begin_list = [x for x in range(2, n + 1)]
ans = [] # this list will be returns.
# actual sieve of erathostenes
for i in range(len(begin_list)):
for j in range(i + 1, len(begin_list)):
if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
begin_list[j] = 0
# filters actual prime numbers.
ans = [x for x in begin_list if x != 0]
# precondition
assert isinstance(ans, list), "'ans' must been from type list"
return ans
# --------------------------------
def get_prime_numbers(n):
"""
input: positive integer 'N' > 2
returns a list of prime numbers from 2 up to N (inclusive)
This function is more efficient as function 'sieveEr(...)'
"""
# precondition
assert isinstance(n, int) and (n > 2), "'N' must been an int and > 2"
ans = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2, n + 1):
if is_prime(number):
ans.append(number)
# precondition
assert isinstance(ans, list), "'ans' must been from type list"
return ans
# -----------------------------------------
def prime_factorization(number):
"""
input: positive integer 'number'
returns a list of the prime number factors of 'number'
"""
# precondition
assert isinstance(number, int) and number >= 0, "'number' must been an int and >= 0"
ans = [] # this list will be returns of the function.
# potential prime number factors.
factor = 2
quotient = number
if number == 0 or number == 1:
ans.append(number)
# if 'number' not prime then builds the prime factorization of 'number'
elif not is_prime(number):
while quotient != 1:
if is_prime(factor) and (quotient % factor == 0):
ans.append(factor)
quotient /= factor
else:
factor += 1
else:
ans.append(number)
# precondition
assert isinstance(ans, list), "'ans' must been from type list"
return ans
# -----------------------------------------
def greatest_prime_factor(number):
"""
input: positive integer 'number' >= 0
returns the greatest prime number factor of 'number'
"""
# precondition
assert isinstance(number, int) and (
number >= 0
), "'number' bust been an int and >= 0"
ans = 0
# prime factorization of 'number'
prime_factors = prime_factorization(number)
ans = max(prime_factors)
# precondition
assert isinstance(ans, int), "'ans' must been from type int"
return ans
# ----------------------------------------------
def smallest_prime_factor(number):
"""
input: integer 'number' >= 0
returns the smallest prime number factor of 'number'
"""
# precondition
assert isinstance(number, int) and (
number >= 0
), "'number' bust been an int and >= 0"
ans = 0
# prime factorization of 'number'
prime_factors = prime_factorization(number)
ans = min(prime_factors)
# precondition
assert isinstance(ans, int), "'ans' must been from type int"
return ans
# ----------------------
def is_even(number):
"""
input: integer 'number'
returns true if 'number' is even, otherwise false.
"""
# precondition
assert isinstance(number, int), "'number' must been an int"
assert isinstance(number % 2 == 0, bool), "compare bust been from type bool"
return number % 2 == 0
# ------------------------
def is_odd(number):
"""
input: integer 'number'
returns true if 'number' is odd, otherwise false.
"""
# precondition
assert isinstance(number, int), "'number' must been an int"
assert isinstance(number % 2 != 0, bool), "compare bust been from type bool"
return number % 2 != 0
# ------------------------
def goldbach(number):
"""
Goldbach's assumption
input: a even positive integer 'number' > 2
returns a list of two prime numbers whose sum is equal to 'number'
"""
# precondition
assert (
isinstance(number, int) and (number > 2) and is_even(number)
), "'number' must been an int, even and > 2"
ans = [] # this list will returned
# creates a list of prime numbers between 2 up to 'number'
prime_numbers = get_prime_numbers(number)
len_pn = len(prime_numbers)
# run variable for while-loops.
i = 0
j = None
# exit variable. for break up the loops
loop = True
while i < len_pn and loop:
j = i + 1
while j < len_pn and loop:
if prime_numbers[i] + prime_numbers[j] == number:
loop = False
ans.append(prime_numbers[i])
ans.append(prime_numbers[j])
j += 1
i += 1
# precondition
assert (
isinstance(ans, list)
and (len(ans) == 2)
and (ans[0] + ans[1] == number)
and is_prime(ans[0])
and is_prime(ans[1])
), "'ans' must contains two primes. And sum of elements must been eq 'number'"
return ans
# ----------------------------------------------
def gcd(number1, number2):
"""
Greatest common divisor
input: two positive integer 'number1' and 'number2'
returns the greatest common divisor of 'number1' and 'number2'
"""
# precondition
assert (
isinstance(number1, int)
and isinstance(number2, int)
and (number1 >= 0)
and (number2 >= 0)
), "'number1' and 'number2' must been positive integer."
rest = 0
while number2 != 0:
rest = number1 % number2
number1 = number2
number2 = rest
# precondition
assert isinstance(number1, int) and (
number1 >= 0
), "'number' must been from type int and positive"
return number1
# ----------------------------------------------------
def kg_v(number1, number2):
"""
Least common multiple
input: two positive integer 'number1' and 'number2'
returns the least common multiple of 'number1' and 'number2'
"""
# precondition
assert (
isinstance(number1, int)
and isinstance(number2, int)
and (number1 >= 1)
and (number2 >= 1)
), "'number1' and 'number2' must been positive integer."
ans = 1 # actual answer that will be return.
# for kgV (x,1)
if number1 > 1 and number2 > 1:
# builds the prime factorization of 'number1' and 'number2'
prime_fac_1 = prime_factorization(number1)
prime_fac_2 = prime_factorization(number2)
elif number1 == 1 or number2 == 1:
prime_fac_1 = []
prime_fac_2 = []
ans = max(number1, number2)
count1 = 0
count2 = 0
done = [] # captured numbers int both 'primeFac1' and 'primeFac2'
# iterates through primeFac1
for n in prime_fac_1:
if n not in done:
if n in prime_fac_2:
count1 = prime_fac_1.count(n)
count2 = prime_fac_2.count(n)
for _ in range(max(count1, count2)):
ans *= n
else:
count1 = prime_fac_1.count(n)
for _ in range(count1):
ans *= n
done.append(n)
# iterates through primeFac2
for n in prime_fac_2:
if n not in done:
count2 = prime_fac_2.count(n)
for _ in range(count2):
ans *= n
done.append(n)
# precondition
assert isinstance(ans, int) and (
ans >= 0
), "'ans' must been from type int and positive"
return ans
# ----------------------------------
def get_prime(n):
"""
Gets the n-th prime number.
input: positive integer 'n' >= 0
returns the n-th prime number, beginning at index 0
"""
# precondition
assert isinstance(n, int) and (n >= 0), "'number' must been a positive int"
index = 0
ans = 2 # this variable holds the answer
while index < n:
index += 1
ans += 1 # counts to the next number
# if ans not prime then
# runs to the next prime number.
while not is_prime(ans):
ans += 1
# precondition
assert isinstance(ans, int) and is_prime(
ans
), "'ans' must been a prime number and from type int"
return ans
# ---------------------------------------------------
def get_primes_between(p_number_1, p_number_2):
"""
input: prime numbers 'pNumber1' and 'pNumber2'
pNumber1 < pNumber2
returns a list of all prime numbers between 'pNumber1' (exclusive)
and 'pNumber2' (exclusive)
"""
# precondition
assert (
is_prime(p_number_1) and is_prime(p_number_2) and (p_number_1 < p_number_2)
), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"
number = p_number_1 + 1 # jump to the next number
ans = [] # this list will be returns.
# if number is not prime then
# fetch the next prime number.
while not is_prime(number):
number += 1
while number < p_number_2:
ans.append(number)
number += 1
# fetch the next prime number.
while not is_prime(number):
number += 1
# precondition
assert (
isinstance(ans, list)
and ans[0] != p_number_1
and ans[len(ans) - 1] != p_number_2
), "'ans' must been a list without the arguments"
# 'ans' contains not 'pNumber1' and 'pNumber2' !
return ans
# ----------------------------------------------------
def get_divisors(n):
"""
input: positive integer 'n' >= 1
returns all divisors of n (inclusive 1 and 'n')
"""
# precondition
assert isinstance(n, int) and (n >= 1), "'n' must been int and >= 1"
ans = [] # will be returned.
for divisor in range(1, n + 1):
if n % divisor == 0:
ans.append(divisor)
# precondition
assert ans[0] == 1 and ans[len(ans) - 1] == n, "Error in function getDivisiors(...)"
return ans
# ----------------------------------------------------
def is_perfect_number(number):
"""
input: positive integer 'number' > 1
returns true if 'number' is a perfect number otherwise false.
"""
# precondition
assert isinstance(number, int) and (
number > 1
), "'number' must been an int and >= 1"
divisors = get_divisors(number)
# precondition
assert (
isinstance(divisors, list)
and (divisors[0] == 1)
and (divisors[len(divisors) - 1] == number)
), "Error in help-function getDivisiors(...)"
# summed all divisors up to 'number' (exclusive), hence [:-1]
return sum(divisors[:-1]) == number
# ------------------------------------------------------------
def simplify_fraction(numerator, denominator):
"""
input: two integer 'numerator' and 'denominator'
assumes: 'denominator' != 0
returns: a tuple with simplify numerator and denominator.
"""
# precondition
assert (
isinstance(numerator, int)
and isinstance(denominator, int)
and (denominator != 0)
), "The arguments must been from type int and 'denominator' != 0"
# build the greatest common divisor of numerator and denominator.
gcd_of_fraction = gcd(abs(numerator), abs(denominator))
# precondition
assert (
isinstance(gcd_of_fraction, int)
and (numerator % gcd_of_fraction == 0)
and (denominator % gcd_of_fraction == 0)
), "Error in function gcd(...,...)"
return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
# -----------------------------------------------------------------
def factorial(n):
"""
input: positive integer 'n'
returns the factorial of 'n' (n!)
"""
# precondition
assert isinstance(n, int) and (n >= 0), "'n' must been a int and >= 0"
ans = 1 # this will be return.
for factor in range(1, n + 1):
ans *= factor
return ans
# -------------------------------------------------------------------
def fib(n):
"""
input: positive integer 'n'
returns the n-th fibonacci term , indexing by 0
"""
# precondition
assert isinstance(n, int) and (n >= 0), "'n' must been an int and >= 0"
tmp = 0
fib1 = 1
ans = 1 # this will be return
for _ in range(n - 1):
tmp = ans
ans += fib1
fib1 = tmp
return ans
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
from typing import Generic, TypeVar
T = TypeVar("T")
class StackOverflowError(BaseException):
pass
class StackUnderflowError(BaseException):
pass
class Stack(Generic[T]):
"""A stack is an abstract data type that serves as a collection of
elements with two principal operations: push() and pop(). push() adds an
element to the top of the stack, and pop() removes an element from the top
of a stack. The order in which elements come off of a stack are
Last In, First Out (LIFO).
https://en.wikipedia.org/wiki/Stack_(abstract_data_type)
"""
def __init__(self, limit: int = 10):
self.stack: list[T] = []
self.limit = limit
def __bool__(self) -> bool:
return bool(self.stack)
def __str__(self) -> str:
return str(self.stack)
def push(self, data: T) -> None:
"""Push an element to the top of the stack."""
if len(self.stack) >= self.limit:
raise StackOverflowError
self.stack.append(data)
def pop(self) -> T:
"""
Pop an element off of the top of the stack.
>>> Stack().pop()
Traceback (most recent call last):
...
data_structures.stacks.stack.StackUnderflowError
"""
if not self.stack:
raise StackUnderflowError
return self.stack.pop()
def peek(self) -> T:
"""
Peek at the top-most element of the stack.
>>> Stack().pop()
Traceback (most recent call last):
...
data_structures.stacks.stack.StackUnderflowError
"""
if not self.stack:
raise StackUnderflowError
return self.stack[-1]
def is_empty(self) -> bool:
"""Check if a stack is empty."""
return not bool(self.stack)
def is_full(self) -> bool:
return self.size() == self.limit
def size(self) -> int:
"""Return the size of the stack."""
return len(self.stack)
def __contains__(self, item: T) -> bool:
"""Check if item is in stack"""
return item in self.stack
def test_stack() -> None:
"""
>>> test_stack()
"""
stack: Stack[int] = Stack(10)
assert bool(stack) is False
assert stack.is_empty() is True
assert stack.is_full() is False
assert str(stack) == "[]"
try:
_ = stack.pop()
raise AssertionError() # This should not happen
except StackUnderflowError:
assert True # This should happen
try:
_ = stack.peek()
raise AssertionError() # This should not happen
except StackUnderflowError:
assert True # This should happen
for i in range(10):
assert stack.size() == i
stack.push(i)
assert bool(stack)
assert not stack.is_empty()
assert stack.is_full()
assert str(stack) == str(list(range(10)))
assert stack.pop() == 9
assert stack.peek() == 8
stack.push(100)
assert str(stack) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100])
try:
stack.push(200)
raise AssertionError() # This should not happen
except StackOverflowError:
assert True # This should happen
assert not stack.is_empty()
assert stack.size() == 10
assert 5 in stack
assert 55 not in stack
if __name__ == "__main__":
test_stack()
| from __future__ import annotations
from typing import Generic, TypeVar
T = TypeVar("T")
class StackOverflowError(BaseException):
pass
class StackUnderflowError(BaseException):
pass
class Stack(Generic[T]):
"""A stack is an abstract data type that serves as a collection of
elements with two principal operations: push() and pop(). push() adds an
element to the top of the stack, and pop() removes an element from the top
of a stack. The order in which elements come off of a stack are
Last In, First Out (LIFO).
https://en.wikipedia.org/wiki/Stack_(abstract_data_type)
"""
def __init__(self, limit: int = 10):
self.stack: list[T] = []
self.limit = limit
def __bool__(self) -> bool:
return bool(self.stack)
def __str__(self) -> str:
return str(self.stack)
def push(self, data: T) -> None:
"""Push an element to the top of the stack."""
if len(self.stack) >= self.limit:
raise StackOverflowError
self.stack.append(data)
def pop(self) -> T:
"""
Pop an element off of the top of the stack.
>>> Stack().pop()
Traceback (most recent call last):
...
data_structures.stacks.stack.StackUnderflowError
"""
if not self.stack:
raise StackUnderflowError
return self.stack.pop()
def peek(self) -> T:
"""
Peek at the top-most element of the stack.
>>> Stack().pop()
Traceback (most recent call last):
...
data_structures.stacks.stack.StackUnderflowError
"""
if not self.stack:
raise StackUnderflowError
return self.stack[-1]
def is_empty(self) -> bool:
"""Check if a stack is empty."""
return not bool(self.stack)
def is_full(self) -> bool:
return self.size() == self.limit
def size(self) -> int:
"""Return the size of the stack."""
return len(self.stack)
def __contains__(self, item: T) -> bool:
"""Check if item is in stack"""
return item in self.stack
def test_stack() -> None:
"""
>>> test_stack()
"""
stack: Stack[int] = Stack(10)
assert bool(stack) is False
assert stack.is_empty() is True
assert stack.is_full() is False
assert str(stack) == "[]"
try:
_ = stack.pop()
raise AssertionError() # This should not happen
except StackUnderflowError:
assert True # This should happen
try:
_ = stack.peek()
raise AssertionError() # This should not happen
except StackUnderflowError:
assert True # This should happen
for i in range(10):
assert stack.size() == i
stack.push(i)
assert bool(stack)
assert not stack.is_empty()
assert stack.is_full()
assert str(stack) == str(list(range(10)))
assert stack.pop() == 9
assert stack.peek() == 8
stack.push(100)
assert str(stack) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100])
try:
stack.push(200)
raise AssertionError() # This should not happen
except StackOverflowError:
assert True # This should happen
assert not stack.is_empty()
assert stack.size() == 10
assert 5 in stack
assert 55 not in stack
if __name__ == "__main__":
test_stack()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 10: https://projecteuler.net/problem=10
Summation of primes
The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17.
Find the sum of all the primes below two million.
References:
- https://en.wikipedia.org/wiki/Prime_number
"""
import math
def is_prime(number: int) -> bool:
"""Checks to see if a number is a prime in O(sqrt(n)).
A number is prime if it has exactly two factors: 1 and itself.
Returns boolean representing primality of given number num (i.e., if the
result is true, then the number is indeed prime else it is not).
>>> is_prime(2)
True
>>> is_prime(3)
True
>>> is_prime(27)
False
>>> is_prime(2999)
True
>>> is_prime(0)
False
>>> is_prime(1)
False
"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5, int(math.sqrt(number) + 1), 6):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def solution(n: int = 2000000) -> int:
"""
Returns the sum of all the primes below n.
>>> solution(1000)
76127
>>> solution(5000)
1548136
>>> solution(10000)
5736396
>>> solution(7)
10
"""
return sum(num for num in range(3, n, 2) if is_prime(num)) + 2 if n > 2 else 0
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 10: https://projecteuler.net/problem=10
Summation of primes
The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17.
Find the sum of all the primes below two million.
References:
- https://en.wikipedia.org/wiki/Prime_number
"""
import math
def is_prime(number: int) -> bool:
"""Checks to see if a number is a prime in O(sqrt(n)).
A number is prime if it has exactly two factors: 1 and itself.
Returns boolean representing primality of given number num (i.e., if the
result is true, then the number is indeed prime else it is not).
>>> is_prime(2)
True
>>> is_prime(3)
True
>>> is_prime(27)
False
>>> is_prime(2999)
True
>>> is_prime(0)
False
>>> is_prime(1)
False
"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5, int(math.sqrt(number) + 1), 6):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def solution(n: int = 2000000) -> int:
"""
Returns the sum of all the primes below n.
>>> solution(1000)
76127
>>> solution(5000)
1548136
>>> solution(10000)
5736396
>>> solution(7)
10
"""
return sum(num for num in range(3, n, 2) if is_prime(num)) + 2 if n > 2 else 0
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from random import randint
from tempfile import TemporaryFile
import numpy as np
def _in_place_quick_sort(a, start, end):
count = 0
if start < end:
pivot = randint(start, end)
temp = a[end]
a[end] = a[pivot]
a[pivot] = temp
p, count = _in_place_partition(a, start, end)
count += _in_place_quick_sort(a, start, p - 1)
count += _in_place_quick_sort(a, p + 1, end)
return count
def _in_place_partition(a, start, end):
count = 0
pivot = randint(start, end)
temp = a[end]
a[end] = a[pivot]
a[pivot] = temp
new_pivot_index = start - 1
for index in range(start, end):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
new_pivot_index = new_pivot_index + 1
temp = a[new_pivot_index]
a[new_pivot_index] = a[index]
a[index] = temp
temp = a[new_pivot_index + 1]
a[new_pivot_index + 1] = a[end]
a[end] = temp
return new_pivot_index + 1, count
outfile = TemporaryFile()
p = 100 # 1000 elements are to be sorted
mu, sigma = 0, 1 # mean and standard deviation
X = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)
outfile.seek(0) # using the same array
M = np.load(outfile)
r = len(M) - 1
z = _in_place_quick_sort(M, 0, r)
print(
"No of Comparisons for 100 elements selected from a standard normal distribution"
"is :"
)
print(z)
| from random import randint
from tempfile import TemporaryFile
import numpy as np
def _in_place_quick_sort(a, start, end):
count = 0
if start < end:
pivot = randint(start, end)
temp = a[end]
a[end] = a[pivot]
a[pivot] = temp
p, count = _in_place_partition(a, start, end)
count += _in_place_quick_sort(a, start, p - 1)
count += _in_place_quick_sort(a, p + 1, end)
return count
def _in_place_partition(a, start, end):
count = 0
pivot = randint(start, end)
temp = a[end]
a[end] = a[pivot]
a[pivot] = temp
new_pivot_index = start - 1
for index in range(start, end):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
new_pivot_index = new_pivot_index + 1
temp = a[new_pivot_index]
a[new_pivot_index] = a[index]
a[index] = temp
temp = a[new_pivot_index + 1]
a[new_pivot_index + 1] = a[end]
a[end] = temp
return new_pivot_index + 1, count
outfile = TemporaryFile()
p = 100 # 1000 elements are to be sorted
mu, sigma = 0, 1 # mean and standard deviation
X = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)
outfile.seek(0) # using the same array
M = np.load(outfile)
r = len(M) - 1
z = _in_place_quick_sort(M, 0, r)
print(
"No of Comparisons for 100 elements selected from a standard normal distribution"
"is :"
)
print(z)
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
If we are presented with the first k terms of a sequence it is impossible to say with
certainty the value of the next term, as there are infinitely many polynomial functions
that can model the sequence.
As an example, let us consider the sequence of cube
numbers. This is defined by the generating function,
u(n) = n3: 1, 8, 27, 64, 125, 216, ...
Suppose we were only given the first two terms of this sequence. Working on the
principle that "simple is best" we should assume a linear relationship and predict the
next term to be 15 (common difference 7). Even if we were presented with the first three
terms, by the same principle of simplicity, a quadratic relationship should be
assumed.
We shall define OP(k, n) to be the nth term of the optimum polynomial
generating function for the first k terms of a sequence. It should be clear that
OP(k, n) will accurately generate the terms of the sequence for n ≤ k, and potentially
the first incorrect term (FIT) will be OP(k, k+1); in which case we shall call it a
bad OP (BOP).
As a basis, if we were only given the first term of sequence, it would be most
sensible to assume constancy; that is, for n ≥ 2, OP(1, n) = u(1).
Hence we obtain the
following OPs for the cubic sequence:
OP(1, n) = 1 1, 1, 1, 1, ...
OP(2, n) = 7n-6 1, 8, 15, ...
OP(3, n) = 6n^2-11n+6 1, 8, 27, 58, ...
OP(4, n) = n^3 1, 8, 27, 64, 125, ...
Clearly no BOPs exist for k ≥ 4.
By considering the sum of FITs generated by the BOPs (indicated in red above), we
obtain 1 + 15 + 58 = 74.
Consider the following tenth degree polynomial generating function:
1 - n + n^2 - n^3 + n^4 - n^5 + n^6 - n^7 + n^8 - n^9 + n^10
Find the sum of FITs for the BOPs.
"""
from __future__ import annotations
from collections.abc import Callable
Matrix = list[list[float | int]]
def solve(matrix: Matrix, vector: Matrix) -> Matrix:
"""
Solve the linear system of equations Ax = b (A = "matrix", b = "vector")
for x using Gaussian elimination and back substitution. We assume that A
is an invertible square matrix and that b is a column vector of the
same height.
>>> solve([[1, 0], [0, 1]], [[1],[2]])
[[1.0], [2.0]]
>>> solve([[2, 1, -1],[-3, -1, 2],[-2, 1, 2]],[[8], [-11],[-3]])
[[2.0], [3.0], [-1.0]]
"""
size: int = len(matrix)
augmented: Matrix = [[0 for _ in range(size + 1)] for _ in range(size)]
row: int
row2: int
col: int
col2: int
pivot_row: int
ratio: float
for row in range(size):
for col in range(size):
augmented[row][col] = matrix[row][col]
augmented[row][size] = vector[row][0]
row = 0
col = 0
while row < size and col < size:
# pivoting
pivot_row = max((abs(augmented[row2][col]), row2) for row2 in range(col, size))[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
augmented[row], augmented[pivot_row] = augmented[pivot_row], augmented[row]
for row2 in range(row + 1, size):
ratio = augmented[row2][col] / augmented[row][col]
augmented[row2][col] = 0
for col2 in range(col + 1, size + 1):
augmented[row2][col2] -= augmented[row][col2] * ratio
row += 1
col += 1
# back substitution
for col in range(1, size):
for row in range(col):
ratio = augmented[row][col] / augmented[col][col]
for col2 in range(col, size + 1):
augmented[row][col2] -= augmented[col][col2] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row], 10)] for row in range(size)
]
def interpolate(y_list: list[int]) -> Callable[[int], int]:
"""
Given a list of data points (1,y0),(2,y1), ..., return a function that
interpolates the data points. We find the coefficients of the interpolating
polynomial by solving a system of linear equations corresponding to
x = 1, 2, 3...
>>> interpolate([1])(3)
1
>>> interpolate([1, 8])(3)
15
>>> interpolate([1, 8, 27])(4)
58
>>> interpolate([1, 8, 27, 64])(6)
216
"""
size: int = len(y_list)
matrix: Matrix = [[0 for _ in range(size)] for _ in range(size)]
vector: Matrix = [[0] for _ in range(size)]
coeffs: Matrix
x_val: int
y_val: int
col: int
for x_val, y_val in enumerate(y_list):
for col in range(size):
matrix[x_val][col] = (x_val + 1) ** (size - col - 1)
vector[x_val][0] = y_val
coeffs = solve(matrix, vector)
def interpolated_func(var: int) -> int:
"""
>>> interpolate([1])(3)
1
>>> interpolate([1, 8])(3)
15
>>> interpolate([1, 8, 27])(4)
58
>>> interpolate([1, 8, 27, 64])(6)
216
"""
return sum(
round(coeffs[x_val][0]) * (var ** (size - x_val - 1))
for x_val in range(size)
)
return interpolated_func
def question_function(variable: int) -> int:
"""
The generating function u as specified in the question.
>>> question_function(0)
1
>>> question_function(1)
1
>>> question_function(5)
8138021
>>> question_function(10)
9090909091
"""
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def solution(func: Callable[[int], int] = question_function, order: int = 10) -> int:
"""
Find the sum of the FITs of the BOPS. For each interpolating polynomial of order
1, 2, ... , 10, find the first x such that the value of the polynomial at x does
not equal u(x).
>>> solution(lambda n: n ** 3, 3)
74
"""
data_points: list[int] = [func(x_val) for x_val in range(1, order + 1)]
polynomials: list[Callable[[int], int]] = [
interpolate(data_points[:max_coeff]) for max_coeff in range(1, order + 1)
]
ret: int = 0
poly: Callable[[int], int]
x_val: int
for poly in polynomials:
x_val = 1
while func(x_val) == poly(x_val):
x_val += 1
ret += poly(x_val)
return ret
if __name__ == "__main__":
print(f"{solution() = }")
| """
If we are presented with the first k terms of a sequence it is impossible to say with
certainty the value of the next term, as there are infinitely many polynomial functions
that can model the sequence.
As an example, let us consider the sequence of cube
numbers. This is defined by the generating function,
u(n) = n3: 1, 8, 27, 64, 125, 216, ...
Suppose we were only given the first two terms of this sequence. Working on the
principle that "simple is best" we should assume a linear relationship and predict the
next term to be 15 (common difference 7). Even if we were presented with the first three
terms, by the same principle of simplicity, a quadratic relationship should be
assumed.
We shall define OP(k, n) to be the nth term of the optimum polynomial
generating function for the first k terms of a sequence. It should be clear that
OP(k, n) will accurately generate the terms of the sequence for n ≤ k, and potentially
the first incorrect term (FIT) will be OP(k, k+1); in which case we shall call it a
bad OP (BOP).
As a basis, if we were only given the first term of sequence, it would be most
sensible to assume constancy; that is, for n ≥ 2, OP(1, n) = u(1).
Hence we obtain the
following OPs for the cubic sequence:
OP(1, n) = 1 1, 1, 1, 1, ...
OP(2, n) = 7n-6 1, 8, 15, ...
OP(3, n) = 6n^2-11n+6 1, 8, 27, 58, ...
OP(4, n) = n^3 1, 8, 27, 64, 125, ...
Clearly no BOPs exist for k ≥ 4.
By considering the sum of FITs generated by the BOPs (indicated in red above), we
obtain 1 + 15 + 58 = 74.
Consider the following tenth degree polynomial generating function:
1 - n + n^2 - n^3 + n^4 - n^5 + n^6 - n^7 + n^8 - n^9 + n^10
Find the sum of FITs for the BOPs.
"""
from __future__ import annotations
from collections.abc import Callable
Matrix = list[list[float | int]]
def solve(matrix: Matrix, vector: Matrix) -> Matrix:
"""
Solve the linear system of equations Ax = b (A = "matrix", b = "vector")
for x using Gaussian elimination and back substitution. We assume that A
is an invertible square matrix and that b is a column vector of the
same height.
>>> solve([[1, 0], [0, 1]], [[1],[2]])
[[1.0], [2.0]]
>>> solve([[2, 1, -1],[-3, -1, 2],[-2, 1, 2]],[[8], [-11],[-3]])
[[2.0], [3.0], [-1.0]]
"""
size: int = len(matrix)
augmented: Matrix = [[0 for _ in range(size + 1)] for _ in range(size)]
row: int
row2: int
col: int
col2: int
pivot_row: int
ratio: float
for row in range(size):
for col in range(size):
augmented[row][col] = matrix[row][col]
augmented[row][size] = vector[row][0]
row = 0
col = 0
while row < size and col < size:
# pivoting
pivot_row = max((abs(augmented[row2][col]), row2) for row2 in range(col, size))[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
augmented[row], augmented[pivot_row] = augmented[pivot_row], augmented[row]
for row2 in range(row + 1, size):
ratio = augmented[row2][col] / augmented[row][col]
augmented[row2][col] = 0
for col2 in range(col + 1, size + 1):
augmented[row2][col2] -= augmented[row][col2] * ratio
row += 1
col += 1
# back substitution
for col in range(1, size):
for row in range(col):
ratio = augmented[row][col] / augmented[col][col]
for col2 in range(col, size + 1):
augmented[row][col2] -= augmented[col][col2] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row], 10)] for row in range(size)
]
def interpolate(y_list: list[int]) -> Callable[[int], int]:
"""
Given a list of data points (1,y0),(2,y1), ..., return a function that
interpolates the data points. We find the coefficients of the interpolating
polynomial by solving a system of linear equations corresponding to
x = 1, 2, 3...
>>> interpolate([1])(3)
1
>>> interpolate([1, 8])(3)
15
>>> interpolate([1, 8, 27])(4)
58
>>> interpolate([1, 8, 27, 64])(6)
216
"""
size: int = len(y_list)
matrix: Matrix = [[0 for _ in range(size)] for _ in range(size)]
vector: Matrix = [[0] for _ in range(size)]
coeffs: Matrix
x_val: int
y_val: int
col: int
for x_val, y_val in enumerate(y_list):
for col in range(size):
matrix[x_val][col] = (x_val + 1) ** (size - col - 1)
vector[x_val][0] = y_val
coeffs = solve(matrix, vector)
def interpolated_func(var: int) -> int:
"""
>>> interpolate([1])(3)
1
>>> interpolate([1, 8])(3)
15
>>> interpolate([1, 8, 27])(4)
58
>>> interpolate([1, 8, 27, 64])(6)
216
"""
return sum(
round(coeffs[x_val][0]) * (var ** (size - x_val - 1))
for x_val in range(size)
)
return interpolated_func
def question_function(variable: int) -> int:
"""
The generating function u as specified in the question.
>>> question_function(0)
1
>>> question_function(1)
1
>>> question_function(5)
8138021
>>> question_function(10)
9090909091
"""
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def solution(func: Callable[[int], int] = question_function, order: int = 10) -> int:
"""
Find the sum of the FITs of the BOPS. For each interpolating polynomial of order
1, 2, ... , 10, find the first x such that the value of the polynomial at x does
not equal u(x).
>>> solution(lambda n: n ** 3, 3)
74
"""
data_points: list[int] = [func(x_val) for x_val in range(1, order + 1)]
polynomials: list[Callable[[int], int]] = [
interpolate(data_points[:max_coeff]) for max_coeff in range(1, order + 1)
]
ret: int = 0
poly: Callable[[int], int]
x_val: int
for poly in polynomials:
x_val = 1
while func(x_val) == poly(x_val):
x_val += 1
ret += poly(x_val)
return ret
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Output:
Enter an Infix Equation = a + b ^c
Symbol | Stack | Postfix
----------------------------
c | | c
^ | ^ | c
b | ^ | cb
+ | + | cb^
a | + | cb^a
| | cb^a+
a+b^c (Infix) -> +a^bc (Prefix)
"""
def infix_2_postfix(infix):
stack = []
post_fix = []
priority = {
"^": 3,
"*": 2,
"/": 2,
"%": 2,
"+": 1,
"-": 1,
} # Priority of each operator
print_width = len(infix) if (len(infix) > 7) else 7
# Print table header for output
print(
"Symbol".center(8),
"Stack".center(print_width),
"Postfix".center(print_width),
sep=" | ",
)
print("-" * (print_width * 3 + 7))
for x in infix:
if x.isalpha() or x.isdigit():
post_fix.append(x) # if x is Alphabet / Digit, add it to Postfix
elif x == "(":
stack.append(x) # if x is "(" push to Stack
elif x == ")": # if x is ")" pop stack until "(" is encountered
while stack[-1] != "(":
post_fix.append(stack.pop()) # Pop stack & add the content to Postfix
stack.pop()
else:
if len(stack) == 0:
stack.append(x) # If stack is empty, push x to stack
else: # while priority of x is not > priority of element in the stack
while len(stack) > 0 and priority[x] <= priority[stack[-1]]:
post_fix.append(stack.pop()) # pop stack & add to Postfix
stack.append(x) # push x to stack
print(
x.center(8),
("".join(stack)).ljust(print_width),
("".join(post_fix)).ljust(print_width),
sep=" | ",
) # Output in tabular format
while len(stack) > 0: # while stack is not empty
post_fix.append(stack.pop()) # pop stack & add to Postfix
print(
" ".center(8),
("".join(stack)).ljust(print_width),
("".join(post_fix)).ljust(print_width),
sep=" | ",
) # Output in tabular format
return "".join(post_fix) # return Postfix as str
def infix_2_prefix(infix):
infix = list(infix[::-1]) # reverse the infix equation
for i in range(len(infix)):
if infix[i] == "(":
infix[i] = ")" # change "(" to ")"
elif infix[i] == ")":
infix[i] = "(" # change ")" to "("
return (infix_2_postfix("".join(infix)))[
::-1
] # call infix_2_postfix on Infix, return reverse of Postfix
if __name__ == "__main__":
Infix = input("\nEnter an Infix Equation = ") # Input an Infix equation
Infix = "".join(Infix.split()) # Remove spaces from the input
print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")
| """
Output:
Enter an Infix Equation = a + b ^c
Symbol | Stack | Postfix
----------------------------
c | | c
^ | ^ | c
b | ^ | cb
+ | + | cb^
a | + | cb^a
| | cb^a+
a+b^c (Infix) -> +a^bc (Prefix)
"""
def infix_2_postfix(infix):
stack = []
post_fix = []
priority = {
"^": 3,
"*": 2,
"/": 2,
"%": 2,
"+": 1,
"-": 1,
} # Priority of each operator
print_width = len(infix) if (len(infix) > 7) else 7
# Print table header for output
print(
"Symbol".center(8),
"Stack".center(print_width),
"Postfix".center(print_width),
sep=" | ",
)
print("-" * (print_width * 3 + 7))
for x in infix:
if x.isalpha() or x.isdigit():
post_fix.append(x) # if x is Alphabet / Digit, add it to Postfix
elif x == "(":
stack.append(x) # if x is "(" push to Stack
elif x == ")": # if x is ")" pop stack until "(" is encountered
while stack[-1] != "(":
post_fix.append(stack.pop()) # Pop stack & add the content to Postfix
stack.pop()
else:
if len(stack) == 0:
stack.append(x) # If stack is empty, push x to stack
else: # while priority of x is not > priority of element in the stack
while len(stack) > 0 and priority[x] <= priority[stack[-1]]:
post_fix.append(stack.pop()) # pop stack & add to Postfix
stack.append(x) # push x to stack
print(
x.center(8),
("".join(stack)).ljust(print_width),
("".join(post_fix)).ljust(print_width),
sep=" | ",
) # Output in tabular format
while len(stack) > 0: # while stack is not empty
post_fix.append(stack.pop()) # pop stack & add to Postfix
print(
" ".center(8),
("".join(stack)).ljust(print_width),
("".join(post_fix)).ljust(print_width),
sep=" | ",
) # Output in tabular format
return "".join(post_fix) # return Postfix as str
def infix_2_prefix(infix):
infix = list(infix[::-1]) # reverse the infix equation
for i in range(len(infix)):
if infix[i] == "(":
infix[i] = ")" # change "(" to ")"
elif infix[i] == ")":
infix[i] = "(" # change ")" to "("
return (infix_2_postfix("".join(infix)))[
::-1
] # call infix_2_postfix on Infix, return reverse of Postfix
if __name__ == "__main__":
Infix = input("\nEnter an Infix Equation = ") # Input an Infix equation
Infix = "".join(Infix.split()) # Remove spaces from the input
print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """ Problem Statement (Digit Fifth Powers): https://projecteuler.net/problem=30
Surprisingly there are only three numbers that can be written as the sum of fourth
powers of their digits:
1634 = 1^4 + 6^4 + 3^4 + 4^4
8208 = 8^4 + 2^4 + 0^4 + 8^4
9474 = 9^4 + 4^4 + 7^4 + 4^4
As 1 = 1^4 is not a sum it is not included.
The sum of these numbers is 1634 + 8208 + 9474 = 19316.
Find the sum of all the numbers that can be written as the sum of fifth powers of their
digits.
9^5 = 59049
59049 * 7 = 413343 (which is only 6 digit number)
So, numbers greater than 999999 are rejected
and also 59049 * 3 = 177147 (which exceeds the criteria of number being 3 digit)
So, number > 999
and hence a number between 1000 and 1000000
"""
DIGITS_FIFTH_POWER = {str(digit): digit**5 for digit in range(10)}
def digits_fifth_powers_sum(number: int) -> int:
"""
>>> digits_fifth_powers_sum(1234)
1300
"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(number))
def solution() -> int:
return sum(
number
for number in range(1000, 1000000)
if number == digits_fifth_powers_sum(number)
)
if __name__ == "__main__":
print(solution())
| """ Problem Statement (Digit Fifth Powers): https://projecteuler.net/problem=30
Surprisingly there are only three numbers that can be written as the sum of fourth
powers of their digits:
1634 = 1^4 + 6^4 + 3^4 + 4^4
8208 = 8^4 + 2^4 + 0^4 + 8^4
9474 = 9^4 + 4^4 + 7^4 + 4^4
As 1 = 1^4 is not a sum it is not included.
The sum of these numbers is 1634 + 8208 + 9474 = 19316.
Find the sum of all the numbers that can be written as the sum of fifth powers of their
digits.
9^5 = 59049
59049 * 7 = 413343 (which is only 6 digit number)
So, numbers greater than 999999 are rejected
and also 59049 * 3 = 177147 (which exceeds the criteria of number being 3 digit)
So, number > 999
and hence a number between 1000 and 1000000
"""
DIGITS_FIFTH_POWER = {str(digit): digit**5 for digit in range(10)}
def digits_fifth_powers_sum(number: int) -> int:
"""
>>> digits_fifth_powers_sum(1234)
1300
"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(number))
def solution() -> int:
return sum(
number
for number in range(1000, 1000000)
if number == digits_fifth_powers_sum(number)
)
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from math import asin, atan, cos, radians, sin, sqrt, tan
def haversine_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
"""
Calculate great circle distance between two points in a sphere,
given longitudes and latitudes https://en.wikipedia.org/wiki/Haversine_formula
We know that the globe is "sort of" spherical, so a path between two points
isn't exactly a straight line. We need to account for the Earth's curvature
when calculating distance from point A to B. This effect is negligible for
small distances but adds up as distance increases. The Haversine method treats
the earth as a sphere which allows us to "project" the two points A and B
onto the surface of that sphere and approximate the spherical distance between
them. Since the Earth is not a perfect sphere, other methods which model the
Earth's ellipsoidal nature are more accurate but a quick and modifiable
computation like Haversine can be handy for shorter range distances.
Args:
lat1, lon1: latitude and longitude of coordinate 1
lat2, lon2: latitude and longitude of coordinate 2
Returns:
geographical distance between two points in metres
>>> from collections import namedtuple
>>> point_2d = namedtuple("point_2d", "lat lon")
>>> SAN_FRANCISCO = point_2d(37.774856, -122.424227)
>>> YOSEMITE = point_2d(37.864742, -119.537521)
>>> f"{haversine_distance(*SAN_FRANCISCO, *YOSEMITE):0,.0f} meters"
'254,352 meters'
"""
# CONSTANTS per WGS84 https://en.wikipedia.org/wiki/World_Geodetic_System
# Distance in metres(m)
AXIS_A = 6378137.0 # noqa: N806
AXIS_B = 6356752.314245 # noqa: N806
RADIUS = 6378137 # noqa: N806
# Equation parameters
# Equation https://en.wikipedia.org/wiki/Haversine_formula#Formulation
flattening = (AXIS_A - AXIS_B) / AXIS_A
phi_1 = atan((1 - flattening) * tan(radians(lat1)))
phi_2 = atan((1 - flattening) * tan(radians(lat2)))
lambda_1 = radians(lon1)
lambda_2 = radians(lon2)
# Equation
sin_sq_phi = sin((phi_2 - phi_1) / 2)
sin_sq_lambda = sin((lambda_2 - lambda_1) / 2)
# Square both values
sin_sq_phi *= sin_sq_phi
sin_sq_lambda *= sin_sq_lambda
h_value = sqrt(sin_sq_phi + (cos(phi_1) * cos(phi_2) * sin_sq_lambda))
return 2 * RADIUS * asin(h_value)
if __name__ == "__main__":
import doctest
doctest.testmod()
| from math import asin, atan, cos, radians, sin, sqrt, tan
def haversine_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
"""
Calculate great circle distance between two points in a sphere,
given longitudes and latitudes https://en.wikipedia.org/wiki/Haversine_formula
We know that the globe is "sort of" spherical, so a path between two points
isn't exactly a straight line. We need to account for the Earth's curvature
when calculating distance from point A to B. This effect is negligible for
small distances but adds up as distance increases. The Haversine method treats
the earth as a sphere which allows us to "project" the two points A and B
onto the surface of that sphere and approximate the spherical distance between
them. Since the Earth is not a perfect sphere, other methods which model the
Earth's ellipsoidal nature are more accurate but a quick and modifiable
computation like Haversine can be handy for shorter range distances.
Args:
lat1, lon1: latitude and longitude of coordinate 1
lat2, lon2: latitude and longitude of coordinate 2
Returns:
geographical distance between two points in metres
>>> from collections import namedtuple
>>> point_2d = namedtuple("point_2d", "lat lon")
>>> SAN_FRANCISCO = point_2d(37.774856, -122.424227)
>>> YOSEMITE = point_2d(37.864742, -119.537521)
>>> f"{haversine_distance(*SAN_FRANCISCO, *YOSEMITE):0,.0f} meters"
'254,352 meters'
"""
# CONSTANTS per WGS84 https://en.wikipedia.org/wiki/World_Geodetic_System
# Distance in metres(m)
AXIS_A = 6378137.0 # noqa: N806
AXIS_B = 6356752.314245 # noqa: N806
RADIUS = 6378137 # noqa: N806
# Equation parameters
# Equation https://en.wikipedia.org/wiki/Haversine_formula#Formulation
flattening = (AXIS_A - AXIS_B) / AXIS_A
phi_1 = atan((1 - flattening) * tan(radians(lat1)))
phi_2 = atan((1 - flattening) * tan(radians(lat2)))
lambda_1 = radians(lon1)
lambda_2 = radians(lon2)
# Equation
sin_sq_phi = sin((phi_2 - phi_1) / 2)
sin_sq_lambda = sin((lambda_2 - lambda_1) / 2)
# Square both values
sin_sq_phi *= sin_sq_phi
sin_sq_lambda *= sin_sq_lambda
h_value = sqrt(sin_sq_phi + (cos(phi_1) * cos(phi_2) * sin_sq_lambda))
return 2 * RADIUS * asin(h_value)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 50: https://projecteuler.net/problem=50
Consecutive prime sum
The prime 41, can be written as the sum of six consecutive primes:
41 = 2 + 3 + 5 + 7 + 11 + 13
This is the longest sum of consecutive primes that adds to a prime below
one-hundred.
The longest sum of consecutive primes below one-thousand that adds to a prime,
contains 21 terms, and is equal to 953.
Which prime, below one-million, can be written as the sum of the most
consecutive primes?
"""
from __future__ import annotations
def prime_sieve(limit: int) -> list[int]:
"""
Sieve of Erotosthenes
Function to return all the prime numbers up to a number 'limit'
https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes
>>> prime_sieve(3)
[2]
>>> prime_sieve(50)
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]
"""
is_prime = [True] * limit
is_prime[0] = False
is_prime[1] = False
is_prime[2] = True
for i in range(3, int(limit**0.5 + 1), 2):
index = i * 2
while index < limit:
is_prime[index] = False
index = index + i
primes = [2]
for i in range(3, limit, 2):
if is_prime[i]:
primes.append(i)
return primes
def solution(ceiling: int = 1_000_000) -> int:
"""
Returns the biggest prime, below the celing, that can be written as the sum
of consecutive the most consecutive primes.
>>> solution(500)
499
>>> solution(1_000)
953
>>> solution(10_000)
9521
"""
primes = prime_sieve(ceiling)
length = 0
largest = 0
for i in range(len(primes)):
for j in range(i + length, len(primes)):
sol = sum(primes[i:j])
if sol >= ceiling:
break
if sol in primes:
length = j - i
largest = sol
return largest
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 50: https://projecteuler.net/problem=50
Consecutive prime sum
The prime 41, can be written as the sum of six consecutive primes:
41 = 2 + 3 + 5 + 7 + 11 + 13
This is the longest sum of consecutive primes that adds to a prime below
one-hundred.
The longest sum of consecutive primes below one-thousand that adds to a prime,
contains 21 terms, and is equal to 953.
Which prime, below one-million, can be written as the sum of the most
consecutive primes?
"""
from __future__ import annotations
def prime_sieve(limit: int) -> list[int]:
"""
Sieve of Erotosthenes
Function to return all the prime numbers up to a number 'limit'
https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes
>>> prime_sieve(3)
[2]
>>> prime_sieve(50)
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]
"""
is_prime = [True] * limit
is_prime[0] = False
is_prime[1] = False
is_prime[2] = True
for i in range(3, int(limit**0.5 + 1), 2):
index = i * 2
while index < limit:
is_prime[index] = False
index = index + i
primes = [2]
for i in range(3, limit, 2):
if is_prime[i]:
primes.append(i)
return primes
def solution(ceiling: int = 1_000_000) -> int:
"""
Returns the biggest prime, below the celing, that can be written as the sum
of consecutive the most consecutive primes.
>>> solution(500)
499
>>> solution(1_000)
953
>>> solution(10_000)
9521
"""
primes = prime_sieve(ceiling)
length = 0
largest = 0
for i in range(len(primes)):
for j in range(i + length, len(primes)):
sol = sum(primes[i:j])
if sol >= ceiling:
break
if sol in primes:
length = j - i
largest = sol
return largest
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Author : Turfa Auliarachman
Date : October 12, 2016
This is a pure Python implementation of Dynamic Programming solution to the edit
distance problem.
The problem is :
Given two strings A and B. Find the minimum number of operations to string B such that
A = B. The permitted operations are removal, insertion, and substitution.
"""
class EditDistance:
"""
Use :
solver = EditDistance()
editDistanceResult = solver.solve(firstString, secondString)
"""
def __init__(self):
self.__prepare__()
def __prepare__(self, n=0, m=0):
self.dp = [[-1 for y in range(0, m)] for x in range(0, n)]
def __solve_dp(self, x, y):
if x == -1:
return y + 1
elif y == -1:
return x + 1
elif self.dp[x][y] > -1:
return self.dp[x][y]
else:
if self.a[x] == self.b[y]:
self.dp[x][y] = self.__solve_dp(x - 1, y - 1)
else:
self.dp[x][y] = 1 + min(
self.__solve_dp(x, y - 1),
self.__solve_dp(x - 1, y),
self.__solve_dp(x - 1, y - 1),
)
return self.dp[x][y]
def solve(self, a, b):
if isinstance(a, bytes):
a = a.decode("ascii")
if isinstance(b, bytes):
b = b.decode("ascii")
self.a = str(a)
self.b = str(b)
self.__prepare__(len(a), len(b))
return self.__solve_dp(len(a) - 1, len(b) - 1)
def min_distance_bottom_up(word1: str, word2: str) -> int:
"""
>>> min_distance_bottom_up("intention", "execution")
5
>>> min_distance_bottom_up("intention", "")
9
>>> min_distance_bottom_up("", "")
0
"""
m = len(word1)
n = len(word2)
dp = [[0 for _ in range(n + 1)] for _ in range(m + 1)]
for i in range(m + 1):
for j in range(n + 1):
if i == 0: # first string is empty
dp[i][j] = j
elif j == 0: # second string is empty
dp[i][j] = i
elif (
word1[i - 1] == word2[j - 1]
): # last character of both substing is equal
dp[i][j] = dp[i - 1][j - 1]
else:
insert = dp[i][j - 1]
delete = dp[i - 1][j]
replace = dp[i - 1][j - 1]
dp[i][j] = 1 + min(insert, delete, replace)
return dp[m][n]
if __name__ == "__main__":
solver = EditDistance()
print("****************** Testing Edit Distance DP Algorithm ******************")
print()
S1 = input("Enter the first string: ").strip()
S2 = input("Enter the second string: ").strip()
print()
print("The minimum Edit Distance is: %d" % (solver.solve(S1, S2)))
print("The minimum Edit Distance is: %d" % (min_distance_bottom_up(S1, S2)))
print()
print("*************** End of Testing Edit Distance DP Algorithm ***************")
| """
Author : Turfa Auliarachman
Date : October 12, 2016
This is a pure Python implementation of Dynamic Programming solution to the edit
distance problem.
The problem is :
Given two strings A and B. Find the minimum number of operations to string B such that
A = B. The permitted operations are removal, insertion, and substitution.
"""
class EditDistance:
"""
Use :
solver = EditDistance()
editDistanceResult = solver.solve(firstString, secondString)
"""
def __init__(self):
self.__prepare__()
def __prepare__(self, n=0, m=0):
self.dp = [[-1 for y in range(0, m)] for x in range(0, n)]
def __solve_dp(self, x, y):
if x == -1:
return y + 1
elif y == -1:
return x + 1
elif self.dp[x][y] > -1:
return self.dp[x][y]
else:
if self.a[x] == self.b[y]:
self.dp[x][y] = self.__solve_dp(x - 1, y - 1)
else:
self.dp[x][y] = 1 + min(
self.__solve_dp(x, y - 1),
self.__solve_dp(x - 1, y),
self.__solve_dp(x - 1, y - 1),
)
return self.dp[x][y]
def solve(self, a, b):
if isinstance(a, bytes):
a = a.decode("ascii")
if isinstance(b, bytes):
b = b.decode("ascii")
self.a = str(a)
self.b = str(b)
self.__prepare__(len(a), len(b))
return self.__solve_dp(len(a) - 1, len(b) - 1)
def min_distance_bottom_up(word1: str, word2: str) -> int:
"""
>>> min_distance_bottom_up("intention", "execution")
5
>>> min_distance_bottom_up("intention", "")
9
>>> min_distance_bottom_up("", "")
0
"""
m = len(word1)
n = len(word2)
dp = [[0 for _ in range(n + 1)] for _ in range(m + 1)]
for i in range(m + 1):
for j in range(n + 1):
if i == 0: # first string is empty
dp[i][j] = j
elif j == 0: # second string is empty
dp[i][j] = i
elif (
word1[i - 1] == word2[j - 1]
): # last character of both substing is equal
dp[i][j] = dp[i - 1][j - 1]
else:
insert = dp[i][j - 1]
delete = dp[i - 1][j]
replace = dp[i - 1][j - 1]
dp[i][j] = 1 + min(insert, delete, replace)
return dp[m][n]
if __name__ == "__main__":
solver = EditDistance()
print("****************** Testing Edit Distance DP Algorithm ******************")
print()
S1 = input("Enter the first string: ").strip()
S2 = input("Enter the second string: ").strip()
print()
print("The minimum Edit Distance is: %d" % (solver.solve(S1, S2)))
print("The minimum Edit Distance is: %d" % (min_distance_bottom_up(S1, S2)))
print()
print("*************** End of Testing Edit Distance DP Algorithm ***************")
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/Best-first_search#Greedy_BFS
"""
from __future__ import annotations
Path = list[tuple[int, int]]
grid = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
delta = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right
class Node:
"""
>>> k = Node(0, 0, 4, 5, 0, None)
>>> k.calculate_heuristic()
9
>>> n = Node(1, 4, 3, 4, 2, None)
>>> n.calculate_heuristic()
2
>>> l = [k, n]
>>> n == l[0]
False
>>> l.sort()
>>> n == l[0]
True
"""
def __init__(
self,
pos_x: int,
pos_y: int,
goal_x: int,
goal_y: int,
g_cost: float,
parent: Node | None,
):
self.pos_x = pos_x
self.pos_y = pos_y
self.pos = (pos_y, pos_x)
self.goal_x = goal_x
self.goal_y = goal_y
self.g_cost = g_cost
self.parent = parent
self.f_cost = self.calculate_heuristic()
def calculate_heuristic(self) -> float:
"""
The heuristic here is the Manhattan Distance
Could elaborate to offer more than one choice
"""
dy = abs(self.pos_x - self.goal_x)
dx = abs(self.pos_y - self.goal_y)
return dx + dy
def __lt__(self, other) -> bool:
return self.f_cost < other.f_cost
class GreedyBestFirst:
"""
>>> gbf = GreedyBestFirst((0, 0), (len(grid) - 1, len(grid[0]) - 1))
>>> [x.pos for x in gbf.get_successors(gbf.start)]
[(1, 0), (0, 1)]
>>> (gbf.start.pos_y + delta[3][0], gbf.start.pos_x + delta[3][1])
(0, 1)
>>> (gbf.start.pos_y + delta[2][0], gbf.start.pos_x + delta[2][1])
(1, 0)
>>> gbf.retrace_path(gbf.start)
[(0, 0)]
>>> gbf.search() # doctest: +NORMALIZE_WHITESPACE
[(0, 0), (1, 0), (2, 0), (3, 0), (3, 1), (4, 1), (5, 1), (6, 1),
(6, 2), (6, 3), (5, 3), (5, 4), (5, 5), (6, 5), (6, 6)]
"""
def __init__(self, start: tuple[int, int], goal: tuple[int, int]):
self.start = Node(start[1], start[0], goal[1], goal[0], 0, None)
self.target = Node(goal[1], goal[0], goal[1], goal[0], 99999, None)
self.open_nodes = [self.start]
self.closed_nodes: list[Node] = []
self.reached = False
def search(self) -> Path | None:
"""
Search for the path,
if a path is not found, only the starting position is returned
"""
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
current_node = self.open_nodes.pop(0)
if current_node.pos == self.target.pos:
self.reached = True
return self.retrace_path(current_node)
self.closed_nodes.append(current_node)
successors = self.get_successors(current_node)
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(child_node)
else:
# retrieve the best current path
better_node = self.open_nodes.pop(self.open_nodes.index(child_node))
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(child_node)
else:
self.open_nodes.append(better_node)
if not self.reached:
return [self.start.pos]
return None
def get_successors(self, parent: Node) -> list[Node]:
"""
Returns a list of successors (both in the grid and free spaces)
"""
successors = []
for action in delta:
pos_x = parent.pos_x + action[1]
pos_y = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(grid) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
pos_x,
pos_y,
self.target.pos_y,
self.target.pos_x,
parent.g_cost + 1,
parent,
)
)
return successors
def retrace_path(self, node: Node | None) -> Path:
"""
Retrace the path from parents to parents until start node
"""
current_node = node
path = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x))
current_node = current_node.parent
path.reverse()
return path
if __name__ == "__main__":
init = (0, 0)
goal = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
print("------")
greedy_bf = GreedyBestFirst(init, goal)
path = greedy_bf.search()
if path:
for pos_x, pos_y in path:
grid[pos_x][pos_y] = 2
for elem in grid:
print(elem)
| """
https://en.wikipedia.org/wiki/Best-first_search#Greedy_BFS
"""
from __future__ import annotations
Path = list[tuple[int, int]]
grid = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
delta = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right
class Node:
"""
>>> k = Node(0, 0, 4, 5, 0, None)
>>> k.calculate_heuristic()
9
>>> n = Node(1, 4, 3, 4, 2, None)
>>> n.calculate_heuristic()
2
>>> l = [k, n]
>>> n == l[0]
False
>>> l.sort()
>>> n == l[0]
True
"""
def __init__(
self,
pos_x: int,
pos_y: int,
goal_x: int,
goal_y: int,
g_cost: float,
parent: Node | None,
):
self.pos_x = pos_x
self.pos_y = pos_y
self.pos = (pos_y, pos_x)
self.goal_x = goal_x
self.goal_y = goal_y
self.g_cost = g_cost
self.parent = parent
self.f_cost = self.calculate_heuristic()
def calculate_heuristic(self) -> float:
"""
The heuristic here is the Manhattan Distance
Could elaborate to offer more than one choice
"""
dy = abs(self.pos_x - self.goal_x)
dx = abs(self.pos_y - self.goal_y)
return dx + dy
def __lt__(self, other) -> bool:
return self.f_cost < other.f_cost
class GreedyBestFirst:
"""
>>> gbf = GreedyBestFirst((0, 0), (len(grid) - 1, len(grid[0]) - 1))
>>> [x.pos for x in gbf.get_successors(gbf.start)]
[(1, 0), (0, 1)]
>>> (gbf.start.pos_y + delta[3][0], gbf.start.pos_x + delta[3][1])
(0, 1)
>>> (gbf.start.pos_y + delta[2][0], gbf.start.pos_x + delta[2][1])
(1, 0)
>>> gbf.retrace_path(gbf.start)
[(0, 0)]
>>> gbf.search() # doctest: +NORMALIZE_WHITESPACE
[(0, 0), (1, 0), (2, 0), (3, 0), (3, 1), (4, 1), (5, 1), (6, 1),
(6, 2), (6, 3), (5, 3), (5, 4), (5, 5), (6, 5), (6, 6)]
"""
def __init__(self, start: tuple[int, int], goal: tuple[int, int]):
self.start = Node(start[1], start[0], goal[1], goal[0], 0, None)
self.target = Node(goal[1], goal[0], goal[1], goal[0], 99999, None)
self.open_nodes = [self.start]
self.closed_nodes: list[Node] = []
self.reached = False
def search(self) -> Path | None:
"""
Search for the path,
if a path is not found, only the starting position is returned
"""
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
current_node = self.open_nodes.pop(0)
if current_node.pos == self.target.pos:
self.reached = True
return self.retrace_path(current_node)
self.closed_nodes.append(current_node)
successors = self.get_successors(current_node)
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(child_node)
else:
# retrieve the best current path
better_node = self.open_nodes.pop(self.open_nodes.index(child_node))
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(child_node)
else:
self.open_nodes.append(better_node)
if not self.reached:
return [self.start.pos]
return None
def get_successors(self, parent: Node) -> list[Node]:
"""
Returns a list of successors (both in the grid and free spaces)
"""
successors = []
for action in delta:
pos_x = parent.pos_x + action[1]
pos_y = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(grid) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
pos_x,
pos_y,
self.target.pos_y,
self.target.pos_x,
parent.g_cost + 1,
parent,
)
)
return successors
def retrace_path(self, node: Node | None) -> Path:
"""
Retrace the path from parents to parents until start node
"""
current_node = node
path = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x))
current_node = current_node.parent
path.reverse()
return path
if __name__ == "__main__":
init = (0, 0)
goal = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
print("------")
greedy_bf = GreedyBestFirst(init, goal)
path = greedy_bf.search()
if path:
for pos_x, pos_y in path:
grid[pos_x][pos_y] = 2
for elem in grid:
print(elem)
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
import requests
valid_terms = set(
"""approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)
def get_subreddit_data(
subreddit: str, limit: int = 1, age: str = "new", wanted_data: list | None = None
) -> dict:
"""
subreddit : Subreddit to query
limit : Number of posts to fetch
age : ["new", "top", "hot"]
wanted_data : Get only the required data in the list
>>> pass
"""
wanted_data = wanted_data or []
if invalid_search_terms := ", ".join(sorted(set(wanted_data) - valid_terms)):
raise ValueError(f"Invalid search term: {invalid_search_terms}")
response = requests.get(
f"https://reddit.com/r/{subreddit}/{age}.json?limit={limit}",
headers={"User-agent": "A random string"},
)
if response.status_code == 429:
raise requests.HTTPError
data = response.json()
if not wanted_data:
return {id_: data["data"]["children"][id_] for id_ in range(limit)}
data_dict = {}
for id_ in range(limit):
data_dict[id_] = {
item: data["data"]["children"][id_]["data"][item] for item in wanted_data
}
return data_dict
if __name__ == "__main__":
# If you get Error 429, that means you are rate limited.Try after some time
print(get_subreddit_data("learnpython", wanted_data=["title", "url", "selftext"]))
| from __future__ import annotations
import requests
valid_terms = set(
"""approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)
def get_subreddit_data(
subreddit: str, limit: int = 1, age: str = "new", wanted_data: list | None = None
) -> dict:
"""
subreddit : Subreddit to query
limit : Number of posts to fetch
age : ["new", "top", "hot"]
wanted_data : Get only the required data in the list
>>> pass
"""
wanted_data = wanted_data or []
if invalid_search_terms := ", ".join(sorted(set(wanted_data) - valid_terms)):
raise ValueError(f"Invalid search term: {invalid_search_terms}")
response = requests.get(
f"https://reddit.com/r/{subreddit}/{age}.json?limit={limit}",
headers={"User-agent": "A random string"},
)
if response.status_code == 429:
raise requests.HTTPError
data = response.json()
if not wanted_data:
return {id_: data["data"]["children"][id_] for id_ in range(limit)}
data_dict = {}
for id_ in range(limit):
data_dict[id_] = {
item: data["data"]["children"][id_]["data"][item] for item in wanted_data
}
return data_dict
if __name__ == "__main__":
# If you get Error 429, that means you are rate limited.Try after some time
print(get_subreddit_data("learnpython", wanted_data=["title", "url", "selftext"]))
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
import requests
giphy_api_key = "YOUR API KEY"
# Can be fetched from https://developers.giphy.com/dashboard/
def get_gifs(query: str, api_key: str = giphy_api_key) -> list:
"""
Get a list of URLs of GIFs based on a given query..
"""
formatted_query = "+".join(query.split())
url = f"http://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"
gifs = requests.get(url).json()["data"]
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print("\n".join(get_gifs("space ship")))
| #!/usr/bin/env python3
import requests
giphy_api_key = "YOUR API KEY"
# Can be fetched from https://developers.giphy.com/dashboard/
def get_gifs(query: str, api_key: str = giphy_api_key) -> list:
"""
Get a list of URLs of GIFs based on a given query..
"""
formatted_query = "+".join(query.split())
url = f"http://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"
gifs = requests.get(url).json()["data"]
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print("\n".join(get_gifs("space ship")))
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Backtracking
Backtracking is a way to speed up the search process by removing candidates when they can't be the solution of a problem.
* <https://en.wikipedia.org/wiki/Backtracking>
* <https://en.wikipedia.org/wiki/Decision_tree_pruning>
* <https://medium.com/@priyankmistry1999/backtracking-sudoku-6e4439e4825c>
* <https://www.geeksforgeeks.org/sudoku-backtracking-7/>
| # Backtracking
Backtracking is a way to speed up the search process by removing candidates when they can't be the solution of a problem.
* <https://en.wikipedia.org/wiki/Backtracking>
* <https://en.wikipedia.org/wiki/Decision_tree_pruning>
* <https://medium.com/@priyankmistry1999/backtracking-sudoku-6e4439e4825c>
* <https://www.geeksforgeeks.org/sudoku-backtracking-7/>
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/String-searching_algorithm#Na%C3%AFve_string_search
this algorithm tries to find the pattern from every position of
the mainString if pattern is found from position i it add it to
the answer and does the same for position i+1
Complexity : O(n*m)
n=length of main string
m=length of pattern string
"""
def naive_pattern_search(s: str, pattern: str) -> list:
"""
>>> naive_pattern_search("ABAAABCDBBABCDDEBCABC", "ABC")
[4, 10, 18]
>>> naive_pattern_search("ABC", "ABAAABCDBBABCDDEBCABC")
[]
>>> naive_pattern_search("", "ABC")
[]
>>> naive_pattern_search("TEST", "TEST")
[0]
>>> naive_pattern_search("ABCDEGFTEST", "TEST")
[7]
"""
pat_len = len(pattern)
position = []
for i in range(len(s) - pat_len + 1):
match_found = True
for j in range(pat_len):
if s[i + j] != pattern[j]:
match_found = False
break
if match_found:
position.append(i)
return position
if __name__ == "__main__":
assert naive_pattern_search("ABCDEFG", "DE") == [3]
print(naive_pattern_search("ABAAABCDBBABCDDEBCABC", "ABC"))
| """
https://en.wikipedia.org/wiki/String-searching_algorithm#Na%C3%AFve_string_search
this algorithm tries to find the pattern from every position of
the mainString if pattern is found from position i it add it to
the answer and does the same for position i+1
Complexity : O(n*m)
n=length of main string
m=length of pattern string
"""
def naive_pattern_search(s: str, pattern: str) -> list:
"""
>>> naive_pattern_search("ABAAABCDBBABCDDEBCABC", "ABC")
[4, 10, 18]
>>> naive_pattern_search("ABC", "ABAAABCDBBABCDDEBCABC")
[]
>>> naive_pattern_search("", "ABC")
[]
>>> naive_pattern_search("TEST", "TEST")
[0]
>>> naive_pattern_search("ABCDEGFTEST", "TEST")
[7]
"""
pat_len = len(pattern)
position = []
for i in range(len(s) - pat_len + 1):
match_found = True
for j in range(pat_len):
if s[i + j] != pattern[j]:
match_found = False
break
if match_found:
position.append(i)
return position
if __name__ == "__main__":
assert naive_pattern_search("ABCDEFG", "DE") == [3]
print(naive_pattern_search("ABAAABCDBBABCDDEBCABC", "ABC"))
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Title : Finding the value of either Gravitational Force, one of the masses or distance
provided that the other three parameters are given.
Description : Newton's Law of Universal Gravitation explains the presence of force of
attraction between bodies having a definite mass situated at a distance. It is usually
stated as that, every particle attracts every other particle in the universe with a
force that is directly proportional to the product of their masses and inversely
proportional to the square of the distance between their centers. The publication of the
theory has become known as the "first great unification", as it marked the unification
of the previously described phenomena of gravity on Earth with known astronomical
behaviors.
The equation for the universal gravitation is as follows:
F = (G * mass_1 * mass_2) / (distance)^2
Source :
- https://en.wikipedia.org/wiki/Newton%27s_law_of_universal_gravitation
- Newton (1687) "Philosophiæ Naturalis Principia Mathematica"
"""
from __future__ import annotations
# Define the Gravitational Constant G and the function
GRAVITATIONAL_CONSTANT = 6.6743e-11 # unit of G : m^3 * kg^-1 * s^-2
def gravitational_law(
force: float, mass_1: float, mass_2: float, distance: float
) -> dict[str, float]:
"""
Input Parameters
----------------
force : magnitude in Newtons
mass_1 : mass in Kilograms
mass_2 : mass in Kilograms
distance : distance in Meters
Returns
-------
result : dict name, value pair of the parameter having Zero as it's value
Returns the value of one of the parameters specified as 0, provided the values of
other parameters are given.
>>> gravitational_law(force=0, mass_1=5, mass_2=10, distance=20)
{'force': 8.342875e-12}
>>> gravitational_law(force=7367.382, mass_1=0, mass_2=74, distance=3048)
{'mass_1': 1.385816317292268e+19}
>>> gravitational_law(force=36337.283, mass_1=0, mass_2=0, distance=35584)
Traceback (most recent call last):
...
ValueError: One and only one argument must be 0
>>> gravitational_law(force=36337.283, mass_1=-674, mass_2=0, distance=35584)
Traceback (most recent call last):
...
ValueError: Mass can not be negative
>>> gravitational_law(force=-847938e12, mass_1=674, mass_2=0, distance=9374)
Traceback (most recent call last):
...
ValueError: Gravitational force can not be negative
"""
product_of_mass = mass_1 * mass_2
if (force, mass_1, mass_2, distance).count(0) != 1:
raise ValueError("One and only one argument must be 0")
if force < 0:
raise ValueError("Gravitational force can not be negative")
if distance < 0:
raise ValueError("Distance can not be negative")
if mass_1 < 0 or mass_2 < 0:
raise ValueError("Mass can not be negative")
if force == 0:
force = GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
return {"force": force}
elif mass_1 == 0:
mass_1 = (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_2)
return {"mass_1": mass_1}
elif mass_2 == 0:
mass_2 = (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_1)
return {"mass_2": mass_2}
elif distance == 0:
distance = (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
return {"distance": distance}
raise ValueError("One and only one argument must be 0")
# Run doctest
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Title : Finding the value of either Gravitational Force, one of the masses or distance
provided that the other three parameters are given.
Description : Newton's Law of Universal Gravitation explains the presence of force of
attraction between bodies having a definite mass situated at a distance. It is usually
stated as that, every particle attracts every other particle in the universe with a
force that is directly proportional to the product of their masses and inversely
proportional to the square of the distance between their centers. The publication of the
theory has become known as the "first great unification", as it marked the unification
of the previously described phenomena of gravity on Earth with known astronomical
behaviors.
The equation for the universal gravitation is as follows:
F = (G * mass_1 * mass_2) / (distance)^2
Source :
- https://en.wikipedia.org/wiki/Newton%27s_law_of_universal_gravitation
- Newton (1687) "Philosophiæ Naturalis Principia Mathematica"
"""
from __future__ import annotations
# Define the Gravitational Constant G and the function
GRAVITATIONAL_CONSTANT = 6.6743e-11 # unit of G : m^3 * kg^-1 * s^-2
def gravitational_law(
force: float, mass_1: float, mass_2: float, distance: float
) -> dict[str, float]:
"""
Input Parameters
----------------
force : magnitude in Newtons
mass_1 : mass in Kilograms
mass_2 : mass in Kilograms
distance : distance in Meters
Returns
-------
result : dict name, value pair of the parameter having Zero as it's value
Returns the value of one of the parameters specified as 0, provided the values of
other parameters are given.
>>> gravitational_law(force=0, mass_1=5, mass_2=10, distance=20)
{'force': 8.342875e-12}
>>> gravitational_law(force=7367.382, mass_1=0, mass_2=74, distance=3048)
{'mass_1': 1.385816317292268e+19}
>>> gravitational_law(force=36337.283, mass_1=0, mass_2=0, distance=35584)
Traceback (most recent call last):
...
ValueError: One and only one argument must be 0
>>> gravitational_law(force=36337.283, mass_1=-674, mass_2=0, distance=35584)
Traceback (most recent call last):
...
ValueError: Mass can not be negative
>>> gravitational_law(force=-847938e12, mass_1=674, mass_2=0, distance=9374)
Traceback (most recent call last):
...
ValueError: Gravitational force can not be negative
"""
product_of_mass = mass_1 * mass_2
if (force, mass_1, mass_2, distance).count(0) != 1:
raise ValueError("One and only one argument must be 0")
if force < 0:
raise ValueError("Gravitational force can not be negative")
if distance < 0:
raise ValueError("Distance can not be negative")
if mass_1 < 0 or mass_2 < 0:
raise ValueError("Mass can not be negative")
if force == 0:
force = GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
return {"force": force}
elif mass_1 == 0:
mass_1 = (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_2)
return {"mass_1": mass_1}
elif mass_2 == 0:
mass_2 = (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_1)
return {"mass_2": mass_2}
elif distance == 0:
distance = (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
return {"distance": distance}
raise ValueError("One and only one argument must be 0")
# Run doctest
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 75: https://projecteuler.net/problem=75
It turns out that 12 cm is the smallest length of wire that can be bent to form an
integer sided right angle triangle in exactly one way, but there are many more examples.
12 cm: (3,4,5)
24 cm: (6,8,10)
30 cm: (5,12,13)
36 cm: (9,12,15)
40 cm: (8,15,17)
48 cm: (12,16,20)
In contrast, some lengths of wire, like 20 cm, cannot be bent to form an integer sided
right angle triangle, and other lengths allow more than one solution to be found; for
example, using 120 cm it is possible to form exactly three different integer sided
right angle triangles.
120 cm: (30,40,50), (20,48,52), (24,45,51)
Given that L is the length of the wire, for how many values of L ≤ 1,500,000 can
exactly one integer sided right angle triangle be formed?
Solution: we generate all pythagorean triples using Euclid's formula and
keep track of the frequencies of the perimeters.
Reference: https://en.wikipedia.org/wiki/Pythagorean_triple#Generating_a_triple
"""
from collections import defaultdict
from math import gcd
from typing import DefaultDict
def solution(limit: int = 1500000) -> int:
"""
Return the number of values of L <= limit such that a wire of length L can be
formmed into an integer sided right angle triangle in exactly one way.
>>> solution(50)
6
>>> solution(1000)
112
>>> solution(50000)
5502
"""
frequencies: DefaultDict = defaultdict(int)
euclid_m = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1, euclid_m, 2):
if gcd(euclid_m, euclid_n) > 1:
continue
primitive_perimeter = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(primitive_perimeter, limit + 1, primitive_perimeter):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 75: https://projecteuler.net/problem=75
It turns out that 12 cm is the smallest length of wire that can be bent to form an
integer sided right angle triangle in exactly one way, but there are many more examples.
12 cm: (3,4,5)
24 cm: (6,8,10)
30 cm: (5,12,13)
36 cm: (9,12,15)
40 cm: (8,15,17)
48 cm: (12,16,20)
In contrast, some lengths of wire, like 20 cm, cannot be bent to form an integer sided
right angle triangle, and other lengths allow more than one solution to be found; for
example, using 120 cm it is possible to form exactly three different integer sided
right angle triangles.
120 cm: (30,40,50), (20,48,52), (24,45,51)
Given that L is the length of the wire, for how many values of L ≤ 1,500,000 can
exactly one integer sided right angle triangle be formed?
Solution: we generate all pythagorean triples using Euclid's formula and
keep track of the frequencies of the perimeters.
Reference: https://en.wikipedia.org/wiki/Pythagorean_triple#Generating_a_triple
"""
from collections import defaultdict
from math import gcd
from typing import DefaultDict
def solution(limit: int = 1500000) -> int:
"""
Return the number of values of L <= limit such that a wire of length L can be
formmed into an integer sided right angle triangle in exactly one way.
>>> solution(50)
6
>>> solution(1000)
112
>>> solution(50000)
5502
"""
frequencies: DefaultDict = defaultdict(int)
euclid_m = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1, euclid_m, 2):
if gcd(euclid_m, euclid_n) > 1:
continue
primitive_perimeter = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(primitive_perimeter, limit + 1, primitive_perimeter):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
# Divide and Conquer algorithm
def find_min(nums: list[int | float], left: int, right: int) -> int | float:
"""
find min value in list
:param nums: contains elements
:param left: index of first element
:param right: index of last element
:return: min in nums
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_min(nums, 0, len(nums) - 1) == min(nums)
True
True
True
True
>>> nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10]
>>> find_min(nums, 0, len(nums) - 1) == min(nums)
True
>>> find_min([], 0, 0)
Traceback (most recent call last):
...
ValueError: find_min() arg is an empty sequence
>>> find_min(nums, 0, len(nums)) == min(nums)
Traceback (most recent call last):
...
IndexError: list index out of range
>>> find_min(nums, -len(nums), -1) == min(nums)
True
>>> find_min(nums, -len(nums) - 1, -1) == min(nums)
Traceback (most recent call last):
...
IndexError: list index out of range
"""
if len(nums) == 0:
raise ValueError("find_min() arg is an empty sequence")
if (
left >= len(nums)
or left < -len(nums)
or right >= len(nums)
or right < -len(nums)
):
raise IndexError("list index out of range")
if left == right:
return nums[left]
mid = (left + right) >> 1 # the middle
left_min = find_min(nums, left, mid) # find min in range[left, mid]
right_min = find_min(nums, mid + 1, right) # find min in range[mid + 1, right]
return left_min if left_min <= right_min else right_min
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| from __future__ import annotations
# Divide and Conquer algorithm
def find_min(nums: list[int | float], left: int, right: int) -> int | float:
"""
find min value in list
:param nums: contains elements
:param left: index of first element
:param right: index of last element
:return: min in nums
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_min(nums, 0, len(nums) - 1) == min(nums)
True
True
True
True
>>> nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10]
>>> find_min(nums, 0, len(nums) - 1) == min(nums)
True
>>> find_min([], 0, 0)
Traceback (most recent call last):
...
ValueError: find_min() arg is an empty sequence
>>> find_min(nums, 0, len(nums)) == min(nums)
Traceback (most recent call last):
...
IndexError: list index out of range
>>> find_min(nums, -len(nums), -1) == min(nums)
True
>>> find_min(nums, -len(nums) - 1, -1) == min(nums)
Traceback (most recent call last):
...
IndexError: list index out of range
"""
if len(nums) == 0:
raise ValueError("find_min() arg is an empty sequence")
if (
left >= len(nums)
or left < -len(nums)
or right >= len(nums)
or right < -len(nums)
):
raise IndexError("list index out of range")
if left == right:
return nums[left]
mid = (left + right) >> 1 # the middle
left_min = find_min(nums, left, mid) # find min in range[left, mid]
right_min = find_min(nums, mid + 1, right) # find min in range[mid + 1, right]
return left_min if left_min <= right_min else right_min
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
import re
def natural_sort(input_list: list[str]) -> list[str]:
"""
Sort the given list of strings in the way that humans expect.
The normal Python sort algorithm sorts lexicographically,
so you might not get the results that you expect...
>>> example1 = ['2 ft 7 in', '1 ft 5 in', '10 ft 2 in', '2 ft 11 in', '7 ft 6 in']
>>> sorted(example1)
['1 ft 5 in', '10 ft 2 in', '2 ft 11 in', '2 ft 7 in', '7 ft 6 in']
>>> # The natural sort algorithm sort based on meaning and not computer code point.
>>> natural_sort(example1)
['1 ft 5 in', '2 ft 7 in', '2 ft 11 in', '7 ft 6 in', '10 ft 2 in']
>>> example2 = ['Elm11', 'Elm12', 'Elm2', 'elm0', 'elm1', 'elm10', 'elm13', 'elm9']
>>> sorted(example2)
['Elm11', 'Elm12', 'Elm2', 'elm0', 'elm1', 'elm10', 'elm13', 'elm9']
>>> natural_sort(example2)
['elm0', 'elm1', 'Elm2', 'elm9', 'elm10', 'Elm11', 'Elm12', 'elm13']
"""
def alphanum_key(key):
return [int(s) if s.isdigit() else s.lower() for s in re.split("([0-9]+)", key)]
return sorted(input_list, key=alphanum_key)
if __name__ == "__main__":
import doctest
doctest.testmod()
| from __future__ import annotations
import re
def natural_sort(input_list: list[str]) -> list[str]:
"""
Sort the given list of strings in the way that humans expect.
The normal Python sort algorithm sorts lexicographically,
so you might not get the results that you expect...
>>> example1 = ['2 ft 7 in', '1 ft 5 in', '10 ft 2 in', '2 ft 11 in', '7 ft 6 in']
>>> sorted(example1)
['1 ft 5 in', '10 ft 2 in', '2 ft 11 in', '2 ft 7 in', '7 ft 6 in']
>>> # The natural sort algorithm sort based on meaning and not computer code point.
>>> natural_sort(example1)
['1 ft 5 in', '2 ft 7 in', '2 ft 11 in', '7 ft 6 in', '10 ft 2 in']
>>> example2 = ['Elm11', 'Elm12', 'Elm2', 'elm0', 'elm1', 'elm10', 'elm13', 'elm9']
>>> sorted(example2)
['Elm11', 'Elm12', 'Elm2', 'elm0', 'elm1', 'elm10', 'elm13', 'elm9']
>>> natural_sort(example2)
['elm0', 'elm1', 'Elm2', 'elm9', 'elm10', 'Elm11', 'Elm12', 'elm13']
"""
def alphanum_key(key):
return [int(s) if s.isdigit() else s.lower() for s in re.split("([0-9]+)", key)]
return sorted(input_list, key=alphanum_key)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # https://www.geeksforgeeks.org/newton-forward-backward-interpolation/
from __future__ import annotations
import math
# for calculating u value
def ucal(u: float, p: int) -> float:
"""
>>> ucal(1, 2)
0
>>> ucal(1.1, 2)
0.11000000000000011
>>> ucal(1.2, 2)
0.23999999999999994
"""
temp = u
for i in range(1, p):
temp = temp * (u - i)
return temp
def main() -> None:
n = int(input("enter the numbers of values: "))
y: list[list[float]] = []
for _ in range(n):
y.append([])
for i in range(n):
for j in range(n):
y[i].append(j)
y[i][j] = 0
print("enter the values of parameters in a list: ")
x = list(map(int, input().split()))
print("enter the values of corresponding parameters: ")
for i in range(n):
y[i][0] = float(input())
value = int(input("enter the value to interpolate: "))
u = (value - x[0]) / (x[1] - x[0])
# for calculating forward difference table
for i in range(1, n):
for j in range(n - i):
y[j][i] = y[j + 1][i - 1] - y[j][i - 1]
summ = y[0][0]
for i in range(1, n):
summ += (ucal(u, i) * y[0][i]) / math.factorial(i)
print(f"the value at {value} is {summ}")
if __name__ == "__main__":
main()
| # https://www.geeksforgeeks.org/newton-forward-backward-interpolation/
from __future__ import annotations
import math
# for calculating u value
def ucal(u: float, p: int) -> float:
"""
>>> ucal(1, 2)
0
>>> ucal(1.1, 2)
0.11000000000000011
>>> ucal(1.2, 2)
0.23999999999999994
"""
temp = u
for i in range(1, p):
temp = temp * (u - i)
return temp
def main() -> None:
n = int(input("enter the numbers of values: "))
y: list[list[float]] = []
for _ in range(n):
y.append([])
for i in range(n):
for j in range(n):
y[i].append(j)
y[i][j] = 0
print("enter the values of parameters in a list: ")
x = list(map(int, input().split()))
print("enter the values of corresponding parameters: ")
for i in range(n):
y[i][0] = float(input())
value = int(input("enter the value to interpolate: "))
u = (value - x[0]) / (x[1] - x[0])
# for calculating forward difference table
for i in range(1, n):
for j in range(n - i):
y[j][i] = y[j + 1][i - 1] - y[j][i - 1]
summ = y[0][0]
for i in range(1, n):
summ += (ucal(u, i) * y[0][i]) / math.factorial(i)
print(f"the value at {value} is {summ}")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://projecteuler.net/problem=51
Prime digit replacements
Problem 51
By replacing the 1st digit of the 2-digit number *3, it turns out that six of
the nine possible values: 13, 23, 43, 53, 73, and 83, are all prime.
By replacing the 3rd and 4th digits of 56**3 with the same digit, this 5-digit
number is the first example having seven primes among the ten generated numbers,
yielding the family: 56003, 56113, 56333, 56443, 56663, 56773, and 56993.
Consequently 56003, being the first member of this family, is the smallest prime
with this property.
Find the smallest prime which, by replacing part of the number (not necessarily
adjacent digits) with the same digit, is part of an eight prime value family.
"""
from __future__ import annotations
from collections import Counter
def prime_sieve(n: int) -> list[int]:
"""
Sieve of Erotosthenes
Function to return all the prime numbers up to a certain number
https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes
>>> prime_sieve(3)
[2]
>>> prime_sieve(50)
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]
"""
is_prime = [True] * n
is_prime[0] = False
is_prime[1] = False
is_prime[2] = True
for i in range(3, int(n**0.5 + 1), 2):
index = i * 2
while index < n:
is_prime[index] = False
index = index + i
primes = [2]
for i in range(3, n, 2):
if is_prime[i]:
primes.append(i)
return primes
def digit_replacements(number: int) -> list[list[int]]:
"""
Returns all the possible families of digit replacements in a number which
contains at least one repeating digit
>>> digit_replacements(544)
[[500, 511, 522, 533, 544, 555, 566, 577, 588, 599]]
>>> digit_replacements(3112)
[[3002, 3112, 3222, 3332, 3442, 3552, 3662, 3772, 3882, 3992]]
"""
number_str = str(number)
replacements = []
digits = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
for duplicate in Counter(number_str) - Counter(set(number_str)):
family = [int(number_str.replace(duplicate, digit)) for digit in digits]
replacements.append(family)
return replacements
def solution(family_length: int = 8) -> int:
"""
Returns the solution of the problem
>>> solution(2)
229399
>>> solution(3)
221311
"""
numbers_checked = set()
# Filter primes with less than 3 replaceable digits
primes = {
x for x in set(prime_sieve(1_000_000)) if len(str(x)) - len(set(str(x))) >= 3
}
for prime in primes:
if prime in numbers_checked:
continue
replacements = digit_replacements(prime)
for family in replacements:
numbers_checked.update(family)
primes_in_family = primes.intersection(family)
if len(primes_in_family) != family_length:
continue
return min(primes_in_family)
return -1
if __name__ == "__main__":
print(solution())
| """
https://projecteuler.net/problem=51
Prime digit replacements
Problem 51
By replacing the 1st digit of the 2-digit number *3, it turns out that six of
the nine possible values: 13, 23, 43, 53, 73, and 83, are all prime.
By replacing the 3rd and 4th digits of 56**3 with the same digit, this 5-digit
number is the first example having seven primes among the ten generated numbers,
yielding the family: 56003, 56113, 56333, 56443, 56663, 56773, and 56993.
Consequently 56003, being the first member of this family, is the smallest prime
with this property.
Find the smallest prime which, by replacing part of the number (not necessarily
adjacent digits) with the same digit, is part of an eight prime value family.
"""
from __future__ import annotations
from collections import Counter
def prime_sieve(n: int) -> list[int]:
"""
Sieve of Erotosthenes
Function to return all the prime numbers up to a certain number
https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes
>>> prime_sieve(3)
[2]
>>> prime_sieve(50)
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]
"""
is_prime = [True] * n
is_prime[0] = False
is_prime[1] = False
is_prime[2] = True
for i in range(3, int(n**0.5 + 1), 2):
index = i * 2
while index < n:
is_prime[index] = False
index = index + i
primes = [2]
for i in range(3, n, 2):
if is_prime[i]:
primes.append(i)
return primes
def digit_replacements(number: int) -> list[list[int]]:
"""
Returns all the possible families of digit replacements in a number which
contains at least one repeating digit
>>> digit_replacements(544)
[[500, 511, 522, 533, 544, 555, 566, 577, 588, 599]]
>>> digit_replacements(3112)
[[3002, 3112, 3222, 3332, 3442, 3552, 3662, 3772, 3882, 3992]]
"""
number_str = str(number)
replacements = []
digits = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
for duplicate in Counter(number_str) - Counter(set(number_str)):
family = [int(number_str.replace(duplicate, digit)) for digit in digits]
replacements.append(family)
return replacements
def solution(family_length: int = 8) -> int:
"""
Returns the solution of the problem
>>> solution(2)
229399
>>> solution(3)
221311
"""
numbers_checked = set()
# Filter primes with less than 3 replaceable digits
primes = {
x for x in set(prime_sieve(1_000_000)) if len(str(x)) - len(set(str(x))) >= 3
}
for prime in primes:
if prime in numbers_checked:
continue
replacements = digit_replacements(prime)
for family in replacements:
numbers_checked.update(family)
primes_in_family = primes.intersection(family)
if len(primes_in_family) != family_length:
continue
return min(primes_in_family)
return -1
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Lower-Upper (LU) Decomposition.
Reference:
- https://en.wikipedia.org/wiki/LU_decomposition
"""
from __future__ import annotations
import numpy as np
from numpy import float64
from numpy.typing import ArrayLike
def lower_upper_decomposition(
table: ArrayLike[float64],
) -> tuple[ArrayLike[float64], ArrayLike[float64]]:
"""Lower-Upper (LU) Decomposition
Example:
>>> matrix = np.array([[2, -2, 1], [0, 1, 2], [5, 3, 1]])
>>> outcome = lower_upper_decomposition(matrix)
>>> outcome[0]
array([[1. , 0. , 0. ],
[0. , 1. , 0. ],
[2.5, 8. , 1. ]])
>>> outcome[1]
array([[ 2. , -2. , 1. ],
[ 0. , 1. , 2. ],
[ 0. , 0. , -17.5]])
>>> matrix = np.array([[2, -2, 1], [0, 1, 2]])
>>> lower_upper_decomposition(matrix)
Traceback (most recent call last):
...
ValueError: 'table' has to be of square shaped array but got a 2x3 array:
[[ 2 -2 1]
[ 0 1 2]]
"""
# Table that contains our data
# Table has to be a square array so we need to check first
rows, columns = np.shape(table)
if rows != columns:
raise ValueError(
f"'table' has to be of square shaped array but got a {rows}x{columns} "
+ f"array:\n{table}"
)
lower = np.zeros((rows, columns))
upper = np.zeros((rows, columns))
for i in range(columns):
for j in range(i):
total = 0
for k in range(j):
total += lower[i][k] * upper[k][j]
lower[i][j] = (table[i][j] - total) / upper[j][j]
lower[i][i] = 1
for j in range(i, columns):
total = 0
for k in range(i):
total += lower[i][k] * upper[k][j]
upper[i][j] = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| """Lower-Upper (LU) Decomposition.
Reference:
- https://en.wikipedia.org/wiki/LU_decomposition
"""
from __future__ import annotations
import numpy as np
from numpy import float64
from numpy.typing import ArrayLike
def lower_upper_decomposition(
table: ArrayLike[float64],
) -> tuple[ArrayLike[float64], ArrayLike[float64]]:
"""Lower-Upper (LU) Decomposition
Example:
>>> matrix = np.array([[2, -2, 1], [0, 1, 2], [5, 3, 1]])
>>> outcome = lower_upper_decomposition(matrix)
>>> outcome[0]
array([[1. , 0. , 0. ],
[0. , 1. , 0. ],
[2.5, 8. , 1. ]])
>>> outcome[1]
array([[ 2. , -2. , 1. ],
[ 0. , 1. , 2. ],
[ 0. , 0. , -17.5]])
>>> matrix = np.array([[2, -2, 1], [0, 1, 2]])
>>> lower_upper_decomposition(matrix)
Traceback (most recent call last):
...
ValueError: 'table' has to be of square shaped array but got a 2x3 array:
[[ 2 -2 1]
[ 0 1 2]]
"""
# Table that contains our data
# Table has to be a square array so we need to check first
rows, columns = np.shape(table)
if rows != columns:
raise ValueError(
f"'table' has to be of square shaped array but got a {rows}x{columns} "
+ f"array:\n{table}"
)
lower = np.zeros((rows, columns))
upper = np.zeros((rows, columns))
for i in range(columns):
for j in range(i):
total = 0
for k in range(j):
total += lower[i][k] * upper[k][j]
lower[i][j] = (table[i][j] - total) / upper[j][j]
lower[i][i] = 1
for j in range(i, columns):
total = 0
for k in range(i):
total += lower[i][k] * upper[k][j]
upper[i][j] = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import math
class Graph:
def __init__(self, n=0): # a graph with Node 0,1,...,N-1
self.n = n
self.w = [
[math.inf for j in range(0, n)] for i in range(0, n)
] # adjacency matrix for weight
self.dp = [
[math.inf for j in range(0, n)] for i in range(0, n)
] # dp[i][j] stores minimum distance from i to j
def add_edge(self, u, v, w):
self.dp[u][v] = w
def floyd_warshall(self):
for k in range(0, self.n):
for i in range(0, self.n):
for j in range(0, self.n):
self.dp[i][j] = min(self.dp[i][j], self.dp[i][k] + self.dp[k][j])
def show_min(self, u, v):
return self.dp[u][v]
if __name__ == "__main__":
graph = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| import math
class Graph:
def __init__(self, n=0): # a graph with Node 0,1,...,N-1
self.n = n
self.w = [
[math.inf for j in range(0, n)] for i in range(0, n)
] # adjacency matrix for weight
self.dp = [
[math.inf for j in range(0, n)] for i in range(0, n)
] # dp[i][j] stores minimum distance from i to j
def add_edge(self, u, v, w):
self.dp[u][v] = w
def floyd_warshall(self):
for k in range(0, self.n):
for i in range(0, self.n):
for j in range(0, self.n):
self.dp[i][j] = min(self.dp[i][j], self.dp[i][k] + self.dp[k][j])
def show_min(self, u, v):
return self.dp[u][v]
if __name__ == "__main__":
graph = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| -1 |
TheAlgorithms/Python | 7,198 | refactor: Replace `list()` and `dict()` calls with literals | ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-14T23:05:46Z" | "2022-10-15T01:07:03Z" | dcca5351c9185bf8c568615782ffb28319a6539d | 6e69181d1f592a08806717058720bf63e241eef2 | refactor: Replace `list()` and `dict()` calls with literals. ### Describe your change:
Replace all `list()` and `dict()` calls with literal `[]` and `{}`s
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 6,912 | Misc fixes across multiple algorithms | ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CenTdemeern1 | "2022-10-10T12:56:00Z" | "2022-10-16T05:25:38Z" | c94e215c8dbdfe1f349eab5708be6b5f337b6ddd | 04698538d816fc5f70c850e8b89c6d1f5599fa84 | Misc fixes across multiple algorithms. ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
import sys
class Letter:
def __init__(self, letter: str, freq: int):
self.letter: str = letter
self.freq: int = freq
self.bitstring: dict[str, str] = {}
def __repr__(self) -> str:
return f"{self.letter}:{self.freq}"
class TreeNode:
def __init__(self, freq: int, left: Letter | TreeNode, right: Letter | TreeNode):
self.freq: int = freq
self.left: Letter | TreeNode = left
self.right: Letter | TreeNode = right
def parse_file(file_path: str) -> list[Letter]:
"""
Read the file and build a dict of all letters and their
frequencies, then convert the dict into a list of Letters.
"""
chars: dict[str, int] = {}
with open(file_path) as f:
while True:
c = f.read(1)
if not c:
break
chars[c] = chars[c] + 1 if c in chars.keys() else 1
return sorted((Letter(c, f) for c, f in chars.items()), key=lambda l: l.freq)
def build_tree(letters: list[Letter]) -> Letter | TreeNode:
"""
Run through the list of Letters and build the min heap
for the Huffman Tree.
"""
response: list[Letter | TreeNode] = letters # type: ignore
while len(response) > 1:
left = response.pop(0)
right = response.pop(0)
total_freq = left.freq + right.freq
node = TreeNode(total_freq, left, right)
response.append(node)
response.sort(key=lambda l: l.freq)
return response[0]
def traverse_tree(root: Letter | TreeNode, bitstring: str) -> list[Letter]:
"""
Recursively traverse the Huffman Tree to set each
Letter's bitstring dictionary, and return the list of Letters
"""
if type(root) is Letter:
root.bitstring[root.letter] = bitstring
return [root]
treenode: TreeNode = root # type: ignore
letters = []
letters += traverse_tree(treenode.left, bitstring + "0")
letters += traverse_tree(treenode.right, bitstring + "1")
return letters
def huffman(file_path: str) -> None:
"""
Parse the file, build the tree, then run through the file
again, using the letters dictionary to find and print out the
bitstring for each letter.
"""
letters_list = parse_file(file_path)
root = build_tree(letters_list)
letters = {
k: v for letter in traverse_tree(root, "") for k, v in letter.bitstring.items()
}
print(f"Huffman Coding of {file_path}: ")
with open(file_path) as f:
while True:
c = f.read(1)
if not c:
break
print(letters[c], end=" ")
print()
if __name__ == "__main__":
# pass the file path to the huffman function
huffman(sys.argv[1])
| from __future__ import annotations
import sys
class Letter:
def __init__(self, letter: str, freq: int):
self.letter: str = letter
self.freq: int = freq
self.bitstring: dict[str, str] = {}
def __repr__(self) -> str:
return f"{self.letter}:{self.freq}"
class TreeNode:
def __init__(self, freq: int, left: Letter | TreeNode, right: Letter | TreeNode):
self.freq: int = freq
self.left: Letter | TreeNode = left
self.right: Letter | TreeNode = right
def parse_file(file_path: str) -> list[Letter]:
"""
Read the file and build a dict of all letters and their
frequencies, then convert the dict into a list of Letters.
"""
chars: dict[str, int] = {}
with open(file_path) as f:
while True:
c = f.read(1)
if not c:
break
chars[c] = chars[c] + 1 if c in chars else 1
return sorted((Letter(c, f) for c, f in chars.items()), key=lambda l: l.freq)
def build_tree(letters: list[Letter]) -> Letter | TreeNode:
"""
Run through the list of Letters and build the min heap
for the Huffman Tree.
"""
response: list[Letter | TreeNode] = letters # type: ignore
while len(response) > 1:
left = response.pop(0)
right = response.pop(0)
total_freq = left.freq + right.freq
node = TreeNode(total_freq, left, right)
response.append(node)
response.sort(key=lambda l: l.freq)
return response[0]
def traverse_tree(root: Letter | TreeNode, bitstring: str) -> list[Letter]:
"""
Recursively traverse the Huffman Tree to set each
Letter's bitstring dictionary, and return the list of Letters
"""
if type(root) is Letter:
root.bitstring[root.letter] = bitstring
return [root]
treenode: TreeNode = root # type: ignore
letters = []
letters += traverse_tree(treenode.left, bitstring + "0")
letters += traverse_tree(treenode.right, bitstring + "1")
return letters
def huffman(file_path: str) -> None:
"""
Parse the file, build the tree, then run through the file
again, using the letters dictionary to find and print out the
bitstring for each letter.
"""
letters_list = parse_file(file_path)
root = build_tree(letters_list)
letters = {
k: v for letter in traverse_tree(root, "") for k, v in letter.bitstring.items()
}
print(f"Huffman Coding of {file_path}: ")
with open(file_path) as f:
while True:
c = f.read(1)
if not c:
break
print(letters[c], end=" ")
print()
if __name__ == "__main__":
# pass the file path to the huffman function
huffman(sys.argv[1])
| 1 |
TheAlgorithms/Python | 6,912 | Misc fixes across multiple algorithms | ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CenTdemeern1 | "2022-10-10T12:56:00Z" | "2022-10-16T05:25:38Z" | c94e215c8dbdfe1f349eab5708be6b5f337b6ddd | 04698538d816fc5f70c850e8b89c6d1f5599fa84 | Misc fixes across multiple algorithms. ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def is_palindrome(head):
if not head:
return True
# split the list to two parts
fast, slow = head.next, head
while fast and fast.next:
fast = fast.next.next
slow = slow.next
second = slow.next
slow.next = None # Don't forget here! But forget still works!
# reverse the second part
node = None
while second:
nxt = second.next
second.next = node
node = second
second = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
node = node.next
head = head.next
return True
def is_palindrome_stack(head):
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
slow = fast = cur = head
while fast and fast.next:
fast, slow = fast.next.next, slow.next
# 2. Push the second half into the stack
stack = [slow.val]
while slow.next:
slow = slow.next
stack.append(slow.val)
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
cur = cur.next
return True
def is_palindrome_dict(head):
if not head or not head.next:
return True
d = {}
pos = 0
while head:
if head.val in d.keys():
d[head.val].append(pos)
else:
d[head.val] = [pos]
head = head.next
pos += 1
checksum = pos - 1
middle = 0
for v in d.values():
if len(v) % 2 != 0:
middle += 1
else:
step = 0
for i in range(0, len(v)):
if v[i] + v[len(v) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| def is_palindrome(head):
if not head:
return True
# split the list to two parts
fast, slow = head.next, head
while fast and fast.next:
fast = fast.next.next
slow = slow.next
second = slow.next
slow.next = None # Don't forget here! But forget still works!
# reverse the second part
node = None
while second:
nxt = second.next
second.next = node
node = second
second = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
node = node.next
head = head.next
return True
def is_palindrome_stack(head):
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
slow = fast = cur = head
while fast and fast.next:
fast, slow = fast.next.next, slow.next
# 2. Push the second half into the stack
stack = [slow.val]
while slow.next:
slow = slow.next
stack.append(slow.val)
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
cur = cur.next
return True
def is_palindrome_dict(head):
if not head or not head.next:
return True
d = {}
pos = 0
while head:
if head.val in d:
d[head.val].append(pos)
else:
d[head.val] = [pos]
head = head.next
pos += 1
checksum = pos - 1
middle = 0
for v in d.values():
if len(v) % 2 != 0:
middle += 1
else:
step = 0
for i in range(0, len(v)):
if v[i] + v[len(v) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 1 |
TheAlgorithms/Python | 6,912 | Misc fixes across multiple algorithms | ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CenTdemeern1 | "2022-10-10T12:56:00Z" | "2022-10-16T05:25:38Z" | c94e215c8dbdfe1f349eab5708be6b5f337b6ddd | 04698538d816fc5f70c850e8b89c6d1f5599fa84 | Misc fixes across multiple algorithms. ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import cv2
import numpy as np
def get_neighbors_pixel(
image: np.ndarray, x_coordinate: int, y_coordinate: int, center: int
) -> int:
"""
Comparing local neighborhood pixel value with threshold value of centre pixel.
Exception is required when neighborhood value of a center pixel value is null.
i.e. values present at boundaries.
:param image: The image we're working with
:param x_coordinate: x-coordinate of the pixel
:param y_coordinate: The y coordinate of the pixel
:param center: center pixel value
:return: The value of the pixel is being returned.
"""
try:
return int(image[x_coordinate][y_coordinate] >= center)
except (IndexError, TypeError):
return 0
def local_binary_value(image: np.ndarray, x_coordinate: int, y_coordinate: int) -> int:
"""
It takes an image, an x and y coordinate, and returns the
decimal value of the local binary patternof the pixel
at that coordinate
:param image: the image to be processed
:param x_coordinate: x coordinate of the pixel
:param y_coordinate: the y coordinate of the pixel
:return: The decimal value of the binary value of the pixels
around the center pixel.
"""
center = image[x_coordinate][y_coordinate]
powers = [1, 2, 4, 8, 16, 32, 64, 128]
# skip get_neighbors_pixel if center is null
if center is None:
return 0
# Starting from the top right, assigning value to pixels clockwise
binary_values = [
get_neighbors_pixel(image, x_coordinate - 1, y_coordinate + 1, center),
get_neighbors_pixel(image, x_coordinate, y_coordinate + 1, center),
get_neighbors_pixel(image, x_coordinate - 1, y_coordinate, center),
get_neighbors_pixel(image, x_coordinate + 1, y_coordinate + 1, center),
get_neighbors_pixel(image, x_coordinate + 1, y_coordinate, center),
get_neighbors_pixel(image, x_coordinate + 1, y_coordinate - 1, center),
get_neighbors_pixel(image, x_coordinate, y_coordinate - 1, center),
get_neighbors_pixel(image, x_coordinate - 1, y_coordinate - 1, center),
]
# Converting the binary value to decimal.
return sum(
binary_value * power for binary_value, power in zip(binary_values, powers)
)
if __name__ == "main":
# Reading the image and converting it to grayscale.
image = cv2.imread(
"digital_image_processing/image_data/lena.jpg", cv2.IMREAD_GRAYSCALE
)
# Create a numpy array as the same height and width of read image
lbp_image = np.zeros((image.shape[0], image.shape[1]))
# Iterating through the image and calculating the
# local binary pattern value for each pixel.
for i in range(0, image.shape[0]):
for j in range(0, image.shape[1]):
lbp_image[i][j] = local_binary_value(image, i, j)
cv2.imshow("local binary pattern", lbp_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
| import cv2
import numpy as np
def get_neighbors_pixel(
image: np.ndarray, x_coordinate: int, y_coordinate: int, center: int
) -> int:
"""
Comparing local neighborhood pixel value with threshold value of centre pixel.
Exception is required when neighborhood value of a center pixel value is null.
i.e. values present at boundaries.
:param image: The image we're working with
:param x_coordinate: x-coordinate of the pixel
:param y_coordinate: The y coordinate of the pixel
:param center: center pixel value
:return: The value of the pixel is being returned.
"""
try:
return int(image[x_coordinate][y_coordinate] >= center)
except (IndexError, TypeError):
return 0
def local_binary_value(image: np.ndarray, x_coordinate: int, y_coordinate: int) -> int:
"""
It takes an image, an x and y coordinate, and returns the
decimal value of the local binary patternof the pixel
at that coordinate
:param image: the image to be processed
:param x_coordinate: x coordinate of the pixel
:param y_coordinate: the y coordinate of the pixel
:return: The decimal value of the binary value of the pixels
around the center pixel.
"""
center = image[x_coordinate][y_coordinate]
powers = [1, 2, 4, 8, 16, 32, 64, 128]
# skip get_neighbors_pixel if center is null
if center is None:
return 0
# Starting from the top right, assigning value to pixels clockwise
binary_values = [
get_neighbors_pixel(image, x_coordinate - 1, y_coordinate + 1, center),
get_neighbors_pixel(image, x_coordinate, y_coordinate + 1, center),
get_neighbors_pixel(image, x_coordinate - 1, y_coordinate, center),
get_neighbors_pixel(image, x_coordinate + 1, y_coordinate + 1, center),
get_neighbors_pixel(image, x_coordinate + 1, y_coordinate, center),
get_neighbors_pixel(image, x_coordinate + 1, y_coordinate - 1, center),
get_neighbors_pixel(image, x_coordinate, y_coordinate - 1, center),
get_neighbors_pixel(image, x_coordinate - 1, y_coordinate - 1, center),
]
# Converting the binary value to decimal.
return sum(
binary_value * power for binary_value, power in zip(binary_values, powers)
)
if __name__ == "__main__":
# Reading the image and converting it to grayscale.
image = cv2.imread(
"digital_image_processing/image_data/lena.jpg", cv2.IMREAD_GRAYSCALE
)
# Create a numpy array as the same height and width of read image
lbp_image = np.zeros((image.shape[0], image.shape[1]))
# Iterating through the image and calculating the
# local binary pattern value for each pixel.
for i in range(0, image.shape[0]):
for j in range(0, image.shape[1]):
lbp_image[i][j] = local_binary_value(image, i, j)
cv2.imshow("local binary pattern", lbp_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
| 1 |
TheAlgorithms/Python | 6,912 | Misc fixes across multiple algorithms | ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CenTdemeern1 | "2022-10-10T12:56:00Z" | "2022-10-16T05:25:38Z" | c94e215c8dbdfe1f349eab5708be6b5f337b6ddd | 04698538d816fc5f70c850e8b89c6d1f5599fa84 | Misc fixes across multiple algorithms. ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
README, Author - Jigyasa Gandhi(mailto:[email protected])
Requirements:
- scikit-fuzzy
- numpy
- matplotlib
Python:
- 3.5
"""
import numpy as np
try:
import skfuzzy as fuzz
except ImportError:
fuzz = None
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
X = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
abc1 = [0, 25, 50]
abc2 = [25, 50, 75]
young = fuzz.membership.trimf(X, abc1)
middle_aged = fuzz.membership.trimf(X, abc2)
# Compute the different operations using inbuilt functions.
one = np.ones(75)
zero = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
union = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
intersection = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
complement_a = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
difference = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
alg_sum = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
alg_product = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
bdd_sum = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
bdd_difference = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title("Young")
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title("Middle aged")
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title("union")
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title("intersection")
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title("complement_a")
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title("difference a/b")
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title("alg_sum")
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title("alg_product")
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title("bdd_sum")
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title("bdd_difference")
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| """
README, Author - Jigyasa Gandhi(mailto:[email protected])
Requirements:
- scikit-fuzzy
- numpy
- matplotlib
Python:
- 3.5
"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
X = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
abc1 = [0, 25, 50]
abc2 = [25, 50, 75]
young = fuzz.membership.trimf(X, abc1)
middle_aged = fuzz.membership.trimf(X, abc2)
# Compute the different operations using inbuilt functions.
one = np.ones(75)
zero = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
union = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
intersection = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
complement_a = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
difference = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
alg_sum = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
alg_product = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
bdd_sum = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
bdd_difference = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title("Young")
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title("Middle aged")
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title("union")
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title("intersection")
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title("complement_a")
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title("difference a/b")
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title("alg_sum")
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title("alg_product")
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title("bdd_sum")
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title("bdd_difference")
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 1 |
TheAlgorithms/Python | 6,912 | Misc fixes across multiple algorithms | ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CenTdemeern1 | "2022-10-10T12:56:00Z" | "2022-10-16T05:25:38Z" | c94e215c8dbdfe1f349eab5708be6b5f337b6ddd | 04698538d816fc5f70c850e8b89c6d1f5599fa84 | Misc fixes across multiple algorithms. ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Title: Dijkstra's Algorithm for finding single source shortest path from scratch
# Author: Shubham Malik
# References: https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm
import math
import sys
# For storing the vertex set to retrieve node with the lowest distance
class PriorityQueue:
# Based on Min Heap
def __init__(self):
self.cur_size = 0
self.array = []
self.pos = {} # To store the pos of node in array
def is_empty(self):
return self.cur_size == 0
def min_heapify(self, idx):
lc = self.left(idx)
rc = self.right(idx)
if lc < self.cur_size and self.array(lc)[0] < self.array(idx)[0]:
smallest = lc
else:
smallest = idx
if rc < self.cur_size and self.array(rc)[0] < self.array(smallest)[0]:
smallest = rc
if smallest != idx:
self.swap(idx, smallest)
self.min_heapify(smallest)
def insert(self, tup):
# Inserts a node into the Priority Queue
self.pos[tup[1]] = self.cur_size
self.cur_size += 1
self.array.append((sys.maxsize, tup[1]))
self.decrease_key((sys.maxsize, tup[1]), tup[0])
def extract_min(self):
# Removes and returns the min element at top of priority queue
min_node = self.array[0][1]
self.array[0] = self.array[self.cur_size - 1]
self.cur_size -= 1
self.min_heapify(1)
del self.pos[min_node]
return min_node
def left(self, i):
# returns the index of left child
return 2 * i + 1
def right(self, i):
# returns the index of right child
return 2 * i + 2
def par(self, i):
# returns the index of parent
return math.floor(i / 2)
def swap(self, i, j):
# swaps array elements at indices i and j
# update the pos{}
self.pos[self.array[i][1]] = j
self.pos[self.array[j][1]] = i
temp = self.array[i]
self.array[i] = self.array[j]
self.array[j] = temp
def decrease_key(self, tup, new_d):
idx = self.pos[tup[1]]
# assuming the new_d is atmost old_d
self.array[idx] = (new_d, tup[1])
while idx > 0 and self.array[self.par(idx)][0] > self.array[idx][0]:
self.swap(idx, self.par(idx))
idx = self.par(idx)
class Graph:
def __init__(self, num):
self.adjList = {} # To store graph: u -> (v,w)
self.num_nodes = num # Number of nodes in graph
# To store the distance from source vertex
self.dist = [0] * self.num_nodes
self.par = [-1] * self.num_nodes # To store the path
def add_edge(self, u, v, w):
# Edge going from node u to v and v to u with weight w
# u (w)-> v, v (w) -> u
# Check if u already in graph
if u in self.adjList.keys():
self.adjList[u].append((v, w))
else:
self.adjList[u] = [(v, w)]
# Assuming undirected graph
if v in self.adjList.keys():
self.adjList[v].append((u, w))
else:
self.adjList[v] = [(u, w)]
def show_graph(self):
# u -> v(w)
for u in self.adjList:
print(u, "->", " -> ".join(str(f"{v}({w})") for v, w in self.adjList[u]))
def dijkstra(self, src):
# Flush old junk values in par[]
self.par = [-1] * self.num_nodes
# src is the source node
self.dist[src] = 0
q = PriorityQueue()
q.insert((0, src)) # (dist from src, node)
for u in self.adjList.keys():
if u != src:
self.dist[u] = sys.maxsize # Infinity
self.par[u] = -1
while not q.is_empty():
u = q.extract_min() # Returns node with the min dist from source
# Update the distance of all the neighbours of u and
# if their prev dist was INFINITY then push them in Q
for v, w in self.adjList[u]:
new_dist = self.dist[u] + w
if self.dist[v] > new_dist:
if self.dist[v] == sys.maxsize:
q.insert((new_dist, v))
else:
q.decrease_key((self.dist[v], v), new_dist)
self.dist[v] = new_dist
self.par[v] = u
# Show the shortest distances from src
self.show_distances(src)
def show_distances(self, src):
print(f"Distance from node: {src}")
for u in range(self.num_nodes):
print(f"Node {u} has distance: {self.dist[u]}")
def show_path(self, src, dest):
# To show the shortest path from src to dest
# WARNING: Use it *after* calling dijkstra
path = []
cost = 0
temp = dest
# Backtracking from dest to src
while self.par[temp] != -1:
path.append(temp)
if temp != src:
for v, w in self.adjList[temp]:
if v == self.par[temp]:
cost += w
break
temp = self.par[temp]
path.append(src)
path.reverse()
print(f"----Path to reach {dest} from {src}----")
for u in path:
print(f"{u}", end=" ")
if u != dest:
print("-> ", end="")
print("\nTotal cost of path: ", cost)
if __name__ == "__main__":
graph = Graph(9)
graph.add_edge(0, 1, 4)
graph.add_edge(0, 7, 8)
graph.add_edge(1, 2, 8)
graph.add_edge(1, 7, 11)
graph.add_edge(2, 3, 7)
graph.add_edge(2, 8, 2)
graph.add_edge(2, 5, 4)
graph.add_edge(3, 4, 9)
graph.add_edge(3, 5, 14)
graph.add_edge(4, 5, 10)
graph.add_edge(5, 6, 2)
graph.add_edge(6, 7, 1)
graph.add_edge(6, 8, 6)
graph.add_edge(7, 8, 7)
graph.show_graph()
graph.dijkstra(0)
graph.show_path(0, 4)
# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path: 21
| # Title: Dijkstra's Algorithm for finding single source shortest path from scratch
# Author: Shubham Malik
# References: https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm
import math
import sys
# For storing the vertex set to retrieve node with the lowest distance
class PriorityQueue:
# Based on Min Heap
def __init__(self):
self.cur_size = 0
self.array = []
self.pos = {} # To store the pos of node in array
def is_empty(self):
return self.cur_size == 0
def min_heapify(self, idx):
lc = self.left(idx)
rc = self.right(idx)
if lc < self.cur_size and self.array(lc)[0] < self.array(idx)[0]:
smallest = lc
else:
smallest = idx
if rc < self.cur_size and self.array(rc)[0] < self.array(smallest)[0]:
smallest = rc
if smallest != idx:
self.swap(idx, smallest)
self.min_heapify(smallest)
def insert(self, tup):
# Inserts a node into the Priority Queue
self.pos[tup[1]] = self.cur_size
self.cur_size += 1
self.array.append((sys.maxsize, tup[1]))
self.decrease_key((sys.maxsize, tup[1]), tup[0])
def extract_min(self):
# Removes and returns the min element at top of priority queue
min_node = self.array[0][1]
self.array[0] = self.array[self.cur_size - 1]
self.cur_size -= 1
self.min_heapify(1)
del self.pos[min_node]
return min_node
def left(self, i):
# returns the index of left child
return 2 * i + 1
def right(self, i):
# returns the index of right child
return 2 * i + 2
def par(self, i):
# returns the index of parent
return math.floor(i / 2)
def swap(self, i, j):
# swaps array elements at indices i and j
# update the pos{}
self.pos[self.array[i][1]] = j
self.pos[self.array[j][1]] = i
temp = self.array[i]
self.array[i] = self.array[j]
self.array[j] = temp
def decrease_key(self, tup, new_d):
idx = self.pos[tup[1]]
# assuming the new_d is atmost old_d
self.array[idx] = (new_d, tup[1])
while idx > 0 and self.array[self.par(idx)][0] > self.array[idx][0]:
self.swap(idx, self.par(idx))
idx = self.par(idx)
class Graph:
def __init__(self, num):
self.adjList = {} # To store graph: u -> (v,w)
self.num_nodes = num # Number of nodes in graph
# To store the distance from source vertex
self.dist = [0] * self.num_nodes
self.par = [-1] * self.num_nodes # To store the path
def add_edge(self, u, v, w):
# Edge going from node u to v and v to u with weight w
# u (w)-> v, v (w) -> u
# Check if u already in graph
if u in self.adjList:
self.adjList[u].append((v, w))
else:
self.adjList[u] = [(v, w)]
# Assuming undirected graph
if v in self.adjList:
self.adjList[v].append((u, w))
else:
self.adjList[v] = [(u, w)]
def show_graph(self):
# u -> v(w)
for u in self.adjList:
print(u, "->", " -> ".join(str(f"{v}({w})") for v, w in self.adjList[u]))
def dijkstra(self, src):
# Flush old junk values in par[]
self.par = [-1] * self.num_nodes
# src is the source node
self.dist[src] = 0
q = PriorityQueue()
q.insert((0, src)) # (dist from src, node)
for u in self.adjList.keys():
if u != src:
self.dist[u] = sys.maxsize # Infinity
self.par[u] = -1
while not q.is_empty():
u = q.extract_min() # Returns node with the min dist from source
# Update the distance of all the neighbours of u and
# if their prev dist was INFINITY then push them in Q
for v, w in self.adjList[u]:
new_dist = self.dist[u] + w
if self.dist[v] > new_dist:
if self.dist[v] == sys.maxsize:
q.insert((new_dist, v))
else:
q.decrease_key((self.dist[v], v), new_dist)
self.dist[v] = new_dist
self.par[v] = u
# Show the shortest distances from src
self.show_distances(src)
def show_distances(self, src):
print(f"Distance from node: {src}")
for u in range(self.num_nodes):
print(f"Node {u} has distance: {self.dist[u]}")
def show_path(self, src, dest):
# To show the shortest path from src to dest
# WARNING: Use it *after* calling dijkstra
path = []
cost = 0
temp = dest
# Backtracking from dest to src
while self.par[temp] != -1:
path.append(temp)
if temp != src:
for v, w in self.adjList[temp]:
if v == self.par[temp]:
cost += w
break
temp = self.par[temp]
path.append(src)
path.reverse()
print(f"----Path to reach {dest} from {src}----")
for u in path:
print(f"{u}", end=" ")
if u != dest:
print("-> ", end="")
print("\nTotal cost of path: ", cost)
if __name__ == "__main__":
graph = Graph(9)
graph.add_edge(0, 1, 4)
graph.add_edge(0, 7, 8)
graph.add_edge(1, 2, 8)
graph.add_edge(1, 7, 11)
graph.add_edge(2, 3, 7)
graph.add_edge(2, 8, 2)
graph.add_edge(2, 5, 4)
graph.add_edge(3, 4, 9)
graph.add_edge(3, 5, 14)
graph.add_edge(4, 5, 10)
graph.add_edge(5, 6, 2)
graph.add_edge(6, 7, 1)
graph.add_edge(6, 8, 6)
graph.add_edge(7, 8, 7)
graph.show_graph()
graph.dijkstra(0)
graph.show_path(0, 4)
# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path: 21
| 1 |
TheAlgorithms/Python | 6,912 | Misc fixes across multiple algorithms | ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CenTdemeern1 | "2022-10-10T12:56:00Z" | "2022-10-16T05:25:38Z" | c94e215c8dbdfe1f349eab5708be6b5f337b6ddd | 04698538d816fc5f70c850e8b89c6d1f5599fa84 | Misc fixes across multiple algorithms. ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from collections import deque
from math import floor
from random import random
from time import time
# the default weight is 1 if not assigned but all the implementation is weighted
class DirectedGraph:
def __init__(self):
self.graph = {}
# adding vertices and edges
# adding the weight is optional
# handles repetition
def add_pair(self, u, v, w=1):
if self.graph.get(u):
if self.graph[u].count([w, v]) == 0:
self.graph[u].append([w, v])
else:
self.graph[u] = [[w, v]]
if not self.graph.get(v):
self.graph[v] = []
def all_nodes(self):
return list(self.graph)
# handles if the input does not exist
def remove_pair(self, u, v):
if self.graph.get(u):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(_)
# if no destination is meant the default value is -1
def dfs(self, s=-2, d=-1):
if s == d:
return []
stack = []
visited = []
if s == -2:
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
ss = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if visited.count(node[1]) < 1:
if node[1] == d:
visited.append(d)
return visited
else:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return visited
# c is the count of nodes you want and if you leave it or pass -1 to the function
# the count will be random from 10 to 10000
def fill_graph_randomly(self, c=-1):
if c == -1:
c = floor(random() * 10000) + 10
for i in range(c):
# every vertex has max 100 edges
for _ in range(floor(random() * 102) + 1):
n = floor(random() * c) + 1
if n != i:
self.add_pair(i, n, 1)
def bfs(self, s=-2):
d = deque()
visited = []
if s == -2:
s = list(self.graph)[0]
d.append(s)
visited.append(s)
while d:
s = d.popleft()
if len(self.graph[s]) != 0:
for node in self.graph[s]:
if visited.count(node[1]) < 1:
d.append(node[1])
visited.append(node[1])
return visited
def in_degree(self, u):
count = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def out_degree(self, u):
return len(self.graph[u])
def topological_sort(self, s=-2):
stack = []
visited = []
if s == -2:
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
ss = s
sorted_nodes = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop())
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return sorted_nodes
def cycle_nodes(self):
stack = []
visited = []
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if (
visited.count(node[1]) > 0
and node[1] != parent
and indirect_parents.count(node[1]) > 0
and not on_the_way_back
):
len_stack = len(stack) - 1
while True and len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1])
break
else:
anticipating_nodes.add(stack[len_stack])
len_stack -= 1
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return list(anticipating_nodes)
def has_cycle(self):
stack = []
visited = []
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if (
visited.count(node[1]) > 0
and node[1] != parent
and indirect_parents.count(node[1]) > 0
and not on_the_way_back
):
len_stack_minus_one = len(stack) - 1
while True and len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1])
break
else:
return True
# TODO:The following code is unreachable.
anticipating_nodes.add(stack[len_stack_minus_one])
len_stack_minus_one -= 1
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return False
def dfs_time(self, s=-2, e=-1):
begin = time()
self.dfs(s, e)
end = time()
return end - begin
def bfs_time(self, s=-2):
begin = time()
self.bfs(s)
end = time()
return end - begin
class Graph:
def __init__(self):
self.graph = {}
# adding vertices and edges
# adding the weight is optional
# handles repetition
def add_pair(self, u, v, w=1):
# check if the u exists
if self.graph.get(u):
# if there already is a edge
if self.graph[u].count([w, v]) == 0:
self.graph[u].append([w, v])
else:
# if u does not exist
self.graph[u] = [[w, v]]
# add the other way
if self.graph.get(v):
# if there already is a edge
if self.graph[v].count([w, u]) == 0:
self.graph[v].append([w, u])
else:
# if u does not exist
self.graph[v] = [[w, u]]
# handles if the input does not exist
def remove_pair(self, u, v):
if self.graph.get(u):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(_)
# the other way round
if self.graph.get(v):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(_)
# if no destination is meant the default value is -1
def dfs(self, s=-2, d=-1):
if s == d:
return []
stack = []
visited = []
if s == -2:
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
ss = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if visited.count(node[1]) < 1:
if node[1] == d:
visited.append(d)
return visited
else:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return visited
# c is the count of nodes you want and if you leave it or pass -1 to the function
# the count will be random from 10 to 10000
def fill_graph_randomly(self, c=-1):
if c == -1:
c = floor(random() * 10000) + 10
for i in range(c):
# every vertex has max 100 edges
for _ in range(floor(random() * 102) + 1):
n = floor(random() * c) + 1
if n != i:
self.add_pair(i, n, 1)
def bfs(self, s=-2):
d = deque()
visited = []
if s == -2:
s = list(self.graph)[0]
d.append(s)
visited.append(s)
while d:
s = d.popleft()
if len(self.graph[s]) != 0:
for node in self.graph[s]:
if visited.count(node[1]) < 1:
d.append(node[1])
visited.append(node[1])
return visited
def degree(self, u):
return len(self.graph[u])
def cycle_nodes(self):
stack = []
visited = []
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if (
visited.count(node[1]) > 0
and node[1] != parent
and indirect_parents.count(node[1]) > 0
and not on_the_way_back
):
len_stack = len(stack) - 1
while True and len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1])
break
else:
anticipating_nodes.add(stack[len_stack])
len_stack -= 1
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return list(anticipating_nodes)
def has_cycle(self):
stack = []
visited = []
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if (
visited.count(node[1]) > 0
and node[1] != parent
and indirect_parents.count(node[1]) > 0
and not on_the_way_back
):
len_stack_minus_one = len(stack) - 1
while True and len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1])
break
else:
return True
# TODO: the following code is unreachable
# is this meant to be called in the else ?
anticipating_nodes.add(stack[len_stack_minus_one])
len_stack_minus_one -= 1
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return False
def all_nodes(self):
return list(self.graph)
def dfs_time(self, s=-2, e=-1):
begin = time()
self.dfs(s, e)
end = time()
return end - begin
def bfs_time(self, s=-2):
begin = time()
self.bfs(s)
end = time()
return end - begin
| from collections import deque
from math import floor
from random import random
from time import time
# the default weight is 1 if not assigned but all the implementation is weighted
class DirectedGraph:
def __init__(self):
self.graph = {}
# adding vertices and edges
# adding the weight is optional
# handles repetition
def add_pair(self, u, v, w=1):
if self.graph.get(u):
if self.graph[u].count([w, v]) == 0:
self.graph[u].append([w, v])
else:
self.graph[u] = [[w, v]]
if not self.graph.get(v):
self.graph[v] = []
def all_nodes(self):
return list(self.graph)
# handles if the input does not exist
def remove_pair(self, u, v):
if self.graph.get(u):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(_)
# if no destination is meant the default value is -1
def dfs(self, s=-2, d=-1):
if s == d:
return []
stack = []
visited = []
if s == -2:
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
ss = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if visited.count(node[1]) < 1:
if node[1] == d:
visited.append(d)
return visited
else:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return visited
# c is the count of nodes you want and if you leave it or pass -1 to the function
# the count will be random from 10 to 10000
def fill_graph_randomly(self, c=-1):
if c == -1:
c = floor(random() * 10000) + 10
for i in range(c):
# every vertex has max 100 edges
for _ in range(floor(random() * 102) + 1):
n = floor(random() * c) + 1
if n != i:
self.add_pair(i, n, 1)
def bfs(self, s=-2):
d = deque()
visited = []
if s == -2:
s = list(self.graph)[0]
d.append(s)
visited.append(s)
while d:
s = d.popleft()
if len(self.graph[s]) != 0:
for node in self.graph[s]:
if visited.count(node[1]) < 1:
d.append(node[1])
visited.append(node[1])
return visited
def in_degree(self, u):
count = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def out_degree(self, u):
return len(self.graph[u])
def topological_sort(self, s=-2):
stack = []
visited = []
if s == -2:
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
ss = s
sorted_nodes = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop())
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return sorted_nodes
def cycle_nodes(self):
stack = []
visited = []
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if (
visited.count(node[1]) > 0
and node[1] != parent
and indirect_parents.count(node[1]) > 0
and not on_the_way_back
):
len_stack = len(stack) - 1
while True and len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1])
break
else:
anticipating_nodes.add(stack[len_stack])
len_stack -= 1
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return list(anticipating_nodes)
def has_cycle(self):
stack = []
visited = []
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if (
visited.count(node[1]) > 0
and node[1] != parent
and indirect_parents.count(node[1]) > 0
and not on_the_way_back
):
len_stack_minus_one = len(stack) - 1
while True and len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1])
break
else:
return True
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return False
def dfs_time(self, s=-2, e=-1):
begin = time()
self.dfs(s, e)
end = time()
return end - begin
def bfs_time(self, s=-2):
begin = time()
self.bfs(s)
end = time()
return end - begin
class Graph:
def __init__(self):
self.graph = {}
# adding vertices and edges
# adding the weight is optional
# handles repetition
def add_pair(self, u, v, w=1):
# check if the u exists
if self.graph.get(u):
# if there already is a edge
if self.graph[u].count([w, v]) == 0:
self.graph[u].append([w, v])
else:
# if u does not exist
self.graph[u] = [[w, v]]
# add the other way
if self.graph.get(v):
# if there already is a edge
if self.graph[v].count([w, u]) == 0:
self.graph[v].append([w, u])
else:
# if u does not exist
self.graph[v] = [[w, u]]
# handles if the input does not exist
def remove_pair(self, u, v):
if self.graph.get(u):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(_)
# the other way round
if self.graph.get(v):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(_)
# if no destination is meant the default value is -1
def dfs(self, s=-2, d=-1):
if s == d:
return []
stack = []
visited = []
if s == -2:
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
ss = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if visited.count(node[1]) < 1:
if node[1] == d:
visited.append(d)
return visited
else:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return visited
# c is the count of nodes you want and if you leave it or pass -1 to the function
# the count will be random from 10 to 10000
def fill_graph_randomly(self, c=-1):
if c == -1:
c = floor(random() * 10000) + 10
for i in range(c):
# every vertex has max 100 edges
for _ in range(floor(random() * 102) + 1):
n = floor(random() * c) + 1
if n != i:
self.add_pair(i, n, 1)
def bfs(self, s=-2):
d = deque()
visited = []
if s == -2:
s = list(self.graph)[0]
d.append(s)
visited.append(s)
while d:
s = d.popleft()
if len(self.graph[s]) != 0:
for node in self.graph[s]:
if visited.count(node[1]) < 1:
d.append(node[1])
visited.append(node[1])
return visited
def degree(self, u):
return len(self.graph[u])
def cycle_nodes(self):
stack = []
visited = []
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if (
visited.count(node[1]) > 0
and node[1] != parent
and indirect_parents.count(node[1]) > 0
and not on_the_way_back
):
len_stack = len(stack) - 1
while True and len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1])
break
else:
anticipating_nodes.add(stack[len_stack])
len_stack -= 1
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return list(anticipating_nodes)
def has_cycle(self):
stack = []
visited = []
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if (
visited.count(node[1]) > 0
and node[1] != parent
and indirect_parents.count(node[1]) > 0
and not on_the_way_back
):
len_stack_minus_one = len(stack) - 1
while True and len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1])
break
else:
return True
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return False
def all_nodes(self):
return list(self.graph)
def dfs_time(self, s=-2, e=-1):
begin = time()
self.dfs(s, e)
end = time()
return end - begin
def bfs_time(self, s=-2):
begin = time()
self.bfs(s)
end = time()
return end - begin
| 1 |
TheAlgorithms/Python | 6,912 | Misc fixes across multiple algorithms | ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CenTdemeern1 | "2022-10-10T12:56:00Z" | "2022-10-16T05:25:38Z" | c94e215c8dbdfe1f349eab5708be6b5f337b6ddd | 04698538d816fc5f70c850e8b89c6d1f5599fa84 | Misc fixes across multiple algorithms. ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Author: 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 emitter_converter(size_par, data):
"""
:param size_par: 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
>>> emitter_converter(4, "101010111111")
['1', '1', '1', '1', '0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1']
"""
if size_par + len(data) <= 2**size_par - (len(data) - 1):
print("ERROR - size of parity don't match with size of data")
exit(0)
data_out = []
parity = []
bin_pos = [bin(x)[2:] for x in range(1, size_par + len(data) + 1)]
# sorted information data for the size of the output data
data_ord = []
# data position template + parity
data_out_gab = []
# parity bit counter
qtd_bp = 0
# counter position of data bits
cont_data = 0
for x in range(1, size_par + len(data) + 1):
# Performs a template of bit positions - who should be given,
# and who should be parity
if qtd_bp < size_par:
if (np.log(x) / np.log(2)).is_integer():
data_out_gab.append("P")
qtd_bp = qtd_bp + 1
else:
data_out_gab.append("D")
else:
data_out_gab.append("D")
# Sorts the data to the new output size
if data_out_gab[-1] == "D":
data_ord.append(data[cont_data])
cont_data += 1
else:
data_ord.append(None)
# Calculates parity
qtd_bp = 0 # parity bit counter
for bp in range(1, size_par + 1):
# Bit counter one for a given parity
cont_bo = 0
# counter to control the loop reading
cont_loop = 0
for x in data_ord:
if x is not None:
try:
aux = (bin_pos[cont_loop])[-1 * (bp)]
except IndexError:
aux = "0"
if aux == "1":
if x == "1":
cont_bo += 1
cont_loop += 1
parity.append(cont_bo % 2)
qtd_bp += 1
# Mount the message
cont_bp = 0 # parity bit counter
for x in range(0, size_par + len(data)):
if data_ord[x] is None:
data_out.append(str(parity[cont_bp]))
cont_bp += 1
else:
data_out.append(data_ord[x])
return data_out
def receptor_converter(size_par, data):
"""
>>> receptor_converter(4, "1111010010111111")
(['1', '0', '1', '0', '1', '0', '1', '1', '1', '1', '1', '1'], True)
"""
# data position template + parity
data_out_gab = []
# Parity bit counter
qtd_bp = 0
# Counter p data bit reading
cont_data = 0
# list of parity received
parity_received = []
data_output = []
for x in range(1, len(data) + 1):
# Performs a template of bit positions - who should be given,
# and who should be parity
if qtd_bp < size_par and (np.log(x) / np.log(2)).is_integer():
data_out_gab.append("P")
qtd_bp = qtd_bp + 1
else:
data_out_gab.append("D")
# Sorts the data to the new output size
if data_out_gab[-1] == "D":
data_output.append(data[cont_data])
else:
parity_received.append(data[cont_data])
cont_data += 1
# -----------calculates the parity with the data
data_out = []
parity = []
bin_pos = [bin(x)[2:] for x in range(1, size_par + len(data_output) + 1)]
# sorted information data for the size of the output data
data_ord = []
# Data position feedback + parity
data_out_gab = []
# Parity bit counter
qtd_bp = 0
# Counter p data bit reading
cont_data = 0
for x in range(1, size_par + len(data_output) + 1):
# Performs a template position of bits - who should be given,
# and who should be parity
if qtd_bp < size_par and (np.log(x) / np.log(2)).is_integer():
data_out_gab.append("P")
qtd_bp = qtd_bp + 1
else:
data_out_gab.append("D")
# Sorts the data to the new output size
if data_out_gab[-1] == "D":
data_ord.append(data_output[cont_data])
cont_data += 1
else:
data_ord.append(None)
# Calculates parity
qtd_bp = 0 # parity bit counter
for bp in range(1, size_par + 1):
# Bit counter one for a certain parity
cont_bo = 0
# Counter to control loop reading
cont_loop = 0
for x in data_ord:
if x is not None:
try:
aux = (bin_pos[cont_loop])[-1 * (bp)]
except IndexError:
aux = "0"
if aux == "1" and x == "1":
cont_bo += 1
cont_loop += 1
parity.append(str(cont_bo % 2))
qtd_bp += 1
# Mount the message
cont_bp = 0 # Parity bit counter
for x in range(0, size_par + len(data_output)):
if data_ord[x] is None:
data_out.append(str(parity[cont_bp]))
cont_bp += 1
else:
data_out.append(data_ord[x])
ack = parity_received == parity
return data_output, 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 emitter_converter(size_par, data):
"""
:param size_par: 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
>>> emitter_converter(4, "101010111111")
['1', '1', '1', '1', '0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1']
"""
if size_par + len(data) <= 2**size_par - (len(data) - 1):
raise ValueError("size of parity don't match with size of data")
data_out = []
parity = []
bin_pos = [bin(x)[2:] for x in range(1, size_par + len(data) + 1)]
# sorted information data for the size of the output data
data_ord = []
# data position template + parity
data_out_gab = []
# parity bit counter
qtd_bp = 0
# counter position of data bits
cont_data = 0
for x in range(1, size_par + len(data) + 1):
# Performs a template of bit positions - who should be given,
# and who should be parity
if qtd_bp < size_par:
if (np.log(x) / np.log(2)).is_integer():
data_out_gab.append("P")
qtd_bp = qtd_bp + 1
else:
data_out_gab.append("D")
else:
data_out_gab.append("D")
# Sorts the data to the new output size
if data_out_gab[-1] == "D":
data_ord.append(data[cont_data])
cont_data += 1
else:
data_ord.append(None)
# Calculates parity
qtd_bp = 0 # parity bit counter
for bp in range(1, size_par + 1):
# Bit counter one for a given parity
cont_bo = 0
# counter to control the loop reading
cont_loop = 0
for x in data_ord:
if x is not None:
try:
aux = (bin_pos[cont_loop])[-1 * (bp)]
except IndexError:
aux = "0"
if aux == "1":
if x == "1":
cont_bo += 1
cont_loop += 1
parity.append(cont_bo % 2)
qtd_bp += 1
# Mount the message
cont_bp = 0 # parity bit counter
for x in range(0, size_par + len(data)):
if data_ord[x] is None:
data_out.append(str(parity[cont_bp]))
cont_bp += 1
else:
data_out.append(data_ord[x])
return data_out
def receptor_converter(size_par, data):
"""
>>> receptor_converter(4, "1111010010111111")
(['1', '0', '1', '0', '1', '0', '1', '1', '1', '1', '1', '1'], True)
"""
# data position template + parity
data_out_gab = []
# Parity bit counter
qtd_bp = 0
# Counter p data bit reading
cont_data = 0
# list of parity received
parity_received = []
data_output = []
for x in range(1, len(data) + 1):
# Performs a template of bit positions - who should be given,
# and who should be parity
if qtd_bp < size_par and (np.log(x) / np.log(2)).is_integer():
data_out_gab.append("P")
qtd_bp = qtd_bp + 1
else:
data_out_gab.append("D")
# Sorts the data to the new output size
if data_out_gab[-1] == "D":
data_output.append(data[cont_data])
else:
parity_received.append(data[cont_data])
cont_data += 1
# -----------calculates the parity with the data
data_out = []
parity = []
bin_pos = [bin(x)[2:] for x in range(1, size_par + len(data_output) + 1)]
# sorted information data for the size of the output data
data_ord = []
# Data position feedback + parity
data_out_gab = []
# Parity bit counter
qtd_bp = 0
# Counter p data bit reading
cont_data = 0
for x in range(1, size_par + len(data_output) + 1):
# Performs a template position of bits - who should be given,
# and who should be parity
if qtd_bp < size_par and (np.log(x) / np.log(2)).is_integer():
data_out_gab.append("P")
qtd_bp = qtd_bp + 1
else:
data_out_gab.append("D")
# Sorts the data to the new output size
if data_out_gab[-1] == "D":
data_ord.append(data_output[cont_data])
cont_data += 1
else:
data_ord.append(None)
# Calculates parity
qtd_bp = 0 # parity bit counter
for bp in range(1, size_par + 1):
# Bit counter one for a certain parity
cont_bo = 0
# Counter to control loop reading
cont_loop = 0
for x in data_ord:
if x is not None:
try:
aux = (bin_pos[cont_loop])[-1 * (bp)]
except IndexError:
aux = "0"
if aux == "1" and x == "1":
cont_bo += 1
cont_loop += 1
parity.append(str(cont_bo % 2))
qtd_bp += 1
# Mount the message
cont_bp = 0 # Parity bit counter
for x in range(0, size_par + len(data_output)):
if data_ord[x] is None:
data_out.append(str(parity[cont_bp]))
cont_bp += 1
else:
data_out.append(data_ord[x])
ack = parity_received == parity
return data_output, 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 | 6,912 | Misc fixes across multiple algorithms | ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CenTdemeern1 | "2022-10-10T12:56:00Z" | "2022-10-16T05:25:38Z" | c94e215c8dbdfe1f349eab5708be6b5f337b6ddd | 04698538d816fc5f70c850e8b89c6d1f5599fa84 | Misc fixes across multiple algorithms. ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Created on Mon Feb 26 15:40:07 2018
@author: Christian Bender
@license: MIT-license
This file contains the test-suite for the linear algebra library.
"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class Test(unittest.TestCase):
def test_component(self) -> None:
"""
test for method component()
"""
x = Vector([1, 2, 3])
self.assertEqual(x.component(0), 1)
self.assertEqual(x.component(2), 3)
_ = Vector()
def test_str(self) -> None:
"""
test for method toString()
"""
x = Vector([0, 0, 0, 0, 0, 1])
self.assertEqual(str(x), "(0,0,0,0,0,1)")
def test_size(self) -> None:
"""
test for method size()
"""
x = Vector([1, 2, 3, 4])
self.assertEqual(len(x), 4)
def test_euclidean_length(self) -> None:
"""
test for method euclidean_length()
"""
x = Vector([1, 2])
y = Vector([1, 2, 3, 4, 5])
z = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
w = Vector([1, -1, 1, -1, 2, -3, 4, -5])
self.assertAlmostEqual(x.euclidean_length(), 2.236, 3)
self.assertAlmostEqual(y.euclidean_length(), 7.416, 3)
self.assertEqual(z.euclidean_length(), 0)
self.assertAlmostEqual(w.euclidean_length(), 7.616, 3)
def test_add(self) -> None:
"""
test for + operator
"""
x = Vector([1, 2, 3])
y = Vector([1, 1, 1])
self.assertEqual((x + y).component(0), 2)
self.assertEqual((x + y).component(1), 3)
self.assertEqual((x + y).component(2), 4)
def test_sub(self) -> None:
"""
test for - operator
"""
x = Vector([1, 2, 3])
y = Vector([1, 1, 1])
self.assertEqual((x - y).component(0), 0)
self.assertEqual((x - y).component(1), 1)
self.assertEqual((x - y).component(2), 2)
def test_mul(self) -> None:
"""
test for * operator
"""
x = Vector([1, 2, 3])
a = Vector([2, -1, 4]) # for test of dot product
b = Vector([1, -2, -1])
self.assertEqual(str(x * 3.0), "(3.0,6.0,9.0)")
self.assertEqual((a * b), 0)
def test_zero_vector(self) -> None:
"""
test for global function zero_vector()
"""
self.assertTrue(str(zero_vector(10)).count("0") == 10)
def test_unit_basis_vector(self) -> None:
"""
test for global function unit_basis_vector()
"""
self.assertEqual(str(unit_basis_vector(3, 1)), "(0,1,0)")
def test_axpy(self) -> None:
"""
test for global function axpy() (operation)
"""
x = Vector([1, 2, 3])
y = Vector([1, 0, 1])
self.assertEqual(str(axpy(2, x, y)), "(3,4,7)")
def test_copy(self) -> None:
"""
test for method copy()
"""
x = Vector([1, 0, 0, 0, 0, 0])
y = x.copy()
self.assertEqual(str(x), str(y))
def test_change_component(self) -> None:
"""
test for method change_component()
"""
x = Vector([1, 0, 0])
x.change_component(0, 0)
x.change_component(1, 1)
self.assertEqual(str(x), "(0,1,0)")
def test_str_matrix(self) -> None:
"""
test for Matrix method str()
"""
a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
self.assertEqual("|1,2,3|\n|2,4,5|\n|6,7,8|\n", str(a))
def test_minor(self) -> None:
"""
test for Matrix method minor()
"""
a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
minors = [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
for x in range(a.height()):
for y in range(a.width()):
self.assertEqual(minors[x][y], a.minor(x, y))
def test_cofactor(self) -> None:
"""
test for Matrix method cofactor()
"""
a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
cofactors = [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
for x in range(a.height()):
for y in range(a.width()):
self.assertEqual(cofactors[x][y], a.cofactor(x, y))
def test_determinant(self) -> None:
"""
test for Matrix method determinant()
"""
a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
self.assertEqual(-5, a.determinant())
def test__mul__matrix(self) -> None:
"""
test for Matrix * operator
"""
a = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]], 3, 3)
x = Vector([1, 2, 3])
self.assertEqual("(14,32,50)", str(a * x))
self.assertEqual("|2,4,6|\n|8,10,12|\n|14,16,18|\n", str(a * 2))
def test_change_component_matrix(self) -> None:
"""
test for Matrix method change_component()
"""
a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
a.change_component(0, 2, 5)
self.assertEqual("|1,2,5|\n|2,4,5|\n|6,7,8|\n", str(a))
def test_component_matrix(self) -> None:
"""
test for Matrix method component()
"""
a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
self.assertEqual(7, a.component(2, 1), 0.01)
def test__add__matrix(self) -> None:
"""
test for Matrix + operator
"""
a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
b = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]], 3, 3)
self.assertEqual("|2,4,10|\n|4,8,10|\n|12,14,18|\n", str(a + b))
def test__sub__matrix(self) -> None:
"""
test for Matrix - operator
"""
a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
b = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]], 3, 3)
self.assertEqual("|0,0,-4|\n|0,0,0|\n|0,0,-2|\n", str(a - b))
def test_square_zero_matrix(self) -> None:
"""
test for global function square_zero_matrix()
"""
self.assertEqual(
"|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n",
str(square_zero_matrix(5)),
)
if __name__ == "__main__":
unittest.main()
| """
Created on Mon Feb 26 15:40:07 2018
@author: Christian Bender
@license: MIT-license
This file contains the test-suite for the linear algebra library.
"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class Test(unittest.TestCase):
def test_component(self) -> None:
"""
test for method component()
"""
x = Vector([1, 2, 3])
self.assertEqual(x.component(0), 1)
self.assertEqual(x.component(2), 3)
_ = Vector()
def test_str(self) -> None:
"""
test for method toString()
"""
x = Vector([0, 0, 0, 0, 0, 1])
self.assertEqual(str(x), "(0,0,0,0,0,1)")
def test_size(self) -> None:
"""
test for method size()
"""
x = Vector([1, 2, 3, 4])
self.assertEqual(len(x), 4)
def test_euclidean_length(self) -> None:
"""
test for method euclidean_length()
"""
x = Vector([1, 2])
y = Vector([1, 2, 3, 4, 5])
z = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
w = Vector([1, -1, 1, -1, 2, -3, 4, -5])
self.assertAlmostEqual(x.euclidean_length(), 2.236, 3)
self.assertAlmostEqual(y.euclidean_length(), 7.416, 3)
self.assertEqual(z.euclidean_length(), 0)
self.assertAlmostEqual(w.euclidean_length(), 7.616, 3)
def test_add(self) -> None:
"""
test for + operator
"""
x = Vector([1, 2, 3])
y = Vector([1, 1, 1])
self.assertEqual((x + y).component(0), 2)
self.assertEqual((x + y).component(1), 3)
self.assertEqual((x + y).component(2), 4)
def test_sub(self) -> None:
"""
test for - operator
"""
x = Vector([1, 2, 3])
y = Vector([1, 1, 1])
self.assertEqual((x - y).component(0), 0)
self.assertEqual((x - y).component(1), 1)
self.assertEqual((x - y).component(2), 2)
def test_mul(self) -> None:
"""
test for * operator
"""
x = Vector([1, 2, 3])
a = Vector([2, -1, 4]) # for test of dot product
b = Vector([1, -2, -1])
self.assertEqual(str(x * 3.0), "(3.0,6.0,9.0)")
self.assertEqual((a * b), 0)
def test_zero_vector(self) -> None:
"""
test for global function zero_vector()
"""
self.assertEqual(str(zero_vector(10)).count("0"), 10)
def test_unit_basis_vector(self) -> None:
"""
test for global function unit_basis_vector()
"""
self.assertEqual(str(unit_basis_vector(3, 1)), "(0,1,0)")
def test_axpy(self) -> None:
"""
test for global function axpy() (operation)
"""
x = Vector([1, 2, 3])
y = Vector([1, 0, 1])
self.assertEqual(str(axpy(2, x, y)), "(3,4,7)")
def test_copy(self) -> None:
"""
test for method copy()
"""
x = Vector([1, 0, 0, 0, 0, 0])
y = x.copy()
self.assertEqual(str(x), str(y))
def test_change_component(self) -> None:
"""
test for method change_component()
"""
x = Vector([1, 0, 0])
x.change_component(0, 0)
x.change_component(1, 1)
self.assertEqual(str(x), "(0,1,0)")
def test_str_matrix(self) -> None:
"""
test for Matrix method str()
"""
a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
self.assertEqual("|1,2,3|\n|2,4,5|\n|6,7,8|\n", str(a))
def test_minor(self) -> None:
"""
test for Matrix method minor()
"""
a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
minors = [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
for x in range(a.height()):
for y in range(a.width()):
self.assertEqual(minors[x][y], a.minor(x, y))
def test_cofactor(self) -> None:
"""
test for Matrix method cofactor()
"""
a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
cofactors = [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
for x in range(a.height()):
for y in range(a.width()):
self.assertEqual(cofactors[x][y], a.cofactor(x, y))
def test_determinant(self) -> None:
"""
test for Matrix method determinant()
"""
a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
self.assertEqual(-5, a.determinant())
def test__mul__matrix(self) -> None:
"""
test for Matrix * operator
"""
a = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]], 3, 3)
x = Vector([1, 2, 3])
self.assertEqual("(14,32,50)", str(a * x))
self.assertEqual("|2,4,6|\n|8,10,12|\n|14,16,18|\n", str(a * 2))
def test_change_component_matrix(self) -> None:
"""
test for Matrix method change_component()
"""
a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
a.change_component(0, 2, 5)
self.assertEqual("|1,2,5|\n|2,4,5|\n|6,7,8|\n", str(a))
def test_component_matrix(self) -> None:
"""
test for Matrix method component()
"""
a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
self.assertEqual(7, a.component(2, 1), 0.01)
def test__add__matrix(self) -> None:
"""
test for Matrix + operator
"""
a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
b = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]], 3, 3)
self.assertEqual("|2,4,10|\n|4,8,10|\n|12,14,18|\n", str(a + b))
def test__sub__matrix(self) -> None:
"""
test for Matrix - operator
"""
a = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3)
b = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]], 3, 3)
self.assertEqual("|0,0,-4|\n|0,0,0|\n|0,0,-2|\n", str(a - b))
def test_square_zero_matrix(self) -> None:
"""
test for global function square_zero_matrix()
"""
self.assertEqual(
"|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n",
str(square_zero_matrix(5)),
)
if __name__ == "__main__":
unittest.main()
| 1 |
TheAlgorithms/Python | 6,912 | Misc fixes across multiple algorithms | ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CenTdemeern1 | "2022-10-10T12:56:00Z" | "2022-10-16T05:25:38Z" | c94e215c8dbdfe1f349eab5708be6b5f337b6ddd | 04698538d816fc5f70c850e8b89c6d1f5599fa84 | Misc fixes across multiple algorithms. ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Extended Euclidean Algorithm.
Finds 2 numbers a and b such that it satisfies
the equation am + bn = gcd(m, n) (a.k.a Bezout's Identity)
https://en.wikipedia.org/wiki/Extended_Euclidean_algorithm
"""
# @Author: S. Sharma <silentcat>
# @Date: 2019-02-25T12:08:53-06:00
# @Email: [email protected]
# @Last modified by: pikulet
# @Last modified time: 2020-10-02
from __future__ import annotations
import sys
def extended_euclidean_algorithm(a: int, b: int) -> tuple[int, int]:
"""
Extended Euclidean Algorithm.
Finds 2 numbers a and b such that it satisfies
the equation am + bn = gcd(m, n) (a.k.a Bezout's Identity)
>>> extended_euclidean_algorithm(1, 24)
(1, 0)
>>> extended_euclidean_algorithm(8, 14)
(2, -1)
>>> extended_euclidean_algorithm(240, 46)
(-9, 47)
>>> extended_euclidean_algorithm(1, -4)
(1, 0)
>>> extended_euclidean_algorithm(-2, -4)
(-1, 0)
>>> extended_euclidean_algorithm(0, -4)
(0, -1)
>>> extended_euclidean_algorithm(2, 0)
(1, 0)
"""
# base cases
if abs(a) == 1:
return a, 0
elif abs(b) == 1:
return 0, b
old_remainder, remainder = a, b
old_coeff_a, coeff_a = 1, 0
old_coeff_b, coeff_b = 0, 1
while remainder != 0:
quotient = old_remainder // remainder
old_remainder, remainder = remainder, old_remainder - quotient * remainder
old_coeff_a, coeff_a = coeff_a, old_coeff_a - quotient * coeff_a
old_coeff_b, coeff_b = coeff_b, old_coeff_b - quotient * coeff_b
# sign correction for negative numbers
if a < 0:
old_coeff_a = -old_coeff_a
if b < 0:
old_coeff_b = -old_coeff_b
return old_coeff_a, old_coeff_b
def main():
"""Call Extended Euclidean Algorithm."""
if len(sys.argv) < 3:
print("2 integer arguments required")
exit(1)
a = int(sys.argv[1])
b = int(sys.argv[2])
print(extended_euclidean_algorithm(a, b))
if __name__ == "__main__":
main()
| """
Extended Euclidean Algorithm.
Finds 2 numbers a and b such that it satisfies
the equation am + bn = gcd(m, n) (a.k.a Bezout's Identity)
https://en.wikipedia.org/wiki/Extended_Euclidean_algorithm
"""
# @Author: S. Sharma <silentcat>
# @Date: 2019-02-25T12:08:53-06:00
# @Email: [email protected]
# @Last modified by: pikulet
# @Last modified time: 2020-10-02
from __future__ import annotations
import sys
def extended_euclidean_algorithm(a: int, b: int) -> tuple[int, int]:
"""
Extended Euclidean Algorithm.
Finds 2 numbers a and b such that it satisfies
the equation am + bn = gcd(m, n) (a.k.a Bezout's Identity)
>>> extended_euclidean_algorithm(1, 24)
(1, 0)
>>> extended_euclidean_algorithm(8, 14)
(2, -1)
>>> extended_euclidean_algorithm(240, 46)
(-9, 47)
>>> extended_euclidean_algorithm(1, -4)
(1, 0)
>>> extended_euclidean_algorithm(-2, -4)
(-1, 0)
>>> extended_euclidean_algorithm(0, -4)
(0, -1)
>>> extended_euclidean_algorithm(2, 0)
(1, 0)
"""
# base cases
if abs(a) == 1:
return a, 0
elif abs(b) == 1:
return 0, b
old_remainder, remainder = a, b
old_coeff_a, coeff_a = 1, 0
old_coeff_b, coeff_b = 0, 1
while remainder != 0:
quotient = old_remainder // remainder
old_remainder, remainder = remainder, old_remainder - quotient * remainder
old_coeff_a, coeff_a = coeff_a, old_coeff_a - quotient * coeff_a
old_coeff_b, coeff_b = coeff_b, old_coeff_b - quotient * coeff_b
# sign correction for negative numbers
if a < 0:
old_coeff_a = -old_coeff_a
if b < 0:
old_coeff_b = -old_coeff_b
return old_coeff_a, old_coeff_b
def main():
"""Call Extended Euclidean Algorithm."""
if len(sys.argv) < 3:
print("2 integer arguments required")
return 1
a = int(sys.argv[1])
b = int(sys.argv[2])
print(extended_euclidean_algorithm(a, b))
return 0
if __name__ == "__main__":
raise SystemExit(main())
| 1 |
TheAlgorithms/Python | 6,912 | Misc fixes across multiple algorithms | ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CenTdemeern1 | "2022-10-10T12:56:00Z" | "2022-10-16T05:25:38Z" | c94e215c8dbdfe1f349eab5708be6b5f337b6ddd | 04698538d816fc5f70c850e8b89c6d1f5599fa84 | Misc fixes across multiple algorithms. ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
The Jaccard similarity coefficient is a commonly used indicator of the
similarity between two sets. Let U be a set and A and B be subsets of U,
then the Jaccard index/similarity is defined to be the ratio of the number
of elements of their intersection and the number of elements of their union.
Inspired from Wikipedia and
the book Mining of Massive Datasets [MMDS 2nd Edition, Chapter 3]
https://en.wikipedia.org/wiki/Jaccard_index
https://mmds.org
Jaccard similarity is widely used with MinHashing.
"""
def jaccard_similariy(set_a, set_b, alternative_union=False):
"""
Finds the jaccard similarity between two sets.
Essentially, its intersection over union.
The alternative way to calculate this is to take union as sum of the
number of items in the two sets. This will lead to jaccard similarity
of a set with itself be 1/2 instead of 1. [MMDS 2nd Edition, Page 77]
Parameters:
:set_a (set,list,tuple): A non-empty set/list
:set_b (set,list,tuple): A non-empty set/list
:alternativeUnion (boolean): If True, use sum of number of
items as union
Output:
(float) The jaccard similarity between the two sets.
Examples:
>>> set_a = {'a', 'b', 'c', 'd', 'e'}
>>> set_b = {'c', 'd', 'e', 'f', 'h', 'i'}
>>> jaccard_similariy(set_a, set_b)
0.375
>>> jaccard_similariy(set_a, set_a)
1.0
>>> jaccard_similariy(set_a, set_a, True)
0.5
>>> set_a = ['a', 'b', 'c', 'd', 'e']
>>> set_b = ('c', 'd', 'e', 'f', 'h', 'i')
>>> jaccard_similariy(set_a, set_b)
0.375
"""
if isinstance(set_a, set) and isinstance(set_b, set):
intersection = len(set_a.intersection(set_b))
if alternative_union:
union = len(set_a) + len(set_b)
else:
union = len(set_a.union(set_b))
return intersection / union
if isinstance(set_a, (list, tuple)) and isinstance(set_b, (list, tuple)):
intersection = [element for element in set_a if element in set_b]
if alternative_union:
union = len(set_a) + len(set_b)
else:
union = set_a + [element for element in set_b if element not in set_a]
return len(intersection) / len(union)
if __name__ == "__main__":
set_a = {"a", "b", "c", "d", "e"}
set_b = {"c", "d", "e", "f", "h", "i"}
print(jaccard_similariy(set_a, set_b))
| """
The Jaccard similarity coefficient is a commonly used indicator of the
similarity between two sets. Let U be a set and A and B be subsets of U,
then the Jaccard index/similarity is defined to be the ratio of the number
of elements of their intersection and the number of elements of their union.
Inspired from Wikipedia and
the book Mining of Massive Datasets [MMDS 2nd Edition, Chapter 3]
https://en.wikipedia.org/wiki/Jaccard_index
https://mmds.org
Jaccard similarity is widely used with MinHashing.
"""
def jaccard_similarity(set_a, set_b, alternative_union=False):
"""
Finds the jaccard similarity between two sets.
Essentially, its intersection over union.
The alternative way to calculate this is to take union as sum of the
number of items in the two sets. This will lead to jaccard similarity
of a set with itself be 1/2 instead of 1. [MMDS 2nd Edition, Page 77]
Parameters:
:set_a (set,list,tuple): A non-empty set/list
:set_b (set,list,tuple): A non-empty set/list
:alternativeUnion (boolean): If True, use sum of number of
items as union
Output:
(float) The jaccard similarity between the two sets.
Examples:
>>> set_a = {'a', 'b', 'c', 'd', 'e'}
>>> set_b = {'c', 'd', 'e', 'f', 'h', 'i'}
>>> jaccard_similarity(set_a, set_b)
0.375
>>> jaccard_similarity(set_a, set_a)
1.0
>>> jaccard_similarity(set_a, set_a, True)
0.5
>>> set_a = ['a', 'b', 'c', 'd', 'e']
>>> set_b = ('c', 'd', 'e', 'f', 'h', 'i')
>>> jaccard_similarity(set_a, set_b)
0.375
"""
if isinstance(set_a, set) and isinstance(set_b, set):
intersection = len(set_a.intersection(set_b))
if alternative_union:
union = len(set_a) + len(set_b)
else:
union = len(set_a.union(set_b))
return intersection / union
if isinstance(set_a, (list, tuple)) and isinstance(set_b, (list, tuple)):
intersection = [element for element in set_a if element in set_b]
if alternative_union:
union = len(set_a) + len(set_b)
return len(intersection) / union
else:
union = set_a + [element for element in set_b if element not in set_a]
return len(intersection) / len(union)
return len(intersection) / len(union)
if __name__ == "__main__":
set_a = {"a", "b", "c", "d", "e"}
set_b = {"c", "d", "e", "f", "h", "i"}
print(jaccard_similarity(set_a, set_b))
| 1 |
TheAlgorithms/Python | 6,912 | Misc fixes across multiple algorithms | ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CenTdemeern1 | "2022-10-10T12:56:00Z" | "2022-10-16T05:25:38Z" | c94e215c8dbdfe1f349eab5708be6b5f337b6ddd | 04698538d816fc5f70c850e8b89c6d1f5599fa84 | Misc fixes across multiple algorithms. ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # An OOP approach to representing and manipulating matrices
from __future__ import annotations
class Matrix:
"""
Matrix object generated from a 2D array where each element is an array representing
a row.
Rows can contain type int or float.
Common operations and information available.
>>> rows = [
... [1, 2, 3],
... [4, 5, 6],
... [7, 8, 9]
... ]
>>> matrix = Matrix(rows)
>>> print(matrix)
[[1. 2. 3.]
[4. 5. 6.]
[7. 8. 9.]]
Matrix rows and columns are available as 2D arrays
>>> print(matrix.rows)
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
>>> print(matrix.columns())
[[1, 4, 7], [2, 5, 8], [3, 6, 9]]
Order is returned as a tuple
>>> matrix.order
(3, 3)
Squareness and invertability are represented as bool
>>> matrix.is_square
True
>>> matrix.is_invertable()
False
Identity, Minors, Cofactors and Adjugate are returned as Matrices. Inverse can be
a Matrix or Nonetype
>>> print(matrix.identity())
[[1. 0. 0.]
[0. 1. 0.]
[0. 0. 1.]]
>>> print(matrix.minors())
[[-3. -6. -3.]
[-6. -12. -6.]
[-3. -6. -3.]]
>>> print(matrix.cofactors())
[[-3. 6. -3.]
[6. -12. 6.]
[-3. 6. -3.]]
>>> # won't be apparent due to the nature of the cofactor matrix
>>> print(matrix.adjugate())
[[-3. 6. -3.]
[6. -12. 6.]
[-3. 6. -3.]]
>>> print(matrix.inverse())
Traceback (most recent call last):
...
TypeError: Only matrices with a non-zero determinant have an inverse
Determinant is an int, float, or Nonetype
>>> matrix.determinant()
0
Negation, scalar multiplication, addition, subtraction, multiplication and
exponentiation are available and all return a Matrix
>>> print(-matrix)
[[-1. -2. -3.]
[-4. -5. -6.]
[-7. -8. -9.]]
>>> matrix2 = matrix * 3
>>> print(matrix2)
[[3. 6. 9.]
[12. 15. 18.]
[21. 24. 27.]]
>>> print(matrix + matrix2)
[[4. 8. 12.]
[16. 20. 24.]
[28. 32. 36.]]
>>> print(matrix - matrix2)
[[-2. -4. -6.]
[-8. -10. -12.]
[-14. -16. -18.]]
>>> print(matrix ** 3)
[[468. 576. 684.]
[1062. 1305. 1548.]
[1656. 2034. 2412.]]
Matrices can also be modified
>>> matrix.add_row([10, 11, 12])
>>> print(matrix)
[[1. 2. 3.]
[4. 5. 6.]
[7. 8. 9.]
[10. 11. 12.]]
>>> matrix2.add_column([8, 16, 32])
>>> print(matrix2)
[[3. 6. 9. 8.]
[12. 15. 18. 16.]
[21. 24. 27. 32.]]
>>> print(matrix * matrix2)
[[90. 108. 126. 136.]
[198. 243. 288. 304.]
[306. 378. 450. 472.]
[414. 513. 612. 640.]]
"""
def __init__(self, rows: list[list[int]]):
error = TypeError(
"Matrices must be formed from a list of zero or more lists containing at "
"least one and the same number of values, each of which must be of type "
"int or float."
)
if len(rows) != 0:
cols = len(rows[0])
if cols == 0:
raise error
for row in rows:
if len(row) != cols:
raise error
for value in row:
if not isinstance(value, (int, float)):
raise error
self.rows = rows
else:
self.rows = []
# MATRIX INFORMATION
def columns(self) -> list[list[int]]:
return [[row[i] for row in self.rows] for i in range(len(self.rows[0]))]
@property
def num_rows(self) -> int:
return len(self.rows)
@property
def num_columns(self) -> int:
return len(self.rows[0])
@property
def order(self) -> tuple[int, int]:
return (self.num_rows, self.num_columns)
@property
def is_square(self) -> bool:
return self.order[0] == self.order[1]
def identity(self) -> Matrix:
values = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows)]
for row_num in range(self.num_rows)
]
return Matrix(values)
def determinant(self) -> int:
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0])
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0])
)
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns)
)
def is_invertable(self) -> bool:
return bool(self.determinant())
def get_minor(self, row: int, column: int) -> int:
values = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns)
if other_column != column
]
for other_row in range(self.num_rows)
if other_row != row
]
return Matrix(values).determinant()
def get_cofactor(self, row: int, column: int) -> int:
if (row + column) % 2 == 0:
return self.get_minor(row, column)
return -1 * self.get_minor(row, column)
def minors(self) -> Matrix:
return Matrix(
[
[self.get_minor(row, column) for column in range(self.num_columns)]
for row in range(self.num_rows)
]
)
def cofactors(self) -> Matrix:
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns)
]
for row in range(self.minors().num_rows)
]
)
def adjugate(self) -> Matrix:
values = [
[self.cofactors().rows[column][row] for column in range(self.num_columns)]
for row in range(self.num_rows)
]
return Matrix(values)
def inverse(self) -> Matrix:
determinant = self.determinant()
if not determinant:
raise TypeError("Only matrices with a non-zero determinant have an inverse")
return self.adjugate() * (1 / determinant)
def __repr__(self) -> str:
return str(self.rows)
def __str__(self) -> str:
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0])) + "]]"
return (
"["
+ "\n ".join(
[
"[" + ". ".join([str(value) for value in row]) + ".]"
for row in self.rows
]
)
+ "]"
)
# MATRIX MANIPULATION
def add_row(self, row: list[int], position: int | None = None) -> None:
type_error = TypeError("Row must be a list containing all ints and/or floats")
if not isinstance(row, list):
raise type_error
for value in row:
if not isinstance(value, (int, float)):
raise type_error
if len(row) != self.num_columns:
raise ValueError(
"Row must be equal in length to the other rows in the matrix"
)
if position is None:
self.rows.append(row)
else:
self.rows = self.rows[0:position] + [row] + self.rows[position:]
def add_column(self, column: list[int], position: int | None = None) -> None:
type_error = TypeError(
"Column must be a list containing all ints and/or floats"
)
if not isinstance(column, list):
raise type_error
for value in column:
if not isinstance(value, (int, float)):
raise type_error
if len(column) != self.num_rows:
raise ValueError(
"Column must be equal in length to the other columns in the matrix"
)
if position is None:
self.rows = [self.rows[i] + [column[i]] for i in range(self.num_rows)]
else:
self.rows = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows)
]
# MATRIX OPERATIONS
def __eq__(self, other: object) -> bool:
if not isinstance(other, Matrix):
raise TypeError("A Matrix can only be compared with another Matrix")
return self.rows == other.rows
def __ne__(self, other: object) -> bool:
return not self == other
def __neg__(self) -> Matrix:
return self * -1
def __add__(self, other: Matrix) -> Matrix:
if self.order != other.order:
raise ValueError("Addition requires matrices of the same order")
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns)]
for i in range(self.num_rows)
]
)
def __sub__(self, other: Matrix) -> Matrix:
if self.order != other.order:
raise ValueError("Subtraction requires matrices of the same order")
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns)]
for i in range(self.num_rows)
]
)
def __mul__(self, other: Matrix | int | float) -> Matrix:
if isinstance(other, (int, float)):
return Matrix(
[[int(element * other) for element in row] for row in self.rows]
)
elif isinstance(other, Matrix):
if self.num_columns != other.num_rows:
raise ValueError(
"The number of columns in the first matrix must "
"be equal to the number of rows in the second"
)
return Matrix(
[
[Matrix.dot_product(row, column) for column in other.columns()]
for row in self.rows
]
)
else:
raise TypeError(
"A Matrix can only be multiplied by an int, float, or another matrix"
)
def __pow__(self, other: int) -> Matrix:
if not isinstance(other, int):
raise TypeError("A Matrix can only be raised to the power of an int")
if not self.is_square:
raise ValueError("Only square matrices can be raised to a power")
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable:
return self.inverse() ** (-other)
raise ValueError(
"Only invertable matrices can be raised to a negative power"
)
result = self
for _ in range(other - 1):
result *= self
return result
@classmethod
def dot_product(cls, row: list[int], column: list[int]) -> int:
return sum(row[i] * column[i] for i in range(len(row)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| # An OOP approach to representing and manipulating matrices
from __future__ import annotations
class Matrix:
"""
Matrix object generated from a 2D array where each element is an array representing
a row.
Rows can contain type int or float.
Common operations and information available.
>>> rows = [
... [1, 2, 3],
... [4, 5, 6],
... [7, 8, 9]
... ]
>>> matrix = Matrix(rows)
>>> print(matrix)
[[1. 2. 3.]
[4. 5. 6.]
[7. 8. 9.]]
Matrix rows and columns are available as 2D arrays
>>> print(matrix.rows)
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
>>> print(matrix.columns())
[[1, 4, 7], [2, 5, 8], [3, 6, 9]]
Order is returned as a tuple
>>> matrix.order
(3, 3)
Squareness and invertability are represented as bool
>>> matrix.is_square
True
>>> matrix.is_invertable()
False
Identity, Minors, Cofactors and Adjugate are returned as Matrices. Inverse can be
a Matrix or Nonetype
>>> print(matrix.identity())
[[1. 0. 0.]
[0. 1. 0.]
[0. 0. 1.]]
>>> print(matrix.minors())
[[-3. -6. -3.]
[-6. -12. -6.]
[-3. -6. -3.]]
>>> print(matrix.cofactors())
[[-3. 6. -3.]
[6. -12. 6.]
[-3. 6. -3.]]
>>> # won't be apparent due to the nature of the cofactor matrix
>>> print(matrix.adjugate())
[[-3. 6. -3.]
[6. -12. 6.]
[-3. 6. -3.]]
>>> print(matrix.inverse())
Traceback (most recent call last):
...
TypeError: Only matrices with a non-zero determinant have an inverse
Determinant is an int, float, or Nonetype
>>> matrix.determinant()
0
Negation, scalar multiplication, addition, subtraction, multiplication and
exponentiation are available and all return a Matrix
>>> print(-matrix)
[[-1. -2. -3.]
[-4. -5. -6.]
[-7. -8. -9.]]
>>> matrix2 = matrix * 3
>>> print(matrix2)
[[3. 6. 9.]
[12. 15. 18.]
[21. 24. 27.]]
>>> print(matrix + matrix2)
[[4. 8. 12.]
[16. 20. 24.]
[28. 32. 36.]]
>>> print(matrix - matrix2)
[[-2. -4. -6.]
[-8. -10. -12.]
[-14. -16. -18.]]
>>> print(matrix ** 3)
[[468. 576. 684.]
[1062. 1305. 1548.]
[1656. 2034. 2412.]]
Matrices can also be modified
>>> matrix.add_row([10, 11, 12])
>>> print(matrix)
[[1. 2. 3.]
[4. 5. 6.]
[7. 8. 9.]
[10. 11. 12.]]
>>> matrix2.add_column([8, 16, 32])
>>> print(matrix2)
[[3. 6. 9. 8.]
[12. 15. 18. 16.]
[21. 24. 27. 32.]]
>>> print(matrix * matrix2)
[[90. 108. 126. 136.]
[198. 243. 288. 304.]
[306. 378. 450. 472.]
[414. 513. 612. 640.]]
"""
def __init__(self, rows: list[list[int]]):
error = TypeError(
"Matrices must be formed from a list of zero or more lists containing at "
"least one and the same number of values, each of which must be of type "
"int or float."
)
if len(rows) != 0:
cols = len(rows[0])
if cols == 0:
raise error
for row in rows:
if len(row) != cols:
raise error
for value in row:
if not isinstance(value, (int, float)):
raise error
self.rows = rows
else:
self.rows = []
# MATRIX INFORMATION
def columns(self) -> list[list[int]]:
return [[row[i] for row in self.rows] for i in range(len(self.rows[0]))]
@property
def num_rows(self) -> int:
return len(self.rows)
@property
def num_columns(self) -> int:
return len(self.rows[0])
@property
def order(self) -> tuple[int, int]:
return (self.num_rows, self.num_columns)
@property
def is_square(self) -> bool:
return self.order[0] == self.order[1]
def identity(self) -> Matrix:
values = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows)]
for row_num in range(self.num_rows)
]
return Matrix(values)
def determinant(self) -> int:
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0])
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0])
)
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns)
)
def is_invertable(self) -> bool:
return bool(self.determinant())
def get_minor(self, row: int, column: int) -> int:
values = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns)
if other_column != column
]
for other_row in range(self.num_rows)
if other_row != row
]
return Matrix(values).determinant()
def get_cofactor(self, row: int, column: int) -> int:
if (row + column) % 2 == 0:
return self.get_minor(row, column)
return -1 * self.get_minor(row, column)
def minors(self) -> Matrix:
return Matrix(
[
[self.get_minor(row, column) for column in range(self.num_columns)]
for row in range(self.num_rows)
]
)
def cofactors(self) -> Matrix:
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns)
]
for row in range(self.minors().num_rows)
]
)
def adjugate(self) -> Matrix:
values = [
[self.cofactors().rows[column][row] for column in range(self.num_columns)]
for row in range(self.num_rows)
]
return Matrix(values)
def inverse(self) -> Matrix:
determinant = self.determinant()
if not determinant:
raise TypeError("Only matrices with a non-zero determinant have an inverse")
return self.adjugate() * (1 / determinant)
def __repr__(self) -> str:
return str(self.rows)
def __str__(self) -> str:
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0])) + "]]"
return (
"["
+ "\n ".join(
[
"[" + ". ".join([str(value) for value in row]) + ".]"
for row in self.rows
]
)
+ "]"
)
# MATRIX MANIPULATION
def add_row(self, row: list[int], position: int | None = None) -> None:
type_error = TypeError("Row must be a list containing all ints and/or floats")
if not isinstance(row, list):
raise type_error
for value in row:
if not isinstance(value, (int, float)):
raise type_error
if len(row) != self.num_columns:
raise ValueError(
"Row must be equal in length to the other rows in the matrix"
)
if position is None:
self.rows.append(row)
else:
self.rows = self.rows[0:position] + [row] + self.rows[position:]
def add_column(self, column: list[int], position: int | None = None) -> None:
type_error = TypeError(
"Column must be a list containing all ints and/or floats"
)
if not isinstance(column, list):
raise type_error
for value in column:
if not isinstance(value, (int, float)):
raise type_error
if len(column) != self.num_rows:
raise ValueError(
"Column must be equal in length to the other columns in the matrix"
)
if position is None:
self.rows = [self.rows[i] + [column[i]] for i in range(self.num_rows)]
else:
self.rows = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows)
]
# MATRIX OPERATIONS
def __eq__(self, other: object) -> bool:
if not isinstance(other, Matrix):
return NotImplemented
return self.rows == other.rows
def __ne__(self, other: object) -> bool:
return not self == other
def __neg__(self) -> Matrix:
return self * -1
def __add__(self, other: Matrix) -> Matrix:
if self.order != other.order:
raise ValueError("Addition requires matrices of the same order")
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns)]
for i in range(self.num_rows)
]
)
def __sub__(self, other: Matrix) -> Matrix:
if self.order != other.order:
raise ValueError("Subtraction requires matrices of the same order")
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns)]
for i in range(self.num_rows)
]
)
def __mul__(self, other: Matrix | int | float) -> Matrix:
if isinstance(other, (int, float)):
return Matrix(
[[int(element * other) for element in row] for row in self.rows]
)
elif isinstance(other, Matrix):
if self.num_columns != other.num_rows:
raise ValueError(
"The number of columns in the first matrix must "
"be equal to the number of rows in the second"
)
return Matrix(
[
[Matrix.dot_product(row, column) for column in other.columns()]
for row in self.rows
]
)
else:
raise TypeError(
"A Matrix can only be multiplied by an int, float, or another matrix"
)
def __pow__(self, other: int) -> Matrix:
if not isinstance(other, int):
raise TypeError("A Matrix can only be raised to the power of an int")
if not self.is_square:
raise ValueError("Only square matrices can be raised to a power")
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable:
return self.inverse() ** (-other)
raise ValueError(
"Only invertable matrices can be raised to a negative power"
)
result = self
for _ in range(other - 1):
result *= self
return result
@classmethod
def dot_product(cls, row: list[int], column: list[int]) -> int:
return sum(row[i] * column[i] for i in range(len(row)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 6,912 | Misc fixes across multiple algorithms | ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CenTdemeern1 | "2022-10-10T12:56:00Z" | "2022-10-16T05:25:38Z" | c94e215c8dbdfe1f349eab5708be6b5f337b6ddd | 04698538d816fc5f70c850e8b89c6d1f5599fa84 | Misc fixes across multiple algorithms. ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 1: https://projecteuler.net/problem=1
Multiples of 3 and 5
If we list all the natural numbers below 10 that are multiples of 3 or 5,
we get 3, 5, 6 and 9. The sum of these multiples is 23.
Find the sum of all the multiples of 3 or 5 below 1000.
"""
def solution(n: int = 1000) -> int:
"""
Returns the sum of all the multiples of 3 or 5 below n.
>>> solution(3)
0
>>> solution(4)
3
>>> solution(10)
23
>>> solution(600)
83700
"""
result = 0
for i in range(n):
if i % 3 == 0:
result += i
elif i % 5 == 0:
result += i
return result
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 1: https://projecteuler.net/problem=1
Multiples of 3 and 5
If we list all the natural numbers below 10 that are multiples of 3 or 5,
we get 3, 5, 6 and 9. The sum of these multiples is 23.
Find the sum of all the multiples of 3 or 5 below 1000.
"""
def solution(n: int = 1000) -> int:
"""
Returns the sum of all the multiples of 3 or 5 below n.
>>> solution(3)
0
>>> solution(4)
3
>>> solution(10)
23
>>> solution(600)
83700
"""
result = 0
for i in range(n):
if i % 3 == 0 or i % 5 == 0:
result += i
return result
if __name__ == "__main__":
print(f"{solution() = }")
| 1 |
TheAlgorithms/Python | 6,912 | Misc fixes across multiple algorithms | ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CenTdemeern1 | "2022-10-10T12:56:00Z" | "2022-10-16T05:25:38Z" | c94e215c8dbdfe1f349eab5708be6b5f337b6ddd | 04698538d816fc5f70c850e8b89c6d1f5599fa84 | Misc fixes across multiple algorithms. ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
The nth term of the sequence of triangle numbers is given by, tn = ½n(n+1); so
the first ten triangle numbers are:
1, 3, 6, 10, 15, 21, 28, 36, 45, 55, ...
By converting each letter in a word to a number corresponding to its
alphabetical position and adding these values we form a word value. For example,
the word value for SKY is 19 + 11 + 25 = 55 = t10. If the word value is a
triangle number then we shall call the word a triangle word.
Using words.txt (right click and 'Save Link/Target As...'), a 16K text file
containing nearly two-thousand common English words, how many are triangle
words?
"""
import os
# Precomputes a list of the 100 first triangular numbers
TRIANGULAR_NUMBERS = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def solution():
"""
Finds the amount of triangular words in the words file.
>>> solution()
162
"""
script_dir = os.path.dirname(os.path.realpath(__file__))
words_file_path = os.path.join(script_dir, "words.txt")
words = ""
with open(words_file_path) as f:
words = f.readline()
words = [word.strip('"') for word in words.strip("\r\n").split(",")]
words = list(
filter(
lambda word: word in TRIANGULAR_NUMBERS,
(sum(ord(x) - 64 for x in word) for word in words),
)
)
return len(words)
if __name__ == "__main__":
print(solution())
| """
The nth term of the sequence of triangle numbers is given by, tn = ½n(n+1); so
the first ten triangle numbers are:
1, 3, 6, 10, 15, 21, 28, 36, 45, 55, ...
By converting each letter in a word to a number corresponding to its
alphabetical position and adding these values we form a word value. For example,
the word value for SKY is 19 + 11 + 25 = 55 = t10. If the word value is a
triangle number then we shall call the word a triangle word.
Using words.txt (right click and 'Save Link/Target As...'), a 16K text file
containing nearly two-thousand common English words, how many are triangle
words?
"""
import os
# Precomputes a list of the 100 first triangular numbers
TRIANGULAR_NUMBERS = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def solution():
"""
Finds the amount of triangular words in the words file.
>>> solution()
162
"""
script_dir = os.path.dirname(os.path.realpath(__file__))
words_file_path = os.path.join(script_dir, "words.txt")
words = ""
with open(words_file_path) as f:
words = f.readline()
words = [word.strip('"') for word in words.strip("\r\n").split(",")]
words = [
word
for word in [sum(ord(x) - 64 for x in word) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(words)
if __name__ == "__main__":
print(solution())
| 1 |
TheAlgorithms/Python | 6,912 | Misc fixes across multiple algorithms | ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CenTdemeern1 | "2022-10-10T12:56:00Z" | "2022-10-16T05:25:38Z" | c94e215c8dbdfe1f349eab5708be6b5f337b6ddd | 04698538d816fc5f70c850e8b89c6d1f5599fa84 | Misc fixes across multiple algorithms. ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem Statement:
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 in triangle.txt (right click and
'Save Link/Target As...'), a 15K text file containing a triangle with
one-hundred rows.
"""
import os
def solution():
"""
Finds the maximum total in a triangle as described by the problem statement
above.
>>> solution()
7273
"""
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 = (x.rstrip("\r\n").split(" ") for x in triangle)
a = [list(map(int, x)) for x in a]
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())
| """
Problem Statement:
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 in triangle.txt (right click and
'Save Link/Target As...'), a 15K text file containing a triangle with
one-hundred rows.
"""
import os
def solution():
"""
Finds the maximum total in a triangle as described by the problem statement
above.
>>> solution()
7273
"""
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 = []
for line in triangle:
numbers_from_line = []
for number in line.strip().split(" "):
numbers_from_line.append(int(number))
a.append(numbers_from_line)
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 | 6,912 | Misc fixes across multiple algorithms | ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CenTdemeern1 | "2022-10-10T12:56:00Z" | "2022-10-16T05:25:38Z" | c94e215c8dbdfe1f349eab5708be6b5f337b6ddd | 04698538d816fc5f70c850e8b89c6d1f5599fa84 | Misc fixes across multiple algorithms. ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 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)
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
with open(os.path.dirname(__file__) + roman_numerals_filename) as file1:
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 | 6,912 | Misc fixes across multiple algorithms | ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CenTdemeern1 | "2022-10-10T12:56:00Z" | "2022-10-16T05:25:38Z" | c94e215c8dbdfe1f349eab5708be6b5f337b6ddd | 04698538d816fc5f70c850e8b89c6d1f5599fa84 | Misc fixes across multiple algorithms. ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| beautifulsoup4
fake_useragent
keras
lxml
matplotlib
numpy
opencv-python
pandas
pillow
qiskit
requests
# scikit-fuzzy # Causing broken builds
sklearn
statsmodels
sympy
tensorflow
texttable
tweepy
xgboost
yulewalker
| beautifulsoup4
fake_useragent
keras
lxml
matplotlib
numpy
opencv-python
pandas
pillow
qiskit
requests
scikit-fuzzy
sklearn
statsmodels
sympy
tensorflow
texttable
tweepy
xgboost
yulewalker
| 1 |
TheAlgorithms/Python | 6,912 | Misc fixes across multiple algorithms | ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CenTdemeern1 | "2022-10-10T12:56:00Z" | "2022-10-16T05:25:38Z" | c94e215c8dbdfe1f349eab5708be6b5f337b6ddd | 04698538d816fc5f70c850e8b89c6d1f5599fa84 | Misc fixes across multiple algorithms. ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Implementation of First Come First Served scheduling algorithm
# In this Algorithm we just care about the order that the processes arrived
# without carring about their duration time
# https://en.wikipedia.org/wiki/Scheduling_(computing)#First_come,_first_served
from __future__ import annotations
def calculate_waiting_times(duration_times: list[int]) -> list[int]:
"""
This function calculates the waiting time of some processes that have a
specified duration time.
Return: The waiting time for each process.
>>> calculate_waiting_times([5, 10, 15])
[0, 5, 15]
>>> calculate_waiting_times([1, 2, 3, 4, 5])
[0, 1, 3, 6, 10]
>>> calculate_waiting_times([10, 3])
[0, 10]
"""
waiting_times = [0] * len(duration_times)
for i in range(1, len(duration_times)):
waiting_times[i] = duration_times[i - 1] + waiting_times[i - 1]
return waiting_times
def calculate_turnaround_times(
duration_times: list[int], waiting_times: list[int]
) -> list[int]:
"""
This function calculates the turnaround time of some processes.
Return: The time difference between the completion time and the
arrival time.
Practically waiting_time + duration_time
>>> calculate_turnaround_times([5, 10, 15], [0, 5, 15])
[5, 15, 30]
>>> calculate_turnaround_times([1, 2, 3, 4, 5], [0, 1, 3, 6, 10])
[1, 3, 6, 10, 15]
>>> calculate_turnaround_times([10, 3], [0, 10])
[10, 13]
"""
return [
duration_time + waiting_times[i]
for i, duration_time in enumerate(duration_times)
]
def calculate_average_turnaround_time(turnaround_times: list[int]) -> float:
"""
This function calculates the average of the turnaround times
Return: The average of the turnaround times.
>>> calculate_average_turnaround_time([0, 5, 16])
7.0
>>> calculate_average_turnaround_time([1, 5, 8, 12])
6.5
>>> calculate_average_turnaround_time([10, 24])
17.0
"""
return sum(turnaround_times) / len(turnaround_times)
def calculate_average_waiting_time(waiting_times: list[int]) -> float:
"""
This function calculates the average of the waiting times
Return: The average of the waiting times.
>>> calculate_average_waiting_time([0, 5, 16])
7.0
>>> calculate_average_waiting_time([1, 5, 8, 12])
6.5
>>> calculate_average_waiting_time([10, 24])
17.0
"""
return sum(waiting_times) / len(waiting_times)
if __name__ == "__main__":
# process id's
processes = [1, 2, 3]
# ensure that we actually have processes
if len(processes) == 0:
print("Zero amount of processes")
exit()
# duration time of all processes
duration_times = [19, 8, 9]
# ensure we can match each id to a duration time
if len(duration_times) != len(processes):
print("Unable to match all id's with their duration time")
exit()
# get the waiting times and the turnaround times
waiting_times = calculate_waiting_times(duration_times)
turnaround_times = calculate_turnaround_times(duration_times, waiting_times)
# get the average times
average_waiting_time = calculate_average_waiting_time(waiting_times)
average_turnaround_time = calculate_average_turnaround_time(turnaround_times)
# print all the results
print("Process ID\tDuration Time\tWaiting Time\tTurnaround Time")
for i, process in enumerate(processes):
print(
f"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
f"{turnaround_times[i]}"
)
print(f"Average waiting time = {average_waiting_time}")
print(f"Average turn around time = {average_turnaround_time}")
| # Implementation of First Come First Served scheduling algorithm
# In this Algorithm we just care about the order that the processes arrived
# without carring about their duration time
# https://en.wikipedia.org/wiki/Scheduling_(computing)#First_come,_first_served
from __future__ import annotations
def calculate_waiting_times(duration_times: list[int]) -> list[int]:
"""
This function calculates the waiting time of some processes that have a
specified duration time.
Return: The waiting time for each process.
>>> calculate_waiting_times([5, 10, 15])
[0, 5, 15]
>>> calculate_waiting_times([1, 2, 3, 4, 5])
[0, 1, 3, 6, 10]
>>> calculate_waiting_times([10, 3])
[0, 10]
"""
waiting_times = [0] * len(duration_times)
for i in range(1, len(duration_times)):
waiting_times[i] = duration_times[i - 1] + waiting_times[i - 1]
return waiting_times
def calculate_turnaround_times(
duration_times: list[int], waiting_times: list[int]
) -> list[int]:
"""
This function calculates the turnaround time of some processes.
Return: The time difference between the completion time and the
arrival time.
Practically waiting_time + duration_time
>>> calculate_turnaround_times([5, 10, 15], [0, 5, 15])
[5, 15, 30]
>>> calculate_turnaround_times([1, 2, 3, 4, 5], [0, 1, 3, 6, 10])
[1, 3, 6, 10, 15]
>>> calculate_turnaround_times([10, 3], [0, 10])
[10, 13]
"""
return [
duration_time + waiting_times[i]
for i, duration_time in enumerate(duration_times)
]
def calculate_average_turnaround_time(turnaround_times: list[int]) -> float:
"""
This function calculates the average of the turnaround times
Return: The average of the turnaround times.
>>> calculate_average_turnaround_time([0, 5, 16])
7.0
>>> calculate_average_turnaround_time([1, 5, 8, 12])
6.5
>>> calculate_average_turnaround_time([10, 24])
17.0
"""
return sum(turnaround_times) / len(turnaround_times)
def calculate_average_waiting_time(waiting_times: list[int]) -> float:
"""
This function calculates the average of the waiting times
Return: The average of the waiting times.
>>> calculate_average_waiting_time([0, 5, 16])
7.0
>>> calculate_average_waiting_time([1, 5, 8, 12])
6.5
>>> calculate_average_waiting_time([10, 24])
17.0
"""
return sum(waiting_times) / len(waiting_times)
if __name__ == "__main__":
# process id's
processes = [1, 2, 3]
# ensure that we actually have processes
if len(processes) == 0:
print("Zero amount of processes")
raise SystemExit(0)
# duration time of all processes
duration_times = [19, 8, 9]
# ensure we can match each id to a duration time
if len(duration_times) != len(processes):
print("Unable to match all id's with their duration time")
raise SystemExit(0)
# get the waiting times and the turnaround times
waiting_times = calculate_waiting_times(duration_times)
turnaround_times = calculate_turnaround_times(duration_times, waiting_times)
# get the average times
average_waiting_time = calculate_average_waiting_time(waiting_times)
average_turnaround_time = calculate_average_turnaround_time(turnaround_times)
# print all the results
print("Process ID\tDuration Time\tWaiting Time\tTurnaround Time")
for i, process in enumerate(processes):
print(
f"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
f"{turnaround_times[i]}"
)
print(f"Average waiting time = {average_waiting_time}")
print(f"Average turn around time = {average_turnaround_time}")
| 1 |
TheAlgorithms/Python | 6,912 | Misc fixes across multiple algorithms | ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CenTdemeern1 | "2022-10-10T12:56:00Z" | "2022-10-16T05:25:38Z" | c94e215c8dbdfe1f349eab5708be6b5f337b6ddd | 04698538d816fc5f70c850e8b89c6d1f5599fa84 | Misc fixes across multiple algorithms. ### Describe your change:
This change fixes a typo where `__name__ == "__main__"` was accidentally written as `__name__ == "main"`.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from collections import deque
class Process:
def __init__(self, process_name: str, arrival_time: int, burst_time: int) -> None:
self.process_name = process_name # process name
self.arrival_time = arrival_time # arrival time of the process
# completion time of finished process or last interrupted time
self.stop_time = arrival_time
self.burst_time = burst_time # remaining burst time
self.waiting_time = 0 # total time of the process wait in ready queue
self.turnaround_time = 0 # time from arrival time to completion time
class MLFQ:
"""
MLFQ(Multi Level Feedback Queue)
https://en.wikipedia.org/wiki/Multilevel_feedback_queue
MLFQ has a lot of queues that have different priority
In this MLFQ,
The first Queue(0) to last second Queue(N-2) of MLFQ have Round Robin Algorithm
The last Queue(N-1) has First Come, First Served Algorithm
"""
def __init__(
self,
number_of_queues: int,
time_slices: list[int],
queue: deque[Process],
current_time: int,
) -> None:
# total number of mlfq's queues
self.number_of_queues = number_of_queues
# time slice of queues that round robin algorithm applied
self.time_slices = time_slices
# unfinished process is in this ready_queue
self.ready_queue = queue
# current time
self.current_time = current_time
# finished process is in this sequence queue
self.finish_queue: deque[Process] = deque()
def calculate_sequence_of_finish_queue(self) -> list[str]:
"""
This method returns the sequence of finished processes
>>> P1 = Process("P1", 0, 53)
>>> P2 = Process("P2", 0, 17)
>>> P3 = Process("P3", 0, 68)
>>> P4 = Process("P4", 0, 24)
>>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
>>> _ = mlfq.multi_level_feedback_queue()
>>> mlfq.calculate_sequence_of_finish_queue()
['P2', 'P4', 'P1', 'P3']
"""
sequence = []
for i in range(len(self.finish_queue)):
sequence.append(self.finish_queue[i].process_name)
return sequence
def calculate_waiting_time(self, queue: list[Process]) -> list[int]:
"""
This method calculates waiting time of processes
>>> P1 = Process("P1", 0, 53)
>>> P2 = Process("P2", 0, 17)
>>> P3 = Process("P3", 0, 68)
>>> P4 = Process("P4", 0, 24)
>>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
>>> _ = mlfq.multi_level_feedback_queue()
>>> mlfq.calculate_waiting_time([P1, P2, P3, P4])
[83, 17, 94, 101]
"""
waiting_times = []
for i in range(len(queue)):
waiting_times.append(queue[i].waiting_time)
return waiting_times
def calculate_turnaround_time(self, queue: list[Process]) -> list[int]:
"""
This method calculates turnaround time of processes
>>> P1 = Process("P1", 0, 53)
>>> P2 = Process("P2", 0, 17)
>>> P3 = Process("P3", 0, 68)
>>> P4 = Process("P4", 0, 24)
>>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
>>> _ = mlfq.multi_level_feedback_queue()
>>> mlfq.calculate_turnaround_time([P1, P2, P3, P4])
[136, 34, 162, 125]
"""
turnaround_times = []
for i in range(len(queue)):
turnaround_times.append(queue[i].turnaround_time)
return turnaround_times
def calculate_completion_time(self, queue: list[Process]) -> list[int]:
"""
This method calculates completion time of processes
>>> P1 = Process("P1", 0, 53)
>>> P2 = Process("P2", 0, 17)
>>> P3 = Process("P3", 0, 68)
>>> P4 = Process("P4", 0, 24)
>>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
>>> _ = mlfq.multi_level_feedback_queue()
>>> mlfq.calculate_turnaround_time([P1, P2, P3, P4])
[136, 34, 162, 125]
"""
completion_times = []
for i in range(len(queue)):
completion_times.append(queue[i].stop_time)
return completion_times
def calculate_remaining_burst_time_of_processes(
self, queue: deque[Process]
) -> list[int]:
"""
This method calculate remaining burst time of processes
>>> P1 = Process("P1", 0, 53)
>>> P2 = Process("P2", 0, 17)
>>> P3 = Process("P3", 0, 68)
>>> P4 = Process("P4", 0, 24)
>>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
>>> finish_queue, ready_queue = mlfq.round_robin(deque([P1, P2, P3, P4]), 17)
>>> mlfq.calculate_remaining_burst_time_of_processes(mlfq.finish_queue)
[0]
>>> mlfq.calculate_remaining_burst_time_of_processes(ready_queue)
[36, 51, 7]
>>> finish_queue, ready_queue = mlfq.round_robin(ready_queue, 25)
>>> mlfq.calculate_remaining_burst_time_of_processes(mlfq.finish_queue)
[0, 0]
>>> mlfq.calculate_remaining_burst_time_of_processes(ready_queue)
[11, 26]
"""
return [q.burst_time for q in queue]
def update_waiting_time(self, process: Process) -> int:
"""
This method updates waiting times of unfinished processes
>>> P1 = Process("P1", 0, 53)
>>> P2 = Process("P2", 0, 17)
>>> P3 = Process("P3", 0, 68)
>>> P4 = Process("P4", 0, 24)
>>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
>>> mlfq.current_time = 10
>>> P1.stop_time = 5
>>> mlfq.update_waiting_time(P1)
5
"""
process.waiting_time += self.current_time - process.stop_time
return process.waiting_time
def first_come_first_served(self, ready_queue: deque[Process]) -> deque[Process]:
"""
FCFS(First Come, First Served)
FCFS will be applied to MLFQ's last queue
A first came process will be finished at first
>>> P1 = Process("P1", 0, 53)
>>> P2 = Process("P2", 0, 17)
>>> P3 = Process("P3", 0, 68)
>>> P4 = Process("P4", 0, 24)
>>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
>>> _ = mlfq.first_come_first_served(mlfq.ready_queue)
>>> mlfq.calculate_sequence_of_finish_queue()
['P1', 'P2', 'P3', 'P4']
"""
finished: deque[Process] = deque() # sequence deque of finished process
while len(ready_queue) != 0:
cp = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of current process
self.update_waiting_time(cp)
# update current time
self.current_time += cp.burst_time
# finish the process and set the process's burst-time 0
cp.burst_time = 0
# set the process's turnaround time because it is finished
cp.turnaround_time = self.current_time - cp.arrival_time
# set the completion time
cp.stop_time = self.current_time
# add the process to queue that has finished queue
finished.append(cp)
self.finish_queue.extend(finished) # add finished process to finish queue
# FCFS will finish all remaining processes
return finished
def round_robin(
self, ready_queue: deque[Process], time_slice: int
) -> tuple[deque[Process], deque[Process]]:
"""
RR(Round Robin)
RR will be applied to MLFQ's all queues except last queue
All processes can't use CPU for time more than time_slice
If the process consume CPU up to time_slice, it will go back to ready queue
>>> P1 = Process("P1", 0, 53)
>>> P2 = Process("P2", 0, 17)
>>> P3 = Process("P3", 0, 68)
>>> P4 = Process("P4", 0, 24)
>>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
>>> finish_queue, ready_queue = mlfq.round_robin(mlfq.ready_queue, 17)
>>> mlfq.calculate_sequence_of_finish_queue()
['P2']
"""
finished: deque[Process] = deque() # sequence deque of terminated process
# just for 1 cycle and unfinished processes will go back to queue
for _ in range(len(ready_queue)):
cp = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of unfinished processes
self.update_waiting_time(cp)
# if the burst time of process is bigger than time-slice
if cp.burst_time > time_slice:
# use CPU for only time-slice
self.current_time += time_slice
# update remaining burst time
cp.burst_time -= time_slice
# update end point time
cp.stop_time = self.current_time
# locate the process behind the queue because it is not finished
ready_queue.append(cp)
else:
# use CPU for remaining burst time
self.current_time += cp.burst_time
# set burst time 0 because the process is finished
cp.burst_time = 0
# set the finish time
cp.stop_time = self.current_time
# update the process' turnaround time because it is finished
cp.turnaround_time = self.current_time - cp.arrival_time
# add the process to queue that has finished queue
finished.append(cp)
self.finish_queue.extend(finished) # add finished process to finish queue
# return finished processes queue and remaining processes queue
return finished, ready_queue
def multi_level_feedback_queue(self) -> deque[Process]:
"""
MLFQ(Multi Level Feedback Queue)
>>> P1 = Process("P1", 0, 53)
>>> P2 = Process("P2", 0, 17)
>>> P3 = Process("P3", 0, 68)
>>> P4 = Process("P4", 0, 24)
>>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
>>> finish_queue = mlfq.multi_level_feedback_queue()
>>> mlfq.calculate_sequence_of_finish_queue()
['P2', 'P4', 'P1', 'P3']
"""
# all queues except last one have round_robin algorithm
for i in range(self.number_of_queues - 1):
finished, self.ready_queue = self.round_robin(
self.ready_queue, self.time_slices[i]
)
# the last queue has first_come_first_served algorithm
self.first_come_first_served(self.ready_queue)
return self.finish_queue
if __name__ == "__main__":
import doctest
P1 = Process("P1", 0, 53)
P2 = Process("P2", 0, 17)
P3 = Process("P3", 0, 68)
P4 = Process("P4", 0, 24)
number_of_queues = 3
time_slices = [17, 25]
queue = deque([P1, P2, P3, P4])
if len(time_slices) != number_of_queues - 1:
exit()
doctest.testmod(extraglobs={"queue": deque([P1, P2, P3, P4])})
P1 = Process("P1", 0, 53)
P2 = Process("P2", 0, 17)
P3 = Process("P3", 0, 68)
P4 = Process("P4", 0, 24)
number_of_queues = 3
time_slices = [17, 25]
queue = deque([P1, P2, P3, P4])
mlfq = MLFQ(number_of_queues, time_slices, queue, 0)
finish_queue = mlfq.multi_level_feedback_queue()
# print total waiting times of processes(P1, P2, P3, P4)
print(
f"waiting time:\
\t\t\t{MLFQ.calculate_waiting_time(mlfq, [P1, P2, P3, P4])}"
)
# print completion times of processes(P1, P2, P3, P4)
print(
f"completion time:\
\t\t{MLFQ.calculate_completion_time(mlfq, [P1, P2, P3, P4])}"
)
# print total turnaround times of processes(P1, P2, P3, P4)
print(
f"turnaround time:\
\t\t{MLFQ.calculate_turnaround_time(mlfq, [P1, P2, P3, P4])}"
)
# print sequence of finished processes
print(
f"sequence of finished processes:\
{mlfq.calculate_sequence_of_finish_queue()}"
)
| from collections import deque
class Process:
def __init__(self, process_name: str, arrival_time: int, burst_time: int) -> None:
self.process_name = process_name # process name
self.arrival_time = arrival_time # arrival time of the process
# completion time of finished process or last interrupted time
self.stop_time = arrival_time
self.burst_time = burst_time # remaining burst time
self.waiting_time = 0 # total time of the process wait in ready queue
self.turnaround_time = 0 # time from arrival time to completion time
class MLFQ:
"""
MLFQ(Multi Level Feedback Queue)
https://en.wikipedia.org/wiki/Multilevel_feedback_queue
MLFQ has a lot of queues that have different priority
In this MLFQ,
The first Queue(0) to last second Queue(N-2) of MLFQ have Round Robin Algorithm
The last Queue(N-1) has First Come, First Served Algorithm
"""
def __init__(
self,
number_of_queues: int,
time_slices: list[int],
queue: deque[Process],
current_time: int,
) -> None:
# total number of mlfq's queues
self.number_of_queues = number_of_queues
# time slice of queues that round robin algorithm applied
self.time_slices = time_slices
# unfinished process is in this ready_queue
self.ready_queue = queue
# current time
self.current_time = current_time
# finished process is in this sequence queue
self.finish_queue: deque[Process] = deque()
def calculate_sequence_of_finish_queue(self) -> list[str]:
"""
This method returns the sequence of finished processes
>>> P1 = Process("P1", 0, 53)
>>> P2 = Process("P2", 0, 17)
>>> P3 = Process("P3", 0, 68)
>>> P4 = Process("P4", 0, 24)
>>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
>>> _ = mlfq.multi_level_feedback_queue()
>>> mlfq.calculate_sequence_of_finish_queue()
['P2', 'P4', 'P1', 'P3']
"""
sequence = []
for i in range(len(self.finish_queue)):
sequence.append(self.finish_queue[i].process_name)
return sequence
def calculate_waiting_time(self, queue: list[Process]) -> list[int]:
"""
This method calculates waiting time of processes
>>> P1 = Process("P1", 0, 53)
>>> P2 = Process("P2", 0, 17)
>>> P3 = Process("P3", 0, 68)
>>> P4 = Process("P4", 0, 24)
>>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
>>> _ = mlfq.multi_level_feedback_queue()
>>> mlfq.calculate_waiting_time([P1, P2, P3, P4])
[83, 17, 94, 101]
"""
waiting_times = []
for i in range(len(queue)):
waiting_times.append(queue[i].waiting_time)
return waiting_times
def calculate_turnaround_time(self, queue: list[Process]) -> list[int]:
"""
This method calculates turnaround time of processes
>>> P1 = Process("P1", 0, 53)
>>> P2 = Process("P2", 0, 17)
>>> P3 = Process("P3", 0, 68)
>>> P4 = Process("P4", 0, 24)
>>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
>>> _ = mlfq.multi_level_feedback_queue()
>>> mlfq.calculate_turnaround_time([P1, P2, P3, P4])
[136, 34, 162, 125]
"""
turnaround_times = []
for i in range(len(queue)):
turnaround_times.append(queue[i].turnaround_time)
return turnaround_times
def calculate_completion_time(self, queue: list[Process]) -> list[int]:
"""
This method calculates completion time of processes
>>> P1 = Process("P1", 0, 53)
>>> P2 = Process("P2", 0, 17)
>>> P3 = Process("P3", 0, 68)
>>> P4 = Process("P4", 0, 24)
>>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
>>> _ = mlfq.multi_level_feedback_queue()
>>> mlfq.calculate_turnaround_time([P1, P2, P3, P4])
[136, 34, 162, 125]
"""
completion_times = []
for i in range(len(queue)):
completion_times.append(queue[i].stop_time)
return completion_times
def calculate_remaining_burst_time_of_processes(
self, queue: deque[Process]
) -> list[int]:
"""
This method calculate remaining burst time of processes
>>> P1 = Process("P1", 0, 53)
>>> P2 = Process("P2", 0, 17)
>>> P3 = Process("P3", 0, 68)
>>> P4 = Process("P4", 0, 24)
>>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
>>> finish_queue, ready_queue = mlfq.round_robin(deque([P1, P2, P3, P4]), 17)
>>> mlfq.calculate_remaining_burst_time_of_processes(mlfq.finish_queue)
[0]
>>> mlfq.calculate_remaining_burst_time_of_processes(ready_queue)
[36, 51, 7]
>>> finish_queue, ready_queue = mlfq.round_robin(ready_queue, 25)
>>> mlfq.calculate_remaining_burst_time_of_processes(mlfq.finish_queue)
[0, 0]
>>> mlfq.calculate_remaining_burst_time_of_processes(ready_queue)
[11, 26]
"""
return [q.burst_time for q in queue]
def update_waiting_time(self, process: Process) -> int:
"""
This method updates waiting times of unfinished processes
>>> P1 = Process("P1", 0, 53)
>>> P2 = Process("P2", 0, 17)
>>> P3 = Process("P3", 0, 68)
>>> P4 = Process("P4", 0, 24)
>>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
>>> mlfq.current_time = 10
>>> P1.stop_time = 5
>>> mlfq.update_waiting_time(P1)
5
"""
process.waiting_time += self.current_time - process.stop_time
return process.waiting_time
def first_come_first_served(self, ready_queue: deque[Process]) -> deque[Process]:
"""
FCFS(First Come, First Served)
FCFS will be applied to MLFQ's last queue
A first came process will be finished at first
>>> P1 = Process("P1", 0, 53)
>>> P2 = Process("P2", 0, 17)
>>> P3 = Process("P3", 0, 68)
>>> P4 = Process("P4", 0, 24)
>>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
>>> _ = mlfq.first_come_first_served(mlfq.ready_queue)
>>> mlfq.calculate_sequence_of_finish_queue()
['P1', 'P2', 'P3', 'P4']
"""
finished: deque[Process] = deque() # sequence deque of finished process
while len(ready_queue) != 0:
cp = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of current process
self.update_waiting_time(cp)
# update current time
self.current_time += cp.burst_time
# finish the process and set the process's burst-time 0
cp.burst_time = 0
# set the process's turnaround time because it is finished
cp.turnaround_time = self.current_time - cp.arrival_time
# set the completion time
cp.stop_time = self.current_time
# add the process to queue that has finished queue
finished.append(cp)
self.finish_queue.extend(finished) # add finished process to finish queue
# FCFS will finish all remaining processes
return finished
def round_robin(
self, ready_queue: deque[Process], time_slice: int
) -> tuple[deque[Process], deque[Process]]:
"""
RR(Round Robin)
RR will be applied to MLFQ's all queues except last queue
All processes can't use CPU for time more than time_slice
If the process consume CPU up to time_slice, it will go back to ready queue
>>> P1 = Process("P1", 0, 53)
>>> P2 = Process("P2", 0, 17)
>>> P3 = Process("P3", 0, 68)
>>> P4 = Process("P4", 0, 24)
>>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
>>> finish_queue, ready_queue = mlfq.round_robin(mlfq.ready_queue, 17)
>>> mlfq.calculate_sequence_of_finish_queue()
['P2']
"""
finished: deque[Process] = deque() # sequence deque of terminated process
# just for 1 cycle and unfinished processes will go back to queue
for _ in range(len(ready_queue)):
cp = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of unfinished processes
self.update_waiting_time(cp)
# if the burst time of process is bigger than time-slice
if cp.burst_time > time_slice:
# use CPU for only time-slice
self.current_time += time_slice
# update remaining burst time
cp.burst_time -= time_slice
# update end point time
cp.stop_time = self.current_time
# locate the process behind the queue because it is not finished
ready_queue.append(cp)
else:
# use CPU for remaining burst time
self.current_time += cp.burst_time
# set burst time 0 because the process is finished
cp.burst_time = 0
# set the finish time
cp.stop_time = self.current_time
# update the process' turnaround time because it is finished
cp.turnaround_time = self.current_time - cp.arrival_time
# add the process to queue that has finished queue
finished.append(cp)
self.finish_queue.extend(finished) # add finished process to finish queue
# return finished processes queue and remaining processes queue
return finished, ready_queue
def multi_level_feedback_queue(self) -> deque[Process]:
"""
MLFQ(Multi Level Feedback Queue)
>>> P1 = Process("P1", 0, 53)
>>> P2 = Process("P2", 0, 17)
>>> P3 = Process("P3", 0, 68)
>>> P4 = Process("P4", 0, 24)
>>> mlfq = MLFQ(3, [17, 25], deque([P1, P2, P3, P4]), 0)
>>> finish_queue = mlfq.multi_level_feedback_queue()
>>> mlfq.calculate_sequence_of_finish_queue()
['P2', 'P4', 'P1', 'P3']
"""
# all queues except last one have round_robin algorithm
for i in range(self.number_of_queues - 1):
finished, self.ready_queue = self.round_robin(
self.ready_queue, self.time_slices[i]
)
# the last queue has first_come_first_served algorithm
self.first_come_first_served(self.ready_queue)
return self.finish_queue
if __name__ == "__main__":
import doctest
P1 = Process("P1", 0, 53)
P2 = Process("P2", 0, 17)
P3 = Process("P3", 0, 68)
P4 = Process("P4", 0, 24)
number_of_queues = 3
time_slices = [17, 25]
queue = deque([P1, P2, P3, P4])
if len(time_slices) != number_of_queues - 1:
raise SystemExit(0)
doctest.testmod(extraglobs={"queue": deque([P1, P2, P3, P4])})
P1 = Process("P1", 0, 53)
P2 = Process("P2", 0, 17)
P3 = Process("P3", 0, 68)
P4 = Process("P4", 0, 24)
number_of_queues = 3
time_slices = [17, 25]
queue = deque([P1, P2, P3, P4])
mlfq = MLFQ(number_of_queues, time_slices, queue, 0)
finish_queue = mlfq.multi_level_feedback_queue()
# print total waiting times of processes(P1, P2, P3, P4)
print(
f"waiting time:\
\t\t\t{MLFQ.calculate_waiting_time(mlfq, [P1, P2, P3, P4])}"
)
# print completion times of processes(P1, P2, P3, P4)
print(
f"completion time:\
\t\t{MLFQ.calculate_completion_time(mlfq, [P1, P2, P3, P4])}"
)
# print total turnaround times of processes(P1, P2, P3, P4)
print(
f"turnaround time:\
\t\t{MLFQ.calculate_turnaround_time(mlfq, [P1, P2, P3, P4])}"
)
# print sequence of finished processes
print(
f"sequence of finished processes:\
{mlfq.calculate_sequence_of_finish_queue()}"
)
| 1 |
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