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TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/Breadth-first_search
pseudo-code:
breadth_first_search(graph G, start vertex s):
// all nodes initially unexplored
mark s as explored
let Q = queue data structure, initialized with s
while Q is non-empty:
remove the first node of Q, call it v
for each edge(v, w): // for w in graph[v]
if w unexplored:
mark w as explored
add w to Q (at the end)
"""
from __future__ import annotations
from collections import deque
from queue import Queue
from timeit import timeit
G = {
"A": ["B", "C"],
"B": ["A", "D", "E"],
"C": ["A", "F"],
"D": ["B"],
"E": ["B", "F"],
"F": ["C", "E"],
}
def breadth_first_search(graph: dict, start: str) -> list[str]:
"""
Implementation of breadth first search using queue.Queue.
>>> ''.join(breadth_first_search(G, 'A'))
'ABCDEF'
"""
explored = {start}
result = [start]
queue: Queue = Queue()
queue.put(start)
while not queue.empty():
v = queue.get()
for w in graph[v]:
if w not in explored:
explored.add(w)
result.append(w)
queue.put(w)
return result
def breadth_first_search_with_deque(graph: dict, start: str) -> list[str]:
"""
Implementation of breadth first search using collection.queue.
>>> ''.join(breadth_first_search_with_deque(G, 'A'))
'ABCDEF'
"""
visited = {start}
result = [start]
queue = deque([start])
while queue:
v = queue.popleft()
for child in graph[v]:
if child not in visited:
visited.add(child)
result.append(child)
queue.append(child)
return result
def benchmark_function(name: str) -> None:
setup = f"from __main__ import G, {name}"
number = 10000
res = timeit(f"{name}(G, 'A')", setup=setup, number=number)
print(f"{name:<35} finished {number} runs in {res:.5f} seconds")
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark_function("breadth_first_search")
benchmark_function("breadth_first_search_with_deque")
# breadth_first_search finished 10000 runs in 0.20999 seconds
# breadth_first_search_with_deque finished 10000 runs in 0.01421 seconds
| """
https://en.wikipedia.org/wiki/Breadth-first_search
pseudo-code:
breadth_first_search(graph G, start vertex s):
// all nodes initially unexplored
mark s as explored
let Q = queue data structure, initialized with s
while Q is non-empty:
remove the first node of Q, call it v
for each edge(v, w): // for w in graph[v]
if w unexplored:
mark w as explored
add w to Q (at the end)
"""
from __future__ import annotations
from collections import deque
from queue import Queue
from timeit import timeit
G = {
"A": ["B", "C"],
"B": ["A", "D", "E"],
"C": ["A", "F"],
"D": ["B"],
"E": ["B", "F"],
"F": ["C", "E"],
}
def breadth_first_search(graph: dict, start: str) -> list[str]:
"""
Implementation of breadth first search using queue.Queue.
>>> ''.join(breadth_first_search(G, 'A'))
'ABCDEF'
"""
explored = {start}
result = [start]
queue: Queue = Queue()
queue.put(start)
while not queue.empty():
v = queue.get()
for w in graph[v]:
if w not in explored:
explored.add(w)
result.append(w)
queue.put(w)
return result
def breadth_first_search_with_deque(graph: dict, start: str) -> list[str]:
"""
Implementation of breadth first search using collection.queue.
>>> ''.join(breadth_first_search_with_deque(G, 'A'))
'ABCDEF'
"""
visited = {start}
result = [start]
queue = deque([start])
while queue:
v = queue.popleft()
for child in graph[v]:
if child not in visited:
visited.add(child)
result.append(child)
queue.append(child)
return result
def benchmark_function(name: str) -> None:
setup = f"from __main__ import G, {name}"
number = 10000
res = timeit(f"{name}(G, 'A')", setup=setup, number=number)
print(f"{name:<35} finished {number} runs in {res:.5f} seconds")
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark_function("breadth_first_search")
benchmark_function("breadth_first_search_with_deque")
# breadth_first_search finished 10000 runs in 0.20999 seconds
# breadth_first_search_with_deque finished 10000 runs in 0.01421 seconds
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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 | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # This theorem states that the number of prime factors of n
# will be approximately log(log(n)) for most natural numbers n
import math
def exact_prime_factor_count(n):
"""
>>> exact_prime_factor_count(51242183)
3
"""
count = 0
if n % 2 == 0:
count += 1
while n % 2 == 0:
n = int(n / 2)
# the n input value must be odd so that
# we can skip one element (ie i += 2)
i = 3
while i <= int(math.sqrt(n)):
if n % i == 0:
count += 1
while n % i == 0:
n = int(n / i)
i = i + 2
# this condition checks the prime
# number n is greater than 2
if n > 2:
count += 1
return count
if __name__ == "__main__":
n = 51242183
print(f"The number of distinct prime factors is/are {exact_prime_factor_count(n)}")
print(f"The value of log(log(n)) is {math.log(math.log(n)):.4f}")
"""
The number of distinct prime factors is/are 3
The value of log(log(n)) is 2.8765
"""
| # This theorem states that the number of prime factors of n
# will be approximately log(log(n)) for most natural numbers n
import math
def exact_prime_factor_count(n):
"""
>>> exact_prime_factor_count(51242183)
3
"""
count = 0
if n % 2 == 0:
count += 1
while n % 2 == 0:
n = int(n / 2)
# the n input value must be odd so that
# we can skip one element (ie i += 2)
i = 3
while i <= int(math.sqrt(n)):
if n % i == 0:
count += 1
while n % i == 0:
n = int(n / i)
i = i + 2
# this condition checks the prime
# number n is greater than 2
if n > 2:
count += 1
return count
if __name__ == "__main__":
n = 51242183
print(f"The number of distinct prime factors is/are {exact_prime_factor_count(n)}")
print(f"The value of log(log(n)) is {math.log(math.log(n)):.4f}")
"""
The number of distinct prime factors is/are 3
The value of log(log(n)) is 2.8765
"""
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Gaussian elimination method for solving a system of linear equations.
Gaussian elimination - https://en.wikipedia.org/wiki/Gaussian_elimination
"""
import numpy as np
from numpy import float64
from numpy.typing import NDArray
def retroactive_resolution(
coefficients: NDArray[float64], vector: NDArray[float64]
) -> NDArray[float64]:
"""
This function performs a retroactive linear system resolution
for triangular matrix
Examples:
2x1 + 2x2 - 1x3 = 5 2x1 + 2x2 = -1
0x1 - 2x2 - 1x3 = -7 0x1 - 2x2 = -1
0x1 + 0x2 + 5x3 = 15
>>> gaussian_elimination([[2, 2, -1], [0, -2, -1], [0, 0, 5]], [[5], [-7], [15]])
array([[2.],
[2.],
[3.]])
>>> gaussian_elimination([[2, 2], [0, -2]], [[-1], [-1]])
array([[-1. ],
[ 0.5]])
"""
rows, columns = np.shape(coefficients)
x: NDArray[float64] = np.zeros((rows, 1), dtype=float)
for row in reversed(range(rows)):
total = 0
for col in range(row + 1, columns):
total += coefficients[row, col] * x[col]
x[row, 0] = (vector[row] - total) / coefficients[row, row]
return x
def gaussian_elimination(
coefficients: NDArray[float64], vector: NDArray[float64]
) -> NDArray[float64]:
"""
This function performs Gaussian elimination method
Examples:
1x1 - 4x2 - 2x3 = -2 1x1 + 2x2 = 5
5x1 + 2x2 - 2x3 = -3 5x1 + 2x2 = 5
1x1 - 1x2 + 0x3 = 4
>>> gaussian_elimination([[1, -4, -2], [5, 2, -2], [1, -1, 0]], [[-2], [-3], [4]])
array([[ 2.3 ],
[-1.7 ],
[ 5.55]])
>>> gaussian_elimination([[1, 2], [5, 2]], [[5], [5]])
array([[0. ],
[2.5]])
"""
# coefficients must to be a square matrix so we need to check first
rows, columns = np.shape(coefficients)
if rows != columns:
return np.array((), dtype=float)
# augmented matrix
augmented_mat: NDArray[float64] = np.concatenate((coefficients, vector), axis=1)
augmented_mat = augmented_mat.astype("float64")
# scale the matrix leaving it triangular
for row in range(rows - 1):
pivot = augmented_mat[row, row]
for col in range(row + 1, columns):
factor = augmented_mat[col, row] / pivot
augmented_mat[col, :] -= factor * augmented_mat[row, :]
x = retroactive_resolution(
augmented_mat[:, 0:columns], augmented_mat[:, columns : columns + 1]
)
return x
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Gaussian elimination method for solving a system of linear equations.
Gaussian elimination - https://en.wikipedia.org/wiki/Gaussian_elimination
"""
import numpy as np
from numpy import float64
from numpy.typing import NDArray
def retroactive_resolution(
coefficients: NDArray[float64], vector: NDArray[float64]
) -> NDArray[float64]:
"""
This function performs a retroactive linear system resolution
for triangular matrix
Examples:
2x1 + 2x2 - 1x3 = 5 2x1 + 2x2 = -1
0x1 - 2x2 - 1x3 = -7 0x1 - 2x2 = -1
0x1 + 0x2 + 5x3 = 15
>>> gaussian_elimination([[2, 2, -1], [0, -2, -1], [0, 0, 5]], [[5], [-7], [15]])
array([[2.],
[2.],
[3.]])
>>> gaussian_elimination([[2, 2], [0, -2]], [[-1], [-1]])
array([[-1. ],
[ 0.5]])
"""
rows, columns = np.shape(coefficients)
x: NDArray[float64] = np.zeros((rows, 1), dtype=float)
for row in reversed(range(rows)):
total = 0
for col in range(row + 1, columns):
total += coefficients[row, col] * x[col]
x[row, 0] = (vector[row] - total) / coefficients[row, row]
return x
def gaussian_elimination(
coefficients: NDArray[float64], vector: NDArray[float64]
) -> NDArray[float64]:
"""
This function performs Gaussian elimination method
Examples:
1x1 - 4x2 - 2x3 = -2 1x1 + 2x2 = 5
5x1 + 2x2 - 2x3 = -3 5x1 + 2x2 = 5
1x1 - 1x2 + 0x3 = 4
>>> gaussian_elimination([[1, -4, -2], [5, 2, -2], [1, -1, 0]], [[-2], [-3], [4]])
array([[ 2.3 ],
[-1.7 ],
[ 5.55]])
>>> gaussian_elimination([[1, 2], [5, 2]], [[5], [5]])
array([[0. ],
[2.5]])
"""
# coefficients must to be a square matrix so we need to check first
rows, columns = np.shape(coefficients)
if rows != columns:
return np.array((), dtype=float)
# augmented matrix
augmented_mat: NDArray[float64] = np.concatenate((coefficients, vector), axis=1)
augmented_mat = augmented_mat.astype("float64")
# scale the matrix leaving it triangular
for row in range(rows - 1):
pivot = augmented_mat[row, row]
for col in range(row + 1, columns):
factor = augmented_mat[col, row] / pivot
augmented_mat[col, :] -= factor * augmented_mat[row, :]
x = retroactive_resolution(
augmented_mat[:, 0:columns], augmented_mat[:, columns : columns + 1]
)
return x
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Combinatoric selections
Problem 53
There are exactly ten ways of selecting three from five, 12345:
123, 124, 125, 134, 135, 145, 234, 235, 245, and 345
In combinatorics, we use the notation, 5C3 = 10.
In general,
nCr = n!/(r!(n−r)!),where r ≤ n, n! = n×(n−1)×...×3×2×1, and 0! = 1.
It is not until n = 23, that a value exceeds one-million: 23C10 = 1144066.
How many, not necessarily distinct, values of nCr, for 1 ≤ n ≤ 100, are greater
than one-million?
"""
from math import factorial
def combinations(n, r):
return factorial(n) / (factorial(r) * factorial(n - r))
def solution():
"""Returns the number of values of nCr, for 1 ≤ n ≤ 100, are greater than
one-million
>>> solution()
4075
"""
total = 0
for i in range(1, 101):
for j in range(1, i + 1):
if combinations(i, j) > 1e6:
total += 1
return total
if __name__ == "__main__":
print(solution())
| """
Combinatoric selections
Problem 53
There are exactly ten ways of selecting three from five, 12345:
123, 124, 125, 134, 135, 145, 234, 235, 245, and 345
In combinatorics, we use the notation, 5C3 = 10.
In general,
nCr = n!/(r!(n−r)!),where r ≤ n, n! = n×(n−1)×...×3×2×1, and 0! = 1.
It is not until n = 23, that a value exceeds one-million: 23C10 = 1144066.
How many, not necessarily distinct, values of nCr, for 1 ≤ n ≤ 100, are greater
than one-million?
"""
from math import factorial
def combinations(n, r):
return factorial(n) / (factorial(r) * factorial(n - r))
def solution():
"""Returns the number of values of nCr, for 1 ≤ n ≤ 100, are greater than
one-million
>>> solution()
4075
"""
total = 0
for i in range(1, 101):
for j in range(1, i + 1):
if combinations(i, j) > 1e6:
total += 1
return total
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from collections import Counter
def sock_merchant(colors: list[int]) -> int:
"""
>>> sock_merchant([10, 20, 20, 10, 10, 30, 50, 10, 20])
3
>>> sock_merchant([1, 1, 3, 3])
2
"""
return sum(socks_by_color // 2 for socks_by_color in Counter(colors).values())
if __name__ == "__main__":
import doctest
doctest.testmod()
colors = [int(x) for x in input("Enter socks by color :").rstrip().split()]
print(f"sock_merchant({colors}) = {sock_merchant(colors)}")
| from collections import Counter
def sock_merchant(colors: list[int]) -> int:
"""
>>> sock_merchant([10, 20, 20, 10, 10, 30, 50, 10, 20])
3
>>> sock_merchant([1, 1, 3, 3])
2
"""
return sum(socks_by_color // 2 for socks_by_color in Counter(colors).values())
if __name__ == "__main__":
import doctest
doctest.testmod()
colors = [int(x) for x in input("Enter socks by color :").rstrip().split()]
print(f"sock_merchant({colors}) = {sock_merchant(colors)}")
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
def find_max(nums: list[int | float]) -> int | float:
"""
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_max(nums) == max(nums)
True
True
True
True
>>> find_max([2, 4, 9, 7, 19, 94, 5])
94
>>> find_max([])
Traceback (most recent call last):
...
ValueError: find_max() arg is an empty sequence
"""
if len(nums) == 0:
raise ValueError("find_max() arg is an empty sequence")
max_num = nums[0]
for x in nums:
if x > max_num:
max_num = x
return max_num
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| from __future__ import annotations
def find_max(nums: list[int | float]) -> int | float:
"""
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_max(nums) == max(nums)
True
True
True
True
>>> find_max([2, 4, 9, 7, 19, 94, 5])
94
>>> find_max([])
Traceback (most recent call last):
...
ValueError: find_max() arg is an empty sequence
"""
if len(nums) == 0:
raise ValueError("find_max() arg is an empty sequence")
max_num = nums[0]
for x in nums:
if x > max_num:
max_num = x
return max_num
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
import random
class Dice:
NUM_SIDES = 6
def __init__(self):
"""Initialize a six sided dice"""
self.sides = list(range(1, Dice.NUM_SIDES + 1))
def roll(self):
return random.choice(self.sides)
def throw_dice(num_throws: int, num_dice: int = 2) -> list[float]:
"""
Return probability list of all possible sums when throwing dice.
>>> random.seed(0)
>>> throw_dice(10, 1)
[10.0, 0.0, 30.0, 50.0, 10.0, 0.0]
>>> throw_dice(100, 1)
[19.0, 17.0, 17.0, 11.0, 23.0, 13.0]
>>> throw_dice(1000, 1)
[18.8, 15.5, 16.3, 17.6, 14.2, 17.6]
>>> throw_dice(10000, 1)
[16.35, 16.89, 16.93, 16.6, 16.52, 16.71]
>>> throw_dice(10000, 2)
[2.74, 5.6, 7.99, 11.26, 13.92, 16.7, 14.44, 10.63, 8.05, 5.92, 2.75]
"""
dices = [Dice() for i in range(num_dice)]
count_of_sum = [0] * (len(dices) * Dice.NUM_SIDES + 1)
for _ in range(num_throws):
count_of_sum[sum(dice.roll() for dice in dices)] += 1
probability = [round((count * 100) / num_throws, 2) for count in count_of_sum]
return probability[num_dice:] # remove probability of sums that never appear
if __name__ == "__main__":
import doctest
doctest.testmod()
| from __future__ import annotations
import random
class Dice:
NUM_SIDES = 6
def __init__(self):
"""Initialize a six sided dice"""
self.sides = list(range(1, Dice.NUM_SIDES + 1))
def roll(self):
return random.choice(self.sides)
def throw_dice(num_throws: int, num_dice: int = 2) -> list[float]:
"""
Return probability list of all possible sums when throwing dice.
>>> random.seed(0)
>>> throw_dice(10, 1)
[10.0, 0.0, 30.0, 50.0, 10.0, 0.0]
>>> throw_dice(100, 1)
[19.0, 17.0, 17.0, 11.0, 23.0, 13.0]
>>> throw_dice(1000, 1)
[18.8, 15.5, 16.3, 17.6, 14.2, 17.6]
>>> throw_dice(10000, 1)
[16.35, 16.89, 16.93, 16.6, 16.52, 16.71]
>>> throw_dice(10000, 2)
[2.74, 5.6, 7.99, 11.26, 13.92, 16.7, 14.44, 10.63, 8.05, 5.92, 2.75]
"""
dices = [Dice() for i in range(num_dice)]
count_of_sum = [0] * (len(dices) * Dice.NUM_SIDES + 1)
for _ in range(num_throws):
count_of_sum[sum(dice.roll() for dice in dices)] += 1
probability = [round((count * 100) / num_throws, 2) for count in count_of_sum]
return probability[num_dice:] # remove probability of sums that never appear
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Description
The Koch snowflake is a fractal curve and one of the earliest fractals to
have been described. The Koch snowflake can be built up iteratively, in a
sequence of stages. The first stage is an equilateral triangle, and each
successive stage is formed by adding outward bends to each side of the
previous stage, making smaller equilateral triangles.
This can be achieved through the following steps for each line:
1. divide the line segment into three segments of equal length.
2. draw an equilateral triangle that has the middle segment from step 1
as its base and points outward.
3. remove the line segment that is the base of the triangle from step 2.
(description adapted from https://en.wikipedia.org/wiki/Koch_snowflake )
(for a more detailed explanation and an implementation in the
Processing language, see https://natureofcode.com/book/chapter-8-fractals/
#84-the-koch-curve-and-the-arraylist-technique )
Requirements (pip):
- matplotlib
- numpy
"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
VECTOR_1 = numpy.array([0, 0])
VECTOR_2 = numpy.array([0.5, 0.8660254])
VECTOR_3 = numpy.array([1, 0])
INITIAL_VECTORS = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
# uncomment for simple Koch curve instead of Koch snowflake
# INITIAL_VECTORS = [VECTOR_1, VECTOR_3]
def iterate(initial_vectors: list[numpy.ndarray], steps: int) -> list[numpy.ndarray]:
"""
Go through the number of iterations determined by the argument "steps".
Be careful with high values (above 5) since the time to calculate increases
exponentially.
>>> iterate([numpy.array([0, 0]), numpy.array([1, 0])], 1)
[array([0, 0]), array([0.33333333, 0. ]), array([0.5 , \
0.28867513]), array([0.66666667, 0. ]), array([1, 0])]
"""
vectors = initial_vectors
for _ in range(steps):
vectors = iteration_step(vectors)
return vectors
def iteration_step(vectors: list[numpy.ndarray]) -> list[numpy.ndarray]:
"""
Loops through each pair of adjacent vectors. Each line between two adjacent
vectors is divided into 4 segments by adding 3 additional vectors in-between
the original two vectors. The vector in the middle is constructed through a
60 degree rotation so it is bent outwards.
>>> iteration_step([numpy.array([0, 0]), numpy.array([1, 0])])
[array([0, 0]), array([0.33333333, 0. ]), array([0.5 , \
0.28867513]), array([0.66666667, 0. ]), array([1, 0])]
"""
new_vectors = []
for i, start_vector in enumerate(vectors[:-1]):
end_vector = vectors[i + 1]
new_vectors.append(start_vector)
difference_vector = end_vector - start_vector
new_vectors.append(start_vector + difference_vector / 3)
new_vectors.append(
start_vector + difference_vector / 3 + rotate(difference_vector / 3, 60)
)
new_vectors.append(start_vector + difference_vector * 2 / 3)
new_vectors.append(vectors[-1])
return new_vectors
def rotate(vector: numpy.ndarray, angle_in_degrees: float) -> numpy.ndarray:
"""
Standard rotation of a 2D vector with a rotation matrix
(see https://en.wikipedia.org/wiki/Rotation_matrix )
>>> rotate(numpy.array([1, 0]), 60)
array([0.5 , 0.8660254])
>>> rotate(numpy.array([1, 0]), 90)
array([6.123234e-17, 1.000000e+00])
"""
theta = numpy.radians(angle_in_degrees)
c, s = numpy.cos(theta), numpy.sin(theta)
rotation_matrix = numpy.array(((c, -s), (s, c)))
return numpy.dot(rotation_matrix, vector)
def plot(vectors: list[numpy.ndarray]) -> None:
"""
Utility function to plot the vectors using matplotlib.pyplot
No doctest was implemented since this function does not have a return value
"""
# avoid stretched display of graph
axes = plt.gca()
axes.set_aspect("equal")
# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
# y-coordinates as inputs, which are constructed from the vector-list using
# zip()
x_coordinates, y_coordinates = zip(*vectors)
plt.plot(x_coordinates, y_coordinates)
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
processed_vectors = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| """
Description
The Koch snowflake is a fractal curve and one of the earliest fractals to
have been described. The Koch snowflake can be built up iteratively, in a
sequence of stages. The first stage is an equilateral triangle, and each
successive stage is formed by adding outward bends to each side of the
previous stage, making smaller equilateral triangles.
This can be achieved through the following steps for each line:
1. divide the line segment into three segments of equal length.
2. draw an equilateral triangle that has the middle segment from step 1
as its base and points outward.
3. remove the line segment that is the base of the triangle from step 2.
(description adapted from https://en.wikipedia.org/wiki/Koch_snowflake )
(for a more detailed explanation and an implementation in the
Processing language, see https://natureofcode.com/book/chapter-8-fractals/
#84-the-koch-curve-and-the-arraylist-technique )
Requirements (pip):
- matplotlib
- numpy
"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
VECTOR_1 = numpy.array([0, 0])
VECTOR_2 = numpy.array([0.5, 0.8660254])
VECTOR_3 = numpy.array([1, 0])
INITIAL_VECTORS = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
# uncomment for simple Koch curve instead of Koch snowflake
# INITIAL_VECTORS = [VECTOR_1, VECTOR_3]
def iterate(initial_vectors: list[numpy.ndarray], steps: int) -> list[numpy.ndarray]:
"""
Go through the number of iterations determined by the argument "steps".
Be careful with high values (above 5) since the time to calculate increases
exponentially.
>>> iterate([numpy.array([0, 0]), numpy.array([1, 0])], 1)
[array([0, 0]), array([0.33333333, 0. ]), array([0.5 , \
0.28867513]), array([0.66666667, 0. ]), array([1, 0])]
"""
vectors = initial_vectors
for _ in range(steps):
vectors = iteration_step(vectors)
return vectors
def iteration_step(vectors: list[numpy.ndarray]) -> list[numpy.ndarray]:
"""
Loops through each pair of adjacent vectors. Each line between two adjacent
vectors is divided into 4 segments by adding 3 additional vectors in-between
the original two vectors. The vector in the middle is constructed through a
60 degree rotation so it is bent outwards.
>>> iteration_step([numpy.array([0, 0]), numpy.array([1, 0])])
[array([0, 0]), array([0.33333333, 0. ]), array([0.5 , \
0.28867513]), array([0.66666667, 0. ]), array([1, 0])]
"""
new_vectors = []
for i, start_vector in enumerate(vectors[:-1]):
end_vector = vectors[i + 1]
new_vectors.append(start_vector)
difference_vector = end_vector - start_vector
new_vectors.append(start_vector + difference_vector / 3)
new_vectors.append(
start_vector + difference_vector / 3 + rotate(difference_vector / 3, 60)
)
new_vectors.append(start_vector + difference_vector * 2 / 3)
new_vectors.append(vectors[-1])
return new_vectors
def rotate(vector: numpy.ndarray, angle_in_degrees: float) -> numpy.ndarray:
"""
Standard rotation of a 2D vector with a rotation matrix
(see https://en.wikipedia.org/wiki/Rotation_matrix )
>>> rotate(numpy.array([1, 0]), 60)
array([0.5 , 0.8660254])
>>> rotate(numpy.array([1, 0]), 90)
array([6.123234e-17, 1.000000e+00])
"""
theta = numpy.radians(angle_in_degrees)
c, s = numpy.cos(theta), numpy.sin(theta)
rotation_matrix = numpy.array(((c, -s), (s, c)))
return numpy.dot(rotation_matrix, vector)
def plot(vectors: list[numpy.ndarray]) -> None:
"""
Utility function to plot the vectors using matplotlib.pyplot
No doctest was implemented since this function does not have a return value
"""
# avoid stretched display of graph
axes = plt.gca()
axes.set_aspect("equal")
# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
# y-coordinates as inputs, which are constructed from the vector-list using
# zip()
x_coordinates, y_coordinates = zip(*vectors)
plt.plot(x_coordinates, y_coordinates)
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
processed_vectors = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """ Luhn Algorithm """
from __future__ import annotations
def is_luhn(string: str) -> bool:
"""
Perform Luhn validation on an input string
Algorithm:
* Double every other digit starting from 2nd last digit.
* Subtract 9 if number is greater than 9.
* Sum the numbers
*
>>> test_cases = (79927398710, 79927398711, 79927398712, 79927398713,
... 79927398714, 79927398715, 79927398716, 79927398717, 79927398718,
... 79927398719)
>>> [is_luhn(str(test_case)) for test_case in test_cases]
[False, False, False, True, False, False, False, False, False, False]
"""
check_digit: int
_vector: list[str] = list(string)
__vector, check_digit = _vector[:-1], int(_vector[-1])
vector: list[int] = [int(digit) for digit in __vector]
vector.reverse()
for i, digit in enumerate(vector):
if i & 1 == 0:
doubled: int = digit * 2
if doubled > 9:
doubled -= 9
check_digit += doubled
else:
check_digit += digit
return check_digit % 10 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
assert is_luhn("79927398713")
assert not is_luhn("79927398714")
| """ Luhn Algorithm """
from __future__ import annotations
def is_luhn(string: str) -> bool:
"""
Perform Luhn validation on an input string
Algorithm:
* Double every other digit starting from 2nd last digit.
* Subtract 9 if number is greater than 9.
* Sum the numbers
*
>>> test_cases = (79927398710, 79927398711, 79927398712, 79927398713,
... 79927398714, 79927398715, 79927398716, 79927398717, 79927398718,
... 79927398719)
>>> [is_luhn(str(test_case)) for test_case in test_cases]
[False, False, False, True, False, False, False, False, False, False]
"""
check_digit: int
_vector: list[str] = list(string)
__vector, check_digit = _vector[:-1], int(_vector[-1])
vector: list[int] = [int(digit) for digit in __vector]
vector.reverse()
for i, digit in enumerate(vector):
if i & 1 == 0:
doubled: int = digit * 2
if doubled > 9:
doubled -= 9
check_digit += doubled
else:
check_digit += digit
return check_digit % 10 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
assert is_luhn("79927398713")
assert not is_luhn("79927398714")
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Find the area of various geometric shapes
Wikipedia reference: https://en.wikipedia.org/wiki/Area
"""
from math import pi, sqrt, tan
def surface_area_cube(side_length: float) -> float:
"""
Calculate the Surface Area of a Cube.
>>> surface_area_cube(1)
6
>>> surface_area_cube(1.6)
15.360000000000003
>>> surface_area_cube(0)
0
>>> surface_area_cube(3)
54
>>> surface_area_cube(-1)
Traceback (most recent call last):
...
ValueError: surface_area_cube() only accepts non-negative values
"""
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def surface_area_cuboid(length: float, breadth: float, height: float) -> float:
"""
Calculate the Surface Area of a Cuboid.
>>> surface_area_cuboid(1, 2, 3)
22
>>> surface_area_cuboid(0, 0, 0)
0
>>> surface_area_cuboid(1.6, 2.6, 3.6)
38.56
>>> surface_area_cuboid(-1, 2, 3)
Traceback (most recent call last):
...
ValueError: surface_area_cuboid() only accepts non-negative values
>>> surface_area_cuboid(1, -2, 3)
Traceback (most recent call last):
...
ValueError: surface_area_cuboid() only accepts non-negative values
>>> surface_area_cuboid(1, 2, -3)
Traceback (most recent call last):
...
ValueError: surface_area_cuboid() only accepts non-negative values
"""
if length < 0 or breadth < 0 or height < 0:
raise ValueError("surface_area_cuboid() only accepts non-negative values")
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def surface_area_sphere(radius: float) -> float:
"""
Calculate the Surface Area of a Sphere.
Wikipedia reference: https://en.wikipedia.org/wiki/Sphere
Formula: 4 * pi * r^2
>>> surface_area_sphere(5)
314.1592653589793
>>> surface_area_sphere(1)
12.566370614359172
>>> surface_area_sphere(1.6)
32.169908772759484
>>> surface_area_sphere(0)
0.0
>>> surface_area_sphere(-1)
Traceback (most recent call last):
...
ValueError: surface_area_sphere() only accepts non-negative values
"""
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values")
return 4 * pi * radius**2
def surface_area_hemisphere(radius: float) -> float:
"""
Calculate the Surface Area of a Hemisphere.
Formula: 3 * pi * r^2
>>> surface_area_hemisphere(5)
235.61944901923448
>>> surface_area_hemisphere(1)
9.42477796076938
>>> surface_area_hemisphere(0)
0.0
>>> surface_area_hemisphere(1.1)
11.40398133253095
>>> surface_area_hemisphere(-1)
Traceback (most recent call last):
...
ValueError: surface_area_hemisphere() only accepts non-negative values
"""
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values")
return 3 * pi * radius**2
def surface_area_cone(radius: float, height: float) -> float:
"""
Calculate the Surface Area of a Cone.
Wikipedia reference: https://en.wikipedia.org/wiki/Cone
Formula: pi * r * (r + (h ** 2 + r ** 2) ** 0.5)
>>> surface_area_cone(10, 24)
1130.9733552923256
>>> surface_area_cone(6, 8)
301.59289474462014
>>> surface_area_cone(1.6, 2.6)
23.387862992395807
>>> surface_area_cone(0, 0)
0.0
>>> surface_area_cone(-1, -2)
Traceback (most recent call last):
...
ValueError: surface_area_cone() only accepts non-negative values
>>> surface_area_cone(1, -2)
Traceback (most recent call last):
...
ValueError: surface_area_cone() only accepts non-negative values
>>> surface_area_cone(-1, 2)
Traceback (most recent call last):
...
ValueError: surface_area_cone() only accepts non-negative values
"""
if radius < 0 or height < 0:
raise ValueError("surface_area_cone() only accepts non-negative values")
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def surface_area_conical_frustum(
radius_1: float, radius_2: float, height: float
) -> float:
"""
Calculate the Surface Area of a Conical Frustum.
>>> surface_area_conical_frustum(1, 2, 3)
45.511728065337266
>>> surface_area_conical_frustum(4, 5, 6)
300.7913575056268
>>> surface_area_conical_frustum(0, 0, 0)
0.0
>>> surface_area_conical_frustum(1.6, 2.6, 3.6)
78.57907060751548
>>> surface_area_conical_frustum(-1, 2, 3)
Traceback (most recent call last):
...
ValueError: surface_area_conical_frustum() only accepts non-negative values
>>> surface_area_conical_frustum(1, -2, 3)
Traceback (most recent call last):
...
ValueError: surface_area_conical_frustum() only accepts non-negative values
>>> surface_area_conical_frustum(1, 2, -3)
Traceback (most recent call last):
...
ValueError: surface_area_conical_frustum() only accepts non-negative values
"""
if radius_1 < 0 or radius_2 < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values"
)
slant_height = (height**2 + (radius_1 - radius_2) ** 2) ** 0.5
return pi * ((slant_height * (radius_1 + radius_2)) + radius_1**2 + radius_2**2)
def surface_area_cylinder(radius: float, height: float) -> float:
"""
Calculate the Surface Area of a Cylinder.
Wikipedia reference: https://en.wikipedia.org/wiki/Cylinder
Formula: 2 * pi * r * (h + r)
>>> surface_area_cylinder(7, 10)
747.6990515543707
>>> surface_area_cylinder(1.6, 2.6)
42.22300526424682
>>> surface_area_cylinder(0, 0)
0.0
>>> surface_area_cylinder(6, 8)
527.7875658030853
>>> surface_area_cylinder(-1, -2)
Traceback (most recent call last):
...
ValueError: surface_area_cylinder() only accepts non-negative values
>>> surface_area_cylinder(1, -2)
Traceback (most recent call last):
...
ValueError: surface_area_cylinder() only accepts non-negative values
>>> surface_area_cylinder(-1, 2)
Traceback (most recent call last):
...
ValueError: surface_area_cylinder() only accepts non-negative values
"""
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values")
return 2 * pi * radius * (height + radius)
def surface_area_torus(torus_radius: float, tube_radius: float) -> float:
"""Calculate the Area of a Torus.
Wikipedia reference: https://en.wikipedia.org/wiki/Torus
:return 4pi^2 * torus_radius * tube_radius
>>> surface_area_torus(1, 1)
39.47841760435743
>>> surface_area_torus(4, 3)
473.7410112522892
>>> surface_area_torus(3, 4)
Traceback (most recent call last):
...
ValueError: surface_area_torus() does not support spindle or self intersecting tori
>>> surface_area_torus(1.6, 1.6)
101.06474906715503
>>> surface_area_torus(0, 0)
0.0
>>> surface_area_torus(-1, 1)
Traceback (most recent call last):
...
ValueError: surface_area_torus() only accepts non-negative values
>>> surface_area_torus(1, -1)
Traceback (most recent call last):
...
ValueError: surface_area_torus() only accepts non-negative values
"""
if torus_radius < 0 or tube_radius < 0:
raise ValueError("surface_area_torus() only accepts non-negative values")
if torus_radius < tube_radius:
raise ValueError(
"surface_area_torus() does not support spindle or self intersecting tori"
)
return 4 * pow(pi, 2) * torus_radius * tube_radius
def area_rectangle(length: float, width: float) -> float:
"""
Calculate the area of a rectangle.
>>> area_rectangle(10, 20)
200
>>> area_rectangle(1.6, 2.6)
4.16
>>> area_rectangle(0, 0)
0
>>> area_rectangle(-1, -2)
Traceback (most recent call last):
...
ValueError: area_rectangle() only accepts non-negative values
>>> area_rectangle(1, -2)
Traceback (most recent call last):
...
ValueError: area_rectangle() only accepts non-negative values
>>> area_rectangle(-1, 2)
Traceback (most recent call last):
...
ValueError: area_rectangle() only accepts non-negative values
"""
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values")
return length * width
def area_square(side_length: float) -> float:
"""
Calculate the area of a square.
>>> area_square(10)
100
>>> area_square(0)
0
>>> area_square(1.6)
2.5600000000000005
>>> area_square(-1)
Traceback (most recent call last):
...
ValueError: area_square() only accepts non-negative values
"""
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values")
return side_length**2
def area_triangle(base: float, height: float) -> float:
"""
Calculate the area of a triangle given the base and height.
>>> area_triangle(10, 10)
50.0
>>> area_triangle(1.6, 2.6)
2.08
>>> area_triangle(0, 0)
0.0
>>> area_triangle(-1, -2)
Traceback (most recent call last):
...
ValueError: area_triangle() only accepts non-negative values
>>> area_triangle(1, -2)
Traceback (most recent call last):
...
ValueError: area_triangle() only accepts non-negative values
>>> area_triangle(-1, 2)
Traceback (most recent call last):
...
ValueError: area_triangle() only accepts non-negative values
"""
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values")
return (base * height) / 2
def area_triangle_three_sides(side1: float, side2: float, side3: float) -> float:
"""
Calculate area of triangle when the length of 3 sides are known.
This function uses Heron's formula: https://en.wikipedia.org/wiki/Heron%27s_formula
>>> area_triangle_three_sides(5, 12, 13)
30.0
>>> area_triangle_three_sides(10, 11, 12)
51.521233486786784
>>> area_triangle_three_sides(0, 0, 0)
0.0
>>> area_triangle_three_sides(1.6, 2.6, 3.6)
1.8703742940919619
>>> area_triangle_three_sides(-1, -2, -1)
Traceback (most recent call last):
...
ValueError: area_triangle_three_sides() only accepts non-negative values
>>> area_triangle_three_sides(1, -2, 1)
Traceback (most recent call last):
...
ValueError: area_triangle_three_sides() only accepts non-negative values
>>> area_triangle_three_sides(2, 4, 7)
Traceback (most recent call last):
...
ValueError: Given three sides do not form a triangle
>>> area_triangle_three_sides(2, 7, 4)
Traceback (most recent call last):
...
ValueError: Given three sides do not form a triangle
>>> area_triangle_three_sides(7, 2, 4)
Traceback (most recent call last):
...
ValueError: Given three sides do not form a triangle
"""
if side1 < 0 or side2 < 0 or side3 < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values")
elif side1 + side2 < side3 or side1 + side3 < side2 or side2 + side3 < side1:
raise ValueError("Given three sides do not form a triangle")
semi_perimeter = (side1 + side2 + side3) / 2
area = sqrt(
semi_perimeter
* (semi_perimeter - side1)
* (semi_perimeter - side2)
* (semi_perimeter - side3)
)
return area
def area_parallelogram(base: float, height: float) -> float:
"""
Calculate the area of a parallelogram.
>>> area_parallelogram(10, 20)
200
>>> area_parallelogram(1.6, 2.6)
4.16
>>> area_parallelogram(0, 0)
0
>>> area_parallelogram(-1, -2)
Traceback (most recent call last):
...
ValueError: area_parallelogram() only accepts non-negative values
>>> area_parallelogram(1, -2)
Traceback (most recent call last):
...
ValueError: area_parallelogram() only accepts non-negative values
>>> area_parallelogram(-1, 2)
Traceback (most recent call last):
...
ValueError: area_parallelogram() only accepts non-negative values
"""
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values")
return base * height
def area_trapezium(base1: float, base2: float, height: float) -> float:
"""
Calculate the area of a trapezium.
>>> area_trapezium(10, 20, 30)
450.0
>>> area_trapezium(1.6, 2.6, 3.6)
7.5600000000000005
>>> area_trapezium(0, 0, 0)
0.0
>>> area_trapezium(-1, -2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(-1, 2, 3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(1, -2, 3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(1, 2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(-1, -2, 3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(1, -2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(-1, 2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
"""
if base1 < 0 or base2 < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values")
return 1 / 2 * (base1 + base2) * height
def area_circle(radius: float) -> float:
"""
Calculate the area of a circle.
>>> area_circle(20)
1256.6370614359173
>>> area_circle(1.6)
8.042477193189871
>>> area_circle(0)
0.0
>>> area_circle(-1)
Traceback (most recent call last):
...
ValueError: area_circle() only accepts non-negative values
"""
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values")
return pi * radius**2
def area_ellipse(radius_x: float, radius_y: float) -> float:
"""
Calculate the area of a ellipse.
>>> area_ellipse(10, 10)
314.1592653589793
>>> area_ellipse(10, 20)
628.3185307179587
>>> area_ellipse(0, 0)
0.0
>>> area_ellipse(1.6, 2.6)
13.06902543893354
>>> area_ellipse(-10, 20)
Traceback (most recent call last):
...
ValueError: area_ellipse() only accepts non-negative values
>>> area_ellipse(10, -20)
Traceback (most recent call last):
...
ValueError: area_ellipse() only accepts non-negative values
>>> area_ellipse(-10, -20)
Traceback (most recent call last):
...
ValueError: area_ellipse() only accepts non-negative values
"""
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values")
return pi * radius_x * radius_y
def area_rhombus(diagonal_1: float, diagonal_2: float) -> float:
"""
Calculate the area of a rhombus.
>>> area_rhombus(10, 20)
100.0
>>> area_rhombus(1.6, 2.6)
2.08
>>> area_rhombus(0, 0)
0.0
>>> area_rhombus(-1, -2)
Traceback (most recent call last):
...
ValueError: area_rhombus() only accepts non-negative values
>>> area_rhombus(1, -2)
Traceback (most recent call last):
...
ValueError: area_rhombus() only accepts non-negative values
>>> area_rhombus(-1, 2)
Traceback (most recent call last):
...
ValueError: area_rhombus() only accepts non-negative values
"""
if diagonal_1 < 0 or diagonal_2 < 0:
raise ValueError("area_rhombus() only accepts non-negative values")
return 1 / 2 * diagonal_1 * diagonal_2
def area_reg_polygon(sides: int, length: float) -> float:
"""
Calculate the area of a regular polygon.
Wikipedia reference: https://en.wikipedia.org/wiki/Polygon#Regular_polygons
Formula: (n*s^2*cot(pi/n))/4
>>> area_reg_polygon(3, 10)
43.301270189221945
>>> area_reg_polygon(4, 10)
100.00000000000001
>>> area_reg_polygon(0, 0)
Traceback (most recent call last):
...
ValueError: area_reg_polygon() only accepts integers greater than or equal to \
three as number of sides
>>> area_reg_polygon(-1, -2)
Traceback (most recent call last):
...
ValueError: area_reg_polygon() only accepts integers greater than or equal to \
three as number of sides
>>> area_reg_polygon(5, -2)
Traceback (most recent call last):
...
ValueError: area_reg_polygon() only accepts non-negative values as \
length of a side
>>> area_reg_polygon(-1, 2)
Traceback (most recent call last):
...
ValueError: area_reg_polygon() only accepts integers greater than or equal to \
three as number of sides
"""
if not isinstance(sides, int) or sides < 3:
raise ValueError(
"area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides"
)
elif length < 0:
raise ValueError(
"area_reg_polygon() only accepts non-negative values as \
length of a side"
)
return (sides * length**2) / (4 * tan(pi / sides))
return (sides * length**2) / (4 * tan(pi / sides))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print("\nSurface Areas of various geometric shapes: \n")
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| """
Find the area of various geometric shapes
Wikipedia reference: https://en.wikipedia.org/wiki/Area
"""
from math import pi, sqrt, tan
def surface_area_cube(side_length: float) -> float:
"""
Calculate the Surface Area of a Cube.
>>> surface_area_cube(1)
6
>>> surface_area_cube(1.6)
15.360000000000003
>>> surface_area_cube(0)
0
>>> surface_area_cube(3)
54
>>> surface_area_cube(-1)
Traceback (most recent call last):
...
ValueError: surface_area_cube() only accepts non-negative values
"""
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def surface_area_cuboid(length: float, breadth: float, height: float) -> float:
"""
Calculate the Surface Area of a Cuboid.
>>> surface_area_cuboid(1, 2, 3)
22
>>> surface_area_cuboid(0, 0, 0)
0
>>> surface_area_cuboid(1.6, 2.6, 3.6)
38.56
>>> surface_area_cuboid(-1, 2, 3)
Traceback (most recent call last):
...
ValueError: surface_area_cuboid() only accepts non-negative values
>>> surface_area_cuboid(1, -2, 3)
Traceback (most recent call last):
...
ValueError: surface_area_cuboid() only accepts non-negative values
>>> surface_area_cuboid(1, 2, -3)
Traceback (most recent call last):
...
ValueError: surface_area_cuboid() only accepts non-negative values
"""
if length < 0 or breadth < 0 or height < 0:
raise ValueError("surface_area_cuboid() only accepts non-negative values")
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def surface_area_sphere(radius: float) -> float:
"""
Calculate the Surface Area of a Sphere.
Wikipedia reference: https://en.wikipedia.org/wiki/Sphere
Formula: 4 * pi * r^2
>>> surface_area_sphere(5)
314.1592653589793
>>> surface_area_sphere(1)
12.566370614359172
>>> surface_area_sphere(1.6)
32.169908772759484
>>> surface_area_sphere(0)
0.0
>>> surface_area_sphere(-1)
Traceback (most recent call last):
...
ValueError: surface_area_sphere() only accepts non-negative values
"""
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values")
return 4 * pi * radius**2
def surface_area_hemisphere(radius: float) -> float:
"""
Calculate the Surface Area of a Hemisphere.
Formula: 3 * pi * r^2
>>> surface_area_hemisphere(5)
235.61944901923448
>>> surface_area_hemisphere(1)
9.42477796076938
>>> surface_area_hemisphere(0)
0.0
>>> surface_area_hemisphere(1.1)
11.40398133253095
>>> surface_area_hemisphere(-1)
Traceback (most recent call last):
...
ValueError: surface_area_hemisphere() only accepts non-negative values
"""
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values")
return 3 * pi * radius**2
def surface_area_cone(radius: float, height: float) -> float:
"""
Calculate the Surface Area of a Cone.
Wikipedia reference: https://en.wikipedia.org/wiki/Cone
Formula: pi * r * (r + (h ** 2 + r ** 2) ** 0.5)
>>> surface_area_cone(10, 24)
1130.9733552923256
>>> surface_area_cone(6, 8)
301.59289474462014
>>> surface_area_cone(1.6, 2.6)
23.387862992395807
>>> surface_area_cone(0, 0)
0.0
>>> surface_area_cone(-1, -2)
Traceback (most recent call last):
...
ValueError: surface_area_cone() only accepts non-negative values
>>> surface_area_cone(1, -2)
Traceback (most recent call last):
...
ValueError: surface_area_cone() only accepts non-negative values
>>> surface_area_cone(-1, 2)
Traceback (most recent call last):
...
ValueError: surface_area_cone() only accepts non-negative values
"""
if radius < 0 or height < 0:
raise ValueError("surface_area_cone() only accepts non-negative values")
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def surface_area_conical_frustum(
radius_1: float, radius_2: float, height: float
) -> float:
"""
Calculate the Surface Area of a Conical Frustum.
>>> surface_area_conical_frustum(1, 2, 3)
45.511728065337266
>>> surface_area_conical_frustum(4, 5, 6)
300.7913575056268
>>> surface_area_conical_frustum(0, 0, 0)
0.0
>>> surface_area_conical_frustum(1.6, 2.6, 3.6)
78.57907060751548
>>> surface_area_conical_frustum(-1, 2, 3)
Traceback (most recent call last):
...
ValueError: surface_area_conical_frustum() only accepts non-negative values
>>> surface_area_conical_frustum(1, -2, 3)
Traceback (most recent call last):
...
ValueError: surface_area_conical_frustum() only accepts non-negative values
>>> surface_area_conical_frustum(1, 2, -3)
Traceback (most recent call last):
...
ValueError: surface_area_conical_frustum() only accepts non-negative values
"""
if radius_1 < 0 or radius_2 < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values"
)
slant_height = (height**2 + (radius_1 - radius_2) ** 2) ** 0.5
return pi * ((slant_height * (radius_1 + radius_2)) + radius_1**2 + radius_2**2)
def surface_area_cylinder(radius: float, height: float) -> float:
"""
Calculate the Surface Area of a Cylinder.
Wikipedia reference: https://en.wikipedia.org/wiki/Cylinder
Formula: 2 * pi * r * (h + r)
>>> surface_area_cylinder(7, 10)
747.6990515543707
>>> surface_area_cylinder(1.6, 2.6)
42.22300526424682
>>> surface_area_cylinder(0, 0)
0.0
>>> surface_area_cylinder(6, 8)
527.7875658030853
>>> surface_area_cylinder(-1, -2)
Traceback (most recent call last):
...
ValueError: surface_area_cylinder() only accepts non-negative values
>>> surface_area_cylinder(1, -2)
Traceback (most recent call last):
...
ValueError: surface_area_cylinder() only accepts non-negative values
>>> surface_area_cylinder(-1, 2)
Traceback (most recent call last):
...
ValueError: surface_area_cylinder() only accepts non-negative values
"""
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values")
return 2 * pi * radius * (height + radius)
def surface_area_torus(torus_radius: float, tube_radius: float) -> float:
"""Calculate the Area of a Torus.
Wikipedia reference: https://en.wikipedia.org/wiki/Torus
:return 4pi^2 * torus_radius * tube_radius
>>> surface_area_torus(1, 1)
39.47841760435743
>>> surface_area_torus(4, 3)
473.7410112522892
>>> surface_area_torus(3, 4)
Traceback (most recent call last):
...
ValueError: surface_area_torus() does not support spindle or self intersecting tori
>>> surface_area_torus(1.6, 1.6)
101.06474906715503
>>> surface_area_torus(0, 0)
0.0
>>> surface_area_torus(-1, 1)
Traceback (most recent call last):
...
ValueError: surface_area_torus() only accepts non-negative values
>>> surface_area_torus(1, -1)
Traceback (most recent call last):
...
ValueError: surface_area_torus() only accepts non-negative values
"""
if torus_radius < 0 or tube_radius < 0:
raise ValueError("surface_area_torus() only accepts non-negative values")
if torus_radius < tube_radius:
raise ValueError(
"surface_area_torus() does not support spindle or self intersecting tori"
)
return 4 * pow(pi, 2) * torus_radius * tube_radius
def area_rectangle(length: float, width: float) -> float:
"""
Calculate the area of a rectangle.
>>> area_rectangle(10, 20)
200
>>> area_rectangle(1.6, 2.6)
4.16
>>> area_rectangle(0, 0)
0
>>> area_rectangle(-1, -2)
Traceback (most recent call last):
...
ValueError: area_rectangle() only accepts non-negative values
>>> area_rectangle(1, -2)
Traceback (most recent call last):
...
ValueError: area_rectangle() only accepts non-negative values
>>> area_rectangle(-1, 2)
Traceback (most recent call last):
...
ValueError: area_rectangle() only accepts non-negative values
"""
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values")
return length * width
def area_square(side_length: float) -> float:
"""
Calculate the area of a square.
>>> area_square(10)
100
>>> area_square(0)
0
>>> area_square(1.6)
2.5600000000000005
>>> area_square(-1)
Traceback (most recent call last):
...
ValueError: area_square() only accepts non-negative values
"""
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values")
return side_length**2
def area_triangle(base: float, height: float) -> float:
"""
Calculate the area of a triangle given the base and height.
>>> area_triangle(10, 10)
50.0
>>> area_triangle(1.6, 2.6)
2.08
>>> area_triangle(0, 0)
0.0
>>> area_triangle(-1, -2)
Traceback (most recent call last):
...
ValueError: area_triangle() only accepts non-negative values
>>> area_triangle(1, -2)
Traceback (most recent call last):
...
ValueError: area_triangle() only accepts non-negative values
>>> area_triangle(-1, 2)
Traceback (most recent call last):
...
ValueError: area_triangle() only accepts non-negative values
"""
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values")
return (base * height) / 2
def area_triangle_three_sides(side1: float, side2: float, side3: float) -> float:
"""
Calculate area of triangle when the length of 3 sides are known.
This function uses Heron's formula: https://en.wikipedia.org/wiki/Heron%27s_formula
>>> area_triangle_three_sides(5, 12, 13)
30.0
>>> area_triangle_three_sides(10, 11, 12)
51.521233486786784
>>> area_triangle_three_sides(0, 0, 0)
0.0
>>> area_triangle_three_sides(1.6, 2.6, 3.6)
1.8703742940919619
>>> area_triangle_three_sides(-1, -2, -1)
Traceback (most recent call last):
...
ValueError: area_triangle_three_sides() only accepts non-negative values
>>> area_triangle_three_sides(1, -2, 1)
Traceback (most recent call last):
...
ValueError: area_triangle_three_sides() only accepts non-negative values
>>> area_triangle_three_sides(2, 4, 7)
Traceback (most recent call last):
...
ValueError: Given three sides do not form a triangle
>>> area_triangle_three_sides(2, 7, 4)
Traceback (most recent call last):
...
ValueError: Given three sides do not form a triangle
>>> area_triangle_three_sides(7, 2, 4)
Traceback (most recent call last):
...
ValueError: Given three sides do not form a triangle
"""
if side1 < 0 or side2 < 0 or side3 < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values")
elif side1 + side2 < side3 or side1 + side3 < side2 or side2 + side3 < side1:
raise ValueError("Given three sides do not form a triangle")
semi_perimeter = (side1 + side2 + side3) / 2
area = sqrt(
semi_perimeter
* (semi_perimeter - side1)
* (semi_perimeter - side2)
* (semi_perimeter - side3)
)
return area
def area_parallelogram(base: float, height: float) -> float:
"""
Calculate the area of a parallelogram.
>>> area_parallelogram(10, 20)
200
>>> area_parallelogram(1.6, 2.6)
4.16
>>> area_parallelogram(0, 0)
0
>>> area_parallelogram(-1, -2)
Traceback (most recent call last):
...
ValueError: area_parallelogram() only accepts non-negative values
>>> area_parallelogram(1, -2)
Traceback (most recent call last):
...
ValueError: area_parallelogram() only accepts non-negative values
>>> area_parallelogram(-1, 2)
Traceback (most recent call last):
...
ValueError: area_parallelogram() only accepts non-negative values
"""
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values")
return base * height
def area_trapezium(base1: float, base2: float, height: float) -> float:
"""
Calculate the area of a trapezium.
>>> area_trapezium(10, 20, 30)
450.0
>>> area_trapezium(1.6, 2.6, 3.6)
7.5600000000000005
>>> area_trapezium(0, 0, 0)
0.0
>>> area_trapezium(-1, -2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(-1, 2, 3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(1, -2, 3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(1, 2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(-1, -2, 3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(1, -2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(-1, 2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
"""
if base1 < 0 or base2 < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values")
return 1 / 2 * (base1 + base2) * height
def area_circle(radius: float) -> float:
"""
Calculate the area of a circle.
>>> area_circle(20)
1256.6370614359173
>>> area_circle(1.6)
8.042477193189871
>>> area_circle(0)
0.0
>>> area_circle(-1)
Traceback (most recent call last):
...
ValueError: area_circle() only accepts non-negative values
"""
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values")
return pi * radius**2
def area_ellipse(radius_x: float, radius_y: float) -> float:
"""
Calculate the area of a ellipse.
>>> area_ellipse(10, 10)
314.1592653589793
>>> area_ellipse(10, 20)
628.3185307179587
>>> area_ellipse(0, 0)
0.0
>>> area_ellipse(1.6, 2.6)
13.06902543893354
>>> area_ellipse(-10, 20)
Traceback (most recent call last):
...
ValueError: area_ellipse() only accepts non-negative values
>>> area_ellipse(10, -20)
Traceback (most recent call last):
...
ValueError: area_ellipse() only accepts non-negative values
>>> area_ellipse(-10, -20)
Traceback (most recent call last):
...
ValueError: area_ellipse() only accepts non-negative values
"""
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values")
return pi * radius_x * radius_y
def area_rhombus(diagonal_1: float, diagonal_2: float) -> float:
"""
Calculate the area of a rhombus.
>>> area_rhombus(10, 20)
100.0
>>> area_rhombus(1.6, 2.6)
2.08
>>> area_rhombus(0, 0)
0.0
>>> area_rhombus(-1, -2)
Traceback (most recent call last):
...
ValueError: area_rhombus() only accepts non-negative values
>>> area_rhombus(1, -2)
Traceback (most recent call last):
...
ValueError: area_rhombus() only accepts non-negative values
>>> area_rhombus(-1, 2)
Traceback (most recent call last):
...
ValueError: area_rhombus() only accepts non-negative values
"""
if diagonal_1 < 0 or diagonal_2 < 0:
raise ValueError("area_rhombus() only accepts non-negative values")
return 1 / 2 * diagonal_1 * diagonal_2
def area_reg_polygon(sides: int, length: float) -> float:
"""
Calculate the area of a regular polygon.
Wikipedia reference: https://en.wikipedia.org/wiki/Polygon#Regular_polygons
Formula: (n*s^2*cot(pi/n))/4
>>> area_reg_polygon(3, 10)
43.301270189221945
>>> area_reg_polygon(4, 10)
100.00000000000001
>>> area_reg_polygon(0, 0)
Traceback (most recent call last):
...
ValueError: area_reg_polygon() only accepts integers greater than or equal to \
three as number of sides
>>> area_reg_polygon(-1, -2)
Traceback (most recent call last):
...
ValueError: area_reg_polygon() only accepts integers greater than or equal to \
three as number of sides
>>> area_reg_polygon(5, -2)
Traceback (most recent call last):
...
ValueError: area_reg_polygon() only accepts non-negative values as \
length of a side
>>> area_reg_polygon(-1, 2)
Traceback (most recent call last):
...
ValueError: area_reg_polygon() only accepts integers greater than or equal to \
three as number of sides
"""
if not isinstance(sides, int) or sides < 3:
raise ValueError(
"area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides"
)
elif length < 0:
raise ValueError(
"area_reg_polygon() only accepts non-negative values as \
length of a side"
)
return (sides * length**2) / (4 * tan(pi / sides))
return (sides * length**2) / (4 * tan(pi / sides))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print("\nSurface Areas of various geometric shapes: \n")
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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 | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Graph Coloring also called "m coloring problem"
consists of coloring a given graph with at most m colors
such that no adjacent vertices are assigned the same color
Wikipedia: https://en.wikipedia.org/wiki/Graph_coloring
"""
def valid_coloring(
neighbours: list[int], colored_vertices: list[int], color: int
) -> bool:
"""
For each neighbour check if the coloring constraint is satisfied
If any of the neighbours fail the constraint return False
If all neighbours validate the constraint return True
>>> neighbours = [0,1,0,1,0]
>>> colored_vertices = [0, 2, 1, 2, 0]
>>> color = 1
>>> valid_coloring(neighbours, colored_vertices, color)
True
>>> color = 2
>>> valid_coloring(neighbours, colored_vertices, color)
False
"""
# Does any neighbour not satisfy the constraints
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(neighbours)
)
def util_color(
graph: list[list[int]], max_colors: int, colored_vertices: list[int], index: int
) -> bool:
"""
Pseudo-Code
Base Case:
1. Check if coloring is complete
1.1 If complete return True (meaning that we successfully colored the graph)
Recursive Step:
2. Iterates over each color:
Check if the current coloring is valid:
2.1. Color given vertex
2.2. Do recursive call, check if this coloring leads to a solution
2.4. if current coloring leads to a solution return
2.5. Uncolor given vertex
>>> graph = [[0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1],
... [0, 1, 0, 1, 0],
... [0, 1, 1, 0, 0],
... [0, 1, 0, 0, 0]]
>>> max_colors = 3
>>> colored_vertices = [0, 1, 0, 0, 0]
>>> index = 3
>>> util_color(graph, max_colors, colored_vertices, index)
True
>>> max_colors = 2
>>> util_color(graph, max_colors, colored_vertices, index)
False
"""
# Base Case
if index == len(graph):
return True
# Recursive Step
for i in range(max_colors):
if valid_coloring(graph[index], colored_vertices, i):
# Color current vertex
colored_vertices[index] = i
# Validate coloring
if util_color(graph, max_colors, colored_vertices, index + 1):
return True
# Backtrack
colored_vertices[index] = -1
return False
def color(graph: list[list[int]], max_colors: int) -> list[int]:
"""
Wrapper function to call subroutine called util_color
which will either return True or False.
If True is returned colored_vertices list is filled with correct colorings
>>> graph = [[0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1],
... [0, 1, 0, 1, 0],
... [0, 1, 1, 0, 0],
... [0, 1, 0, 0, 0]]
>>> max_colors = 3
>>> color(graph, max_colors)
[0, 1, 0, 2, 0]
>>> max_colors = 2
>>> color(graph, max_colors)
[]
"""
colored_vertices = [-1] * len(graph)
if util_color(graph, max_colors, colored_vertices, 0):
return colored_vertices
return []
| """
Graph Coloring also called "m coloring problem"
consists of coloring a given graph with at most m colors
such that no adjacent vertices are assigned the same color
Wikipedia: https://en.wikipedia.org/wiki/Graph_coloring
"""
def valid_coloring(
neighbours: list[int], colored_vertices: list[int], color: int
) -> bool:
"""
For each neighbour check if the coloring constraint is satisfied
If any of the neighbours fail the constraint return False
If all neighbours validate the constraint return True
>>> neighbours = [0,1,0,1,0]
>>> colored_vertices = [0, 2, 1, 2, 0]
>>> color = 1
>>> valid_coloring(neighbours, colored_vertices, color)
True
>>> color = 2
>>> valid_coloring(neighbours, colored_vertices, color)
False
"""
# Does any neighbour not satisfy the constraints
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(neighbours)
)
def util_color(
graph: list[list[int]], max_colors: int, colored_vertices: list[int], index: int
) -> bool:
"""
Pseudo-Code
Base Case:
1. Check if coloring is complete
1.1 If complete return True (meaning that we successfully colored the graph)
Recursive Step:
2. Iterates over each color:
Check if the current coloring is valid:
2.1. Color given vertex
2.2. Do recursive call, check if this coloring leads to a solution
2.4. if current coloring leads to a solution return
2.5. Uncolor given vertex
>>> graph = [[0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1],
... [0, 1, 0, 1, 0],
... [0, 1, 1, 0, 0],
... [0, 1, 0, 0, 0]]
>>> max_colors = 3
>>> colored_vertices = [0, 1, 0, 0, 0]
>>> index = 3
>>> util_color(graph, max_colors, colored_vertices, index)
True
>>> max_colors = 2
>>> util_color(graph, max_colors, colored_vertices, index)
False
"""
# Base Case
if index == len(graph):
return True
# Recursive Step
for i in range(max_colors):
if valid_coloring(graph[index], colored_vertices, i):
# Color current vertex
colored_vertices[index] = i
# Validate coloring
if util_color(graph, max_colors, colored_vertices, index + 1):
return True
# Backtrack
colored_vertices[index] = -1
return False
def color(graph: list[list[int]], max_colors: int) -> list[int]:
"""
Wrapper function to call subroutine called util_color
which will either return True or False.
If True is returned colored_vertices list is filled with correct colorings
>>> graph = [[0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1],
... [0, 1, 0, 1, 0],
... [0, 1, 1, 0, 0],
... [0, 1, 0, 0, 0]]
>>> max_colors = 3
>>> color(graph, max_colors)
[0, 1, 0, 2, 0]
>>> max_colors = 2
>>> color(graph, max_colors)
[]
"""
colored_vertices = [-1] * len(graph)
if util_color(graph, max_colors, colored_vertices, 0):
return colored_vertices
return []
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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 | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Get CO2 emission data from the UK CarbonIntensity API
"""
from datetime import date
import requests
BASE_URL = "https://api.carbonintensity.org.uk/intensity"
# Emission in the last half hour
def fetch_last_half_hour() -> str:
last_half_hour = requests.get(BASE_URL).json()["data"][0]
return last_half_hour["intensity"]["actual"]
# Emissions in a specific date range
def fetch_from_to(start, end) -> list:
return requests.get(f"{BASE_URL}/{start}/{end}").json()["data"]
if __name__ == "__main__":
for entry in fetch_from_to(start=date(2020, 10, 1), end=date(2020, 10, 3)):
print("from {from} to {to}: {intensity[actual]}".format(**entry))
print(f"{fetch_last_half_hour() = }")
| """
Get CO2 emission data from the UK CarbonIntensity API
"""
from datetime import date
import requests
BASE_URL = "https://api.carbonintensity.org.uk/intensity"
# Emission in the last half hour
def fetch_last_half_hour() -> str:
last_half_hour = requests.get(BASE_URL).json()["data"][0]
return last_half_hour["intensity"]["actual"]
# Emissions in a specific date range
def fetch_from_to(start, end) -> list:
return requests.get(f"{BASE_URL}/{start}/{end}").json()["data"]
if __name__ == "__main__":
for entry in fetch_from_to(start=date(2020, 10, 1), end=date(2020, 10, 3)):
print("from {from} to {to}: {intensity[actual]}".format(**entry))
print(f"{fetch_last_half_hour() = }")
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import requests
APPID = "" # <-- Put your OpenWeatherMap appid here!
URL_BASE = "https://api.openweathermap.org/data/2.5/"
def current_weather(q: str = "Chicago", appid: str = APPID) -> dict:
"""https://openweathermap.org/api"""
return requests.get(URL_BASE + "weather", params=locals()).json()
def weather_forecast(q: str = "Kolkata, India", appid: str = APPID) -> dict:
"""https://openweathermap.org/forecast5"""
return requests.get(URL_BASE + "forecast", params=locals()).json()
def weather_onecall(lat: float = 55.68, lon: float = 12.57, appid: str = APPID) -> dict:
"""https://openweathermap.org/api/one-call-api"""
return requests.get(URL_BASE + "onecall", params=locals()).json()
if __name__ == "__main__":
from pprint import pprint
while True:
location = input("Enter a location:").strip()
if location:
pprint(current_weather(location))
else:
break
| import requests
APPID = "" # <-- Put your OpenWeatherMap appid here!
URL_BASE = "https://api.openweathermap.org/data/2.5/"
def current_weather(q: str = "Chicago", appid: str = APPID) -> dict:
"""https://openweathermap.org/api"""
return requests.get(URL_BASE + "weather", params=locals()).json()
def weather_forecast(q: str = "Kolkata, India", appid: str = APPID) -> dict:
"""https://openweathermap.org/forecast5"""
return requests.get(URL_BASE + "forecast", params=locals()).json()
def weather_onecall(lat: float = 55.68, lon: float = 12.57, appid: str = APPID) -> dict:
"""https://openweathermap.org/api/one-call-api"""
return requests.get(URL_BASE + "onecall", params=locals()).json()
if __name__ == "__main__":
from pprint import pprint
while True:
location = input("Enter a location:").strip()
if location:
pprint(current_weather(location))
else:
break
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def upper(word: str) -> str:
"""
Will convert the entire string to uppercase letters
>>> upper("wow")
'WOW'
>>> upper("Hello")
'HELLO'
>>> upper("WHAT")
'WHAT'
>>> upper("wh[]32")
'WH[]32'
"""
# Converting to ascii value int value and checking to see if char is a lower letter
# if it is a lowercase letter it is getting shift by 32 which makes it an uppercase
# case letter
return "".join(chr(ord(char) - 32) if "a" <= char <= "z" else char for char in word)
if __name__ == "__main__":
from doctest import testmod
testmod()
| def upper(word: str) -> str:
"""
Will convert the entire string to uppercase letters
>>> upper("wow")
'WOW'
>>> upper("Hello")
'HELLO'
>>> upper("WHAT")
'WHAT'
>>> upper("wh[]32")
'WH[]32'
"""
# Converting to ascii value int value and checking to see if char is a lower letter
# if it is a lowercase letter it is getting shift by 32 which makes it an uppercase
# case letter
return "".join(chr(ord(char) - 32) if "a" <= char <= "z" else char for char in word)
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/Check_digit#Algorithms
"""
def get_check_digit(barcode: int) -> int:
"""
Returns the last digit of barcode by excluding the last digit first
and then computing to reach the actual last digit from the remaining
12 digits.
>>> get_check_digit(8718452538119)
9
>>> get_check_digit(87184523)
5
>>> get_check_digit(87193425381086)
9
>>> [get_check_digit(x) for x in range(0, 100, 10)]
[0, 7, 4, 1, 8, 5, 2, 9, 6, 3]
"""
barcode //= 10 # exclude the last digit
checker = False
s = 0
# extract and check each digit
while barcode != 0:
mult = 1 if checker else 3
s += mult * (barcode % 10)
barcode //= 10
checker = not checker
return (10 - (s % 10)) % 10
def is_valid(barcode: int) -> bool:
"""
Checks for length of barcode and last-digit
Returns boolean value of validity of barcode
>>> is_valid(8718452538119)
True
>>> is_valid(87184525)
False
>>> is_valid(87193425381089)
False
>>> is_valid(0)
False
>>> is_valid(dwefgiweuf)
Traceback (most recent call last):
...
NameError: name 'dwefgiweuf' is not defined
"""
return len(str(barcode)) == 13 and get_check_digit(barcode) == barcode % 10
def get_barcode(barcode: str) -> int:
"""
Returns the barcode as an integer
>>> get_barcode("8718452538119")
8718452538119
>>> get_barcode("dwefgiweuf")
Traceback (most recent call last):
...
ValueError: Barcode 'dwefgiweuf' has alphabetic characters.
"""
if str(barcode).isalpha():
raise ValueError(f"Barcode '{barcode}' has alphabetic characters.")
elif int(barcode) < 0:
raise ValueError("The entered barcode has a negative value. Try again.")
else:
return int(barcode)
if __name__ == "__main__":
import doctest
doctest.testmod()
"""
Enter a barcode.
"""
barcode = get_barcode(input("Barcode: ").strip())
if is_valid(barcode):
print(f"'{barcode}' is a valid barcode.")
else:
print(f"'{barcode}' is NOT a valid barcode.")
| """
https://en.wikipedia.org/wiki/Check_digit#Algorithms
"""
def get_check_digit(barcode: int) -> int:
"""
Returns the last digit of barcode by excluding the last digit first
and then computing to reach the actual last digit from the remaining
12 digits.
>>> get_check_digit(8718452538119)
9
>>> get_check_digit(87184523)
5
>>> get_check_digit(87193425381086)
9
>>> [get_check_digit(x) for x in range(0, 100, 10)]
[0, 7, 4, 1, 8, 5, 2, 9, 6, 3]
"""
barcode //= 10 # exclude the last digit
checker = False
s = 0
# extract and check each digit
while barcode != 0:
mult = 1 if checker else 3
s += mult * (barcode % 10)
barcode //= 10
checker = not checker
return (10 - (s % 10)) % 10
def is_valid(barcode: int) -> bool:
"""
Checks for length of barcode and last-digit
Returns boolean value of validity of barcode
>>> is_valid(8718452538119)
True
>>> is_valid(87184525)
False
>>> is_valid(87193425381089)
False
>>> is_valid(0)
False
>>> is_valid(dwefgiweuf)
Traceback (most recent call last):
...
NameError: name 'dwefgiweuf' is not defined
"""
return len(str(barcode)) == 13 and get_check_digit(barcode) == barcode % 10
def get_barcode(barcode: str) -> int:
"""
Returns the barcode as an integer
>>> get_barcode("8718452538119")
8718452538119
>>> get_barcode("dwefgiweuf")
Traceback (most recent call last):
...
ValueError: Barcode 'dwefgiweuf' has alphabetic characters.
"""
if str(barcode).isalpha():
raise ValueError(f"Barcode '{barcode}' has alphabetic characters.")
elif int(barcode) < 0:
raise ValueError("The entered barcode has a negative value. Try again.")
else:
return int(barcode)
if __name__ == "__main__":
import doctest
doctest.testmod()
"""
Enter a barcode.
"""
barcode = get_barcode(input("Barcode: ").strip())
if is_valid(barcode):
print(f"'{barcode}' is a valid barcode.")
else:
print(f"'{barcode}' is NOT a valid barcode.")
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def password_generator(length: int = 8) -> str:
"""
Password Generator allows you to generate a random password of length N.
>>> len(password_generator())
8
>>> len(password_generator(length=16))
16
>>> len(password_generator(257))
257
>>> len(password_generator(length=0))
0
>>> len(password_generator(-1))
0
"""
chars = ascii_letters + digits + punctuation
return "".join(secrets.choice(chars) for _ in range(length))
# ALTERNATIVE METHODS
# chars_incl= characters that must be in password
# i= how many letters or characters the password length will be
def alternative_password_generator(chars_incl: str, i: int) -> str:
# Password Generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i -= len(chars_incl)
quotient = i // 3
remainder = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
chars = (
chars_incl
+ random(ascii_letters, quotient + remainder)
+ random(digits, quotient)
+ random(punctuation, quotient)
)
list_of_chars = list(chars)
shuffle(list_of_chars)
return "".join(list_of_chars)
# random is a generalised function for letters, characters and numbers
def random(chars_incl: str, i: int) -> str:
return "".join(secrets.choice(chars_incl) for _ in range(i))
def random_number(chars_incl, i):
pass # Put your code here...
def random_letters(chars_incl, i):
pass # Put your code here...
def random_characters(chars_incl, i):
pass # Put your code here...
# This Will Check Whether A Given Password Is Strong Or Not
# It Follows The Rule that Length Of Password Should Be At Least 8 Characters
# And At Least 1 Lower, 1 Upper, 1 Number And 1 Special Character
def is_strong_password(password: str, min_length: int = 8) -> bool:
"""
>>> is_strong_password('Hwea7$2!')
True
>>> is_strong_password('Sh0r1')
False
>>> is_strong_password('Hello123')
False
>>> is_strong_password('Hello1238udfhiaf038fajdvjjf!jaiuFhkqi1')
True
>>> is_strong_password('0')
False
"""
if len(password) < min_length:
# Your Password must be at least 8 characters long
return False
upper = any(char in ascii_uppercase for char in password)
lower = any(char in ascii_lowercase for char in password)
num = any(char in digits for char in password)
spec_char = any(char in punctuation for char in password)
if upper and lower and num and spec_char:
return True
else:
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
return False
def main():
length = int(input("Please indicate the max length of your password: ").strip())
chars_incl = input(
"Please indicate the characters that must be in your password: "
).strip()
print("Password generated:", password_generator(length))
print(
"Alternative Password generated:",
alternative_password_generator(chars_incl, length),
)
print("[If you are thinking of using this passsword, You better save it.]")
if __name__ == "__main__":
main()
| import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def password_generator(length: int = 8) -> str:
"""
Password Generator allows you to generate a random password of length N.
>>> len(password_generator())
8
>>> len(password_generator(length=16))
16
>>> len(password_generator(257))
257
>>> len(password_generator(length=0))
0
>>> len(password_generator(-1))
0
"""
chars = ascii_letters + digits + punctuation
return "".join(secrets.choice(chars) for _ in range(length))
# ALTERNATIVE METHODS
# chars_incl= characters that must be in password
# i= how many letters or characters the password length will be
def alternative_password_generator(chars_incl: str, i: int) -> str:
# Password Generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i -= len(chars_incl)
quotient = i // 3
remainder = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
chars = (
chars_incl
+ random(ascii_letters, quotient + remainder)
+ random(digits, quotient)
+ random(punctuation, quotient)
)
list_of_chars = list(chars)
shuffle(list_of_chars)
return "".join(list_of_chars)
# random is a generalised function for letters, characters and numbers
def random(chars_incl: str, i: int) -> str:
return "".join(secrets.choice(chars_incl) for _ in range(i))
def random_number(chars_incl, i):
pass # Put your code here...
def random_letters(chars_incl, i):
pass # Put your code here...
def random_characters(chars_incl, i):
pass # Put your code here...
# This Will Check Whether A Given Password Is Strong Or Not
# It Follows The Rule that Length Of Password Should Be At Least 8 Characters
# And At Least 1 Lower, 1 Upper, 1 Number And 1 Special Character
def is_strong_password(password: str, min_length: int = 8) -> bool:
"""
>>> is_strong_password('Hwea7$2!')
True
>>> is_strong_password('Sh0r1')
False
>>> is_strong_password('Hello123')
False
>>> is_strong_password('Hello1238udfhiaf038fajdvjjf!jaiuFhkqi1')
True
>>> is_strong_password('0')
False
"""
if len(password) < min_length:
# Your Password must be at least 8 characters long
return False
upper = any(char in ascii_uppercase for char in password)
lower = any(char in ascii_lowercase for char in password)
num = any(char in digits for char in password)
spec_char = any(char in punctuation for char in password)
if upper and lower and num and spec_char:
return True
else:
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
return False
def main():
length = int(input("Please indicate the max length of your password: ").strip())
chars_incl = input(
"Please indicate the characters that must be in your password: "
).strip()
print("Password generated:", password_generator(length))
print(
"Alternative Password generated:",
alternative_password_generator(chars_incl, length),
)
print("[If you are thinking of using this passsword, You better save it.]")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] 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 | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
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4QEzRi_5P,4[# | -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Find the kinetic energy of an object, given its mass and velocity.
Description : In physics, the kinetic energy of an object is the energy that it
possesses due to its motion.It is defined as the work needed to accelerate a body of a
given mass from rest to its stated velocity.Having gained this energy during its
acceleration, the body maintains this kinetic energy unless its speed changes.The same
amount of work is done by the body when decelerating from its current speed to a state
of rest.Formally, a kinetic energy is any term in a system's Lagrangian which includes
a derivative with respect to time.
In classical mechanics, the kinetic energy of a non-rotating object of mass m traveling
at a speed v is ½mv².In relativistic mechanics, this is a good approximation only when
v is much less than the speed of light.The standard unit of kinetic energy is the
joule, while the English unit of kinetic energy is the foot-pound.
Reference : https://en.m.wikipedia.org/wiki/Kinetic_energy
"""
def kinetic_energy(mass: float, velocity: float) -> float:
"""
Calculate kinetic energy.
The kinetic energy of a non-rotating object of mass m traveling at a speed v is ½mv²
>>> kinetic_energy(10,10)
500.0
>>> kinetic_energy(0,10)
0.0
>>> kinetic_energy(10,0)
0.0
>>> kinetic_energy(20,-20)
4000.0
>>> kinetic_energy(0,0)
0.0
>>> kinetic_energy(2,2)
4.0
>>> kinetic_energy(100,100)
500000.0
"""
if mass < 0:
raise ValueError("The mass of a body cannot be negative")
return 0.5 * mass * abs(velocity) * abs(velocity)
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| """
Find the kinetic energy of an object, given its mass and velocity.
Description : In physics, the kinetic energy of an object is the energy that it
possesses due to its motion.It is defined as the work needed to accelerate a body of a
given mass from rest to its stated velocity.Having gained this energy during its
acceleration, the body maintains this kinetic energy unless its speed changes.The same
amount of work is done by the body when decelerating from its current speed to a state
of rest.Formally, a kinetic energy is any term in a system's Lagrangian which includes
a derivative with respect to time.
In classical mechanics, the kinetic energy of a non-rotating object of mass m traveling
at a speed v is ½mv².In relativistic mechanics, this is a good approximation only when
v is much less than the speed of light.The standard unit of kinetic energy is the
joule, while the English unit of kinetic energy is the foot-pound.
Reference : https://en.m.wikipedia.org/wiki/Kinetic_energy
"""
def kinetic_energy(mass: float, velocity: float) -> float:
"""
Calculate kinetic energy.
The kinetic energy of a non-rotating object of mass m traveling at a speed v is ½mv²
>>> kinetic_energy(10,10)
500.0
>>> kinetic_energy(0,10)
0.0
>>> kinetic_energy(10,0)
0.0
>>> kinetic_energy(20,-20)
4000.0
>>> kinetic_energy(0,0)
0.0
>>> kinetic_energy(2,2)
4.0
>>> kinetic_energy(100,100)
500000.0
"""
if mass < 0:
raise ValueError("The mass of a body cannot be negative")
return 0.5 * mass * abs(velocity) * abs(velocity)
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] 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 | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
A pure Python implementation of the quick sort algorithm
For doctests run following command:
python3 -m doctest -v quick_sort.py
For manual testing run:
python3 quick_sort.py
"""
from __future__ import annotations
from random import randrange
def quick_sort(collection: list) -> list:
"""A pure Python implementation of quick sort algorithm
:param collection: a mutable collection of comparable items
:return: the same collection ordered by ascending
Examples:
>>> quick_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> quick_sort([])
[]
>>> quick_sort([-2, 5, 0, -45])
[-45, -2, 0, 5]
"""
if len(collection) < 2:
return collection
pivot_index = randrange(len(collection)) # Use random element as pivot
pivot = collection[pivot_index]
greater: list[int] = [] # All elements greater than pivot
lesser: list[int] = [] # All elements less than or equal to pivot
for element in collection[:pivot_index]:
(greater if element > pivot else lesser).append(element)
for element in collection[pivot_index + 1 :]:
(greater if element > pivot else lesser).append(element)
return quick_sort(lesser) + [pivot] + quick_sort(greater)
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(quick_sort(unsorted))
| """
A pure Python implementation of the quick sort algorithm
For doctests run following command:
python3 -m doctest -v quick_sort.py
For manual testing run:
python3 quick_sort.py
"""
from __future__ import annotations
from random import randrange
def quick_sort(collection: list) -> list:
"""A pure Python implementation of quick sort algorithm
:param collection: a mutable collection of comparable items
:return: the same collection ordered by ascending
Examples:
>>> quick_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> quick_sort([])
[]
>>> quick_sort([-2, 5, 0, -45])
[-45, -2, 0, 5]
"""
if len(collection) < 2:
return collection
pivot_index = randrange(len(collection)) # Use random element as pivot
pivot = collection[pivot_index]
greater: list[int] = [] # All elements greater than pivot
lesser: list[int] = [] # All elements less than or equal to pivot
for element in collection[:pivot_index]:
(greater if element > pivot else lesser).append(element)
for element in collection[pivot_index + 1 :]:
(greater if element > pivot else lesser).append(element)
return quick_sort(lesser) + [pivot] + quick_sort(greater)
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(quick_sort(unsorted))
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Python program to implement Pigeonhole Sorting in python
# Algorithm for the pigeonhole sorting
def pigeonhole_sort(a):
"""
>>> a = [8, 3, 2, 7, 4, 6, 8]
>>> b = sorted(a) # a nondestructive sort
>>> pigeonhole_sort(a) # a destructive sort
>>> a == b
True
"""
# size of range of values in the list (ie, number of pigeonholes we need)
min_val = min(a) # min() finds the minimum value
max_val = max(a) # max() finds the maximum value
size = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
holes = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(x, int), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
i = 0
for count in range(size):
while holes[count] > 0:
holes[count] -= 1
a[i] = count + min_val
i += 1
def main():
a = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(a)
print("Sorted order is:", " ".join(a))
if __name__ == "__main__":
main()
| # Python program to implement Pigeonhole Sorting in python
# Algorithm for the pigeonhole sorting
def pigeonhole_sort(a):
"""
>>> a = [8, 3, 2, 7, 4, 6, 8]
>>> b = sorted(a) # a nondestructive sort
>>> pigeonhole_sort(a) # a destructive sort
>>> a == b
True
"""
# size of range of values in the list (ie, number of pigeonholes we need)
min_val = min(a) # min() finds the minimum value
max_val = max(a) # max() finds the maximum value
size = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
holes = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(x, int), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
i = 0
for count in range(size):
while holes[count] > 0:
holes[count] -= 1
a[i] = count + min_val
i += 1
def main():
a = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(a)
print("Sorted order is:", " ".join(a))
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def binary_search(array: list, lower_bound: int, upper_bound: int, value: int) -> int:
"""
This function carries out Binary search on a 1d array and
return -1 if it do not exist
array: A 1d sorted array
value : the value meant to be searched
>>> matrix = [1, 4, 7, 11, 15]
>>> binary_search(matrix, 0, len(matrix) - 1, 1)
0
>>> binary_search(matrix, 0, len(matrix) - 1, 23)
-1
"""
r = int((lower_bound + upper_bound) // 2)
if array[r] == value:
return r
if lower_bound >= upper_bound:
return -1
if array[r] < value:
return binary_search(array, r + 1, upper_bound, value)
else:
return binary_search(array, lower_bound, r - 1, value)
def mat_bin_search(value: int, matrix: list) -> list:
"""
This function loops over a 2d matrix and calls binarySearch on
the selected 1d array and returns [-1, -1] is it do not exist
value : value meant to be searched
matrix = a sorted 2d matrix
>>> matrix = [[1, 4, 7, 11, 15],
... [2, 5, 8, 12, 19],
... [3, 6, 9, 16, 22],
... [10, 13, 14, 17, 24],
... [18, 21, 23, 26, 30]]
>>> target = 1
>>> mat_bin_search(target, matrix)
[0, 0]
>>> target = 34
>>> mat_bin_search(target, matrix)
[-1, -1]
"""
index = 0
if matrix[index][0] == value:
return [index, 0]
while index < len(matrix) and matrix[index][0] < value:
r = binary_search(matrix[index], 0, len(matrix[index]) - 1, value)
if r != -1:
return [index, r]
index += 1
return [-1, -1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| def binary_search(array: list, lower_bound: int, upper_bound: int, value: int) -> int:
"""
This function carries out Binary search on a 1d array and
return -1 if it do not exist
array: A 1d sorted array
value : the value meant to be searched
>>> matrix = [1, 4, 7, 11, 15]
>>> binary_search(matrix, 0, len(matrix) - 1, 1)
0
>>> binary_search(matrix, 0, len(matrix) - 1, 23)
-1
"""
r = int((lower_bound + upper_bound) // 2)
if array[r] == value:
return r
if lower_bound >= upper_bound:
return -1
if array[r] < value:
return binary_search(array, r + 1, upper_bound, value)
else:
return binary_search(array, lower_bound, r - 1, value)
def mat_bin_search(value: int, matrix: list) -> list:
"""
This function loops over a 2d matrix and calls binarySearch on
the selected 1d array and returns [-1, -1] is it do not exist
value : value meant to be searched
matrix = a sorted 2d matrix
>>> matrix = [[1, 4, 7, 11, 15],
... [2, 5, 8, 12, 19],
... [3, 6, 9, 16, 22],
... [10, 13, 14, 17, 24],
... [18, 21, 23, 26, 30]]
>>> target = 1
>>> mat_bin_search(target, matrix)
[0, 0]
>>> target = 34
>>> mat_bin_search(target, matrix)
[-1, -1]
"""
index = 0
if matrix[index][0] == value:
return [index, 0]
while index < len(matrix) and matrix[index][0] < value:
r = binary_search(matrix[index], 0, len(matrix[index]) - 1, value)
if r != -1:
return [index, r]
index += 1
return [-1, -1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import random
import sys
LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def main() -> None:
message = input("Enter message: ")
key = "LFWOAYUISVKMNXPBDCRJTQEGHZ"
resp = input("Encrypt/Decrypt [e/d]: ")
check_valid_key(key)
if resp.lower().startswith("e"):
mode = "encrypt"
translated = encrypt_message(key, message)
elif resp.lower().startswith("d"):
mode = "decrypt"
translated = decrypt_message(key, message)
print(f"\n{mode.title()}ion: \n{translated}")
def check_valid_key(key: str) -> None:
key_list = list(key)
letters_list = list(LETTERS)
key_list.sort()
letters_list.sort()
if key_list != letters_list:
sys.exit("Error in the key or symbol set.")
def encrypt_message(key: str, message: str) -> str:
"""
>>> encrypt_message('LFWOAYUISVKMNXPBDCRJTQEGHZ', 'Harshil Darji')
'Ilcrism Olcvs'
"""
return translate_message(key, message, "encrypt")
def decrypt_message(key: str, message: str) -> str:
"""
>>> decrypt_message('LFWOAYUISVKMNXPBDCRJTQEGHZ', 'Ilcrism Olcvs')
'Harshil Darji'
"""
return translate_message(key, message, "decrypt")
def translate_message(key: str, message: str, mode: str) -> str:
translated = ""
chars_a = LETTERS
chars_b = key
if mode == "decrypt":
chars_a, chars_b = chars_b, chars_a
for symbol in message:
if symbol.upper() in chars_a:
sym_index = chars_a.find(symbol.upper())
if symbol.isupper():
translated += chars_b[sym_index].upper()
else:
translated += chars_b[sym_index].lower()
else:
translated += symbol
return translated
def get_random_key() -> str:
key = list(LETTERS)
random.shuffle(key)
return "".join(key)
if __name__ == "__main__":
main()
| import random
import sys
LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def main() -> None:
message = input("Enter message: ")
key = "LFWOAYUISVKMNXPBDCRJTQEGHZ"
resp = input("Encrypt/Decrypt [e/d]: ")
check_valid_key(key)
if resp.lower().startswith("e"):
mode = "encrypt"
translated = encrypt_message(key, message)
elif resp.lower().startswith("d"):
mode = "decrypt"
translated = decrypt_message(key, message)
print(f"\n{mode.title()}ion: \n{translated}")
def check_valid_key(key: str) -> None:
key_list = list(key)
letters_list = list(LETTERS)
key_list.sort()
letters_list.sort()
if key_list != letters_list:
sys.exit("Error in the key or symbol set.")
def encrypt_message(key: str, message: str) -> str:
"""
>>> encrypt_message('LFWOAYUISVKMNXPBDCRJTQEGHZ', 'Harshil Darji')
'Ilcrism Olcvs'
"""
return translate_message(key, message, "encrypt")
def decrypt_message(key: str, message: str) -> str:
"""
>>> decrypt_message('LFWOAYUISVKMNXPBDCRJTQEGHZ', 'Ilcrism Olcvs')
'Harshil Darji'
"""
return translate_message(key, message, "decrypt")
def translate_message(key: str, message: str, mode: str) -> str:
translated = ""
chars_a = LETTERS
chars_b = key
if mode == "decrypt":
chars_a, chars_b = chars_b, chars_a
for symbol in message:
if symbol.upper() in chars_a:
sym_index = chars_a.find(symbol.upper())
if symbol.isupper():
translated += chars_b[sym_index].upper()
else:
translated += chars_b[sym_index].lower()
else:
translated += symbol
return translated
def get_random_key() -> str:
key = list(LETTERS)
random.shuffle(key)
return "".join(key)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] 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 | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
== Perfect Number ==
In number theory, a perfect number is a positive integer that is equal to the sum of
its positive divisors, excluding the number itself.
For example: 6 ==> divisors[1, 2, 3, 6]
Excluding 6, the sum(divisors) is 1 + 2 + 3 = 6
So, 6 is a Perfect Number
Other examples of Perfect Numbers: 28, 486, ...
https://en.wikipedia.org/wiki/Perfect_number
"""
def perfect(number: int) -> bool:
"""
>>> perfect(27)
False
>>> perfect(28)
True
>>> perfect(29)
False
Start from 1 because dividing by 0 will raise ZeroDivisionError.
A number at most can be divisible by the half of the number except the number
itself. For example, 6 is at most can be divisible by 3 except by 6 itself.
"""
return sum(i for i in range(1, number // 2 + 1) if number % i == 0) == number
if __name__ == "__main__":
print("Program to check whether a number is a Perfect number or not...")
number = int(input("Enter number: ").strip())
print(f"{number} is {'' if perfect(number) else 'not '}a Perfect Number.")
| """
== Perfect Number ==
In number theory, a perfect number is a positive integer that is equal to the sum of
its positive divisors, excluding the number itself.
For example: 6 ==> divisors[1, 2, 3, 6]
Excluding 6, the sum(divisors) is 1 + 2 + 3 = 6
So, 6 is a Perfect Number
Other examples of Perfect Numbers: 28, 486, ...
https://en.wikipedia.org/wiki/Perfect_number
"""
def perfect(number: int) -> bool:
"""
>>> perfect(27)
False
>>> perfect(28)
True
>>> perfect(29)
False
Start from 1 because dividing by 0 will raise ZeroDivisionError.
A number at most can be divisible by the half of the number except the number
itself. For example, 6 is at most can be divisible by 3 except by 6 itself.
"""
return sum(i for i in range(1, number // 2 + 1) if number % i == 0) == number
if __name__ == "__main__":
print("Program to check whether a number is a Perfect number or not...")
number = int(input("Enter number: ").strip())
print(f"{number} is {'' if perfect(number) else 'not '}a Perfect Number.")
| -1 |
TheAlgorithms/Python | 7,984 | fix: no implicit optional | https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2022-11-15T03:45:16Z" | "2022-11-15T13:55:14Z" | 316e71b03448b6adb8a32d96cb4d6488ee7b7787 | 3bf86b91e7d438eb2b9ecbab68060c007d270332 | fix: no implicit optional. https://mypy-lang.blogspot.com/2022/11/mypy-0990-released.html
### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [ ] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [ ] This pull request is all my own work -- I have not plagiarized.
* [ ] 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is a Python implementation of the levenshtein distance.
Levenshtein distance is a string metric for measuring the
difference between two sequences.
For doctests run following command:
python -m doctest -v levenshtein-distance.py
or
python3 -m doctest -v levenshtein-distance.py
For manual testing run:
python levenshtein-distance.py
"""
def levenshtein_distance(first_word: str, second_word: str) -> int:
"""Implementation of the levenshtein distance in Python.
:param first_word: the first word to measure the difference.
:param second_word: the second word to measure the difference.
:return: the levenshtein distance between the two words.
Examples:
>>> levenshtein_distance("planet", "planetary")
3
>>> levenshtein_distance("", "test")
4
>>> levenshtein_distance("book", "back")
2
>>> levenshtein_distance("book", "book")
0
>>> levenshtein_distance("test", "")
4
>>> levenshtein_distance("", "")
0
>>> levenshtein_distance("orchestration", "container")
10
"""
# The longer word should come first
if len(first_word) < len(second_word):
return levenshtein_distance(second_word, first_word)
if len(second_word) == 0:
return len(first_word)
previous_row = list(range(len(second_word) + 1))
for i, c1 in enumerate(first_word):
current_row = [i + 1]
for j, c2 in enumerate(second_word):
# Calculate insertions, deletions and substitutions
insertions = previous_row[j + 1] + 1
deletions = current_row[j] + 1
substitutions = previous_row[j] + (c1 != c2)
# Get the minimum to append to the current row
current_row.append(min(insertions, deletions, substitutions))
# Store the previous row
previous_row = current_row
# Returns the last element (distance)
return previous_row[-1]
if __name__ == "__main__":
first_word = input("Enter the first word:\n").strip()
second_word = input("Enter the second word:\n").strip()
result = levenshtein_distance(first_word, second_word)
print(f"Levenshtein distance between {first_word} and {second_word} is {result}")
| """
This is a Python implementation of the levenshtein distance.
Levenshtein distance is a string metric for measuring the
difference between two sequences.
For doctests run following command:
python -m doctest -v levenshtein-distance.py
or
python3 -m doctest -v levenshtein-distance.py
For manual testing run:
python levenshtein-distance.py
"""
def levenshtein_distance(first_word: str, second_word: str) -> int:
"""Implementation of the levenshtein distance in Python.
:param first_word: the first word to measure the difference.
:param second_word: the second word to measure the difference.
:return: the levenshtein distance between the two words.
Examples:
>>> levenshtein_distance("planet", "planetary")
3
>>> levenshtein_distance("", "test")
4
>>> levenshtein_distance("book", "back")
2
>>> levenshtein_distance("book", "book")
0
>>> levenshtein_distance("test", "")
4
>>> levenshtein_distance("", "")
0
>>> levenshtein_distance("orchestration", "container")
10
"""
# The longer word should come first
if len(first_word) < len(second_word):
return levenshtein_distance(second_word, first_word)
if len(second_word) == 0:
return len(first_word)
previous_row = list(range(len(second_word) + 1))
for i, c1 in enumerate(first_word):
current_row = [i + 1]
for j, c2 in enumerate(second_word):
# Calculate insertions, deletions and substitutions
insertions = previous_row[j + 1] + 1
deletions = current_row[j] + 1
substitutions = previous_row[j] + (c1 != c2)
# Get the minimum to append to the current row
current_row.append(min(insertions, deletions, substitutions))
# Store the previous row
previous_row = current_row
# Returns the last element (distance)
return previous_row[-1]
if __name__ == "__main__":
first_word = input("Enter the first word:\n").strip()
second_word = input("Enter the second word:\n").strip()
result = levenshtein_distance(first_word, second_word)
print(f"Levenshtein distance between {first_word} and {second_word} is {result}")
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| [flake8]
max-line-length = 88
max-complexity = 25
extend-ignore =
A003 # Class attribute is shadowing a python builtin
# Formatting style for `black`
E203 # Whitespace before ':'
W503 # Line break occurred before a binary operator
| [flake8]
max-line-length = 88
# max-complexity should be 10
max-complexity = 23
extend-ignore =
# Formatting style for `black`
E203 # Whitespace before ':'
W503 # Line break occurred before a binary operator
| 1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
|
## Arithmetic Analysis
* [Bisection](arithmetic_analysis/bisection.py)
* [Gaussian Elimination](arithmetic_analysis/gaussian_elimination.py)
* [In Static Equilibrium](arithmetic_analysis/in_static_equilibrium.py)
* [Intersection](arithmetic_analysis/intersection.py)
* [Jacobi Iteration Method](arithmetic_analysis/jacobi_iteration_method.py)
* [Lu Decomposition](arithmetic_analysis/lu_decomposition.py)
* [Newton Forward Interpolation](arithmetic_analysis/newton_forward_interpolation.py)
* [Newton Method](arithmetic_analysis/newton_method.py)
* [Newton Raphson](arithmetic_analysis/newton_raphson.py)
* [Newton Raphson New](arithmetic_analysis/newton_raphson_new.py)
* [Secant Method](arithmetic_analysis/secant_method.py)
## Audio Filters
* [Butterworth Filter](audio_filters/butterworth_filter.py)
* [Equal Loudness Filter](audio_filters/equal_loudness_filter.py)
* [Iir Filter](audio_filters/iir_filter.py)
* [Show Response](audio_filters/show_response.py)
## Backtracking
* [All Combinations](backtracking/all_combinations.py)
* [All Permutations](backtracking/all_permutations.py)
* [All Subsequences](backtracking/all_subsequences.py)
* [Coloring](backtracking/coloring.py)
* [Combination Sum](backtracking/combination_sum.py)
* [Hamiltonian Cycle](backtracking/hamiltonian_cycle.py)
* [Knight Tour](backtracking/knight_tour.py)
* [Minimax](backtracking/minimax.py)
* [Minmax](backtracking/minmax.py)
* [N Queens](backtracking/n_queens.py)
* [N Queens Math](backtracking/n_queens_math.py)
* [Rat In Maze](backtracking/rat_in_maze.py)
* [Sudoku](backtracking/sudoku.py)
* [Sum Of Subsets](backtracking/sum_of_subsets.py)
## Bit Manipulation
* [Binary And Operator](bit_manipulation/binary_and_operator.py)
* [Binary Count Setbits](bit_manipulation/binary_count_setbits.py)
* [Binary Count Trailing Zeros](bit_manipulation/binary_count_trailing_zeros.py)
* [Binary Or Operator](bit_manipulation/binary_or_operator.py)
* [Binary Shifts](bit_manipulation/binary_shifts.py)
* [Binary Twos Complement](bit_manipulation/binary_twos_complement.py)
* [Binary Xor Operator](bit_manipulation/binary_xor_operator.py)
* [Count 1S Brian Kernighan Method](bit_manipulation/count_1s_brian_kernighan_method.py)
* [Count Number Of One Bits](bit_manipulation/count_number_of_one_bits.py)
* [Gray Code Sequence](bit_manipulation/gray_code_sequence.py)
* [Highest Set Bit](bit_manipulation/highest_set_bit.py)
* [Index Of Rightmost Set Bit](bit_manipulation/index_of_rightmost_set_bit.py)
* [Is Even](bit_manipulation/is_even.py)
* [Reverse Bits](bit_manipulation/reverse_bits.py)
* [Single Bit Manipulation Operations](bit_manipulation/single_bit_manipulation_operations.py)
## Blockchain
* [Chinese Remainder Theorem](blockchain/chinese_remainder_theorem.py)
* [Diophantine Equation](blockchain/diophantine_equation.py)
* [Modular Division](blockchain/modular_division.py)
## Boolean Algebra
* [And Gate](boolean_algebra/and_gate.py)
* [Nand Gate](boolean_algebra/nand_gate.py)
* [Norgate](boolean_algebra/norgate.py)
* [Not Gate](boolean_algebra/not_gate.py)
* [Or Gate](boolean_algebra/or_gate.py)
* [Quine Mc Cluskey](boolean_algebra/quine_mc_cluskey.py)
* [Xnor Gate](boolean_algebra/xnor_gate.py)
* [Xor Gate](boolean_algebra/xor_gate.py)
## Cellular Automata
* [Conways Game Of Life](cellular_automata/conways_game_of_life.py)
* [Game Of Life](cellular_automata/game_of_life.py)
* [Nagel Schrekenberg](cellular_automata/nagel_schrekenberg.py)
* [One Dimensional](cellular_automata/one_dimensional.py)
## Ciphers
* [A1Z26](ciphers/a1z26.py)
* [Affine Cipher](ciphers/affine_cipher.py)
* [Atbash](ciphers/atbash.py)
* [Baconian Cipher](ciphers/baconian_cipher.py)
* [Base16](ciphers/base16.py)
* [Base32](ciphers/base32.py)
* [Base64](ciphers/base64.py)
* [Base85](ciphers/base85.py)
* [Beaufort Cipher](ciphers/beaufort_cipher.py)
* [Bifid](ciphers/bifid.py)
* [Brute Force Caesar Cipher](ciphers/brute_force_caesar_cipher.py)
* [Caesar Cipher](ciphers/caesar_cipher.py)
* [Cryptomath Module](ciphers/cryptomath_module.py)
* [Decrypt Caesar With Chi Squared](ciphers/decrypt_caesar_with_chi_squared.py)
* [Deterministic Miller Rabin](ciphers/deterministic_miller_rabin.py)
* [Diffie](ciphers/diffie.py)
* [Diffie Hellman](ciphers/diffie_hellman.py)
* [Elgamal Key Generator](ciphers/elgamal_key_generator.py)
* [Enigma Machine2](ciphers/enigma_machine2.py)
* [Hill Cipher](ciphers/hill_cipher.py)
* [Mixed Keyword Cypher](ciphers/mixed_keyword_cypher.py)
* [Mono Alphabetic Ciphers](ciphers/mono_alphabetic_ciphers.py)
* [Morse Code](ciphers/morse_code.py)
* [Onepad Cipher](ciphers/onepad_cipher.py)
* [Playfair Cipher](ciphers/playfair_cipher.py)
* [Polybius](ciphers/polybius.py)
* [Porta Cipher](ciphers/porta_cipher.py)
* [Rabin Miller](ciphers/rabin_miller.py)
* [Rail Fence Cipher](ciphers/rail_fence_cipher.py)
* [Rot13](ciphers/rot13.py)
* [Rsa Cipher](ciphers/rsa_cipher.py)
* [Rsa Factorization](ciphers/rsa_factorization.py)
* [Rsa Key Generator](ciphers/rsa_key_generator.py)
* [Shuffled Shift Cipher](ciphers/shuffled_shift_cipher.py)
* [Simple Keyword Cypher](ciphers/simple_keyword_cypher.py)
* [Simple Substitution Cipher](ciphers/simple_substitution_cipher.py)
* [Trafid Cipher](ciphers/trafid_cipher.py)
* [Transposition Cipher](ciphers/transposition_cipher.py)
* [Transposition Cipher Encrypt Decrypt File](ciphers/transposition_cipher_encrypt_decrypt_file.py)
* [Vigenere Cipher](ciphers/vigenere_cipher.py)
* [Xor Cipher](ciphers/xor_cipher.py)
## Compression
* [Burrows Wheeler](compression/burrows_wheeler.py)
* [Huffman](compression/huffman.py)
* [Lempel Ziv](compression/lempel_ziv.py)
* [Lempel Ziv Decompress](compression/lempel_ziv_decompress.py)
* [Peak Signal To Noise Ratio](compression/peak_signal_to_noise_ratio.py)
* [Run Length Encoding](compression/run_length_encoding.py)
## Computer Vision
* [Cnn Classification](computer_vision/cnn_classification.py)
* [Flip Augmentation](computer_vision/flip_augmentation.py)
* [Harris Corner](computer_vision/harris_corner.py)
* [Horn Schunck](computer_vision/horn_schunck.py)
* [Mean Threshold](computer_vision/mean_threshold.py)
* [Mosaic Augmentation](computer_vision/mosaic_augmentation.py)
* [Pooling Functions](computer_vision/pooling_functions.py)
## Conversions
* [Astronomical Length Scale Conversion](conversions/astronomical_length_scale_conversion.py)
* [Binary To Decimal](conversions/binary_to_decimal.py)
* [Binary To Hexadecimal](conversions/binary_to_hexadecimal.py)
* [Binary To Octal](conversions/binary_to_octal.py)
* [Decimal To Any](conversions/decimal_to_any.py)
* [Decimal To Binary](conversions/decimal_to_binary.py)
* [Decimal To Binary Recursion](conversions/decimal_to_binary_recursion.py)
* [Decimal To Hexadecimal](conversions/decimal_to_hexadecimal.py)
* [Decimal To Octal](conversions/decimal_to_octal.py)
* [Excel Title To Column](conversions/excel_title_to_column.py)
* [Hex To Bin](conversions/hex_to_bin.py)
* [Hexadecimal To Decimal](conversions/hexadecimal_to_decimal.py)
* [Length Conversion](conversions/length_conversion.py)
* [Molecular Chemistry](conversions/molecular_chemistry.py)
* [Octal To Decimal](conversions/octal_to_decimal.py)
* [Prefix Conversions](conversions/prefix_conversions.py)
* [Prefix Conversions String](conversions/prefix_conversions_string.py)
* [Pressure Conversions](conversions/pressure_conversions.py)
* [Rgb Hsv Conversion](conversions/rgb_hsv_conversion.py)
* [Roman Numerals](conversions/roman_numerals.py)
* [Speed Conversions](conversions/speed_conversions.py)
* [Temperature Conversions](conversions/temperature_conversions.py)
* [Volume Conversions](conversions/volume_conversions.py)
* [Weight Conversion](conversions/weight_conversion.py)
## Data Structures
* Arrays
* [Permutations](data_structures/arrays/permutations.py)
* Binary Tree
* [Avl Tree](data_structures/binary_tree/avl_tree.py)
* [Basic Binary Tree](data_structures/binary_tree/basic_binary_tree.py)
* [Binary Search Tree](data_structures/binary_tree/binary_search_tree.py)
* [Binary Search Tree Recursive](data_structures/binary_tree/binary_search_tree_recursive.py)
* [Binary Tree Mirror](data_structures/binary_tree/binary_tree_mirror.py)
* [Binary Tree Node Sum](data_structures/binary_tree/binary_tree_node_sum.py)
* [Binary Tree Path Sum](data_structures/binary_tree/binary_tree_path_sum.py)
* [Binary Tree Traversals](data_structures/binary_tree/binary_tree_traversals.py)
* [Diff Views Of Binary Tree](data_structures/binary_tree/diff_views_of_binary_tree.py)
* [Fenwick Tree](data_structures/binary_tree/fenwick_tree.py)
* [Inorder Tree Traversal 2022](data_structures/binary_tree/inorder_tree_traversal_2022.py)
* [Lazy Segment Tree](data_structures/binary_tree/lazy_segment_tree.py)
* [Lowest Common Ancestor](data_structures/binary_tree/lowest_common_ancestor.py)
* [Maximum Fenwick Tree](data_structures/binary_tree/maximum_fenwick_tree.py)
* [Merge Two Binary Trees](data_structures/binary_tree/merge_two_binary_trees.py)
* [Non Recursive Segment Tree](data_structures/binary_tree/non_recursive_segment_tree.py)
* [Number Of Possible Binary Trees](data_structures/binary_tree/number_of_possible_binary_trees.py)
* [Red Black Tree](data_structures/binary_tree/red_black_tree.py)
* [Segment Tree](data_structures/binary_tree/segment_tree.py)
* [Segment Tree Other](data_structures/binary_tree/segment_tree_other.py)
* [Treap](data_structures/binary_tree/treap.py)
* [Wavelet Tree](data_structures/binary_tree/wavelet_tree.py)
* Disjoint Set
* [Alternate Disjoint Set](data_structures/disjoint_set/alternate_disjoint_set.py)
* [Disjoint Set](data_structures/disjoint_set/disjoint_set.py)
* Hashing
* [Double Hash](data_structures/hashing/double_hash.py)
* [Hash Table](data_structures/hashing/hash_table.py)
* [Hash Table With Linked List](data_structures/hashing/hash_table_with_linked_list.py)
* Number Theory
* [Prime Numbers](data_structures/hashing/number_theory/prime_numbers.py)
* [Quadratic Probing](data_structures/hashing/quadratic_probing.py)
* Heap
* [Binomial Heap](data_structures/heap/binomial_heap.py)
* [Heap](data_structures/heap/heap.py)
* [Heap Generic](data_structures/heap/heap_generic.py)
* [Max Heap](data_structures/heap/max_heap.py)
* [Min Heap](data_structures/heap/min_heap.py)
* [Randomized Heap](data_structures/heap/randomized_heap.py)
* [Skew Heap](data_structures/heap/skew_heap.py)
* Linked List
* [Circular Linked List](data_structures/linked_list/circular_linked_list.py)
* [Deque Doubly](data_structures/linked_list/deque_doubly.py)
* [Doubly Linked List](data_structures/linked_list/doubly_linked_list.py)
* [Doubly Linked List Two](data_structures/linked_list/doubly_linked_list_two.py)
* [From Sequence](data_structures/linked_list/from_sequence.py)
* [Has Loop](data_structures/linked_list/has_loop.py)
* [Is Palindrome](data_structures/linked_list/is_palindrome.py)
* [Merge Two Lists](data_structures/linked_list/merge_two_lists.py)
* [Middle Element Of Linked List](data_structures/linked_list/middle_element_of_linked_list.py)
* [Print Reverse](data_structures/linked_list/print_reverse.py)
* [Singly Linked List](data_structures/linked_list/singly_linked_list.py)
* [Skip List](data_structures/linked_list/skip_list.py)
* [Swap Nodes](data_structures/linked_list/swap_nodes.py)
* Queue
* [Circular Queue](data_structures/queue/circular_queue.py)
* [Circular Queue Linked List](data_structures/queue/circular_queue_linked_list.py)
* [Double Ended Queue](data_structures/queue/double_ended_queue.py)
* [Linked Queue](data_structures/queue/linked_queue.py)
* [Priority Queue Using List](data_structures/queue/priority_queue_using_list.py)
* [Queue On List](data_structures/queue/queue_on_list.py)
* [Queue On Pseudo Stack](data_structures/queue/queue_on_pseudo_stack.py)
* Stacks
* [Balanced Parentheses](data_structures/stacks/balanced_parentheses.py)
* [Dijkstras Two Stack Algorithm](data_structures/stacks/dijkstras_two_stack_algorithm.py)
* [Evaluate Postfix Notations](data_structures/stacks/evaluate_postfix_notations.py)
* [Infix To Postfix Conversion](data_structures/stacks/infix_to_postfix_conversion.py)
* [Infix To Prefix Conversion](data_structures/stacks/infix_to_prefix_conversion.py)
* [Next Greater Element](data_structures/stacks/next_greater_element.py)
* [Postfix Evaluation](data_structures/stacks/postfix_evaluation.py)
* [Prefix Evaluation](data_structures/stacks/prefix_evaluation.py)
* [Stack](data_structures/stacks/stack.py)
* [Stack With Doubly Linked List](data_structures/stacks/stack_with_doubly_linked_list.py)
* [Stack With Singly Linked List](data_structures/stacks/stack_with_singly_linked_list.py)
* [Stock Span Problem](data_structures/stacks/stock_span_problem.py)
* Trie
* [Radix Tree](data_structures/trie/radix_tree.py)
* [Trie](data_structures/trie/trie.py)
## Digital Image Processing
* [Change Brightness](digital_image_processing/change_brightness.py)
* [Change Contrast](digital_image_processing/change_contrast.py)
* [Convert To Negative](digital_image_processing/convert_to_negative.py)
* Dithering
* [Burkes](digital_image_processing/dithering/burkes.py)
* Edge Detection
* [Canny](digital_image_processing/edge_detection/canny.py)
* Filters
* [Bilateral Filter](digital_image_processing/filters/bilateral_filter.py)
* [Convolve](digital_image_processing/filters/convolve.py)
* [Gabor Filter](digital_image_processing/filters/gabor_filter.py)
* [Gaussian Filter](digital_image_processing/filters/gaussian_filter.py)
* [Local Binary Pattern](digital_image_processing/filters/local_binary_pattern.py)
* [Median Filter](digital_image_processing/filters/median_filter.py)
* [Sobel Filter](digital_image_processing/filters/sobel_filter.py)
* Histogram Equalization
* [Histogram Stretch](digital_image_processing/histogram_equalization/histogram_stretch.py)
* [Index Calculation](digital_image_processing/index_calculation.py)
* Morphological Operations
* [Dilation Operation](digital_image_processing/morphological_operations/dilation_operation.py)
* [Erosion Operation](digital_image_processing/morphological_operations/erosion_operation.py)
* Resize
* [Resize](digital_image_processing/resize/resize.py)
* Rotation
* [Rotation](digital_image_processing/rotation/rotation.py)
* [Sepia](digital_image_processing/sepia.py)
* [Test Digital Image Processing](digital_image_processing/test_digital_image_processing.py)
## Divide And Conquer
* [Closest Pair Of Points](divide_and_conquer/closest_pair_of_points.py)
* [Convex Hull](divide_and_conquer/convex_hull.py)
* [Heaps Algorithm](divide_and_conquer/heaps_algorithm.py)
* [Heaps Algorithm Iterative](divide_and_conquer/heaps_algorithm_iterative.py)
* [Inversions](divide_and_conquer/inversions.py)
* [Kth Order Statistic](divide_and_conquer/kth_order_statistic.py)
* [Max Difference Pair](divide_and_conquer/max_difference_pair.py)
* [Max Subarray Sum](divide_and_conquer/max_subarray_sum.py)
* [Mergesort](divide_and_conquer/mergesort.py)
* [Peak](divide_and_conquer/peak.py)
* [Power](divide_and_conquer/power.py)
* [Strassen Matrix Multiplication](divide_and_conquer/strassen_matrix_multiplication.py)
## Dynamic Programming
* [Abbreviation](dynamic_programming/abbreviation.py)
* [All Construct](dynamic_programming/all_construct.py)
* [Bitmask](dynamic_programming/bitmask.py)
* [Catalan Numbers](dynamic_programming/catalan_numbers.py)
* [Climbing Stairs](dynamic_programming/climbing_stairs.py)
* [Combination Sum Iv](dynamic_programming/combination_sum_iv.py)
* [Edit Distance](dynamic_programming/edit_distance.py)
* [Factorial](dynamic_programming/factorial.py)
* [Fast Fibonacci](dynamic_programming/fast_fibonacci.py)
* [Fibonacci](dynamic_programming/fibonacci.py)
* [Fizz Buzz](dynamic_programming/fizz_buzz.py)
* [Floyd Warshall](dynamic_programming/floyd_warshall.py)
* [Integer Partition](dynamic_programming/integer_partition.py)
* [Iterating Through Submasks](dynamic_programming/iterating_through_submasks.py)
* [Knapsack](dynamic_programming/knapsack.py)
* [Longest Common Subsequence](dynamic_programming/longest_common_subsequence.py)
* [Longest Common Substring](dynamic_programming/longest_common_substring.py)
* [Longest Increasing Subsequence](dynamic_programming/longest_increasing_subsequence.py)
* [Longest Increasing Subsequence O(Nlogn)](dynamic_programming/longest_increasing_subsequence_o(nlogn).py)
* [Longest Sub Array](dynamic_programming/longest_sub_array.py)
* [Matrix Chain Order](dynamic_programming/matrix_chain_order.py)
* [Max Non Adjacent Sum](dynamic_programming/max_non_adjacent_sum.py)
* [Max Sub Array](dynamic_programming/max_sub_array.py)
* [Max Sum Contiguous Subsequence](dynamic_programming/max_sum_contiguous_subsequence.py)
* [Min Distance Up Bottom](dynamic_programming/min_distance_up_bottom.py)
* [Minimum Coin Change](dynamic_programming/minimum_coin_change.py)
* [Minimum Cost Path](dynamic_programming/minimum_cost_path.py)
* [Minimum Partition](dynamic_programming/minimum_partition.py)
* [Minimum Squares To Represent A Number](dynamic_programming/minimum_squares_to_represent_a_number.py)
* [Minimum Steps To One](dynamic_programming/minimum_steps_to_one.py)
* [Optimal Binary Search Tree](dynamic_programming/optimal_binary_search_tree.py)
* [Palindrome Partitioning](dynamic_programming/palindrome_partitioning.py)
* [Rod Cutting](dynamic_programming/rod_cutting.py)
* [Subset Generation](dynamic_programming/subset_generation.py)
* [Sum Of Subset](dynamic_programming/sum_of_subset.py)
* [Viterbi](dynamic_programming/viterbi.py)
## Electronics
* [Builtin Voltage](electronics/builtin_voltage.py)
* [Carrier Concentration](electronics/carrier_concentration.py)
* [Coulombs Law](electronics/coulombs_law.py)
* [Electric Conductivity](electronics/electric_conductivity.py)
* [Electric Power](electronics/electric_power.py)
* [Electrical Impedance](electronics/electrical_impedance.py)
* [Ind Reactance](electronics/ind_reactance.py)
* [Ohms Law](electronics/ohms_law.py)
* [Resistor Equivalence](electronics/resistor_equivalence.py)
* [Resonant Frequency](electronics/resonant_frequency.py)
## File Transfer
* [Receive File](file_transfer/receive_file.py)
* [Send File](file_transfer/send_file.py)
* Tests
* [Test Send File](file_transfer/tests/test_send_file.py)
## Financial
* [Equated Monthly Installments](financial/equated_monthly_installments.py)
* [Interest](financial/interest.py)
* [Price Plus Tax](financial/price_plus_tax.py)
## Fractals
* [Julia Sets](fractals/julia_sets.py)
* [Koch Snowflake](fractals/koch_snowflake.py)
* [Mandelbrot](fractals/mandelbrot.py)
* [Sierpinski Triangle](fractals/sierpinski_triangle.py)
## Fuzzy Logic
* [Fuzzy Operations](fuzzy_logic/fuzzy_operations.py)
## Genetic Algorithm
* [Basic String](genetic_algorithm/basic_string.py)
## Geodesy
* [Haversine Distance](geodesy/haversine_distance.py)
* [Lamberts Ellipsoidal Distance](geodesy/lamberts_ellipsoidal_distance.py)
## Graphics
* [Bezier Curve](graphics/bezier_curve.py)
* [Vector3 For 2D Rendering](graphics/vector3_for_2d_rendering.py)
## Graphs
* [A Star](graphs/a_star.py)
* [Articulation Points](graphs/articulation_points.py)
* [Basic Graphs](graphs/basic_graphs.py)
* [Bellman Ford](graphs/bellman_ford.py)
* [Bidirectional A Star](graphs/bidirectional_a_star.py)
* [Bidirectional Breadth First Search](graphs/bidirectional_breadth_first_search.py)
* [Boruvka](graphs/boruvka.py)
* [Breadth First Search](graphs/breadth_first_search.py)
* [Breadth First Search 2](graphs/breadth_first_search_2.py)
* [Breadth First Search Shortest Path](graphs/breadth_first_search_shortest_path.py)
* [Breadth First Search Shortest Path 2](graphs/breadth_first_search_shortest_path_2.py)
* [Breadth First Search Zero One Shortest Path](graphs/breadth_first_search_zero_one_shortest_path.py)
* [Check Bipartite Graph Bfs](graphs/check_bipartite_graph_bfs.py)
* [Check Bipartite Graph Dfs](graphs/check_bipartite_graph_dfs.py)
* [Check Cycle](graphs/check_cycle.py)
* [Connected Components](graphs/connected_components.py)
* [Depth First Search](graphs/depth_first_search.py)
* [Depth First Search 2](graphs/depth_first_search_2.py)
* [Dijkstra](graphs/dijkstra.py)
* [Dijkstra 2](graphs/dijkstra_2.py)
* [Dijkstra Algorithm](graphs/dijkstra_algorithm.py)
* [Dijkstra Alternate](graphs/dijkstra_alternate.py)
* [Dinic](graphs/dinic.py)
* [Directed And Undirected (Weighted) Graph](graphs/directed_and_undirected_(weighted)_graph.py)
* [Edmonds Karp Multiple Source And Sink](graphs/edmonds_karp_multiple_source_and_sink.py)
* [Eulerian Path And Circuit For Undirected Graph](graphs/eulerian_path_and_circuit_for_undirected_graph.py)
* [Even Tree](graphs/even_tree.py)
* [Finding Bridges](graphs/finding_bridges.py)
* [Frequent Pattern Graph Miner](graphs/frequent_pattern_graph_miner.py)
* [G Topological Sort](graphs/g_topological_sort.py)
* [Gale Shapley Bigraph](graphs/gale_shapley_bigraph.py)
* [Graph List](graphs/graph_list.py)
* [Graph Matrix](graphs/graph_matrix.py)
* [Graphs Floyd Warshall](graphs/graphs_floyd_warshall.py)
* [Greedy Best First](graphs/greedy_best_first.py)
* [Greedy Min Vertex Cover](graphs/greedy_min_vertex_cover.py)
* [Kahns Algorithm Long](graphs/kahns_algorithm_long.py)
* [Kahns Algorithm Topo](graphs/kahns_algorithm_topo.py)
* [Karger](graphs/karger.py)
* [Markov Chain](graphs/markov_chain.py)
* [Matching Min Vertex Cover](graphs/matching_min_vertex_cover.py)
* [Minimum Path Sum](graphs/minimum_path_sum.py)
* [Minimum Spanning Tree Boruvka](graphs/minimum_spanning_tree_boruvka.py)
* [Minimum Spanning Tree Kruskal](graphs/minimum_spanning_tree_kruskal.py)
* [Minimum Spanning Tree Kruskal2](graphs/minimum_spanning_tree_kruskal2.py)
* [Minimum Spanning Tree Prims](graphs/minimum_spanning_tree_prims.py)
* [Minimum Spanning Tree Prims2](graphs/minimum_spanning_tree_prims2.py)
* [Multi Heuristic Astar](graphs/multi_heuristic_astar.py)
* [Page Rank](graphs/page_rank.py)
* [Prim](graphs/prim.py)
* [Random Graph Generator](graphs/random_graph_generator.py)
* [Scc Kosaraju](graphs/scc_kosaraju.py)
* [Strongly Connected Components](graphs/strongly_connected_components.py)
* [Tarjans Scc](graphs/tarjans_scc.py)
* Tests
* [Test Min Spanning Tree Kruskal](graphs/tests/test_min_spanning_tree_kruskal.py)
* [Test Min Spanning Tree Prim](graphs/tests/test_min_spanning_tree_prim.py)
## Greedy Methods
* [Fractional Knapsack](greedy_methods/fractional_knapsack.py)
* [Fractional Knapsack 2](greedy_methods/fractional_knapsack_2.py)
* [Optimal Merge Pattern](greedy_methods/optimal_merge_pattern.py)
## Hashes
* [Adler32](hashes/adler32.py)
* [Chaos Machine](hashes/chaos_machine.py)
* [Djb2](hashes/djb2.py)
* [Elf](hashes/elf.py)
* [Enigma Machine](hashes/enigma_machine.py)
* [Hamming Code](hashes/hamming_code.py)
* [Luhn](hashes/luhn.py)
* [Md5](hashes/md5.py)
* [Sdbm](hashes/sdbm.py)
* [Sha1](hashes/sha1.py)
* [Sha256](hashes/sha256.py)
## Knapsack
* [Greedy Knapsack](knapsack/greedy_knapsack.py)
* [Knapsack](knapsack/knapsack.py)
* [Recursive Approach Knapsack](knapsack/recursive_approach_knapsack.py)
* Tests
* [Test Greedy Knapsack](knapsack/tests/test_greedy_knapsack.py)
* [Test Knapsack](knapsack/tests/test_knapsack.py)
## Linear Algebra
* Src
* [Conjugate Gradient](linear_algebra/src/conjugate_gradient.py)
* [Lib](linear_algebra/src/lib.py)
* [Polynom For Points](linear_algebra/src/polynom_for_points.py)
* [Power Iteration](linear_algebra/src/power_iteration.py)
* [Rayleigh Quotient](linear_algebra/src/rayleigh_quotient.py)
* [Schur Complement](linear_algebra/src/schur_complement.py)
* [Test Linear Algebra](linear_algebra/src/test_linear_algebra.py)
* [Transformations 2D](linear_algebra/src/transformations_2d.py)
## Machine Learning
* [Astar](machine_learning/astar.py)
* [Data Transformations](machine_learning/data_transformations.py)
* [Decision Tree](machine_learning/decision_tree.py)
* Forecasting
* [Run](machine_learning/forecasting/run.py)
* [Gaussian Naive Bayes](machine_learning/gaussian_naive_bayes.py)
* [Gradient Boosting Regressor](machine_learning/gradient_boosting_regressor.py)
* [Gradient Descent](machine_learning/gradient_descent.py)
* [K Means Clust](machine_learning/k_means_clust.py)
* [K Nearest Neighbours](machine_learning/k_nearest_neighbours.py)
* [Knn Sklearn](machine_learning/knn_sklearn.py)
* [Linear Discriminant Analysis](machine_learning/linear_discriminant_analysis.py)
* [Linear Regression](machine_learning/linear_regression.py)
* Local Weighted Learning
* [Local Weighted Learning](machine_learning/local_weighted_learning/local_weighted_learning.py)
* [Logistic Regression](machine_learning/logistic_regression.py)
* Lstm
* [Lstm Prediction](machine_learning/lstm/lstm_prediction.py)
* [Multilayer Perceptron Classifier](machine_learning/multilayer_perceptron_classifier.py)
* [Polymonial Regression](machine_learning/polymonial_regression.py)
* [Random Forest Classifier](machine_learning/random_forest_classifier.py)
* [Random Forest Regressor](machine_learning/random_forest_regressor.py)
* [Scoring Functions](machine_learning/scoring_functions.py)
* [Self Organizing Map](machine_learning/self_organizing_map.py)
* [Sequential Minimum Optimization](machine_learning/sequential_minimum_optimization.py)
* [Similarity Search](machine_learning/similarity_search.py)
* [Support Vector Machines](machine_learning/support_vector_machines.py)
* [Word Frequency Functions](machine_learning/word_frequency_functions.py)
* [Xgboost Classifier](machine_learning/xgboost_classifier.py)
* [Xgboost Regressor](machine_learning/xgboost_regressor.py)
## Maths
* [3N Plus 1](maths/3n_plus_1.py)
* [Abs](maths/abs.py)
* [Abs Max](maths/abs_max.py)
* [Abs Min](maths/abs_min.py)
* [Add](maths/add.py)
* [Addition Without Arithmetic](maths/addition_without_arithmetic.py)
* [Aliquot Sum](maths/aliquot_sum.py)
* [Allocation Number](maths/allocation_number.py)
* [Arc Length](maths/arc_length.py)
* [Area](maths/area.py)
* [Area Under Curve](maths/area_under_curve.py)
* [Armstrong Numbers](maths/armstrong_numbers.py)
* [Average Absolute Deviation](maths/average_absolute_deviation.py)
* [Average Mean](maths/average_mean.py)
* [Average Median](maths/average_median.py)
* [Average Mode](maths/average_mode.py)
* [Bailey Borwein Plouffe](maths/bailey_borwein_plouffe.py)
* [Basic Maths](maths/basic_maths.py)
* [Binary Exp Mod](maths/binary_exp_mod.py)
* [Binary Exponentiation](maths/binary_exponentiation.py)
* [Binary Exponentiation 2](maths/binary_exponentiation_2.py)
* [Binary Exponentiation 3](maths/binary_exponentiation_3.py)
* [Binomial Coefficient](maths/binomial_coefficient.py)
* [Binomial Distribution](maths/binomial_distribution.py)
* [Bisection](maths/bisection.py)
* [Carmichael Number](maths/carmichael_number.py)
* [Catalan Number](maths/catalan_number.py)
* [Ceil](maths/ceil.py)
* [Check Polygon](maths/check_polygon.py)
* [Chudnovsky Algorithm](maths/chudnovsky_algorithm.py)
* [Collatz Sequence](maths/collatz_sequence.py)
* [Combinations](maths/combinations.py)
* [Decimal Isolate](maths/decimal_isolate.py)
* [Dodecahedron](maths/dodecahedron.py)
* [Double Factorial Iterative](maths/double_factorial_iterative.py)
* [Double Factorial Recursive](maths/double_factorial_recursive.py)
* [Entropy](maths/entropy.py)
* [Euclidean Distance](maths/euclidean_distance.py)
* [Euclidean Gcd](maths/euclidean_gcd.py)
* [Euler Method](maths/euler_method.py)
* [Euler Modified](maths/euler_modified.py)
* [Eulers Totient](maths/eulers_totient.py)
* [Extended Euclidean Algorithm](maths/extended_euclidean_algorithm.py)
* [Factorial Iterative](maths/factorial_iterative.py)
* [Factorial Recursive](maths/factorial_recursive.py)
* [Factors](maths/factors.py)
* [Fermat Little Theorem](maths/fermat_little_theorem.py)
* [Fibonacci](maths/fibonacci.py)
* [Find Max](maths/find_max.py)
* [Find Max Recursion](maths/find_max_recursion.py)
* [Find Min](maths/find_min.py)
* [Find Min Recursion](maths/find_min_recursion.py)
* [Floor](maths/floor.py)
* [Gamma](maths/gamma.py)
* [Gamma Recursive](maths/gamma_recursive.py)
* [Gaussian](maths/gaussian.py)
* [Gaussian Error Linear Unit](maths/gaussian_error_linear_unit.py)
* [Greatest Common Divisor](maths/greatest_common_divisor.py)
* [Greedy Coin Change](maths/greedy_coin_change.py)
* [Hamming Numbers](maths/hamming_numbers.py)
* [Hardy Ramanujanalgo](maths/hardy_ramanujanalgo.py)
* [Integration By Simpson Approx](maths/integration_by_simpson_approx.py)
* [Is Ip V4 Address Valid](maths/is_ip_v4_address_valid.py)
* [Is Square Free](maths/is_square_free.py)
* [Jaccard Similarity](maths/jaccard_similarity.py)
* [Kadanes](maths/kadanes.py)
* [Karatsuba](maths/karatsuba.py)
* [Krishnamurthy Number](maths/krishnamurthy_number.py)
* [Kth Lexicographic Permutation](maths/kth_lexicographic_permutation.py)
* [Largest Of Very Large Numbers](maths/largest_of_very_large_numbers.py)
* [Largest Subarray Sum](maths/largest_subarray_sum.py)
* [Least Common Multiple](maths/least_common_multiple.py)
* [Line Length](maths/line_length.py)
* [Lucas Lehmer Primality Test](maths/lucas_lehmer_primality_test.py)
* [Lucas Series](maths/lucas_series.py)
* [Maclaurin Series](maths/maclaurin_series.py)
* [Manhattan Distance](maths/manhattan_distance.py)
* [Matrix Exponentiation](maths/matrix_exponentiation.py)
* [Max Sum Sliding Window](maths/max_sum_sliding_window.py)
* [Median Of Two Arrays](maths/median_of_two_arrays.py)
* [Miller Rabin](maths/miller_rabin.py)
* [Mobius Function](maths/mobius_function.py)
* [Modular Exponential](maths/modular_exponential.py)
* [Monte Carlo](maths/monte_carlo.py)
* [Monte Carlo Dice](maths/monte_carlo_dice.py)
* [Nevilles Method](maths/nevilles_method.py)
* [Newton Raphson](maths/newton_raphson.py)
* [Number Of Digits](maths/number_of_digits.py)
* [Numerical Integration](maths/numerical_integration.py)
* [Perfect Cube](maths/perfect_cube.py)
* [Perfect Number](maths/perfect_number.py)
* [Perfect Square](maths/perfect_square.py)
* [Persistence](maths/persistence.py)
* [Pi Monte Carlo Estimation](maths/pi_monte_carlo_estimation.py)
* [Points Are Collinear 3D](maths/points_are_collinear_3d.py)
* [Pollard Rho](maths/pollard_rho.py)
* [Polynomial Evaluation](maths/polynomial_evaluation.py)
* Polynomials
* [Single Indeterminate Operations](maths/polynomials/single_indeterminate_operations.py)
* [Power Using Recursion](maths/power_using_recursion.py)
* [Prime Check](maths/prime_check.py)
* [Prime Factors](maths/prime_factors.py)
* [Prime Numbers](maths/prime_numbers.py)
* [Prime Sieve Eratosthenes](maths/prime_sieve_eratosthenes.py)
* [Primelib](maths/primelib.py)
* [Print Multiplication Table](maths/print_multiplication_table.py)
* [Proth Number](maths/proth_number.py)
* [Pythagoras](maths/pythagoras.py)
* [Qr Decomposition](maths/qr_decomposition.py)
* [Quadratic Equations Complex Numbers](maths/quadratic_equations_complex_numbers.py)
* [Radians](maths/radians.py)
* [Radix2 Fft](maths/radix2_fft.py)
* [Relu](maths/relu.py)
* [Runge Kutta](maths/runge_kutta.py)
* [Segmented Sieve](maths/segmented_sieve.py)
* Series
* [Arithmetic](maths/series/arithmetic.py)
* [Geometric](maths/series/geometric.py)
* [Geometric Series](maths/series/geometric_series.py)
* [Harmonic](maths/series/harmonic.py)
* [Harmonic Series](maths/series/harmonic_series.py)
* [Hexagonal Numbers](maths/series/hexagonal_numbers.py)
* [P Series](maths/series/p_series.py)
* [Sieve Of Eratosthenes](maths/sieve_of_eratosthenes.py)
* [Sigmoid](maths/sigmoid.py)
* [Sigmoid Linear Unit](maths/sigmoid_linear_unit.py)
* [Signum](maths/signum.py)
* [Simpson Rule](maths/simpson_rule.py)
* [Sin](maths/sin.py)
* [Sock Merchant](maths/sock_merchant.py)
* [Softmax](maths/softmax.py)
* [Square Root](maths/square_root.py)
* [Sum Of Arithmetic Series](maths/sum_of_arithmetic_series.py)
* [Sum Of Digits](maths/sum_of_digits.py)
* [Sum Of Geometric Progression](maths/sum_of_geometric_progression.py)
* [Sum Of Harmonic Series](maths/sum_of_harmonic_series.py)
* [Sumset](maths/sumset.py)
* [Sylvester Sequence](maths/sylvester_sequence.py)
* [Test Prime Check](maths/test_prime_check.py)
* [Trapezoidal Rule](maths/trapezoidal_rule.py)
* [Triplet Sum](maths/triplet_sum.py)
* [Two Pointer](maths/two_pointer.py)
* [Two Sum](maths/two_sum.py)
* [Ugly Numbers](maths/ugly_numbers.py)
* [Volume](maths/volume.py)
* [Weird Number](maths/weird_number.py)
* [Zellers Congruence](maths/zellers_congruence.py)
## Matrix
* [Binary Search Matrix](matrix/binary_search_matrix.py)
* [Count Islands In Matrix](matrix/count_islands_in_matrix.py)
* [Count Paths](matrix/count_paths.py)
* [Cramers Rule 2X2](matrix/cramers_rule_2x2.py)
* [Inverse Of Matrix](matrix/inverse_of_matrix.py)
* [Largest Square Area In Matrix](matrix/largest_square_area_in_matrix.py)
* [Matrix Class](matrix/matrix_class.py)
* [Matrix Operation](matrix/matrix_operation.py)
* [Max Area Of Island](matrix/max_area_of_island.py)
* [Nth Fibonacci Using Matrix Exponentiation](matrix/nth_fibonacci_using_matrix_exponentiation.py)
* [Rotate Matrix](matrix/rotate_matrix.py)
* [Searching In Sorted Matrix](matrix/searching_in_sorted_matrix.py)
* [Sherman Morrison](matrix/sherman_morrison.py)
* [Spiral Print](matrix/spiral_print.py)
* Tests
* [Test Matrix Operation](matrix/tests/test_matrix_operation.py)
## Networking Flow
* [Ford Fulkerson](networking_flow/ford_fulkerson.py)
* [Minimum Cut](networking_flow/minimum_cut.py)
## Neural Network
* [2 Hidden Layers Neural Network](neural_network/2_hidden_layers_neural_network.py)
* [Back Propagation Neural Network](neural_network/back_propagation_neural_network.py)
* [Convolution Neural Network](neural_network/convolution_neural_network.py)
* [Perceptron](neural_network/perceptron.py)
* [Simple Neural Network](neural_network/simple_neural_network.py)
## Other
* [Activity Selection](other/activity_selection.py)
* [Alternative List Arrange](other/alternative_list_arrange.py)
* [Check Strong Password](other/check_strong_password.py)
* [Davisb Putnamb Logemannb Loveland](other/davisb_putnamb_logemannb_loveland.py)
* [Dijkstra Bankers Algorithm](other/dijkstra_bankers_algorithm.py)
* [Doomsday](other/doomsday.py)
* [Fischer Yates Shuffle](other/fischer_yates_shuffle.py)
* [Gauss Easter](other/gauss_easter.py)
* [Graham Scan](other/graham_scan.py)
* [Greedy](other/greedy.py)
* [Least Recently Used](other/least_recently_used.py)
* [Lfu Cache](other/lfu_cache.py)
* [Linear Congruential Generator](other/linear_congruential_generator.py)
* [Lru Cache](other/lru_cache.py)
* [Magicdiamondpattern](other/magicdiamondpattern.py)
* [Maximum Subarray](other/maximum_subarray.py)
* [Nested Brackets](other/nested_brackets.py)
* [Pascal Triangle](other/pascal_triangle.py)
* [Password Generator](other/password_generator.py)
* [Quine](other/quine.py)
* [Scoring Algorithm](other/scoring_algorithm.py)
* [Sdes](other/sdes.py)
* [Tower Of Hanoi](other/tower_of_hanoi.py)
## Physics
* [Archimedes Principle](physics/archimedes_principle.py)
* [Casimir Effect](physics/casimir_effect.py)
* [Centripetal Force](physics/centripetal_force.py)
* [Horizontal Projectile Motion](physics/horizontal_projectile_motion.py)
* [Ideal Gas Law](physics/ideal_gas_law.py)
* [Kinetic Energy](physics/kinetic_energy.py)
* [Lorentz Transformation Four Vector](physics/lorentz_transformation_four_vector.py)
* [Malus Law](physics/malus_law.py)
* [N Body Simulation](physics/n_body_simulation.py)
* [Newtons Law Of Gravitation](physics/newtons_law_of_gravitation.py)
* [Newtons Second Law Of Motion](physics/newtons_second_law_of_motion.py)
* [Potential Energy](physics/potential_energy.py)
* [Rms Speed Of Molecule](physics/rms_speed_of_molecule.py)
* [Shear Stress](physics/shear_stress.py)
## Project Euler
* Problem 001
* [Sol1](project_euler/problem_001/sol1.py)
* [Sol2](project_euler/problem_001/sol2.py)
* [Sol3](project_euler/problem_001/sol3.py)
* [Sol4](project_euler/problem_001/sol4.py)
* [Sol5](project_euler/problem_001/sol5.py)
* [Sol6](project_euler/problem_001/sol6.py)
* [Sol7](project_euler/problem_001/sol7.py)
* Problem 002
* [Sol1](project_euler/problem_002/sol1.py)
* [Sol2](project_euler/problem_002/sol2.py)
* [Sol3](project_euler/problem_002/sol3.py)
* [Sol4](project_euler/problem_002/sol4.py)
* [Sol5](project_euler/problem_002/sol5.py)
* Problem 003
* [Sol1](project_euler/problem_003/sol1.py)
* [Sol2](project_euler/problem_003/sol2.py)
* [Sol3](project_euler/problem_003/sol3.py)
* Problem 004
* [Sol1](project_euler/problem_004/sol1.py)
* [Sol2](project_euler/problem_004/sol2.py)
* Problem 005
* [Sol1](project_euler/problem_005/sol1.py)
* [Sol2](project_euler/problem_005/sol2.py)
* Problem 006
* [Sol1](project_euler/problem_006/sol1.py)
* [Sol2](project_euler/problem_006/sol2.py)
* [Sol3](project_euler/problem_006/sol3.py)
* [Sol4](project_euler/problem_006/sol4.py)
* Problem 007
* [Sol1](project_euler/problem_007/sol1.py)
* [Sol2](project_euler/problem_007/sol2.py)
* [Sol3](project_euler/problem_007/sol3.py)
* Problem 008
* [Sol1](project_euler/problem_008/sol1.py)
* [Sol2](project_euler/problem_008/sol2.py)
* [Sol3](project_euler/problem_008/sol3.py)
* Problem 009
* [Sol1](project_euler/problem_009/sol1.py)
* [Sol2](project_euler/problem_009/sol2.py)
* [Sol3](project_euler/problem_009/sol3.py)
* Problem 010
* [Sol1](project_euler/problem_010/sol1.py)
* [Sol2](project_euler/problem_010/sol2.py)
* [Sol3](project_euler/problem_010/sol3.py)
* Problem 011
* [Sol1](project_euler/problem_011/sol1.py)
* [Sol2](project_euler/problem_011/sol2.py)
* Problem 012
* [Sol1](project_euler/problem_012/sol1.py)
* [Sol2](project_euler/problem_012/sol2.py)
* Problem 013
* [Sol1](project_euler/problem_013/sol1.py)
* Problem 014
* [Sol1](project_euler/problem_014/sol1.py)
* [Sol2](project_euler/problem_014/sol2.py)
* Problem 015
* [Sol1](project_euler/problem_015/sol1.py)
* Problem 016
* [Sol1](project_euler/problem_016/sol1.py)
* [Sol2](project_euler/problem_016/sol2.py)
* Problem 017
* [Sol1](project_euler/problem_017/sol1.py)
* Problem 018
* [Solution](project_euler/problem_018/solution.py)
* Problem 019
* [Sol1](project_euler/problem_019/sol1.py)
* Problem 020
* [Sol1](project_euler/problem_020/sol1.py)
* [Sol2](project_euler/problem_020/sol2.py)
* [Sol3](project_euler/problem_020/sol3.py)
* [Sol4](project_euler/problem_020/sol4.py)
* Problem 021
* [Sol1](project_euler/problem_021/sol1.py)
* Problem 022
* [Sol1](project_euler/problem_022/sol1.py)
* [Sol2](project_euler/problem_022/sol2.py)
* Problem 023
* [Sol1](project_euler/problem_023/sol1.py)
* Problem 024
* [Sol1](project_euler/problem_024/sol1.py)
* Problem 025
* [Sol1](project_euler/problem_025/sol1.py)
* [Sol2](project_euler/problem_025/sol2.py)
* [Sol3](project_euler/problem_025/sol3.py)
* Problem 026
* [Sol1](project_euler/problem_026/sol1.py)
* Problem 027
* [Sol1](project_euler/problem_027/sol1.py)
* Problem 028
* [Sol1](project_euler/problem_028/sol1.py)
* Problem 029
* [Sol1](project_euler/problem_029/sol1.py)
* Problem 030
* [Sol1](project_euler/problem_030/sol1.py)
* Problem 031
* [Sol1](project_euler/problem_031/sol1.py)
* [Sol2](project_euler/problem_031/sol2.py)
* Problem 032
* [Sol32](project_euler/problem_032/sol32.py)
* Problem 033
* [Sol1](project_euler/problem_033/sol1.py)
* Problem 034
* [Sol1](project_euler/problem_034/sol1.py)
* Problem 035
* [Sol1](project_euler/problem_035/sol1.py)
* Problem 036
* [Sol1](project_euler/problem_036/sol1.py)
* Problem 037
* [Sol1](project_euler/problem_037/sol1.py)
* Problem 038
* [Sol1](project_euler/problem_038/sol1.py)
* Problem 039
* [Sol1](project_euler/problem_039/sol1.py)
* Problem 040
* [Sol1](project_euler/problem_040/sol1.py)
* Problem 041
* [Sol1](project_euler/problem_041/sol1.py)
* Problem 042
* [Solution42](project_euler/problem_042/solution42.py)
* Problem 043
* [Sol1](project_euler/problem_043/sol1.py)
* Problem 044
* [Sol1](project_euler/problem_044/sol1.py)
* Problem 045
* [Sol1](project_euler/problem_045/sol1.py)
* Problem 046
* [Sol1](project_euler/problem_046/sol1.py)
* Problem 047
* [Sol1](project_euler/problem_047/sol1.py)
* Problem 048
* [Sol1](project_euler/problem_048/sol1.py)
* Problem 049
* [Sol1](project_euler/problem_049/sol1.py)
* Problem 050
* [Sol1](project_euler/problem_050/sol1.py)
* Problem 051
* [Sol1](project_euler/problem_051/sol1.py)
* Problem 052
* [Sol1](project_euler/problem_052/sol1.py)
* Problem 053
* [Sol1](project_euler/problem_053/sol1.py)
* Problem 054
* [Sol1](project_euler/problem_054/sol1.py)
* [Test Poker Hand](project_euler/problem_054/test_poker_hand.py)
* Problem 055
* [Sol1](project_euler/problem_055/sol1.py)
* Problem 056
* [Sol1](project_euler/problem_056/sol1.py)
* Problem 057
* [Sol1](project_euler/problem_057/sol1.py)
* Problem 058
* [Sol1](project_euler/problem_058/sol1.py)
* Problem 059
* [Sol1](project_euler/problem_059/sol1.py)
* Problem 062
* [Sol1](project_euler/problem_062/sol1.py)
* Problem 063
* [Sol1](project_euler/problem_063/sol1.py)
* Problem 064
* [Sol1](project_euler/problem_064/sol1.py)
* Problem 065
* [Sol1](project_euler/problem_065/sol1.py)
* Problem 067
* [Sol1](project_euler/problem_067/sol1.py)
* [Sol2](project_euler/problem_067/sol2.py)
* Problem 068
* [Sol1](project_euler/problem_068/sol1.py)
* Problem 069
* [Sol1](project_euler/problem_069/sol1.py)
* Problem 070
* [Sol1](project_euler/problem_070/sol1.py)
* Problem 071
* [Sol1](project_euler/problem_071/sol1.py)
* Problem 072
* [Sol1](project_euler/problem_072/sol1.py)
* [Sol2](project_euler/problem_072/sol2.py)
* Problem 073
* [Sol1](project_euler/problem_073/sol1.py)
* Problem 074
* [Sol1](project_euler/problem_074/sol1.py)
* [Sol2](project_euler/problem_074/sol2.py)
* Problem 075
* [Sol1](project_euler/problem_075/sol1.py)
* Problem 076
* [Sol1](project_euler/problem_076/sol1.py)
* Problem 077
* [Sol1](project_euler/problem_077/sol1.py)
* Problem 078
* [Sol1](project_euler/problem_078/sol1.py)
* Problem 080
* [Sol1](project_euler/problem_080/sol1.py)
* Problem 081
* [Sol1](project_euler/problem_081/sol1.py)
* Problem 085
* [Sol1](project_euler/problem_085/sol1.py)
* Problem 086
* [Sol1](project_euler/problem_086/sol1.py)
* Problem 087
* [Sol1](project_euler/problem_087/sol1.py)
* Problem 089
* [Sol1](project_euler/problem_089/sol1.py)
* Problem 091
* [Sol1](project_euler/problem_091/sol1.py)
* Problem 092
* [Sol1](project_euler/problem_092/sol1.py)
* Problem 097
* [Sol1](project_euler/problem_097/sol1.py)
* Problem 099
* [Sol1](project_euler/problem_099/sol1.py)
* Problem 101
* [Sol1](project_euler/problem_101/sol1.py)
* Problem 102
* [Sol1](project_euler/problem_102/sol1.py)
* Problem 104
* [Sol1](project_euler/problem_104/sol1.py)
* Problem 107
* [Sol1](project_euler/problem_107/sol1.py)
* Problem 109
* [Sol1](project_euler/problem_109/sol1.py)
* Problem 112
* [Sol1](project_euler/problem_112/sol1.py)
* Problem 113
* [Sol1](project_euler/problem_113/sol1.py)
* Problem 114
* [Sol1](project_euler/problem_114/sol1.py)
* Problem 115
* [Sol1](project_euler/problem_115/sol1.py)
* Problem 116
* [Sol1](project_euler/problem_116/sol1.py)
* Problem 119
* [Sol1](project_euler/problem_119/sol1.py)
* Problem 120
* [Sol1](project_euler/problem_120/sol1.py)
* Problem 121
* [Sol1](project_euler/problem_121/sol1.py)
* Problem 123
* [Sol1](project_euler/problem_123/sol1.py)
* Problem 125
* [Sol1](project_euler/problem_125/sol1.py)
* Problem 129
* [Sol1](project_euler/problem_129/sol1.py)
* Problem 135
* [Sol1](project_euler/problem_135/sol1.py)
* Problem 144
* [Sol1](project_euler/problem_144/sol1.py)
* Problem 145
* [Sol1](project_euler/problem_145/sol1.py)
* Problem 173
* [Sol1](project_euler/problem_173/sol1.py)
* Problem 174
* [Sol1](project_euler/problem_174/sol1.py)
* Problem 180
* [Sol1](project_euler/problem_180/sol1.py)
* Problem 188
* [Sol1](project_euler/problem_188/sol1.py)
* Problem 191
* [Sol1](project_euler/problem_191/sol1.py)
* Problem 203
* [Sol1](project_euler/problem_203/sol1.py)
* Problem 205
* [Sol1](project_euler/problem_205/sol1.py)
* Problem 206
* [Sol1](project_euler/problem_206/sol1.py)
* Problem 207
* [Sol1](project_euler/problem_207/sol1.py)
* Problem 234
* [Sol1](project_euler/problem_234/sol1.py)
* Problem 301
* [Sol1](project_euler/problem_301/sol1.py)
* Problem 493
* [Sol1](project_euler/problem_493/sol1.py)
* Problem 551
* [Sol1](project_euler/problem_551/sol1.py)
* Problem 587
* [Sol1](project_euler/problem_587/sol1.py)
* Problem 686
* [Sol1](project_euler/problem_686/sol1.py)
## Quantum
* [Deutsch Jozsa](quantum/deutsch_jozsa.py)
* [Half Adder](quantum/half_adder.py)
* [Not Gate](quantum/not_gate.py)
* [Q Fourier Transform](quantum/q_fourier_transform.py)
* [Q Full Adder](quantum/q_full_adder.py)
* [Quantum Entanglement](quantum/quantum_entanglement.py)
* [Quantum Teleportation](quantum/quantum_teleportation.py)
* [Ripple Adder Classic](quantum/ripple_adder_classic.py)
* [Single Qubit Measure](quantum/single_qubit_measure.py)
* [Superdense Coding](quantum/superdense_coding.py)
## Scheduling
* [First Come First Served](scheduling/first_come_first_served.py)
* [Highest Response Ratio Next](scheduling/highest_response_ratio_next.py)
* [Job Sequencing With Deadline](scheduling/job_sequencing_with_deadline.py)
* [Multi Level Feedback Queue](scheduling/multi_level_feedback_queue.py)
* [Non Preemptive Shortest Job First](scheduling/non_preemptive_shortest_job_first.py)
* [Round Robin](scheduling/round_robin.py)
* [Shortest Job First](scheduling/shortest_job_first.py)
## Searches
* [Binary Search](searches/binary_search.py)
* [Binary Tree Traversal](searches/binary_tree_traversal.py)
* [Double Linear Search](searches/double_linear_search.py)
* [Double Linear Search Recursion](searches/double_linear_search_recursion.py)
* [Fibonacci Search](searches/fibonacci_search.py)
* [Hill Climbing](searches/hill_climbing.py)
* [Interpolation Search](searches/interpolation_search.py)
* [Jump Search](searches/jump_search.py)
* [Linear Search](searches/linear_search.py)
* [Quick Select](searches/quick_select.py)
* [Sentinel Linear Search](searches/sentinel_linear_search.py)
* [Simple Binary Search](searches/simple_binary_search.py)
* [Simulated Annealing](searches/simulated_annealing.py)
* [Tabu Search](searches/tabu_search.py)
* [Ternary Search](searches/ternary_search.py)
## Sorts
* [Bead Sort](sorts/bead_sort.py)
* [Bitonic Sort](sorts/bitonic_sort.py)
* [Bogo Sort](sorts/bogo_sort.py)
* [Bubble Sort](sorts/bubble_sort.py)
* [Bucket Sort](sorts/bucket_sort.py)
* [Circle Sort](sorts/circle_sort.py)
* [Cocktail Shaker Sort](sorts/cocktail_shaker_sort.py)
* [Comb Sort](sorts/comb_sort.py)
* [Counting Sort](sorts/counting_sort.py)
* [Cycle Sort](sorts/cycle_sort.py)
* [Double Sort](sorts/double_sort.py)
* [Dutch National Flag Sort](sorts/dutch_national_flag_sort.py)
* [Exchange Sort](sorts/exchange_sort.py)
* [External Sort](sorts/external_sort.py)
* [Gnome Sort](sorts/gnome_sort.py)
* [Heap Sort](sorts/heap_sort.py)
* [Insertion Sort](sorts/insertion_sort.py)
* [Intro Sort](sorts/intro_sort.py)
* [Iterative Merge Sort](sorts/iterative_merge_sort.py)
* [Merge Insertion Sort](sorts/merge_insertion_sort.py)
* [Merge Sort](sorts/merge_sort.py)
* [Msd Radix Sort](sorts/msd_radix_sort.py)
* [Natural Sort](sorts/natural_sort.py)
* [Odd Even Sort](sorts/odd_even_sort.py)
* [Odd Even Transposition Parallel](sorts/odd_even_transposition_parallel.py)
* [Odd Even Transposition Single Threaded](sorts/odd_even_transposition_single_threaded.py)
* [Pancake Sort](sorts/pancake_sort.py)
* [Patience Sort](sorts/patience_sort.py)
* [Pigeon Sort](sorts/pigeon_sort.py)
* [Pigeonhole Sort](sorts/pigeonhole_sort.py)
* [Quick Sort](sorts/quick_sort.py)
* [Quick Sort 3 Partition](sorts/quick_sort_3_partition.py)
* [Radix Sort](sorts/radix_sort.py)
* [Random Normal Distribution Quicksort](sorts/random_normal_distribution_quicksort.py)
* [Random Pivot Quick Sort](sorts/random_pivot_quick_sort.py)
* [Recursive Bubble Sort](sorts/recursive_bubble_sort.py)
* [Recursive Insertion Sort](sorts/recursive_insertion_sort.py)
* [Recursive Mergesort Array](sorts/recursive_mergesort_array.py)
* [Recursive Quick Sort](sorts/recursive_quick_sort.py)
* [Selection Sort](sorts/selection_sort.py)
* [Shell Sort](sorts/shell_sort.py)
* [Shrink Shell Sort](sorts/shrink_shell_sort.py)
* [Slowsort](sorts/slowsort.py)
* [Stooge Sort](sorts/stooge_sort.py)
* [Strand Sort](sorts/strand_sort.py)
* [Tim Sort](sorts/tim_sort.py)
* [Topological Sort](sorts/topological_sort.py)
* [Tree Sort](sorts/tree_sort.py)
* [Unknown Sort](sorts/unknown_sort.py)
* [Wiggle Sort](sorts/wiggle_sort.py)
## Strings
* [Aho Corasick](strings/aho_corasick.py)
* [Alternative String Arrange](strings/alternative_string_arrange.py)
* [Anagrams](strings/anagrams.py)
* [Autocomplete Using Trie](strings/autocomplete_using_trie.py)
* [Barcode Validator](strings/barcode_validator.py)
* [Boyer Moore Search](strings/boyer_moore_search.py)
* [Can String Be Rearranged As Palindrome](strings/can_string_be_rearranged_as_palindrome.py)
* [Capitalize](strings/capitalize.py)
* [Check Anagrams](strings/check_anagrams.py)
* [Credit Card Validator](strings/credit_card_validator.py)
* [Detecting English Programmatically](strings/detecting_english_programmatically.py)
* [Dna](strings/dna.py)
* [Frequency Finder](strings/frequency_finder.py)
* [Hamming Distance](strings/hamming_distance.py)
* [Indian Phone Validator](strings/indian_phone_validator.py)
* [Is Contains Unique Chars](strings/is_contains_unique_chars.py)
* [Is Isogram](strings/is_isogram.py)
* [Is Palindrome](strings/is_palindrome.py)
* [Is Pangram](strings/is_pangram.py)
* [Is Spain National Id](strings/is_spain_national_id.py)
* [Is Srilankan Phone Number](strings/is_srilankan_phone_number.py)
* [Jaro Winkler](strings/jaro_winkler.py)
* [Join](strings/join.py)
* [Knuth Morris Pratt](strings/knuth_morris_pratt.py)
* [Levenshtein Distance](strings/levenshtein_distance.py)
* [Lower](strings/lower.py)
* [Manacher](strings/manacher.py)
* [Min Cost String Conversion](strings/min_cost_string_conversion.py)
* [Naive String Search](strings/naive_string_search.py)
* [Ngram](strings/ngram.py)
* [Palindrome](strings/palindrome.py)
* [Prefix Function](strings/prefix_function.py)
* [Rabin Karp](strings/rabin_karp.py)
* [Remove Duplicate](strings/remove_duplicate.py)
* [Reverse Letters](strings/reverse_letters.py)
* [Reverse Long Words](strings/reverse_long_words.py)
* [Reverse Words](strings/reverse_words.py)
* [Snake Case To Camel Pascal Case](strings/snake_case_to_camel_pascal_case.py)
* [Split](strings/split.py)
* [Text Justification](strings/text_justification.py)
* [Upper](strings/upper.py)
* [Wave](strings/wave.py)
* [Wildcard Pattern Matching](strings/wildcard_pattern_matching.py)
* [Word Occurrence](strings/word_occurrence.py)
* [Word Patterns](strings/word_patterns.py)
* [Z Function](strings/z_function.py)
## Web Programming
* [Co2 Emission](web_programming/co2_emission.py)
* [Convert Number To Words](web_programming/convert_number_to_words.py)
* [Covid Stats Via Xpath](web_programming/covid_stats_via_xpath.py)
* [Crawl Google Results](web_programming/crawl_google_results.py)
* [Crawl Google Scholar Citation](web_programming/crawl_google_scholar_citation.py)
* [Currency Converter](web_programming/currency_converter.py)
* [Current Stock Price](web_programming/current_stock_price.py)
* [Current Weather](web_programming/current_weather.py)
* [Daily Horoscope](web_programming/daily_horoscope.py)
* [Download Images From Google Query](web_programming/download_images_from_google_query.py)
* [Emails From Url](web_programming/emails_from_url.py)
* [Fetch Anime And Play](web_programming/fetch_anime_and_play.py)
* [Fetch Bbc News](web_programming/fetch_bbc_news.py)
* [Fetch Github Info](web_programming/fetch_github_info.py)
* [Fetch Jobs](web_programming/fetch_jobs.py)
* [Fetch Quotes](web_programming/fetch_quotes.py)
* [Fetch Well Rx Price](web_programming/fetch_well_rx_price.py)
* [Get Amazon Product Data](web_programming/get_amazon_product_data.py)
* [Get Imdb Top 250 Movies Csv](web_programming/get_imdb_top_250_movies_csv.py)
* [Get Imdbtop](web_programming/get_imdbtop.py)
* [Get Top Billioners](web_programming/get_top_billioners.py)
* [Get Top Hn Posts](web_programming/get_top_hn_posts.py)
* [Get User Tweets](web_programming/get_user_tweets.py)
* [Giphy](web_programming/giphy.py)
* [Instagram Crawler](web_programming/instagram_crawler.py)
* [Instagram Pic](web_programming/instagram_pic.py)
* [Instagram Video](web_programming/instagram_video.py)
* [Nasa Data](web_programming/nasa_data.py)
* [Open Google Results](web_programming/open_google_results.py)
* [Random Anime Character](web_programming/random_anime_character.py)
* [Recaptcha Verification](web_programming/recaptcha_verification.py)
* [Reddit](web_programming/reddit.py)
* [Search Books By Isbn](web_programming/search_books_by_isbn.py)
* [Slack Message](web_programming/slack_message.py)
* [Test Fetch Github Info](web_programming/test_fetch_github_info.py)
* [World Covid19 Stats](web_programming/world_covid19_stats.py)
|
## Arithmetic Analysis
* [Bisection](arithmetic_analysis/bisection.py)
* [Gaussian Elimination](arithmetic_analysis/gaussian_elimination.py)
* [In Static Equilibrium](arithmetic_analysis/in_static_equilibrium.py)
* [Intersection](arithmetic_analysis/intersection.py)
* [Jacobi Iteration Method](arithmetic_analysis/jacobi_iteration_method.py)
* [Lu Decomposition](arithmetic_analysis/lu_decomposition.py)
* [Newton Forward Interpolation](arithmetic_analysis/newton_forward_interpolation.py)
* [Newton Method](arithmetic_analysis/newton_method.py)
* [Newton Raphson](arithmetic_analysis/newton_raphson.py)
* [Newton Raphson New](arithmetic_analysis/newton_raphson_new.py)
* [Secant Method](arithmetic_analysis/secant_method.py)
## Audio Filters
* [Butterworth Filter](audio_filters/butterworth_filter.py)
* [Equal Loudness Filter](audio_filters/equal_loudness_filter.py)
* [Iir Filter](audio_filters/iir_filter.py)
* [Show Response](audio_filters/show_response.py)
## Backtracking
* [All Combinations](backtracking/all_combinations.py)
* [All Permutations](backtracking/all_permutations.py)
* [All Subsequences](backtracking/all_subsequences.py)
* [Coloring](backtracking/coloring.py)
* [Combination Sum](backtracking/combination_sum.py)
* [Hamiltonian Cycle](backtracking/hamiltonian_cycle.py)
* [Knight Tour](backtracking/knight_tour.py)
* [Minimax](backtracking/minimax.py)
* [Minmax](backtracking/minmax.py)
* [N Queens](backtracking/n_queens.py)
* [N Queens Math](backtracking/n_queens_math.py)
* [Rat In Maze](backtracking/rat_in_maze.py)
* [Sudoku](backtracking/sudoku.py)
* [Sum Of Subsets](backtracking/sum_of_subsets.py)
## Bit Manipulation
* [Binary And Operator](bit_manipulation/binary_and_operator.py)
* [Binary Count Setbits](bit_manipulation/binary_count_setbits.py)
* [Binary Count Trailing Zeros](bit_manipulation/binary_count_trailing_zeros.py)
* [Binary Or Operator](bit_manipulation/binary_or_operator.py)
* [Binary Shifts](bit_manipulation/binary_shifts.py)
* [Binary Twos Complement](bit_manipulation/binary_twos_complement.py)
* [Binary Xor Operator](bit_manipulation/binary_xor_operator.py)
* [Count 1S Brian Kernighan Method](bit_manipulation/count_1s_brian_kernighan_method.py)
* [Count Number Of One Bits](bit_manipulation/count_number_of_one_bits.py)
* [Gray Code Sequence](bit_manipulation/gray_code_sequence.py)
* [Highest Set Bit](bit_manipulation/highest_set_bit.py)
* [Index Of Rightmost Set Bit](bit_manipulation/index_of_rightmost_set_bit.py)
* [Is Even](bit_manipulation/is_even.py)
* [Is Power Of Two](bit_manipulation/is_power_of_two.py)
* [Reverse Bits](bit_manipulation/reverse_bits.py)
* [Single Bit Manipulation Operations](bit_manipulation/single_bit_manipulation_operations.py)
## Blockchain
* [Chinese Remainder Theorem](blockchain/chinese_remainder_theorem.py)
* [Diophantine Equation](blockchain/diophantine_equation.py)
* [Modular Division](blockchain/modular_division.py)
## Boolean Algebra
* [And Gate](boolean_algebra/and_gate.py)
* [Nand Gate](boolean_algebra/nand_gate.py)
* [Norgate](boolean_algebra/norgate.py)
* [Not Gate](boolean_algebra/not_gate.py)
* [Or Gate](boolean_algebra/or_gate.py)
* [Quine Mc Cluskey](boolean_algebra/quine_mc_cluskey.py)
* [Xnor Gate](boolean_algebra/xnor_gate.py)
* [Xor Gate](boolean_algebra/xor_gate.py)
## Cellular Automata
* [Conways Game Of Life](cellular_automata/conways_game_of_life.py)
* [Game Of Life](cellular_automata/game_of_life.py)
* [Nagel Schrekenberg](cellular_automata/nagel_schrekenberg.py)
* [One Dimensional](cellular_automata/one_dimensional.py)
## Ciphers
* [A1Z26](ciphers/a1z26.py)
* [Affine Cipher](ciphers/affine_cipher.py)
* [Atbash](ciphers/atbash.py)
* [Baconian Cipher](ciphers/baconian_cipher.py)
* [Base16](ciphers/base16.py)
* [Base32](ciphers/base32.py)
* [Base64](ciphers/base64.py)
* [Base85](ciphers/base85.py)
* [Beaufort Cipher](ciphers/beaufort_cipher.py)
* [Bifid](ciphers/bifid.py)
* [Brute Force Caesar Cipher](ciphers/brute_force_caesar_cipher.py)
* [Caesar Cipher](ciphers/caesar_cipher.py)
* [Cryptomath Module](ciphers/cryptomath_module.py)
* [Decrypt Caesar With Chi Squared](ciphers/decrypt_caesar_with_chi_squared.py)
* [Deterministic Miller Rabin](ciphers/deterministic_miller_rabin.py)
* [Diffie](ciphers/diffie.py)
* [Diffie Hellman](ciphers/diffie_hellman.py)
* [Elgamal Key Generator](ciphers/elgamal_key_generator.py)
* [Enigma Machine2](ciphers/enigma_machine2.py)
* [Hill Cipher](ciphers/hill_cipher.py)
* [Mixed Keyword Cypher](ciphers/mixed_keyword_cypher.py)
* [Mono Alphabetic Ciphers](ciphers/mono_alphabetic_ciphers.py)
* [Morse Code](ciphers/morse_code.py)
* [Onepad Cipher](ciphers/onepad_cipher.py)
* [Playfair Cipher](ciphers/playfair_cipher.py)
* [Polybius](ciphers/polybius.py)
* [Porta Cipher](ciphers/porta_cipher.py)
* [Rabin Miller](ciphers/rabin_miller.py)
* [Rail Fence Cipher](ciphers/rail_fence_cipher.py)
* [Rot13](ciphers/rot13.py)
* [Rsa Cipher](ciphers/rsa_cipher.py)
* [Rsa Factorization](ciphers/rsa_factorization.py)
* [Rsa Key Generator](ciphers/rsa_key_generator.py)
* [Shuffled Shift Cipher](ciphers/shuffled_shift_cipher.py)
* [Simple Keyword Cypher](ciphers/simple_keyword_cypher.py)
* [Simple Substitution Cipher](ciphers/simple_substitution_cipher.py)
* [Trafid Cipher](ciphers/trafid_cipher.py)
* [Transposition Cipher](ciphers/transposition_cipher.py)
* [Transposition Cipher Encrypt Decrypt File](ciphers/transposition_cipher_encrypt_decrypt_file.py)
* [Vigenere Cipher](ciphers/vigenere_cipher.py)
* [Xor Cipher](ciphers/xor_cipher.py)
## Compression
* [Burrows Wheeler](compression/burrows_wheeler.py)
* [Huffman](compression/huffman.py)
* [Lempel Ziv](compression/lempel_ziv.py)
* [Lempel Ziv Decompress](compression/lempel_ziv_decompress.py)
* [Peak Signal To Noise Ratio](compression/peak_signal_to_noise_ratio.py)
* [Run Length Encoding](compression/run_length_encoding.py)
## Computer Vision
* [Cnn Classification](computer_vision/cnn_classification.py)
* [Flip Augmentation](computer_vision/flip_augmentation.py)
* [Harris Corner](computer_vision/harris_corner.py)
* [Horn Schunck](computer_vision/horn_schunck.py)
* [Mean Threshold](computer_vision/mean_threshold.py)
* [Mosaic Augmentation](computer_vision/mosaic_augmentation.py)
* [Pooling Functions](computer_vision/pooling_functions.py)
## Conversions
* [Astronomical Length Scale Conversion](conversions/astronomical_length_scale_conversion.py)
* [Binary To Decimal](conversions/binary_to_decimal.py)
* [Binary To Hexadecimal](conversions/binary_to_hexadecimal.py)
* [Binary To Octal](conversions/binary_to_octal.py)
* [Decimal To Any](conversions/decimal_to_any.py)
* [Decimal To Binary](conversions/decimal_to_binary.py)
* [Decimal To Binary Recursion](conversions/decimal_to_binary_recursion.py)
* [Decimal To Hexadecimal](conversions/decimal_to_hexadecimal.py)
* [Decimal To Octal](conversions/decimal_to_octal.py)
* [Excel Title To Column](conversions/excel_title_to_column.py)
* [Hex To Bin](conversions/hex_to_bin.py)
* [Hexadecimal To Decimal](conversions/hexadecimal_to_decimal.py)
* [Length Conversion](conversions/length_conversion.py)
* [Molecular Chemistry](conversions/molecular_chemistry.py)
* [Octal To Decimal](conversions/octal_to_decimal.py)
* [Prefix Conversions](conversions/prefix_conversions.py)
* [Prefix Conversions String](conversions/prefix_conversions_string.py)
* [Pressure Conversions](conversions/pressure_conversions.py)
* [Rgb Hsv Conversion](conversions/rgb_hsv_conversion.py)
* [Roman Numerals](conversions/roman_numerals.py)
* [Speed Conversions](conversions/speed_conversions.py)
* [Temperature Conversions](conversions/temperature_conversions.py)
* [Volume Conversions](conversions/volume_conversions.py)
* [Weight Conversion](conversions/weight_conversion.py)
## Data Structures
* Arrays
* [Permutations](data_structures/arrays/permutations.py)
* Binary Tree
* [Avl Tree](data_structures/binary_tree/avl_tree.py)
* [Basic Binary Tree](data_structures/binary_tree/basic_binary_tree.py)
* [Binary Search Tree](data_structures/binary_tree/binary_search_tree.py)
* [Binary Search Tree Recursive](data_structures/binary_tree/binary_search_tree_recursive.py)
* [Binary Tree Mirror](data_structures/binary_tree/binary_tree_mirror.py)
* [Binary Tree Node Sum](data_structures/binary_tree/binary_tree_node_sum.py)
* [Binary Tree Path Sum](data_structures/binary_tree/binary_tree_path_sum.py)
* [Binary Tree Traversals](data_structures/binary_tree/binary_tree_traversals.py)
* [Diff Views Of Binary Tree](data_structures/binary_tree/diff_views_of_binary_tree.py)
* [Fenwick Tree](data_structures/binary_tree/fenwick_tree.py)
* [Inorder Tree Traversal 2022](data_structures/binary_tree/inorder_tree_traversal_2022.py)
* [Lazy Segment Tree](data_structures/binary_tree/lazy_segment_tree.py)
* [Lowest Common Ancestor](data_structures/binary_tree/lowest_common_ancestor.py)
* [Maximum Fenwick Tree](data_structures/binary_tree/maximum_fenwick_tree.py)
* [Merge Two Binary Trees](data_structures/binary_tree/merge_two_binary_trees.py)
* [Non Recursive Segment Tree](data_structures/binary_tree/non_recursive_segment_tree.py)
* [Number Of Possible Binary Trees](data_structures/binary_tree/number_of_possible_binary_trees.py)
* [Red Black Tree](data_structures/binary_tree/red_black_tree.py)
* [Segment Tree](data_structures/binary_tree/segment_tree.py)
* [Segment Tree Other](data_structures/binary_tree/segment_tree_other.py)
* [Treap](data_structures/binary_tree/treap.py)
* [Wavelet Tree](data_structures/binary_tree/wavelet_tree.py)
* Disjoint Set
* [Alternate Disjoint Set](data_structures/disjoint_set/alternate_disjoint_set.py)
* [Disjoint Set](data_structures/disjoint_set/disjoint_set.py)
* Hashing
* [Double Hash](data_structures/hashing/double_hash.py)
* [Hash Table](data_structures/hashing/hash_table.py)
* [Hash Table With Linked List](data_structures/hashing/hash_table_with_linked_list.py)
* Number Theory
* [Prime Numbers](data_structures/hashing/number_theory/prime_numbers.py)
* [Quadratic Probing](data_structures/hashing/quadratic_probing.py)
* Heap
* [Binomial Heap](data_structures/heap/binomial_heap.py)
* [Heap](data_structures/heap/heap.py)
* [Heap Generic](data_structures/heap/heap_generic.py)
* [Max Heap](data_structures/heap/max_heap.py)
* [Min Heap](data_structures/heap/min_heap.py)
* [Randomized Heap](data_structures/heap/randomized_heap.py)
* [Skew Heap](data_structures/heap/skew_heap.py)
* Linked List
* [Circular Linked List](data_structures/linked_list/circular_linked_list.py)
* [Deque Doubly](data_structures/linked_list/deque_doubly.py)
* [Doubly Linked List](data_structures/linked_list/doubly_linked_list.py)
* [Doubly Linked List Two](data_structures/linked_list/doubly_linked_list_two.py)
* [From Sequence](data_structures/linked_list/from_sequence.py)
* [Has Loop](data_structures/linked_list/has_loop.py)
* [Is Palindrome](data_structures/linked_list/is_palindrome.py)
* [Merge Two Lists](data_structures/linked_list/merge_two_lists.py)
* [Middle Element Of Linked List](data_structures/linked_list/middle_element_of_linked_list.py)
* [Print Reverse](data_structures/linked_list/print_reverse.py)
* [Singly Linked List](data_structures/linked_list/singly_linked_list.py)
* [Skip List](data_structures/linked_list/skip_list.py)
* [Swap Nodes](data_structures/linked_list/swap_nodes.py)
* Queue
* [Circular Queue](data_structures/queue/circular_queue.py)
* [Circular Queue Linked List](data_structures/queue/circular_queue_linked_list.py)
* [Double Ended Queue](data_structures/queue/double_ended_queue.py)
* [Linked Queue](data_structures/queue/linked_queue.py)
* [Priority Queue Using List](data_structures/queue/priority_queue_using_list.py)
* [Queue On List](data_structures/queue/queue_on_list.py)
* [Queue On Pseudo Stack](data_structures/queue/queue_on_pseudo_stack.py)
* Stacks
* [Balanced Parentheses](data_structures/stacks/balanced_parentheses.py)
* [Dijkstras Two Stack Algorithm](data_structures/stacks/dijkstras_two_stack_algorithm.py)
* [Evaluate Postfix Notations](data_structures/stacks/evaluate_postfix_notations.py)
* [Infix To Postfix Conversion](data_structures/stacks/infix_to_postfix_conversion.py)
* [Infix To Prefix Conversion](data_structures/stacks/infix_to_prefix_conversion.py)
* [Next Greater Element](data_structures/stacks/next_greater_element.py)
* [Postfix Evaluation](data_structures/stacks/postfix_evaluation.py)
* [Prefix Evaluation](data_structures/stacks/prefix_evaluation.py)
* [Stack](data_structures/stacks/stack.py)
* [Stack With Doubly Linked List](data_structures/stacks/stack_with_doubly_linked_list.py)
* [Stack With Singly Linked List](data_structures/stacks/stack_with_singly_linked_list.py)
* [Stock Span Problem](data_structures/stacks/stock_span_problem.py)
* Trie
* [Radix Tree](data_structures/trie/radix_tree.py)
* [Trie](data_structures/trie/trie.py)
## Digital Image Processing
* [Change Brightness](digital_image_processing/change_brightness.py)
* [Change Contrast](digital_image_processing/change_contrast.py)
* [Convert To Negative](digital_image_processing/convert_to_negative.py)
* Dithering
* [Burkes](digital_image_processing/dithering/burkes.py)
* Edge Detection
* [Canny](digital_image_processing/edge_detection/canny.py)
* Filters
* [Bilateral Filter](digital_image_processing/filters/bilateral_filter.py)
* [Convolve](digital_image_processing/filters/convolve.py)
* [Gabor Filter](digital_image_processing/filters/gabor_filter.py)
* [Gaussian Filter](digital_image_processing/filters/gaussian_filter.py)
* [Local Binary Pattern](digital_image_processing/filters/local_binary_pattern.py)
* [Median Filter](digital_image_processing/filters/median_filter.py)
* [Sobel Filter](digital_image_processing/filters/sobel_filter.py)
* Histogram Equalization
* [Histogram Stretch](digital_image_processing/histogram_equalization/histogram_stretch.py)
* [Index Calculation](digital_image_processing/index_calculation.py)
* Morphological Operations
* [Dilation Operation](digital_image_processing/morphological_operations/dilation_operation.py)
* [Erosion Operation](digital_image_processing/morphological_operations/erosion_operation.py)
* Resize
* [Resize](digital_image_processing/resize/resize.py)
* Rotation
* [Rotation](digital_image_processing/rotation/rotation.py)
* [Sepia](digital_image_processing/sepia.py)
* [Test Digital Image Processing](digital_image_processing/test_digital_image_processing.py)
## Divide And Conquer
* [Closest Pair Of Points](divide_and_conquer/closest_pair_of_points.py)
* [Convex Hull](divide_and_conquer/convex_hull.py)
* [Heaps Algorithm](divide_and_conquer/heaps_algorithm.py)
* [Heaps Algorithm Iterative](divide_and_conquer/heaps_algorithm_iterative.py)
* [Inversions](divide_and_conquer/inversions.py)
* [Kth Order Statistic](divide_and_conquer/kth_order_statistic.py)
* [Max Difference Pair](divide_and_conquer/max_difference_pair.py)
* [Max Subarray Sum](divide_and_conquer/max_subarray_sum.py)
* [Mergesort](divide_and_conquer/mergesort.py)
* [Peak](divide_and_conquer/peak.py)
* [Power](divide_and_conquer/power.py)
* [Strassen Matrix Multiplication](divide_and_conquer/strassen_matrix_multiplication.py)
## Dynamic Programming
* [Abbreviation](dynamic_programming/abbreviation.py)
* [All Construct](dynamic_programming/all_construct.py)
* [Bitmask](dynamic_programming/bitmask.py)
* [Catalan Numbers](dynamic_programming/catalan_numbers.py)
* [Climbing Stairs](dynamic_programming/climbing_stairs.py)
* [Combination Sum Iv](dynamic_programming/combination_sum_iv.py)
* [Edit Distance](dynamic_programming/edit_distance.py)
* [Factorial](dynamic_programming/factorial.py)
* [Fast Fibonacci](dynamic_programming/fast_fibonacci.py)
* [Fibonacci](dynamic_programming/fibonacci.py)
* [Fizz Buzz](dynamic_programming/fizz_buzz.py)
* [Floyd Warshall](dynamic_programming/floyd_warshall.py)
* [Integer Partition](dynamic_programming/integer_partition.py)
* [Iterating Through Submasks](dynamic_programming/iterating_through_submasks.py)
* [Knapsack](dynamic_programming/knapsack.py)
* [Longest Common Subsequence](dynamic_programming/longest_common_subsequence.py)
* [Longest Common Substring](dynamic_programming/longest_common_substring.py)
* [Longest Increasing Subsequence](dynamic_programming/longest_increasing_subsequence.py)
* [Longest Increasing Subsequence O(Nlogn)](dynamic_programming/longest_increasing_subsequence_o(nlogn).py)
* [Longest Sub Array](dynamic_programming/longest_sub_array.py)
* [Matrix Chain Order](dynamic_programming/matrix_chain_order.py)
* [Max Non Adjacent Sum](dynamic_programming/max_non_adjacent_sum.py)
* [Max Sub Array](dynamic_programming/max_sub_array.py)
* [Max Sum Contiguous Subsequence](dynamic_programming/max_sum_contiguous_subsequence.py)
* [Min Distance Up Bottom](dynamic_programming/min_distance_up_bottom.py)
* [Minimum Coin Change](dynamic_programming/minimum_coin_change.py)
* [Minimum Cost Path](dynamic_programming/minimum_cost_path.py)
* [Minimum Partition](dynamic_programming/minimum_partition.py)
* [Minimum Squares To Represent A Number](dynamic_programming/minimum_squares_to_represent_a_number.py)
* [Minimum Steps To One](dynamic_programming/minimum_steps_to_one.py)
* [Minimum Tickets Cost](dynamic_programming/minimum_tickets_cost.py)
* [Optimal Binary Search Tree](dynamic_programming/optimal_binary_search_tree.py)
* [Palindrome Partitioning](dynamic_programming/palindrome_partitioning.py)
* [Rod Cutting](dynamic_programming/rod_cutting.py)
* [Subset Generation](dynamic_programming/subset_generation.py)
* [Sum Of Subset](dynamic_programming/sum_of_subset.py)
* [Viterbi](dynamic_programming/viterbi.py)
## Electronics
* [Builtin Voltage](electronics/builtin_voltage.py)
* [Carrier Concentration](electronics/carrier_concentration.py)
* [Coulombs Law](electronics/coulombs_law.py)
* [Electric Conductivity](electronics/electric_conductivity.py)
* [Electric Power](electronics/electric_power.py)
* [Electrical Impedance](electronics/electrical_impedance.py)
* [Ind Reactance](electronics/ind_reactance.py)
* [Ohms Law](electronics/ohms_law.py)
* [Resistor Equivalence](electronics/resistor_equivalence.py)
* [Resonant Frequency](electronics/resonant_frequency.py)
## File Transfer
* [Receive File](file_transfer/receive_file.py)
* [Send File](file_transfer/send_file.py)
* Tests
* [Test Send File](file_transfer/tests/test_send_file.py)
## Financial
* [Equated Monthly Installments](financial/equated_monthly_installments.py)
* [Interest](financial/interest.py)
* [Price Plus Tax](financial/price_plus_tax.py)
## Fractals
* [Julia Sets](fractals/julia_sets.py)
* [Koch Snowflake](fractals/koch_snowflake.py)
* [Mandelbrot](fractals/mandelbrot.py)
* [Sierpinski Triangle](fractals/sierpinski_triangle.py)
## Fuzzy Logic
* [Fuzzy Operations](fuzzy_logic/fuzzy_operations.py)
## Genetic Algorithm
* [Basic String](genetic_algorithm/basic_string.py)
## Geodesy
* [Haversine Distance](geodesy/haversine_distance.py)
* [Lamberts Ellipsoidal Distance](geodesy/lamberts_ellipsoidal_distance.py)
## Graphics
* [Bezier Curve](graphics/bezier_curve.py)
* [Vector3 For 2D Rendering](graphics/vector3_for_2d_rendering.py)
## Graphs
* [A Star](graphs/a_star.py)
* [Articulation Points](graphs/articulation_points.py)
* [Basic Graphs](graphs/basic_graphs.py)
* [Bellman Ford](graphs/bellman_ford.py)
* [Bidirectional A Star](graphs/bidirectional_a_star.py)
* [Bidirectional Breadth First Search](graphs/bidirectional_breadth_first_search.py)
* [Boruvka](graphs/boruvka.py)
* [Breadth First Search](graphs/breadth_first_search.py)
* [Breadth First Search 2](graphs/breadth_first_search_2.py)
* [Breadth First Search Shortest Path](graphs/breadth_first_search_shortest_path.py)
* [Breadth First Search Shortest Path 2](graphs/breadth_first_search_shortest_path_2.py)
* [Breadth First Search Zero One Shortest Path](graphs/breadth_first_search_zero_one_shortest_path.py)
* [Check Bipartite Graph Bfs](graphs/check_bipartite_graph_bfs.py)
* [Check Bipartite Graph Dfs](graphs/check_bipartite_graph_dfs.py)
* [Check Cycle](graphs/check_cycle.py)
* [Connected Components](graphs/connected_components.py)
* [Depth First Search](graphs/depth_first_search.py)
* [Depth First Search 2](graphs/depth_first_search_2.py)
* [Dijkstra](graphs/dijkstra.py)
* [Dijkstra 2](graphs/dijkstra_2.py)
* [Dijkstra Algorithm](graphs/dijkstra_algorithm.py)
* [Dijkstra Alternate](graphs/dijkstra_alternate.py)
* [Dinic](graphs/dinic.py)
* [Directed And Undirected (Weighted) Graph](graphs/directed_and_undirected_(weighted)_graph.py)
* [Edmonds Karp Multiple Source And Sink](graphs/edmonds_karp_multiple_source_and_sink.py)
* [Eulerian Path And Circuit For Undirected Graph](graphs/eulerian_path_and_circuit_for_undirected_graph.py)
* [Even Tree](graphs/even_tree.py)
* [Finding Bridges](graphs/finding_bridges.py)
* [Frequent Pattern Graph Miner](graphs/frequent_pattern_graph_miner.py)
* [G Topological Sort](graphs/g_topological_sort.py)
* [Gale Shapley Bigraph](graphs/gale_shapley_bigraph.py)
* [Graph List](graphs/graph_list.py)
* [Graph Matrix](graphs/graph_matrix.py)
* [Graphs Floyd Warshall](graphs/graphs_floyd_warshall.py)
* [Greedy Best First](graphs/greedy_best_first.py)
* [Greedy Min Vertex Cover](graphs/greedy_min_vertex_cover.py)
* [Kahns Algorithm Long](graphs/kahns_algorithm_long.py)
* [Kahns Algorithm Topo](graphs/kahns_algorithm_topo.py)
* [Karger](graphs/karger.py)
* [Markov Chain](graphs/markov_chain.py)
* [Matching Min Vertex Cover](graphs/matching_min_vertex_cover.py)
* [Minimum Path Sum](graphs/minimum_path_sum.py)
* [Minimum Spanning Tree Boruvka](graphs/minimum_spanning_tree_boruvka.py)
* [Minimum Spanning Tree Kruskal](graphs/minimum_spanning_tree_kruskal.py)
* [Minimum Spanning Tree Kruskal2](graphs/minimum_spanning_tree_kruskal2.py)
* [Minimum Spanning Tree Prims](graphs/minimum_spanning_tree_prims.py)
* [Minimum Spanning Tree Prims2](graphs/minimum_spanning_tree_prims2.py)
* [Multi Heuristic Astar](graphs/multi_heuristic_astar.py)
* [Page Rank](graphs/page_rank.py)
* [Prim](graphs/prim.py)
* [Random Graph Generator](graphs/random_graph_generator.py)
* [Scc Kosaraju](graphs/scc_kosaraju.py)
* [Strongly Connected Components](graphs/strongly_connected_components.py)
* [Tarjans Scc](graphs/tarjans_scc.py)
* Tests
* [Test Min Spanning Tree Kruskal](graphs/tests/test_min_spanning_tree_kruskal.py)
* [Test Min Spanning Tree Prim](graphs/tests/test_min_spanning_tree_prim.py)
## Greedy Methods
* [Fractional Knapsack](greedy_methods/fractional_knapsack.py)
* [Fractional Knapsack 2](greedy_methods/fractional_knapsack_2.py)
* [Optimal Merge Pattern](greedy_methods/optimal_merge_pattern.py)
## Hashes
* [Adler32](hashes/adler32.py)
* [Chaos Machine](hashes/chaos_machine.py)
* [Djb2](hashes/djb2.py)
* [Elf](hashes/elf.py)
* [Enigma Machine](hashes/enigma_machine.py)
* [Hamming Code](hashes/hamming_code.py)
* [Luhn](hashes/luhn.py)
* [Md5](hashes/md5.py)
* [Sdbm](hashes/sdbm.py)
* [Sha1](hashes/sha1.py)
* [Sha256](hashes/sha256.py)
## Knapsack
* [Greedy Knapsack](knapsack/greedy_knapsack.py)
* [Knapsack](knapsack/knapsack.py)
* [Recursive Approach Knapsack](knapsack/recursive_approach_knapsack.py)
* Tests
* [Test Greedy Knapsack](knapsack/tests/test_greedy_knapsack.py)
* [Test Knapsack](knapsack/tests/test_knapsack.py)
## Linear Algebra
* Src
* [Conjugate Gradient](linear_algebra/src/conjugate_gradient.py)
* [Lib](linear_algebra/src/lib.py)
* [Polynom For Points](linear_algebra/src/polynom_for_points.py)
* [Power Iteration](linear_algebra/src/power_iteration.py)
* [Rayleigh Quotient](linear_algebra/src/rayleigh_quotient.py)
* [Schur Complement](linear_algebra/src/schur_complement.py)
* [Test Linear Algebra](linear_algebra/src/test_linear_algebra.py)
* [Transformations 2D](linear_algebra/src/transformations_2d.py)
## Machine Learning
* [Astar](machine_learning/astar.py)
* [Data Transformations](machine_learning/data_transformations.py)
* [Decision Tree](machine_learning/decision_tree.py)
* Forecasting
* [Run](machine_learning/forecasting/run.py)
* [Gaussian Naive Bayes](machine_learning/gaussian_naive_bayes.py)
* [Gradient Boosting Regressor](machine_learning/gradient_boosting_regressor.py)
* [Gradient Descent](machine_learning/gradient_descent.py)
* [K Means Clust](machine_learning/k_means_clust.py)
* [K Nearest Neighbours](machine_learning/k_nearest_neighbours.py)
* [Knn Sklearn](machine_learning/knn_sklearn.py)
* [Linear Discriminant Analysis](machine_learning/linear_discriminant_analysis.py)
* [Linear Regression](machine_learning/linear_regression.py)
* Local Weighted Learning
* [Local Weighted Learning](machine_learning/local_weighted_learning/local_weighted_learning.py)
* [Logistic Regression](machine_learning/logistic_regression.py)
* Lstm
* [Lstm Prediction](machine_learning/lstm/lstm_prediction.py)
* [Multilayer Perceptron Classifier](machine_learning/multilayer_perceptron_classifier.py)
* [Polymonial Regression](machine_learning/polymonial_regression.py)
* [Random Forest Classifier](machine_learning/random_forest_classifier.py)
* [Random Forest Regressor](machine_learning/random_forest_regressor.py)
* [Scoring Functions](machine_learning/scoring_functions.py)
* [Self Organizing Map](machine_learning/self_organizing_map.py)
* [Sequential Minimum Optimization](machine_learning/sequential_minimum_optimization.py)
* [Similarity Search](machine_learning/similarity_search.py)
* [Support Vector Machines](machine_learning/support_vector_machines.py)
* [Word Frequency Functions](machine_learning/word_frequency_functions.py)
* [Xgboost Classifier](machine_learning/xgboost_classifier.py)
* [Xgboost Regressor](machine_learning/xgboost_regressor.py)
## Maths
* [3N Plus 1](maths/3n_plus_1.py)
* [Abs](maths/abs.py)
* [Add](maths/add.py)
* [Addition Without Arithmetic](maths/addition_without_arithmetic.py)
* [Aliquot Sum](maths/aliquot_sum.py)
* [Allocation Number](maths/allocation_number.py)
* [Arc Length](maths/arc_length.py)
* [Area](maths/area.py)
* [Area Under Curve](maths/area_under_curve.py)
* [Armstrong Numbers](maths/armstrong_numbers.py)
* [Average Absolute Deviation](maths/average_absolute_deviation.py)
* [Average Mean](maths/average_mean.py)
* [Average Median](maths/average_median.py)
* [Average Mode](maths/average_mode.py)
* [Bailey Borwein Plouffe](maths/bailey_borwein_plouffe.py)
* [Basic Maths](maths/basic_maths.py)
* [Binary Exp Mod](maths/binary_exp_mod.py)
* [Binary Exponentiation](maths/binary_exponentiation.py)
* [Binary Exponentiation 2](maths/binary_exponentiation_2.py)
* [Binary Exponentiation 3](maths/binary_exponentiation_3.py)
* [Binomial Coefficient](maths/binomial_coefficient.py)
* [Binomial Distribution](maths/binomial_distribution.py)
* [Bisection](maths/bisection.py)
* [Carmichael Number](maths/carmichael_number.py)
* [Catalan Number](maths/catalan_number.py)
* [Ceil](maths/ceil.py)
* [Check Polygon](maths/check_polygon.py)
* [Chudnovsky Algorithm](maths/chudnovsky_algorithm.py)
* [Collatz Sequence](maths/collatz_sequence.py)
* [Combinations](maths/combinations.py)
* [Decimal Isolate](maths/decimal_isolate.py)
* [Dodecahedron](maths/dodecahedron.py)
* [Double Factorial Iterative](maths/double_factorial_iterative.py)
* [Double Factorial Recursive](maths/double_factorial_recursive.py)
* [Entropy](maths/entropy.py)
* [Euclidean Distance](maths/euclidean_distance.py)
* [Euclidean Gcd](maths/euclidean_gcd.py)
* [Euler Method](maths/euler_method.py)
* [Euler Modified](maths/euler_modified.py)
* [Eulers Totient](maths/eulers_totient.py)
* [Extended Euclidean Algorithm](maths/extended_euclidean_algorithm.py)
* [Factorial Iterative](maths/factorial_iterative.py)
* [Factorial Recursive](maths/factorial_recursive.py)
* [Factors](maths/factors.py)
* [Fermat Little Theorem](maths/fermat_little_theorem.py)
* [Fibonacci](maths/fibonacci.py)
* [Find Max](maths/find_max.py)
* [Find Max Recursion](maths/find_max_recursion.py)
* [Find Min](maths/find_min.py)
* [Find Min Recursion](maths/find_min_recursion.py)
* [Floor](maths/floor.py)
* [Gamma](maths/gamma.py)
* [Gamma Recursive](maths/gamma_recursive.py)
* [Gaussian](maths/gaussian.py)
* [Gaussian Error Linear Unit](maths/gaussian_error_linear_unit.py)
* [Greatest Common Divisor](maths/greatest_common_divisor.py)
* [Greedy Coin Change](maths/greedy_coin_change.py)
* [Hamming Numbers](maths/hamming_numbers.py)
* [Hardy Ramanujanalgo](maths/hardy_ramanujanalgo.py)
* [Integration By Simpson Approx](maths/integration_by_simpson_approx.py)
* [Is Ip V4 Address Valid](maths/is_ip_v4_address_valid.py)
* [Is Square Free](maths/is_square_free.py)
* [Jaccard Similarity](maths/jaccard_similarity.py)
* [Kadanes](maths/kadanes.py)
* [Karatsuba](maths/karatsuba.py)
* [Krishnamurthy Number](maths/krishnamurthy_number.py)
* [Kth Lexicographic Permutation](maths/kth_lexicographic_permutation.py)
* [Largest Of Very Large Numbers](maths/largest_of_very_large_numbers.py)
* [Largest Subarray Sum](maths/largest_subarray_sum.py)
* [Least Common Multiple](maths/least_common_multiple.py)
* [Line Length](maths/line_length.py)
* [Lucas Lehmer Primality Test](maths/lucas_lehmer_primality_test.py)
* [Lucas Series](maths/lucas_series.py)
* [Maclaurin Series](maths/maclaurin_series.py)
* [Manhattan Distance](maths/manhattan_distance.py)
* [Matrix Exponentiation](maths/matrix_exponentiation.py)
* [Max Sum Sliding Window](maths/max_sum_sliding_window.py)
* [Median Of Two Arrays](maths/median_of_two_arrays.py)
* [Miller Rabin](maths/miller_rabin.py)
* [Mobius Function](maths/mobius_function.py)
* [Modular Exponential](maths/modular_exponential.py)
* [Monte Carlo](maths/monte_carlo.py)
* [Monte Carlo Dice](maths/monte_carlo_dice.py)
* [Nevilles Method](maths/nevilles_method.py)
* [Newton Raphson](maths/newton_raphson.py)
* [Number Of Digits](maths/number_of_digits.py)
* [Numerical Integration](maths/numerical_integration.py)
* [Perfect Cube](maths/perfect_cube.py)
* [Perfect Number](maths/perfect_number.py)
* [Perfect Square](maths/perfect_square.py)
* [Persistence](maths/persistence.py)
* [Pi Monte Carlo Estimation](maths/pi_monte_carlo_estimation.py)
* [Points Are Collinear 3D](maths/points_are_collinear_3d.py)
* [Pollard Rho](maths/pollard_rho.py)
* [Polynomial Evaluation](maths/polynomial_evaluation.py)
* Polynomials
* [Single Indeterminate Operations](maths/polynomials/single_indeterminate_operations.py)
* [Power Using Recursion](maths/power_using_recursion.py)
* [Prime Check](maths/prime_check.py)
* [Prime Factors](maths/prime_factors.py)
* [Prime Numbers](maths/prime_numbers.py)
* [Prime Sieve Eratosthenes](maths/prime_sieve_eratosthenes.py)
* [Primelib](maths/primelib.py)
* [Print Multiplication Table](maths/print_multiplication_table.py)
* [Proth Number](maths/proth_number.py)
* [Pythagoras](maths/pythagoras.py)
* [Qr Decomposition](maths/qr_decomposition.py)
* [Quadratic Equations Complex Numbers](maths/quadratic_equations_complex_numbers.py)
* [Radians](maths/radians.py)
* [Radix2 Fft](maths/radix2_fft.py)
* [Relu](maths/relu.py)
* [Runge Kutta](maths/runge_kutta.py)
* [Segmented Sieve](maths/segmented_sieve.py)
* Series
* [Arithmetic](maths/series/arithmetic.py)
* [Geometric](maths/series/geometric.py)
* [Geometric Series](maths/series/geometric_series.py)
* [Harmonic](maths/series/harmonic.py)
* [Harmonic Series](maths/series/harmonic_series.py)
* [Hexagonal Numbers](maths/series/hexagonal_numbers.py)
* [P Series](maths/series/p_series.py)
* [Sieve Of Eratosthenes](maths/sieve_of_eratosthenes.py)
* [Sigmoid](maths/sigmoid.py)
* [Sigmoid Linear Unit](maths/sigmoid_linear_unit.py)
* [Signum](maths/signum.py)
* [Simpson Rule](maths/simpson_rule.py)
* [Sin](maths/sin.py)
* [Sock Merchant](maths/sock_merchant.py)
* [Softmax](maths/softmax.py)
* [Square Root](maths/square_root.py)
* [Sum Of Arithmetic Series](maths/sum_of_arithmetic_series.py)
* [Sum Of Digits](maths/sum_of_digits.py)
* [Sum Of Geometric Progression](maths/sum_of_geometric_progression.py)
* [Sum Of Harmonic Series](maths/sum_of_harmonic_series.py)
* [Sumset](maths/sumset.py)
* [Sylvester Sequence](maths/sylvester_sequence.py)
* [Test Prime Check](maths/test_prime_check.py)
* [Trapezoidal Rule](maths/trapezoidal_rule.py)
* [Triplet Sum](maths/triplet_sum.py)
* [Two Pointer](maths/two_pointer.py)
* [Two Sum](maths/two_sum.py)
* [Ugly Numbers](maths/ugly_numbers.py)
* [Volume](maths/volume.py)
* [Weird Number](maths/weird_number.py)
* [Zellers Congruence](maths/zellers_congruence.py)
## Matrix
* [Binary Search Matrix](matrix/binary_search_matrix.py)
* [Count Islands In Matrix](matrix/count_islands_in_matrix.py)
* [Count Paths](matrix/count_paths.py)
* [Cramers Rule 2X2](matrix/cramers_rule_2x2.py)
* [Inverse Of Matrix](matrix/inverse_of_matrix.py)
* [Largest Square Area In Matrix](matrix/largest_square_area_in_matrix.py)
* [Matrix Class](matrix/matrix_class.py)
* [Matrix Operation](matrix/matrix_operation.py)
* [Max Area Of Island](matrix/max_area_of_island.py)
* [Nth Fibonacci Using Matrix Exponentiation](matrix/nth_fibonacci_using_matrix_exponentiation.py)
* [Pascal Triangle](matrix/pascal_triangle.py)
* [Rotate Matrix](matrix/rotate_matrix.py)
* [Searching In Sorted Matrix](matrix/searching_in_sorted_matrix.py)
* [Sherman Morrison](matrix/sherman_morrison.py)
* [Spiral Print](matrix/spiral_print.py)
* Tests
* [Test Matrix Operation](matrix/tests/test_matrix_operation.py)
## Networking Flow
* [Ford Fulkerson](networking_flow/ford_fulkerson.py)
* [Minimum Cut](networking_flow/minimum_cut.py)
## Neural Network
* [2 Hidden Layers Neural Network](neural_network/2_hidden_layers_neural_network.py)
* [Back Propagation Neural Network](neural_network/back_propagation_neural_network.py)
* [Convolution Neural Network](neural_network/convolution_neural_network.py)
* [Perceptron](neural_network/perceptron.py)
* [Simple Neural Network](neural_network/simple_neural_network.py)
## Other
* [Activity Selection](other/activity_selection.py)
* [Alternative List Arrange](other/alternative_list_arrange.py)
* [Davisb Putnamb Logemannb Loveland](other/davisb_putnamb_logemannb_loveland.py)
* [Dijkstra Bankers Algorithm](other/dijkstra_bankers_algorithm.py)
* [Doomsday](other/doomsday.py)
* [Fischer Yates Shuffle](other/fischer_yates_shuffle.py)
* [Gauss Easter](other/gauss_easter.py)
* [Graham Scan](other/graham_scan.py)
* [Greedy](other/greedy.py)
* [Least Recently Used](other/least_recently_used.py)
* [Lfu Cache](other/lfu_cache.py)
* [Linear Congruential Generator](other/linear_congruential_generator.py)
* [Lru Cache](other/lru_cache.py)
* [Magicdiamondpattern](other/magicdiamondpattern.py)
* [Maximum Subarray](other/maximum_subarray.py)
* [Nested Brackets](other/nested_brackets.py)
* [Password](other/password.py)
* [Quine](other/quine.py)
* [Scoring Algorithm](other/scoring_algorithm.py)
* [Sdes](other/sdes.py)
* [Tower Of Hanoi](other/tower_of_hanoi.py)
## Physics
* [Archimedes Principle](physics/archimedes_principle.py)
* [Casimir Effect](physics/casimir_effect.py)
* [Centripetal Force](physics/centripetal_force.py)
* [Horizontal Projectile Motion](physics/horizontal_projectile_motion.py)
* [Hubble Parameter](physics/hubble_parameter.py)
* [Ideal Gas Law](physics/ideal_gas_law.py)
* [Kinetic Energy](physics/kinetic_energy.py)
* [Lorentz Transformation Four Vector](physics/lorentz_transformation_four_vector.py)
* [Malus Law](physics/malus_law.py)
* [N Body Simulation](physics/n_body_simulation.py)
* [Newtons Law Of Gravitation](physics/newtons_law_of_gravitation.py)
* [Newtons Second Law Of Motion](physics/newtons_second_law_of_motion.py)
* [Potential Energy](physics/potential_energy.py)
* [Rms Speed Of Molecule](physics/rms_speed_of_molecule.py)
* [Shear Stress](physics/shear_stress.py)
## Project Euler
* Problem 001
* [Sol1](project_euler/problem_001/sol1.py)
* [Sol2](project_euler/problem_001/sol2.py)
* [Sol3](project_euler/problem_001/sol3.py)
* [Sol4](project_euler/problem_001/sol4.py)
* [Sol5](project_euler/problem_001/sol5.py)
* [Sol6](project_euler/problem_001/sol6.py)
* [Sol7](project_euler/problem_001/sol7.py)
* Problem 002
* [Sol1](project_euler/problem_002/sol1.py)
* [Sol2](project_euler/problem_002/sol2.py)
* [Sol3](project_euler/problem_002/sol3.py)
* [Sol4](project_euler/problem_002/sol4.py)
* [Sol5](project_euler/problem_002/sol5.py)
* Problem 003
* [Sol1](project_euler/problem_003/sol1.py)
* [Sol2](project_euler/problem_003/sol2.py)
* [Sol3](project_euler/problem_003/sol3.py)
* Problem 004
* [Sol1](project_euler/problem_004/sol1.py)
* [Sol2](project_euler/problem_004/sol2.py)
* Problem 005
* [Sol1](project_euler/problem_005/sol1.py)
* [Sol2](project_euler/problem_005/sol2.py)
* Problem 006
* [Sol1](project_euler/problem_006/sol1.py)
* [Sol2](project_euler/problem_006/sol2.py)
* [Sol3](project_euler/problem_006/sol3.py)
* [Sol4](project_euler/problem_006/sol4.py)
* Problem 007
* [Sol1](project_euler/problem_007/sol1.py)
* [Sol2](project_euler/problem_007/sol2.py)
* [Sol3](project_euler/problem_007/sol3.py)
* Problem 008
* [Sol1](project_euler/problem_008/sol1.py)
* [Sol2](project_euler/problem_008/sol2.py)
* [Sol3](project_euler/problem_008/sol3.py)
* Problem 009
* [Sol1](project_euler/problem_009/sol1.py)
* [Sol2](project_euler/problem_009/sol2.py)
* [Sol3](project_euler/problem_009/sol3.py)
* Problem 010
* [Sol1](project_euler/problem_010/sol1.py)
* [Sol2](project_euler/problem_010/sol2.py)
* [Sol3](project_euler/problem_010/sol3.py)
* Problem 011
* [Sol1](project_euler/problem_011/sol1.py)
* [Sol2](project_euler/problem_011/sol2.py)
* Problem 012
* [Sol1](project_euler/problem_012/sol1.py)
* [Sol2](project_euler/problem_012/sol2.py)
* Problem 013
* [Sol1](project_euler/problem_013/sol1.py)
* Problem 014
* [Sol1](project_euler/problem_014/sol1.py)
* [Sol2](project_euler/problem_014/sol2.py)
* Problem 015
* [Sol1](project_euler/problem_015/sol1.py)
* Problem 016
* [Sol1](project_euler/problem_016/sol1.py)
* [Sol2](project_euler/problem_016/sol2.py)
* Problem 017
* [Sol1](project_euler/problem_017/sol1.py)
* Problem 018
* [Solution](project_euler/problem_018/solution.py)
* Problem 019
* [Sol1](project_euler/problem_019/sol1.py)
* Problem 020
* [Sol1](project_euler/problem_020/sol1.py)
* [Sol2](project_euler/problem_020/sol2.py)
* [Sol3](project_euler/problem_020/sol3.py)
* [Sol4](project_euler/problem_020/sol4.py)
* Problem 021
* [Sol1](project_euler/problem_021/sol1.py)
* Problem 022
* [Sol1](project_euler/problem_022/sol1.py)
* [Sol2](project_euler/problem_022/sol2.py)
* Problem 023
* [Sol1](project_euler/problem_023/sol1.py)
* Problem 024
* [Sol1](project_euler/problem_024/sol1.py)
* Problem 025
* [Sol1](project_euler/problem_025/sol1.py)
* [Sol2](project_euler/problem_025/sol2.py)
* [Sol3](project_euler/problem_025/sol3.py)
* Problem 026
* [Sol1](project_euler/problem_026/sol1.py)
* Problem 027
* [Sol1](project_euler/problem_027/sol1.py)
* Problem 028
* [Sol1](project_euler/problem_028/sol1.py)
* Problem 029
* [Sol1](project_euler/problem_029/sol1.py)
* Problem 030
* [Sol1](project_euler/problem_030/sol1.py)
* Problem 031
* [Sol1](project_euler/problem_031/sol1.py)
* [Sol2](project_euler/problem_031/sol2.py)
* Problem 032
* [Sol32](project_euler/problem_032/sol32.py)
* Problem 033
* [Sol1](project_euler/problem_033/sol1.py)
* Problem 034
* [Sol1](project_euler/problem_034/sol1.py)
* Problem 035
* [Sol1](project_euler/problem_035/sol1.py)
* Problem 036
* [Sol1](project_euler/problem_036/sol1.py)
* Problem 037
* [Sol1](project_euler/problem_037/sol1.py)
* Problem 038
* [Sol1](project_euler/problem_038/sol1.py)
* Problem 039
* [Sol1](project_euler/problem_039/sol1.py)
* Problem 040
* [Sol1](project_euler/problem_040/sol1.py)
* Problem 041
* [Sol1](project_euler/problem_041/sol1.py)
* Problem 042
* [Solution42](project_euler/problem_042/solution42.py)
* Problem 043
* [Sol1](project_euler/problem_043/sol1.py)
* Problem 044
* [Sol1](project_euler/problem_044/sol1.py)
* Problem 045
* [Sol1](project_euler/problem_045/sol1.py)
* Problem 046
* [Sol1](project_euler/problem_046/sol1.py)
* Problem 047
* [Sol1](project_euler/problem_047/sol1.py)
* Problem 048
* [Sol1](project_euler/problem_048/sol1.py)
* Problem 049
* [Sol1](project_euler/problem_049/sol1.py)
* Problem 050
* [Sol1](project_euler/problem_050/sol1.py)
* Problem 051
* [Sol1](project_euler/problem_051/sol1.py)
* Problem 052
* [Sol1](project_euler/problem_052/sol1.py)
* Problem 053
* [Sol1](project_euler/problem_053/sol1.py)
* Problem 054
* [Sol1](project_euler/problem_054/sol1.py)
* [Test Poker Hand](project_euler/problem_054/test_poker_hand.py)
* Problem 055
* [Sol1](project_euler/problem_055/sol1.py)
* Problem 056
* [Sol1](project_euler/problem_056/sol1.py)
* Problem 057
* [Sol1](project_euler/problem_057/sol1.py)
* Problem 058
* [Sol1](project_euler/problem_058/sol1.py)
* Problem 059
* [Sol1](project_euler/problem_059/sol1.py)
* Problem 062
* [Sol1](project_euler/problem_062/sol1.py)
* Problem 063
* [Sol1](project_euler/problem_063/sol1.py)
* Problem 064
* [Sol1](project_euler/problem_064/sol1.py)
* Problem 065
* [Sol1](project_euler/problem_065/sol1.py)
* Problem 067
* [Sol1](project_euler/problem_067/sol1.py)
* [Sol2](project_euler/problem_067/sol2.py)
* Problem 068
* [Sol1](project_euler/problem_068/sol1.py)
* Problem 069
* [Sol1](project_euler/problem_069/sol1.py)
* Problem 070
* [Sol1](project_euler/problem_070/sol1.py)
* Problem 071
* [Sol1](project_euler/problem_071/sol1.py)
* Problem 072
* [Sol1](project_euler/problem_072/sol1.py)
* [Sol2](project_euler/problem_072/sol2.py)
* Problem 073
* [Sol1](project_euler/problem_073/sol1.py)
* Problem 074
* [Sol1](project_euler/problem_074/sol1.py)
* [Sol2](project_euler/problem_074/sol2.py)
* Problem 075
* [Sol1](project_euler/problem_075/sol1.py)
* Problem 076
* [Sol1](project_euler/problem_076/sol1.py)
* Problem 077
* [Sol1](project_euler/problem_077/sol1.py)
* Problem 078
* [Sol1](project_euler/problem_078/sol1.py)
* Problem 080
* [Sol1](project_euler/problem_080/sol1.py)
* Problem 081
* [Sol1](project_euler/problem_081/sol1.py)
* Problem 085
* [Sol1](project_euler/problem_085/sol1.py)
* Problem 086
* [Sol1](project_euler/problem_086/sol1.py)
* Problem 087
* [Sol1](project_euler/problem_087/sol1.py)
* Problem 089
* [Sol1](project_euler/problem_089/sol1.py)
* Problem 091
* [Sol1](project_euler/problem_091/sol1.py)
* Problem 092
* [Sol1](project_euler/problem_092/sol1.py)
* Problem 097
* [Sol1](project_euler/problem_097/sol1.py)
* Problem 099
* [Sol1](project_euler/problem_099/sol1.py)
* Problem 101
* [Sol1](project_euler/problem_101/sol1.py)
* Problem 102
* [Sol1](project_euler/problem_102/sol1.py)
* Problem 104
* [Sol1](project_euler/problem_104/sol1.py)
* Problem 107
* [Sol1](project_euler/problem_107/sol1.py)
* Problem 109
* [Sol1](project_euler/problem_109/sol1.py)
* Problem 112
* [Sol1](project_euler/problem_112/sol1.py)
* Problem 113
* [Sol1](project_euler/problem_113/sol1.py)
* Problem 114
* [Sol1](project_euler/problem_114/sol1.py)
* Problem 115
* [Sol1](project_euler/problem_115/sol1.py)
* Problem 116
* [Sol1](project_euler/problem_116/sol1.py)
* Problem 119
* [Sol1](project_euler/problem_119/sol1.py)
* Problem 120
* [Sol1](project_euler/problem_120/sol1.py)
* Problem 121
* [Sol1](project_euler/problem_121/sol1.py)
* Problem 123
* [Sol1](project_euler/problem_123/sol1.py)
* Problem 125
* [Sol1](project_euler/problem_125/sol1.py)
* Problem 129
* [Sol1](project_euler/problem_129/sol1.py)
* Problem 135
* [Sol1](project_euler/problem_135/sol1.py)
* Problem 144
* [Sol1](project_euler/problem_144/sol1.py)
* Problem 145
* [Sol1](project_euler/problem_145/sol1.py)
* Problem 173
* [Sol1](project_euler/problem_173/sol1.py)
* Problem 174
* [Sol1](project_euler/problem_174/sol1.py)
* Problem 180
* [Sol1](project_euler/problem_180/sol1.py)
* Problem 188
* [Sol1](project_euler/problem_188/sol1.py)
* Problem 191
* [Sol1](project_euler/problem_191/sol1.py)
* Problem 203
* [Sol1](project_euler/problem_203/sol1.py)
* Problem 205
* [Sol1](project_euler/problem_205/sol1.py)
* Problem 206
* [Sol1](project_euler/problem_206/sol1.py)
* Problem 207
* [Sol1](project_euler/problem_207/sol1.py)
* Problem 234
* [Sol1](project_euler/problem_234/sol1.py)
* Problem 301
* [Sol1](project_euler/problem_301/sol1.py)
* Problem 493
* [Sol1](project_euler/problem_493/sol1.py)
* Problem 551
* [Sol1](project_euler/problem_551/sol1.py)
* Problem 587
* [Sol1](project_euler/problem_587/sol1.py)
* Problem 686
* [Sol1](project_euler/problem_686/sol1.py)
## Quantum
* [Deutsch Jozsa](quantum/deutsch_jozsa.py)
* [Half Adder](quantum/half_adder.py)
* [Not Gate](quantum/not_gate.py)
* [Q Fourier Transform](quantum/q_fourier_transform.py)
* [Q Full Adder](quantum/q_full_adder.py)
* [Quantum Entanglement](quantum/quantum_entanglement.py)
* [Quantum Teleportation](quantum/quantum_teleportation.py)
* [Ripple Adder Classic](quantum/ripple_adder_classic.py)
* [Single Qubit Measure](quantum/single_qubit_measure.py)
* [Superdense Coding](quantum/superdense_coding.py)
## Scheduling
* [First Come First Served](scheduling/first_come_first_served.py)
* [Highest Response Ratio Next](scheduling/highest_response_ratio_next.py)
* [Job Sequencing With Deadline](scheduling/job_sequencing_with_deadline.py)
* [Multi Level Feedback Queue](scheduling/multi_level_feedback_queue.py)
* [Non Preemptive Shortest Job First](scheduling/non_preemptive_shortest_job_first.py)
* [Round Robin](scheduling/round_robin.py)
* [Shortest Job First](scheduling/shortest_job_first.py)
## Searches
* [Binary Search](searches/binary_search.py)
* [Binary Tree Traversal](searches/binary_tree_traversal.py)
* [Double Linear Search](searches/double_linear_search.py)
* [Double Linear Search Recursion](searches/double_linear_search_recursion.py)
* [Fibonacci Search](searches/fibonacci_search.py)
* [Hill Climbing](searches/hill_climbing.py)
* [Interpolation Search](searches/interpolation_search.py)
* [Jump Search](searches/jump_search.py)
* [Linear Search](searches/linear_search.py)
* [Quick Select](searches/quick_select.py)
* [Sentinel Linear Search](searches/sentinel_linear_search.py)
* [Simple Binary Search](searches/simple_binary_search.py)
* [Simulated Annealing](searches/simulated_annealing.py)
* [Tabu Search](searches/tabu_search.py)
* [Ternary Search](searches/ternary_search.py)
## Sorts
* [Bead Sort](sorts/bead_sort.py)
* [Bitonic Sort](sorts/bitonic_sort.py)
* [Bogo Sort](sorts/bogo_sort.py)
* [Bubble Sort](sorts/bubble_sort.py)
* [Bucket Sort](sorts/bucket_sort.py)
* [Circle Sort](sorts/circle_sort.py)
* [Cocktail Shaker Sort](sorts/cocktail_shaker_sort.py)
* [Comb Sort](sorts/comb_sort.py)
* [Counting Sort](sorts/counting_sort.py)
* [Cycle Sort](sorts/cycle_sort.py)
* [Double Sort](sorts/double_sort.py)
* [Dutch National Flag Sort](sorts/dutch_national_flag_sort.py)
* [Exchange Sort](sorts/exchange_sort.py)
* [External Sort](sorts/external_sort.py)
* [Gnome Sort](sorts/gnome_sort.py)
* [Heap Sort](sorts/heap_sort.py)
* [Insertion Sort](sorts/insertion_sort.py)
* [Intro Sort](sorts/intro_sort.py)
* [Iterative Merge Sort](sorts/iterative_merge_sort.py)
* [Merge Insertion Sort](sorts/merge_insertion_sort.py)
* [Merge Sort](sorts/merge_sort.py)
* [Msd Radix Sort](sorts/msd_radix_sort.py)
* [Natural Sort](sorts/natural_sort.py)
* [Odd Even Sort](sorts/odd_even_sort.py)
* [Odd Even Transposition Parallel](sorts/odd_even_transposition_parallel.py)
* [Odd Even Transposition Single Threaded](sorts/odd_even_transposition_single_threaded.py)
* [Pancake Sort](sorts/pancake_sort.py)
* [Patience Sort](sorts/patience_sort.py)
* [Pigeon Sort](sorts/pigeon_sort.py)
* [Pigeonhole Sort](sorts/pigeonhole_sort.py)
* [Quick Sort](sorts/quick_sort.py)
* [Quick Sort 3 Partition](sorts/quick_sort_3_partition.py)
* [Radix Sort](sorts/radix_sort.py)
* [Random Normal Distribution Quicksort](sorts/random_normal_distribution_quicksort.py)
* [Random Pivot Quick Sort](sorts/random_pivot_quick_sort.py)
* [Recursive Bubble Sort](sorts/recursive_bubble_sort.py)
* [Recursive Insertion Sort](sorts/recursive_insertion_sort.py)
* [Recursive Mergesort Array](sorts/recursive_mergesort_array.py)
* [Recursive Quick Sort](sorts/recursive_quick_sort.py)
* [Selection Sort](sorts/selection_sort.py)
* [Shell Sort](sorts/shell_sort.py)
* [Shrink Shell Sort](sorts/shrink_shell_sort.py)
* [Slowsort](sorts/slowsort.py)
* [Stooge Sort](sorts/stooge_sort.py)
* [Strand Sort](sorts/strand_sort.py)
* [Tim Sort](sorts/tim_sort.py)
* [Topological Sort](sorts/topological_sort.py)
* [Tree Sort](sorts/tree_sort.py)
* [Unknown Sort](sorts/unknown_sort.py)
* [Wiggle Sort](sorts/wiggle_sort.py)
## Strings
* [Aho Corasick](strings/aho_corasick.py)
* [Alternative String Arrange](strings/alternative_string_arrange.py)
* [Anagrams](strings/anagrams.py)
* [Autocomplete Using Trie](strings/autocomplete_using_trie.py)
* [Barcode Validator](strings/barcode_validator.py)
* [Boyer Moore Search](strings/boyer_moore_search.py)
* [Can String Be Rearranged As Palindrome](strings/can_string_be_rearranged_as_palindrome.py)
* [Capitalize](strings/capitalize.py)
* [Check Anagrams](strings/check_anagrams.py)
* [Credit Card Validator](strings/credit_card_validator.py)
* [Detecting English Programmatically](strings/detecting_english_programmatically.py)
* [Dna](strings/dna.py)
* [Frequency Finder](strings/frequency_finder.py)
* [Hamming Distance](strings/hamming_distance.py)
* [Indian Phone Validator](strings/indian_phone_validator.py)
* [Is Contains Unique Chars](strings/is_contains_unique_chars.py)
* [Is Isogram](strings/is_isogram.py)
* [Is Palindrome](strings/is_palindrome.py)
* [Is Pangram](strings/is_pangram.py)
* [Is Spain National Id](strings/is_spain_national_id.py)
* [Is Srilankan Phone Number](strings/is_srilankan_phone_number.py)
* [Jaro Winkler](strings/jaro_winkler.py)
* [Join](strings/join.py)
* [Knuth Morris Pratt](strings/knuth_morris_pratt.py)
* [Levenshtein Distance](strings/levenshtein_distance.py)
* [Lower](strings/lower.py)
* [Manacher](strings/manacher.py)
* [Min Cost String Conversion](strings/min_cost_string_conversion.py)
* [Naive String Search](strings/naive_string_search.py)
* [Ngram](strings/ngram.py)
* [Palindrome](strings/palindrome.py)
* [Prefix Function](strings/prefix_function.py)
* [Rabin Karp](strings/rabin_karp.py)
* [Remove Duplicate](strings/remove_duplicate.py)
* [Reverse Letters](strings/reverse_letters.py)
* [Reverse Long Words](strings/reverse_long_words.py)
* [Reverse Words](strings/reverse_words.py)
* [Snake Case To Camel Pascal Case](strings/snake_case_to_camel_pascal_case.py)
* [Split](strings/split.py)
* [Text Justification](strings/text_justification.py)
* [Upper](strings/upper.py)
* [Wave](strings/wave.py)
* [Wildcard Pattern Matching](strings/wildcard_pattern_matching.py)
* [Word Occurrence](strings/word_occurrence.py)
* [Word Patterns](strings/word_patterns.py)
* [Z Function](strings/z_function.py)
## Web Programming
* [Co2 Emission](web_programming/co2_emission.py)
* [Convert Number To Words](web_programming/convert_number_to_words.py)
* [Covid Stats Via Xpath](web_programming/covid_stats_via_xpath.py)
* [Crawl Google Results](web_programming/crawl_google_results.py)
* [Crawl Google Scholar Citation](web_programming/crawl_google_scholar_citation.py)
* [Currency Converter](web_programming/currency_converter.py)
* [Current Stock Price](web_programming/current_stock_price.py)
* [Current Weather](web_programming/current_weather.py)
* [Daily Horoscope](web_programming/daily_horoscope.py)
* [Download Images From Google Query](web_programming/download_images_from_google_query.py)
* [Emails From Url](web_programming/emails_from_url.py)
* [Fetch Anime And Play](web_programming/fetch_anime_and_play.py)
* [Fetch Bbc News](web_programming/fetch_bbc_news.py)
* [Fetch Github Info](web_programming/fetch_github_info.py)
* [Fetch Jobs](web_programming/fetch_jobs.py)
* [Fetch Quotes](web_programming/fetch_quotes.py)
* [Fetch Well Rx Price](web_programming/fetch_well_rx_price.py)
* [Get Amazon Product Data](web_programming/get_amazon_product_data.py)
* [Get Imdb Top 250 Movies Csv](web_programming/get_imdb_top_250_movies_csv.py)
* [Get Imdbtop](web_programming/get_imdbtop.py)
* [Get Top Billioners](web_programming/get_top_billioners.py)
* [Get Top Hn Posts](web_programming/get_top_hn_posts.py)
* [Get User Tweets](web_programming/get_user_tweets.py)
* [Giphy](web_programming/giphy.py)
* [Instagram Crawler](web_programming/instagram_crawler.py)
* [Instagram Pic](web_programming/instagram_pic.py)
* [Instagram Video](web_programming/instagram_video.py)
* [Nasa Data](web_programming/nasa_data.py)
* [Open Google Results](web_programming/open_google_results.py)
* [Random Anime Character](web_programming/random_anime_character.py)
* [Recaptcha Verification](web_programming/recaptcha_verification.py)
* [Reddit](web_programming/reddit.py)
* [Search Books By Isbn](web_programming/search_books_by_isbn.py)
* [Slack Message](web_programming/slack_message.py)
* [Test Fetch Github Info](web_programming/test_fetch_github_info.py)
* [World Covid19 Stats](web_programming/world_covid19_stats.py)
| 1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from copy import deepcopy
class FenwickTree:
"""
Fenwick Tree
More info: https://en.wikipedia.org/wiki/Fenwick_tree
"""
def __init__(self, arr: list[int] = None, size: int = None) -> None:
"""
Constructor for the Fenwick tree
Parameters:
arr (list): list of elements to initialize the tree with (optional)
size (int): size of the Fenwick tree (if arr is None)
"""
if arr is None and size is not None:
self.size = size
self.tree = [0] * size
elif arr is not None:
self.init(arr)
else:
raise ValueError("Either arr or size must be specified")
def init(self, arr: list[int]) -> None:
"""
Initialize the Fenwick tree with arr in O(N)
Parameters:
arr (list): list of elements to initialize the tree with
Returns:
None
>>> a = [1, 2, 3, 4, 5]
>>> f1 = FenwickTree(a)
>>> f2 = FenwickTree(size=len(a))
>>> for index, value in enumerate(a):
... f2.add(index, value)
>>> f1.tree == f2.tree
True
"""
self.size = len(arr)
self.tree = deepcopy(arr)
for i in range(1, self.size):
j = self.next(i)
if j < self.size:
self.tree[j] += self.tree[i]
def get_array(self) -> list[int]:
"""
Get the Normal Array of the Fenwick tree in O(N)
Returns:
list: Normal Array of the Fenwick tree
>>> a = [i for i in range(128)]
>>> f = FenwickTree(a)
>>> f.get_array() == a
True
"""
arr = self.tree[:]
for i in range(self.size - 1, 0, -1):
j = self.next(i)
if j < self.size:
arr[j] -= arr[i]
return arr
@staticmethod
def next(index: int) -> int:
return index + (index & (-index))
@staticmethod
def prev(index: int) -> int:
return index - (index & (-index))
def add(self, index: int, value: int) -> None:
"""
Add a value to index in O(lg N)
Parameters:
index (int): index to add value to
value (int): value to add to index
Returns:
None
>>> f = FenwickTree([1, 2, 3, 4, 5])
>>> f.add(0, 1)
>>> f.add(1, 2)
>>> f.add(2, 3)
>>> f.add(3, 4)
>>> f.add(4, 5)
>>> f.get_array()
[2, 4, 6, 8, 10]
"""
if index == 0:
self.tree[0] += value
return
while index < self.size:
self.tree[index] += value
index = self.next(index)
def update(self, index: int, value: int) -> None:
"""
Set the value of index in O(lg N)
Parameters:
index (int): index to set value to
value (int): value to set in index
Returns:
None
>>> f = FenwickTree([5, 4, 3, 2, 1])
>>> f.update(0, 1)
>>> f.update(1, 2)
>>> f.update(2, 3)
>>> f.update(3, 4)
>>> f.update(4, 5)
>>> f.get_array()
[1, 2, 3, 4, 5]
"""
self.add(index, value - self.get(index))
def prefix(self, right: int) -> int:
"""
Prefix sum of all elements in [0, right) in O(lg N)
Parameters:
right (int): right bound of the query (exclusive)
Returns:
int: sum of all elements in [0, right)
>>> a = [i for i in range(128)]
>>> f = FenwickTree(a)
>>> res = True
>>> for i in range(len(a)):
... res = res and f.prefix(i) == sum(a[:i])
>>> res
True
"""
if right == 0:
return 0
result = self.tree[0]
right -= 1 # make right inclusive
while right > 0:
result += self.tree[right]
right = self.prev(right)
return result
def query(self, left: int, right: int) -> int:
"""
Query the sum of all elements in [left, right) in O(lg N)
Parameters:
left (int): left bound of the query (inclusive)
right (int): right bound of the query (exclusive)
Returns:
int: sum of all elements in [left, right)
>>> a = [i for i in range(128)]
>>> f = FenwickTree(a)
>>> res = True
>>> for i in range(len(a)):
... for j in range(i + 1, len(a)):
... res = res and f.query(i, j) == sum(a[i:j])
>>> res
True
"""
return self.prefix(right) - self.prefix(left)
def get(self, index: int) -> int:
"""
Get value at index in O(lg N)
Parameters:
index (int): index to get the value
Returns:
int: Value of element at index
>>> a = [i for i in range(128)]
>>> f = FenwickTree(a)
>>> res = True
>>> for i in range(len(a)):
... res = res and f.get(i) == a[i]
>>> res
True
"""
return self.query(index, index + 1)
def rank_query(self, value: int) -> int:
"""
Find the largest index with prefix(i) <= value in O(lg N)
NOTE: Requires that all values are non-negative!
Parameters:
value (int): value to find the largest index of
Returns:
-1: if value is smaller than all elements in prefix sum
int: largest index with prefix(i) <= value
>>> f = FenwickTree([1, 2, 0, 3, 0, 5])
>>> f.rank_query(0)
-1
>>> f.rank_query(2)
0
>>> f.rank_query(1)
0
>>> f.rank_query(3)
2
>>> f.rank_query(5)
2
>>> f.rank_query(6)
4
>>> f.rank_query(11)
5
"""
value -= self.tree[0]
if value < 0:
return -1
j = 1 # Largest power of 2 <= size
while j * 2 < self.size:
j *= 2
i = 0
while j > 0:
if i + j < self.size and self.tree[i + j] <= value:
value -= self.tree[i + j]
i += j
j //= 2
return i
if __name__ == "__main__":
import doctest
doctest.testmod()
| from copy import deepcopy
class FenwickTree:
"""
Fenwick Tree
More info: https://en.wikipedia.org/wiki/Fenwick_tree
"""
def __init__(self, arr: list[int] = None, size: int = None) -> None:
"""
Constructor for the Fenwick tree
Parameters:
arr (list): list of elements to initialize the tree with (optional)
size (int): size of the Fenwick tree (if arr is None)
"""
if arr is None and size is not None:
self.size = size
self.tree = [0] * size
elif arr is not None:
self.init(arr)
else:
raise ValueError("Either arr or size must be specified")
def init(self, arr: list[int]) -> None:
"""
Initialize the Fenwick tree with arr in O(N)
Parameters:
arr (list): list of elements to initialize the tree with
Returns:
None
>>> a = [1, 2, 3, 4, 5]
>>> f1 = FenwickTree(a)
>>> f2 = FenwickTree(size=len(a))
>>> for index, value in enumerate(a):
... f2.add(index, value)
>>> f1.tree == f2.tree
True
"""
self.size = len(arr)
self.tree = deepcopy(arr)
for i in range(1, self.size):
j = self.next_(i)
if j < self.size:
self.tree[j] += self.tree[i]
def get_array(self) -> list[int]:
"""
Get the Normal Array of the Fenwick tree in O(N)
Returns:
list: Normal Array of the Fenwick tree
>>> a = [i for i in range(128)]
>>> f = FenwickTree(a)
>>> f.get_array() == a
True
"""
arr = self.tree[:]
for i in range(self.size - 1, 0, -1):
j = self.next_(i)
if j < self.size:
arr[j] -= arr[i]
return arr
@staticmethod
def next_(index: int) -> int:
return index + (index & (-index))
@staticmethod
def prev(index: int) -> int:
return index - (index & (-index))
def add(self, index: int, value: int) -> None:
"""
Add a value to index in O(lg N)
Parameters:
index (int): index to add value to
value (int): value to add to index
Returns:
None
>>> f = FenwickTree([1, 2, 3, 4, 5])
>>> f.add(0, 1)
>>> f.add(1, 2)
>>> f.add(2, 3)
>>> f.add(3, 4)
>>> f.add(4, 5)
>>> f.get_array()
[2, 4, 6, 8, 10]
"""
if index == 0:
self.tree[0] += value
return
while index < self.size:
self.tree[index] += value
index = self.next_(index)
def update(self, index: int, value: int) -> None:
"""
Set the value of index in O(lg N)
Parameters:
index (int): index to set value to
value (int): value to set in index
Returns:
None
>>> f = FenwickTree([5, 4, 3, 2, 1])
>>> f.update(0, 1)
>>> f.update(1, 2)
>>> f.update(2, 3)
>>> f.update(3, 4)
>>> f.update(4, 5)
>>> f.get_array()
[1, 2, 3, 4, 5]
"""
self.add(index, value - self.get(index))
def prefix(self, right: int) -> int:
"""
Prefix sum of all elements in [0, right) in O(lg N)
Parameters:
right (int): right bound of the query (exclusive)
Returns:
int: sum of all elements in [0, right)
>>> a = [i for i in range(128)]
>>> f = FenwickTree(a)
>>> res = True
>>> for i in range(len(a)):
... res = res and f.prefix(i) == sum(a[:i])
>>> res
True
"""
if right == 0:
return 0
result = self.tree[0]
right -= 1 # make right inclusive
while right > 0:
result += self.tree[right]
right = self.prev(right)
return result
def query(self, left: int, right: int) -> int:
"""
Query the sum of all elements in [left, right) in O(lg N)
Parameters:
left (int): left bound of the query (inclusive)
right (int): right bound of the query (exclusive)
Returns:
int: sum of all elements in [left, right)
>>> a = [i for i in range(128)]
>>> f = FenwickTree(a)
>>> res = True
>>> for i in range(len(a)):
... for j in range(i + 1, len(a)):
... res = res and f.query(i, j) == sum(a[i:j])
>>> res
True
"""
return self.prefix(right) - self.prefix(left)
def get(self, index: int) -> int:
"""
Get value at index in O(lg N)
Parameters:
index (int): index to get the value
Returns:
int: Value of element at index
>>> a = [i for i in range(128)]
>>> f = FenwickTree(a)
>>> res = True
>>> for i in range(len(a)):
... res = res and f.get(i) == a[i]
>>> res
True
"""
return self.query(index, index + 1)
def rank_query(self, value: int) -> int:
"""
Find the largest index with prefix(i) <= value in O(lg N)
NOTE: Requires that all values are non-negative!
Parameters:
value (int): value to find the largest index of
Returns:
-1: if value is smaller than all elements in prefix sum
int: largest index with prefix(i) <= value
>>> f = FenwickTree([1, 2, 0, 3, 0, 5])
>>> f.rank_query(0)
-1
>>> f.rank_query(2)
0
>>> f.rank_query(1)
0
>>> f.rank_query(3)
2
>>> f.rank_query(5)
2
>>> f.rank_query(6)
4
>>> f.rank_query(11)
5
"""
value -= self.tree[0]
if value < 0:
return -1
j = 1 # Largest power of 2 <= size
while j * 2 < self.size:
j *= 2
i = 0
while j > 0:
if i + j < self.size and self.tree[i + j] <= value:
value -= self.tree[i + j]
i += j
j //= 2
return i
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] 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
class Heap:
"""A Max Heap Implementation
>>> unsorted = [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5]
>>> h = Heap()
>>> h.build_max_heap(unsorted)
>>> h
[209, 201, 25, 103, 107, 15, 1, 9, 7, 11, 5]
>>>
>>> h.extract_max()
209
>>> h
[201, 107, 25, 103, 11, 15, 1, 9, 7, 5]
>>>
>>> h.insert(100)
>>> h
[201, 107, 25, 103, 100, 15, 1, 9, 7, 5, 11]
>>>
>>> h.heap_sort()
>>> h
[1, 5, 7, 9, 11, 15, 25, 100, 103, 107, 201]
"""
def __init__(self) -> None:
self.h: list[float] = []
self.heap_size: int = 0
def __repr__(self) -> str:
return str(self.h)
def parent_index(self, child_idx: int) -> int | None:
"""return the parent index of given child"""
if child_idx > 0:
return (child_idx - 1) // 2
return None
def left_child_idx(self, parent_idx: int) -> int | None:
"""
return the left child index if the left child exists.
if not, return None.
"""
left_child_index = 2 * parent_idx + 1
if left_child_index < self.heap_size:
return left_child_index
return None
def right_child_idx(self, parent_idx: int) -> int | None:
"""
return the right child index if the right child exists.
if not, return None.
"""
right_child_index = 2 * parent_idx + 2
if right_child_index < self.heap_size:
return right_child_index
return None
def max_heapify(self, index: int) -> None:
"""
correct a single violation of the heap property in a subtree's root.
"""
if index < self.heap_size:
violation: int = index
left_child = self.left_child_idx(index)
right_child = self.right_child_idx(index)
# check which child is larger than its parent
if left_child is not None and self.h[left_child] > self.h[violation]:
violation = left_child
if right_child is not None and self.h[right_child] > self.h[violation]:
violation = right_child
# if violation indeed exists
if violation != index:
# swap to fix the violation
self.h[violation], self.h[index] = self.h[index], self.h[violation]
# fix the subsequent violation recursively if any
self.max_heapify(violation)
def build_max_heap(self, collection: Iterable[float]) -> None:
"""build max heap from an unsorted array"""
self.h = list(collection)
self.heap_size = len(self.h)
if self.heap_size > 1:
# max_heapify from right to left but exclude leaves (last level)
for i in range(self.heap_size // 2 - 1, -1, -1):
self.max_heapify(i)
def max(self) -> float:
"""return the max in the heap"""
if self.heap_size >= 1:
return self.h[0]
else:
raise Exception("Empty heap")
def extract_max(self) -> float:
"""get and remove max from heap"""
if self.heap_size >= 2:
me = self.h[0]
self.h[0] = self.h.pop(-1)
self.heap_size -= 1
self.max_heapify(0)
return me
elif self.heap_size == 1:
self.heap_size -= 1
return self.h.pop(-1)
else:
raise Exception("Empty heap")
def insert(self, value: float) -> None:
"""insert a new value into the max heap"""
self.h.append(value)
idx = (self.heap_size - 1) // 2
self.heap_size += 1
while idx >= 0:
self.max_heapify(idx)
idx = (idx - 1) // 2
def heap_sort(self) -> None:
size = self.heap_size
for j in range(size - 1, 0, -1):
self.h[0], self.h[j] = self.h[j], self.h[0]
self.heap_size -= 1
self.max_heapify(0)
self.heap_size = size
if __name__ == "__main__":
import doctest
# run doc test
doctest.testmod()
# demo
for unsorted in [
[0],
[2],
[3, 5],
[5, 3],
[5, 5],
[0, 0, 0, 0],
[1, 1, 1, 1],
[2, 2, 3, 5],
[0, 2, 2, 3, 5],
[2, 5, 3, 0, 2, 3, 0, 3],
[6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
[103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
[-45, -2, -5],
]:
print(f"unsorted array: {unsorted}")
heap = Heap()
heap.build_max_heap(unsorted)
print(f"after build heap: {heap}")
print(f"max value: {heap.extract_max()}")
print(f"after max value removed: {heap}")
heap.insert(100)
print(f"after new value 100 inserted: {heap}")
heap.heap_sort()
print(f"heap-sorted array: {heap}\n")
| from __future__ import annotations
from collections.abc import Iterable
class Heap:
"""A Max Heap Implementation
>>> unsorted = [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5]
>>> h = Heap()
>>> h.build_max_heap(unsorted)
>>> h
[209, 201, 25, 103, 107, 15, 1, 9, 7, 11, 5]
>>>
>>> h.extract_max()
209
>>> h
[201, 107, 25, 103, 11, 15, 1, 9, 7, 5]
>>>
>>> h.insert(100)
>>> h
[201, 107, 25, 103, 100, 15, 1, 9, 7, 5, 11]
>>>
>>> h.heap_sort()
>>> h
[1, 5, 7, 9, 11, 15, 25, 100, 103, 107, 201]
"""
def __init__(self) -> None:
self.h: list[float] = []
self.heap_size: int = 0
def __repr__(self) -> str:
return str(self.h)
def parent_index(self, child_idx: int) -> int | None:
"""return the parent index of given child"""
if child_idx > 0:
return (child_idx - 1) // 2
return None
def left_child_idx(self, parent_idx: int) -> int | None:
"""
return the left child index if the left child exists.
if not, return None.
"""
left_child_index = 2 * parent_idx + 1
if left_child_index < self.heap_size:
return left_child_index
return None
def right_child_idx(self, parent_idx: int) -> int | None:
"""
return the right child index if the right child exists.
if not, return None.
"""
right_child_index = 2 * parent_idx + 2
if right_child_index < self.heap_size:
return right_child_index
return None
def max_heapify(self, index: int) -> None:
"""
correct a single violation of the heap property in a subtree's root.
"""
if index < self.heap_size:
violation: int = index
left_child = self.left_child_idx(index)
right_child = self.right_child_idx(index)
# check which child is larger than its parent
if left_child is not None and self.h[left_child] > self.h[violation]:
violation = left_child
if right_child is not None and self.h[right_child] > self.h[violation]:
violation = right_child
# if violation indeed exists
if violation != index:
# swap to fix the violation
self.h[violation], self.h[index] = self.h[index], self.h[violation]
# fix the subsequent violation recursively if any
self.max_heapify(violation)
def build_max_heap(self, collection: Iterable[float]) -> None:
"""build max heap from an unsorted array"""
self.h = list(collection)
self.heap_size = len(self.h)
if self.heap_size > 1:
# max_heapify from right to left but exclude leaves (last level)
for i in range(self.heap_size // 2 - 1, -1, -1):
self.max_heapify(i)
def extract_max(self) -> float:
"""get and remove max from heap"""
if self.heap_size >= 2:
me = self.h[0]
self.h[0] = self.h.pop(-1)
self.heap_size -= 1
self.max_heapify(0)
return me
elif self.heap_size == 1:
self.heap_size -= 1
return self.h.pop(-1)
else:
raise Exception("Empty heap")
def insert(self, value: float) -> None:
"""insert a new value into the max heap"""
self.h.append(value)
idx = (self.heap_size - 1) // 2
self.heap_size += 1
while idx >= 0:
self.max_heapify(idx)
idx = (idx - 1) // 2
def heap_sort(self) -> None:
size = self.heap_size
for j in range(size - 1, 0, -1):
self.h[0], self.h[j] = self.h[j], self.h[0]
self.heap_size -= 1
self.max_heapify(0)
self.heap_size = size
if __name__ == "__main__":
import doctest
# run doc test
doctest.testmod()
# demo
for unsorted in [
[0],
[2],
[3, 5],
[5, 3],
[5, 5],
[0, 0, 0, 0],
[1, 1, 1, 1],
[2, 2, 3, 5],
[0, 2, 2, 3, 5],
[2, 5, 3, 0, 2, 3, 0, 3],
[6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
[103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
[-45, -2, -5],
]:
print(f"unsorted array: {unsorted}")
heap = Heap()
heap.build_max_heap(unsorted)
print(f"after build heap: {heap}")
print(f"max value: {heap.extract_max()}")
print(f"after max value removed: {heap}")
heap.insert(100)
print(f"after new value 100 inserted: {heap}")
heap.heap_sort()
print(f"heap-sorted array: {heap}\n")
| 1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Algorithm that merges two sorted linked lists into one sorted linked list.
"""
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
test_data_odd = (3, 9, -11, 0, 7, 5, 1, -1)
test_data_even = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class Node:
data: int
next: Node | None
class SortedLinkedList:
def __init__(self, ints: Iterable[int]) -> None:
self.head: Node | None = None
for i in sorted(ints, reverse=True):
self.head = Node(i, self.head)
def __iter__(self) -> Iterator[int]:
"""
>>> tuple(SortedLinkedList(test_data_odd)) == tuple(sorted(test_data_odd))
True
>>> tuple(SortedLinkedList(test_data_even)) == tuple(sorted(test_data_even))
True
"""
node = self.head
while node:
yield node.data
node = node.next
def __len__(self) -> int:
"""
>>> for i in range(3):
... len(SortedLinkedList(range(i))) == i
True
True
True
>>> len(SortedLinkedList(test_data_odd))
8
"""
return len(tuple(iter(self)))
def __str__(self) -> str:
"""
>>> str(SortedLinkedList([]))
''
>>> str(SortedLinkedList(test_data_odd))
'-11 -> -1 -> 0 -> 1 -> 3 -> 5 -> 7 -> 9'
>>> str(SortedLinkedList(test_data_even))
'-2 -> 0 -> 2 -> 3 -> 4 -> 6 -> 8 -> 10'
"""
return " -> ".join([str(node) for node in self])
def merge_lists(
sll_one: SortedLinkedList, sll_two: SortedLinkedList
) -> SortedLinkedList:
"""
>>> SSL = SortedLinkedList
>>> merged = merge_lists(SSL(test_data_odd), SSL(test_data_even))
>>> len(merged)
16
>>> str(merged)
'-11 -> -2 -> -1 -> 0 -> 0 -> 1 -> 2 -> 3 -> 3 -> 4 -> 5 -> 6 -> 7 -> 8 -> 9 -> 10'
>>> list(merged) == list(sorted(test_data_odd + test_data_even))
True
"""
return SortedLinkedList(list(sll_one) + list(sll_two))
if __name__ == "__main__":
import doctest
doctest.testmod()
SSL = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| """
Algorithm that merges two sorted linked lists into one sorted linked list.
"""
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
test_data_odd = (3, 9, -11, 0, 7, 5, 1, -1)
test_data_even = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class Node:
data: int
next_node: Node | None
class SortedLinkedList:
def __init__(self, ints: Iterable[int]) -> None:
self.head: Node | None = None
for i in sorted(ints, reverse=True):
self.head = Node(i, self.head)
def __iter__(self) -> Iterator[int]:
"""
>>> tuple(SortedLinkedList(test_data_odd)) == tuple(sorted(test_data_odd))
True
>>> tuple(SortedLinkedList(test_data_even)) == tuple(sorted(test_data_even))
True
"""
node = self.head
while node:
yield node.data
node = node.next_node
def __len__(self) -> int:
"""
>>> for i in range(3):
... len(SortedLinkedList(range(i))) == i
True
True
True
>>> len(SortedLinkedList(test_data_odd))
8
"""
return len(tuple(iter(self)))
def __str__(self) -> str:
"""
>>> str(SortedLinkedList([]))
''
>>> str(SortedLinkedList(test_data_odd))
'-11 -> -1 -> 0 -> 1 -> 3 -> 5 -> 7 -> 9'
>>> str(SortedLinkedList(test_data_even))
'-2 -> 0 -> 2 -> 3 -> 4 -> 6 -> 8 -> 10'
"""
return " -> ".join([str(node) for node in self])
def merge_lists(
sll_one: SortedLinkedList, sll_two: SortedLinkedList
) -> SortedLinkedList:
"""
>>> SSL = SortedLinkedList
>>> merged = merge_lists(SSL(test_data_odd), SSL(test_data_even))
>>> len(merged)
16
>>> str(merged)
'-11 -> -2 -> -1 -> 0 -> 0 -> 1 -> 2 -> 3 -> 3 -> 4 -> 5 -> 6 -> 7 -> 8 -> 9 -> 10'
>>> list(merged) == list(sorted(test_data_odd + test_data_even))
True
"""
return SortedLinkedList(list(sll_one) + list(sll_two))
if __name__ == "__main__":
import doctest
doctest.testmod()
SSL = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Implementation of double ended queue.
"""
from __future__ import annotations
from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any
class Deque:
"""
Deque data structure.
Operations
----------
append(val: Any) -> None
appendleft(val: Any) -> None
extend(iterable: Iterable) -> None
extendleft(iterable: Iterable) -> None
pop() -> Any
popleft() -> Any
Observers
---------
is_empty() -> bool
Attributes
----------
_front: _Node
front of the deque a.k.a. the first element
_back: _Node
back of the element a.k.a. the last element
_len: int
the number of nodes
"""
__slots__ = ["_front", "_back", "_len"]
@dataclass
class _Node:
"""
Representation of a node.
Contains a value and a pointer to the next node as well as to the previous one.
"""
val: Any = None
next: Deque._Node | None = None
prev: Deque._Node | None = None
class _Iterator:
"""
Helper class for iteration. Will be used to implement iteration.
Attributes
----------
_cur: _Node
the current node of the iteration.
"""
__slots__ = ["_cur"]
def __init__(self, cur: Deque._Node | None) -> None:
self._cur = cur
def __iter__(self) -> Deque._Iterator:
"""
>>> our_deque = Deque([1, 2, 3])
>>> iterator = iter(our_deque)
"""
return self
def __next__(self) -> Any:
"""
>>> our_deque = Deque([1, 2, 3])
>>> iterator = iter(our_deque)
>>> next(iterator)
1
>>> next(iterator)
2
>>> next(iterator)
3
"""
if self._cur is None:
# finished iterating
raise StopIteration
val = self._cur.val
self._cur = self._cur.next
return val
def __init__(self, iterable: Iterable[Any] | None = None) -> None:
self._front: Any = None
self._back: Any = None
self._len: int = 0
if iterable is not None:
# append every value to the deque
for val in iterable:
self.append(val)
def append(self, val: Any) -> None:
"""
Adds val to the end of the deque.
Time complexity: O(1)
>>> our_deque_1 = Deque([1, 2, 3])
>>> our_deque_1.append(4)
>>> our_deque_1
[1, 2, 3, 4]
>>> our_deque_2 = Deque('ab')
>>> our_deque_2.append('c')
>>> our_deque_2
['a', 'b', 'c']
>>> from collections import deque
>>> deque_collections_1 = deque([1, 2, 3])
>>> deque_collections_1.append(4)
>>> deque_collections_1
deque([1, 2, 3, 4])
>>> deque_collections_2 = deque('ab')
>>> deque_collections_2.append('c')
>>> deque_collections_2
deque(['a', 'b', 'c'])
>>> list(our_deque_1) == list(deque_collections_1)
True
>>> list(our_deque_2) == list(deque_collections_2)
True
"""
node = self._Node(val, None, None)
if self.is_empty():
# front = back
self._front = self._back = node
self._len = 1
else:
# connect nodes
self._back.next = node
node.prev = self._back
self._back = node # assign new back to the new node
self._len += 1
# make sure there were no errors
assert not self.is_empty(), "Error on appending value."
def appendleft(self, val: Any) -> None:
"""
Adds val to the beginning of the deque.
Time complexity: O(1)
>>> our_deque_1 = Deque([2, 3])
>>> our_deque_1.appendleft(1)
>>> our_deque_1
[1, 2, 3]
>>> our_deque_2 = Deque('bc')
>>> our_deque_2.appendleft('a')
>>> our_deque_2
['a', 'b', 'c']
>>> from collections import deque
>>> deque_collections_1 = deque([2, 3])
>>> deque_collections_1.appendleft(1)
>>> deque_collections_1
deque([1, 2, 3])
>>> deque_collections_2 = deque('bc')
>>> deque_collections_2.appendleft('a')
>>> deque_collections_2
deque(['a', 'b', 'c'])
>>> list(our_deque_1) == list(deque_collections_1)
True
>>> list(our_deque_2) == list(deque_collections_2)
True
"""
node = self._Node(val, None, None)
if self.is_empty():
# front = back
self._front = self._back = node
self._len = 1
else:
# connect nodes
node.next = self._front
self._front.prev = node
self._front = node # assign new front to the new node
self._len += 1
# make sure there were no errors
assert not self.is_empty(), "Error on appending value."
def extend(self, iterable: Iterable[Any]) -> None:
"""
Appends every value of iterable to the end of the deque.
Time complexity: O(n)
>>> our_deque_1 = Deque([1, 2, 3])
>>> our_deque_1.extend([4, 5])
>>> our_deque_1
[1, 2, 3, 4, 5]
>>> our_deque_2 = Deque('ab')
>>> our_deque_2.extend('cd')
>>> our_deque_2
['a', 'b', 'c', 'd']
>>> from collections import deque
>>> deque_collections_1 = deque([1, 2, 3])
>>> deque_collections_1.extend([4, 5])
>>> deque_collections_1
deque([1, 2, 3, 4, 5])
>>> deque_collections_2 = deque('ab')
>>> deque_collections_2.extend('cd')
>>> deque_collections_2
deque(['a', 'b', 'c', 'd'])
>>> list(our_deque_1) == list(deque_collections_1)
True
>>> list(our_deque_2) == list(deque_collections_2)
True
"""
for val in iterable:
self.append(val)
def extendleft(self, iterable: Iterable[Any]) -> None:
"""
Appends every value of iterable to the beginning of the deque.
Time complexity: O(n)
>>> our_deque_1 = Deque([1, 2, 3])
>>> our_deque_1.extendleft([0, -1])
>>> our_deque_1
[-1, 0, 1, 2, 3]
>>> our_deque_2 = Deque('cd')
>>> our_deque_2.extendleft('ba')
>>> our_deque_2
['a', 'b', 'c', 'd']
>>> from collections import deque
>>> deque_collections_1 = deque([1, 2, 3])
>>> deque_collections_1.extendleft([0, -1])
>>> deque_collections_1
deque([-1, 0, 1, 2, 3])
>>> deque_collections_2 = deque('cd')
>>> deque_collections_2.extendleft('ba')
>>> deque_collections_2
deque(['a', 'b', 'c', 'd'])
>>> list(our_deque_1) == list(deque_collections_1)
True
>>> list(our_deque_2) == list(deque_collections_2)
True
"""
for val in iterable:
self.appendleft(val)
def pop(self) -> Any:
"""
Removes the last element of the deque and returns it.
Time complexity: O(1)
@returns topop.val: the value of the node to pop.
>>> our_deque = Deque([1, 2, 3, 15182])
>>> our_popped = our_deque.pop()
>>> our_popped
15182
>>> our_deque
[1, 2, 3]
>>> from collections import deque
>>> deque_collections = deque([1, 2, 3, 15182])
>>> collections_popped = deque_collections.pop()
>>> collections_popped
15182
>>> deque_collections
deque([1, 2, 3])
>>> list(our_deque) == list(deque_collections)
True
>>> our_popped == collections_popped
True
"""
# make sure the deque has elements to pop
assert not self.is_empty(), "Deque is empty."
topop = self._back
self._back = self._back.prev # set new back
self._back.next = (
None # drop the last node - python will deallocate memory automatically
)
self._len -= 1
return topop.val
def popleft(self) -> Any:
"""
Removes the first element of the deque and returns it.
Time complexity: O(1)
@returns topop.val: the value of the node to pop.
>>> our_deque = Deque([15182, 1, 2, 3])
>>> our_popped = our_deque.popleft()
>>> our_popped
15182
>>> our_deque
[1, 2, 3]
>>> from collections import deque
>>> deque_collections = deque([15182, 1, 2, 3])
>>> collections_popped = deque_collections.popleft()
>>> collections_popped
15182
>>> deque_collections
deque([1, 2, 3])
>>> list(our_deque) == list(deque_collections)
True
>>> our_popped == collections_popped
True
"""
# make sure the deque has elements to pop
assert not self.is_empty(), "Deque is empty."
topop = self._front
self._front = self._front.next # set new front and drop the first node
self._front.prev = None
self._len -= 1
return topop.val
def is_empty(self) -> bool:
"""
Checks if the deque is empty.
Time complexity: O(1)
>>> our_deque = Deque([1, 2, 3])
>>> our_deque.is_empty()
False
>>> our_empty_deque = Deque()
>>> our_empty_deque.is_empty()
True
>>> from collections import deque
>>> empty_deque_collections = deque()
>>> list(our_empty_deque) == list(empty_deque_collections)
True
"""
return self._front is None
def __len__(self) -> int:
"""
Implements len() function. Returns the length of the deque.
Time complexity: O(1)
>>> our_deque = Deque([1, 2, 3])
>>> len(our_deque)
3
>>> our_empty_deque = Deque()
>>> len(our_empty_deque)
0
>>> from collections import deque
>>> deque_collections = deque([1, 2, 3])
>>> len(deque_collections)
3
>>> empty_deque_collections = deque()
>>> len(empty_deque_collections)
0
>>> len(our_empty_deque) == len(empty_deque_collections)
True
"""
return self._len
def __eq__(self, other: object) -> bool:
"""
Implements "==" operator. Returns if *self* is equal to *other*.
Time complexity: O(n)
>>> our_deque_1 = Deque([1, 2, 3])
>>> our_deque_2 = Deque([1, 2, 3])
>>> our_deque_1 == our_deque_2
True
>>> our_deque_3 = Deque([1, 2])
>>> our_deque_1 == our_deque_3
False
>>> from collections import deque
>>> deque_collections_1 = deque([1, 2, 3])
>>> deque_collections_2 = deque([1, 2, 3])
>>> deque_collections_1 == deque_collections_2
True
>>> deque_collections_3 = deque([1, 2])
>>> deque_collections_1 == deque_collections_3
False
>>> (our_deque_1 == our_deque_2) == (deque_collections_1 == deque_collections_2)
True
>>> (our_deque_1 == our_deque_3) == (deque_collections_1 == deque_collections_3)
True
"""
if not isinstance(other, Deque):
return NotImplemented
me = self._front
oth = other._front
# if the length of the dequeues are not the same, they are not equal
if len(self) != len(other):
return False
while me is not None and oth is not None:
# compare every value
if me.val != oth.val:
return False
me = me.next
oth = oth.next
return True
def __iter__(self) -> Deque._Iterator:
"""
Implements iteration.
Time complexity: O(1)
>>> our_deque = Deque([1, 2, 3])
>>> for v in our_deque:
... print(v)
1
2
3
>>> from collections import deque
>>> deque_collections = deque([1, 2, 3])
>>> for v in deque_collections:
... print(v)
1
2
3
"""
return Deque._Iterator(self._front)
def __repr__(self) -> str:
"""
Implements representation of the deque.
Represents it as a list, with its values between '[' and ']'.
Time complexity: O(n)
>>> our_deque = Deque([1, 2, 3])
>>> our_deque
[1, 2, 3]
"""
values_list = []
aux = self._front
while aux is not None:
# append the values in a list to display
values_list.append(aux.val)
aux = aux.next
return "[" + ", ".join(repr(val) for val in values_list) + "]"
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Implementation of double ended queue.
"""
from __future__ import annotations
from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any
class Deque:
"""
Deque data structure.
Operations
----------
append(val: Any) -> None
appendleft(val: Any) -> None
extend(iterable: Iterable) -> None
extendleft(iterable: Iterable) -> None
pop() -> Any
popleft() -> Any
Observers
---------
is_empty() -> bool
Attributes
----------
_front: _Node
front of the deque a.k.a. the first element
_back: _Node
back of the element a.k.a. the last element
_len: int
the number of nodes
"""
__slots__ = ["_front", "_back", "_len"]
@dataclass
class _Node:
"""
Representation of a node.
Contains a value and a pointer to the next node as well as to the previous one.
"""
val: Any = None
next_node: Deque._Node | None = None
prev_node: Deque._Node | None = None
class _Iterator:
"""
Helper class for iteration. Will be used to implement iteration.
Attributes
----------
_cur: _Node
the current node of the iteration.
"""
__slots__ = ["_cur"]
def __init__(self, cur: Deque._Node | None) -> None:
self._cur = cur
def __iter__(self) -> Deque._Iterator:
"""
>>> our_deque = Deque([1, 2, 3])
>>> iterator = iter(our_deque)
"""
return self
def __next__(self) -> Any:
"""
>>> our_deque = Deque([1, 2, 3])
>>> iterator = iter(our_deque)
>>> next(iterator)
1
>>> next(iterator)
2
>>> next(iterator)
3
"""
if self._cur is None:
# finished iterating
raise StopIteration
val = self._cur.val
self._cur = self._cur.next_node
return val
def __init__(self, iterable: Iterable[Any] | None = None) -> None:
self._front: Any = None
self._back: Any = None
self._len: int = 0
if iterable is not None:
# append every value to the deque
for val in iterable:
self.append(val)
def append(self, val: Any) -> None:
"""
Adds val to the end of the deque.
Time complexity: O(1)
>>> our_deque_1 = Deque([1, 2, 3])
>>> our_deque_1.append(4)
>>> our_deque_1
[1, 2, 3, 4]
>>> our_deque_2 = Deque('ab')
>>> our_deque_2.append('c')
>>> our_deque_2
['a', 'b', 'c']
>>> from collections import deque
>>> deque_collections_1 = deque([1, 2, 3])
>>> deque_collections_1.append(4)
>>> deque_collections_1
deque([1, 2, 3, 4])
>>> deque_collections_2 = deque('ab')
>>> deque_collections_2.append('c')
>>> deque_collections_2
deque(['a', 'b', 'c'])
>>> list(our_deque_1) == list(deque_collections_1)
True
>>> list(our_deque_2) == list(deque_collections_2)
True
"""
node = self._Node(val, None, None)
if self.is_empty():
# front = back
self._front = self._back = node
self._len = 1
else:
# connect nodes
self._back.next_node = node
node.prev_node = self._back
self._back = node # assign new back to the new node
self._len += 1
# make sure there were no errors
assert not self.is_empty(), "Error on appending value."
def appendleft(self, val: Any) -> None:
"""
Adds val to the beginning of the deque.
Time complexity: O(1)
>>> our_deque_1 = Deque([2, 3])
>>> our_deque_1.appendleft(1)
>>> our_deque_1
[1, 2, 3]
>>> our_deque_2 = Deque('bc')
>>> our_deque_2.appendleft('a')
>>> our_deque_2
['a', 'b', 'c']
>>> from collections import deque
>>> deque_collections_1 = deque([2, 3])
>>> deque_collections_1.appendleft(1)
>>> deque_collections_1
deque([1, 2, 3])
>>> deque_collections_2 = deque('bc')
>>> deque_collections_2.appendleft('a')
>>> deque_collections_2
deque(['a', 'b', 'c'])
>>> list(our_deque_1) == list(deque_collections_1)
True
>>> list(our_deque_2) == list(deque_collections_2)
True
"""
node = self._Node(val, None, None)
if self.is_empty():
# front = back
self._front = self._back = node
self._len = 1
else:
# connect nodes
node.next_node = self._front
self._front.prev_node = node
self._front = node # assign new front to the new node
self._len += 1
# make sure there were no errors
assert not self.is_empty(), "Error on appending value."
def extend(self, iterable: Iterable[Any]) -> None:
"""
Appends every value of iterable to the end of the deque.
Time complexity: O(n)
>>> our_deque_1 = Deque([1, 2, 3])
>>> our_deque_1.extend([4, 5])
>>> our_deque_1
[1, 2, 3, 4, 5]
>>> our_deque_2 = Deque('ab')
>>> our_deque_2.extend('cd')
>>> our_deque_2
['a', 'b', 'c', 'd']
>>> from collections import deque
>>> deque_collections_1 = deque([1, 2, 3])
>>> deque_collections_1.extend([4, 5])
>>> deque_collections_1
deque([1, 2, 3, 4, 5])
>>> deque_collections_2 = deque('ab')
>>> deque_collections_2.extend('cd')
>>> deque_collections_2
deque(['a', 'b', 'c', 'd'])
>>> list(our_deque_1) == list(deque_collections_1)
True
>>> list(our_deque_2) == list(deque_collections_2)
True
"""
for val in iterable:
self.append(val)
def extendleft(self, iterable: Iterable[Any]) -> None:
"""
Appends every value of iterable to the beginning of the deque.
Time complexity: O(n)
>>> our_deque_1 = Deque([1, 2, 3])
>>> our_deque_1.extendleft([0, -1])
>>> our_deque_1
[-1, 0, 1, 2, 3]
>>> our_deque_2 = Deque('cd')
>>> our_deque_2.extendleft('ba')
>>> our_deque_2
['a', 'b', 'c', 'd']
>>> from collections import deque
>>> deque_collections_1 = deque([1, 2, 3])
>>> deque_collections_1.extendleft([0, -1])
>>> deque_collections_1
deque([-1, 0, 1, 2, 3])
>>> deque_collections_2 = deque('cd')
>>> deque_collections_2.extendleft('ba')
>>> deque_collections_2
deque(['a', 'b', 'c', 'd'])
>>> list(our_deque_1) == list(deque_collections_1)
True
>>> list(our_deque_2) == list(deque_collections_2)
True
"""
for val in iterable:
self.appendleft(val)
def pop(self) -> Any:
"""
Removes the last element of the deque and returns it.
Time complexity: O(1)
@returns topop.val: the value of the node to pop.
>>> our_deque = Deque([1, 2, 3, 15182])
>>> our_popped = our_deque.pop()
>>> our_popped
15182
>>> our_deque
[1, 2, 3]
>>> from collections import deque
>>> deque_collections = deque([1, 2, 3, 15182])
>>> collections_popped = deque_collections.pop()
>>> collections_popped
15182
>>> deque_collections
deque([1, 2, 3])
>>> list(our_deque) == list(deque_collections)
True
>>> our_popped == collections_popped
True
"""
# make sure the deque has elements to pop
assert not self.is_empty(), "Deque is empty."
topop = self._back
self._back = self._back.prev_node # set new back
# drop the last node - python will deallocate memory automatically
self._back.next_node = None
self._len -= 1
return topop.val
def popleft(self) -> Any:
"""
Removes the first element of the deque and returns it.
Time complexity: O(1)
@returns topop.val: the value of the node to pop.
>>> our_deque = Deque([15182, 1, 2, 3])
>>> our_popped = our_deque.popleft()
>>> our_popped
15182
>>> our_deque
[1, 2, 3]
>>> from collections import deque
>>> deque_collections = deque([15182, 1, 2, 3])
>>> collections_popped = deque_collections.popleft()
>>> collections_popped
15182
>>> deque_collections
deque([1, 2, 3])
>>> list(our_deque) == list(deque_collections)
True
>>> our_popped == collections_popped
True
"""
# make sure the deque has elements to pop
assert not self.is_empty(), "Deque is empty."
topop = self._front
self._front = self._front.next_node # set new front and drop the first node
self._front.prev_node = None
self._len -= 1
return topop.val
def is_empty(self) -> bool:
"""
Checks if the deque is empty.
Time complexity: O(1)
>>> our_deque = Deque([1, 2, 3])
>>> our_deque.is_empty()
False
>>> our_empty_deque = Deque()
>>> our_empty_deque.is_empty()
True
>>> from collections import deque
>>> empty_deque_collections = deque()
>>> list(our_empty_deque) == list(empty_deque_collections)
True
"""
return self._front is None
def __len__(self) -> int:
"""
Implements len() function. Returns the length of the deque.
Time complexity: O(1)
>>> our_deque = Deque([1, 2, 3])
>>> len(our_deque)
3
>>> our_empty_deque = Deque()
>>> len(our_empty_deque)
0
>>> from collections import deque
>>> deque_collections = deque([1, 2, 3])
>>> len(deque_collections)
3
>>> empty_deque_collections = deque()
>>> len(empty_deque_collections)
0
>>> len(our_empty_deque) == len(empty_deque_collections)
True
"""
return self._len
def __eq__(self, other: object) -> bool:
"""
Implements "==" operator. Returns if *self* is equal to *other*.
Time complexity: O(n)
>>> our_deque_1 = Deque([1, 2, 3])
>>> our_deque_2 = Deque([1, 2, 3])
>>> our_deque_1 == our_deque_2
True
>>> our_deque_3 = Deque([1, 2])
>>> our_deque_1 == our_deque_3
False
>>> from collections import deque
>>> deque_collections_1 = deque([1, 2, 3])
>>> deque_collections_2 = deque([1, 2, 3])
>>> deque_collections_1 == deque_collections_2
True
>>> deque_collections_3 = deque([1, 2])
>>> deque_collections_1 == deque_collections_3
False
>>> (our_deque_1 == our_deque_2) == (deque_collections_1 == deque_collections_2)
True
>>> (our_deque_1 == our_deque_3) == (deque_collections_1 == deque_collections_3)
True
"""
if not isinstance(other, Deque):
return NotImplemented
me = self._front
oth = other._front
# if the length of the dequeues are not the same, they are not equal
if len(self) != len(other):
return False
while me is not None and oth is not None:
# compare every value
if me.val != oth.val:
return False
me = me.next_node
oth = oth.next_node
return True
def __iter__(self) -> Deque._Iterator:
"""
Implements iteration.
Time complexity: O(1)
>>> our_deque = Deque([1, 2, 3])
>>> for v in our_deque:
... print(v)
1
2
3
>>> from collections import deque
>>> deque_collections = deque([1, 2, 3])
>>> for v in deque_collections:
... print(v)
1
2
3
"""
return Deque._Iterator(self._front)
def __repr__(self) -> str:
"""
Implements representation of the deque.
Represents it as a list, with its values between '[' and ']'.
Time complexity: O(n)
>>> our_deque = Deque([1, 2, 3])
>>> our_deque
[1, 2, 3]
"""
values_list = []
aux = self._front
while aux is not None:
# append the values in a list to display
values_list.append(aux.val)
aux = aux.next_node
return "[" + ", ".join(repr(val) for val in values_list) + "]"
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Created on Mon Feb 26 14:29:11 2018
@author: Christian Bender
@license: MIT-license
This module contains some useful classes and functions for dealing
with linear algebra in python.
Overview:
- class Vector
- function zero_vector(dimension)
- function unit_basis_vector(dimension, pos)
- function axpy(scalar, vector1, vector2)
- function random_vector(N, a, b)
- class Matrix
- function square_zero_matrix(N)
- function random_matrix(W, H, a, b)
"""
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class Vector:
"""
This class represents a vector of arbitrary size.
You need to give the vector components.
Overview of the methods:
__init__(components: Collection[float] | None): init the vector
__len__(): gets the size of the vector (number of components)
__str__(): returns a string representation
__add__(other: Vector): vector addition
__sub__(other: Vector): vector subtraction
__mul__(other: float): scalar multiplication
__mul__(other: Vector): dot product
set(components: Collection[float]): changes the vector components
copy(): copies this vector and returns it
component(i): gets the i-th component (0-indexed)
change_component(pos: int, value: float): changes specified component
euclidean_length(): returns the euclidean length of the vector
angle(other: Vector, deg: bool): returns the angle between two vectors
TODO: compare-operator
"""
def __init__(self, components: Collection[float] | None = None) -> None:
"""
input: components or nothing
simple constructor for init the vector
"""
if components is None:
components = []
self.__components = list(components)
def __len__(self) -> int:
"""
returns the size of the vector
"""
return len(self.__components)
def __str__(self) -> str:
"""
returns a string representation of the vector
"""
return "(" + ",".join(map(str, self.__components)) + ")"
def __add__(self, other: Vector) -> Vector:
"""
input: other vector
assumes: other vector has the same size
returns a new vector that represents the sum.
"""
size = len(self)
if size == len(other):
result = [self.__components[i] + other.component(i) for i in range(size)]
return Vector(result)
else:
raise Exception("must have the same size")
def __sub__(self, other: Vector) -> Vector:
"""
input: other vector
assumes: other vector has the same size
returns a new vector that represents the difference.
"""
size = len(self)
if size == len(other):
result = [self.__components[i] - other.component(i) for i in range(size)]
return Vector(result)
else: # error case
raise Exception("must have the same size")
@overload
def __mul__(self, other: float) -> Vector:
...
@overload
def __mul__(self, other: Vector) -> float:
...
def __mul__(self, other: float | Vector) -> float | Vector:
"""
mul implements the scalar multiplication
and the dot-product
"""
if isinstance(other, float) or isinstance(other, int):
ans = [c * other for c in self.__components]
return Vector(ans)
elif isinstance(other, Vector) and len(self) == len(other):
size = len(self)
prods = [self.__components[i] * other.component(i) for i in range(size)]
return sum(prods)
else: # error case
raise Exception("invalid operand!")
def set(self, components: Collection[float]) -> None:
"""
input: new components
changes the components of the vector.
replaces the components with newer one.
"""
if len(components) > 0:
self.__components = list(components)
else:
raise Exception("please give any vector")
def copy(self) -> Vector:
"""
copies this vector and returns it.
"""
return Vector(self.__components)
def component(self, i: int) -> float:
"""
input: index (0-indexed)
output: the i-th component of the vector.
"""
if type(i) is int and -len(self.__components) <= i < len(self.__components):
return self.__components[i]
else:
raise Exception("index out of range")
def change_component(self, pos: int, value: float) -> None:
"""
input: an index (pos) and a value
changes the specified component (pos) with the
'value'
"""
# precondition
assert -len(self.__components) <= pos < len(self.__components)
self.__components[pos] = value
def euclidean_length(self) -> float:
"""
returns the euclidean length of the vector
>>> Vector([2, 3, 4]).euclidean_length()
5.385164807134504
>>> Vector([1]).euclidean_length()
1.0
>>> Vector([0, -1, -2, -3, 4, 5, 6]).euclidean_length()
9.539392014169456
>>> Vector([]).euclidean_length()
Traceback (most recent call last):
...
Exception: Vector is empty
"""
if len(self.__components) == 0:
raise Exception("Vector is empty")
squares = [c**2 for c in self.__components]
return math.sqrt(sum(squares))
def angle(self, other: Vector, deg: bool = False) -> float:
"""
find angle between two Vector (self, Vector)
>>> Vector([3, 4, -1]).angle(Vector([2, -1, 1]))
1.4906464636572374
>>> Vector([3, 4, -1]).angle(Vector([2, -1, 1]), deg = True)
85.40775111366095
>>> Vector([3, 4, -1]).angle(Vector([2, -1]))
Traceback (most recent call last):
...
Exception: invalid operand!
"""
num = self * other
den = self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den))
else:
return math.acos(num / den)
def zero_vector(dimension: int) -> Vector:
"""
returns a zero-vector of size 'dimension'
"""
# precondition
assert isinstance(dimension, int)
return Vector([0] * dimension)
def unit_basis_vector(dimension: int, pos: int) -> Vector:
"""
returns a unit basis vector with a One
at index 'pos' (indexing at 0)
"""
# precondition
assert isinstance(dimension, int) and (isinstance(pos, int))
ans = [0] * dimension
ans[pos] = 1
return Vector(ans)
def axpy(scalar: float, x: Vector, y: Vector) -> Vector:
"""
input: a 'scalar' and two vectors 'x' and 'y'
output: a vector
computes the axpy operation
"""
# precondition
assert (
isinstance(x, Vector)
and isinstance(y, Vector)
and (isinstance(scalar, int) or isinstance(scalar, float))
)
return x * scalar + y
def random_vector(n: int, a: int, b: int) -> Vector:
"""
input: size (N) of the vector.
random range (a,b)
output: returns a random vector of size N, with
random integer components between 'a' and 'b'.
"""
random.seed(None)
ans = [random.randint(a, b) for _ in range(n)]
return Vector(ans)
class Matrix:
"""
class: Matrix
This class represents an arbitrary matrix.
Overview of the methods:
__init__():
__str__(): returns a string representation
__add__(other: Matrix): matrix addition
__sub__(other: Matrix): matrix subtraction
__mul__(other: float): scalar multiplication
__mul__(other: Vector): vector multiplication
height() : returns height
width() : returns width
component(x: int, y: int): returns specified component
change_component(x: int, y: int, value: float): changes specified component
minor(x: int, y: int): returns minor along (x, y)
cofactor(x: int, y: int): returns cofactor along (x, y)
determinant() : returns determinant
"""
def __init__(self, matrix: list[list[float]], w: int, h: int) -> None:
"""
simple constructor for initializing the matrix with components.
"""
self.__matrix = matrix
self.__width = w
self.__height = h
def __str__(self) -> str:
"""
returns a string representation of this matrix.
"""
ans = ""
for i in range(self.__height):
ans += "|"
for j in range(self.__width):
if j < self.__width - 1:
ans += str(self.__matrix[i][j]) + ","
else:
ans += str(self.__matrix[i][j]) + "|\n"
return ans
def __add__(self, other: Matrix) -> Matrix:
"""
implements matrix addition.
"""
if self.__width == other.width() and self.__height == other.height():
matrix = []
for i in range(self.__height):
row = [
self.__matrix[i][j] + other.component(i, j)
for j in range(self.__width)
]
matrix.append(row)
return Matrix(matrix, self.__width, self.__height)
else:
raise Exception("matrix must have the same dimension!")
def __sub__(self, other: Matrix) -> Matrix:
"""
implements matrix subtraction.
"""
if self.__width == other.width() and self.__height == other.height():
matrix = []
for i in range(self.__height):
row = [
self.__matrix[i][j] - other.component(i, j)
for j in range(self.__width)
]
matrix.append(row)
return Matrix(matrix, self.__width, self.__height)
else:
raise Exception("matrices must have the same dimension!")
@overload
def __mul__(self, other: float) -> Matrix:
...
@overload
def __mul__(self, other: Vector) -> Vector:
...
def __mul__(self, other: float | Vector) -> Vector | Matrix:
"""
implements the matrix-vector multiplication.
implements the matrix-scalar multiplication
"""
if isinstance(other, Vector): # matrix-vector
if len(other) == self.__width:
ans = zero_vector(self.__height)
for i in range(self.__height):
prods = [
self.__matrix[i][j] * other.component(j)
for j in range(self.__width)
]
ans.change_component(i, sum(prods))
return ans
else:
raise Exception(
"vector must have the same size as the "
"number of columns of the matrix!"
)
elif isinstance(other, int) or isinstance(other, float): # matrix-scalar
matrix = [
[self.__matrix[i][j] * other for j in range(self.__width)]
for i in range(self.__height)
]
return Matrix(matrix, self.__width, self.__height)
def height(self) -> int:
"""
getter for the height
"""
return self.__height
def width(self) -> int:
"""
getter for the width
"""
return self.__width
def component(self, x: int, y: int) -> float:
"""
returns the specified (x,y) component
"""
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception("change_component: indices out of bounds")
def change_component(self, x: int, y: int, value: float) -> None:
"""
changes the x-y component of this matrix
"""
if 0 <= x < self.__height and 0 <= y < self.__width:
self.__matrix[x][y] = value
else:
raise Exception("change_component: indices out of bounds")
def minor(self, x: int, y: int) -> float:
"""
returns the minor along (x, y)
"""
if self.__height != self.__width:
raise Exception("Matrix is not square")
minor = self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(minor)):
minor[i] = minor[i][:y] + minor[i][y + 1 :]
return Matrix(minor, self.__width - 1, self.__height - 1).determinant()
def cofactor(self, x: int, y: int) -> float:
"""
returns the cofactor (signed minor) along (x, y)
"""
if self.__height != self.__width:
raise Exception("Matrix is not square")
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(x, y)
else:
raise Exception("Indices out of bounds")
def determinant(self) -> float:
"""
returns the determinant of an nxn matrix using Laplace expansion
"""
if self.__height != self.__width:
raise Exception("Matrix is not square")
if self.__height < 1:
raise Exception("Matrix has no element")
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
cofactor_prods = [
self.__matrix[0][y] * self.cofactor(0, y) for y in range(self.__width)
]
return sum(cofactor_prods)
def square_zero_matrix(n: int) -> Matrix:
"""
returns a square zero-matrix of dimension NxN
"""
ans: list[list[float]] = [[0] * n for _ in range(n)]
return Matrix(ans, n, n)
def random_matrix(width: int, height: int, a: int, b: int) -> Matrix:
"""
returns a random matrix WxH with integer components
between 'a' and 'b'
"""
random.seed(None)
matrix: list[list[float]] = [
[random.randint(a, b) for _ in range(width)] for _ in range(height)
]
return Matrix(matrix, width, height)
| """
Created on Mon Feb 26 14:29:11 2018
@author: Christian Bender
@license: MIT-license
This module contains some useful classes and functions for dealing
with linear algebra in python.
Overview:
- class Vector
- function zero_vector(dimension)
- function unit_basis_vector(dimension, pos)
- function axpy(scalar, vector1, vector2)
- function random_vector(N, a, b)
- class Matrix
- function square_zero_matrix(N)
- function random_matrix(W, H, a, b)
"""
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class Vector:
"""
This class represents a vector of arbitrary size.
You need to give the vector components.
Overview of the methods:
__init__(components: Collection[float] | None): init the vector
__len__(): gets the size of the vector (number of components)
__str__(): returns a string representation
__add__(other: Vector): vector addition
__sub__(other: Vector): vector subtraction
__mul__(other: float): scalar multiplication
__mul__(other: Vector): dot product
copy(): copies this vector and returns it
component(i): gets the i-th component (0-indexed)
change_component(pos: int, value: float): changes specified component
euclidean_length(): returns the euclidean length of the vector
angle(other: Vector, deg: bool): returns the angle between two vectors
TODO: compare-operator
"""
def __init__(self, components: Collection[float] | None = None) -> None:
"""
input: components or nothing
simple constructor for init the vector
"""
if components is None:
components = []
self.__components = list(components)
def __len__(self) -> int:
"""
returns the size of the vector
"""
return len(self.__components)
def __str__(self) -> str:
"""
returns a string representation of the vector
"""
return "(" + ",".join(map(str, self.__components)) + ")"
def __add__(self, other: Vector) -> Vector:
"""
input: other vector
assumes: other vector has the same size
returns a new vector that represents the sum.
"""
size = len(self)
if size == len(other):
result = [self.__components[i] + other.component(i) for i in range(size)]
return Vector(result)
else:
raise Exception("must have the same size")
def __sub__(self, other: Vector) -> Vector:
"""
input: other vector
assumes: other vector has the same size
returns a new vector that represents the difference.
"""
size = len(self)
if size == len(other):
result = [self.__components[i] - other.component(i) for i in range(size)]
return Vector(result)
else: # error case
raise Exception("must have the same size")
@overload
def __mul__(self, other: float) -> Vector:
...
@overload
def __mul__(self, other: Vector) -> float:
...
def __mul__(self, other: float | Vector) -> float | Vector:
"""
mul implements the scalar multiplication
and the dot-product
"""
if isinstance(other, float) or isinstance(other, int):
ans = [c * other for c in self.__components]
return Vector(ans)
elif isinstance(other, Vector) and len(self) == len(other):
size = len(self)
prods = [self.__components[i] * other.component(i) for i in range(size)]
return sum(prods)
else: # error case
raise Exception("invalid operand!")
def copy(self) -> Vector:
"""
copies this vector and returns it.
"""
return Vector(self.__components)
def component(self, i: int) -> float:
"""
input: index (0-indexed)
output: the i-th component of the vector.
"""
if type(i) is int and -len(self.__components) <= i < len(self.__components):
return self.__components[i]
else:
raise Exception("index out of range")
def change_component(self, pos: int, value: float) -> None:
"""
input: an index (pos) and a value
changes the specified component (pos) with the
'value'
"""
# precondition
assert -len(self.__components) <= pos < len(self.__components)
self.__components[pos] = value
def euclidean_length(self) -> float:
"""
returns the euclidean length of the vector
>>> Vector([2, 3, 4]).euclidean_length()
5.385164807134504
>>> Vector([1]).euclidean_length()
1.0
>>> Vector([0, -1, -2, -3, 4, 5, 6]).euclidean_length()
9.539392014169456
>>> Vector([]).euclidean_length()
Traceback (most recent call last):
...
Exception: Vector is empty
"""
if len(self.__components) == 0:
raise Exception("Vector is empty")
squares = [c**2 for c in self.__components]
return math.sqrt(sum(squares))
def angle(self, other: Vector, deg: bool = False) -> float:
"""
find angle between two Vector (self, Vector)
>>> Vector([3, 4, -1]).angle(Vector([2, -1, 1]))
1.4906464636572374
>>> Vector([3, 4, -1]).angle(Vector([2, -1, 1]), deg = True)
85.40775111366095
>>> Vector([3, 4, -1]).angle(Vector([2, -1]))
Traceback (most recent call last):
...
Exception: invalid operand!
"""
num = self * other
den = self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den))
else:
return math.acos(num / den)
def zero_vector(dimension: int) -> Vector:
"""
returns a zero-vector of size 'dimension'
"""
# precondition
assert isinstance(dimension, int)
return Vector([0] * dimension)
def unit_basis_vector(dimension: int, pos: int) -> Vector:
"""
returns a unit basis vector with a One
at index 'pos' (indexing at 0)
"""
# precondition
assert isinstance(dimension, int) and (isinstance(pos, int))
ans = [0] * dimension
ans[pos] = 1
return Vector(ans)
def axpy(scalar: float, x: Vector, y: Vector) -> Vector:
"""
input: a 'scalar' and two vectors 'x' and 'y'
output: a vector
computes the axpy operation
"""
# precondition
assert (
isinstance(x, Vector)
and isinstance(y, Vector)
and (isinstance(scalar, int) or isinstance(scalar, float))
)
return x * scalar + y
def random_vector(n: int, a: int, b: int) -> Vector:
"""
input: size (N) of the vector.
random range (a,b)
output: returns a random vector of size N, with
random integer components between 'a' and 'b'.
"""
random.seed(None)
ans = [random.randint(a, b) for _ in range(n)]
return Vector(ans)
class Matrix:
"""
class: Matrix
This class represents an arbitrary matrix.
Overview of the methods:
__init__():
__str__(): returns a string representation
__add__(other: Matrix): matrix addition
__sub__(other: Matrix): matrix subtraction
__mul__(other: float): scalar multiplication
__mul__(other: Vector): vector multiplication
height() : returns height
width() : returns width
component(x: int, y: int): returns specified component
change_component(x: int, y: int, value: float): changes specified component
minor(x: int, y: int): returns minor along (x, y)
cofactor(x: int, y: int): returns cofactor along (x, y)
determinant() : returns determinant
"""
def __init__(self, matrix: list[list[float]], w: int, h: int) -> None:
"""
simple constructor for initializing the matrix with components.
"""
self.__matrix = matrix
self.__width = w
self.__height = h
def __str__(self) -> str:
"""
returns a string representation of this matrix.
"""
ans = ""
for i in range(self.__height):
ans += "|"
for j in range(self.__width):
if j < self.__width - 1:
ans += str(self.__matrix[i][j]) + ","
else:
ans += str(self.__matrix[i][j]) + "|\n"
return ans
def __add__(self, other: Matrix) -> Matrix:
"""
implements matrix addition.
"""
if self.__width == other.width() and self.__height == other.height():
matrix = []
for i in range(self.__height):
row = [
self.__matrix[i][j] + other.component(i, j)
for j in range(self.__width)
]
matrix.append(row)
return Matrix(matrix, self.__width, self.__height)
else:
raise Exception("matrix must have the same dimension!")
def __sub__(self, other: Matrix) -> Matrix:
"""
implements matrix subtraction.
"""
if self.__width == other.width() and self.__height == other.height():
matrix = []
for i in range(self.__height):
row = [
self.__matrix[i][j] - other.component(i, j)
for j in range(self.__width)
]
matrix.append(row)
return Matrix(matrix, self.__width, self.__height)
else:
raise Exception("matrices must have the same dimension!")
@overload
def __mul__(self, other: float) -> Matrix:
...
@overload
def __mul__(self, other: Vector) -> Vector:
...
def __mul__(self, other: float | Vector) -> Vector | Matrix:
"""
implements the matrix-vector multiplication.
implements the matrix-scalar multiplication
"""
if isinstance(other, Vector): # matrix-vector
if len(other) == self.__width:
ans = zero_vector(self.__height)
for i in range(self.__height):
prods = [
self.__matrix[i][j] * other.component(j)
for j in range(self.__width)
]
ans.change_component(i, sum(prods))
return ans
else:
raise Exception(
"vector must have the same size as the "
"number of columns of the matrix!"
)
elif isinstance(other, int) or isinstance(other, float): # matrix-scalar
matrix = [
[self.__matrix[i][j] * other for j in range(self.__width)]
for i in range(self.__height)
]
return Matrix(matrix, self.__width, self.__height)
def height(self) -> int:
"""
getter for the height
"""
return self.__height
def width(self) -> int:
"""
getter for the width
"""
return self.__width
def component(self, x: int, y: int) -> float:
"""
returns the specified (x,y) component
"""
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception("change_component: indices out of bounds")
def change_component(self, x: int, y: int, value: float) -> None:
"""
changes the x-y component of this matrix
"""
if 0 <= x < self.__height and 0 <= y < self.__width:
self.__matrix[x][y] = value
else:
raise Exception("change_component: indices out of bounds")
def minor(self, x: int, y: int) -> float:
"""
returns the minor along (x, y)
"""
if self.__height != self.__width:
raise Exception("Matrix is not square")
minor = self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(minor)):
minor[i] = minor[i][:y] + minor[i][y + 1 :]
return Matrix(minor, self.__width - 1, self.__height - 1).determinant()
def cofactor(self, x: int, y: int) -> float:
"""
returns the cofactor (signed minor) along (x, y)
"""
if self.__height != self.__width:
raise Exception("Matrix is not square")
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(x, y)
else:
raise Exception("Indices out of bounds")
def determinant(self) -> float:
"""
returns the determinant of an nxn matrix using Laplace expansion
"""
if self.__height != self.__width:
raise Exception("Matrix is not square")
if self.__height < 1:
raise Exception("Matrix has no element")
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
cofactor_prods = [
self.__matrix[0][y] * self.cofactor(0, y) for y in range(self.__width)
]
return sum(cofactor_prods)
def square_zero_matrix(n: int) -> Matrix:
"""
returns a square zero-matrix of dimension NxN
"""
ans: list[list[float]] = [[0] * n for _ in range(n)]
return Matrix(ans, n, n)
def random_matrix(width: int, height: int, a: int, b: int) -> Matrix:
"""
returns a random matrix WxH with integer components
between 'a' and 'b'
"""
random.seed(None)
matrix: list[list[float]] = [
[random.randint(a, b) for _ in range(width)] for _ in range(height)
]
return Matrix(matrix, width, height)
| 1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] 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 Callable
from typing import Generic, TypeVar
T = TypeVar("T")
U = TypeVar("U")
class DoubleLinkedListNode(Generic[T, U]):
"""
Double Linked List Node built specifically for LFU Cache
>>> node = DoubleLinkedListNode(1,1)
>>> node
Node: key: 1, val: 1, freq: 0, has next: False, has prev: False
"""
def __init__(self, key: T | None, val: U | None):
self.key = key
self.val = val
self.freq: int = 0
self.next: DoubleLinkedListNode[T, U] | None = None
self.prev: DoubleLinkedListNode[T, U] | None = None
def __repr__(self) -> str:
return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
self.key, self.val, self.freq, self.next is not None, self.prev is not None
)
class DoubleLinkedList(Generic[T, U]):
"""
Double Linked List built specifically for LFU Cache
>>> dll: DoubleLinkedList = DoubleLinkedList()
>>> dll
DoubleLinkedList,
Node: key: None, val: None, freq: 0, has next: True, has prev: False,
Node: key: None, val: None, freq: 0, has next: False, has prev: True
>>> first_node = DoubleLinkedListNode(1,10)
>>> first_node
Node: key: 1, val: 10, freq: 0, has next: False, has prev: False
>>> dll.add(first_node)
>>> dll
DoubleLinkedList,
Node: key: None, val: None, freq: 0, has next: True, has prev: False,
Node: key: 1, val: 10, freq: 1, has next: True, has prev: True,
Node: key: None, val: None, freq: 0, has next: False, has prev: True
>>> # node is mutated
>>> first_node
Node: key: 1, val: 10, freq: 1, has next: True, has prev: True
>>> second_node = DoubleLinkedListNode(2,20)
>>> second_node
Node: key: 2, val: 20, freq: 0, has next: False, has prev: False
>>> dll.add(second_node)
>>> dll
DoubleLinkedList,
Node: key: None, val: None, freq: 0, has next: True, has prev: False,
Node: key: 1, val: 10, freq: 1, has next: True, has prev: True,
Node: key: 2, val: 20, freq: 1, has next: True, has prev: True,
Node: key: None, val: None, freq: 0, has next: False, has prev: True
>>> removed_node = dll.remove(first_node)
>>> assert removed_node == first_node
>>> dll
DoubleLinkedList,
Node: key: None, val: None, freq: 0, has next: True, has prev: False,
Node: key: 2, val: 20, freq: 1, has next: True, has prev: True,
Node: key: None, val: None, freq: 0, has next: False, has prev: True
>>> # Attempt to remove node not on list
>>> removed_node = dll.remove(first_node)
>>> removed_node is None
True
>>> # Attempt to remove head or rear
>>> dll.head
Node: key: None, val: None, freq: 0, has next: True, has prev: False
>>> dll.remove(dll.head) is None
True
>>> # Attempt to remove head or rear
>>> dll.rear
Node: key: None, val: None, freq: 0, has next: False, has prev: True
>>> dll.remove(dll.rear) is None
True
"""
def __init__(self) -> None:
self.head: DoubleLinkedListNode[T, U] = DoubleLinkedListNode(None, None)
self.rear: DoubleLinkedListNode[T, U] = DoubleLinkedListNode(None, None)
self.head.next, self.rear.prev = self.rear, self.head
def __repr__(self) -> str:
rep = ["DoubleLinkedList"]
node = self.head
while node.next is not None:
rep.append(str(node))
node = node.next
rep.append(str(self.rear))
return ",\n ".join(rep)
def add(self, node: DoubleLinkedListNode[T, U]) -> None:
"""
Adds the given node at the tail of the list and shifting it to proper position
"""
previous = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
previous.next = node
node.prev = previous
self.rear.prev = node
node.next = self.rear
node.freq += 1
self._position_node(node)
def _position_node(self, node: DoubleLinkedListNode[T, U]) -> None:
"""
Moves node forward to maintain invariant of sort by freq value
"""
while node.prev is not None and node.prev.freq > node.freq:
# swap node with previous node
previous_node = node.prev
node.prev = previous_node.prev
previous_node.next = node.prev
node.next = previous_node
previous_node.prev = node
def remove(
self, node: DoubleLinkedListNode[T, U]
) -> DoubleLinkedListNode[T, U] | None:
"""
Removes and returns the given node from the list
Returns None if node.prev or node.next is None
"""
if node.prev is None or node.next is None:
return None
node.prev.next = node.next
node.next.prev = node.prev
node.prev = None
node.next = None
return node
class LFUCache(Generic[T, U]):
"""
LFU Cache to store a given capacity of data. Can be used as a stand-alone object
or as a function decorator.
>>> cache = LFUCache(2)
>>> cache.set(1, 1)
>>> cache.set(2, 2)
>>> cache.get(1)
1
>>> cache.set(3, 3)
>>> cache.get(2) is None
True
>>> cache.set(4, 4)
>>> cache.get(1) is None
True
>>> cache.get(3)
3
>>> cache.get(4)
4
>>> cache
CacheInfo(hits=3, misses=2, capacity=2, current_size=2)
>>> @LFUCache.decorator(100)
... def fib(num):
... if num in (1, 2):
... return 1
... return fib(num - 1) + fib(num - 2)
>>> for i in range(1, 101):
... res = fib(i)
>>> fib.cache_info()
CacheInfo(hits=196, misses=100, capacity=100, current_size=100)
"""
# class variable to map the decorator functions to their respective instance
decorator_function_to_instance_map: dict[Callable[[T], U], LFUCache[T, U]] = {}
def __init__(self, capacity: int):
self.list: DoubleLinkedList[T, U] = DoubleLinkedList()
self.capacity = capacity
self.num_keys = 0
self.hits = 0
self.miss = 0
self.cache: dict[T, DoubleLinkedListNode[T, U]] = {}
def __repr__(self) -> str:
"""
Return the details for the cache instance
[hits, misses, capacity, current_size]
"""
return (
f"CacheInfo(hits={self.hits}, misses={self.miss}, "
f"capacity={self.capacity}, current_size={self.num_keys})"
)
def __contains__(self, key: T) -> bool:
"""
>>> cache = LFUCache(1)
>>> 1 in cache
False
>>> cache.set(1, 1)
>>> 1 in cache
True
"""
return key in self.cache
def get(self, key: T) -> U | None:
"""
Returns the value for the input key and updates the Double Linked List. Returns
Returns None if key is not present in cache
"""
if key in self.cache:
self.hits += 1
value_node: DoubleLinkedListNode[T, U] = self.cache[key]
node = self.list.remove(self.cache[key])
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(node)
return node.val
self.miss += 1
return None
def set(self, key: T, value: U) -> None:
"""
Sets the value for the input key and updates the Double Linked List
"""
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node when over capacity
first_node = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert self.list.remove(first_node) is not None
# first_node guaranteed to be in list
del self.cache[first_node.key]
self.num_keys -= 1
self.cache[key] = DoubleLinkedListNode(key, value)
self.list.add(self.cache[key])
self.num_keys += 1
else:
node = self.list.remove(self.cache[key])
assert node is not None # node guaranteed to be in list
node.val = value
self.list.add(node)
@classmethod
def decorator(
cls: type[LFUCache[T, U]], size: int = 128
) -> Callable[[Callable[[T], U]], Callable[..., U]]:
"""
Decorator version of LFU Cache
Decorated function must be function of T -> U
"""
def cache_decorator_inner(func: Callable[[T], U]) -> Callable[..., U]:
def cache_decorator_wrapper(*args: T) -> U:
if func not in cls.decorator_function_to_instance_map:
cls.decorator_function_to_instance_map[func] = LFUCache(size)
result = cls.decorator_function_to_instance_map[func].get(args[0])
if result is None:
result = func(*args)
cls.decorator_function_to_instance_map[func].set(args[0], result)
return result
def cache_info() -> LFUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(cache_decorator_wrapper, "cache_info", cache_info) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
T = TypeVar("T")
U = TypeVar("U")
class DoubleLinkedListNode(Generic[T, U]):
"""
Double Linked List Node built specifically for LFU Cache
>>> node = DoubleLinkedListNode(1,1)
>>> node
Node: key: 1, val: 1, freq: 0, has next: False, has prev: False
"""
def __init__(self, key: T | None, val: U | None):
self.key = key
self.val = val
self.freq: int = 0
self.next: DoubleLinkedListNode[T, U] | None = None
self.prev: DoubleLinkedListNode[T, U] | None = None
def __repr__(self) -> str:
return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
self.key, self.val, self.freq, self.next is not None, self.prev is not None
)
class DoubleLinkedList(Generic[T, U]):
"""
Double Linked List built specifically for LFU Cache
>>> dll: DoubleLinkedList = DoubleLinkedList()
>>> dll
DoubleLinkedList,
Node: key: None, val: None, freq: 0, has next: True, has prev: False,
Node: key: None, val: None, freq: 0, has next: False, has prev: True
>>> first_node = DoubleLinkedListNode(1,10)
>>> first_node
Node: key: 1, val: 10, freq: 0, has next: False, has prev: False
>>> dll.add(first_node)
>>> dll
DoubleLinkedList,
Node: key: None, val: None, freq: 0, has next: True, has prev: False,
Node: key: 1, val: 10, freq: 1, has next: True, has prev: True,
Node: key: None, val: None, freq: 0, has next: False, has prev: True
>>> # node is mutated
>>> first_node
Node: key: 1, val: 10, freq: 1, has next: True, has prev: True
>>> second_node = DoubleLinkedListNode(2,20)
>>> second_node
Node: key: 2, val: 20, freq: 0, has next: False, has prev: False
>>> dll.add(second_node)
>>> dll
DoubleLinkedList,
Node: key: None, val: None, freq: 0, has next: True, has prev: False,
Node: key: 1, val: 10, freq: 1, has next: True, has prev: True,
Node: key: 2, val: 20, freq: 1, has next: True, has prev: True,
Node: key: None, val: None, freq: 0, has next: False, has prev: True
>>> removed_node = dll.remove(first_node)
>>> assert removed_node == first_node
>>> dll
DoubleLinkedList,
Node: key: None, val: None, freq: 0, has next: True, has prev: False,
Node: key: 2, val: 20, freq: 1, has next: True, has prev: True,
Node: key: None, val: None, freq: 0, has next: False, has prev: True
>>> # Attempt to remove node not on list
>>> removed_node = dll.remove(first_node)
>>> removed_node is None
True
>>> # Attempt to remove head or rear
>>> dll.head
Node: key: None, val: None, freq: 0, has next: True, has prev: False
>>> dll.remove(dll.head) is None
True
>>> # Attempt to remove head or rear
>>> dll.rear
Node: key: None, val: None, freq: 0, has next: False, has prev: True
>>> dll.remove(dll.rear) is None
True
"""
def __init__(self) -> None:
self.head: DoubleLinkedListNode[T, U] = DoubleLinkedListNode(None, None)
self.rear: DoubleLinkedListNode[T, U] = DoubleLinkedListNode(None, None)
self.head.next, self.rear.prev = self.rear, self.head
def __repr__(self) -> str:
rep = ["DoubleLinkedList"]
node = self.head
while node.next is not None:
rep.append(str(node))
node = node.next
rep.append(str(self.rear))
return ",\n ".join(rep)
def add(self, node: DoubleLinkedListNode[T, U]) -> None:
"""
Adds the given node at the tail of the list and shifting it to proper position
"""
previous = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
previous.next = node
node.prev = previous
self.rear.prev = node
node.next = self.rear
node.freq += 1
self._position_node(node)
def _position_node(self, node: DoubleLinkedListNode[T, U]) -> None:
"""
Moves node forward to maintain invariant of sort by freq value
"""
while node.prev is not None and node.prev.freq > node.freq:
# swap node with previous node
previous_node = node.prev
node.prev = previous_node.prev
previous_node.next = node.prev
node.next = previous_node
previous_node.prev = node
def remove(
self, node: DoubleLinkedListNode[T, U]
) -> DoubleLinkedListNode[T, U] | None:
"""
Removes and returns the given node from the list
Returns None if node.prev or node.next is None
"""
if node.prev is None or node.next is None:
return None
node.prev.next = node.next
node.next.prev = node.prev
node.prev = None
node.next = None
return node
class LFUCache(Generic[T, U]):
"""
LFU Cache to store a given capacity of data. Can be used as a stand-alone object
or as a function decorator.
>>> cache = LFUCache(2)
>>> cache.put(1, 1)
>>> cache.put(2, 2)
>>> cache.get(1)
1
>>> cache.put(3, 3)
>>> cache.get(2) is None
True
>>> cache.put(4, 4)
>>> cache.get(1) is None
True
>>> cache.get(3)
3
>>> cache.get(4)
4
>>> cache
CacheInfo(hits=3, misses=2, capacity=2, current_size=2)
>>> @LFUCache.decorator(100)
... def fib(num):
... if num in (1, 2):
... return 1
... return fib(num - 1) + fib(num - 2)
>>> for i in range(1, 101):
... res = fib(i)
>>> fib.cache_info()
CacheInfo(hits=196, misses=100, capacity=100, current_size=100)
"""
# class variable to map the decorator functions to their respective instance
decorator_function_to_instance_map: dict[Callable[[T], U], LFUCache[T, U]] = {}
def __init__(self, capacity: int):
self.list: DoubleLinkedList[T, U] = DoubleLinkedList()
self.capacity = capacity
self.num_keys = 0
self.hits = 0
self.miss = 0
self.cache: dict[T, DoubleLinkedListNode[T, U]] = {}
def __repr__(self) -> str:
"""
Return the details for the cache instance
[hits, misses, capacity, current_size]
"""
return (
f"CacheInfo(hits={self.hits}, misses={self.miss}, "
f"capacity={self.capacity}, current_size={self.num_keys})"
)
def __contains__(self, key: T) -> bool:
"""
>>> cache = LFUCache(1)
>>> 1 in cache
False
>>> cache.put(1, 1)
>>> 1 in cache
True
"""
return key in self.cache
def get(self, key: T) -> U | None:
"""
Returns the value for the input key and updates the Double Linked List. Returns
Returns None if key is not present in cache
"""
if key in self.cache:
self.hits += 1
value_node: DoubleLinkedListNode[T, U] = self.cache[key]
node = self.list.remove(self.cache[key])
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(node)
return node.val
self.miss += 1
return None
def put(self, key: T, value: U) -> None:
"""
Sets the value for the input key and updates the Double Linked List
"""
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node when over capacity
first_node = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert self.list.remove(first_node) is not None
# first_node guaranteed to be in list
del self.cache[first_node.key]
self.num_keys -= 1
self.cache[key] = DoubleLinkedListNode(key, value)
self.list.add(self.cache[key])
self.num_keys += 1
else:
node = self.list.remove(self.cache[key])
assert node is not None # node guaranteed to be in list
node.val = value
self.list.add(node)
@classmethod
def decorator(
cls: type[LFUCache[T, U]], size: int = 128
) -> Callable[[Callable[[T], U]], Callable[..., U]]:
"""
Decorator version of LFU Cache
Decorated function must be function of T -> U
"""
def cache_decorator_inner(func: Callable[[T], U]) -> Callable[..., U]:
def cache_decorator_wrapper(*args: T) -> U:
if func not in cls.decorator_function_to_instance_map:
cls.decorator_function_to_instance_map[func] = LFUCache(size)
result = cls.decorator_function_to_instance_map[func].get(args[0])
if result is None:
result = func(*args)
cls.decorator_function_to_instance_map[func].put(args[0], result)
return result
def cache_info() -> LFUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(cache_decorator_wrapper, "cache_info", cache_info) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] 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 Callable
from typing import Generic, TypeVar
T = TypeVar("T")
U = TypeVar("U")
class DoubleLinkedListNode(Generic[T, U]):
"""
Double Linked List Node built specifically for LRU Cache
>>> DoubleLinkedListNode(1,1)
Node: key: 1, val: 1, has next: False, has prev: False
"""
def __init__(self, key: T | None, val: U | None):
self.key = key
self.val = val
self.next: DoubleLinkedListNode[T, U] | None = None
self.prev: DoubleLinkedListNode[T, U] | None = None
def __repr__(self) -> str:
return (
f"Node: key: {self.key}, val: {self.val}, "
f"has next: {bool(self.next)}, has prev: {bool(self.prev)}"
)
class DoubleLinkedList(Generic[T, U]):
"""
Double Linked List built specifically for LRU Cache
>>> dll: DoubleLinkedList = DoubleLinkedList()
>>> dll
DoubleLinkedList,
Node: key: None, val: None, has next: True, has prev: False,
Node: key: None, val: None, has next: False, has prev: True
>>> first_node = DoubleLinkedListNode(1,10)
>>> first_node
Node: key: 1, val: 10, has next: False, has prev: False
>>> dll.add(first_node)
>>> dll
DoubleLinkedList,
Node: key: None, val: None, has next: True, has prev: False,
Node: key: 1, val: 10, has next: True, has prev: True,
Node: key: None, val: None, has next: False, has prev: True
>>> # node is mutated
>>> first_node
Node: key: 1, val: 10, has next: True, has prev: True
>>> second_node = DoubleLinkedListNode(2,20)
>>> second_node
Node: key: 2, val: 20, has next: False, has prev: False
>>> dll.add(second_node)
>>> dll
DoubleLinkedList,
Node: key: None, val: None, has next: True, has prev: False,
Node: key: 1, val: 10, has next: True, has prev: True,
Node: key: 2, val: 20, has next: True, has prev: True,
Node: key: None, val: None, has next: False, has prev: True
>>> removed_node = dll.remove(first_node)
>>> assert removed_node == first_node
>>> dll
DoubleLinkedList,
Node: key: None, val: None, has next: True, has prev: False,
Node: key: 2, val: 20, has next: True, has prev: True,
Node: key: None, val: None, has next: False, has prev: True
>>> # Attempt to remove node not on list
>>> removed_node = dll.remove(first_node)
>>> removed_node is None
True
>>> # Attempt to remove head or rear
>>> dll.head
Node: key: None, val: None, has next: True, has prev: False
>>> dll.remove(dll.head) is None
True
>>> # Attempt to remove head or rear
>>> dll.rear
Node: key: None, val: None, has next: False, has prev: True
>>> dll.remove(dll.rear) is None
True
"""
def __init__(self) -> None:
self.head: DoubleLinkedListNode[T, U] = DoubleLinkedListNode(None, None)
self.rear: DoubleLinkedListNode[T, U] = DoubleLinkedListNode(None, None)
self.head.next, self.rear.prev = self.rear, self.head
def __repr__(self) -> str:
rep = ["DoubleLinkedList"]
node = self.head
while node.next is not None:
rep.append(str(node))
node = node.next
rep.append(str(self.rear))
return ",\n ".join(rep)
def add(self, node: DoubleLinkedListNode[T, U]) -> None:
"""
Adds the given node to the end of the list (before rear)
"""
previous = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
previous.next = node
node.prev = previous
self.rear.prev = node
node.next = self.rear
def remove(
self, node: DoubleLinkedListNode[T, U]
) -> DoubleLinkedListNode[T, U] | None:
"""
Removes and returns the given node from the list
Returns None if node.prev or node.next is None
"""
if node.prev is None or node.next is None:
return None
node.prev.next = node.next
node.next.prev = node.prev
node.prev = None
node.next = None
return node
class LRUCache(Generic[T, U]):
"""
LRU Cache to store a given capacity of data. Can be used as a stand-alone object
or as a function decorator.
>>> cache = LRUCache(2)
>>> cache.set(1, 1)
>>> cache.set(2, 2)
>>> cache.get(1)
1
>>> cache.list
DoubleLinkedList,
Node: key: None, val: None, has next: True, has prev: False,
Node: key: 2, val: 2, has next: True, has prev: True,
Node: key: 1, val: 1, has next: True, has prev: True,
Node: key: None, val: None, has next: False, has prev: True
>>> cache.cache # doctest: +NORMALIZE_WHITESPACE
{1: Node: key: 1, val: 1, has next: True, has prev: True, \
2: Node: key: 2, val: 2, has next: True, has prev: True}
>>> cache.set(3, 3)
>>> cache.list
DoubleLinkedList,
Node: key: None, val: None, has next: True, has prev: False,
Node: key: 1, val: 1, has next: True, has prev: True,
Node: key: 3, val: 3, has next: True, has prev: True,
Node: key: None, val: None, has next: False, has prev: True
>>> cache.cache # doctest: +NORMALIZE_WHITESPACE
{1: Node: key: 1, val: 1, has next: True, has prev: True, \
3: Node: key: 3, val: 3, has next: True, has prev: True}
>>> cache.get(2) is None
True
>>> cache.set(4, 4)
>>> cache.get(1) is None
True
>>> cache.get(3)
3
>>> cache.get(4)
4
>>> cache
CacheInfo(hits=3, misses=2, capacity=2, current size=2)
>>> @LRUCache.decorator(100)
... def fib(num):
... if num in (1, 2):
... return 1
... return fib(num - 1) + fib(num - 2)
>>> for i in range(1, 100):
... res = fib(i)
>>> fib.cache_info()
CacheInfo(hits=194, misses=99, capacity=100, current size=99)
"""
# class variable to map the decorator functions to their respective instance
decorator_function_to_instance_map: dict[Callable[[T], U], LRUCache[T, U]] = {}
def __init__(self, capacity: int):
self.list: DoubleLinkedList[T, U] = DoubleLinkedList()
self.capacity = capacity
self.num_keys = 0
self.hits = 0
self.miss = 0
self.cache: dict[T, DoubleLinkedListNode[T, U]] = {}
def __repr__(self) -> str:
"""
Return the details for the cache instance
[hits, misses, capacity, current_size]
"""
return (
f"CacheInfo(hits={self.hits}, misses={self.miss}, "
f"capacity={self.capacity}, current size={self.num_keys})"
)
def __contains__(self, key: T) -> bool:
"""
>>> cache = LRUCache(1)
>>> 1 in cache
False
>>> cache.set(1, 1)
>>> 1 in cache
True
"""
return key in self.cache
def get(self, key: T) -> U | None:
"""
Returns the value for the input key and updates the Double Linked List.
Returns None if key is not present in cache
"""
# Note: pythonic interface would throw KeyError rather than return None
if key in self.cache:
self.hits += 1
value_node: DoubleLinkedListNode[T, U] = self.cache[key]
node = self.list.remove(self.cache[key])
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(node)
return node.val
self.miss += 1
return None
def set(self, key: T, value: U) -> None:
"""
Sets the value for the input key and updates the Double Linked List
"""
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
first_node = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(first_node) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
self.cache[key] = DoubleLinkedListNode(key, value)
self.list.add(self.cache[key])
self.num_keys += 1
else:
# bump node to the end of the list, update value
node = self.list.remove(self.cache[key])
assert node is not None # node guaranteed to be in list
node.val = value
self.list.add(node)
@classmethod
def decorator(
cls, size: int = 128
) -> Callable[[Callable[[T], U]], Callable[..., U]]:
"""
Decorator version of LRU Cache
Decorated function must be function of T -> U
"""
def cache_decorator_inner(func: Callable[[T], U]) -> Callable[..., U]:
def cache_decorator_wrapper(*args: T) -> U:
if func not in cls.decorator_function_to_instance_map:
cls.decorator_function_to_instance_map[func] = LRUCache(size)
result = cls.decorator_function_to_instance_map[func].get(args[0])
if result is None:
result = func(*args)
cls.decorator_function_to_instance_map[func].set(args[0], result)
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(cache_decorator_wrapper, "cache_info", cache_info) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
T = TypeVar("T")
U = TypeVar("U")
class DoubleLinkedListNode(Generic[T, U]):
"""
Double Linked List Node built specifically for LRU Cache
>>> DoubleLinkedListNode(1,1)
Node: key: 1, val: 1, has next: False, has prev: False
"""
def __init__(self, key: T | None, val: U | None):
self.key = key
self.val = val
self.next: DoubleLinkedListNode[T, U] | None = None
self.prev: DoubleLinkedListNode[T, U] | None = None
def __repr__(self) -> str:
return (
f"Node: key: {self.key}, val: {self.val}, "
f"has next: {bool(self.next)}, has prev: {bool(self.prev)}"
)
class DoubleLinkedList(Generic[T, U]):
"""
Double Linked List built specifically for LRU Cache
>>> dll: DoubleLinkedList = DoubleLinkedList()
>>> dll
DoubleLinkedList,
Node: key: None, val: None, has next: True, has prev: False,
Node: key: None, val: None, has next: False, has prev: True
>>> first_node = DoubleLinkedListNode(1,10)
>>> first_node
Node: key: 1, val: 10, has next: False, has prev: False
>>> dll.add(first_node)
>>> dll
DoubleLinkedList,
Node: key: None, val: None, has next: True, has prev: False,
Node: key: 1, val: 10, has next: True, has prev: True,
Node: key: None, val: None, has next: False, has prev: True
>>> # node is mutated
>>> first_node
Node: key: 1, val: 10, has next: True, has prev: True
>>> second_node = DoubleLinkedListNode(2,20)
>>> second_node
Node: key: 2, val: 20, has next: False, has prev: False
>>> dll.add(second_node)
>>> dll
DoubleLinkedList,
Node: key: None, val: None, has next: True, has prev: False,
Node: key: 1, val: 10, has next: True, has prev: True,
Node: key: 2, val: 20, has next: True, has prev: True,
Node: key: None, val: None, has next: False, has prev: True
>>> removed_node = dll.remove(first_node)
>>> assert removed_node == first_node
>>> dll
DoubleLinkedList,
Node: key: None, val: None, has next: True, has prev: False,
Node: key: 2, val: 20, has next: True, has prev: True,
Node: key: None, val: None, has next: False, has prev: True
>>> # Attempt to remove node not on list
>>> removed_node = dll.remove(first_node)
>>> removed_node is None
True
>>> # Attempt to remove head or rear
>>> dll.head
Node: key: None, val: None, has next: True, has prev: False
>>> dll.remove(dll.head) is None
True
>>> # Attempt to remove head or rear
>>> dll.rear
Node: key: None, val: None, has next: False, has prev: True
>>> dll.remove(dll.rear) is None
True
"""
def __init__(self) -> None:
self.head: DoubleLinkedListNode[T, U] = DoubleLinkedListNode(None, None)
self.rear: DoubleLinkedListNode[T, U] = DoubleLinkedListNode(None, None)
self.head.next, self.rear.prev = self.rear, self.head
def __repr__(self) -> str:
rep = ["DoubleLinkedList"]
node = self.head
while node.next is not None:
rep.append(str(node))
node = node.next
rep.append(str(self.rear))
return ",\n ".join(rep)
def add(self, node: DoubleLinkedListNode[T, U]) -> None:
"""
Adds the given node to the end of the list (before rear)
"""
previous = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
previous.next = node
node.prev = previous
self.rear.prev = node
node.next = self.rear
def remove(
self, node: DoubleLinkedListNode[T, U]
) -> DoubleLinkedListNode[T, U] | None:
"""
Removes and returns the given node from the list
Returns None if node.prev or node.next is None
"""
if node.prev is None or node.next is None:
return None
node.prev.next = node.next
node.next.prev = node.prev
node.prev = None
node.next = None
return node
class LRUCache(Generic[T, U]):
"""
LRU Cache to store a given capacity of data. Can be used as a stand-alone object
or as a function decorator.
>>> cache = LRUCache(2)
>>> cache.put(1, 1)
>>> cache.put(2, 2)
>>> cache.get(1)
1
>>> cache.list
DoubleLinkedList,
Node: key: None, val: None, has next: True, has prev: False,
Node: key: 2, val: 2, has next: True, has prev: True,
Node: key: 1, val: 1, has next: True, has prev: True,
Node: key: None, val: None, has next: False, has prev: True
>>> cache.cache # doctest: +NORMALIZE_WHITESPACE
{1: Node: key: 1, val: 1, has next: True, has prev: True, \
2: Node: key: 2, val: 2, has next: True, has prev: True}
>>> cache.put(3, 3)
>>> cache.list
DoubleLinkedList,
Node: key: None, val: None, has next: True, has prev: False,
Node: key: 1, val: 1, has next: True, has prev: True,
Node: key: 3, val: 3, has next: True, has prev: True,
Node: key: None, val: None, has next: False, has prev: True
>>> cache.cache # doctest: +NORMALIZE_WHITESPACE
{1: Node: key: 1, val: 1, has next: True, has prev: True, \
3: Node: key: 3, val: 3, has next: True, has prev: True}
>>> cache.get(2) is None
True
>>> cache.put(4, 4)
>>> cache.get(1) is None
True
>>> cache.get(3)
3
>>> cache.get(4)
4
>>> cache
CacheInfo(hits=3, misses=2, capacity=2, current size=2)
>>> @LRUCache.decorator(100)
... def fib(num):
... if num in (1, 2):
... return 1
... return fib(num - 1) + fib(num - 2)
>>> for i in range(1, 100):
... res = fib(i)
>>> fib.cache_info()
CacheInfo(hits=194, misses=99, capacity=100, current size=99)
"""
# class variable to map the decorator functions to their respective instance
decorator_function_to_instance_map: dict[Callable[[T], U], LRUCache[T, U]] = {}
def __init__(self, capacity: int):
self.list: DoubleLinkedList[T, U] = DoubleLinkedList()
self.capacity = capacity
self.num_keys = 0
self.hits = 0
self.miss = 0
self.cache: dict[T, DoubleLinkedListNode[T, U]] = {}
def __repr__(self) -> str:
"""
Return the details for the cache instance
[hits, misses, capacity, current_size]
"""
return (
f"CacheInfo(hits={self.hits}, misses={self.miss}, "
f"capacity={self.capacity}, current size={self.num_keys})"
)
def __contains__(self, key: T) -> bool:
"""
>>> cache = LRUCache(1)
>>> 1 in cache
False
>>> cache.put(1, 1)
>>> 1 in cache
True
"""
return key in self.cache
def get(self, key: T) -> U | None:
"""
Returns the value for the input key and updates the Double Linked List.
Returns None if key is not present in cache
"""
# Note: pythonic interface would throw KeyError rather than return None
if key in self.cache:
self.hits += 1
value_node: DoubleLinkedListNode[T, U] = self.cache[key]
node = self.list.remove(self.cache[key])
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(node)
return node.val
self.miss += 1
return None
def put(self, key: T, value: U) -> None:
"""
Sets the value for the input key and updates the Double Linked List
"""
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
first_node = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(first_node) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
self.cache[key] = DoubleLinkedListNode(key, value)
self.list.add(self.cache[key])
self.num_keys += 1
else:
# bump node to the end of the list, update value
node = self.list.remove(self.cache[key])
assert node is not None # node guaranteed to be in list
node.val = value
self.list.add(node)
@classmethod
def decorator(
cls, size: int = 128
) -> Callable[[Callable[[T], U]], Callable[..., U]]:
"""
Decorator version of LRU Cache
Decorated function must be function of T -> U
"""
def cache_decorator_inner(func: Callable[[T], U]) -> Callable[..., U]:
def cache_decorator_wrapper(*args: T) -> U:
if func not in cls.decorator_function_to_instance_map:
cls.decorator_function_to_instance_map[func] = LRUCache(size)
result = cls.decorator_function_to_instance_map[func].get(args[0])
if result is None:
result = func(*args)
cls.decorator_function_to_instance_map[func].put(args[0], result)
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(cache_decorator_wrapper, "cache_info", cache_info) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] 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 | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Created by Nathan Damon, @bizzfitch on github
>>> test_miller_rabin()
"""
def miller_rabin(n: int, allow_probable: bool = False) -> bool:
"""Deterministic Miller-Rabin algorithm for primes ~< 3.32e24.
Uses numerical analysis results to return whether or not the passed number
is prime. If the passed number is above the upper limit, and
allow_probable is True, then a return value of True indicates that n is
probably prime. This test does not allow False negatives- a return value
of False is ALWAYS composite.
Parameters
----------
n : int
The integer to be tested. Since we usually care if a number is prime,
n < 2 returns False instead of raising a ValueError.
allow_probable: bool, default False
Whether or not to test n above the upper bound of the deterministic test.
Raises
------
ValueError
Reference
---------
https://en.wikipedia.org/wiki/Miller%E2%80%93Rabin_primality_test
"""
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_317_044_064_679_887_385_961_981 and not allow_probable:
raise ValueError(
"Warning: upper bound of deterministic test is exceeded. "
"Pass allow_probable=True to allow probabilistic test. "
"A return value of True indicates a probable prime."
)
# array bounds provided by analysis
bounds = [
2_047,
1_373_653,
25_326_001,
3_215_031_751,
2_152_302_898_747,
3_474_749_660_383,
341_550_071_728_321,
1,
3_825_123_056_546_413_051,
1,
1,
318_665_857_834_031_151_167_461,
3_317_044_064_679_887_385_961_981,
]
primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
for idx, _p in enumerate(bounds, 1):
if n < _p:
# then we have our last prime to check
plist = primes[:idx]
break
d, s = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
pr = False
for r in range(s):
m = pow(prime, d * 2**r, n)
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
pr = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def test_miller_rabin() -> None:
"""Testing a nontrivial (ends in 1, 3, 7, 9) composite
and a prime in each range.
"""
assert not miller_rabin(561)
assert miller_rabin(563)
# 2047
assert not miller_rabin(838_201)
assert miller_rabin(838_207)
# 1_373_653
assert not miller_rabin(17_316_001)
assert miller_rabin(17_316_017)
# 25_326_001
assert not miller_rabin(3_078_386_641)
assert miller_rabin(3_078_386_653)
# 3_215_031_751
assert not miller_rabin(1_713_045_574_801)
assert miller_rabin(1_713_045_574_819)
# 2_152_302_898_747
assert not miller_rabin(2_779_799_728_307)
assert miller_rabin(2_779_799_728_327)
# 3_474_749_660_383
assert not miller_rabin(113_850_023_909_441)
assert miller_rabin(113_850_023_909_527)
# 341_550_071_728_321
assert not miller_rabin(1_275_041_018_848_804_351)
assert miller_rabin(1_275_041_018_848_804_391)
# 3_825_123_056_546_413_051
assert not miller_rabin(79_666_464_458_507_787_791_867)
assert miller_rabin(79_666_464_458_507_787_791_951)
# 318_665_857_834_031_151_167_461
assert not miller_rabin(552_840_677_446_647_897_660_333)
assert miller_rabin(552_840_677_446_647_897_660_359)
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| """Created by Nathan Damon, @bizzfitch on github
>>> test_miller_rabin()
"""
def miller_rabin(n: int, allow_probable: bool = False) -> bool:
"""Deterministic Miller-Rabin algorithm for primes ~< 3.32e24.
Uses numerical analysis results to return whether or not the passed number
is prime. If the passed number is above the upper limit, and
allow_probable is True, then a return value of True indicates that n is
probably prime. This test does not allow False negatives- a return value
of False is ALWAYS composite.
Parameters
----------
n : int
The integer to be tested. Since we usually care if a number is prime,
n < 2 returns False instead of raising a ValueError.
allow_probable: bool, default False
Whether or not to test n above the upper bound of the deterministic test.
Raises
------
ValueError
Reference
---------
https://en.wikipedia.org/wiki/Miller%E2%80%93Rabin_primality_test
"""
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_317_044_064_679_887_385_961_981 and not allow_probable:
raise ValueError(
"Warning: upper bound of deterministic test is exceeded. "
"Pass allow_probable=True to allow probabilistic test. "
"A return value of True indicates a probable prime."
)
# array bounds provided by analysis
bounds = [
2_047,
1_373_653,
25_326_001,
3_215_031_751,
2_152_302_898_747,
3_474_749_660_383,
341_550_071_728_321,
1,
3_825_123_056_546_413_051,
1,
1,
318_665_857_834_031_151_167_461,
3_317_044_064_679_887_385_961_981,
]
primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
for idx, _p in enumerate(bounds, 1):
if n < _p:
# then we have our last prime to check
plist = primes[:idx]
break
d, s = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
pr = False
for r in range(s):
m = pow(prime, d * 2**r, n)
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
pr = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def test_miller_rabin() -> None:
"""Testing a nontrivial (ends in 1, 3, 7, 9) composite
and a prime in each range.
"""
assert not miller_rabin(561)
assert miller_rabin(563)
# 2047
assert not miller_rabin(838_201)
assert miller_rabin(838_207)
# 1_373_653
assert not miller_rabin(17_316_001)
assert miller_rabin(17_316_017)
# 25_326_001
assert not miller_rabin(3_078_386_641)
assert miller_rabin(3_078_386_653)
# 3_215_031_751
assert not miller_rabin(1_713_045_574_801)
assert miller_rabin(1_713_045_574_819)
# 2_152_302_898_747
assert not miller_rabin(2_779_799_728_307)
assert miller_rabin(2_779_799_728_327)
# 3_474_749_660_383
assert not miller_rabin(113_850_023_909_441)
assert miller_rabin(113_850_023_909_527)
# 341_550_071_728_321
assert not miller_rabin(1_275_041_018_848_804_351)
assert miller_rabin(1_275_041_018_848_804_391)
# 3_825_123_056_546_413_051
assert not miller_rabin(79_666_464_458_507_787_791_867)
assert miller_rabin(79_666_464_458_507_787_791_951)
# 318_665_857_834_031_151_167_461
assert not miller_rabin(552_840_677_446_647_897_660_333)
assert miller_rabin(552_840_677_446_647_897_660_359)
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Normalization.
Wikipedia: https://en.wikipedia.org/wiki/Normalization
Normalization is the process of converting numerical data to a standard range of values.
This range is typically between [0, 1] or [-1, 1]. The equation for normalization is
x_norm = (x - x_min)/(x_max - x_min) where x_norm is the normalized value, x is the
value, x_min is the minimum value within the column or list of data, and x_max is the
maximum value within the column or list of data. Normalization is used to speed up the
training of data and put all of the data on a similar scale. This is useful because
variance in the range of values of a dataset can heavily impact optimization
(particularly Gradient Descent).
Standardization Wikipedia: https://en.wikipedia.org/wiki/Standardization
Standardization is the process of converting numerical data to a normally distributed
range of values. This range will have a mean of 0 and standard deviation of 1. This is
also known as z-score normalization. The equation for standardization is
x_std = (x - mu)/(sigma) where mu is the mean of the column or list of values and sigma
is the standard deviation of the column or list of values.
Choosing between Normalization & Standardization is more of an art of a science, but it
is often recommended to run experiments with both to see which performs better.
Additionally, a few rules of thumb are:
1. gaussian (normal) distributions work better with standardization
2. non-gaussian (non-normal) distributions work better with normalization
3. If a column or list of values has extreme values / outliers, use standardization
"""
from statistics import mean, stdev
def normalization(data: list, ndigits: int = 3) -> list:
"""
Return a normalized list of values.
@params: data, a list of values to normalize
@returns: a list of normalized values (rounded to ndigits decimal places)
@examples:
>>> normalization([2, 7, 10, 20, 30, 50])
[0.0, 0.104, 0.167, 0.375, 0.583, 1.0]
>>> normalization([5, 10, 15, 20, 25])
[0.0, 0.25, 0.5, 0.75, 1.0]
"""
# variables for calculation
x_min = min(data)
x_max = max(data)
# normalize data
return [round((x - x_min) / (x_max - x_min), ndigits) for x in data]
def standardization(data: list, ndigits: int = 3) -> list:
"""
Return a standardized list of values.
@params: data, a list of values to standardize
@returns: a list of standardized values (rounded to ndigits decimal places)
@examples:
>>> standardization([2, 7, 10, 20, 30, 50])
[-0.999, -0.719, -0.551, 0.009, 0.57, 1.69]
>>> standardization([5, 10, 15, 20, 25])
[-1.265, -0.632, 0.0, 0.632, 1.265]
"""
# variables for calculation
mu = mean(data)
sigma = stdev(data)
# standardize data
return [round((x - mu) / (sigma), ndigits) for x in data]
| """
Normalization.
Wikipedia: https://en.wikipedia.org/wiki/Normalization
Normalization is the process of converting numerical data to a standard range of values.
This range is typically between [0, 1] or [-1, 1]. The equation for normalization is
x_norm = (x - x_min)/(x_max - x_min) where x_norm is the normalized value, x is the
value, x_min is the minimum value within the column or list of data, and x_max is the
maximum value within the column or list of data. Normalization is used to speed up the
training of data and put all of the data on a similar scale. This is useful because
variance in the range of values of a dataset can heavily impact optimization
(particularly Gradient Descent).
Standardization Wikipedia: https://en.wikipedia.org/wiki/Standardization
Standardization is the process of converting numerical data to a normally distributed
range of values. This range will have a mean of 0 and standard deviation of 1. This is
also known as z-score normalization. The equation for standardization is
x_std = (x - mu)/(sigma) where mu is the mean of the column or list of values and sigma
is the standard deviation of the column or list of values.
Choosing between Normalization & Standardization is more of an art of a science, but it
is often recommended to run experiments with both to see which performs better.
Additionally, a few rules of thumb are:
1. gaussian (normal) distributions work better with standardization
2. non-gaussian (non-normal) distributions work better with normalization
3. If a column or list of values has extreme values / outliers, use standardization
"""
from statistics import mean, stdev
def normalization(data: list, ndigits: int = 3) -> list:
"""
Return a normalized list of values.
@params: data, a list of values to normalize
@returns: a list of normalized values (rounded to ndigits decimal places)
@examples:
>>> normalization([2, 7, 10, 20, 30, 50])
[0.0, 0.104, 0.167, 0.375, 0.583, 1.0]
>>> normalization([5, 10, 15, 20, 25])
[0.0, 0.25, 0.5, 0.75, 1.0]
"""
# variables for calculation
x_min = min(data)
x_max = max(data)
# normalize data
return [round((x - x_min) / (x_max - x_min), ndigits) for x in data]
def standardization(data: list, ndigits: int = 3) -> list:
"""
Return a standardized list of values.
@params: data, a list of values to standardize
@returns: a list of standardized values (rounded to ndigits decimal places)
@examples:
>>> standardization([2, 7, 10, 20, 30, 50])
[-0.999, -0.719, -0.551, 0.009, 0.57, 1.69]
>>> standardization([5, 10, 15, 20, 25])
[-1.265, -0.632, 0.0, 0.632, 1.265]
"""
# variables for calculation
mu = mean(data)
sigma = stdev(data)
# standardize data
return [round((x - mu) / (sigma), ndigits) for x in data]
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
LCS Problem Statement: Given two sequences, find the length of longest subsequence
present in both of them. A subsequence is a sequence that appears in the same relative
order, but not necessarily continuous.
Example:"abc", "abg" are subsequences of "abcdefgh".
"""
def longest_common_subsequence(x: str, y: str):
"""
Finds the longest common subsequence between two strings. Also returns the
The subsequence found
Parameters
----------
x: str, one of the strings
y: str, the other string
Returns
-------
L[m][n]: int, the length of the longest subsequence. Also equal to len(seq)
Seq: str, the subsequence found
>>> longest_common_subsequence("programming", "gaming")
(6, 'gaming')
>>> longest_common_subsequence("physics", "smartphone")
(2, 'ph')
>>> longest_common_subsequence("computer", "food")
(1, 'o')
"""
# find the length of strings
assert x is not None
assert y is not None
m = len(x)
n = len(y)
# declaring the array for storing the dp values
l = [[0] * (n + 1) for _ in range(m + 1)] # noqa: E741
for i in range(1, m + 1):
for j in range(1, n + 1):
if x[i - 1] == y[j - 1]:
match = 1
else:
match = 0
l[i][j] = max(l[i - 1][j], l[i][j - 1], l[i - 1][j - 1] + match)
seq = ""
i, j = m, n
while i > 0 and j > 0:
if x[i - 1] == y[j - 1]:
match = 1
else:
match = 0
if l[i][j] == l[i - 1][j - 1] + match:
if match == 1:
seq = x[i - 1] + seq
i -= 1
j -= 1
elif l[i][j] == l[i - 1][j]:
i -= 1
else:
j -= 1
return l[m][n], seq
if __name__ == "__main__":
a = "AGGTAB"
b = "GXTXAYB"
expected_ln = 4
expected_subseq = "GTAB"
ln, subseq = longest_common_subsequence(a, b)
print("len =", ln, ", sub-sequence =", subseq)
import doctest
doctest.testmod()
| """
LCS Problem Statement: Given two sequences, find the length of longest subsequence
present in both of them. A subsequence is a sequence that appears in the same relative
order, but not necessarily continuous.
Example:"abc", "abg" are subsequences of "abcdefgh".
"""
def longest_common_subsequence(x: str, y: str):
"""
Finds the longest common subsequence between two strings. Also returns the
The subsequence found
Parameters
----------
x: str, one of the strings
y: str, the other string
Returns
-------
L[m][n]: int, the length of the longest subsequence. Also equal to len(seq)
Seq: str, the subsequence found
>>> longest_common_subsequence("programming", "gaming")
(6, 'gaming')
>>> longest_common_subsequence("physics", "smartphone")
(2, 'ph')
>>> longest_common_subsequence("computer", "food")
(1, 'o')
"""
# find the length of strings
assert x is not None
assert y is not None
m = len(x)
n = len(y)
# declaring the array for storing the dp values
l = [[0] * (n + 1) for _ in range(m + 1)] # noqa: E741
for i in range(1, m + 1):
for j in range(1, n + 1):
if x[i - 1] == y[j - 1]:
match = 1
else:
match = 0
l[i][j] = max(l[i - 1][j], l[i][j - 1], l[i - 1][j - 1] + match)
seq = ""
i, j = m, n
while i > 0 and j > 0:
if x[i - 1] == y[j - 1]:
match = 1
else:
match = 0
if l[i][j] == l[i - 1][j - 1] + match:
if match == 1:
seq = x[i - 1] + seq
i -= 1
j -= 1
elif l[i][j] == l[i - 1][j]:
i -= 1
else:
j -= 1
return l[m][n], seq
if __name__ == "__main__":
a = "AGGTAB"
b = "GXTXAYB"
expected_ln = 4
expected_subseq = "GTAB"
ln, subseq = longest_common_subsequence(a, b)
print("len =", ln, ", sub-sequence =", subseq)
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Sum of digits sequence
Problem 551
Let a(0), a(1),... be an integer sequence defined by:
a(0) = 1
for n >= 1, a(n) is the sum of the digits of all preceding terms
The sequence starts with 1, 1, 2, 4, 8, ...
You are given a(10^6) = 31054319.
Find a(10^15)
"""
ks = range(2, 20 + 1)
base = [10**k for k in range(ks[-1] + 1)]
memo: dict[int, dict[int, list[list[int]]]] = {}
def next_term(a_i, k, i, n):
"""
Calculates and updates a_i in-place to either the n-th term or the
smallest term for which c > 10^k when the terms are written in the form:
a(i) = b * 10^k + c
For any a(i), if digitsum(b) and c have the same value, the difference
between subsequent terms will be the same until c >= 10^k. This difference
is cached to greatly speed up the computation.
Arguments:
a_i -- array of digits starting from the one's place that represent
the i-th term in the sequence
k -- k when terms are written in the from a(i) = b*10^k + c.
Term are calulcated until c > 10^k or the n-th term is reached.
i -- position along the sequence
n -- term to calculate up to if k is large enough
Return: a tuple of difference between ending term and starting term, and
the number of terms calculated. ex. if starting term is a_0=1, and
ending term is a_10=62, then (61, 9) is returned.
"""
# ds_b - digitsum(b)
ds_b = sum(a_i[j] for j in range(k, len(a_i)))
c = sum(a_i[j] * base[j] for j in range(min(len(a_i), k)))
diff, dn = 0, 0
max_dn = n - i
sub_memo = memo.get(ds_b)
if sub_memo is not None:
jumps = sub_memo.get(c)
if jumps is not None and len(jumps) > 0:
# find and make the largest jump without going over
max_jump = -1
for _k in range(len(jumps) - 1, -1, -1):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
max_jump = _k
break
if max_jump >= 0:
diff, dn, _kk = jumps[max_jump]
# since the difference between jumps is cached, add c
new_c = diff + c
for j in range(min(k, len(a_i))):
new_c, a_i[j] = divmod(new_c, 10)
if new_c > 0:
add(a_i, k, new_c)
else:
sub_memo[c] = []
else:
sub_memo = {c: []}
memo[ds_b] = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
_diff, terms_jumped = next_term(a_i, k - 1, i + dn, n)
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
_diff, terms_jumped = compute(a_i, k, i + dn, n)
diff += _diff
dn += terms_jumped
jumps = sub_memo[c]
# keep jumps sorted by # of terms skipped
j = 0
while j < len(jumps):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(j, (diff, dn, k))
return (diff, dn)
def compute(a_i, k, i, n):
"""
same as next_term(a_i, k, i, n) but computes terms without memoizing results.
"""
if i >= n:
return 0, i
if k > len(a_i):
a_i.extend([0 for _ in range(k - len(a_i))])
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
start_i = i
ds_b, ds_c, diff = 0, 0, 0
for j in range(len(a_i)):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
addend = ds_c + ds_b
diff += addend
ds_c = 0
for j in range(k):
s = a_i[j] + addend
addend, a_i[j] = divmod(s, 10)
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(a_i, k, addend)
return diff, i - start_i
def add(digits, k, addend):
"""
adds addend to digit array given in digits
starting at index k
"""
for j in range(k, len(digits)):
s = digits[j] + addend
if s >= 10:
quotient, digits[j] = divmod(s, 10)
addend = addend // 10 + quotient
else:
digits[j] = s
addend = addend // 10
if addend == 0:
break
while addend > 0:
addend, digit = divmod(addend, 10)
digits.append(digit)
def solution(n: int = 10**15) -> int:
"""
returns n-th term of sequence
>>> solution(10)
62
>>> solution(10**6)
31054319
>>> solution(10**15)
73597483551591773
"""
digits = [1]
i = 1
dn = 0
while True:
diff, terms_jumped = next_term(digits, 20, i + dn, n)
dn += terms_jumped
if dn == n - i:
break
a_n = 0
for j in range(len(digits)):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(f"{solution() = }")
| """
Sum of digits sequence
Problem 551
Let a(0), a(1),... be an integer sequence defined by:
a(0) = 1
for n >= 1, a(n) is the sum of the digits of all preceding terms
The sequence starts with 1, 1, 2, 4, 8, ...
You are given a(10^6) = 31054319.
Find a(10^15)
"""
ks = range(2, 20 + 1)
base = [10**k for k in range(ks[-1] + 1)]
memo: dict[int, dict[int, list[list[int]]]] = {}
def next_term(a_i, k, i, n):
"""
Calculates and updates a_i in-place to either the n-th term or the
smallest term for which c > 10^k when the terms are written in the form:
a(i) = b * 10^k + c
For any a(i), if digitsum(b) and c have the same value, the difference
between subsequent terms will be the same until c >= 10^k. This difference
is cached to greatly speed up the computation.
Arguments:
a_i -- array of digits starting from the one's place that represent
the i-th term in the sequence
k -- k when terms are written in the from a(i) = b*10^k + c.
Term are calulcated until c > 10^k or the n-th term is reached.
i -- position along the sequence
n -- term to calculate up to if k is large enough
Return: a tuple of difference between ending term and starting term, and
the number of terms calculated. ex. if starting term is a_0=1, and
ending term is a_10=62, then (61, 9) is returned.
"""
# ds_b - digitsum(b)
ds_b = sum(a_i[j] for j in range(k, len(a_i)))
c = sum(a_i[j] * base[j] for j in range(min(len(a_i), k)))
diff, dn = 0, 0
max_dn = n - i
sub_memo = memo.get(ds_b)
if sub_memo is not None:
jumps = sub_memo.get(c)
if jumps is not None and len(jumps) > 0:
# find and make the largest jump without going over
max_jump = -1
for _k in range(len(jumps) - 1, -1, -1):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
max_jump = _k
break
if max_jump >= 0:
diff, dn, _kk = jumps[max_jump]
# since the difference between jumps is cached, add c
new_c = diff + c
for j in range(min(k, len(a_i))):
new_c, a_i[j] = divmod(new_c, 10)
if new_c > 0:
add(a_i, k, new_c)
else:
sub_memo[c] = []
else:
sub_memo = {c: []}
memo[ds_b] = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
_diff, terms_jumped = next_term(a_i, k - 1, i + dn, n)
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
_diff, terms_jumped = compute(a_i, k, i + dn, n)
diff += _diff
dn += terms_jumped
jumps = sub_memo[c]
# keep jumps sorted by # of terms skipped
j = 0
while j < len(jumps):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(j, (diff, dn, k))
return (diff, dn)
def compute(a_i, k, i, n):
"""
same as next_term(a_i, k, i, n) but computes terms without memoizing results.
"""
if i >= n:
return 0, i
if k > len(a_i):
a_i.extend([0 for _ in range(k - len(a_i))])
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
start_i = i
ds_b, ds_c, diff = 0, 0, 0
for j in range(len(a_i)):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
addend = ds_c + ds_b
diff += addend
ds_c = 0
for j in range(k):
s = a_i[j] + addend
addend, a_i[j] = divmod(s, 10)
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(a_i, k, addend)
return diff, i - start_i
def add(digits, k, addend):
"""
adds addend to digit array given in digits
starting at index k
"""
for j in range(k, len(digits)):
s = digits[j] + addend
if s >= 10:
quotient, digits[j] = divmod(s, 10)
addend = addend // 10 + quotient
else:
digits[j] = s
addend = addend // 10
if addend == 0:
break
while addend > 0:
addend, digit = divmod(addend, 10)
digits.append(digit)
def solution(n: int = 10**15) -> int:
"""
returns n-th term of sequence
>>> solution(10)
62
>>> solution(10**6)
31054319
>>> solution(10**15)
73597483551591773
"""
digits = [1]
i = 1
dn = 0
while True:
diff, terms_jumped = next_term(digits, 20, i + dn, n)
dn += terms_jumped
if dn == n - i:
break
a_n = 0
for j in range(len(digits)):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import numpy as np
def runge_kutta(f, y0, x0, h, x_end):
"""
Calculate the numeric solution at each step to the ODE f(x, y) using RK4
https://en.wikipedia.org/wiki/Runge-Kutta_methods
Arguments:
f -- The ode as a function of x and y
y0 -- the initial value for y
x0 -- the initial value for x
h -- the stepsize
x_end -- the end value for x
>>> # the exact solution is math.exp(x)
>>> def f(x, y):
... return y
>>> y0 = 1
>>> y = runge_kutta(f, y0, 0.0, 0.01, 5)
>>> y[-1]
148.41315904125113
"""
n = int(np.ceil((x_end - x0) / h))
y = np.zeros((n + 1,))
y[0] = y0
x = x0
for k in range(n):
k1 = f(x, y[k])
k2 = f(x + 0.5 * h, y[k] + 0.5 * h * k1)
k3 = f(x + 0.5 * h, y[k] + 0.5 * h * k2)
k4 = f(x + h, y[k] + h * k3)
y[k + 1] = y[k] + (1 / 6) * h * (k1 + 2 * k2 + 2 * k3 + k4)
x += h
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| import numpy as np
def runge_kutta(f, y0, x0, h, x_end):
"""
Calculate the numeric solution at each step to the ODE f(x, y) using RK4
https://en.wikipedia.org/wiki/Runge-Kutta_methods
Arguments:
f -- The ode as a function of x and y
y0 -- the initial value for y
x0 -- the initial value for x
h -- the stepsize
x_end -- the end value for x
>>> # the exact solution is math.exp(x)
>>> def f(x, y):
... return y
>>> y0 = 1
>>> y = runge_kutta(f, y0, 0.0, 0.01, 5)
>>> y[-1]
148.41315904125113
"""
n = int(np.ceil((x_end - x0) / h))
y = np.zeros((n + 1,))
y[0] = y0
x = x0
for k in range(n):
k1 = f(x, y[k])
k2 = f(x + 0.5 * h, y[k] + 0.5 * h * k1)
k3 = f(x + 0.5 * h, y[k] + 0.5 * h * k2)
k4 = f(x + h, y[k] + h * k3)
y[k + 1] = y[k] + (1 / 6) * h * (k1 + 2 * k2 + 2 * k3 + k4)
x += h
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Resources:
- https://en.wikipedia.org/wiki/Conjugate_gradient_method
- https://en.wikipedia.org/wiki/Definite_symmetric_matrix
"""
from typing import Any
import numpy as np
def _is_matrix_spd(matrix: np.ndarray) -> bool:
"""
Returns True if input matrix is symmetric positive definite.
Returns False otherwise.
For a matrix to be SPD, all eigenvalues must be positive.
>>> import numpy as np
>>> matrix = np.array([
... [4.12401784, -5.01453636, -0.63865857],
... [-5.01453636, 12.33347422, -3.40493586],
... [-0.63865857, -3.40493586, 5.78591885]])
>>> _is_matrix_spd(matrix)
True
>>> matrix = np.array([
... [0.34634879, 1.96165514, 2.18277744],
... [0.74074469, -1.19648894, -1.34223498],
... [-0.7687067 , 0.06018373, -1.16315631]])
>>> _is_matrix_spd(matrix)
False
"""
# Ensure matrix is square.
assert np.shape(matrix)[0] == np.shape(matrix)[1]
# If matrix not symmetric, exit right away.
if np.allclose(matrix, matrix.T) is False:
return False
# Get eigenvalues and eignevectors for a symmetric matrix.
eigen_values, _ = np.linalg.eigh(matrix)
# Check sign of all eigenvalues.
# np.all returns a value of type np.bool_
return bool(np.all(eigen_values > 0))
def _create_spd_matrix(dimension: int) -> Any:
"""
Returns a symmetric positive definite matrix given a dimension.
Input:
dimension gives the square matrix dimension.
Output:
spd_matrix is an diminesion x dimensions symmetric positive definite (SPD) matrix.
>>> import numpy as np
>>> dimension = 3
>>> spd_matrix = _create_spd_matrix(dimension)
>>> _is_matrix_spd(spd_matrix)
True
"""
random_matrix = np.random.randn(dimension, dimension)
spd_matrix = np.dot(random_matrix, random_matrix.T)
assert _is_matrix_spd(spd_matrix)
return spd_matrix
def conjugate_gradient(
spd_matrix: np.ndarray,
load_vector: np.ndarray,
max_iterations: int = 1000,
tol: float = 1e-8,
) -> Any:
"""
Returns solution to the linear system np.dot(spd_matrix, x) = b.
Input:
spd_matrix is an NxN Symmetric Positive Definite (SPD) matrix.
load_vector is an Nx1 vector.
Output:
x is an Nx1 vector that is the solution vector.
>>> import numpy as np
>>> spd_matrix = np.array([
... [8.73256573, -5.02034289, -2.68709226],
... [-5.02034289, 3.78188322, 0.91980451],
... [-2.68709226, 0.91980451, 1.94746467]])
>>> b = np.array([
... [-5.80872761],
... [ 3.23807431],
... [ 1.95381422]])
>>> conjugate_gradient(spd_matrix, b)
array([[-0.63114139],
[-0.01561498],
[ 0.13979294]])
"""
# Ensure proper dimensionality.
assert np.shape(spd_matrix)[0] == np.shape(spd_matrix)[1]
assert np.shape(load_vector)[0] == np.shape(spd_matrix)[0]
assert _is_matrix_spd(spd_matrix)
# Initialize solution guess, residual, search direction.
x0 = np.zeros((np.shape(load_vector)[0], 1))
r0 = np.copy(load_vector)
p0 = np.copy(r0)
# Set initial errors in solution guess and residual.
error_residual = 1e9
error_x_solution = 1e9
error = 1e9
# Set iteration counter to threshold number of iterations.
iterations = 0
while error > tol:
# Save this value so we only calculate the matrix-vector product once.
w = np.dot(spd_matrix, p0)
# The main algorithm.
# Update search direction magnitude.
alpha = np.dot(r0.T, r0) / np.dot(p0.T, w)
# Update solution guess.
x = x0 + alpha * p0
# Calculate new residual.
r = r0 - alpha * w
# Calculate new Krylov subspace scale.
beta = np.dot(r.T, r) / np.dot(r0.T, r0)
# Calculate new A conjuage search direction.
p = r + beta * p0
# Calculate errors.
error_residual = np.linalg.norm(r - r0)
error_x_solution = np.linalg.norm(x - x0)
error = np.maximum(error_residual, error_x_solution)
# Update variables.
x0 = np.copy(x)
r0 = np.copy(r)
p0 = np.copy(p)
# Update number of iterations.
iterations += 1
if iterations > max_iterations:
break
return x
def test_conjugate_gradient() -> None:
"""
>>> test_conjugate_gradient() # self running tests
"""
# Create linear system with SPD matrix and known solution x_true.
dimension = 3
spd_matrix = _create_spd_matrix(dimension)
x_true = np.random.randn(dimension, 1)
b = np.dot(spd_matrix, x_true)
# Numpy solution.
x_numpy = np.linalg.solve(spd_matrix, b)
# Our implementation.
x_conjugate_gradient = conjugate_gradient(spd_matrix, b)
# Ensure both solutions are close to x_true (and therefore one another).
assert np.linalg.norm(x_numpy - x_true) <= 1e-6
assert np.linalg.norm(x_conjugate_gradient - x_true) <= 1e-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_conjugate_gradient()
| """
Resources:
- https://en.wikipedia.org/wiki/Conjugate_gradient_method
- https://en.wikipedia.org/wiki/Definite_symmetric_matrix
"""
from typing import Any
import numpy as np
def _is_matrix_spd(matrix: np.ndarray) -> bool:
"""
Returns True if input matrix is symmetric positive definite.
Returns False otherwise.
For a matrix to be SPD, all eigenvalues must be positive.
>>> import numpy as np
>>> matrix = np.array([
... [4.12401784, -5.01453636, -0.63865857],
... [-5.01453636, 12.33347422, -3.40493586],
... [-0.63865857, -3.40493586, 5.78591885]])
>>> _is_matrix_spd(matrix)
True
>>> matrix = np.array([
... [0.34634879, 1.96165514, 2.18277744],
... [0.74074469, -1.19648894, -1.34223498],
... [-0.7687067 , 0.06018373, -1.16315631]])
>>> _is_matrix_spd(matrix)
False
"""
# Ensure matrix is square.
assert np.shape(matrix)[0] == np.shape(matrix)[1]
# If matrix not symmetric, exit right away.
if np.allclose(matrix, matrix.T) is False:
return False
# Get eigenvalues and eignevectors for a symmetric matrix.
eigen_values, _ = np.linalg.eigh(matrix)
# Check sign of all eigenvalues.
# np.all returns a value of type np.bool_
return bool(np.all(eigen_values > 0))
def _create_spd_matrix(dimension: int) -> Any:
"""
Returns a symmetric positive definite matrix given a dimension.
Input:
dimension gives the square matrix dimension.
Output:
spd_matrix is an diminesion x dimensions symmetric positive definite (SPD) matrix.
>>> import numpy as np
>>> dimension = 3
>>> spd_matrix = _create_spd_matrix(dimension)
>>> _is_matrix_spd(spd_matrix)
True
"""
random_matrix = np.random.randn(dimension, dimension)
spd_matrix = np.dot(random_matrix, random_matrix.T)
assert _is_matrix_spd(spd_matrix)
return spd_matrix
def conjugate_gradient(
spd_matrix: np.ndarray,
load_vector: np.ndarray,
max_iterations: int = 1000,
tol: float = 1e-8,
) -> Any:
"""
Returns solution to the linear system np.dot(spd_matrix, x) = b.
Input:
spd_matrix is an NxN Symmetric Positive Definite (SPD) matrix.
load_vector is an Nx1 vector.
Output:
x is an Nx1 vector that is the solution vector.
>>> import numpy as np
>>> spd_matrix = np.array([
... [8.73256573, -5.02034289, -2.68709226],
... [-5.02034289, 3.78188322, 0.91980451],
... [-2.68709226, 0.91980451, 1.94746467]])
>>> b = np.array([
... [-5.80872761],
... [ 3.23807431],
... [ 1.95381422]])
>>> conjugate_gradient(spd_matrix, b)
array([[-0.63114139],
[-0.01561498],
[ 0.13979294]])
"""
# Ensure proper dimensionality.
assert np.shape(spd_matrix)[0] == np.shape(spd_matrix)[1]
assert np.shape(load_vector)[0] == np.shape(spd_matrix)[0]
assert _is_matrix_spd(spd_matrix)
# Initialize solution guess, residual, search direction.
x0 = np.zeros((np.shape(load_vector)[0], 1))
r0 = np.copy(load_vector)
p0 = np.copy(r0)
# Set initial errors in solution guess and residual.
error_residual = 1e9
error_x_solution = 1e9
error = 1e9
# Set iteration counter to threshold number of iterations.
iterations = 0
while error > tol:
# Save this value so we only calculate the matrix-vector product once.
w = np.dot(spd_matrix, p0)
# The main algorithm.
# Update search direction magnitude.
alpha = np.dot(r0.T, r0) / np.dot(p0.T, w)
# Update solution guess.
x = x0 + alpha * p0
# Calculate new residual.
r = r0 - alpha * w
# Calculate new Krylov subspace scale.
beta = np.dot(r.T, r) / np.dot(r0.T, r0)
# Calculate new A conjuage search direction.
p = r + beta * p0
# Calculate errors.
error_residual = np.linalg.norm(r - r0)
error_x_solution = np.linalg.norm(x - x0)
error = np.maximum(error_residual, error_x_solution)
# Update variables.
x0 = np.copy(x)
r0 = np.copy(r)
p0 = np.copy(p)
# Update number of iterations.
iterations += 1
if iterations > max_iterations:
break
return x
def test_conjugate_gradient() -> None:
"""
>>> test_conjugate_gradient() # self running tests
"""
# Create linear system with SPD matrix and known solution x_true.
dimension = 3
spd_matrix = _create_spd_matrix(dimension)
x_true = np.random.randn(dimension, 1)
b = np.dot(spd_matrix, x_true)
# Numpy solution.
x_numpy = np.linalg.solve(spd_matrix, b)
# Our implementation.
x_conjugate_gradient = conjugate_gradient(spd_matrix, b)
# Ensure both solutions are close to x_true (and therefore one another).
assert np.linalg.norm(x_numpy - x_true) <= 1e-6
assert np.linalg.norm(x_conjugate_gradient - x_true) <= 1e-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_conjugate_gradient()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Numbers of alphabet which we call base
alphabet_size = 256
# Modulus to hash a string
modulus = 1000003
def rabin_karp(pattern: str, text: str) -> bool:
"""
The Rabin-Karp Algorithm for finding a pattern within a piece of text
with complexity O(nm), most efficient when it is used with multiple patterns
as it is able to check if any of a set of patterns match a section of text in o(1)
given the precomputed hashes.
This will be the simple version which only assumes one pattern is being searched
for but it's not hard to modify
1) Calculate pattern hash
2) Step through the text one character at a time passing a window with the same
length as the pattern
calculating the hash of the text within the window compare it with the hash
of the pattern. Only testing equality if the hashes match
"""
p_len = len(pattern)
t_len = len(text)
if p_len > t_len:
return False
p_hash = 0
text_hash = 0
modulus_power = 1
# Calculating the hash of pattern and substring of text
for i in range(p_len):
p_hash = (ord(pattern[i]) + p_hash * alphabet_size) % modulus
text_hash = (ord(text[i]) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
modulus_power = (modulus_power * alphabet_size) % modulus
for i in range(0, t_len - p_len + 1):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
text_hash = (
(text_hash - ord(text[i]) * modulus_power) * alphabet_size
+ ord(text[i + p_len])
) % modulus
return False
def test_rabin_karp() -> None:
"""
>>> test_rabin_karp()
Success.
"""
# Test 1)
pattern = "abc1abc12"
text1 = "alskfjaldsabc1abc1abc12k23adsfabcabc"
text2 = "alskfjaldsk23adsfabcabc"
assert rabin_karp(pattern, text1) and not rabin_karp(pattern, text2)
# Test 2)
pattern = "ABABX"
text = "ABABZABABYABABX"
assert rabin_karp(pattern, text)
# Test 3)
pattern = "AAAB"
text = "ABAAAAAB"
assert rabin_karp(pattern, text)
# Test 4)
pattern = "abcdabcy"
text = "abcxabcdabxabcdabcdabcy"
assert rabin_karp(pattern, text)
# Test 5)
pattern = "Lü"
text = "Lüsai"
assert rabin_karp(pattern, text)
pattern = "Lue"
assert not rabin_karp(pattern, text)
print("Success.")
if __name__ == "__main__":
test_rabin_karp()
| # Numbers of alphabet which we call base
alphabet_size = 256
# Modulus to hash a string
modulus = 1000003
def rabin_karp(pattern: str, text: str) -> bool:
"""
The Rabin-Karp Algorithm for finding a pattern within a piece of text
with complexity O(nm), most efficient when it is used with multiple patterns
as it is able to check if any of a set of patterns match a section of text in o(1)
given the precomputed hashes.
This will be the simple version which only assumes one pattern is being searched
for but it's not hard to modify
1) Calculate pattern hash
2) Step through the text one character at a time passing a window with the same
length as the pattern
calculating the hash of the text within the window compare it with the hash
of the pattern. Only testing equality if the hashes match
"""
p_len = len(pattern)
t_len = len(text)
if p_len > t_len:
return False
p_hash = 0
text_hash = 0
modulus_power = 1
# Calculating the hash of pattern and substring of text
for i in range(p_len):
p_hash = (ord(pattern[i]) + p_hash * alphabet_size) % modulus
text_hash = (ord(text[i]) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
modulus_power = (modulus_power * alphabet_size) % modulus
for i in range(0, t_len - p_len + 1):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
text_hash = (
(text_hash - ord(text[i]) * modulus_power) * alphabet_size
+ ord(text[i + p_len])
) % modulus
return False
def test_rabin_karp() -> None:
"""
>>> test_rabin_karp()
Success.
"""
# Test 1)
pattern = "abc1abc12"
text1 = "alskfjaldsabc1abc1abc12k23adsfabcabc"
text2 = "alskfjaldsk23adsfabcabc"
assert rabin_karp(pattern, text1) and not rabin_karp(pattern, text2)
# Test 2)
pattern = "ABABX"
text = "ABABZABABYABABX"
assert rabin_karp(pattern, text)
# Test 3)
pattern = "AAAB"
text = "ABAAAAAB"
assert rabin_karp(pattern, text)
# Test 4)
pattern = "abcdabcy"
text = "abcxabcdabxabcdabcdabcy"
assert rabin_karp(pattern, text)
# Test 5)
pattern = "Lü"
text = "Lüsai"
assert rabin_karp(pattern, text)
pattern = "Lue"
assert not rabin_karp(pattern, text)
print("Success.")
if __name__ == "__main__":
test_rabin_karp()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| class Things:
def __init__(self, name, value, weight):
self.name = name
self.value = value
self.weight = weight
def __repr__(self):
return f"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"
def get_value(self):
return self.value
def get_name(self):
return self.name
def get_weight(self):
return self.weight
def value_weight(self):
return self.value / self.weight
def build_menu(name, value, weight):
menu = []
for i in range(len(value)):
menu.append(Things(name[i], value[i], weight[i]))
return menu
def greedy(item, max_cost, key_func):
items_copy = sorted(item, key=key_func, reverse=True)
result = []
total_value, total_cost = 0.0, 0.0
for i in range(len(items_copy)):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i])
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def test_greedy():
"""
>>> food = ["Burger", "Pizza", "Coca Cola", "Rice",
... "Sambhar", "Chicken", "Fries", "Milk"]
>>> value = [80, 100, 60, 70, 50, 110, 90, 60]
>>> weight = [40, 60, 40, 70, 100, 85, 55, 70]
>>> foods = build_menu(food, value, weight)
>>> foods # doctest: +NORMALIZE_WHITESPACE
[Things(Burger, 80, 40), Things(Pizza, 100, 60), Things(Coca Cola, 60, 40),
Things(Rice, 70, 70), Things(Sambhar, 50, 100), Things(Chicken, 110, 85),
Things(Fries, 90, 55), Things(Milk, 60, 70)]
>>> greedy(foods, 500, Things.get_value) # doctest: +NORMALIZE_WHITESPACE
([Things(Chicken, 110, 85), Things(Pizza, 100, 60), Things(Fries, 90, 55),
Things(Burger, 80, 40), Things(Rice, 70, 70), Things(Coca Cola, 60, 40),
Things(Milk, 60, 70)], 570.0)
"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| class Things:
def __init__(self, name, value, weight):
self.name = name
self.value = value
self.weight = weight
def __repr__(self):
return f"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"
def get_value(self):
return self.value
def get_name(self):
return self.name
def get_weight(self):
return self.weight
def value_weight(self):
return self.value / self.weight
def build_menu(name, value, weight):
menu = []
for i in range(len(value)):
menu.append(Things(name[i], value[i], weight[i]))
return menu
def greedy(item, max_cost, key_func):
items_copy = sorted(item, key=key_func, reverse=True)
result = []
total_value, total_cost = 0.0, 0.0
for i in range(len(items_copy)):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i])
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def test_greedy():
"""
>>> food = ["Burger", "Pizza", "Coca Cola", "Rice",
... "Sambhar", "Chicken", "Fries", "Milk"]
>>> value = [80, 100, 60, 70, 50, 110, 90, 60]
>>> weight = [40, 60, 40, 70, 100, 85, 55, 70]
>>> foods = build_menu(food, value, weight)
>>> foods # doctest: +NORMALIZE_WHITESPACE
[Things(Burger, 80, 40), Things(Pizza, 100, 60), Things(Coca Cola, 60, 40),
Things(Rice, 70, 70), Things(Sambhar, 50, 100), Things(Chicken, 110, 85),
Things(Fries, 90, 55), Things(Milk, 60, 70)]
>>> greedy(foods, 500, Things.get_value) # doctest: +NORMALIZE_WHITESPACE
([Things(Chicken, 110, 85), Things(Pizza, 100, 60), Things(Fries, 90, 55),
Things(Burger, 80, 40), Things(Rice, 70, 70), Things(Coca Cola, 60, 40),
Things(Milk, 60, 70)], 570.0)
"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
arr = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
expect = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def next_greatest_element_slow(arr: list[float]) -> list[float]:
"""
Get the Next Greatest Element (NGE) for all elements in a list.
Maximum element present after the current one which is also greater than the
current one.
>>> next_greatest_element_slow(arr) == expect
True
"""
result = []
arr_size = len(arr)
for i in range(arr_size):
next_element: float = -1
for j in range(i + 1, arr_size):
if arr[i] < arr[j]:
next_element = arr[j]
break
result.append(next_element)
return result
def next_greatest_element_fast(arr: list[float]) -> list[float]:
"""
Like next_greatest_element_slow() but changes the loops to use
enumerate() instead of range(len()) for the outer loop and
for in a slice of arr for the inner loop.
>>> next_greatest_element_fast(arr) == expect
True
"""
result = []
for i, outer in enumerate(arr):
next_item: float = -1
for inner in arr[i + 1 :]:
if outer < inner:
next_item = inner
break
result.append(next_item)
return result
def next_greatest_element(arr: list[float]) -> list[float]:
"""
Get the Next Greatest Element (NGE) for all elements in a list.
Maximum element present after the current one which is also greater than the
current one.
A naive way to solve this is to take two loops and check for the next bigger
number but that will make the time complexity as O(n^2). The better way to solve
this would be to use a stack to keep track of maximum number giving a linear time
solution.
>>> next_greatest_element(arr) == expect
True
"""
arr_size = len(arr)
stack: list[float] = []
result: list[float] = [-1] * arr_size
for index in reversed(range(arr_size)):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
result[index] = stack[-1]
stack.append(arr[index])
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
setup = (
"from __main__ import arr, next_greatest_element_slow, "
"next_greatest_element_fast, next_greatest_element"
)
print(
"next_greatest_element_slow():",
timeit("next_greatest_element_slow(arr)", setup=setup),
)
print(
"next_greatest_element_fast():",
timeit("next_greatest_element_fast(arr)", setup=setup),
)
print(
" next_greatest_element():",
timeit("next_greatest_element(arr)", setup=setup),
)
| from __future__ import annotations
arr = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
expect = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def next_greatest_element_slow(arr: list[float]) -> list[float]:
"""
Get the Next Greatest Element (NGE) for all elements in a list.
Maximum element present after the current one which is also greater than the
current one.
>>> next_greatest_element_slow(arr) == expect
True
"""
result = []
arr_size = len(arr)
for i in range(arr_size):
next_element: float = -1
for j in range(i + 1, arr_size):
if arr[i] < arr[j]:
next_element = arr[j]
break
result.append(next_element)
return result
def next_greatest_element_fast(arr: list[float]) -> list[float]:
"""
Like next_greatest_element_slow() but changes the loops to use
enumerate() instead of range(len()) for the outer loop and
for in a slice of arr for the inner loop.
>>> next_greatest_element_fast(arr) == expect
True
"""
result = []
for i, outer in enumerate(arr):
next_item: float = -1
for inner in arr[i + 1 :]:
if outer < inner:
next_item = inner
break
result.append(next_item)
return result
def next_greatest_element(arr: list[float]) -> list[float]:
"""
Get the Next Greatest Element (NGE) for all elements in a list.
Maximum element present after the current one which is also greater than the
current one.
A naive way to solve this is to take two loops and check for the next bigger
number but that will make the time complexity as O(n^2). The better way to solve
this would be to use a stack to keep track of maximum number giving a linear time
solution.
>>> next_greatest_element(arr) == expect
True
"""
arr_size = len(arr)
stack: list[float] = []
result: list[float] = [-1] * arr_size
for index in reversed(range(arr_size)):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
result[index] = stack[-1]
stack.append(arr[index])
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
setup = (
"from __main__ import arr, next_greatest_element_slow, "
"next_greatest_element_fast, next_greatest_element"
)
print(
"next_greatest_element_slow():",
timeit("next_greatest_element_slow(arr)", setup=setup),
)
print(
"next_greatest_element_fast():",
timeit("next_greatest_element_fast(arr)", setup=setup),
)
print(
" next_greatest_element():",
timeit("next_greatest_element(arr)", setup=setup),
)
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # https://en.wikipedia.org/wiki/Trifid_cipher
from __future__ import annotations
def __encrypt_part(message_part: str, character_to_number: dict[str, str]) -> str:
one, two, three = "", "", ""
tmp = []
for character in message_part:
tmp.append(character_to_number[character])
for each in tmp:
one += each[0]
two += each[1]
three += each[2]
return one + two + three
def __decrypt_part(
message_part: str, character_to_number: dict[str, str]
) -> tuple[str, str, str]:
tmp, this_part = "", ""
result = []
for character in message_part:
this_part += character_to_number[character]
for digit in this_part:
tmp += digit
if len(tmp) == len(message_part):
result.append(tmp)
tmp = ""
return result[0], result[1], result[2]
def __prepare(
message: str, alphabet: str
) -> tuple[str, str, dict[str, str], dict[str, str]]:
# Validate message and alphabet, set to upper and remove spaces
alphabet = alphabet.replace(" ", "").upper()
message = message.replace(" ", "").upper()
# Check length and characters
if len(alphabet) != 27:
raise KeyError("Length of alphabet has to be 27.")
for each in message:
if each not in alphabet:
raise ValueError("Each message character has to be included in alphabet!")
# Generate dictionares
numbers = (
"111",
"112",
"113",
"121",
"122",
"123",
"131",
"132",
"133",
"211",
"212",
"213",
"221",
"222",
"223",
"231",
"232",
"233",
"311",
"312",
"313",
"321",
"322",
"323",
"331",
"332",
"333",
)
character_to_number = {}
number_to_character = {}
for letter, number in zip(alphabet, numbers):
character_to_number[letter] = number
number_to_character[number] = letter
return message, alphabet, character_to_number, number_to_character
def encrypt_message(
message: str, alphabet: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period: int = 5
) -> str:
message, alphabet, character_to_number, number_to_character = __prepare(
message, alphabet
)
encrypted, encrypted_numeric = "", ""
for i in range(0, len(message) + 1, period):
encrypted_numeric += __encrypt_part(
message[i : i + period], character_to_number
)
for i in range(0, len(encrypted_numeric), 3):
encrypted += number_to_character[encrypted_numeric[i : i + 3]]
return encrypted
def decrypt_message(
message: str, alphabet: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period: int = 5
) -> str:
message, alphabet, character_to_number, number_to_character = __prepare(
message, alphabet
)
decrypted_numeric = []
decrypted = ""
for i in range(0, len(message) + 1, period):
a, b, c = __decrypt_part(message[i : i + period], character_to_number)
for j in range(0, len(a)):
decrypted_numeric.append(a[j] + b[j] + c[j])
for each in decrypted_numeric:
decrypted += number_to_character[each]
return decrypted
if __name__ == "__main__":
msg = "DEFEND THE EAST WALL OF THE CASTLE."
encrypted = encrypt_message(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
decrypted = decrypt_message(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
print(f"Encrypted: {encrypted}\nDecrypted: {decrypted}")
| # https://en.wikipedia.org/wiki/Trifid_cipher
from __future__ import annotations
def __encrypt_part(message_part: str, character_to_number: dict[str, str]) -> str:
one, two, three = "", "", ""
tmp = []
for character in message_part:
tmp.append(character_to_number[character])
for each in tmp:
one += each[0]
two += each[1]
three += each[2]
return one + two + three
def __decrypt_part(
message_part: str, character_to_number: dict[str, str]
) -> tuple[str, str, str]:
tmp, this_part = "", ""
result = []
for character in message_part:
this_part += character_to_number[character]
for digit in this_part:
tmp += digit
if len(tmp) == len(message_part):
result.append(tmp)
tmp = ""
return result[0], result[1], result[2]
def __prepare(
message: str, alphabet: str
) -> tuple[str, str, dict[str, str], dict[str, str]]:
# Validate message and alphabet, set to upper and remove spaces
alphabet = alphabet.replace(" ", "").upper()
message = message.replace(" ", "").upper()
# Check length and characters
if len(alphabet) != 27:
raise KeyError("Length of alphabet has to be 27.")
for each in message:
if each not in alphabet:
raise ValueError("Each message character has to be included in alphabet!")
# Generate dictionares
numbers = (
"111",
"112",
"113",
"121",
"122",
"123",
"131",
"132",
"133",
"211",
"212",
"213",
"221",
"222",
"223",
"231",
"232",
"233",
"311",
"312",
"313",
"321",
"322",
"323",
"331",
"332",
"333",
)
character_to_number = {}
number_to_character = {}
for letter, number in zip(alphabet, numbers):
character_to_number[letter] = number
number_to_character[number] = letter
return message, alphabet, character_to_number, number_to_character
def encrypt_message(
message: str, alphabet: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period: int = 5
) -> str:
message, alphabet, character_to_number, number_to_character = __prepare(
message, alphabet
)
encrypted, encrypted_numeric = "", ""
for i in range(0, len(message) + 1, period):
encrypted_numeric += __encrypt_part(
message[i : i + period], character_to_number
)
for i in range(0, len(encrypted_numeric), 3):
encrypted += number_to_character[encrypted_numeric[i : i + 3]]
return encrypted
def decrypt_message(
message: str, alphabet: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period: int = 5
) -> str:
message, alphabet, character_to_number, number_to_character = __prepare(
message, alphabet
)
decrypted_numeric = []
decrypted = ""
for i in range(0, len(message) + 1, period):
a, b, c = __decrypt_part(message[i : i + period], character_to_number)
for j in range(0, len(a)):
decrypted_numeric.append(a[j] + b[j] + c[j])
for each in decrypted_numeric:
decrypted += number_to_character[each]
return decrypted
if __name__ == "__main__":
msg = "DEFEND THE EAST WALL OF THE CASTLE."
encrypted = encrypt_message(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
decrypted = decrypt_message(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
print(f"Encrypted: {encrypted}\nDecrypted: {decrypted}")
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Primality Testing with the Rabin-Miller Algorithm
import random
def rabin_miller(num: int) -> bool:
s = num - 1
t = 0
while s % 2 == 0:
s = s // 2
t += 1
for _ in range(5):
a = random.randrange(2, num - 1)
v = pow(a, s, num)
if v != 1:
i = 0
while v != (num - 1):
if i == t - 1:
return False
else:
i = i + 1
v = (v**2) % num
return True
def is_prime_low_num(num: int) -> bool:
if num < 2:
return False
low_primes = [
2,
3,
5,
7,
11,
13,
17,
19,
23,
29,
31,
37,
41,
43,
47,
53,
59,
61,
67,
71,
73,
79,
83,
89,
97,
101,
103,
107,
109,
113,
127,
131,
137,
139,
149,
151,
157,
163,
167,
173,
179,
181,
191,
193,
197,
199,
211,
223,
227,
229,
233,
239,
241,
251,
257,
263,
269,
271,
277,
281,
283,
293,
307,
311,
313,
317,
331,
337,
347,
349,
353,
359,
367,
373,
379,
383,
389,
397,
401,
409,
419,
421,
431,
433,
439,
443,
449,
457,
461,
463,
467,
479,
487,
491,
499,
503,
509,
521,
523,
541,
547,
557,
563,
569,
571,
577,
587,
593,
599,
601,
607,
613,
617,
619,
631,
641,
643,
647,
653,
659,
661,
673,
677,
683,
691,
701,
709,
719,
727,
733,
739,
743,
751,
757,
761,
769,
773,
787,
797,
809,
811,
821,
823,
827,
829,
839,
853,
857,
859,
863,
877,
881,
883,
887,
907,
911,
919,
929,
937,
941,
947,
953,
967,
971,
977,
983,
991,
997,
]
if num in low_primes:
return True
for prime in low_primes:
if (num % prime) == 0:
return False
return rabin_miller(num)
def generate_large_prime(keysize: int = 1024) -> int:
while True:
num = random.randrange(2 ** (keysize - 1), 2 ** (keysize))
if is_prime_low_num(num):
return num
if __name__ == "__main__":
num = generate_large_prime()
print(("Prime number:", num))
print(("is_prime_low_num:", is_prime_low_num(num)))
| # Primality Testing with the Rabin-Miller Algorithm
import random
def rabin_miller(num: int) -> bool:
s = num - 1
t = 0
while s % 2 == 0:
s = s // 2
t += 1
for _ in range(5):
a = random.randrange(2, num - 1)
v = pow(a, s, num)
if v != 1:
i = 0
while v != (num - 1):
if i == t - 1:
return False
else:
i = i + 1
v = (v**2) % num
return True
def is_prime_low_num(num: int) -> bool:
if num < 2:
return False
low_primes = [
2,
3,
5,
7,
11,
13,
17,
19,
23,
29,
31,
37,
41,
43,
47,
53,
59,
61,
67,
71,
73,
79,
83,
89,
97,
101,
103,
107,
109,
113,
127,
131,
137,
139,
149,
151,
157,
163,
167,
173,
179,
181,
191,
193,
197,
199,
211,
223,
227,
229,
233,
239,
241,
251,
257,
263,
269,
271,
277,
281,
283,
293,
307,
311,
313,
317,
331,
337,
347,
349,
353,
359,
367,
373,
379,
383,
389,
397,
401,
409,
419,
421,
431,
433,
439,
443,
449,
457,
461,
463,
467,
479,
487,
491,
499,
503,
509,
521,
523,
541,
547,
557,
563,
569,
571,
577,
587,
593,
599,
601,
607,
613,
617,
619,
631,
641,
643,
647,
653,
659,
661,
673,
677,
683,
691,
701,
709,
719,
727,
733,
739,
743,
751,
757,
761,
769,
773,
787,
797,
809,
811,
821,
823,
827,
829,
839,
853,
857,
859,
863,
877,
881,
883,
887,
907,
911,
919,
929,
937,
941,
947,
953,
967,
971,
977,
983,
991,
997,
]
if num in low_primes:
return True
for prime in low_primes:
if (num % prime) == 0:
return False
return rabin_miller(num)
def generate_large_prime(keysize: int = 1024) -> int:
while True:
num = random.randrange(2 ** (keysize - 1), 2 ** (keysize))
if is_prime_low_num(num):
return num
if __name__ == "__main__":
num = generate_large_prime()
print(("Prime number:", num))
print(("is_prime_low_num:", is_prime_low_num(num)))
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Find the area of various geometric shapes
Wikipedia reference: https://en.wikipedia.org/wiki/Area
"""
from math import pi, sqrt, tan
def surface_area_cube(side_length: float) -> float:
"""
Calculate the Surface Area of a Cube.
>>> surface_area_cube(1)
6
>>> surface_area_cube(1.6)
15.360000000000003
>>> surface_area_cube(0)
0
>>> surface_area_cube(3)
54
>>> surface_area_cube(-1)
Traceback (most recent call last):
...
ValueError: surface_area_cube() only accepts non-negative values
"""
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def surface_area_cuboid(length: float, breadth: float, height: float) -> float:
"""
Calculate the Surface Area of a Cuboid.
>>> surface_area_cuboid(1, 2, 3)
22
>>> surface_area_cuboid(0, 0, 0)
0
>>> surface_area_cuboid(1.6, 2.6, 3.6)
38.56
>>> surface_area_cuboid(-1, 2, 3)
Traceback (most recent call last):
...
ValueError: surface_area_cuboid() only accepts non-negative values
>>> surface_area_cuboid(1, -2, 3)
Traceback (most recent call last):
...
ValueError: surface_area_cuboid() only accepts non-negative values
>>> surface_area_cuboid(1, 2, -3)
Traceback (most recent call last):
...
ValueError: surface_area_cuboid() only accepts non-negative values
"""
if length < 0 or breadth < 0 or height < 0:
raise ValueError("surface_area_cuboid() only accepts non-negative values")
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def surface_area_sphere(radius: float) -> float:
"""
Calculate the Surface Area of a Sphere.
Wikipedia reference: https://en.wikipedia.org/wiki/Sphere
Formula: 4 * pi * r^2
>>> surface_area_sphere(5)
314.1592653589793
>>> surface_area_sphere(1)
12.566370614359172
>>> surface_area_sphere(1.6)
32.169908772759484
>>> surface_area_sphere(0)
0.0
>>> surface_area_sphere(-1)
Traceback (most recent call last):
...
ValueError: surface_area_sphere() only accepts non-negative values
"""
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values")
return 4 * pi * radius**2
def surface_area_hemisphere(radius: float) -> float:
"""
Calculate the Surface Area of a Hemisphere.
Formula: 3 * pi * r^2
>>> surface_area_hemisphere(5)
235.61944901923448
>>> surface_area_hemisphere(1)
9.42477796076938
>>> surface_area_hemisphere(0)
0.0
>>> surface_area_hemisphere(1.1)
11.40398133253095
>>> surface_area_hemisphere(-1)
Traceback (most recent call last):
...
ValueError: surface_area_hemisphere() only accepts non-negative values
"""
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values")
return 3 * pi * radius**2
def surface_area_cone(radius: float, height: float) -> float:
"""
Calculate the Surface Area of a Cone.
Wikipedia reference: https://en.wikipedia.org/wiki/Cone
Formula: pi * r * (r + (h ** 2 + r ** 2) ** 0.5)
>>> surface_area_cone(10, 24)
1130.9733552923256
>>> surface_area_cone(6, 8)
301.59289474462014
>>> surface_area_cone(1.6, 2.6)
23.387862992395807
>>> surface_area_cone(0, 0)
0.0
>>> surface_area_cone(-1, -2)
Traceback (most recent call last):
...
ValueError: surface_area_cone() only accepts non-negative values
>>> surface_area_cone(1, -2)
Traceback (most recent call last):
...
ValueError: surface_area_cone() only accepts non-negative values
>>> surface_area_cone(-1, 2)
Traceback (most recent call last):
...
ValueError: surface_area_cone() only accepts non-negative values
"""
if radius < 0 or height < 0:
raise ValueError("surface_area_cone() only accepts non-negative values")
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def surface_area_conical_frustum(
radius_1: float, radius_2: float, height: float
) -> float:
"""
Calculate the Surface Area of a Conical Frustum.
>>> surface_area_conical_frustum(1, 2, 3)
45.511728065337266
>>> surface_area_conical_frustum(4, 5, 6)
300.7913575056268
>>> surface_area_conical_frustum(0, 0, 0)
0.0
>>> surface_area_conical_frustum(1.6, 2.6, 3.6)
78.57907060751548
>>> surface_area_conical_frustum(-1, 2, 3)
Traceback (most recent call last):
...
ValueError: surface_area_conical_frustum() only accepts non-negative values
>>> surface_area_conical_frustum(1, -2, 3)
Traceback (most recent call last):
...
ValueError: surface_area_conical_frustum() only accepts non-negative values
>>> surface_area_conical_frustum(1, 2, -3)
Traceback (most recent call last):
...
ValueError: surface_area_conical_frustum() only accepts non-negative values
"""
if radius_1 < 0 or radius_2 < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values"
)
slant_height = (height**2 + (radius_1 - radius_2) ** 2) ** 0.5
return pi * ((slant_height * (radius_1 + radius_2)) + radius_1**2 + radius_2**2)
def surface_area_cylinder(radius: float, height: float) -> float:
"""
Calculate the Surface Area of a Cylinder.
Wikipedia reference: https://en.wikipedia.org/wiki/Cylinder
Formula: 2 * pi * r * (h + r)
>>> surface_area_cylinder(7, 10)
747.6990515543707
>>> surface_area_cylinder(1.6, 2.6)
42.22300526424682
>>> surface_area_cylinder(0, 0)
0.0
>>> surface_area_cylinder(6, 8)
527.7875658030853
>>> surface_area_cylinder(-1, -2)
Traceback (most recent call last):
...
ValueError: surface_area_cylinder() only accepts non-negative values
>>> surface_area_cylinder(1, -2)
Traceback (most recent call last):
...
ValueError: surface_area_cylinder() only accepts non-negative values
>>> surface_area_cylinder(-1, 2)
Traceback (most recent call last):
...
ValueError: surface_area_cylinder() only accepts non-negative values
"""
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values")
return 2 * pi * radius * (height + radius)
def surface_area_torus(torus_radius: float, tube_radius: float) -> float:
"""Calculate the Area of a Torus.
Wikipedia reference: https://en.wikipedia.org/wiki/Torus
:return 4pi^2 * torus_radius * tube_radius
>>> surface_area_torus(1, 1)
39.47841760435743
>>> surface_area_torus(4, 3)
473.7410112522892
>>> surface_area_torus(3, 4)
Traceback (most recent call last):
...
ValueError: surface_area_torus() does not support spindle or self intersecting tori
>>> surface_area_torus(1.6, 1.6)
101.06474906715503
>>> surface_area_torus(0, 0)
0.0
>>> surface_area_torus(-1, 1)
Traceback (most recent call last):
...
ValueError: surface_area_torus() only accepts non-negative values
>>> surface_area_torus(1, -1)
Traceback (most recent call last):
...
ValueError: surface_area_torus() only accepts non-negative values
"""
if torus_radius < 0 or tube_radius < 0:
raise ValueError("surface_area_torus() only accepts non-negative values")
if torus_radius < tube_radius:
raise ValueError(
"surface_area_torus() does not support spindle or self intersecting tori"
)
return 4 * pow(pi, 2) * torus_radius * tube_radius
def area_rectangle(length: float, width: float) -> float:
"""
Calculate the area of a rectangle.
>>> area_rectangle(10, 20)
200
>>> area_rectangle(1.6, 2.6)
4.16
>>> area_rectangle(0, 0)
0
>>> area_rectangle(-1, -2)
Traceback (most recent call last):
...
ValueError: area_rectangle() only accepts non-negative values
>>> area_rectangle(1, -2)
Traceback (most recent call last):
...
ValueError: area_rectangle() only accepts non-negative values
>>> area_rectangle(-1, 2)
Traceback (most recent call last):
...
ValueError: area_rectangle() only accepts non-negative values
"""
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values")
return length * width
def area_square(side_length: float) -> float:
"""
Calculate the area of a square.
>>> area_square(10)
100
>>> area_square(0)
0
>>> area_square(1.6)
2.5600000000000005
>>> area_square(-1)
Traceback (most recent call last):
...
ValueError: area_square() only accepts non-negative values
"""
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values")
return side_length**2
def area_triangle(base: float, height: float) -> float:
"""
Calculate the area of a triangle given the base and height.
>>> area_triangle(10, 10)
50.0
>>> area_triangle(1.6, 2.6)
2.08
>>> area_triangle(0, 0)
0.0
>>> area_triangle(-1, -2)
Traceback (most recent call last):
...
ValueError: area_triangle() only accepts non-negative values
>>> area_triangle(1, -2)
Traceback (most recent call last):
...
ValueError: area_triangle() only accepts non-negative values
>>> area_triangle(-1, 2)
Traceback (most recent call last):
...
ValueError: area_triangle() only accepts non-negative values
"""
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values")
return (base * height) / 2
def area_triangle_three_sides(side1: float, side2: float, side3: float) -> float:
"""
Calculate area of triangle when the length of 3 sides are known.
This function uses Heron's formula: https://en.wikipedia.org/wiki/Heron%27s_formula
>>> area_triangle_three_sides(5, 12, 13)
30.0
>>> area_triangle_three_sides(10, 11, 12)
51.521233486786784
>>> area_triangle_three_sides(0, 0, 0)
0.0
>>> area_triangle_three_sides(1.6, 2.6, 3.6)
1.8703742940919619
>>> area_triangle_three_sides(-1, -2, -1)
Traceback (most recent call last):
...
ValueError: area_triangle_three_sides() only accepts non-negative values
>>> area_triangle_three_sides(1, -2, 1)
Traceback (most recent call last):
...
ValueError: area_triangle_three_sides() only accepts non-negative values
>>> area_triangle_three_sides(2, 4, 7)
Traceback (most recent call last):
...
ValueError: Given three sides do not form a triangle
>>> area_triangle_three_sides(2, 7, 4)
Traceback (most recent call last):
...
ValueError: Given three sides do not form a triangle
>>> area_triangle_three_sides(7, 2, 4)
Traceback (most recent call last):
...
ValueError: Given three sides do not form a triangle
"""
if side1 < 0 or side2 < 0 or side3 < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values")
elif side1 + side2 < side3 or side1 + side3 < side2 or side2 + side3 < side1:
raise ValueError("Given three sides do not form a triangle")
semi_perimeter = (side1 + side2 + side3) / 2
area = sqrt(
semi_perimeter
* (semi_perimeter - side1)
* (semi_perimeter - side2)
* (semi_perimeter - side3)
)
return area
def area_parallelogram(base: float, height: float) -> float:
"""
Calculate the area of a parallelogram.
>>> area_parallelogram(10, 20)
200
>>> area_parallelogram(1.6, 2.6)
4.16
>>> area_parallelogram(0, 0)
0
>>> area_parallelogram(-1, -2)
Traceback (most recent call last):
...
ValueError: area_parallelogram() only accepts non-negative values
>>> area_parallelogram(1, -2)
Traceback (most recent call last):
...
ValueError: area_parallelogram() only accepts non-negative values
>>> area_parallelogram(-1, 2)
Traceback (most recent call last):
...
ValueError: area_parallelogram() only accepts non-negative values
"""
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values")
return base * height
def area_trapezium(base1: float, base2: float, height: float) -> float:
"""
Calculate the area of a trapezium.
>>> area_trapezium(10, 20, 30)
450.0
>>> area_trapezium(1.6, 2.6, 3.6)
7.5600000000000005
>>> area_trapezium(0, 0, 0)
0.0
>>> area_trapezium(-1, -2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(-1, 2, 3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(1, -2, 3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(1, 2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(-1, -2, 3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(1, -2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(-1, 2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
"""
if base1 < 0 or base2 < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values")
return 1 / 2 * (base1 + base2) * height
def area_circle(radius: float) -> float:
"""
Calculate the area of a circle.
>>> area_circle(20)
1256.6370614359173
>>> area_circle(1.6)
8.042477193189871
>>> area_circle(0)
0.0
>>> area_circle(-1)
Traceback (most recent call last):
...
ValueError: area_circle() only accepts non-negative values
"""
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values")
return pi * radius**2
def area_ellipse(radius_x: float, radius_y: float) -> float:
"""
Calculate the area of a ellipse.
>>> area_ellipse(10, 10)
314.1592653589793
>>> area_ellipse(10, 20)
628.3185307179587
>>> area_ellipse(0, 0)
0.0
>>> area_ellipse(1.6, 2.6)
13.06902543893354
>>> area_ellipse(-10, 20)
Traceback (most recent call last):
...
ValueError: area_ellipse() only accepts non-negative values
>>> area_ellipse(10, -20)
Traceback (most recent call last):
...
ValueError: area_ellipse() only accepts non-negative values
>>> area_ellipse(-10, -20)
Traceback (most recent call last):
...
ValueError: area_ellipse() only accepts non-negative values
"""
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values")
return pi * radius_x * radius_y
def area_rhombus(diagonal_1: float, diagonal_2: float) -> float:
"""
Calculate the area of a rhombus.
>>> area_rhombus(10, 20)
100.0
>>> area_rhombus(1.6, 2.6)
2.08
>>> area_rhombus(0, 0)
0.0
>>> area_rhombus(-1, -2)
Traceback (most recent call last):
...
ValueError: area_rhombus() only accepts non-negative values
>>> area_rhombus(1, -2)
Traceback (most recent call last):
...
ValueError: area_rhombus() only accepts non-negative values
>>> area_rhombus(-1, 2)
Traceback (most recent call last):
...
ValueError: area_rhombus() only accepts non-negative values
"""
if diagonal_1 < 0 or diagonal_2 < 0:
raise ValueError("area_rhombus() only accepts non-negative values")
return 1 / 2 * diagonal_1 * diagonal_2
def area_reg_polygon(sides: int, length: float) -> float:
"""
Calculate the area of a regular polygon.
Wikipedia reference: https://en.wikipedia.org/wiki/Polygon#Regular_polygons
Formula: (n*s^2*cot(pi/n))/4
>>> area_reg_polygon(3, 10)
43.301270189221945
>>> area_reg_polygon(4, 10)
100.00000000000001
>>> area_reg_polygon(0, 0)
Traceback (most recent call last):
...
ValueError: area_reg_polygon() only accepts integers greater than or equal to \
three as number of sides
>>> area_reg_polygon(-1, -2)
Traceback (most recent call last):
...
ValueError: area_reg_polygon() only accepts integers greater than or equal to \
three as number of sides
>>> area_reg_polygon(5, -2)
Traceback (most recent call last):
...
ValueError: area_reg_polygon() only accepts non-negative values as \
length of a side
>>> area_reg_polygon(-1, 2)
Traceback (most recent call last):
...
ValueError: area_reg_polygon() only accepts integers greater than or equal to \
three as number of sides
"""
if not isinstance(sides, int) or sides < 3:
raise ValueError(
"area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides"
)
elif length < 0:
raise ValueError(
"area_reg_polygon() only accepts non-negative values as \
length of a side"
)
return (sides * length**2) / (4 * tan(pi / sides))
return (sides * length**2) / (4 * tan(pi / sides))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print("\nSurface Areas of various geometric shapes: \n")
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| """
Find the area of various geometric shapes
Wikipedia reference: https://en.wikipedia.org/wiki/Area
"""
from math import pi, sqrt, tan
def surface_area_cube(side_length: float) -> float:
"""
Calculate the Surface Area of a Cube.
>>> surface_area_cube(1)
6
>>> surface_area_cube(1.6)
15.360000000000003
>>> surface_area_cube(0)
0
>>> surface_area_cube(3)
54
>>> surface_area_cube(-1)
Traceback (most recent call last):
...
ValueError: surface_area_cube() only accepts non-negative values
"""
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def surface_area_cuboid(length: float, breadth: float, height: float) -> float:
"""
Calculate the Surface Area of a Cuboid.
>>> surface_area_cuboid(1, 2, 3)
22
>>> surface_area_cuboid(0, 0, 0)
0
>>> surface_area_cuboid(1.6, 2.6, 3.6)
38.56
>>> surface_area_cuboid(-1, 2, 3)
Traceback (most recent call last):
...
ValueError: surface_area_cuboid() only accepts non-negative values
>>> surface_area_cuboid(1, -2, 3)
Traceback (most recent call last):
...
ValueError: surface_area_cuboid() only accepts non-negative values
>>> surface_area_cuboid(1, 2, -3)
Traceback (most recent call last):
...
ValueError: surface_area_cuboid() only accepts non-negative values
"""
if length < 0 or breadth < 0 or height < 0:
raise ValueError("surface_area_cuboid() only accepts non-negative values")
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def surface_area_sphere(radius: float) -> float:
"""
Calculate the Surface Area of a Sphere.
Wikipedia reference: https://en.wikipedia.org/wiki/Sphere
Formula: 4 * pi * r^2
>>> surface_area_sphere(5)
314.1592653589793
>>> surface_area_sphere(1)
12.566370614359172
>>> surface_area_sphere(1.6)
32.169908772759484
>>> surface_area_sphere(0)
0.0
>>> surface_area_sphere(-1)
Traceback (most recent call last):
...
ValueError: surface_area_sphere() only accepts non-negative values
"""
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values")
return 4 * pi * radius**2
def surface_area_hemisphere(radius: float) -> float:
"""
Calculate the Surface Area of a Hemisphere.
Formula: 3 * pi * r^2
>>> surface_area_hemisphere(5)
235.61944901923448
>>> surface_area_hemisphere(1)
9.42477796076938
>>> surface_area_hemisphere(0)
0.0
>>> surface_area_hemisphere(1.1)
11.40398133253095
>>> surface_area_hemisphere(-1)
Traceback (most recent call last):
...
ValueError: surface_area_hemisphere() only accepts non-negative values
"""
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values")
return 3 * pi * radius**2
def surface_area_cone(radius: float, height: float) -> float:
"""
Calculate the Surface Area of a Cone.
Wikipedia reference: https://en.wikipedia.org/wiki/Cone
Formula: pi * r * (r + (h ** 2 + r ** 2) ** 0.5)
>>> surface_area_cone(10, 24)
1130.9733552923256
>>> surface_area_cone(6, 8)
301.59289474462014
>>> surface_area_cone(1.6, 2.6)
23.387862992395807
>>> surface_area_cone(0, 0)
0.0
>>> surface_area_cone(-1, -2)
Traceback (most recent call last):
...
ValueError: surface_area_cone() only accepts non-negative values
>>> surface_area_cone(1, -2)
Traceback (most recent call last):
...
ValueError: surface_area_cone() only accepts non-negative values
>>> surface_area_cone(-1, 2)
Traceback (most recent call last):
...
ValueError: surface_area_cone() only accepts non-negative values
"""
if radius < 0 or height < 0:
raise ValueError("surface_area_cone() only accepts non-negative values")
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def surface_area_conical_frustum(
radius_1: float, radius_2: float, height: float
) -> float:
"""
Calculate the Surface Area of a Conical Frustum.
>>> surface_area_conical_frustum(1, 2, 3)
45.511728065337266
>>> surface_area_conical_frustum(4, 5, 6)
300.7913575056268
>>> surface_area_conical_frustum(0, 0, 0)
0.0
>>> surface_area_conical_frustum(1.6, 2.6, 3.6)
78.57907060751548
>>> surface_area_conical_frustum(-1, 2, 3)
Traceback (most recent call last):
...
ValueError: surface_area_conical_frustum() only accepts non-negative values
>>> surface_area_conical_frustum(1, -2, 3)
Traceback (most recent call last):
...
ValueError: surface_area_conical_frustum() only accepts non-negative values
>>> surface_area_conical_frustum(1, 2, -3)
Traceback (most recent call last):
...
ValueError: surface_area_conical_frustum() only accepts non-negative values
"""
if radius_1 < 0 or radius_2 < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values"
)
slant_height = (height**2 + (radius_1 - radius_2) ** 2) ** 0.5
return pi * ((slant_height * (radius_1 + radius_2)) + radius_1**2 + radius_2**2)
def surface_area_cylinder(radius: float, height: float) -> float:
"""
Calculate the Surface Area of a Cylinder.
Wikipedia reference: https://en.wikipedia.org/wiki/Cylinder
Formula: 2 * pi * r * (h + r)
>>> surface_area_cylinder(7, 10)
747.6990515543707
>>> surface_area_cylinder(1.6, 2.6)
42.22300526424682
>>> surface_area_cylinder(0, 0)
0.0
>>> surface_area_cylinder(6, 8)
527.7875658030853
>>> surface_area_cylinder(-1, -2)
Traceback (most recent call last):
...
ValueError: surface_area_cylinder() only accepts non-negative values
>>> surface_area_cylinder(1, -2)
Traceback (most recent call last):
...
ValueError: surface_area_cylinder() only accepts non-negative values
>>> surface_area_cylinder(-1, 2)
Traceback (most recent call last):
...
ValueError: surface_area_cylinder() only accepts non-negative values
"""
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values")
return 2 * pi * radius * (height + radius)
def surface_area_torus(torus_radius: float, tube_radius: float) -> float:
"""Calculate the Area of a Torus.
Wikipedia reference: https://en.wikipedia.org/wiki/Torus
:return 4pi^2 * torus_radius * tube_radius
>>> surface_area_torus(1, 1)
39.47841760435743
>>> surface_area_torus(4, 3)
473.7410112522892
>>> surface_area_torus(3, 4)
Traceback (most recent call last):
...
ValueError: surface_area_torus() does not support spindle or self intersecting tori
>>> surface_area_torus(1.6, 1.6)
101.06474906715503
>>> surface_area_torus(0, 0)
0.0
>>> surface_area_torus(-1, 1)
Traceback (most recent call last):
...
ValueError: surface_area_torus() only accepts non-negative values
>>> surface_area_torus(1, -1)
Traceback (most recent call last):
...
ValueError: surface_area_torus() only accepts non-negative values
"""
if torus_radius < 0 or tube_radius < 0:
raise ValueError("surface_area_torus() only accepts non-negative values")
if torus_radius < tube_radius:
raise ValueError(
"surface_area_torus() does not support spindle or self intersecting tori"
)
return 4 * pow(pi, 2) * torus_radius * tube_radius
def area_rectangle(length: float, width: float) -> float:
"""
Calculate the area of a rectangle.
>>> area_rectangle(10, 20)
200
>>> area_rectangle(1.6, 2.6)
4.16
>>> area_rectangle(0, 0)
0
>>> area_rectangle(-1, -2)
Traceback (most recent call last):
...
ValueError: area_rectangle() only accepts non-negative values
>>> area_rectangle(1, -2)
Traceback (most recent call last):
...
ValueError: area_rectangle() only accepts non-negative values
>>> area_rectangle(-1, 2)
Traceback (most recent call last):
...
ValueError: area_rectangle() only accepts non-negative values
"""
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values")
return length * width
def area_square(side_length: float) -> float:
"""
Calculate the area of a square.
>>> area_square(10)
100
>>> area_square(0)
0
>>> area_square(1.6)
2.5600000000000005
>>> area_square(-1)
Traceback (most recent call last):
...
ValueError: area_square() only accepts non-negative values
"""
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values")
return side_length**2
def area_triangle(base: float, height: float) -> float:
"""
Calculate the area of a triangle given the base and height.
>>> area_triangle(10, 10)
50.0
>>> area_triangle(1.6, 2.6)
2.08
>>> area_triangle(0, 0)
0.0
>>> area_triangle(-1, -2)
Traceback (most recent call last):
...
ValueError: area_triangle() only accepts non-negative values
>>> area_triangle(1, -2)
Traceback (most recent call last):
...
ValueError: area_triangle() only accepts non-negative values
>>> area_triangle(-1, 2)
Traceback (most recent call last):
...
ValueError: area_triangle() only accepts non-negative values
"""
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values")
return (base * height) / 2
def area_triangle_three_sides(side1: float, side2: float, side3: float) -> float:
"""
Calculate area of triangle when the length of 3 sides are known.
This function uses Heron's formula: https://en.wikipedia.org/wiki/Heron%27s_formula
>>> area_triangle_three_sides(5, 12, 13)
30.0
>>> area_triangle_three_sides(10, 11, 12)
51.521233486786784
>>> area_triangle_three_sides(0, 0, 0)
0.0
>>> area_triangle_three_sides(1.6, 2.6, 3.6)
1.8703742940919619
>>> area_triangle_three_sides(-1, -2, -1)
Traceback (most recent call last):
...
ValueError: area_triangle_three_sides() only accepts non-negative values
>>> area_triangle_three_sides(1, -2, 1)
Traceback (most recent call last):
...
ValueError: area_triangle_three_sides() only accepts non-negative values
>>> area_triangle_three_sides(2, 4, 7)
Traceback (most recent call last):
...
ValueError: Given three sides do not form a triangle
>>> area_triangle_three_sides(2, 7, 4)
Traceback (most recent call last):
...
ValueError: Given three sides do not form a triangle
>>> area_triangle_three_sides(7, 2, 4)
Traceback (most recent call last):
...
ValueError: Given three sides do not form a triangle
"""
if side1 < 0 or side2 < 0 or side3 < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values")
elif side1 + side2 < side3 or side1 + side3 < side2 or side2 + side3 < side1:
raise ValueError("Given three sides do not form a triangle")
semi_perimeter = (side1 + side2 + side3) / 2
area = sqrt(
semi_perimeter
* (semi_perimeter - side1)
* (semi_perimeter - side2)
* (semi_perimeter - side3)
)
return area
def area_parallelogram(base: float, height: float) -> float:
"""
Calculate the area of a parallelogram.
>>> area_parallelogram(10, 20)
200
>>> area_parallelogram(1.6, 2.6)
4.16
>>> area_parallelogram(0, 0)
0
>>> area_parallelogram(-1, -2)
Traceback (most recent call last):
...
ValueError: area_parallelogram() only accepts non-negative values
>>> area_parallelogram(1, -2)
Traceback (most recent call last):
...
ValueError: area_parallelogram() only accepts non-negative values
>>> area_parallelogram(-1, 2)
Traceback (most recent call last):
...
ValueError: area_parallelogram() only accepts non-negative values
"""
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values")
return base * height
def area_trapezium(base1: float, base2: float, height: float) -> float:
"""
Calculate the area of a trapezium.
>>> area_trapezium(10, 20, 30)
450.0
>>> area_trapezium(1.6, 2.6, 3.6)
7.5600000000000005
>>> area_trapezium(0, 0, 0)
0.0
>>> area_trapezium(-1, -2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(-1, 2, 3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(1, -2, 3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(1, 2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(-1, -2, 3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(1, -2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(-1, 2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
"""
if base1 < 0 or base2 < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values")
return 1 / 2 * (base1 + base2) * height
def area_circle(radius: float) -> float:
"""
Calculate the area of a circle.
>>> area_circle(20)
1256.6370614359173
>>> area_circle(1.6)
8.042477193189871
>>> area_circle(0)
0.0
>>> area_circle(-1)
Traceback (most recent call last):
...
ValueError: area_circle() only accepts non-negative values
"""
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values")
return pi * radius**2
def area_ellipse(radius_x: float, radius_y: float) -> float:
"""
Calculate the area of a ellipse.
>>> area_ellipse(10, 10)
314.1592653589793
>>> area_ellipse(10, 20)
628.3185307179587
>>> area_ellipse(0, 0)
0.0
>>> area_ellipse(1.6, 2.6)
13.06902543893354
>>> area_ellipse(-10, 20)
Traceback (most recent call last):
...
ValueError: area_ellipse() only accepts non-negative values
>>> area_ellipse(10, -20)
Traceback (most recent call last):
...
ValueError: area_ellipse() only accepts non-negative values
>>> area_ellipse(-10, -20)
Traceback (most recent call last):
...
ValueError: area_ellipse() only accepts non-negative values
"""
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values")
return pi * radius_x * radius_y
def area_rhombus(diagonal_1: float, diagonal_2: float) -> float:
"""
Calculate the area of a rhombus.
>>> area_rhombus(10, 20)
100.0
>>> area_rhombus(1.6, 2.6)
2.08
>>> area_rhombus(0, 0)
0.0
>>> area_rhombus(-1, -2)
Traceback (most recent call last):
...
ValueError: area_rhombus() only accepts non-negative values
>>> area_rhombus(1, -2)
Traceback (most recent call last):
...
ValueError: area_rhombus() only accepts non-negative values
>>> area_rhombus(-1, 2)
Traceback (most recent call last):
...
ValueError: area_rhombus() only accepts non-negative values
"""
if diagonal_1 < 0 or diagonal_2 < 0:
raise ValueError("area_rhombus() only accepts non-negative values")
return 1 / 2 * diagonal_1 * diagonal_2
def area_reg_polygon(sides: int, length: float) -> float:
"""
Calculate the area of a regular polygon.
Wikipedia reference: https://en.wikipedia.org/wiki/Polygon#Regular_polygons
Formula: (n*s^2*cot(pi/n))/4
>>> area_reg_polygon(3, 10)
43.301270189221945
>>> area_reg_polygon(4, 10)
100.00000000000001
>>> area_reg_polygon(0, 0)
Traceback (most recent call last):
...
ValueError: area_reg_polygon() only accepts integers greater than or equal to \
three as number of sides
>>> area_reg_polygon(-1, -2)
Traceback (most recent call last):
...
ValueError: area_reg_polygon() only accepts integers greater than or equal to \
three as number of sides
>>> area_reg_polygon(5, -2)
Traceback (most recent call last):
...
ValueError: area_reg_polygon() only accepts non-negative values as \
length of a side
>>> area_reg_polygon(-1, 2)
Traceback (most recent call last):
...
ValueError: area_reg_polygon() only accepts integers greater than or equal to \
three as number of sides
"""
if not isinstance(sides, int) or sides < 3:
raise ValueError(
"area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides"
)
elif length < 0:
raise ValueError(
"area_reg_polygon() only accepts non-negative values as \
length of a side"
)
return (sides * length**2) / (4 * tan(pi / sides))
return (sides * length**2) / (4 * tan(pi / sides))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print("\nSurface Areas of various geometric shapes: \n")
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] 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 Any
class Matrix:
"""
<class Matrix>
Matrix structure.
"""
def __init__(self, row: int, column: int, default_value: float = 0) -> None:
"""
<method Matrix.__init__>
Initialize matrix with given size and default value.
Example:
>>> a = Matrix(2, 3, 1)
>>> a
Matrix consist of 2 rows and 3 columns
[1, 1, 1]
[1, 1, 1]
"""
self.row, self.column = row, column
self.array = [[default_value for c in range(column)] for r in range(row)]
def __str__(self) -> str:
"""
<method Matrix.__str__>
Return string representation of this matrix.
"""
# Prefix
s = f"Matrix consist of {self.row} rows and {self.column} columns\n"
# Make string identifier
max_element_length = 0
for row_vector in self.array:
for obj in row_vector:
max_element_length = max(max_element_length, len(str(obj)))
string_format_identifier = f"%{max_element_length}s"
# Make string and return
def single_line(row_vector: list[float]) -> str:
nonlocal string_format_identifier
line = "["
line += ", ".join(string_format_identifier % (obj,) for obj in row_vector)
line += "]"
return line
s += "\n".join(single_line(row_vector) for row_vector in self.array)
return s
def __repr__(self) -> str:
return str(self)
def validate_indicies(self, loc: tuple[int, int]) -> bool:
"""
<method Matrix.validate_indicies>
Check if given indices are valid to pick element from matrix.
Example:
>>> a = Matrix(2, 6, 0)
>>> a.validate_indicies((2, 7))
False
>>> a.validate_indicies((0, 0))
True
"""
if not (isinstance(loc, (list, tuple)) and len(loc) == 2):
return False
elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
return False
else:
return True
def __getitem__(self, loc: tuple[int, int]) -> Any:
"""
<method Matrix.__getitem__>
Return array[row][column] where loc = (row, column).
Example:
>>> a = Matrix(3, 2, 7)
>>> a[1, 0]
7
"""
assert self.validate_indicies(loc)
return self.array[loc[0]][loc[1]]
def __setitem__(self, loc: tuple[int, int], value: float) -> None:
"""
<method Matrix.__setitem__>
Set array[row][column] = value where loc = (row, column).
Example:
>>> a = Matrix(2, 3, 1)
>>> a[1, 2] = 51
>>> a
Matrix consist of 2 rows and 3 columns
[ 1, 1, 1]
[ 1, 1, 51]
"""
assert self.validate_indicies(loc)
self.array[loc[0]][loc[1]] = value
def __add__(self, another: Matrix) -> Matrix:
"""
<method Matrix.__add__>
Return self + another.
Example:
>>> a = Matrix(2, 1, -4)
>>> b = Matrix(2, 1, 3)
>>> a+b
Matrix consist of 2 rows and 1 columns
[-1]
[-1]
"""
# Validation
assert isinstance(another, Matrix)
assert self.row == another.row and self.column == another.column
# Add
result = Matrix(self.row, self.column)
for r in range(self.row):
for c in range(self.column):
result[r, c] = self[r, c] + another[r, c]
return result
def __neg__(self) -> Matrix:
"""
<method Matrix.__neg__>
Return -self.
Example:
>>> a = Matrix(2, 2, 3)
>>> a[0, 1] = a[1, 0] = -2
>>> -a
Matrix consist of 2 rows and 2 columns
[-3, 2]
[ 2, -3]
"""
result = Matrix(self.row, self.column)
for r in range(self.row):
for c in range(self.column):
result[r, c] = -self[r, c]
return result
def __sub__(self, another: Matrix) -> Matrix:
return self + (-another)
def __mul__(self, another: int | float | Matrix) -> Matrix:
"""
<method Matrix.__mul__>
Return self * another.
Example:
>>> a = Matrix(2, 3, 1)
>>> a[0,2] = a[1,2] = 3
>>> a * -2
Matrix consist of 2 rows and 3 columns
[-2, -2, -6]
[-2, -2, -6]
"""
if isinstance(another, (int, float)): # Scalar multiplication
result = Matrix(self.row, self.column)
for r in range(self.row):
for c in range(self.column):
result[r, c] = self[r, c] * another
return result
elif isinstance(another, Matrix): # Matrix multiplication
assert self.column == another.row
result = Matrix(self.row, another.column)
for r in range(self.row):
for c in range(another.column):
for i in range(self.column):
result[r, c] += self[r, i] * another[i, c]
return result
else:
raise TypeError(f"Unsupported type given for another ({type(another)})")
def transpose(self) -> Matrix:
"""
<method Matrix.transpose>
Return self^T.
Example:
>>> a = Matrix(2, 3)
>>> for r in range(2):
... for c in range(3):
... a[r,c] = r*c
...
>>> a.transpose()
Matrix consist of 3 rows and 2 columns
[0, 0]
[0, 1]
[0, 2]
"""
result = Matrix(self.column, self.row)
for r in range(self.row):
for c in range(self.column):
result[c, r] = self[r, c]
return result
def sherman_morrison(self, u: Matrix, v: Matrix) -> Any:
"""
<method Matrix.sherman_morrison>
Apply Sherman-Morrison formula in O(n^2).
To learn this formula, please look this:
https://en.wikipedia.org/wiki/Sherman%E2%80%93Morrison_formula
This method returns (A + uv^T)^(-1) where A^(-1) is self. Returns None if it's
impossible to calculate.
Warning: This method doesn't check if self is invertible.
Make sure self is invertible before execute this method.
Example:
>>> ainv = Matrix(3, 3, 0)
>>> for i in range(3): ainv[i,i] = 1
...
>>> u = Matrix(3, 1, 0)
>>> u[0,0], u[1,0], u[2,0] = 1, 2, -3
>>> v = Matrix(3, 1, 0)
>>> v[0,0], v[1,0], v[2,0] = 4, -2, 5
>>> ainv.sherman_morrison(u, v)
Matrix consist of 3 rows and 3 columns
[ 1.2857142857142856, -0.14285714285714285, 0.3571428571428571]
[ 0.5714285714285714, 0.7142857142857143, 0.7142857142857142]
[ -0.8571428571428571, 0.42857142857142855, -0.0714285714285714]
"""
# Size validation
assert isinstance(u, Matrix) and isinstance(v, Matrix)
assert self.row == self.column == u.row == v.row # u, v should be column vector
assert u.column == v.column == 1 # u, v should be column vector
# Calculate
v_t = v.transpose()
numerator_factor = (v_t * self * u)[0, 0] + 1
if numerator_factor == 0:
return None # It's not invertable
return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))
# Testing
if __name__ == "__main__":
def test1() -> None:
# a^(-1)
ainv = Matrix(3, 3, 0)
for i in range(3):
ainv[i, i] = 1
print(f"a^(-1) is {ainv}")
# u, v
u = Matrix(3, 1, 0)
u[0, 0], u[1, 0], u[2, 0] = 1, 2, -3
v = Matrix(3, 1, 0)
v[0, 0], v[1, 0], v[2, 0] = 4, -2, 5
print(f"u is {u}")
print(f"v is {v}")
print(f"uv^T is {u * v.transpose()}")
# Sherman Morrison
print(f"(a + uv^T)^(-1) is {ainv.sherman_morrison(u, v)}")
def test2() -> None:
import doctest
doctest.testmod()
test2()
| from __future__ import annotations
from typing import Any
class Matrix:
"""
<class Matrix>
Matrix structure.
"""
def __init__(self, row: int, column: int, default_value: float = 0) -> None:
"""
<method Matrix.__init__>
Initialize matrix with given size and default value.
Example:
>>> a = Matrix(2, 3, 1)
>>> a
Matrix consist of 2 rows and 3 columns
[1, 1, 1]
[1, 1, 1]
"""
self.row, self.column = row, column
self.array = [[default_value for c in range(column)] for r in range(row)]
def __str__(self) -> str:
"""
<method Matrix.__str__>
Return string representation of this matrix.
"""
# Prefix
s = f"Matrix consist of {self.row} rows and {self.column} columns\n"
# Make string identifier
max_element_length = 0
for row_vector in self.array:
for obj in row_vector:
max_element_length = max(max_element_length, len(str(obj)))
string_format_identifier = f"%{max_element_length}s"
# Make string and return
def single_line(row_vector: list[float]) -> str:
nonlocal string_format_identifier
line = "["
line += ", ".join(string_format_identifier % (obj,) for obj in row_vector)
line += "]"
return line
s += "\n".join(single_line(row_vector) for row_vector in self.array)
return s
def __repr__(self) -> str:
return str(self)
def validate_indicies(self, loc: tuple[int, int]) -> bool:
"""
<method Matrix.validate_indicies>
Check if given indices are valid to pick element from matrix.
Example:
>>> a = Matrix(2, 6, 0)
>>> a.validate_indicies((2, 7))
False
>>> a.validate_indicies((0, 0))
True
"""
if not (isinstance(loc, (list, tuple)) and len(loc) == 2):
return False
elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
return False
else:
return True
def __getitem__(self, loc: tuple[int, int]) -> Any:
"""
<method Matrix.__getitem__>
Return array[row][column] where loc = (row, column).
Example:
>>> a = Matrix(3, 2, 7)
>>> a[1, 0]
7
"""
assert self.validate_indicies(loc)
return self.array[loc[0]][loc[1]]
def __setitem__(self, loc: tuple[int, int], value: float) -> None:
"""
<method Matrix.__setitem__>
Set array[row][column] = value where loc = (row, column).
Example:
>>> a = Matrix(2, 3, 1)
>>> a[1, 2] = 51
>>> a
Matrix consist of 2 rows and 3 columns
[ 1, 1, 1]
[ 1, 1, 51]
"""
assert self.validate_indicies(loc)
self.array[loc[0]][loc[1]] = value
def __add__(self, another: Matrix) -> Matrix:
"""
<method Matrix.__add__>
Return self + another.
Example:
>>> a = Matrix(2, 1, -4)
>>> b = Matrix(2, 1, 3)
>>> a+b
Matrix consist of 2 rows and 1 columns
[-1]
[-1]
"""
# Validation
assert isinstance(another, Matrix)
assert self.row == another.row and self.column == another.column
# Add
result = Matrix(self.row, self.column)
for r in range(self.row):
for c in range(self.column):
result[r, c] = self[r, c] + another[r, c]
return result
def __neg__(self) -> Matrix:
"""
<method Matrix.__neg__>
Return -self.
Example:
>>> a = Matrix(2, 2, 3)
>>> a[0, 1] = a[1, 0] = -2
>>> -a
Matrix consist of 2 rows and 2 columns
[-3, 2]
[ 2, -3]
"""
result = Matrix(self.row, self.column)
for r in range(self.row):
for c in range(self.column):
result[r, c] = -self[r, c]
return result
def __sub__(self, another: Matrix) -> Matrix:
return self + (-another)
def __mul__(self, another: int | float | Matrix) -> Matrix:
"""
<method Matrix.__mul__>
Return self * another.
Example:
>>> a = Matrix(2, 3, 1)
>>> a[0,2] = a[1,2] = 3
>>> a * -2
Matrix consist of 2 rows and 3 columns
[-2, -2, -6]
[-2, -2, -6]
"""
if isinstance(another, (int, float)): # Scalar multiplication
result = Matrix(self.row, self.column)
for r in range(self.row):
for c in range(self.column):
result[r, c] = self[r, c] * another
return result
elif isinstance(another, Matrix): # Matrix multiplication
assert self.column == another.row
result = Matrix(self.row, another.column)
for r in range(self.row):
for c in range(another.column):
for i in range(self.column):
result[r, c] += self[r, i] * another[i, c]
return result
else:
raise TypeError(f"Unsupported type given for another ({type(another)})")
def transpose(self) -> Matrix:
"""
<method Matrix.transpose>
Return self^T.
Example:
>>> a = Matrix(2, 3)
>>> for r in range(2):
... for c in range(3):
... a[r,c] = r*c
...
>>> a.transpose()
Matrix consist of 3 rows and 2 columns
[0, 0]
[0, 1]
[0, 2]
"""
result = Matrix(self.column, self.row)
for r in range(self.row):
for c in range(self.column):
result[c, r] = self[r, c]
return result
def sherman_morrison(self, u: Matrix, v: Matrix) -> Any:
"""
<method Matrix.sherman_morrison>
Apply Sherman-Morrison formula in O(n^2).
To learn this formula, please look this:
https://en.wikipedia.org/wiki/Sherman%E2%80%93Morrison_formula
This method returns (A + uv^T)^(-1) where A^(-1) is self. Returns None if it's
impossible to calculate.
Warning: This method doesn't check if self is invertible.
Make sure self is invertible before execute this method.
Example:
>>> ainv = Matrix(3, 3, 0)
>>> for i in range(3): ainv[i,i] = 1
...
>>> u = Matrix(3, 1, 0)
>>> u[0,0], u[1,0], u[2,0] = 1, 2, -3
>>> v = Matrix(3, 1, 0)
>>> v[0,0], v[1,0], v[2,0] = 4, -2, 5
>>> ainv.sherman_morrison(u, v)
Matrix consist of 3 rows and 3 columns
[ 1.2857142857142856, -0.14285714285714285, 0.3571428571428571]
[ 0.5714285714285714, 0.7142857142857143, 0.7142857142857142]
[ -0.8571428571428571, 0.42857142857142855, -0.0714285714285714]
"""
# Size validation
assert isinstance(u, Matrix) and isinstance(v, Matrix)
assert self.row == self.column == u.row == v.row # u, v should be column vector
assert u.column == v.column == 1 # u, v should be column vector
# Calculate
v_t = v.transpose()
numerator_factor = (v_t * self * u)[0, 0] + 1
if numerator_factor == 0:
return None # It's not invertable
return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))
# Testing
if __name__ == "__main__":
def test1() -> None:
# a^(-1)
ainv = Matrix(3, 3, 0)
for i in range(3):
ainv[i, i] = 1
print(f"a^(-1) is {ainv}")
# u, v
u = Matrix(3, 1, 0)
u[0, 0], u[1, 0], u[2, 0] = 1, 2, -3
v = Matrix(3, 1, 0)
v[0, 0], v[1, 0], v[2, 0] = 4, -2, 5
print(f"u is {u}")
print(f"v is {v}")
print(f"uv^T is {u * v.transpose()}")
# Sherman Morrison
print(f"(a + uv^T)^(-1) is {ainv.sherman_morrison(u, v)}")
def test2() -> None:
import doctest
doctest.testmod()
test2()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
from collections import deque
class Automaton:
def __init__(self, keywords: list[str]):
self.adlist: list[dict] = []
self.adlist.append(
{"value": "", "next_states": [], "fail_state": 0, "output": []}
)
for keyword in keywords:
self.add_keyword(keyword)
self.set_fail_transitions()
def find_next_state(self, current_state: int, char: str) -> int | None:
for state in self.adlist[current_state]["next_states"]:
if char == self.adlist[state]["value"]:
return state
return None
def add_keyword(self, keyword: str) -> None:
current_state = 0
for character in keyword:
next_state = self.find_next_state(current_state, character)
if next_state is None:
self.adlist.append(
{
"value": character,
"next_states": [],
"fail_state": 0,
"output": [],
}
)
self.adlist[current_state]["next_states"].append(len(self.adlist) - 1)
current_state = len(self.adlist) - 1
else:
current_state = next_state
self.adlist[current_state]["output"].append(keyword)
def set_fail_transitions(self) -> None:
q: deque = deque()
for node in self.adlist[0]["next_states"]:
q.append(node)
self.adlist[node]["fail_state"] = 0
while q:
r = q.popleft()
for child in self.adlist[r]["next_states"]:
q.append(child)
state = self.adlist[r]["fail_state"]
while (
self.find_next_state(state, self.adlist[child]["value"]) is None
and state != 0
):
state = self.adlist[state]["fail_state"]
self.adlist[child]["fail_state"] = self.find_next_state(
state, self.adlist[child]["value"]
)
if self.adlist[child]["fail_state"] is None:
self.adlist[child]["fail_state"] = 0
self.adlist[child]["output"] = (
self.adlist[child]["output"]
+ self.adlist[self.adlist[child]["fail_state"]]["output"]
)
def search_in(self, string: str) -> dict[str, list[int]]:
"""
>>> A = Automaton(["what", "hat", "ver", "er"])
>>> A.search_in("whatever, err ... , wherever")
{'what': [0], 'hat': [1], 'ver': [5, 25], 'er': [6, 10, 22, 26]}
"""
result: dict = {} # returns a dict with keywords and list of its occurrences
current_state = 0
for i in range(len(string)):
while (
self.find_next_state(current_state, string[i]) is None
and current_state != 0
):
current_state = self.adlist[current_state]["fail_state"]
next_state = self.find_next_state(current_state, string[i])
if next_state is None:
current_state = 0
else:
current_state = next_state
for key in self.adlist[current_state]["output"]:
if not (key in result):
result[key] = []
result[key].append(i - len(key) + 1)
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| from __future__ import annotations
from collections import deque
class Automaton:
def __init__(self, keywords: list[str]):
self.adlist: list[dict] = []
self.adlist.append(
{"value": "", "next_states": [], "fail_state": 0, "output": []}
)
for keyword in keywords:
self.add_keyword(keyword)
self.set_fail_transitions()
def find_next_state(self, current_state: int, char: str) -> int | None:
for state in self.adlist[current_state]["next_states"]:
if char == self.adlist[state]["value"]:
return state
return None
def add_keyword(self, keyword: str) -> None:
current_state = 0
for character in keyword:
next_state = self.find_next_state(current_state, character)
if next_state is None:
self.adlist.append(
{
"value": character,
"next_states": [],
"fail_state": 0,
"output": [],
}
)
self.adlist[current_state]["next_states"].append(len(self.adlist) - 1)
current_state = len(self.adlist) - 1
else:
current_state = next_state
self.adlist[current_state]["output"].append(keyword)
def set_fail_transitions(self) -> None:
q: deque = deque()
for node in self.adlist[0]["next_states"]:
q.append(node)
self.adlist[node]["fail_state"] = 0
while q:
r = q.popleft()
for child in self.adlist[r]["next_states"]:
q.append(child)
state = self.adlist[r]["fail_state"]
while (
self.find_next_state(state, self.adlist[child]["value"]) is None
and state != 0
):
state = self.adlist[state]["fail_state"]
self.adlist[child]["fail_state"] = self.find_next_state(
state, self.adlist[child]["value"]
)
if self.adlist[child]["fail_state"] is None:
self.adlist[child]["fail_state"] = 0
self.adlist[child]["output"] = (
self.adlist[child]["output"]
+ self.adlist[self.adlist[child]["fail_state"]]["output"]
)
def search_in(self, string: str) -> dict[str, list[int]]:
"""
>>> A = Automaton(["what", "hat", "ver", "er"])
>>> A.search_in("whatever, err ... , wherever")
{'what': [0], 'hat': [1], 'ver': [5, 25], 'er': [6, 10, 22, 26]}
"""
result: dict = {} # returns a dict with keywords and list of its occurrences
current_state = 0
for i in range(len(string)):
while (
self.find_next_state(current_state, string[i]) is None
and current_state != 0
):
current_state = self.adlist[current_state]["fail_state"]
next_state = self.find_next_state(current_state, string[i])
if next_state is None:
current_state = 0
else:
current_state = next_state
for key in self.adlist[current_state]["output"]:
if not (key in result):
result[key] = []
result[key].append(i - len(key) + 1)
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # DarkCoder
def sum_of_series(first_term: int, common_diff: int, num_of_terms: int) -> float:
"""
Find the sum of n terms in an arithmetic progression.
>>> sum_of_series(1, 1, 10)
55.0
>>> sum_of_series(1, 10, 100)
49600.0
"""
total = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def main():
print(sum_of_series(1, 1, 10))
if __name__ == "__main__":
import doctest
doctest.testmod()
| # DarkCoder
def sum_of_series(first_term: int, common_diff: int, num_of_terms: int) -> float:
"""
Find the sum of n terms in an arithmetic progression.
>>> sum_of_series(1, 1, 10)
55.0
>>> sum_of_series(1, 10, 100)
49600.0
"""
total = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def main():
print(sum_of_series(1, 1, 10))
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] 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 | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
class Node:
"""
A Node has data variable and pointers to Nodes to its left and right.
"""
def __init__(self, data: int) -> None:
self.data = data
self.left: Node | None = None
self.right: Node | None = None
def display(tree: Node | None) -> None: # In Order traversal of the tree
"""
>>> root = Node(1)
>>> root.left = Node(0)
>>> root.right = Node(2)
>>> display(root)
0
1
2
>>> display(root.right)
2
"""
if tree:
display(tree.left)
print(tree.data)
display(tree.right)
def depth_of_tree(tree: Node | None) -> int:
"""
Recursive function that returns the depth of a binary tree.
>>> root = Node(0)
>>> depth_of_tree(root)
1
>>> root.left = Node(0)
>>> depth_of_tree(root)
2
>>> root.right = Node(0)
>>> depth_of_tree(root)
2
>>> root.left.right = Node(0)
>>> depth_of_tree(root)
3
>>> depth_of_tree(root.left)
2
"""
return 1 + max(depth_of_tree(tree.left), depth_of_tree(tree.right)) if tree else 0
def is_full_binary_tree(tree: Node) -> bool:
"""
Returns True if this is a full binary tree
>>> root = Node(0)
>>> is_full_binary_tree(root)
True
>>> root.left = Node(0)
>>> is_full_binary_tree(root)
False
>>> root.right = Node(0)
>>> is_full_binary_tree(root)
True
>>> root.left.left = Node(0)
>>> is_full_binary_tree(root)
False
>>> root.right.right = Node(0)
>>> is_full_binary_tree(root)
False
"""
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left) and is_full_binary_tree(tree.right)
else:
return not tree.left and not tree.right
def main() -> None: # Main function for testing.
tree = Node(1)
tree.left = Node(2)
tree.right = Node(3)
tree.left.left = Node(4)
tree.left.right = Node(5)
tree.left.right.left = Node(6)
tree.right.left = Node(7)
tree.right.left.left = Node(8)
tree.right.left.left.right = Node(9)
print(is_full_binary_tree(tree))
print(depth_of_tree(tree))
print("Tree is: ")
display(tree)
if __name__ == "__main__":
main()
| from __future__ import annotations
class Node:
"""
A Node has data variable and pointers to Nodes to its left and right.
"""
def __init__(self, data: int) -> None:
self.data = data
self.left: Node | None = None
self.right: Node | None = None
def display(tree: Node | None) -> None: # In Order traversal of the tree
"""
>>> root = Node(1)
>>> root.left = Node(0)
>>> root.right = Node(2)
>>> display(root)
0
1
2
>>> display(root.right)
2
"""
if tree:
display(tree.left)
print(tree.data)
display(tree.right)
def depth_of_tree(tree: Node | None) -> int:
"""
Recursive function that returns the depth of a binary tree.
>>> root = Node(0)
>>> depth_of_tree(root)
1
>>> root.left = Node(0)
>>> depth_of_tree(root)
2
>>> root.right = Node(0)
>>> depth_of_tree(root)
2
>>> root.left.right = Node(0)
>>> depth_of_tree(root)
3
>>> depth_of_tree(root.left)
2
"""
return 1 + max(depth_of_tree(tree.left), depth_of_tree(tree.right)) if tree else 0
def is_full_binary_tree(tree: Node) -> bool:
"""
Returns True if this is a full binary tree
>>> root = Node(0)
>>> is_full_binary_tree(root)
True
>>> root.left = Node(0)
>>> is_full_binary_tree(root)
False
>>> root.right = Node(0)
>>> is_full_binary_tree(root)
True
>>> root.left.left = Node(0)
>>> is_full_binary_tree(root)
False
>>> root.right.right = Node(0)
>>> is_full_binary_tree(root)
False
"""
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left) and is_full_binary_tree(tree.right)
else:
return not tree.left and not tree.right
def main() -> None: # Main function for testing.
tree = Node(1)
tree.left = Node(2)
tree.right = Node(3)
tree.left.left = Node(4)
tree.left.right = Node(5)
tree.left.right.left = Node(6)
tree.right.left = Node(7)
tree.right.left.left = Node(8)
tree.right.left.left.right = Node(9)
print(is_full_binary_tree(tree))
print(depth_of_tree(tree))
print("Tree is: ")
display(tree)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Author: Phyllipe Bezerra (https://github.com/pmba)
clothes = {
0: "underwear",
1: "pants",
2: "belt",
3: "suit",
4: "shoe",
5: "socks",
6: "shirt",
7: "tie",
8: "watch",
}
graph = [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]
visited = [0 for x in range(len(graph))]
stack = []
def print_stack(stack, clothes):
order = 1
while stack:
current_clothing = stack.pop()
print(order, clothes[current_clothing])
order += 1
def depth_first_search(u, visited, graph):
visited[u] = 1
for v in graph[u]:
if not visited[v]:
depth_first_search(v, visited, graph)
stack.append(u)
def topological_sort(graph, visited):
for v in range(len(graph)):
if not visited[v]:
depth_first_search(v, visited, graph)
if __name__ == "__main__":
topological_sort(graph, visited)
print(stack)
print_stack(stack, clothes)
| # Author: Phyllipe Bezerra (https://github.com/pmba)
clothes = {
0: "underwear",
1: "pants",
2: "belt",
3: "suit",
4: "shoe",
5: "socks",
6: "shirt",
7: "tie",
8: "watch",
}
graph = [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]
visited = [0 for x in range(len(graph))]
stack = []
def print_stack(stack, clothes):
order = 1
while stack:
current_clothing = stack.pop()
print(order, clothes[current_clothing])
order += 1
def depth_first_search(u, visited, graph):
visited[u] = 1
for v in graph[u]:
if not visited[v]:
depth_first_search(v, visited, graph)
stack.append(u)
def topological_sort(graph, visited):
for v in range(len(graph)):
if not visited[v]:
depth_first_search(v, visited, graph)
if __name__ == "__main__":
topological_sort(graph, visited)
print(stack)
print_stack(stack, clothes)
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 80: https://projecteuler.net/problem=80
Author: Sandeep Gupta
Problem statement: For the first one hundred natural numbers, find the total of
the digital sums of the first one hundred decimal digits for all the irrational
square roots.
Time: 5 October 2020, 18:30
"""
import decimal
def solution() -> int:
"""
To evaluate the sum, Used decimal python module to calculate the decimal
places up to 100, the most important thing would be take calculate
a few extra places for decimal otherwise there will be rounding
error.
>>> solution()
40886
"""
answer = 0
decimal_context = decimal.Context(prec=105)
for i in range(2, 100):
number = decimal.Decimal(i)
sqrt_number = number.sqrt(decimal_context)
if len(str(sqrt_number)) > 1:
answer += int(str(sqrt_number)[0])
sqrt_number_str = str(sqrt_number)[2:101]
answer += sum(int(x) for x in sqrt_number_str)
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{solution() = }")
| """
Project Euler Problem 80: https://projecteuler.net/problem=80
Author: Sandeep Gupta
Problem statement: For the first one hundred natural numbers, find the total of
the digital sums of the first one hundred decimal digits for all the irrational
square roots.
Time: 5 October 2020, 18:30
"""
import decimal
def solution() -> int:
"""
To evaluate the sum, Used decimal python module to calculate the decimal
places up to 100, the most important thing would be take calculate
a few extra places for decimal otherwise there will be rounding
error.
>>> solution()
40886
"""
answer = 0
decimal_context = decimal.Context(prec=105)
for i in range(2, 100):
number = decimal.Decimal(i)
sqrt_number = number.sqrt(decimal_context)
if len(str(sqrt_number)) > 1:
answer += int(str(sqrt_number)[0])
sqrt_number_str = str(sqrt_number)[2:101]
answer += sum(int(x) for x in sqrt_number_str)
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
developed by: markmelnic
original repo: https://github.com/markmelnic/Scoring-Algorithm
Analyse data using a range based percentual proximity algorithm
and calculate the linear maximum likelihood estimation.
The basic principle is that all values supplied will be broken
down to a range from 0 to 1 and each column's score will be added
up to get the total score.
==========
Example for data of vehicles
price|mileage|registration_year
20k |60k |2012
22k |50k |2011
23k |90k |2015
16k |210k |2010
We want the vehicle with the lowest price,
lowest mileage but newest registration year.
Thus the weights for each column are as follows:
[0, 0, 1]
"""
def procentual_proximity(
source_data: list[list[float]], weights: list[int]
) -> list[list[float]]:
"""
weights - int list
possible values - 0 / 1
0 if lower values have higher weight in the data set
1 if higher values have higher weight in the data set
>>> procentual_proximity([[20, 60, 2012],[23, 90, 2015],[22, 50, 2011]], [0, 0, 1])
[[20, 60, 2012, 2.0], [23, 90, 2015, 1.0], [22, 50, 2011, 1.3333333333333335]]
"""
# getting data
data_lists: list[list[float]] = []
for data in source_data:
for i, el in enumerate(data):
if len(data_lists) < i + 1:
data_lists.append([])
data_lists[i].append(float(el))
score_lists: list[list[float]] = []
# calculating each score
for dlist, weight in zip(data_lists, weights):
mind = min(dlist)
maxd = max(dlist)
score: list[float] = []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)))
except ZeroDivisionError:
score.append(1)
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind))
except ZeroDivisionError:
score.append(0)
# weight not 0 or 1
else:
raise ValueError(f"Invalid weight of {weight:f} provided")
score_lists.append(score)
# initialize final scores
final_scores: list[float] = [0 for i in range(len(score_lists[0]))]
# generate final scores
for slist in score_lists:
for j, ele in enumerate(slist):
final_scores[j] = final_scores[j] + ele
# append scores to source data
for i, ele in enumerate(final_scores):
source_data[i].append(ele)
return source_data
| """
developed by: markmelnic
original repo: https://github.com/markmelnic/Scoring-Algorithm
Analyse data using a range based percentual proximity algorithm
and calculate the linear maximum likelihood estimation.
The basic principle is that all values supplied will be broken
down to a range from 0 to 1 and each column's score will be added
up to get the total score.
==========
Example for data of vehicles
price|mileage|registration_year
20k |60k |2012
22k |50k |2011
23k |90k |2015
16k |210k |2010
We want the vehicle with the lowest price,
lowest mileage but newest registration year.
Thus the weights for each column are as follows:
[0, 0, 1]
"""
def procentual_proximity(
source_data: list[list[float]], weights: list[int]
) -> list[list[float]]:
"""
weights - int list
possible values - 0 / 1
0 if lower values have higher weight in the data set
1 if higher values have higher weight in the data set
>>> procentual_proximity([[20, 60, 2012],[23, 90, 2015],[22, 50, 2011]], [0, 0, 1])
[[20, 60, 2012, 2.0], [23, 90, 2015, 1.0], [22, 50, 2011, 1.3333333333333335]]
"""
# getting data
data_lists: list[list[float]] = []
for data in source_data:
for i, el in enumerate(data):
if len(data_lists) < i + 1:
data_lists.append([])
data_lists[i].append(float(el))
score_lists: list[list[float]] = []
# calculating each score
for dlist, weight in zip(data_lists, weights):
mind = min(dlist)
maxd = max(dlist)
score: list[float] = []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)))
except ZeroDivisionError:
score.append(1)
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind))
except ZeroDivisionError:
score.append(0)
# weight not 0 or 1
else:
raise ValueError(f"Invalid weight of {weight:f} provided")
score_lists.append(score)
# initialize final scores
final_scores: list[float] = [0 for i in range(len(score_lists[0]))]
# generate final scores
for slist in score_lists:
for j, ele in enumerate(slist):
final_scores[j] = final_scores[j] + ele
# append scores to source data
for i, ele in enumerate(final_scores):
source_data[i].append(ele)
return source_data
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
An RSA prime factor algorithm.
The program can efficiently factor RSA prime number given the private key d and
public key e.
Source: on page 3 of https://crypto.stanford.edu/~dabo/papers/RSA-survey.pdf
More readable source: https://www.di-mgt.com.au/rsa_factorize_n.html
large number can take minutes to factor, therefore are not included in doctest.
"""
from __future__ import annotations
import math
import random
def rsafactor(d: int, e: int, n: int) -> list[int]:
"""
This function returns the factors of N, where p*q=N
Return: [p, q]
We call N the RSA modulus, e the encryption exponent, and d the decryption exponent.
The pair (N, e) is the public key. As its name suggests, it is public and is used to
encrypt messages.
The pair (N, d) is the secret key or private key and is known only to the recipient
of encrypted messages.
>>> rsafactor(3, 16971, 25777)
[149, 173]
>>> rsafactor(7331, 11, 27233)
[113, 241]
>>> rsafactor(4021, 13, 17711)
[89, 199]
"""
k = d * e - 1
p = 0
q = 0
while p == 0:
g = random.randint(2, n - 1)
t = k
while True:
if t % 2 == 0:
t = t // 2
x = (g**t) % n
y = math.gcd(x - 1, n)
if x > 1 and y > 1:
p = y
q = n // y
break # find the correct factors
else:
break # t is not divisible by 2, break and choose another g
return sorted([p, q])
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
An RSA prime factor algorithm.
The program can efficiently factor RSA prime number given the private key d and
public key e.
Source: on page 3 of https://crypto.stanford.edu/~dabo/papers/RSA-survey.pdf
More readable source: https://www.di-mgt.com.au/rsa_factorize_n.html
large number can take minutes to factor, therefore are not included in doctest.
"""
from __future__ import annotations
import math
import random
def rsafactor(d: int, e: int, n: int) -> list[int]:
"""
This function returns the factors of N, where p*q=N
Return: [p, q]
We call N the RSA modulus, e the encryption exponent, and d the decryption exponent.
The pair (N, e) is the public key. As its name suggests, it is public and is used to
encrypt messages.
The pair (N, d) is the secret key or private key and is known only to the recipient
of encrypted messages.
>>> rsafactor(3, 16971, 25777)
[149, 173]
>>> rsafactor(7331, 11, 27233)
[113, 241]
>>> rsafactor(4021, 13, 17711)
[89, 199]
"""
k = d * e - 1
p = 0
q = 0
while p == 0:
g = random.randint(2, n - 1)
t = k
while True:
if t % 2 == 0:
t = t // 2
x = (g**t) % n
y = math.gcd(x - 1, n)
if x > 1 and y > 1:
p = y
q = n // y
break # find the correct factors
else:
break # t is not divisible by 2, break and choose another g
return sorted([p, q])
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 234: https://projecteuler.net/problem=234
For any integer n, consider the three functions
f1,n(x,y,z) = x^(n+1) + y^(n+1) - z^(n+1)
f2,n(x,y,z) = (xy + yz + zx)*(x^(n-1) + y^(n-1) - z^(n-1))
f3,n(x,y,z) = xyz*(xn-2 + yn-2 - zn-2)
and their combination
fn(x,y,z) = f1,n(x,y,z) + f2,n(x,y,z) - f3,n(x,y,z)
We call (x,y,z) a golden triple of order k if x, y, and z are all rational numbers
of the form a / b with 0 < a < b ≤ k and there is (at least) one integer n,
so that fn(x,y,z) = 0.
Let s(x,y,z) = x + y + z.
Let t = u / v be the sum of all distinct s(x,y,z) for all golden triples
(x,y,z) of order 35.
All the s(x,y,z) and t must be in reduced form.
Find u + v.
Solution:
By expanding the brackets it is easy to show that
fn(x, y, z) = (x + y + z) * (x^n + y^n - z^n).
Since x,y,z are positive, the requirement fn(x, y, z) = 0 is fulfilled if and
only if x^n + y^n = z^n.
By Fermat's Last Theorem, this means that the absolute value of n can not
exceed 2, i.e. n is in {-2, -1, 0, 1, 2}. We can eliminate n = 0 since then the
equation would reduce to 1 + 1 = 1, for which there are no solutions.
So all we have to do is iterate through the possible numerators and denominators
of x and y, calculate the corresponding z, and check if the corresponding numerator and
denominator are integer and satisfy 0 < z_num < z_den <= 0. We use a set "uniquq_s"
to make sure there are no duplicates, and the fractions.Fraction class to make sure
we get the right numerator and denominator.
Reference:
https://en.wikipedia.org/wiki/Fermat%27s_Last_Theorem
"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def is_sq(number: int) -> bool:
"""
Check if number is a perfect square.
>>> is_sq(1)
True
>>> is_sq(1000001)
False
>>> is_sq(1000000)
True
"""
sq: int = int(number**0.5)
return number == sq * sq
def add_three(
x_num: int, x_den: int, y_num: int, y_den: int, z_num: int, z_den: int
) -> tuple[int, int]:
"""
Given the numerators and denominators of three fractions, return the
numerator and denominator of their sum in lowest form.
>>> add_three(1, 3, 1, 3, 1, 3)
(1, 1)
>>> add_three(2, 5, 4, 11, 12, 3)
(262, 55)
"""
top: int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
bottom: int = x_den * y_den * z_den
hcf: int = gcd(top, bottom)
top //= hcf
bottom //= hcf
return top, bottom
def solution(order: int = 35) -> int:
"""
Find the sum of the numerator and denominator of the sum of all s(x,y,z) for
golden triples (x,y,z) of the given order.
>>> solution(5)
296
>>> solution(10)
12519
>>> solution(20)
19408891927
"""
unique_s: set = set()
hcf: int
total: Fraction = Fraction(0)
fraction_sum: tuple[int, int]
for x_num in range(1, order + 1):
for x_den in range(x_num + 1, order + 1):
for y_num in range(1, order + 1):
for y_den in range(y_num + 1, order + 1):
# n=1
z_num = x_num * y_den + x_den * y_num
z_den = x_den * y_den
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
# n=2
z_num = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
z_den = x_den * x_den * y_den * y_den
if is_sq(z_num) and is_sq(z_den):
z_num = int(sqrt(z_num))
z_den = int(sqrt(z_den))
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
# n=-1
z_num = x_num * y_num
z_den = x_den * y_num + x_num * y_den
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
# n=2
z_num = x_num * x_num * y_num * y_num
z_den = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(z_num) and is_sq(z_den):
z_num = int(sqrt(z_num))
z_den = int(sqrt(z_den))
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
for num, den in unique_s:
total += Fraction(num, den)
return total.denominator + total.numerator
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 234: https://projecteuler.net/problem=234
For any integer n, consider the three functions
f1,n(x,y,z) = x^(n+1) + y^(n+1) - z^(n+1)
f2,n(x,y,z) = (xy + yz + zx)*(x^(n-1) + y^(n-1) - z^(n-1))
f3,n(x,y,z) = xyz*(xn-2 + yn-2 - zn-2)
and their combination
fn(x,y,z) = f1,n(x,y,z) + f2,n(x,y,z) - f3,n(x,y,z)
We call (x,y,z) a golden triple of order k if x, y, and z are all rational numbers
of the form a / b with 0 < a < b ≤ k and there is (at least) one integer n,
so that fn(x,y,z) = 0.
Let s(x,y,z) = x + y + z.
Let t = u / v be the sum of all distinct s(x,y,z) for all golden triples
(x,y,z) of order 35.
All the s(x,y,z) and t must be in reduced form.
Find u + v.
Solution:
By expanding the brackets it is easy to show that
fn(x, y, z) = (x + y + z) * (x^n + y^n - z^n).
Since x,y,z are positive, the requirement fn(x, y, z) = 0 is fulfilled if and
only if x^n + y^n = z^n.
By Fermat's Last Theorem, this means that the absolute value of n can not
exceed 2, i.e. n is in {-2, -1, 0, 1, 2}. We can eliminate n = 0 since then the
equation would reduce to 1 + 1 = 1, for which there are no solutions.
So all we have to do is iterate through the possible numerators and denominators
of x and y, calculate the corresponding z, and check if the corresponding numerator and
denominator are integer and satisfy 0 < z_num < z_den <= 0. We use a set "uniquq_s"
to make sure there are no duplicates, and the fractions.Fraction class to make sure
we get the right numerator and denominator.
Reference:
https://en.wikipedia.org/wiki/Fermat%27s_Last_Theorem
"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def is_sq(number: int) -> bool:
"""
Check if number is a perfect square.
>>> is_sq(1)
True
>>> is_sq(1000001)
False
>>> is_sq(1000000)
True
"""
sq: int = int(number**0.5)
return number == sq * sq
def add_three(
x_num: int, x_den: int, y_num: int, y_den: int, z_num: int, z_den: int
) -> tuple[int, int]:
"""
Given the numerators and denominators of three fractions, return the
numerator and denominator of their sum in lowest form.
>>> add_three(1, 3, 1, 3, 1, 3)
(1, 1)
>>> add_three(2, 5, 4, 11, 12, 3)
(262, 55)
"""
top: int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
bottom: int = x_den * y_den * z_den
hcf: int = gcd(top, bottom)
top //= hcf
bottom //= hcf
return top, bottom
def solution(order: int = 35) -> int:
"""
Find the sum of the numerator and denominator of the sum of all s(x,y,z) for
golden triples (x,y,z) of the given order.
>>> solution(5)
296
>>> solution(10)
12519
>>> solution(20)
19408891927
"""
unique_s: set = set()
hcf: int
total: Fraction = Fraction(0)
fraction_sum: tuple[int, int]
for x_num in range(1, order + 1):
for x_den in range(x_num + 1, order + 1):
for y_num in range(1, order + 1):
for y_den in range(y_num + 1, order + 1):
# n=1
z_num = x_num * y_den + x_den * y_num
z_den = x_den * y_den
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
# n=2
z_num = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
z_den = x_den * x_den * y_den * y_den
if is_sq(z_num) and is_sq(z_den):
z_num = int(sqrt(z_num))
z_den = int(sqrt(z_den))
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
# n=-1
z_num = x_num * y_num
z_den = x_den * y_num + x_num * y_den
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
# n=2
z_num = x_num * x_num * y_num * y_num
z_den = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(z_num) and is_sq(z_den):
z_num = int(sqrt(z_num))
z_den = int(sqrt(z_den))
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
for num, den in unique_s:
total += Fraction(num, den)
return total.denominator + total.numerator
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 70: https://projecteuler.net/problem=70
Euler's Totient function, φ(n) [sometimes called the phi function], is used to
determine the number of positive numbers less than or equal to n which are
relatively prime to n. For example, as 1, 2, 4, 5, 7, and 8, are all less than
nine and relatively prime to nine, φ(9)=6.
The number 1 is considered to be relatively prime to every positive number, so
φ(1)=1.
Interestingly, φ(87109)=79180, and it can be seen that 87109 is a permutation
of 79180.
Find the value of n, 1 < n < 10^7, for which φ(n) is a permutation of n and
the ratio n/φ(n) produces a minimum.
-----
This is essentially brute force. Calculate all totients up to 10^7 and
find the minimum ratio of n/φ(n) that way. To minimize the ratio, we want
to minimize n and maximize φ(n) as much as possible, so we can store the
minimum fraction's numerator and denominator and calculate new fractions
with each totient to compare against. To avoid dividing by zero, I opt to
use cross multiplication.
References:
Finding totients
https://en.wikipedia.org/wiki/Euler's_totient_function#Euler's_product_formula
"""
from __future__ import annotations
def get_totients(max_one: int) -> list[int]:
"""
Calculates a list of totients from 0 to max_one exclusive, using the
definition of Euler's product formula.
>>> get_totients(5)
[0, 1, 1, 2, 2]
>>> get_totients(10)
[0, 1, 1, 2, 2, 4, 2, 6, 4, 6]
"""
totients = [0] * max_one
for i in range(0, max_one):
totients[i] = i
for i in range(2, max_one):
if totients[i] == i:
for j in range(i, max_one, i):
totients[j] -= totients[j] // i
return totients
def has_same_digits(num1: int, num2: int) -> bool:
"""
Return True if num1 and num2 have the same frequency of every digit, False
otherwise.
>>> has_same_digits(123456789, 987654321)
True
>>> has_same_digits(123, 23)
False
>>> has_same_digits(1234566, 123456)
False
"""
return sorted(str(num1)) == sorted(str(num2))
def solution(max_n: int = 10000000) -> int:
"""
Finds the value of n from 1 to max such that n/φ(n) produces a minimum.
>>> solution(100)
21
>>> solution(10000)
4435
"""
min_numerator = 1 # i
min_denominator = 0 # φ(i)
totients = get_totients(max_n + 1)
for i in range(2, max_n + 1):
t = totients[i]
if i * min_denominator < min_numerator * t and has_same_digits(i, t):
min_numerator = i
min_denominator = t
return min_numerator
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 70: https://projecteuler.net/problem=70
Euler's Totient function, φ(n) [sometimes called the phi function], is used to
determine the number of positive numbers less than or equal to n which are
relatively prime to n. For example, as 1, 2, 4, 5, 7, and 8, are all less than
nine and relatively prime to nine, φ(9)=6.
The number 1 is considered to be relatively prime to every positive number, so
φ(1)=1.
Interestingly, φ(87109)=79180, and it can be seen that 87109 is a permutation
of 79180.
Find the value of n, 1 < n < 10^7, for which φ(n) is a permutation of n and
the ratio n/φ(n) produces a minimum.
-----
This is essentially brute force. Calculate all totients up to 10^7 and
find the minimum ratio of n/φ(n) that way. To minimize the ratio, we want
to minimize n and maximize φ(n) as much as possible, so we can store the
minimum fraction's numerator and denominator and calculate new fractions
with each totient to compare against. To avoid dividing by zero, I opt to
use cross multiplication.
References:
Finding totients
https://en.wikipedia.org/wiki/Euler's_totient_function#Euler's_product_formula
"""
from __future__ import annotations
def get_totients(max_one: int) -> list[int]:
"""
Calculates a list of totients from 0 to max_one exclusive, using the
definition of Euler's product formula.
>>> get_totients(5)
[0, 1, 1, 2, 2]
>>> get_totients(10)
[0, 1, 1, 2, 2, 4, 2, 6, 4, 6]
"""
totients = [0] * max_one
for i in range(0, max_one):
totients[i] = i
for i in range(2, max_one):
if totients[i] == i:
for j in range(i, max_one, i):
totients[j] -= totients[j] // i
return totients
def has_same_digits(num1: int, num2: int) -> bool:
"""
Return True if num1 and num2 have the same frequency of every digit, False
otherwise.
>>> has_same_digits(123456789, 987654321)
True
>>> has_same_digits(123, 23)
False
>>> has_same_digits(1234566, 123456)
False
"""
return sorted(str(num1)) == sorted(str(num2))
def solution(max_n: int = 10000000) -> int:
"""
Finds the value of n from 1 to max such that n/φ(n) produces a minimum.
>>> solution(100)
21
>>> solution(10000)
4435
"""
min_numerator = 1 # i
min_denominator = 0 # φ(i)
totients = get_totients(max_n + 1)
for i in range(2, max_n + 1):
t = totients[i]
if i * min_denominator < min_numerator * t and has_same_digits(i, t):
min_numerator = i
min_denominator = t
return min_numerator
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
import random
import string
class ShuffledShiftCipher:
"""
This algorithm uses the Caesar Cipher algorithm but removes the option to
use brute force to decrypt the message.
The passcode is a random password from the selection buffer of
1. uppercase letters of the English alphabet
2. lowercase letters of the English alphabet
3. digits from 0 to 9
Using unique characters from the passcode, the normal list of characters,
that can be allowed in the plaintext, is pivoted and shuffled. Refer to docstring
of __make_key_list() to learn more about the shuffling.
Then, using the passcode, a number is calculated which is used to encrypt the
plaintext message with the normal shift cipher method, only in this case, the
reference, to look back at while decrypting, is shuffled.
Each cipher object can possess an optional argument as passcode, without which a
new passcode is generated for that object automatically.
cip1 = ShuffledShiftCipher('d4usr9TWxw9wMD')
cip2 = ShuffledShiftCipher()
"""
def __init__(self, passcode: str | None = None) -> None:
"""
Initializes a cipher object with a passcode as it's entity
Note: No new passcode is generated if user provides a passcode
while creating the object
"""
self.__passcode = passcode or self.__passcode_creator()
self.__key_list = self.__make_key_list()
self.__shift_key = self.__make_shift_key()
def __str__(self) -> str:
"""
:return: passcode of the cipher object
"""
return "Passcode is: " + "".join(self.__passcode)
def __neg_pos(self, iterlist: list[int]) -> list[int]:
"""
Mutates the list by changing the sign of each alternate element
:param iterlist: takes a list iterable
:return: the mutated list
"""
for i in range(1, len(iterlist), 2):
iterlist[i] *= -1
return iterlist
def __passcode_creator(self) -> list[str]:
"""
Creates a random password from the selection buffer of
1. uppercase letters of the English alphabet
2. lowercase letters of the English alphabet
3. digits from 0 to 9
:rtype: list
:return: a password of a random length between 10 to 20
"""
choices = string.ascii_letters + string.digits
password = [random.choice(choices) for _ in range(random.randint(10, 20))]
return password
def __make_key_list(self) -> list[str]:
"""
Shuffles the ordered character choices by pivoting at breakpoints
Breakpoints are the set of characters in the passcode
eg:
if, ABCDEFGHIJKLMNOPQRSTUVWXYZ are the possible characters
and CAMERA is the passcode
then, breakpoints = [A,C,E,M,R] # sorted set of characters from passcode
shuffled parts: [A,CB,ED,MLKJIHGF,RQPON,ZYXWVUTS]
shuffled __key_list : ACBEDMLKJIHGFRQPONZYXWVUTS
Shuffling only 26 letters of the english alphabet can generate 26!
combinations for the shuffled list. In the program we consider, a set of
97 characters (including letters, digits, punctuation and whitespaces),
thereby creating a possibility of 97! combinations (which is a 152 digit number
in itself), thus diminishing the possibility of a brute force approach.
Moreover, shift keys even introduce a multiple of 26 for a brute force approach
for each of the already 97! combinations.
"""
# key_list_options contain nearly all printable except few elements from
# string.whitespace
key_list_options = (
string.ascii_letters + string.digits + string.punctuation + " \t\n"
)
keys_l = []
# creates points known as breakpoints to break the key_list_options at those
# points and pivot each substring
breakpoints = sorted(set(self.__passcode))
temp_list: list[str] = []
# algorithm for creating a new shuffled list, keys_l, out of key_list_options
for i in key_list_options:
temp_list.extend(i)
# checking breakpoints at which to pivot temporary sublist and add it into
# keys_l
if i in breakpoints or i == key_list_options[-1]:
keys_l.extend(temp_list[::-1])
temp_list.clear()
# returning a shuffled keys_l to prevent brute force guessing of shift key
return keys_l
def __make_shift_key(self) -> int:
"""
sum() of the mutated list of ascii values of all characters where the
mutated list is the one returned by __neg_pos()
"""
num = sum(self.__neg_pos([ord(x) for x in self.__passcode]))
return num if num > 0 else len(self.__passcode)
def decrypt(self, encoded_message: str) -> str:
"""
Performs shifting of the encoded_message w.r.t. the shuffled __key_list
to create the decoded_message
>>> ssc = ShuffledShiftCipher('4PYIXyqeQZr44')
>>> ssc.decrypt("d>**-1z6&'5z'5z:z+-='$'>=zp:>5:#z<'.&>#")
'Hello, this is a modified Caesar cipher'
"""
decoded_message = ""
# decoding shift like Caesar cipher algorithm implementing negative shift or
# reverse shift or left shift
for i in encoded_message:
position = self.__key_list.index(i)
decoded_message += self.__key_list[
(position - self.__shift_key) % -len(self.__key_list)
]
return decoded_message
def encrypt(self, plaintext: str) -> str:
"""
Performs shifting of the plaintext w.r.t. the shuffled __key_list
to create the encoded_message
>>> ssc = ShuffledShiftCipher('4PYIXyqeQZr44')
>>> ssc.encrypt('Hello, this is a modified Caesar cipher')
"d>**-1z6&'5z'5z:z+-='$'>=zp:>5:#z<'.&>#"
"""
encoded_message = ""
# encoding shift like Caesar cipher algorithm implementing positive shift or
# forward shift or right shift
for i in plaintext:
position = self.__key_list.index(i)
encoded_message += self.__key_list[
(position + self.__shift_key) % len(self.__key_list)
]
return encoded_message
def test_end_to_end(msg: str = "Hello, this is a modified Caesar cipher") -> str:
"""
>>> test_end_to_end()
'Hello, this is a modified Caesar cipher'
"""
cip1 = ShuffledShiftCipher()
return cip1.decrypt(cip1.encrypt(msg))
if __name__ == "__main__":
import doctest
doctest.testmod()
| from __future__ import annotations
import random
import string
class ShuffledShiftCipher:
"""
This algorithm uses the Caesar Cipher algorithm but removes the option to
use brute force to decrypt the message.
The passcode is a random password from the selection buffer of
1. uppercase letters of the English alphabet
2. lowercase letters of the English alphabet
3. digits from 0 to 9
Using unique characters from the passcode, the normal list of characters,
that can be allowed in the plaintext, is pivoted and shuffled. Refer to docstring
of __make_key_list() to learn more about the shuffling.
Then, using the passcode, a number is calculated which is used to encrypt the
plaintext message with the normal shift cipher method, only in this case, the
reference, to look back at while decrypting, is shuffled.
Each cipher object can possess an optional argument as passcode, without which a
new passcode is generated for that object automatically.
cip1 = ShuffledShiftCipher('d4usr9TWxw9wMD')
cip2 = ShuffledShiftCipher()
"""
def __init__(self, passcode: str | None = None) -> None:
"""
Initializes a cipher object with a passcode as it's entity
Note: No new passcode is generated if user provides a passcode
while creating the object
"""
self.__passcode = passcode or self.__passcode_creator()
self.__key_list = self.__make_key_list()
self.__shift_key = self.__make_shift_key()
def __str__(self) -> str:
"""
:return: passcode of the cipher object
"""
return "Passcode is: " + "".join(self.__passcode)
def __neg_pos(self, iterlist: list[int]) -> list[int]:
"""
Mutates the list by changing the sign of each alternate element
:param iterlist: takes a list iterable
:return: the mutated list
"""
for i in range(1, len(iterlist), 2):
iterlist[i] *= -1
return iterlist
def __passcode_creator(self) -> list[str]:
"""
Creates a random password from the selection buffer of
1. uppercase letters of the English alphabet
2. lowercase letters of the English alphabet
3. digits from 0 to 9
:rtype: list
:return: a password of a random length between 10 to 20
"""
choices = string.ascii_letters + string.digits
password = [random.choice(choices) for _ in range(random.randint(10, 20))]
return password
def __make_key_list(self) -> list[str]:
"""
Shuffles the ordered character choices by pivoting at breakpoints
Breakpoints are the set of characters in the passcode
eg:
if, ABCDEFGHIJKLMNOPQRSTUVWXYZ are the possible characters
and CAMERA is the passcode
then, breakpoints = [A,C,E,M,R] # sorted set of characters from passcode
shuffled parts: [A,CB,ED,MLKJIHGF,RQPON,ZYXWVUTS]
shuffled __key_list : ACBEDMLKJIHGFRQPONZYXWVUTS
Shuffling only 26 letters of the english alphabet can generate 26!
combinations for the shuffled list. In the program we consider, a set of
97 characters (including letters, digits, punctuation and whitespaces),
thereby creating a possibility of 97! combinations (which is a 152 digit number
in itself), thus diminishing the possibility of a brute force approach.
Moreover, shift keys even introduce a multiple of 26 for a brute force approach
for each of the already 97! combinations.
"""
# key_list_options contain nearly all printable except few elements from
# string.whitespace
key_list_options = (
string.ascii_letters + string.digits + string.punctuation + " \t\n"
)
keys_l = []
# creates points known as breakpoints to break the key_list_options at those
# points and pivot each substring
breakpoints = sorted(set(self.__passcode))
temp_list: list[str] = []
# algorithm for creating a new shuffled list, keys_l, out of key_list_options
for i in key_list_options:
temp_list.extend(i)
# checking breakpoints at which to pivot temporary sublist and add it into
# keys_l
if i in breakpoints or i == key_list_options[-1]:
keys_l.extend(temp_list[::-1])
temp_list.clear()
# returning a shuffled keys_l to prevent brute force guessing of shift key
return keys_l
def __make_shift_key(self) -> int:
"""
sum() of the mutated list of ascii values of all characters where the
mutated list is the one returned by __neg_pos()
"""
num = sum(self.__neg_pos([ord(x) for x in self.__passcode]))
return num if num > 0 else len(self.__passcode)
def decrypt(self, encoded_message: str) -> str:
"""
Performs shifting of the encoded_message w.r.t. the shuffled __key_list
to create the decoded_message
>>> ssc = ShuffledShiftCipher('4PYIXyqeQZr44')
>>> ssc.decrypt("d>**-1z6&'5z'5z:z+-='$'>=zp:>5:#z<'.&>#")
'Hello, this is a modified Caesar cipher'
"""
decoded_message = ""
# decoding shift like Caesar cipher algorithm implementing negative shift or
# reverse shift or left shift
for i in encoded_message:
position = self.__key_list.index(i)
decoded_message += self.__key_list[
(position - self.__shift_key) % -len(self.__key_list)
]
return decoded_message
def encrypt(self, plaintext: str) -> str:
"""
Performs shifting of the plaintext w.r.t. the shuffled __key_list
to create the encoded_message
>>> ssc = ShuffledShiftCipher('4PYIXyqeQZr44')
>>> ssc.encrypt('Hello, this is a modified Caesar cipher')
"d>**-1z6&'5z'5z:z+-='$'>=zp:>5:#z<'.&>#"
"""
encoded_message = ""
# encoding shift like Caesar cipher algorithm implementing positive shift or
# forward shift or right shift
for i in plaintext:
position = self.__key_list.index(i)
encoded_message += self.__key_list[
(position + self.__shift_key) % len(self.__key_list)
]
return encoded_message
def test_end_to_end(msg: str = "Hello, this is a modified Caesar cipher") -> str:
"""
>>> test_end_to_end()
'Hello, this is a modified Caesar cipher'
"""
cip1 = ShuffledShiftCipher()
return cip1.decrypt(cip1.encrypt(msg))
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
"""
Davis–Putnam–Logemann–Loveland (DPLL) algorithm is a complete, backtracking-based
search algorithm for deciding the satisfiability of propositional logic formulae in
conjunctive normal form, i.e, for solving the Conjunctive Normal Form SATisfiability
(CNF-SAT) problem.
For more information about the algorithm: https://en.wikipedia.org/wiki/DPLL_algorithm
"""
from __future__ import annotations
import random
from collections.abc import Iterable
class Clause:
"""
A clause represented in Conjunctive Normal Form.
A clause is a set of literals, either complemented or otherwise.
For example:
{A1, A2, A3'} is the clause (A1 v A2 v A3')
{A5', A2', A1} is the clause (A5' v A2' v A1)
Create model
>>> clause = Clause(["A1", "A2'", "A3"])
>>> clause.evaluate({"A1": True})
True
"""
def __init__(self, literals: list[str]) -> None:
"""
Represent the literals and an assignment in a clause."
"""
# Assign all literals to None initially
self.literals: dict[str, bool | None] = {literal: None for literal in literals}
def __str__(self) -> str:
"""
To print a clause as in Conjunctive Normal Form.
>>> str(Clause(["A1", "A2'", "A3"]))
"{A1 , A2' , A3}"
"""
return "{" + " , ".join(self.literals) + "}"
def __len__(self) -> int:
"""
To print a clause as in Conjunctive Normal Form.
>>> len(Clause([]))
0
>>> len(Clause(["A1", "A2'", "A3"]))
3
"""
return len(self.literals)
def assign(self, model: dict[str, bool | None]) -> None:
"""
Assign values to literals of the clause as given by model.
"""
for literal in self.literals:
symbol = literal[:2]
if symbol in model:
value = model[symbol]
else:
continue
if value is not None:
# Complement assignment if literal is in complemented form
if literal.endswith("'"):
value = not value
self.literals[literal] = value
def evaluate(self, model: dict[str, bool | None]) -> bool | None:
"""
Evaluates the clause with the assignments in model.
This has the following steps:
1. Return True if both a literal and its complement exist in the clause.
2. Return True if a single literal has the assignment True.
3. Return None(unable to complete evaluation) if a literal has no assignment.
4. Compute disjunction of all values assigned in clause.
"""
for literal in self.literals:
symbol = literal.rstrip("'") if literal.endswith("'") else literal + "'"
if symbol in self.literals:
return True
self.assign(model)
for value in self.literals.values():
if value in (True, None):
return value
return any(self.literals.values())
class Formula:
"""
A formula represented in Conjunctive Normal Form.
A formula is a set of clauses.
For example,
{{A1, A2, A3'}, {A5', A2', A1}} is ((A1 v A2 v A3') and (A5' v A2' v A1))
"""
def __init__(self, clauses: Iterable[Clause]) -> None:
"""
Represent the number of clauses and the clauses themselves.
"""
self.clauses = list(clauses)
def __str__(self) -> str:
"""
To print a formula as in Conjunctive Normal Form.
str(Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])]))
"{{A1 , A2' , A3} , {A5' , A2' , A1}}"
"""
return "{" + " , ".join(str(clause) for clause in self.clauses) + "}"
def generate_clause() -> Clause:
"""
Randomly generate a clause.
All literals have the name Ax, where x is an integer from 1 to 5.
"""
literals = []
no_of_literals = random.randint(1, 5)
base_var = "A"
i = 0
while i < no_of_literals:
var_no = random.randint(1, 5)
var_name = base_var + str(var_no)
var_complement = random.randint(0, 1)
if var_complement == 1:
var_name += "'"
if var_name in literals:
i -= 1
else:
literals.append(var_name)
i += 1
return Clause(literals)
def generate_formula() -> Formula:
"""
Randomly generate a formula.
"""
clauses: set[Clause] = set()
no_of_clauses = random.randint(1, 10)
while len(clauses) < no_of_clauses:
clauses.add(generate_clause())
return Formula(clauses)
def generate_parameters(formula: Formula) -> tuple[list[Clause], list[str]]:
"""
Return the clauses and symbols from a formula.
A symbol is the uncomplemented form of a literal.
For example,
Symbol of A3 is A3.
Symbol of A5' is A5.
>>> formula = Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])])
>>> clauses, symbols = generate_parameters(formula)
>>> clauses_list = [str(i) for i in clauses]
>>> clauses_list
["{A1 , A2' , A3}", "{A5' , A2' , A1}"]
>>> symbols
['A1', 'A2', 'A3', 'A5']
"""
clauses = formula.clauses
symbols_set = []
for clause in formula.clauses:
for literal in clause.literals:
symbol = literal[:2]
if symbol not in symbols_set:
symbols_set.append(symbol)
return clauses, symbols_set
def find_pure_symbols(
clauses: list[Clause], symbols: list[str], model: dict[str, bool | None]
) -> tuple[list[str], dict[str, bool | None]]:
"""
Return pure symbols and their values to satisfy clause.
Pure symbols are symbols in a formula that exist only
in one form, either complemented or otherwise.
For example,
{ { A4 , A3 , A5' , A1 , A3' } , { A4 } , { A3 } } has
pure symbols A4, A5' and A1.
This has the following steps:
1. Ignore clauses that have already evaluated to be True.
2. Find symbols that occur only in one form in the rest of the clauses.
3. Assign value True or False depending on whether the symbols occurs
in normal or complemented form respectively.
>>> formula = Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])])
>>> clauses, symbols = generate_parameters(formula)
>>> pure_symbols, values = find_pure_symbols(clauses, symbols, {})
>>> pure_symbols
['A1', 'A2', 'A3', 'A5']
>>> values
{'A1': True, 'A2': False, 'A3': True, 'A5': False}
"""
pure_symbols = []
assignment: dict[str, bool | None] = {}
literals = []
for clause in clauses:
if clause.evaluate(model):
continue
for literal in clause.literals:
literals.append(literal)
for s in symbols:
sym = s + "'"
if (s in literals and sym not in literals) or (
s not in literals and sym in literals
):
pure_symbols.append(s)
for p in pure_symbols:
assignment[p] = None
for s in pure_symbols:
sym = s + "'"
if s in literals:
assignment[s] = True
elif sym in literals:
assignment[s] = False
return pure_symbols, assignment
def find_unit_clauses(
clauses: list[Clause], model: dict[str, bool | None]
) -> tuple[list[str], dict[str, bool | None]]:
"""
Returns the unit symbols and their values to satisfy clause.
Unit symbols are symbols in a formula that are:
- Either the only symbol in a clause
- Or all other literals in that clause have been assigned False
This has the following steps:
1. Find symbols that are the only occurrences in a clause.
2. Find symbols in a clause where all other literals are assigned False.
3. Assign True or False depending on whether the symbols occurs in
normal or complemented form respectively.
>>> clause1 = Clause(["A4", "A3", "A5'", "A1", "A3'"])
>>> clause2 = Clause(["A4"])
>>> clause3 = Clause(["A3"])
>>> clauses, symbols = generate_parameters(Formula([clause1, clause2, clause3]))
>>> unit_clauses, values = find_unit_clauses(clauses, {})
>>> unit_clauses
['A4', 'A3']
>>> values
{'A4': True, 'A3': True}
"""
unit_symbols = []
for clause in clauses:
if len(clause) == 1:
unit_symbols.append(list(clause.literals.keys())[0])
else:
f_count, n_count = 0, 0
for literal, value in clause.literals.items():
if value is False:
f_count += 1
elif value is None:
sym = literal
n_count += 1
if f_count == len(clause) - 1 and n_count == 1:
unit_symbols.append(sym)
assignment: dict[str, bool | None] = {}
for i in unit_symbols:
symbol = i[:2]
assignment[symbol] = len(i) == 2
unit_symbols = [i[:2] for i in unit_symbols]
return unit_symbols, assignment
def dpll_algorithm(
clauses: list[Clause], symbols: list[str], model: dict[str, bool | None]
) -> tuple[bool | None, dict[str, bool | None] | None]:
"""
Returns the model if the formula is satisfiable, else None
This has the following steps:
1. If every clause in clauses is True, return True.
2. If some clause in clauses is False, return False.
3. Find pure symbols.
4. Find unit symbols.
>>> formula = Formula([Clause(["A4", "A3", "A5'", "A1", "A3'"]), Clause(["A4"])])
>>> clauses, symbols = generate_parameters(formula)
>>> soln, model = dpll_algorithm(clauses, symbols, {})
>>> soln
True
>>> model
{'A4': True}
"""
check_clause_all_true = True
for clause in clauses:
clause_check = clause.evaluate(model)
if clause_check is False:
return False, None
elif clause_check is None:
check_clause_all_true = False
continue
if check_clause_all_true:
return True, model
try:
pure_symbols, assignment = find_pure_symbols(clauses, symbols, model)
except RecursionError:
print("raises a RecursionError and is")
return None, {}
p = None
if len(pure_symbols) > 0:
p, value = pure_symbols[0], assignment[pure_symbols[0]]
if p:
tmp_model = model
tmp_model[p] = value
tmp_symbols = list(symbols)
if p in tmp_symbols:
tmp_symbols.remove(p)
return dpll_algorithm(clauses, tmp_symbols, tmp_model)
unit_symbols, assignment = find_unit_clauses(clauses, model)
p = None
if len(unit_symbols) > 0:
p, value = unit_symbols[0], assignment[unit_symbols[0]]
if p:
tmp_model = model
tmp_model[p] = value
tmp_symbols = list(symbols)
if p in tmp_symbols:
tmp_symbols.remove(p)
return dpll_algorithm(clauses, tmp_symbols, tmp_model)
p = symbols[0]
rest = symbols[1:]
tmp1, tmp2 = model, model
tmp1[p], tmp2[p] = True, False
return dpll_algorithm(clauses, rest, tmp1) or dpll_algorithm(clauses, rest, tmp2)
if __name__ == "__main__":
import doctest
doctest.testmod()
formula = generate_formula()
print(f"The formula {formula} is", end=" ")
clauses, symbols = generate_parameters(formula)
solution, model = dpll_algorithm(clauses, symbols, {})
if solution:
print(f"satisfiable with the assignment {model}.")
else:
print("not satisfiable.")
| #!/usr/bin/env python3
"""
Davis–Putnam–Logemann–Loveland (DPLL) algorithm is a complete, backtracking-based
search algorithm for deciding the satisfiability of propositional logic formulae in
conjunctive normal form, i.e, for solving the Conjunctive Normal Form SATisfiability
(CNF-SAT) problem.
For more information about the algorithm: https://en.wikipedia.org/wiki/DPLL_algorithm
"""
from __future__ import annotations
import random
from collections.abc import Iterable
class Clause:
"""
A clause represented in Conjunctive Normal Form.
A clause is a set of literals, either complemented or otherwise.
For example:
{A1, A2, A3'} is the clause (A1 v A2 v A3')
{A5', A2', A1} is the clause (A5' v A2' v A1)
Create model
>>> clause = Clause(["A1", "A2'", "A3"])
>>> clause.evaluate({"A1": True})
True
"""
def __init__(self, literals: list[str]) -> None:
"""
Represent the literals and an assignment in a clause."
"""
# Assign all literals to None initially
self.literals: dict[str, bool | None] = {literal: None for literal in literals}
def __str__(self) -> str:
"""
To print a clause as in Conjunctive Normal Form.
>>> str(Clause(["A1", "A2'", "A3"]))
"{A1 , A2' , A3}"
"""
return "{" + " , ".join(self.literals) + "}"
def __len__(self) -> int:
"""
To print a clause as in Conjunctive Normal Form.
>>> len(Clause([]))
0
>>> len(Clause(["A1", "A2'", "A3"]))
3
"""
return len(self.literals)
def assign(self, model: dict[str, bool | None]) -> None:
"""
Assign values to literals of the clause as given by model.
"""
for literal in self.literals:
symbol = literal[:2]
if symbol in model:
value = model[symbol]
else:
continue
if value is not None:
# Complement assignment if literal is in complemented form
if literal.endswith("'"):
value = not value
self.literals[literal] = value
def evaluate(self, model: dict[str, bool | None]) -> bool | None:
"""
Evaluates the clause with the assignments in model.
This has the following steps:
1. Return True if both a literal and its complement exist in the clause.
2. Return True if a single literal has the assignment True.
3. Return None(unable to complete evaluation) if a literal has no assignment.
4. Compute disjunction of all values assigned in clause.
"""
for literal in self.literals:
symbol = literal.rstrip("'") if literal.endswith("'") else literal + "'"
if symbol in self.literals:
return True
self.assign(model)
for value in self.literals.values():
if value in (True, None):
return value
return any(self.literals.values())
class Formula:
"""
A formula represented in Conjunctive Normal Form.
A formula is a set of clauses.
For example,
{{A1, A2, A3'}, {A5', A2', A1}} is ((A1 v A2 v A3') and (A5' v A2' v A1))
"""
def __init__(self, clauses: Iterable[Clause]) -> None:
"""
Represent the number of clauses and the clauses themselves.
"""
self.clauses = list(clauses)
def __str__(self) -> str:
"""
To print a formula as in Conjunctive Normal Form.
str(Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])]))
"{{A1 , A2' , A3} , {A5' , A2' , A1}}"
"""
return "{" + " , ".join(str(clause) for clause in self.clauses) + "}"
def generate_clause() -> Clause:
"""
Randomly generate a clause.
All literals have the name Ax, where x is an integer from 1 to 5.
"""
literals = []
no_of_literals = random.randint(1, 5)
base_var = "A"
i = 0
while i < no_of_literals:
var_no = random.randint(1, 5)
var_name = base_var + str(var_no)
var_complement = random.randint(0, 1)
if var_complement == 1:
var_name += "'"
if var_name in literals:
i -= 1
else:
literals.append(var_name)
i += 1
return Clause(literals)
def generate_formula() -> Formula:
"""
Randomly generate a formula.
"""
clauses: set[Clause] = set()
no_of_clauses = random.randint(1, 10)
while len(clauses) < no_of_clauses:
clauses.add(generate_clause())
return Formula(clauses)
def generate_parameters(formula: Formula) -> tuple[list[Clause], list[str]]:
"""
Return the clauses and symbols from a formula.
A symbol is the uncomplemented form of a literal.
For example,
Symbol of A3 is A3.
Symbol of A5' is A5.
>>> formula = Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])])
>>> clauses, symbols = generate_parameters(formula)
>>> clauses_list = [str(i) for i in clauses]
>>> clauses_list
["{A1 , A2' , A3}", "{A5' , A2' , A1}"]
>>> symbols
['A1', 'A2', 'A3', 'A5']
"""
clauses = formula.clauses
symbols_set = []
for clause in formula.clauses:
for literal in clause.literals:
symbol = literal[:2]
if symbol not in symbols_set:
symbols_set.append(symbol)
return clauses, symbols_set
def find_pure_symbols(
clauses: list[Clause], symbols: list[str], model: dict[str, bool | None]
) -> tuple[list[str], dict[str, bool | None]]:
"""
Return pure symbols and their values to satisfy clause.
Pure symbols are symbols in a formula that exist only
in one form, either complemented or otherwise.
For example,
{ { A4 , A3 , A5' , A1 , A3' } , { A4 } , { A3 } } has
pure symbols A4, A5' and A1.
This has the following steps:
1. Ignore clauses that have already evaluated to be True.
2. Find symbols that occur only in one form in the rest of the clauses.
3. Assign value True or False depending on whether the symbols occurs
in normal or complemented form respectively.
>>> formula = Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])])
>>> clauses, symbols = generate_parameters(formula)
>>> pure_symbols, values = find_pure_symbols(clauses, symbols, {})
>>> pure_symbols
['A1', 'A2', 'A3', 'A5']
>>> values
{'A1': True, 'A2': False, 'A3': True, 'A5': False}
"""
pure_symbols = []
assignment: dict[str, bool | None] = {}
literals = []
for clause in clauses:
if clause.evaluate(model):
continue
for literal in clause.literals:
literals.append(literal)
for s in symbols:
sym = s + "'"
if (s in literals and sym not in literals) or (
s not in literals and sym in literals
):
pure_symbols.append(s)
for p in pure_symbols:
assignment[p] = None
for s in pure_symbols:
sym = s + "'"
if s in literals:
assignment[s] = True
elif sym in literals:
assignment[s] = False
return pure_symbols, assignment
def find_unit_clauses(
clauses: list[Clause], model: dict[str, bool | None]
) -> tuple[list[str], dict[str, bool | None]]:
"""
Returns the unit symbols and their values to satisfy clause.
Unit symbols are symbols in a formula that are:
- Either the only symbol in a clause
- Or all other literals in that clause have been assigned False
This has the following steps:
1. Find symbols that are the only occurrences in a clause.
2. Find symbols in a clause where all other literals are assigned False.
3. Assign True or False depending on whether the symbols occurs in
normal or complemented form respectively.
>>> clause1 = Clause(["A4", "A3", "A5'", "A1", "A3'"])
>>> clause2 = Clause(["A4"])
>>> clause3 = Clause(["A3"])
>>> clauses, symbols = generate_parameters(Formula([clause1, clause2, clause3]))
>>> unit_clauses, values = find_unit_clauses(clauses, {})
>>> unit_clauses
['A4', 'A3']
>>> values
{'A4': True, 'A3': True}
"""
unit_symbols = []
for clause in clauses:
if len(clause) == 1:
unit_symbols.append(list(clause.literals.keys())[0])
else:
f_count, n_count = 0, 0
for literal, value in clause.literals.items():
if value is False:
f_count += 1
elif value is None:
sym = literal
n_count += 1
if f_count == len(clause) - 1 and n_count == 1:
unit_symbols.append(sym)
assignment: dict[str, bool | None] = {}
for i in unit_symbols:
symbol = i[:2]
assignment[symbol] = len(i) == 2
unit_symbols = [i[:2] for i in unit_symbols]
return unit_symbols, assignment
def dpll_algorithm(
clauses: list[Clause], symbols: list[str], model: dict[str, bool | None]
) -> tuple[bool | None, dict[str, bool | None] | None]:
"""
Returns the model if the formula is satisfiable, else None
This has the following steps:
1. If every clause in clauses is True, return True.
2. If some clause in clauses is False, return False.
3. Find pure symbols.
4. Find unit symbols.
>>> formula = Formula([Clause(["A4", "A3", "A5'", "A1", "A3'"]), Clause(["A4"])])
>>> clauses, symbols = generate_parameters(formula)
>>> soln, model = dpll_algorithm(clauses, symbols, {})
>>> soln
True
>>> model
{'A4': True}
"""
check_clause_all_true = True
for clause in clauses:
clause_check = clause.evaluate(model)
if clause_check is False:
return False, None
elif clause_check is None:
check_clause_all_true = False
continue
if check_clause_all_true:
return True, model
try:
pure_symbols, assignment = find_pure_symbols(clauses, symbols, model)
except RecursionError:
print("raises a RecursionError and is")
return None, {}
p = None
if len(pure_symbols) > 0:
p, value = pure_symbols[0], assignment[pure_symbols[0]]
if p:
tmp_model = model
tmp_model[p] = value
tmp_symbols = list(symbols)
if p in tmp_symbols:
tmp_symbols.remove(p)
return dpll_algorithm(clauses, tmp_symbols, tmp_model)
unit_symbols, assignment = find_unit_clauses(clauses, model)
p = None
if len(unit_symbols) > 0:
p, value = unit_symbols[0], assignment[unit_symbols[0]]
if p:
tmp_model = model
tmp_model[p] = value
tmp_symbols = list(symbols)
if p in tmp_symbols:
tmp_symbols.remove(p)
return dpll_algorithm(clauses, tmp_symbols, tmp_model)
p = symbols[0]
rest = symbols[1:]
tmp1, tmp2 = model, model
tmp1[p], tmp2[p] = True, False
return dpll_algorithm(clauses, rest, tmp1) or dpll_algorithm(clauses, rest, tmp2)
if __name__ == "__main__":
import doctest
doctest.testmod()
formula = generate_formula()
print(f"The formula {formula} is", end=" ")
clauses, symbols = generate_parameters(formula)
solution, model = dpll_algorithm(clauses, symbols, {})
if solution:
print(f"satisfiable with the assignment {model}.")
else:
print("not satisfiable.")
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is a Python implementation for questions involving task assignments between people.
Here Bitmasking and DP are used for solving this.
Question :-
We have N tasks and M people. Each person in M can do only certain of these tasks. Also
a person can do only one task and a task is performed only by one person.
Find the total no of ways in which the tasks can be distributed.
"""
from collections import defaultdict
class AssignmentUsingBitmask:
def __init__(self, task_performed, total):
self.total_tasks = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
self.dp = [
[-1 for i in range(total + 1)] for j in range(2 ** len(task_performed))
]
self.task = defaultdict(list) # stores the list of persons for each task
# final_mask is used to check if all persons are included by setting all bits
# to 1
self.final_mask = (1 << len(task_performed)) - 1
def count_ways_until(self, mask, task_no):
# if mask == self.finalmask all persons are distributed tasks, return 1
if mask == self.final_mask:
return 1
# if not everyone gets the task and no more tasks are available, return 0
if task_no > self.total_tasks:
return 0
# if case already considered
if self.dp[mask][task_no] != -1:
return self.dp[mask][task_no]
# Number of ways when we don't this task in the arrangement
total_ways_util = self.count_ways_until(mask, task_no + 1)
# now assign the tasks one by one to all possible persons and recursively
# assign for the remaining tasks.
if task_no in self.task:
for p in self.task[task_no]:
# if p is already given a task
if mask & (1 << p):
continue
# assign this task to p and change the mask value. And recursively
# assign tasks with the new mask value.
total_ways_util += self.count_ways_until(mask | (1 << p), task_no + 1)
# save the value.
self.dp[mask][task_no] = total_ways_util
return self.dp[mask][task_no]
def count_no_of_ways(self, task_performed):
# Store the list of persons for each task
for i in range(len(task_performed)):
for j in task_performed[i]:
self.task[j].append(i)
# call the function to fill the DP table, final answer is stored in dp[0][1]
return self.count_ways_until(0, 1)
if __name__ == "__main__":
total_tasks = 5 # total no of tasks (the value of N)
# the list of tasks that can be done by M persons.
task_performed = [[1, 3, 4], [1, 2, 5], [3, 4]]
print(
AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
task_performed
)
)
"""
For the particular example the tasks can be distributed as
(1,2,3), (1,2,4), (1,5,3), (1,5,4), (3,1,4),
(3,2,4), (3,5,4), (4,1,3), (4,2,3), (4,5,3)
total 10
"""
| """
This is a Python implementation for questions involving task assignments between people.
Here Bitmasking and DP are used for solving this.
Question :-
We have N tasks and M people. Each person in M can do only certain of these tasks. Also
a person can do only one task and a task is performed only by one person.
Find the total no of ways in which the tasks can be distributed.
"""
from collections import defaultdict
class AssignmentUsingBitmask:
def __init__(self, task_performed, total):
self.total_tasks = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
self.dp = [
[-1 for i in range(total + 1)] for j in range(2 ** len(task_performed))
]
self.task = defaultdict(list) # stores the list of persons for each task
# final_mask is used to check if all persons are included by setting all bits
# to 1
self.final_mask = (1 << len(task_performed)) - 1
def count_ways_until(self, mask, task_no):
# if mask == self.finalmask all persons are distributed tasks, return 1
if mask == self.final_mask:
return 1
# if not everyone gets the task and no more tasks are available, return 0
if task_no > self.total_tasks:
return 0
# if case already considered
if self.dp[mask][task_no] != -1:
return self.dp[mask][task_no]
# Number of ways when we don't this task in the arrangement
total_ways_util = self.count_ways_until(mask, task_no + 1)
# now assign the tasks one by one to all possible persons and recursively
# assign for the remaining tasks.
if task_no in self.task:
for p in self.task[task_no]:
# if p is already given a task
if mask & (1 << p):
continue
# assign this task to p and change the mask value. And recursively
# assign tasks with the new mask value.
total_ways_util += self.count_ways_until(mask | (1 << p), task_no + 1)
# save the value.
self.dp[mask][task_no] = total_ways_util
return self.dp[mask][task_no]
def count_no_of_ways(self, task_performed):
# Store the list of persons for each task
for i in range(len(task_performed)):
for j in task_performed[i]:
self.task[j].append(i)
# call the function to fill the DP table, final answer is stored in dp[0][1]
return self.count_ways_until(0, 1)
if __name__ == "__main__":
total_tasks = 5 # total no of tasks (the value of N)
# the list of tasks that can be done by M persons.
task_performed = [[1, 3, 4], [1, 2, 5], [3, 4]]
print(
AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
task_performed
)
)
"""
For the particular example the tasks can be distributed as
(1,2,3), (1,2,4), (1,5,3), (1,5,4), (3,1,4),
(3,2,4), (3,5,4), (4,1,3), (4,2,3), (4,5,3)
total 10
"""
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from sys import maxsize
def max_sub_array_sum(a: list, size: int = 0):
"""
>>> max_sub_array_sum([-13, -3, -25, -20, -3, -16, -23, -12, -5, -22, -15, -4, -7])
-3
"""
size = size or len(a)
max_so_far = -maxsize - 1
max_ending_here = 0
for i in range(0, size):
max_ending_here = max_ending_here + a[i]
if max_so_far < max_ending_here:
max_so_far = max_ending_here
if max_ending_here < 0:
max_ending_here = 0
return max_so_far
if __name__ == "__main__":
a = [-13, -3, -25, -20, 1, -16, -23, -12, -5, -22, -15, -4, -7]
print(("Maximum contiguous sum is", max_sub_array_sum(a, len(a))))
| from sys import maxsize
def max_sub_array_sum(a: list, size: int = 0):
"""
>>> max_sub_array_sum([-13, -3, -25, -20, -3, -16, -23, -12, -5, -22, -15, -4, -7])
-3
"""
size = size or len(a)
max_so_far = -maxsize - 1
max_ending_here = 0
for i in range(0, size):
max_ending_here = max_ending_here + a[i]
if max_so_far < max_ending_here:
max_so_far = max_ending_here
if max_ending_here < 0:
max_ending_here = 0
return max_so_far
if __name__ == "__main__":
a = [-13, -3, -25, -20, 1, -16, -23, -12, -5, -22, -15, -4, -7]
print(("Maximum contiguous sum is", max_sub_array_sum(a, len(a))))
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] 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_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))
| """
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 | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://www.hackerrank.com/challenges/abbr/problem
You can perform the following operation on some string, :
1. Capitalize zero or more of 's lowercase letters at some index i
(i.e., make them uppercase).
2. Delete all of the remaining lowercase letters in .
Example:
a=daBcd and b="ABC"
daBcd -> capitalize a and c(dABCd) -> remove d (ABC)
"""
def abbr(a: str, b: str) -> bool:
"""
>>> abbr("daBcd", "ABC")
True
>>> abbr("dBcd", "ABC")
False
"""
n = len(a)
m = len(b)
dp = [[False for _ in range(m + 1)] for _ in range(n + 1)]
dp[0][0] = True
for i in range(n):
for j in range(m + 1):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
dp[i + 1][j + 1] = True
if a[i].islower():
dp[i + 1][j] = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
https://www.hackerrank.com/challenges/abbr/problem
You can perform the following operation on some string, :
1. Capitalize zero or more of 's lowercase letters at some index i
(i.e., make them uppercase).
2. Delete all of the remaining lowercase letters in .
Example:
a=daBcd and b="ABC"
daBcd -> capitalize a and c(dABCd) -> remove d (ABC)
"""
def abbr(a: str, b: str) -> bool:
"""
>>> abbr("daBcd", "ABC")
True
>>> abbr("dBcd", "ABC")
False
"""
n = len(a)
m = len(b)
dp = [[False for _ in range(m + 1)] for _ in range(n + 1)]
dp[0][0] = True
for i in range(n):
for j in range(m + 1):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
dp[i + 1][j + 1] = True
if a[i].islower():
dp[i + 1][j] = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/Bidirectional_search
"""
from __future__ import annotations
import time
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:
def __init__(
self, pos_x: int, pos_y: int, goal_x: int, goal_y: int, 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.parent = parent
class BreadthFirstSearch:
"""
# Comment out slow pytests...
# 9.15s call graphs/bidirectional_breadth_first_search.py:: \
# graphs.bidirectional_breadth_first_search.BreadthFirstSearch
# >>> bfs = BreadthFirstSearch((0, 0), (len(grid) - 1, len(grid[0]) - 1))
# >>> (bfs.start.pos_y + delta[3][0], bfs.start.pos_x + delta[3][1])
(0, 1)
# >>> [x.pos for x in bfs.get_successors(bfs.start)]
[(1, 0), (0, 1)]
# >>> (bfs.start.pos_y + delta[2][0], bfs.start.pos_x + delta[2][1])
(1, 0)
# >>> bfs.retrace_path(bfs.start)
[(0, 0)]
# >>> bfs.search() # doctest: +NORMALIZE_WHITESPACE
[(0, 0), (1, 0), (2, 0), (3, 0), (3, 1), (4, 1),
(5, 1), (5, 2), (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], None)
self.target = Node(goal[1], goal[0], goal[1], goal[0], None)
self.node_queue = [self.start]
self.reached = False
def search(self) -> Path | None:
while self.node_queue:
current_node = self.node_queue.pop(0)
if current_node.pos == self.target.pos:
self.reached = True
return self.retrace_path(current_node)
successors = self.get_successors(current_node)
for node in successors:
self.node_queue.append(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)
)
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
class BidirectionalBreadthFirstSearch:
"""
>>> bd_bfs = BidirectionalBreadthFirstSearch((0, 0), (len(grid) - 1,
... len(grid[0]) - 1))
>>> bd_bfs.fwd_bfs.start.pos == bd_bfs.bwd_bfs.target.pos
True
>>> bd_bfs.retrace_bidirectional_path(bd_bfs.fwd_bfs.start,
... bd_bfs.bwd_bfs.start)
[(0, 0)]
>>> bd_bfs.search() # doctest: +NORMALIZE_WHITESPACE
[(0, 0), (0, 1), (0, 2), (1, 2), (2, 2), (2, 3),
(2, 4), (3, 4), (3, 5), (3, 6), (4, 6), (5, 6), (6, 6)]
"""
def __init__(self, start, goal):
self.fwd_bfs = BreadthFirstSearch(start, goal)
self.bwd_bfs = BreadthFirstSearch(goal, start)
self.reached = False
def search(self) -> Path | None:
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
current_fwd_node = self.fwd_bfs.node_queue.pop(0)
current_bwd_node = self.bwd_bfs.node_queue.pop(0)
if current_bwd_node.pos == current_fwd_node.pos:
self.reached = True
return self.retrace_bidirectional_path(
current_fwd_node, current_bwd_node
)
self.fwd_bfs.target = current_bwd_node
self.bwd_bfs.target = current_fwd_node
successors = {
self.fwd_bfs: self.fwd_bfs.get_successors(current_fwd_node),
self.bwd_bfs: self.bwd_bfs.get_successors(current_bwd_node),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(node)
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def retrace_bidirectional_path(self, fwd_node: Node, bwd_node: Node) -> Path:
fwd_path = self.fwd_bfs.retrace_path(fwd_node)
bwd_path = self.bwd_bfs.retrace_path(bwd_node)
bwd_path.pop()
bwd_path.reverse()
path = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
init = (0, 0)
goal = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
start_bfs_time = time.time()
bfs = BreadthFirstSearch(init, goal)
path = bfs.search()
bfs_time = time.time() - start_bfs_time
print("Unidirectional BFS computation time : ", bfs_time)
start_bd_bfs_time = time.time()
bd_bfs = BidirectionalBreadthFirstSearch(init, goal)
bd_path = bd_bfs.search()
bd_bfs_time = time.time() - start_bd_bfs_time
print("Bidirectional BFS computation time : ", bd_bfs_time)
| """
https://en.wikipedia.org/wiki/Bidirectional_search
"""
from __future__ import annotations
import time
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:
def __init__(
self, pos_x: int, pos_y: int, goal_x: int, goal_y: int, 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.parent = parent
class BreadthFirstSearch:
"""
# Comment out slow pytests...
# 9.15s call graphs/bidirectional_breadth_first_search.py:: \
# graphs.bidirectional_breadth_first_search.BreadthFirstSearch
# >>> bfs = BreadthFirstSearch((0, 0), (len(grid) - 1, len(grid[0]) - 1))
# >>> (bfs.start.pos_y + delta[3][0], bfs.start.pos_x + delta[3][1])
(0, 1)
# >>> [x.pos for x in bfs.get_successors(bfs.start)]
[(1, 0), (0, 1)]
# >>> (bfs.start.pos_y + delta[2][0], bfs.start.pos_x + delta[2][1])
(1, 0)
# >>> bfs.retrace_path(bfs.start)
[(0, 0)]
# >>> bfs.search() # doctest: +NORMALIZE_WHITESPACE
[(0, 0), (1, 0), (2, 0), (3, 0), (3, 1), (4, 1),
(5, 1), (5, 2), (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], None)
self.target = Node(goal[1], goal[0], goal[1], goal[0], None)
self.node_queue = [self.start]
self.reached = False
def search(self) -> Path | None:
while self.node_queue:
current_node = self.node_queue.pop(0)
if current_node.pos == self.target.pos:
self.reached = True
return self.retrace_path(current_node)
successors = self.get_successors(current_node)
for node in successors:
self.node_queue.append(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)
)
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
class BidirectionalBreadthFirstSearch:
"""
>>> bd_bfs = BidirectionalBreadthFirstSearch((0, 0), (len(grid) - 1,
... len(grid[0]) - 1))
>>> bd_bfs.fwd_bfs.start.pos == bd_bfs.bwd_bfs.target.pos
True
>>> bd_bfs.retrace_bidirectional_path(bd_bfs.fwd_bfs.start,
... bd_bfs.bwd_bfs.start)
[(0, 0)]
>>> bd_bfs.search() # doctest: +NORMALIZE_WHITESPACE
[(0, 0), (0, 1), (0, 2), (1, 2), (2, 2), (2, 3),
(2, 4), (3, 4), (3, 5), (3, 6), (4, 6), (5, 6), (6, 6)]
"""
def __init__(self, start, goal):
self.fwd_bfs = BreadthFirstSearch(start, goal)
self.bwd_bfs = BreadthFirstSearch(goal, start)
self.reached = False
def search(self) -> Path | None:
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
current_fwd_node = self.fwd_bfs.node_queue.pop(0)
current_bwd_node = self.bwd_bfs.node_queue.pop(0)
if current_bwd_node.pos == current_fwd_node.pos:
self.reached = True
return self.retrace_bidirectional_path(
current_fwd_node, current_bwd_node
)
self.fwd_bfs.target = current_bwd_node
self.bwd_bfs.target = current_fwd_node
successors = {
self.fwd_bfs: self.fwd_bfs.get_successors(current_fwd_node),
self.bwd_bfs: self.bwd_bfs.get_successors(current_bwd_node),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(node)
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def retrace_bidirectional_path(self, fwd_node: Node, bwd_node: Node) -> Path:
fwd_path = self.fwd_bfs.retrace_path(fwd_node)
bwd_path = self.bwd_bfs.retrace_path(bwd_node)
bwd_path.pop()
bwd_path.reverse()
path = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
init = (0, 0)
goal = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
start_bfs_time = time.time()
bfs = BreadthFirstSearch(init, goal)
path = bfs.search()
bfs_time = time.time() - start_bfs_time
print("Unidirectional BFS computation time : ", bfs_time)
start_bd_bfs_time = time.time()
bd_bfs = BidirectionalBreadthFirstSearch(init, goal)
bd_path = bd_bfs.search()
bd_bfs_time = time.time() - start_bd_bfs_time
print("Bidirectional BFS computation time : ", bd_bfs_time)
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] 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):
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)
)
"""
| # 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 | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This script demonstrates the implementation of the ReLU function.
It's a kind of activation function defined as the positive part of its argument in the
context of neural network.
The function takes a vector of K real numbers as input and then argmax(x, 0).
After through ReLU, the element of the vector always 0 or real number.
Script inspired from its corresponding Wikipedia article
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
"""
from __future__ import annotations
import numpy as np
def relu(vector: list[float]):
"""
Implements the relu function
Parameters:
vector (np.array,list,tuple): A numpy array of shape (1,n)
consisting of real values or a similar list,tuple
Returns:
relu_vec (np.array): The input numpy array, after applying
relu.
>>> vec = np.array([-1, 0, 5])
>>> relu(vec)
array([0, 0, 5])
"""
# compare two arrays and then return element-wise maxima.
return np.maximum(0, vector)
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| """
This script demonstrates the implementation of the ReLU function.
It's a kind of activation function defined as the positive part of its argument in the
context of neural network.
The function takes a vector of K real numbers as input and then argmax(x, 0).
After through ReLU, the element of the vector always 0 or real number.
Script inspired from its corresponding Wikipedia article
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
"""
from __future__ import annotations
import numpy as np
def relu(vector: list[float]):
"""
Implements the relu function
Parameters:
vector (np.array,list,tuple): A numpy array of shape (1,n)
consisting of real values or a similar list,tuple
Returns:
relu_vec (np.array): The input numpy array, after applying
relu.
>>> vec = np.array([-1, 0, 5])
>>> relu(vec)
array([0, 0, 5])
"""
# compare two arrays and then return element-wise maxima.
return np.maximum(0, vector)
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from collections.abc import Sequence
def max_subarray_sum(nums: Sequence[int]) -> int:
"""Return the maximum possible sum amongst all non - empty subarrays.
Raises:
ValueError: when nums is empty.
>>> max_subarray_sum([1,2,3,4,-2])
10
>>> max_subarray_sum([-2,1,-3,4,-1,2,1,-5,4])
6
"""
if not nums:
raise ValueError("Input sequence should not be empty")
curr_max = ans = nums[0]
nums_len = len(nums)
for i in range(1, nums_len):
num = nums[i]
curr_max = max(curr_max + num, num)
ans = max(curr_max, ans)
return ans
if __name__ == "__main__":
n = int(input("Enter number of elements : ").strip())
array = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
print(max_subarray_sum(array))
| from collections.abc import Sequence
def max_subarray_sum(nums: Sequence[int]) -> int:
"""Return the maximum possible sum amongst all non - empty subarrays.
Raises:
ValueError: when nums is empty.
>>> max_subarray_sum([1,2,3,4,-2])
10
>>> max_subarray_sum([-2,1,-3,4,-1,2,1,-5,4])
6
"""
if not nums:
raise ValueError("Input sequence should not be empty")
curr_max = ans = nums[0]
nums_len = len(nums)
for i in range(1, nums_len):
num = nums[i]
curr_max = max(curr_max + num, num)
ans = max(curr_max, ans)
return ans
if __name__ == "__main__":
n = int(input("Enter number of elements : ").strip())
array = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
print(max_subarray_sum(array))
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Matrix Exponentiation"""
import timeit
"""
Matrix Exponentiation is a technique to solve linear recurrences in logarithmic time.
You read more about it here:
https://zobayer.blogspot.com/2010/11/matrix-exponentiation.html
https://www.hackerearth.com/practice/notes/matrix-exponentiation-1/
"""
class Matrix:
def __init__(self, arg):
if isinstance(arg, list): # Initializes a matrix identical to the one provided.
self.t = arg
self.n = len(arg)
else: # Initializes a square matrix of the given size and set values to zero.
self.n = arg
self.t = [[0 for _ in range(self.n)] for _ in range(self.n)]
def __mul__(self, b):
matrix = Matrix(self.n)
for i in range(self.n):
for j in range(self.n):
for k in range(self.n):
matrix.t[i][j] += self.t[i][k] * b.t[k][j]
return matrix
def modular_exponentiation(a, b):
matrix = Matrix([[1, 0], [0, 1]])
while b > 0:
if b & 1:
matrix *= a
a *= a
b >>= 1
return matrix
def fibonacci_with_matrix_exponentiation(n, f1, f2):
# Trivial Cases
if n == 1:
return f1
elif n == 2:
return f2
matrix = Matrix([[1, 1], [1, 0]])
matrix = modular_exponentiation(matrix, n - 2)
return f2 * matrix.t[0][0] + f1 * matrix.t[0][1]
def simple_fibonacci(n, f1, f2):
# Trivial Cases
if n == 1:
return f1
elif n == 2:
return f2
fn_1 = f1
fn_2 = f2
n -= 2
while n > 0:
fn_1, fn_2 = fn_1 + fn_2, fn_1
n -= 1
return fn_1
def matrix_exponentiation_time():
setup = """
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
"""
code = "fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"
exec_time = timeit.timeit(setup=setup, stmt=code, number=100)
print("With matrix exponentiation the average execution time is ", exec_time / 100)
return exec_time
def simple_fibonacci_time():
setup = """
from random import randint
from __main__ import simple_fibonacci
"""
code = "simple_fibonacci(randint(1,70000), 1, 1)"
exec_time = timeit.timeit(setup=setup, stmt=code, number=100)
print(
"Without matrix exponentiation the average execution time is ", exec_time / 100
)
return exec_time
def main():
matrix_exponentiation_time()
simple_fibonacci_time()
if __name__ == "__main__":
main()
| """Matrix Exponentiation"""
import timeit
"""
Matrix Exponentiation is a technique to solve linear recurrences in logarithmic time.
You read more about it here:
https://zobayer.blogspot.com/2010/11/matrix-exponentiation.html
https://www.hackerearth.com/practice/notes/matrix-exponentiation-1/
"""
class Matrix:
def __init__(self, arg):
if isinstance(arg, list): # Initializes a matrix identical to the one provided.
self.t = arg
self.n = len(arg)
else: # Initializes a square matrix of the given size and set values to zero.
self.n = arg
self.t = [[0 for _ in range(self.n)] for _ in range(self.n)]
def __mul__(self, b):
matrix = Matrix(self.n)
for i in range(self.n):
for j in range(self.n):
for k in range(self.n):
matrix.t[i][j] += self.t[i][k] * b.t[k][j]
return matrix
def modular_exponentiation(a, b):
matrix = Matrix([[1, 0], [0, 1]])
while b > 0:
if b & 1:
matrix *= a
a *= a
b >>= 1
return matrix
def fibonacci_with_matrix_exponentiation(n, f1, f2):
# Trivial Cases
if n == 1:
return f1
elif n == 2:
return f2
matrix = Matrix([[1, 1], [1, 0]])
matrix = modular_exponentiation(matrix, n - 2)
return f2 * matrix.t[0][0] + f1 * matrix.t[0][1]
def simple_fibonacci(n, f1, f2):
# Trivial Cases
if n == 1:
return f1
elif n == 2:
return f2
fn_1 = f1
fn_2 = f2
n -= 2
while n > 0:
fn_1, fn_2 = fn_1 + fn_2, fn_1
n -= 1
return fn_1
def matrix_exponentiation_time():
setup = """
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
"""
code = "fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"
exec_time = timeit.timeit(setup=setup, stmt=code, number=100)
print("With matrix exponentiation the average execution time is ", exec_time / 100)
return exec_time
def simple_fibonacci_time():
setup = """
from random import randint
from __main__ import simple_fibonacci
"""
code = "simple_fibonacci(randint(1,70000), 1, 1)"
exec_time = timeit.timeit(setup=setup, stmt=code, number=100)
print(
"Without matrix exponentiation the average execution time is ", exec_time / 100
)
return exec_time
def main():
matrix_exponentiation_time()
simple_fibonacci_time()
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| B64_CHARSET = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def base64_encode(data: bytes) -> bytes:
"""Encodes data according to RFC4648.
The data is first transformed to binary and appended with binary digits so that its
length becomes a multiple of 6, then each 6 binary digits will match a character in
the B64_CHARSET string. The number of appended binary digits would later determine
how many "=" signs should be added, the padding.
For every 2 binary digits added, a "=" sign is added in the output.
We can add any binary digits to make it a multiple of 6, for instance, consider the
following example:
"AA" -> 0010100100101001 -> 001010 010010 1001
As can be seen above, 2 more binary digits should be added, so there's 4
possibilities here: 00, 01, 10 or 11.
That being said, Base64 encoding can be used in Steganography to hide data in these
appended digits.
>>> from base64 import b64encode
>>> a = b"This pull request is part of Hacktoberfest20!"
>>> b = b"https://tools.ietf.org/html/rfc4648"
>>> c = b"A"
>>> base64_encode(a) == b64encode(a)
True
>>> base64_encode(b) == b64encode(b)
True
>>> base64_encode(c) == b64encode(c)
True
>>> base64_encode("abc")
Traceback (most recent call last):
...
TypeError: a bytes-like object is required, not 'str'
"""
# Make sure the supplied data is a bytes-like object
if not isinstance(data, bytes):
raise TypeError(
f"a bytes-like object is required, not '{data.__class__.__name__}'"
)
binary_stream = "".join(bin(byte)[2:].zfill(8) for byte in data)
padding_needed = len(binary_stream) % 6 != 0
if padding_needed:
# The padding that will be added later
padding = b"=" * ((6 - len(binary_stream) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(binary_stream) % 6)
else:
padding = b""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6], 2)]
for index in range(0, len(binary_stream), 6)
).encode()
+ padding
)
def base64_decode(encoded_data: str) -> bytes:
"""Decodes data according to RFC4648.
This does the reverse operation of base64_encode.
We first transform the encoded data back to a binary stream, take off the
previously appended binary digits according to the padding, at this point we
would have a binary stream whose length is multiple of 8, the last step is
to convert every 8 bits to a byte.
>>> from base64 import b64decode
>>> a = "VGhpcyBwdWxsIHJlcXVlc3QgaXMgcGFydCBvZiBIYWNrdG9iZXJmZXN0MjAh"
>>> b = "aHR0cHM6Ly90b29scy5pZXRmLm9yZy9odG1sL3JmYzQ2NDg="
>>> c = "QQ=="
>>> base64_decode(a) == b64decode(a)
True
>>> base64_decode(b) == b64decode(b)
True
>>> base64_decode(c) == b64decode(c)
True
>>> base64_decode("abc")
Traceback (most recent call last):
...
AssertionError: Incorrect padding
"""
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(encoded_data, bytes) and not isinstance(encoded_data, str):
raise TypeError(
"argument should be a bytes-like object or ASCII string, not "
f"'{encoded_data.__class__.__name__}'"
)
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(encoded_data, bytes):
try:
encoded_data = encoded_data.decode("utf-8")
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters")
padding = encoded_data.count("=")
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding]
), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data
), "Invalid base64 character(s) found."
# Check the padding
assert len(encoded_data) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
encoded_data = encoded_data[:-padding]
binary_stream = "".join(
bin(B64_CHARSET.index(char))[2:].zfill(6) for char in encoded_data
)[: -padding * 2]
else:
binary_stream = "".join(
bin(B64_CHARSET.index(char))[2:].zfill(6) for char in encoded_data
)
data = [
int(binary_stream[index : index + 8], 2)
for index in range(0, len(binary_stream), 8)
]
return bytes(data)
if __name__ == "__main__":
import doctest
doctest.testmod()
| B64_CHARSET = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def base64_encode(data: bytes) -> bytes:
"""Encodes data according to RFC4648.
The data is first transformed to binary and appended with binary digits so that its
length becomes a multiple of 6, then each 6 binary digits will match a character in
the B64_CHARSET string. The number of appended binary digits would later determine
how many "=" signs should be added, the padding.
For every 2 binary digits added, a "=" sign is added in the output.
We can add any binary digits to make it a multiple of 6, for instance, consider the
following example:
"AA" -> 0010100100101001 -> 001010 010010 1001
As can be seen above, 2 more binary digits should be added, so there's 4
possibilities here: 00, 01, 10 or 11.
That being said, Base64 encoding can be used in Steganography to hide data in these
appended digits.
>>> from base64 import b64encode
>>> a = b"This pull request is part of Hacktoberfest20!"
>>> b = b"https://tools.ietf.org/html/rfc4648"
>>> c = b"A"
>>> base64_encode(a) == b64encode(a)
True
>>> base64_encode(b) == b64encode(b)
True
>>> base64_encode(c) == b64encode(c)
True
>>> base64_encode("abc")
Traceback (most recent call last):
...
TypeError: a bytes-like object is required, not 'str'
"""
# Make sure the supplied data is a bytes-like object
if not isinstance(data, bytes):
raise TypeError(
f"a bytes-like object is required, not '{data.__class__.__name__}'"
)
binary_stream = "".join(bin(byte)[2:].zfill(8) for byte in data)
padding_needed = len(binary_stream) % 6 != 0
if padding_needed:
# The padding that will be added later
padding = b"=" * ((6 - len(binary_stream) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(binary_stream) % 6)
else:
padding = b""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6], 2)]
for index in range(0, len(binary_stream), 6)
).encode()
+ padding
)
def base64_decode(encoded_data: str) -> bytes:
"""Decodes data according to RFC4648.
This does the reverse operation of base64_encode.
We first transform the encoded data back to a binary stream, take off the
previously appended binary digits according to the padding, at this point we
would have a binary stream whose length is multiple of 8, the last step is
to convert every 8 bits to a byte.
>>> from base64 import b64decode
>>> a = "VGhpcyBwdWxsIHJlcXVlc3QgaXMgcGFydCBvZiBIYWNrdG9iZXJmZXN0MjAh"
>>> b = "aHR0cHM6Ly90b29scy5pZXRmLm9yZy9odG1sL3JmYzQ2NDg="
>>> c = "QQ=="
>>> base64_decode(a) == b64decode(a)
True
>>> base64_decode(b) == b64decode(b)
True
>>> base64_decode(c) == b64decode(c)
True
>>> base64_decode("abc")
Traceback (most recent call last):
...
AssertionError: Incorrect padding
"""
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(encoded_data, bytes) and not isinstance(encoded_data, str):
raise TypeError(
"argument should be a bytes-like object or ASCII string, not "
f"'{encoded_data.__class__.__name__}'"
)
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(encoded_data, bytes):
try:
encoded_data = encoded_data.decode("utf-8")
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters")
padding = encoded_data.count("=")
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding]
), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data
), "Invalid base64 character(s) found."
# Check the padding
assert len(encoded_data) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
encoded_data = encoded_data[:-padding]
binary_stream = "".join(
bin(B64_CHARSET.index(char))[2:].zfill(6) for char in encoded_data
)[: -padding * 2]
else:
binary_stream = "".join(
bin(B64_CHARSET.index(char))[2:].zfill(6) for char in encoded_data
)
data = [
int(binary_stream[index : index + 8], 2)
for index in range(0, len(binary_stream), 8)
]
return bytes(data)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Reference: https://en.wikipedia.org/wiki/Gaussian_function
"""
from numpy import exp, pi, sqrt
def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int:
"""
>>> gaussian(1)
0.24197072451914337
>>> gaussian(24)
3.342714441794458e-126
>>> gaussian(1, 4, 2)
0.06475879783294587
>>> gaussian(1, 5, 3)
0.05467002489199788
Supports NumPy Arrays
Use numpy.meshgrid with this to generate gaussian blur on images.
>>> import numpy as np
>>> x = np.arange(15)
>>> gaussian(x)
array([3.98942280e-01, 2.41970725e-01, 5.39909665e-02, 4.43184841e-03,
1.33830226e-04, 1.48671951e-06, 6.07588285e-09, 9.13472041e-12,
5.05227108e-15, 1.02797736e-18, 7.69459863e-23, 2.11881925e-27,
2.14638374e-32, 7.99882776e-38, 1.09660656e-43])
>>> gaussian(15)
5.530709549844416e-50
>>> gaussian([1,2, 'string'])
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for -: 'list' and 'float'
>>> gaussian('hello world')
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for -: 'str' and 'float'
>>> gaussian(10**234) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
OverflowError: (34, 'Result too large')
>>> gaussian(10**-326)
0.3989422804014327
>>> gaussian(2523, mu=234234, sigma=3425)
0.0
"""
return 1 / sqrt(2 * pi * sigma**2) * exp(-((x - mu) ** 2) / (2 * sigma**2))
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Reference: https://en.wikipedia.org/wiki/Gaussian_function
"""
from numpy import exp, pi, sqrt
def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int:
"""
>>> gaussian(1)
0.24197072451914337
>>> gaussian(24)
3.342714441794458e-126
>>> gaussian(1, 4, 2)
0.06475879783294587
>>> gaussian(1, 5, 3)
0.05467002489199788
Supports NumPy Arrays
Use numpy.meshgrid with this to generate gaussian blur on images.
>>> import numpy as np
>>> x = np.arange(15)
>>> gaussian(x)
array([3.98942280e-01, 2.41970725e-01, 5.39909665e-02, 4.43184841e-03,
1.33830226e-04, 1.48671951e-06, 6.07588285e-09, 9.13472041e-12,
5.05227108e-15, 1.02797736e-18, 7.69459863e-23, 2.11881925e-27,
2.14638374e-32, 7.99882776e-38, 1.09660656e-43])
>>> gaussian(15)
5.530709549844416e-50
>>> gaussian([1,2, 'string'])
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for -: 'list' and 'float'
>>> gaussian('hello world')
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for -: 'str' and 'float'
>>> gaussian(10**234) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
OverflowError: (34, 'Result too large')
>>> gaussian(10**-326)
0.3989422804014327
>>> gaussian(2523, mu=234234, sigma=3425)
0.0
"""
return 1 / sqrt(2 * pi * sigma**2) * exp(-((x - mu) ** 2) / (2 * sigma**2))
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # https://en.wikipedia.org/wiki/Tree_traversal
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class Node:
data: int
left: Node | None = None
right: Node | None = None
def make_tree() -> Node | None:
r"""
The below tree
1
/ \
2 3
/ \
4 5
"""
tree = Node(1)
tree.left = Node(2)
tree.right = Node(3)
tree.left.left = Node(4)
tree.left.right = Node(5)
return tree
def preorder(root: Node | None) -> list[int]:
"""
Pre-order traversal visits root node, left subtree, right subtree.
>>> preorder(make_tree())
[1, 2, 4, 5, 3]
"""
return [root.data] + preorder(root.left) + preorder(root.right) if root else []
def postorder(root: Node | None) -> list[int]:
"""
Post-order traversal visits left subtree, right subtree, root node.
>>> postorder(make_tree())
[4, 5, 2, 3, 1]
"""
return postorder(root.left) + postorder(root.right) + [root.data] if root else []
def inorder(root: Node | None) -> list[int]:
"""
In-order traversal visits left subtree, root node, right subtree.
>>> inorder(make_tree())
[4, 2, 5, 1, 3]
"""
return inorder(root.left) + [root.data] + inorder(root.right) if root else []
def height(root: Node | None) -> int:
"""
Recursive function for calculating the height of the binary tree.
>>> height(None)
0
>>> height(make_tree())
3
"""
return (max(height(root.left), height(root.right)) + 1) if root else 0
def level_order(root: Node | None) -> Sequence[Node | None]:
"""
Returns a list of nodes value from a whole binary tree in Level Order Traverse.
Level Order traverse: Visit nodes of the tree level-by-level.
"""
output: list[Any] = []
if root is None:
return output
process_queue = deque([root])
while process_queue:
node = process_queue.popleft()
output.append(node.data)
if node.left:
process_queue.append(node.left)
if node.right:
process_queue.append(node.right)
return output
def get_nodes_from_left_to_right(
root: Node | None, level: int
) -> Sequence[Node | None]:
"""
Returns a list of nodes value from a particular level:
Left to right direction of the binary tree.
"""
output: list[Any] = []
def populate_output(root: Node | None, level: int) -> None:
if not root:
return
if level == 1:
output.append(root.data)
elif level > 1:
populate_output(root.left, level - 1)
populate_output(root.right, level - 1)
populate_output(root, level)
return output
def get_nodes_from_right_to_left(
root: Node | None, level: int
) -> Sequence[Node | None]:
"""
Returns a list of nodes value from a particular level:
Right to left direction of the binary tree.
"""
output: list[Any] = []
def populate_output(root: Node | None, level: int) -> None:
if root is None:
return
if level == 1:
output.append(root.data)
elif level > 1:
populate_output(root.right, level - 1)
populate_output(root.left, level - 1)
populate_output(root, level)
return output
def zigzag(root: Node | None) -> Sequence[Node | None] | list[Any]:
"""
ZigZag traverse:
Returns a list of nodes value from left to right and right to left, alternatively.
"""
if root is None:
return []
output: list[Sequence[Node | None]] = []
flag = 0
height_tree = height(root)
for h in range(1, height_tree + 1):
if not flag:
output.append(get_nodes_from_left_to_right(root, h))
flag = 1
else:
output.append(get_nodes_from_right_to_left(root, h))
flag = 0
return output
def main() -> None: # Main function for testing.
"""
Create binary tree.
"""
root = make_tree()
"""
All Traversals of the binary are as follows:
"""
print(f"In-order Traversal: {inorder(root)}")
print(f"Pre-order Traversal: {preorder(root)}")
print(f"Post-order Traversal: {postorder(root)}", "\n")
print(f"Height of Tree: {height(root)}", "\n")
print("Complete Level Order Traversal: ")
print(level_order(root), "\n")
print("Level-wise order Traversal: ")
for level in range(1, height(root) + 1):
print(f"Level {level}:", get_nodes_from_left_to_right(root, level=level))
print("\nZigZag order Traversal: ")
print(zigzag(root))
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| # https://en.wikipedia.org/wiki/Tree_traversal
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class Node:
data: int
left: Node | None = None
right: Node | None = None
def make_tree() -> Node | None:
r"""
The below tree
1
/ \
2 3
/ \
4 5
"""
tree = Node(1)
tree.left = Node(2)
tree.right = Node(3)
tree.left.left = Node(4)
tree.left.right = Node(5)
return tree
def preorder(root: Node | None) -> list[int]:
"""
Pre-order traversal visits root node, left subtree, right subtree.
>>> preorder(make_tree())
[1, 2, 4, 5, 3]
"""
return [root.data] + preorder(root.left) + preorder(root.right) if root else []
def postorder(root: Node | None) -> list[int]:
"""
Post-order traversal visits left subtree, right subtree, root node.
>>> postorder(make_tree())
[4, 5, 2, 3, 1]
"""
return postorder(root.left) + postorder(root.right) + [root.data] if root else []
def inorder(root: Node | None) -> list[int]:
"""
In-order traversal visits left subtree, root node, right subtree.
>>> inorder(make_tree())
[4, 2, 5, 1, 3]
"""
return inorder(root.left) + [root.data] + inorder(root.right) if root else []
def height(root: Node | None) -> int:
"""
Recursive function for calculating the height of the binary tree.
>>> height(None)
0
>>> height(make_tree())
3
"""
return (max(height(root.left), height(root.right)) + 1) if root else 0
def level_order(root: Node | None) -> Sequence[Node | None]:
"""
Returns a list of nodes value from a whole binary tree in Level Order Traverse.
Level Order traverse: Visit nodes of the tree level-by-level.
"""
output: list[Any] = []
if root is None:
return output
process_queue = deque([root])
while process_queue:
node = process_queue.popleft()
output.append(node.data)
if node.left:
process_queue.append(node.left)
if node.right:
process_queue.append(node.right)
return output
def get_nodes_from_left_to_right(
root: Node | None, level: int
) -> Sequence[Node | None]:
"""
Returns a list of nodes value from a particular level:
Left to right direction of the binary tree.
"""
output: list[Any] = []
def populate_output(root: Node | None, level: int) -> None:
if not root:
return
if level == 1:
output.append(root.data)
elif level > 1:
populate_output(root.left, level - 1)
populate_output(root.right, level - 1)
populate_output(root, level)
return output
def get_nodes_from_right_to_left(
root: Node | None, level: int
) -> Sequence[Node | None]:
"""
Returns a list of nodes value from a particular level:
Right to left direction of the binary tree.
"""
output: list[Any] = []
def populate_output(root: Node | None, level: int) -> None:
if root is None:
return
if level == 1:
output.append(root.data)
elif level > 1:
populate_output(root.right, level - 1)
populate_output(root.left, level - 1)
populate_output(root, level)
return output
def zigzag(root: Node | None) -> Sequence[Node | None] | list[Any]:
"""
ZigZag traverse:
Returns a list of nodes value from left to right and right to left, alternatively.
"""
if root is None:
return []
output: list[Sequence[Node | None]] = []
flag = 0
height_tree = height(root)
for h in range(1, height_tree + 1):
if not flag:
output.append(get_nodes_from_left_to_right(root, h))
flag = 1
else:
output.append(get_nodes_from_right_to_left(root, h))
flag = 0
return output
def main() -> None: # Main function for testing.
"""
Create binary tree.
"""
root = make_tree()
"""
All Traversals of the binary are as follows:
"""
print(f"In-order Traversal: {inorder(root)}")
print(f"Pre-order Traversal: {preorder(root)}")
print(f"Post-order Traversal: {postorder(root)}", "\n")
print(f"Height of Tree: {height(root)}", "\n")
print("Complete Level Order Traversal: ")
print(level_order(root), "\n")
print("Level-wise order Traversal: ")
for level in range(1, height(root) + 1):
print(f"Level {level}:", get_nodes_from_left_to_right(root, level=level))
print("\nZigZag order Traversal: ")
print(zigzag(root))
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| hex_table = {hex(i)[2:]: i for i in range(16)} # Use [:2] to strip off the leading '0x'
def hex_to_decimal(hex_string: str) -> int:
"""
Convert a hexadecimal value to its decimal equivalent
#https://www.programiz.com/python-programming/methods/built-in/hex
>>> hex_to_decimal("a")
10
>>> hex_to_decimal("12f")
303
>>> hex_to_decimal(" 12f ")
303
>>> hex_to_decimal("FfFf")
65535
>>> hex_to_decimal("-Ff")
-255
>>> hex_to_decimal("F-f")
Traceback (most recent call last):
...
ValueError: Non-hexadecimal value was passed to the function
>>> hex_to_decimal("")
Traceback (most recent call last):
...
ValueError: Empty string was passed to the function
>>> hex_to_decimal("12m")
Traceback (most recent call last):
...
ValueError: Non-hexadecimal value was passed to the function
"""
hex_string = hex_string.strip().lower()
if not hex_string:
raise ValueError("Empty string was passed to the function")
is_negative = hex_string[0] == "-"
if is_negative:
hex_string = hex_string[1:]
if not all(char in hex_table for char in hex_string):
raise ValueError("Non-hexadecimal value was passed to the function")
decimal_number = 0
for char in hex_string:
decimal_number = 16 * decimal_number + hex_table[char]
return -decimal_number if is_negative else decimal_number
if __name__ == "__main__":
from doctest import testmod
testmod()
| hex_table = {hex(i)[2:]: i for i in range(16)} # Use [:2] to strip off the leading '0x'
def hex_to_decimal(hex_string: str) -> int:
"""
Convert a hexadecimal value to its decimal equivalent
#https://www.programiz.com/python-programming/methods/built-in/hex
>>> hex_to_decimal("a")
10
>>> hex_to_decimal("12f")
303
>>> hex_to_decimal(" 12f ")
303
>>> hex_to_decimal("FfFf")
65535
>>> hex_to_decimal("-Ff")
-255
>>> hex_to_decimal("F-f")
Traceback (most recent call last):
...
ValueError: Non-hexadecimal value was passed to the function
>>> hex_to_decimal("")
Traceback (most recent call last):
...
ValueError: Empty string was passed to the function
>>> hex_to_decimal("12m")
Traceback (most recent call last):
...
ValueError: Non-hexadecimal value was passed to the function
"""
hex_string = hex_string.strip().lower()
if not hex_string:
raise ValueError("Empty string was passed to the function")
is_negative = hex_string[0] == "-"
if is_negative:
hex_string = hex_string[1:]
if not all(char in hex_table for char in hex_string):
raise ValueError("Non-hexadecimal value was passed to the function")
decimal_number = 0
for char in hex_string:
decimal_number = 16 * decimal_number + hex_table[char]
return -decimal_number if is_negative else decimal_number
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] 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 | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
In this problem, we want to determine all possible subsequences
of the given sequence. We use backtracking to solve this problem.
Time complexity: O(2^n),
where n denotes the length of the given sequence.
"""
from __future__ import annotations
from typing import Any
def generate_all_subsequences(sequence: list[Any]) -> None:
create_state_space_tree(sequence, [], 0)
def create_state_space_tree(
sequence: list[Any], current_subsequence: list[Any], index: int
) -> None:
"""
Creates a state space tree to iterate through each branch using DFS.
We know that each state has exactly two children.
It terminates when it reaches the end of the given sequence.
"""
if index == len(sequence):
print(current_subsequence)
return
create_state_space_tree(sequence, current_subsequence, index + 1)
current_subsequence.append(sequence[index])
create_state_space_tree(sequence, current_subsequence, index + 1)
current_subsequence.pop()
if __name__ == "__main__":
seq: list[Any] = [3, 1, 2, 4]
generate_all_subsequences(seq)
seq.clear()
seq.extend(["A", "B", "C"])
generate_all_subsequences(seq)
| """
In this problem, we want to determine all possible subsequences
of the given sequence. We use backtracking to solve this problem.
Time complexity: O(2^n),
where n denotes the length of the given sequence.
"""
from __future__ import annotations
from typing import Any
def generate_all_subsequences(sequence: list[Any]) -> None:
create_state_space_tree(sequence, [], 0)
def create_state_space_tree(
sequence: list[Any], current_subsequence: list[Any], index: int
) -> None:
"""
Creates a state space tree to iterate through each branch using DFS.
We know that each state has exactly two children.
It terminates when it reaches the end of the given sequence.
"""
if index == len(sequence):
print(current_subsequence)
return
create_state_space_tree(sequence, current_subsequence, index + 1)
current_subsequence.append(sequence[index])
create_state_space_tree(sequence, current_subsequence, index + 1)
current_subsequence.pop()
if __name__ == "__main__":
seq: list[Any] = [3, 1, 2, 4]
generate_all_subsequences(seq)
seq.clear()
seq.extend(["A", "B", "C"])
generate_all_subsequences(seq)
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from typing import Literal
LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def translate_message(
key: str, message: str, mode: Literal["encrypt", "decrypt"]
) -> str:
"""
>>> translate_message("QWERTYUIOPASDFGHJKLZXCVBNM","Hello World","encrypt")
'Pcssi Bidsm'
"""
chars_a = LETTERS if mode == "decrypt" else key
chars_b = key if mode == "decrypt" else LETTERS
translated = ""
# loop through each symbol in the message
for symbol in message:
if symbol.upper() in chars_a:
# encrypt/decrypt the symbol
sym_index = chars_a.find(symbol.upper())
if symbol.isupper():
translated += chars_b[sym_index].upper()
else:
translated += chars_b[sym_index].lower()
else:
# symbol is not in LETTERS, just add it
translated += symbol
return translated
def encrypt_message(key: str, message: str) -> str:
"""
>>> encrypt_message("QWERTYUIOPASDFGHJKLZXCVBNM", "Hello World")
'Pcssi Bidsm'
"""
return translate_message(key, message, "encrypt")
def decrypt_message(key: str, message: str) -> str:
"""
>>> decrypt_message("QWERTYUIOPASDFGHJKLZXCVBNM", "Hello World")
'Itssg Vgksr'
"""
return translate_message(key, message, "decrypt")
def main() -> None:
message = "Hello World"
key = "QWERTYUIOPASDFGHJKLZXCVBNM"
mode = "decrypt" # set to 'encrypt' or 'decrypt'
if mode == "encrypt":
translated = encrypt_message(key, message)
elif mode == "decrypt":
translated = decrypt_message(key, message)
print(f"Using the key {key}, the {mode}ed message is: {translated}")
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| from typing import Literal
LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def translate_message(
key: str, message: str, mode: Literal["encrypt", "decrypt"]
) -> str:
"""
>>> translate_message("QWERTYUIOPASDFGHJKLZXCVBNM","Hello World","encrypt")
'Pcssi Bidsm'
"""
chars_a = LETTERS if mode == "decrypt" else key
chars_b = key if mode == "decrypt" else LETTERS
translated = ""
# loop through each symbol in the message
for symbol in message:
if symbol.upper() in chars_a:
# encrypt/decrypt the symbol
sym_index = chars_a.find(symbol.upper())
if symbol.isupper():
translated += chars_b[sym_index].upper()
else:
translated += chars_b[sym_index].lower()
else:
# symbol is not in LETTERS, just add it
translated += symbol
return translated
def encrypt_message(key: str, message: str) -> str:
"""
>>> encrypt_message("QWERTYUIOPASDFGHJKLZXCVBNM", "Hello World")
'Pcssi Bidsm'
"""
return translate_message(key, message, "encrypt")
def decrypt_message(key: str, message: str) -> str:
"""
>>> decrypt_message("QWERTYUIOPASDFGHJKLZXCVBNM", "Hello World")
'Itssg Vgksr'
"""
return translate_message(key, message, "decrypt")
def main() -> None:
message = "Hello World"
key = "QWERTYUIOPASDFGHJKLZXCVBNM"
mode = "decrypt" # set to 'encrypt' or 'decrypt'
if mode == "encrypt":
translated = encrypt_message(key, message)
elif mode == "decrypt":
translated = decrypt_message(key, message)
print(f"Using the key {key}, the {mode}ed message is: {translated}")
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is a Python implementation of the levenshtein distance.
Levenshtein distance is a string metric for measuring the
difference between two sequences.
For doctests run following command:
python -m doctest -v levenshtein-distance.py
or
python3 -m doctest -v levenshtein-distance.py
For manual testing run:
python levenshtein-distance.py
"""
def levenshtein_distance(first_word: str, second_word: str) -> int:
"""Implementation of the levenshtein distance in Python.
:param first_word: the first word to measure the difference.
:param second_word: the second word to measure the difference.
:return: the levenshtein distance between the two words.
Examples:
>>> levenshtein_distance("planet", "planetary")
3
>>> levenshtein_distance("", "test")
4
>>> levenshtein_distance("book", "back")
2
>>> levenshtein_distance("book", "book")
0
>>> levenshtein_distance("test", "")
4
>>> levenshtein_distance("", "")
0
>>> levenshtein_distance("orchestration", "container")
10
"""
# The longer word should come first
if len(first_word) < len(second_word):
return levenshtein_distance(second_word, first_word)
if len(second_word) == 0:
return len(first_word)
previous_row = list(range(len(second_word) + 1))
for i, c1 in enumerate(first_word):
current_row = [i + 1]
for j, c2 in enumerate(second_word):
# Calculate insertions, deletions and substitutions
insertions = previous_row[j + 1] + 1
deletions = current_row[j] + 1
substitutions = previous_row[j] + (c1 != c2)
# Get the minimum to append to the current row
current_row.append(min(insertions, deletions, substitutions))
# Store the previous row
previous_row = current_row
# Returns the last element (distance)
return previous_row[-1]
if __name__ == "__main__":
first_word = input("Enter the first word:\n").strip()
second_word = input("Enter the second word:\n").strip()
result = levenshtein_distance(first_word, second_word)
print(f"Levenshtein distance between {first_word} and {second_word} is {result}")
| """
This is a Python implementation of the levenshtein distance.
Levenshtein distance is a string metric for measuring the
difference between two sequences.
For doctests run following command:
python -m doctest -v levenshtein-distance.py
or
python3 -m doctest -v levenshtein-distance.py
For manual testing run:
python levenshtein-distance.py
"""
def levenshtein_distance(first_word: str, second_word: str) -> int:
"""Implementation of the levenshtein distance in Python.
:param first_word: the first word to measure the difference.
:param second_word: the second word to measure the difference.
:return: the levenshtein distance between the two words.
Examples:
>>> levenshtein_distance("planet", "planetary")
3
>>> levenshtein_distance("", "test")
4
>>> levenshtein_distance("book", "back")
2
>>> levenshtein_distance("book", "book")
0
>>> levenshtein_distance("test", "")
4
>>> levenshtein_distance("", "")
0
>>> levenshtein_distance("orchestration", "container")
10
"""
# The longer word should come first
if len(first_word) < len(second_word):
return levenshtein_distance(second_word, first_word)
if len(second_word) == 0:
return len(first_word)
previous_row = list(range(len(second_word) + 1))
for i, c1 in enumerate(first_word):
current_row = [i + 1]
for j, c2 in enumerate(second_word):
# Calculate insertions, deletions and substitutions
insertions = previous_row[j + 1] + 1
deletions = current_row[j] + 1
substitutions = previous_row[j] + (c1 != c2)
# Get the minimum to append to the current row
current_row.append(min(insertions, deletions, substitutions))
# Store the previous row
previous_row = current_row
# Returns the last element (distance)
return previous_row[-1]
if __name__ == "__main__":
first_word = input("Enter the first word:\n").strip()
second_word = input("Enter the second word:\n").strip()
result = levenshtein_distance(first_word, second_word)
print(f"Levenshtein distance between {first_word} and {second_word} is {result}")
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def permute(nums: list[int]) -> list[list[int]]:
"""
Return all permutations.
>>> from itertools import permutations
>>> numbers= [1,2,3]
>>> all(list(nums) in permute(numbers) for nums in permutations(numbers))
True
"""
result = []
if len(nums) == 1:
return [nums.copy()]
for _ in range(len(nums)):
n = nums.pop(0)
permutations = permute(nums)
for perm in permutations:
perm.append(n)
result.extend(permutations)
nums.append(n)
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| def permute(nums: list[int]) -> list[list[int]]:
"""
Return all permutations.
>>> from itertools import permutations
>>> numbers= [1,2,3]
>>> all(list(nums) in permute(numbers) for nums in permutations(numbers))
True
"""
result = []
if len(nums) == 1:
return [nums.copy()]
for _ in range(len(nums)):
n = nums.pop(0)
permutations = permute(nums)
for perm in permutations:
perm.append(n)
result.extend(permutations)
nums.append(n)
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 104 : https://projecteuler.net/problem=104
The Fibonacci sequence is defined by the recurrence relation:
Fn = Fn−1 + Fn−2, where F1 = 1 and F2 = 1.
It turns out that F541, which contains 113 digits, is the first Fibonacci number
for which the last nine digits are 1-9 pandigital (contain all the digits 1 to 9,
but not necessarily in order). And F2749, which contains 575 digits, is the first
Fibonacci number for which the first nine digits are 1-9 pandigital.
Given that Fk is the first Fibonacci number for which the first nine digits AND
the last nine digits are 1-9 pandigital, find k.
"""
import sys
sys.set_int_max_str_digits(0) # type: ignore
def check(number: int) -> bool:
"""
Takes a number and checks if it is pandigital both from start and end
>>> check(123456789987654321)
True
>>> check(120000987654321)
False
>>> check(1234567895765677987654321)
True
"""
check_last = [0] * 11
check_front = [0] * 11
# mark last 9 numbers
for _ in range(9):
check_last[int(number % 10)] = 1
number = number // 10
# flag
f = True
# check last 9 numbers for pandigitality
for x in range(9):
if not check_last[x + 1]:
f = False
if not f:
return f
# mark first 9 numbers
number = int(str(number)[:9])
for _ in range(9):
check_front[int(number % 10)] = 1
number = number // 10
# check first 9 numbers for pandigitality
for x in range(9):
if not check_front[x + 1]:
f = False
return f
def check1(number: int) -> bool:
"""
Takes a number and checks if it is pandigital from END
>>> check1(123456789987654321)
True
>>> check1(120000987654321)
True
>>> check1(12345678957656779870004321)
False
"""
check_last = [0] * 11
# mark last 9 numbers
for _ in range(9):
check_last[int(number % 10)] = 1
number = number // 10
# flag
f = True
# check last 9 numbers for pandigitality
for x in range(9):
if not check_last[x + 1]:
f = False
return f
def solution() -> int:
"""
Outputs the answer is the least Fibonacci number pandigital from both sides.
>>> solution()
329468
"""
a = 1
b = 1
c = 2
# temporary Fibonacci numbers
a1 = 1
b1 = 1
c1 = 2
# temporary Fibonacci numbers mod 1e9
# mod m=1e9, done for fast optimisation
tocheck = [0] * 1000000
m = 1000000000
for x in range(1000000):
c1 = (a1 + b1) % m
a1 = b1 % m
b1 = c1 % m
if check1(b1):
tocheck[x + 3] = 1
for x in range(1000000):
c = a + b
a = b
b = c
# perform check only if in tocheck
if tocheck[x + 3] and check(b):
return x + 3 # first 2 already done
return -1
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 104 : https://projecteuler.net/problem=104
The Fibonacci sequence is defined by the recurrence relation:
Fn = Fn−1 + Fn−2, where F1 = 1 and F2 = 1.
It turns out that F541, which contains 113 digits, is the first Fibonacci number
for which the last nine digits are 1-9 pandigital (contain all the digits 1 to 9,
but not necessarily in order). And F2749, which contains 575 digits, is the first
Fibonacci number for which the first nine digits are 1-9 pandigital.
Given that Fk is the first Fibonacci number for which the first nine digits AND
the last nine digits are 1-9 pandigital, find k.
"""
import sys
sys.set_int_max_str_digits(0) # type: ignore
def check(number: int) -> bool:
"""
Takes a number and checks if it is pandigital both from start and end
>>> check(123456789987654321)
True
>>> check(120000987654321)
False
>>> check(1234567895765677987654321)
True
"""
check_last = [0] * 11
check_front = [0] * 11
# mark last 9 numbers
for _ in range(9):
check_last[int(number % 10)] = 1
number = number // 10
# flag
f = True
# check last 9 numbers for pandigitality
for x in range(9):
if not check_last[x + 1]:
f = False
if not f:
return f
# mark first 9 numbers
number = int(str(number)[:9])
for _ in range(9):
check_front[int(number % 10)] = 1
number = number // 10
# check first 9 numbers for pandigitality
for x in range(9):
if not check_front[x + 1]:
f = False
return f
def check1(number: int) -> bool:
"""
Takes a number and checks if it is pandigital from END
>>> check1(123456789987654321)
True
>>> check1(120000987654321)
True
>>> check1(12345678957656779870004321)
False
"""
check_last = [0] * 11
# mark last 9 numbers
for _ in range(9):
check_last[int(number % 10)] = 1
number = number // 10
# flag
f = True
# check last 9 numbers for pandigitality
for x in range(9):
if not check_last[x + 1]:
f = False
return f
def solution() -> int:
"""
Outputs the answer is the least Fibonacci number pandigital from both sides.
>>> solution()
329468
"""
a = 1
b = 1
c = 2
# temporary Fibonacci numbers
a1 = 1
b1 = 1
c1 = 2
# temporary Fibonacci numbers mod 1e9
# mod m=1e9, done for fast optimisation
tocheck = [0] * 1000000
m = 1000000000
for x in range(1000000):
c1 = (a1 + b1) % m
a1 = b1 % m
b1 = c1 % m
if check1(b1):
tocheck[x + 3] = 1
for x in range(1000000):
c = a + b
a = b
b = c
# perform check only if in tocheck
if tocheck[x + 3] and check(b):
return x + 3 # first 2 already done
return -1
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 205: https://projecteuler.net/problem=205
Peter has nine four-sided (pyramidal) dice, each with faces numbered 1, 2, 3, 4.
Colin has six six-sided (cubic) dice, each with faces numbered 1, 2, 3, 4, 5, 6.
Peter and Colin roll their dice and compare totals: the highest total wins.
The result is a draw if the totals are equal.
What is the probability that Pyramidal Peter beats Cubic Colin?
Give your answer rounded to seven decimal places in the form 0.abcdefg
"""
from itertools import product
def total_frequency_distribution(sides_number: int, dice_number: int) -> list[int]:
"""
Returns frequency distribution of total
>>> total_frequency_distribution(sides_number=6, dice_number=1)
[0, 1, 1, 1, 1, 1, 1]
>>> total_frequency_distribution(sides_number=4, dice_number=2)
[0, 0, 1, 2, 3, 4, 3, 2, 1]
"""
max_face_number = sides_number
max_total = max_face_number * dice_number
totals_frequencies = [0] * (max_total + 1)
min_face_number = 1
faces_numbers = range(min_face_number, max_face_number + 1)
for dice_numbers in product(faces_numbers, repeat=dice_number):
total = sum(dice_numbers)
totals_frequencies[total] += 1
return totals_frequencies
def solution() -> float:
"""
Returns probability that Pyramidal Peter beats Cubic Colin
rounded to seven decimal places in the form 0.abcdefg
>>> solution()
0.5731441
"""
peter_totals_frequencies = total_frequency_distribution(
sides_number=4, dice_number=9
)
colin_totals_frequencies = total_frequency_distribution(
sides_number=6, dice_number=6
)
peter_wins_count = 0
min_peter_total = 9
max_peter_total = 4 * 9
min_colin_total = 6
for peter_total in range(min_peter_total, max_peter_total + 1):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total]
)
total_games_number = (4**9) * (6**6)
peter_win_probability = peter_wins_count / total_games_number
rounded_peter_win_probability = round(peter_win_probability, ndigits=7)
return rounded_peter_win_probability
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 205: https://projecteuler.net/problem=205
Peter has nine four-sided (pyramidal) dice, each with faces numbered 1, 2, 3, 4.
Colin has six six-sided (cubic) dice, each with faces numbered 1, 2, 3, 4, 5, 6.
Peter and Colin roll their dice and compare totals: the highest total wins.
The result is a draw if the totals are equal.
What is the probability that Pyramidal Peter beats Cubic Colin?
Give your answer rounded to seven decimal places in the form 0.abcdefg
"""
from itertools import product
def total_frequency_distribution(sides_number: int, dice_number: int) -> list[int]:
"""
Returns frequency distribution of total
>>> total_frequency_distribution(sides_number=6, dice_number=1)
[0, 1, 1, 1, 1, 1, 1]
>>> total_frequency_distribution(sides_number=4, dice_number=2)
[0, 0, 1, 2, 3, 4, 3, 2, 1]
"""
max_face_number = sides_number
max_total = max_face_number * dice_number
totals_frequencies = [0] * (max_total + 1)
min_face_number = 1
faces_numbers = range(min_face_number, max_face_number + 1)
for dice_numbers in product(faces_numbers, repeat=dice_number):
total = sum(dice_numbers)
totals_frequencies[total] += 1
return totals_frequencies
def solution() -> float:
"""
Returns probability that Pyramidal Peter beats Cubic Colin
rounded to seven decimal places in the form 0.abcdefg
>>> solution()
0.5731441
"""
peter_totals_frequencies = total_frequency_distribution(
sides_number=4, dice_number=9
)
colin_totals_frequencies = total_frequency_distribution(
sides_number=6, dice_number=6
)
peter_wins_count = 0
min_peter_total = 9
max_peter_total = 4 * 9
min_colin_total = 6
for peter_total in range(min_peter_total, max_peter_total + 1):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total]
)
total_games_number = (4**9) * (6**6)
peter_win_probability = peter_wins_count / total_games_number
rounded_peter_win_probability = round(peter_win_probability, ndigits=7)
return rounded_peter_win_probability
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] 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(list(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(list(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 | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
The sum-of-subsetsproblem states that a set of non-negative integers, and a
value M, determine all possible subsets of the given set whose summation sum
equal to given M.
Summation of the chosen numbers must be equal to given number M and one number
can be used only once.
"""
from __future__ import annotations
def generate_sum_of_subsets_soln(nums: list[int], max_sum: int) -> list[list[int]]:
result: list[list[int]] = []
path: list[int] = []
num_index = 0
remaining_nums_sum = sum(nums)
create_state_space_tree(nums, max_sum, num_index, path, result, remaining_nums_sum)
return result
def create_state_space_tree(
nums: list[int],
max_sum: int,
num_index: int,
path: list[int],
result: list[list[int]],
remaining_nums_sum: int,
) -> None:
"""
Creates a state space tree to iterate through each branch using DFS.
It terminates the branching of a node when any of the two conditions
given below satisfy.
This algorithm follows depth-fist-search and backtracks when the node is not
branchable.
"""
if sum(path) > max_sum or (remaining_nums_sum + sum(path)) < max_sum:
return
if sum(path) == max_sum:
result.append(path)
return
for index in range(num_index, len(nums)):
create_state_space_tree(
nums,
max_sum,
index + 1,
path + [nums[index]],
result,
remaining_nums_sum - nums[index],
)
"""
remove the comment to take an input from the user
print("Enter the elements")
nums = list(map(int, input().split()))
print("Enter max_sum sum")
max_sum = int(input())
"""
nums = [3, 34, 4, 12, 5, 2]
max_sum = 9
result = generate_sum_of_subsets_soln(nums, max_sum)
print(*result)
| """
The sum-of-subsetsproblem states that a set of non-negative integers, and a
value M, determine all possible subsets of the given set whose summation sum
equal to given M.
Summation of the chosen numbers must be equal to given number M and one number
can be used only once.
"""
from __future__ import annotations
def generate_sum_of_subsets_soln(nums: list[int], max_sum: int) -> list[list[int]]:
result: list[list[int]] = []
path: list[int] = []
num_index = 0
remaining_nums_sum = sum(nums)
create_state_space_tree(nums, max_sum, num_index, path, result, remaining_nums_sum)
return result
def create_state_space_tree(
nums: list[int],
max_sum: int,
num_index: int,
path: list[int],
result: list[list[int]],
remaining_nums_sum: int,
) -> None:
"""
Creates a state space tree to iterate through each branch using DFS.
It terminates the branching of a node when any of the two conditions
given below satisfy.
This algorithm follows depth-fist-search and backtracks when the node is not
branchable.
"""
if sum(path) > max_sum or (remaining_nums_sum + sum(path)) < max_sum:
return
if sum(path) == max_sum:
result.append(path)
return
for index in range(num_index, len(nums)):
create_state_space_tree(
nums,
max_sum,
index + 1,
path + [nums[index]],
result,
remaining_nums_sum - nums[index],
)
"""
remove the comment to take an input from the user
print("Enter the elements")
nums = list(map(int, input().split()))
print("Enter max_sum sum")
max_sum = int(input())
"""
nums = [3, 34, 4, 12, 5, 2]
max_sum = 9
result = generate_sum_of_subsets_soln(nums, max_sum)
print(*result)
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Scraping jobs given job title and location from indeed website
"""
from __future__ import annotations
from collections.abc import Generator
import requests
from bs4 import BeautifulSoup
url = "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def fetch_jobs(location: str = "mumbai") -> Generator[tuple[str, str], None, None]:
soup = BeautifulSoup(requests.get(url + location).content, "html.parser")
# This attribute finds out all the specifics listed in a job
for job in soup.find_all("div", attrs={"data-tn-component": "organicJob"}):
job_title = job.find("a", attrs={"data-tn-element": "jobTitle"}).text.strip()
company_name = job.find("span", {"class": "company"}).text.strip()
yield job_title, company_name
if __name__ == "__main__":
for i, job in enumerate(fetch_jobs("Bangalore"), 1):
print(f"Job {i:>2} is {job[0]} at {job[1]}")
| """
Scraping jobs given job title and location from indeed website
"""
from __future__ import annotations
from collections.abc import Generator
import requests
from bs4 import BeautifulSoup
url = "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def fetch_jobs(location: str = "mumbai") -> Generator[tuple[str, str], None, None]:
soup = BeautifulSoup(requests.get(url + location).content, "html.parser")
# This attribute finds out all the specifics listed in a job
for job in soup.find_all("div", attrs={"data-tn-component": "organicJob"}):
job_title = job.find("a", attrs={"data-tn-element": "jobTitle"}).text.strip()
company_name = job.find("span", {"class": "company"}).text.strip()
yield job_title, company_name
if __name__ == "__main__":
for i, job in enumerate(fetch_jobs("Bangalore"), 1):
print(f"Job {i:>2} is {job[0]} at {job[1]}")
| -1 |
TheAlgorithms/Python | 7,949 | Flake8: Drop ignore of issue A003 | ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2022-11-02T18:01:55Z" | "2022-11-02T18:20:46Z" | 598f6a26a14d815f5fd079f43787995b0f076c03 | 45b3383c3952f646e985972d1fcd772d3d9f5d3f | Flake8: Drop ignore of issue A003. ### Describe your change:
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from collections.abc import Callable
class Heap:
"""
A generic Heap class, can be used as min or max by passing the key function
accordingly.
"""
def __init__(self, key: Callable | None = None) -> None:
# Stores actual heap items.
self.arr: list = []
# Stores indexes of each item for supporting updates and deletion.
self.pos_map: dict = {}
# Stores current size of heap.
self.size = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
self.key = key or (lambda x: x)
def _parent(self, i: int) -> int | None:
"""Returns parent index of given index if exists else None"""
return int((i - 1) / 2) if i > 0 else None
def _left(self, i: int) -> int | None:
"""Returns left-child-index of given index if exists else None"""
left = int(2 * i + 1)
return left if 0 < left < self.size else None
def _right(self, i: int) -> int | None:
"""Returns right-child-index of given index if exists else None"""
right = int(2 * i + 2)
return right if 0 < right < self.size else None
def _swap(self, i: int, j: int) -> None:
"""Performs changes required for swapping two elements in the heap"""
# First update the indexes of the items in index map.
self.pos_map[self.arr[i][0]], self.pos_map[self.arr[j][0]] = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
self.arr[i], self.arr[j] = self.arr[j], self.arr[i]
def _cmp(self, i: int, j: int) -> bool:
"""Compares the two items using default comparison"""
return self.arr[i][1] < self.arr[j][1]
def _get_valid_parent(self, i: int) -> int:
"""
Returns index of valid parent as per desired ordering among given index and
both it's children
"""
left = self._left(i)
right = self._right(i)
valid_parent = i
if left is not None and not self._cmp(left, valid_parent):
valid_parent = left
if right is not None and not self._cmp(right, valid_parent):
valid_parent = right
return valid_parent
def _heapify_up(self, index: int) -> None:
"""Fixes the heap in upward direction of given index"""
parent = self._parent(index)
while parent is not None and not self._cmp(index, parent):
self._swap(index, parent)
index, parent = parent, self._parent(parent)
def _heapify_down(self, index: int) -> None:
"""Fixes the heap in downward direction of given index"""
valid_parent = self._get_valid_parent(index)
while valid_parent != index:
self._swap(index, valid_parent)
index, valid_parent = valid_parent, self._get_valid_parent(valid_parent)
def update_item(self, item: int, item_value: int) -> None:
"""Updates given item value in heap if present"""
if item not in self.pos_map:
return
index = self.pos_map[item]
self.arr[index] = [item, self.key(item_value)]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(index)
self._heapify_down(index)
def delete_item(self, item: int) -> None:
"""Deletes given item from heap if present"""
if item not in self.pos_map:
return
index = self.pos_map[item]
del self.pos_map[item]
self.arr[index] = self.arr[self.size - 1]
self.pos_map[self.arr[self.size - 1][0]] = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(index)
self._heapify_down(index)
def insert_item(self, item: int, item_value: int) -> None:
"""Inserts given item with given value in heap"""
arr_len = len(self.arr)
if arr_len == self.size:
self.arr.append([item, self.key(item_value)])
else:
self.arr[self.size] = [item, self.key(item_value)]
self.pos_map[item] = self.size
self.size += 1
self._heapify_up(self.size - 1)
def get_top(self) -> tuple | None:
"""Returns top item tuple (Calculated value, item) from heap if present"""
return self.arr[0] if self.size else None
def extract_top(self) -> tuple | None:
"""
Return top item tuple (Calculated value, item) from heap and removes it as well
if present
"""
top_item_tuple = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0])
return top_item_tuple
def test_heap() -> None:
"""
>>> h = Heap() # Max-heap
>>> h.insert_item(5, 34)
>>> h.insert_item(6, 31)
>>> h.insert_item(7, 37)
>>> h.get_top()
[7, 37]
>>> h.extract_top()
[7, 37]
>>> h.extract_top()
[5, 34]
>>> h.extract_top()
[6, 31]
>>> h = Heap(key=lambda x: -x) # Min heap
>>> h.insert_item(5, 34)
>>> h.insert_item(6, 31)
>>> h.insert_item(7, 37)
>>> h.get_top()
[6, -31]
>>> h.extract_top()
[6, -31]
>>> h.extract_top()
[5, -34]
>>> h.extract_top()
[7, -37]
>>> h.insert_item(8, 45)
>>> h.insert_item(9, 40)
>>> h.insert_item(10, 50)
>>> h.get_top()
[9, -40]
>>> h.update_item(10, 30)
>>> h.get_top()
[10, -30]
>>> h.delete_item(10)
>>> h.get_top()
[9, -40]
"""
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| from collections.abc import Callable
class Heap:
"""
A generic Heap class, can be used as min or max by passing the key function
accordingly.
"""
def __init__(self, key: Callable | None = None) -> None:
# Stores actual heap items.
self.arr: list = []
# Stores indexes of each item for supporting updates and deletion.
self.pos_map: dict = {}
# Stores current size of heap.
self.size = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
self.key = key or (lambda x: x)
def _parent(self, i: int) -> int | None:
"""Returns parent index of given index if exists else None"""
return int((i - 1) / 2) if i > 0 else None
def _left(self, i: int) -> int | None:
"""Returns left-child-index of given index if exists else None"""
left = int(2 * i + 1)
return left if 0 < left < self.size else None
def _right(self, i: int) -> int | None:
"""Returns right-child-index of given index if exists else None"""
right = int(2 * i + 2)
return right if 0 < right < self.size else None
def _swap(self, i: int, j: int) -> None:
"""Performs changes required for swapping two elements in the heap"""
# First update the indexes of the items in index map.
self.pos_map[self.arr[i][0]], self.pos_map[self.arr[j][0]] = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
self.arr[i], self.arr[j] = self.arr[j], self.arr[i]
def _cmp(self, i: int, j: int) -> bool:
"""Compares the two items using default comparison"""
return self.arr[i][1] < self.arr[j][1]
def _get_valid_parent(self, i: int) -> int:
"""
Returns index of valid parent as per desired ordering among given index and
both it's children
"""
left = self._left(i)
right = self._right(i)
valid_parent = i
if left is not None and not self._cmp(left, valid_parent):
valid_parent = left
if right is not None and not self._cmp(right, valid_parent):
valid_parent = right
return valid_parent
def _heapify_up(self, index: int) -> None:
"""Fixes the heap in upward direction of given index"""
parent = self._parent(index)
while parent is not None and not self._cmp(index, parent):
self._swap(index, parent)
index, parent = parent, self._parent(parent)
def _heapify_down(self, index: int) -> None:
"""Fixes the heap in downward direction of given index"""
valid_parent = self._get_valid_parent(index)
while valid_parent != index:
self._swap(index, valid_parent)
index, valid_parent = valid_parent, self._get_valid_parent(valid_parent)
def update_item(self, item: int, item_value: int) -> None:
"""Updates given item value in heap if present"""
if item not in self.pos_map:
return
index = self.pos_map[item]
self.arr[index] = [item, self.key(item_value)]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(index)
self._heapify_down(index)
def delete_item(self, item: int) -> None:
"""Deletes given item from heap if present"""
if item not in self.pos_map:
return
index = self.pos_map[item]
del self.pos_map[item]
self.arr[index] = self.arr[self.size - 1]
self.pos_map[self.arr[self.size - 1][0]] = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(index)
self._heapify_down(index)
def insert_item(self, item: int, item_value: int) -> None:
"""Inserts given item with given value in heap"""
arr_len = len(self.arr)
if arr_len == self.size:
self.arr.append([item, self.key(item_value)])
else:
self.arr[self.size] = [item, self.key(item_value)]
self.pos_map[item] = self.size
self.size += 1
self._heapify_up(self.size - 1)
def get_top(self) -> tuple | None:
"""Returns top item tuple (Calculated value, item) from heap if present"""
return self.arr[0] if self.size else None
def extract_top(self) -> tuple | None:
"""
Return top item tuple (Calculated value, item) from heap and removes it as well
if present
"""
top_item_tuple = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0])
return top_item_tuple
def test_heap() -> None:
"""
>>> h = Heap() # Max-heap
>>> h.insert_item(5, 34)
>>> h.insert_item(6, 31)
>>> h.insert_item(7, 37)
>>> h.get_top()
[7, 37]
>>> h.extract_top()
[7, 37]
>>> h.extract_top()
[5, 34]
>>> h.extract_top()
[6, 31]
>>> h = Heap(key=lambda x: -x) # Min heap
>>> h.insert_item(5, 34)
>>> h.insert_item(6, 31)
>>> h.insert_item(7, 37)
>>> h.get_top()
[6, -31]
>>> h.extract_top()
[6, -31]
>>> h.extract_top()
[5, -34]
>>> h.extract_top()
[7, -37]
>>> h.insert_item(8, 45)
>>> h.insert_item(9, 40)
>>> h.insert_item(10, 50)
>>> h.get_top()
[9, -40]
>>> h.update_item(10, 30)
>>> h.get_top()
[10, -30]
>>> h.delete_item(10)
>>> h.get_top()
[9, -40]
"""
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
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