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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 pure Python implementation of the heap sort algorithm.
For doctests run following command:
python -m doctest -v heap_sort.py
or
python3 -m doctest -v heap_sort.py
For manual testing run:
python heap_sort.py
"""
def heapify(unsorted, index, heap_size):
largest = index
left_index = 2 * index + 1
right_index = 2 * index + 2
if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
largest = left_index
if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
largest = right_index
if largest != index:
unsorted[largest], unsorted[index] = unsorted[index], unsorted[largest]
heapify(unsorted, largest, heap_size)
def heap_sort(unsorted):
"""
Pure implementation of the heap sort algorithm in Python
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> heap_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> heap_sort([])
[]
>>> heap_sort([-2, -5, -45])
[-45, -5, -2]
"""
n = len(unsorted)
for i in range(n // 2 - 1, -1, -1):
heapify(unsorted, i, n)
for i in range(n - 1, 0, -1):
unsorted[0], unsorted[i] = unsorted[i], unsorted[0]
heapify(unsorted, 0, i)
return unsorted
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(heap_sort(unsorted))
| """
This is a pure Python implementation of the heap sort algorithm.
For doctests run following command:
python -m doctest -v heap_sort.py
or
python3 -m doctest -v heap_sort.py
For manual testing run:
python heap_sort.py
"""
def heapify(unsorted, index, heap_size):
largest = index
left_index = 2 * index + 1
right_index = 2 * index + 2
if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
largest = left_index
if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
largest = right_index
if largest != index:
unsorted[largest], unsorted[index] = unsorted[index], unsorted[largest]
heapify(unsorted, largest, heap_size)
def heap_sort(unsorted):
"""
Pure implementation of the heap sort algorithm in Python
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> heap_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> heap_sort([])
[]
>>> heap_sort([-2, -5, -45])
[-45, -5, -2]
"""
n = len(unsorted)
for i in range(n // 2 - 1, -1, -1):
heapify(unsorted, i, n)
for i in range(n - 1, 0, -1):
unsorted[0], unsorted[i] = unsorted[i], unsorted[0]
heapify(unsorted, 0, i)
return unsorted
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(heap_sort(unsorted))
| -1 |
TheAlgorithms/Python | 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}`.
| if __name__ == "__main__":
import socket # Import socket module
sock = socket.socket() # Create a socket object
host = socket.gethostname() # Get local machine name
port = 12312
sock.connect((host, port))
sock.send(b"Hello server!")
with open("Received_file", "wb") as out_file:
print("File opened")
print("Receiving data...")
while True:
data = sock.recv(1024)
print(f"{data = }")
if not data:
break
out_file.write(data) # Write data to a file
print("Successfully got the file")
sock.close()
print("Connection closed")
| if __name__ == "__main__":
import socket # Import socket module
sock = socket.socket() # Create a socket object
host = socket.gethostname() # Get local machine name
port = 12312
sock.connect((host, port))
sock.send(b"Hello server!")
with open("Received_file", "wb") as out_file:
print("File opened")
print("Receiving data...")
while True:
data = sock.recv(1024)
print(f"{data = }")
if not data:
break
out_file.write(data) # Write data to a file
print("Successfully got the file")
sock.close()
print("Connection closed")
| -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 pure Python implementation of fibonacci search.
Resources used:
https://en.wikipedia.org/wiki/Fibonacci_search_technique
For doctests run following command:
python3 -m doctest -v fibonacci_search.py
For manual testing run:
python3 fibonacci_search.py
"""
from functools import lru_cache
@lru_cache
def fibonacci(k: int) -> int:
"""Finds fibonacci number in index k.
Parameters
----------
k :
Index of fibonacci.
Returns
-------
int
Fibonacci number in position k.
>>> fibonacci(0)
0
>>> fibonacci(2)
1
>>> fibonacci(5)
5
>>> fibonacci(15)
610
>>> fibonacci('a')
Traceback (most recent call last):
TypeError: k must be an integer.
>>> fibonacci(-5)
Traceback (most recent call last):
ValueError: k integer must be greater or equal to zero.
"""
if not isinstance(k, int):
raise TypeError("k must be an integer.")
if k < 0:
raise ValueError("k integer must be greater or equal to zero.")
if k == 0:
return 0
elif k == 1:
return 1
else:
return fibonacci(k - 1) + fibonacci(k - 2)
def fibonacci_search(arr: list, val: int) -> int:
"""A pure Python implementation of a fibonacci search algorithm.
Parameters
----------
arr
List of sorted elements.
val
Element to search in list.
Returns
-------
int
The index of the element in the array.
-1 if the element is not found.
>>> fibonacci_search([4, 5, 6, 7], 4)
0
>>> fibonacci_search([4, 5, 6, 7], -10)
-1
>>> fibonacci_search([-18, 2], -18)
0
>>> fibonacci_search([5], 5)
0
>>> fibonacci_search(['a', 'c', 'd'], 'c')
1
>>> fibonacci_search(['a', 'c', 'd'], 'f')
-1
>>> fibonacci_search([], 1)
-1
>>> fibonacci_search([.1, .4 , 7], .4)
1
>>> fibonacci_search([], 9)
-1
>>> fibonacci_search(list(range(100)), 63)
63
>>> fibonacci_search(list(range(100)), 99)
99
>>> fibonacci_search(list(range(-100, 100, 3)), -97)
1
>>> fibonacci_search(list(range(-100, 100, 3)), 0)
-1
>>> fibonacci_search(list(range(-100, 100, 5)), 0)
20
>>> fibonacci_search(list(range(-100, 100, 5)), 95)
39
"""
len_list = len(arr)
# Find m such that F_m >= n where F_i is the i_th fibonacci number.
i = 0
while True:
if fibonacci(i) >= len_list:
fibb_k = i
break
i += 1
offset = 0
while fibb_k > 0:
index_k = min(
offset + fibonacci(fibb_k - 1), len_list - 1
) # Prevent out of range
item_k_1 = arr[index_k]
if item_k_1 == val:
return index_k
elif val < item_k_1:
fibb_k -= 1
elif val > item_k_1:
offset += fibonacci(fibb_k - 1)
fibb_k -= 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
This is pure Python implementation of fibonacci search.
Resources used:
https://en.wikipedia.org/wiki/Fibonacci_search_technique
For doctests run following command:
python3 -m doctest -v fibonacci_search.py
For manual testing run:
python3 fibonacci_search.py
"""
from functools import lru_cache
@lru_cache
def fibonacci(k: int) -> int:
"""Finds fibonacci number in index k.
Parameters
----------
k :
Index of fibonacci.
Returns
-------
int
Fibonacci number in position k.
>>> fibonacci(0)
0
>>> fibonacci(2)
1
>>> fibonacci(5)
5
>>> fibonacci(15)
610
>>> fibonacci('a')
Traceback (most recent call last):
TypeError: k must be an integer.
>>> fibonacci(-5)
Traceback (most recent call last):
ValueError: k integer must be greater or equal to zero.
"""
if not isinstance(k, int):
raise TypeError("k must be an integer.")
if k < 0:
raise ValueError("k integer must be greater or equal to zero.")
if k == 0:
return 0
elif k == 1:
return 1
else:
return fibonacci(k - 1) + fibonacci(k - 2)
def fibonacci_search(arr: list, val: int) -> int:
"""A pure Python implementation of a fibonacci search algorithm.
Parameters
----------
arr
List of sorted elements.
val
Element to search in list.
Returns
-------
int
The index of the element in the array.
-1 if the element is not found.
>>> fibonacci_search([4, 5, 6, 7], 4)
0
>>> fibonacci_search([4, 5, 6, 7], -10)
-1
>>> fibonacci_search([-18, 2], -18)
0
>>> fibonacci_search([5], 5)
0
>>> fibonacci_search(['a', 'c', 'd'], 'c')
1
>>> fibonacci_search(['a', 'c', 'd'], 'f')
-1
>>> fibonacci_search([], 1)
-1
>>> fibonacci_search([.1, .4 , 7], .4)
1
>>> fibonacci_search([], 9)
-1
>>> fibonacci_search(list(range(100)), 63)
63
>>> fibonacci_search(list(range(100)), 99)
99
>>> fibonacci_search(list(range(-100, 100, 3)), -97)
1
>>> fibonacci_search(list(range(-100, 100, 3)), 0)
-1
>>> fibonacci_search(list(range(-100, 100, 5)), 0)
20
>>> fibonacci_search(list(range(-100, 100, 5)), 95)
39
"""
len_list = len(arr)
# Find m such that F_m >= n where F_i is the i_th fibonacci number.
i = 0
while True:
if fibonacci(i) >= len_list:
fibb_k = i
break
i += 1
offset = 0
while fibb_k > 0:
index_k = min(
offset + fibonacci(fibb_k - 1), len_list - 1
) # Prevent out of range
item_k_1 = arr[index_k]
if item_k_1 == val:
return index_k
elif val < item_k_1:
fibb_k -= 1
elif val > item_k_1:
offset += fibonacci(fibb_k - 1)
fibb_k -= 2
else:
return -1
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/Rayleigh_quotient
"""
from typing import Any
import numpy as np
def is_hermitian(matrix: np.ndarray) -> bool:
"""
Checks if a matrix is Hermitian.
>>> import numpy as np
>>> A = np.array([
... [2, 2+1j, 4],
... [2-1j, 3, 1j],
... [4, -1j, 1]])
>>> is_hermitian(A)
True
>>> A = np.array([
... [2, 2+1j, 4+1j],
... [2-1j, 3, 1j],
... [4, -1j, 1]])
>>> is_hermitian(A)
False
"""
return np.array_equal(matrix, matrix.conjugate().T)
def rayleigh_quotient(a: np.ndarray, v: np.ndarray) -> Any:
"""
Returns the Rayleigh quotient of a Hermitian matrix A and
vector v.
>>> import numpy as np
>>> A = np.array([
... [1, 2, 4],
... [2, 3, -1],
... [4, -1, 1]
... ])
>>> v = np.array([
... [1],
... [2],
... [3]
... ])
>>> rayleigh_quotient(A, v)
array([[3.]])
"""
v_star = v.conjugate().T
v_star_dot = v_star.dot(a)
assert isinstance(v_star_dot, np.ndarray)
return (v_star_dot.dot(v)) / (v_star.dot(v))
def tests() -> None:
a = np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]])
v = np.array([[1], [2], [3]])
assert is_hermitian(a), f"{a} is not hermitian."
print(rayleigh_quotient(a, v))
a = np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]])
assert is_hermitian(a), f"{a} is not hermitian."
assert rayleigh_quotient(a, v) == float(3)
if __name__ == "__main__":
import doctest
doctest.testmod()
tests()
| """
https://en.wikipedia.org/wiki/Rayleigh_quotient
"""
from typing import Any
import numpy as np
def is_hermitian(matrix: np.ndarray) -> bool:
"""
Checks if a matrix is Hermitian.
>>> import numpy as np
>>> A = np.array([
... [2, 2+1j, 4],
... [2-1j, 3, 1j],
... [4, -1j, 1]])
>>> is_hermitian(A)
True
>>> A = np.array([
... [2, 2+1j, 4+1j],
... [2-1j, 3, 1j],
... [4, -1j, 1]])
>>> is_hermitian(A)
False
"""
return np.array_equal(matrix, matrix.conjugate().T)
def rayleigh_quotient(a: np.ndarray, v: np.ndarray) -> Any:
"""
Returns the Rayleigh quotient of a Hermitian matrix A and
vector v.
>>> import numpy as np
>>> A = np.array([
... [1, 2, 4],
... [2, 3, -1],
... [4, -1, 1]
... ])
>>> v = np.array([
... [1],
... [2],
... [3]
... ])
>>> rayleigh_quotient(A, v)
array([[3.]])
"""
v_star = v.conjugate().T
v_star_dot = v_star.dot(a)
assert isinstance(v_star_dot, np.ndarray)
return (v_star_dot.dot(v)) / (v_star.dot(v))
def tests() -> None:
a = np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]])
v = np.array([[1], [2], [3]])
assert is_hermitian(a), f"{a} is not hermitian."
print(rayleigh_quotient(a, v))
a = np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]])
assert is_hermitian(a), f"{a} is not hermitian."
assert rayleigh_quotient(a, v) == float(3)
if __name__ == "__main__":
import doctest
doctest.testmod()
tests()
| -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 Sequence
def compare_string(string1: str, string2: str) -> str:
"""
>>> compare_string('0010','0110')
'0_10'
>>> compare_string('0110','1101')
'X'
"""
list1 = list(string1)
list2 = list(string2)
count = 0
for i in range(len(list1)):
if list1[i] != list2[i]:
count += 1
list1[i] = "_"
if count > 1:
return "X"
else:
return "".join(list1)
def check(binary: list[str]) -> list[str]:
"""
>>> check(['0.00.01.5'])
['0.00.01.5']
"""
pi = []
while True:
check1 = ["$"] * len(binary)
temp = []
for i in range(len(binary)):
for j in range(i + 1, len(binary)):
k = compare_string(binary[i], binary[j])
if k != "X":
check1[i] = "*"
check1[j] = "*"
temp.append(k)
for i in range(len(binary)):
if check1[i] == "$":
pi.append(binary[i])
if len(temp) == 0:
return pi
binary = list(set(temp))
def decimal_to_binary(no_of_variable: int, minterms: Sequence[float]) -> list[str]:
"""
>>> decimal_to_binary(3,[1.5])
['0.00.01.5']
"""
temp = []
for minterm in minterms:
string = ""
for _ in range(no_of_variable):
string = str(minterm % 2) + string
minterm //= 2
temp.append(string)
return temp
def is_for_table(string1: str, string2: str, count: int) -> bool:
"""
>>> is_for_table('__1','011',2)
True
>>> is_for_table('01_','001',1)
False
"""
list1 = list(string1)
list2 = list(string2)
count_n = 0
for i in range(len(list1)):
if list1[i] != list2[i]:
count_n += 1
return count_n == count
def selection(chart: list[list[int]], prime_implicants: list[str]) -> list[str]:
"""
>>> selection([[1]],['0.00.01.5'])
['0.00.01.5']
>>> selection([[1]],['0.00.01.5'])
['0.00.01.5']
"""
temp = []
select = [0] * len(chart)
for i in range(len(chart[0])):
count = 0
rem = -1
for j in range(len(chart)):
if chart[j][i] == 1:
count += 1
rem = j
if count == 1:
select[rem] = 1
for i in range(len(select)):
if select[i] == 1:
for j in range(len(chart[0])):
if chart[i][j] == 1:
for k in range(len(chart)):
chart[k][j] = 0
temp.append(prime_implicants[i])
while True:
max_n = 0
rem = -1
count_n = 0
for i in range(len(chart)):
count_n = chart[i].count(1)
if count_n > max_n:
max_n = count_n
rem = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem])
for i in range(len(chart[0])):
if chart[rem][i] == 1:
for j in range(len(chart)):
chart[j][i] = 0
def prime_implicant_chart(
prime_implicants: list[str], binary: list[str]
) -> list[list[int]]:
"""
>>> prime_implicant_chart(['0.00.01.5'],['0.00.01.5'])
[[1]]
"""
chart = [[0 for x in range(len(binary))] for x in range(len(prime_implicants))]
for i in range(len(prime_implicants)):
count = prime_implicants[i].count("_")
for j in range(len(binary)):
if is_for_table(prime_implicants[i], binary[j], count):
chart[i][j] = 1
return chart
def main() -> None:
no_of_variable = int(input("Enter the no. of variables\n"))
minterms = [
float(x)
for x in input(
"Enter the decimal representation of Minterms 'Spaces Separated'\n"
).split()
]
binary = decimal_to_binary(no_of_variable, minterms)
prime_implicants = check(binary)
print("Prime Implicants are:")
print(prime_implicants)
chart = prime_implicant_chart(prime_implicants, binary)
essential_prime_implicants = selection(chart, prime_implicants)
print("Essential Prime Implicants are:")
print(essential_prime_implicants)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| from __future__ import annotations
from collections.abc import Sequence
def compare_string(string1: str, string2: str) -> str:
"""
>>> compare_string('0010','0110')
'0_10'
>>> compare_string('0110','1101')
'X'
"""
list1 = list(string1)
list2 = list(string2)
count = 0
for i in range(len(list1)):
if list1[i] != list2[i]:
count += 1
list1[i] = "_"
if count > 1:
return "X"
else:
return "".join(list1)
def check(binary: list[str]) -> list[str]:
"""
>>> check(['0.00.01.5'])
['0.00.01.5']
"""
pi = []
while True:
check1 = ["$"] * len(binary)
temp = []
for i in range(len(binary)):
for j in range(i + 1, len(binary)):
k = compare_string(binary[i], binary[j])
if k != "X":
check1[i] = "*"
check1[j] = "*"
temp.append(k)
for i in range(len(binary)):
if check1[i] == "$":
pi.append(binary[i])
if len(temp) == 0:
return pi
binary = list(set(temp))
def decimal_to_binary(no_of_variable: int, minterms: Sequence[float]) -> list[str]:
"""
>>> decimal_to_binary(3,[1.5])
['0.00.01.5']
"""
temp = []
for minterm in minterms:
string = ""
for _ in range(no_of_variable):
string = str(minterm % 2) + string
minterm //= 2
temp.append(string)
return temp
def is_for_table(string1: str, string2: str, count: int) -> bool:
"""
>>> is_for_table('__1','011',2)
True
>>> is_for_table('01_','001',1)
False
"""
list1 = list(string1)
list2 = list(string2)
count_n = 0
for i in range(len(list1)):
if list1[i] != list2[i]:
count_n += 1
return count_n == count
def selection(chart: list[list[int]], prime_implicants: list[str]) -> list[str]:
"""
>>> selection([[1]],['0.00.01.5'])
['0.00.01.5']
>>> selection([[1]],['0.00.01.5'])
['0.00.01.5']
"""
temp = []
select = [0] * len(chart)
for i in range(len(chart[0])):
count = 0
rem = -1
for j in range(len(chart)):
if chart[j][i] == 1:
count += 1
rem = j
if count == 1:
select[rem] = 1
for i in range(len(select)):
if select[i] == 1:
for j in range(len(chart[0])):
if chart[i][j] == 1:
for k in range(len(chart)):
chart[k][j] = 0
temp.append(prime_implicants[i])
while True:
max_n = 0
rem = -1
count_n = 0
for i in range(len(chart)):
count_n = chart[i].count(1)
if count_n > max_n:
max_n = count_n
rem = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem])
for i in range(len(chart[0])):
if chart[rem][i] == 1:
for j in range(len(chart)):
chart[j][i] = 0
def prime_implicant_chart(
prime_implicants: list[str], binary: list[str]
) -> list[list[int]]:
"""
>>> prime_implicant_chart(['0.00.01.5'],['0.00.01.5'])
[[1]]
"""
chart = [[0 for x in range(len(binary))] for x in range(len(prime_implicants))]
for i in range(len(prime_implicants)):
count = prime_implicants[i].count("_")
for j in range(len(binary)):
if is_for_table(prime_implicants[i], binary[j], count):
chart[i][j] = 1
return chart
def main() -> None:
no_of_variable = int(input("Enter the no. of variables\n"))
minterms = [
float(x)
for x in input(
"Enter the decimal representation of Minterms 'Spaces Separated'\n"
).split()
]
binary = decimal_to_binary(no_of_variable, minterms)
prime_implicants = check(binary)
print("Prime Implicants are:")
print(prime_implicants)
chart = prime_implicant_chart(prime_implicants, binary)
essential_prime_implicants = selection(chart, prime_implicants)
print("Essential Prime Implicants are:")
print(essential_prime_implicants)
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}`.
| alphabet = {
"A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
"B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
"C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
"D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
"E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
"F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
"G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
"H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
"I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
"J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
"K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
"L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
"M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
"N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
"O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
"P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
"Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
"R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
"S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
"T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
"U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
"V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
"W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
"X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
"Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
"Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}
def generate_table(key: str) -> list[tuple[str, str]]:
"""
>>> generate_table('marvin') # doctest: +NORMALIZE_WHITESPACE
[('ABCDEFGHIJKLM', 'UVWXYZNOPQRST'), ('ABCDEFGHIJKLM', 'NOPQRSTUVWXYZ'),
('ABCDEFGHIJKLM', 'STUVWXYZNOPQR'), ('ABCDEFGHIJKLM', 'QRSTUVWXYZNOP'),
('ABCDEFGHIJKLM', 'WXYZNOPQRSTUV'), ('ABCDEFGHIJKLM', 'UVWXYZNOPQRST')]
"""
return [alphabet[char] for char in key.upper()]
def encrypt(key: str, words: str) -> str:
"""
>>> encrypt('marvin', 'jessica')
'QRACRWU'
"""
cipher = ""
count = 0
table = generate_table(key)
for char in words.upper():
cipher += get_opponent(table[count], char)
count = (count + 1) % len(table)
return cipher
def decrypt(key: str, words: str) -> str:
"""
>>> decrypt('marvin', 'QRACRWU')
'JESSICA'
"""
return encrypt(key, words)
def get_position(table: tuple[str, str], char: str) -> tuple[int, int]:
"""
>>> get_position(generate_table('marvin')[0], 'M')
(0, 12)
"""
# `char` is either in the 0th row or the 1st row
row = 0 if char in table[0] else 1
col = table[row].index(char)
return row, col
def get_opponent(table: tuple[str, str], char: str) -> str:
"""
>>> get_opponent(generate_table('marvin')[0], 'M')
'T'
"""
row, col = get_position(table, char.upper())
if row == 1:
return table[0][col]
else:
return table[1][col] if row == 0 else char
if __name__ == "__main__":
import doctest
doctest.testmod() # Fist ensure that all our tests are passing...
"""
Demo:
Enter key: marvin
Enter text to encrypt: jessica
Encrypted: QRACRWU
Decrypted with key: JESSICA
"""
key = input("Enter key: ").strip()
text = input("Enter text to encrypt: ").strip()
cipher_text = encrypt(key, text)
print(f"Encrypted: {cipher_text}")
print(f"Decrypted with key: {decrypt(key, cipher_text)}")
| alphabet = {
"A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
"B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
"C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
"D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
"E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
"F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
"G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
"H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
"I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
"J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
"K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
"L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
"M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
"N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
"O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
"P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
"Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
"R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
"S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
"T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
"U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
"V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
"W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
"X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
"Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
"Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}
def generate_table(key: str) -> list[tuple[str, str]]:
"""
>>> generate_table('marvin') # doctest: +NORMALIZE_WHITESPACE
[('ABCDEFGHIJKLM', 'UVWXYZNOPQRST'), ('ABCDEFGHIJKLM', 'NOPQRSTUVWXYZ'),
('ABCDEFGHIJKLM', 'STUVWXYZNOPQR'), ('ABCDEFGHIJKLM', 'QRSTUVWXYZNOP'),
('ABCDEFGHIJKLM', 'WXYZNOPQRSTUV'), ('ABCDEFGHIJKLM', 'UVWXYZNOPQRST')]
"""
return [alphabet[char] for char in key.upper()]
def encrypt(key: str, words: str) -> str:
"""
>>> encrypt('marvin', 'jessica')
'QRACRWU'
"""
cipher = ""
count = 0
table = generate_table(key)
for char in words.upper():
cipher += get_opponent(table[count], char)
count = (count + 1) % len(table)
return cipher
def decrypt(key: str, words: str) -> str:
"""
>>> decrypt('marvin', 'QRACRWU')
'JESSICA'
"""
return encrypt(key, words)
def get_position(table: tuple[str, str], char: str) -> tuple[int, int]:
"""
>>> get_position(generate_table('marvin')[0], 'M')
(0, 12)
"""
# `char` is either in the 0th row or the 1st row
row = 0 if char in table[0] else 1
col = table[row].index(char)
return row, col
def get_opponent(table: tuple[str, str], char: str) -> str:
"""
>>> get_opponent(generate_table('marvin')[0], 'M')
'T'
"""
row, col = get_position(table, char.upper())
if row == 1:
return table[0][col]
else:
return table[1][col] if row == 0 else char
if __name__ == "__main__":
import doctest
doctest.testmod() # Fist ensure that all our tests are passing...
"""
Demo:
Enter key: marvin
Enter text to encrypt: jessica
Encrypted: QRACRWU
Decrypted with key: JESSICA
"""
key = input("Enter key: ").strip()
text = input("Enter text to encrypt: ").strip()
cipher_text = encrypt(key, text)
print(f"Encrypted: {cipher_text}")
print(f"Decrypted with key: {decrypt(key, cipher_text)}")
| -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 8: https://projecteuler.net/problem=8
Largest product in a series
The four adjacent digits in the 1000-digit number that have the greatest
product are 9 × 9 × 8 × 9 = 5832.
73167176531330624919225119674426574742355349194934
96983520312774506326239578318016984801869478851843
85861560789112949495459501737958331952853208805511
12540698747158523863050715693290963295227443043557
66896648950445244523161731856403098711121722383113
62229893423380308135336276614282806444486645238749
30358907296290491560440772390713810515859307960866
70172427121883998797908792274921901699720888093776
65727333001053367881220235421809751254540594752243
52584907711670556013604839586446706324415722155397
53697817977846174064955149290862569321978468622482
83972241375657056057490261407972968652414535100474
82166370484403199890008895243450658541227588666881
16427171479924442928230863465674813919123162824586
17866458359124566529476545682848912883142607690042
24219022671055626321111109370544217506941658960408
07198403850962455444362981230987879927244284909188
84580156166097919133875499200524063689912560717606
05886116467109405077541002256983155200055935729725
71636269561882670428252483600823257530420752963450
Find the thirteen adjacent digits in the 1000-digit number that have the
greatest product. What is the value of this product?
"""
import sys
N = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def str_eval(s: str) -> int:
"""
Returns product of digits in given string n
>>> str_eval("987654321")
362880
>>> str_eval("22222222")
256
"""
product = 1
for digit in s:
product *= int(digit)
return product
def solution(n: str = N) -> int:
"""
Find the thirteen adjacent digits in the 1000-digit number n that have
the greatest product and returns it.
"""
largest_product = -sys.maxsize - 1
substr = n[:13]
cur_index = 13
while cur_index < len(n) - 13:
if int(n[cur_index]) >= int(substr[0]):
substr = substr[1:] + n[cur_index]
cur_index += 1
else:
largest_product = max(largest_product, str_eval(substr))
substr = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 8: https://projecteuler.net/problem=8
Largest product in a series
The four adjacent digits in the 1000-digit number that have the greatest
product are 9 × 9 × 8 × 9 = 5832.
73167176531330624919225119674426574742355349194934
96983520312774506326239578318016984801869478851843
85861560789112949495459501737958331952853208805511
12540698747158523863050715693290963295227443043557
66896648950445244523161731856403098711121722383113
62229893423380308135336276614282806444486645238749
30358907296290491560440772390713810515859307960866
70172427121883998797908792274921901699720888093776
65727333001053367881220235421809751254540594752243
52584907711670556013604839586446706324415722155397
53697817977846174064955149290862569321978468622482
83972241375657056057490261407972968652414535100474
82166370484403199890008895243450658541227588666881
16427171479924442928230863465674813919123162824586
17866458359124566529476545682848912883142607690042
24219022671055626321111109370544217506941658960408
07198403850962455444362981230987879927244284909188
84580156166097919133875499200524063689912560717606
05886116467109405077541002256983155200055935729725
71636269561882670428252483600823257530420752963450
Find the thirteen adjacent digits in the 1000-digit number that have the
greatest product. What is the value of this product?
"""
import sys
N = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def str_eval(s: str) -> int:
"""
Returns product of digits in given string n
>>> str_eval("987654321")
362880
>>> str_eval("22222222")
256
"""
product = 1
for digit in s:
product *= int(digit)
return product
def solution(n: str = N) -> int:
"""
Find the thirteen adjacent digits in the 1000-digit number n that have
the greatest product and returns it.
"""
largest_product = -sys.maxsize - 1
substr = n[:13]
cur_index = 13
while cur_index < len(n) - 13:
if int(n[cur_index]) >= int(substr[0]):
substr = substr[1:] + n[cur_index]
cur_index += 1
else:
largest_product = max(largest_product, str_eval(substr))
substr = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
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}`.
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4QEzRi_5P,4[# | -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}`.
| """
Calculate the nth Proth number
Source:
https://handwiki.org/wiki/Proth_number
"""
import math
def proth(number: int) -> int:
"""
:param number: nth number to calculate in the sequence
:return: the nth number in Proth number
Note: indexing starts at 1 i.e. proth(1) gives the first Proth number of 3
>>> proth(6)
25
>>> proth(0)
Traceback (most recent call last):
...
ValueError: Input value of [number=0] must be > 0
>>> proth(-1)
Traceback (most recent call last):
...
ValueError: Input value of [number=-1] must be > 0
>>> proth(6.0)
Traceback (most recent call last):
...
TypeError: Input value of [number=6.0] must be an integer
"""
if not isinstance(number, int):
raise TypeError(f"Input value of [number={number}] must be an integer")
if number < 1:
raise ValueError(f"Input value of [number={number}] must be > 0")
elif number == 1:
return 3
elif number == 2:
return 5
else:
"""
+1 for binary starting at 0 i.e. 2^0, 2^1, etc.
+1 to start the sequence at the 3rd Proth number
Hence, we have a +2 in the below statement
"""
block_index = int(math.log(number // 3, 2)) + 2
proth_list = [3, 5]
proth_index = 2
increment = 3
for block in range(1, block_index):
for _ in range(increment):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1])
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(11):
value = 0
try:
value = proth(number)
except ValueError:
print(f"ValueError: there is no {number}th Proth number")
continue
print(f"The {number}th Proth number: {value}")
| """
Calculate the nth Proth number
Source:
https://handwiki.org/wiki/Proth_number
"""
import math
def proth(number: int) -> int:
"""
:param number: nth number to calculate in the sequence
:return: the nth number in Proth number
Note: indexing starts at 1 i.e. proth(1) gives the first Proth number of 3
>>> proth(6)
25
>>> proth(0)
Traceback (most recent call last):
...
ValueError: Input value of [number=0] must be > 0
>>> proth(-1)
Traceback (most recent call last):
...
ValueError: Input value of [number=-1] must be > 0
>>> proth(6.0)
Traceback (most recent call last):
...
TypeError: Input value of [number=6.0] must be an integer
"""
if not isinstance(number, int):
raise TypeError(f"Input value of [number={number}] must be an integer")
if number < 1:
raise ValueError(f"Input value of [number={number}] must be > 0")
elif number == 1:
return 3
elif number == 2:
return 5
else:
"""
+1 for binary starting at 0 i.e. 2^0, 2^1, etc.
+1 to start the sequence at the 3rd Proth number
Hence, we have a +2 in the below statement
"""
block_index = int(math.log(number // 3, 2)) + 2
proth_list = [3, 5]
proth_index = 2
increment = 3
for block in range(1, block_index):
for _ in range(increment):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1])
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(11):
value = 0
try:
value = proth(number)
except ValueError:
print(f"ValueError: there is no {number}th Proth number")
continue
print(f"The {number}th Proth number: {value}")
| -1 |
TheAlgorithms/Python | 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 pure Python implementation of sentinel linear search algorithm
For doctests run following command:
python -m doctest -v sentinel_linear_search.py
or
python3 -m doctest -v sentinel_linear_search.py
For manual testing run:
python sentinel_linear_search.py
"""
def sentinel_linear_search(sequence, target):
"""Pure implementation of sentinel linear search algorithm in Python
:param sequence: some sequence with comparable items
:param target: item value to search
:return: index of found item or None if item is not found
Examples:
>>> sentinel_linear_search([0, 5, 7, 10, 15], 0)
0
>>> sentinel_linear_search([0, 5, 7, 10, 15], 15)
4
>>> sentinel_linear_search([0, 5, 7, 10, 15], 5)
1
>>> sentinel_linear_search([0, 5, 7, 10, 15], 6)
"""
sequence.append(target)
index = 0
while sequence[index] != target:
index += 1
sequence.pop()
if index == len(sequence):
return None
return index
if __name__ == "__main__":
user_input = input("Enter numbers separated by comma:\n").strip()
sequence = [int(item) for item in user_input.split(",")]
target_input = input("Enter a single number to be found in the list:\n")
target = int(target_input)
result = sentinel_linear_search(sequence, target)
if result is not None:
print(f"{target} found at positions: {result}")
else:
print("Not found")
| """
This is pure Python implementation of sentinel linear search algorithm
For doctests run following command:
python -m doctest -v sentinel_linear_search.py
or
python3 -m doctest -v sentinel_linear_search.py
For manual testing run:
python sentinel_linear_search.py
"""
def sentinel_linear_search(sequence, target):
"""Pure implementation of sentinel linear search algorithm in Python
:param sequence: some sequence with comparable items
:param target: item value to search
:return: index of found item or None if item is not found
Examples:
>>> sentinel_linear_search([0, 5, 7, 10, 15], 0)
0
>>> sentinel_linear_search([0, 5, 7, 10, 15], 15)
4
>>> sentinel_linear_search([0, 5, 7, 10, 15], 5)
1
>>> sentinel_linear_search([0, 5, 7, 10, 15], 6)
"""
sequence.append(target)
index = 0
while sequence[index] != target:
index += 1
sequence.pop()
if index == len(sequence):
return None
return index
if __name__ == "__main__":
user_input = input("Enter numbers separated by comma:\n").strip()
sequence = [int(item) for item in user_input.split(",")]
target_input = input("Enter a single number to be found in the list:\n")
target = int(target_input)
result = sentinel_linear_search(sequence, target)
if result is not None:
print(f"{target} found at positions: {result}")
else:
print("Not found")
| -1 |
TheAlgorithms/Python | 7,949 | 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 GradientBoostingRegressor in sklearn using the
boston dataset which is very popular for regression problem to
predict house price.
"""
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_boston
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
def main():
# loading the dataset from the sklearn
df = load_boston()
print(df.keys())
# now let construct a data frame
df_boston = pd.DataFrame(df.data, columns=df.feature_names)
# let add the target to the dataframe
df_boston["Price"] = df.target
# print the first five rows using the head function
print(df_boston.head())
# Summary statistics
print(df_boston.describe().T)
# Feature selection
x = df_boston.iloc[:, :-1]
y = df_boston.iloc[:, -1] # target variable
# split the data with 75% train and 25% test sets.
x_train, x_test, y_train, y_test = train_test_split(
x, y, random_state=0, test_size=0.25
)
model = GradientBoostingRegressor(
n_estimators=500, max_depth=5, min_samples_split=4, learning_rate=0.01
)
# training the model
model.fit(x_train, y_train)
# to see how good the model fit the data
training_score = model.score(x_train, y_train).round(3)
test_score = model.score(x_test, y_test).round(3)
print("Training score of GradientBoosting is :", training_score)
print("The test score of GradientBoosting is :", test_score)
# Let us evaluation the model by finding the errors
y_pred = model.predict(x_test)
# The mean squared error
print(f"Mean squared error: {mean_squared_error(y_test, y_pred):.2f}")
# Explained variance score: 1 is perfect prediction
print(f"Test Variance score: {r2_score(y_test, y_pred):.2f}")
# So let's run the model against the test data
fig, ax = plt.subplots()
ax.scatter(y_test, y_pred, edgecolors=(0, 0, 0))
ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], "k--", lw=4)
ax.set_xlabel("Actual")
ax.set_ylabel("Predicted")
ax.set_title("Truth vs Predicted")
# this show function will display the plotting
plt.show()
if __name__ == "__main__":
main()
| """Implementation of GradientBoostingRegressor in sklearn using the
boston dataset which is very popular for regression problem to
predict house price.
"""
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_boston
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
def main():
# loading the dataset from the sklearn
df = load_boston()
print(df.keys())
# now let construct a data frame
df_boston = pd.DataFrame(df.data, columns=df.feature_names)
# let add the target to the dataframe
df_boston["Price"] = df.target
# print the first five rows using the head function
print(df_boston.head())
# Summary statistics
print(df_boston.describe().T)
# Feature selection
x = df_boston.iloc[:, :-1]
y = df_boston.iloc[:, -1] # target variable
# split the data with 75% train and 25% test sets.
x_train, x_test, y_train, y_test = train_test_split(
x, y, random_state=0, test_size=0.25
)
model = GradientBoostingRegressor(
n_estimators=500, max_depth=5, min_samples_split=4, learning_rate=0.01
)
# training the model
model.fit(x_train, y_train)
# to see how good the model fit the data
training_score = model.score(x_train, y_train).round(3)
test_score = model.score(x_test, y_test).round(3)
print("Training score of GradientBoosting is :", training_score)
print("The test score of GradientBoosting is :", test_score)
# Let us evaluation the model by finding the errors
y_pred = model.predict(x_test)
# The mean squared error
print(f"Mean squared error: {mean_squared_error(y_test, y_pred):.2f}")
# Explained variance score: 1 is perfect prediction
print(f"Test Variance score: {r2_score(y_test, y_pred):.2f}")
# So let's run the model against the test data
fig, ax = plt.subplots()
ax.scatter(y_test, y_pred, edgecolors=(0, 0, 0))
ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], "k--", lw=4)
ax.set_xlabel("Actual")
ax.set_ylabel("Predicted")
ax.set_title("Truth vs Predicted")
# this show function will display the plotting
plt.show()
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}`.
| """
Implements a disjoint set using Lists and some added heuristics for efficiency
Union by Rank Heuristic and Path Compression
"""
class DisjointSet:
def __init__(self, set_counts: list) -> None:
"""
Initialize with a list of the number of items in each set
and with rank = 1 for each set
"""
self.set_counts = set_counts
self.max_set = max(set_counts)
num_sets = len(set_counts)
self.ranks = [1] * num_sets
self.parents = list(range(num_sets))
def merge(self, src: int, dst: int) -> bool:
"""
Merge two sets together using Union by rank heuristic
Return True if successful
Merge two disjoint sets
>>> A = DisjointSet([1, 1, 1])
>>> A.merge(1, 2)
True
>>> A.merge(0, 2)
True
>>> A.merge(0, 1)
False
"""
src_parent = self.get_parent(src)
dst_parent = self.get_parent(dst)
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
self.set_counts[src_parent] = 0
self.parents[src_parent] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
joined_set_size = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
self.set_counts[dst_parent] = 0
self.parents[dst_parent] = src_parent
joined_set_size = self.set_counts[src_parent]
self.max_set = max(self.max_set, joined_set_size)
return True
def get_parent(self, disj_set: int) -> int:
"""
Find the Parent of a given set
>>> A = DisjointSet([1, 1, 1])
>>> A.merge(1, 2)
True
>>> A.get_parent(0)
0
>>> A.get_parent(1)
2
"""
if self.parents[disj_set] == disj_set:
return disj_set
self.parents[disj_set] = self.get_parent(self.parents[disj_set])
return self.parents[disj_set]
| """
Implements a disjoint set using Lists and some added heuristics for efficiency
Union by Rank Heuristic and Path Compression
"""
class DisjointSet:
def __init__(self, set_counts: list) -> None:
"""
Initialize with a list of the number of items in each set
and with rank = 1 for each set
"""
self.set_counts = set_counts
self.max_set = max(set_counts)
num_sets = len(set_counts)
self.ranks = [1] * num_sets
self.parents = list(range(num_sets))
def merge(self, src: int, dst: int) -> bool:
"""
Merge two sets together using Union by rank heuristic
Return True if successful
Merge two disjoint sets
>>> A = DisjointSet([1, 1, 1])
>>> A.merge(1, 2)
True
>>> A.merge(0, 2)
True
>>> A.merge(0, 1)
False
"""
src_parent = self.get_parent(src)
dst_parent = self.get_parent(dst)
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
self.set_counts[src_parent] = 0
self.parents[src_parent] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
joined_set_size = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
self.set_counts[dst_parent] = 0
self.parents[dst_parent] = src_parent
joined_set_size = self.set_counts[src_parent]
self.max_set = max(self.max_set, joined_set_size)
return True
def get_parent(self, disj_set: int) -> int:
"""
Find the Parent of a given set
>>> A = DisjointSet([1, 1, 1])
>>> A.merge(1, 2)
True
>>> A.get_parent(0)
0
>>> A.get_parent(1)
2
"""
if self.parents[disj_set] == disj_set:
return disj_set
self.parents[disj_set] = self.get_parent(self.parents[disj_set])
return self.parents[disj_set]
| -1 |
TheAlgorithms/Python | 7,949 | 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 1: https://projecteuler.net/problem=1
Multiples of 3 and 5
If we list all the natural numbers below 10 that are multiples of 3 or 5,
we get 3, 5, 6 and 9. The sum of these multiples is 23.
Find the sum of all the multiples of 3 or 5 below 1000.
"""
def solution(n: int = 1000) -> int:
"""
Returns the sum of all the multiples of 3 or 5 below n.
>>> solution(3)
0
>>> solution(4)
3
>>> solution(10)
23
>>> solution(600)
83700
"""
result = 0
for i in range(n):
if i % 3 == 0 or i % 5 == 0:
result += i
return result
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 1: https://projecteuler.net/problem=1
Multiples of 3 and 5
If we list all the natural numbers below 10 that are multiples of 3 or 5,
we get 3, 5, 6 and 9. The sum of these multiples is 23.
Find the sum of all the multiples of 3 or 5 below 1000.
"""
def solution(n: int = 1000) -> int:
"""
Returns the sum of all the multiples of 3 or 5 below n.
>>> solution(3)
0
>>> solution(4)
3
>>> solution(10)
23
>>> solution(600)
83700
"""
result = 0
for i in range(n):
if i % 3 == 0 or i % 5 == 0:
result += i
return result
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 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/Hill_climbing
import math
class SearchProblem:
"""
An interface to define search problems.
The interface will be illustrated using the example of mathematical function.
"""
def __init__(self, x: int, y: int, step_size: int, function_to_optimize):
"""
The constructor of the search problem.
x: the x coordinate of the current search state.
y: the y coordinate of the current search state.
step_size: size of the step to take when looking for neighbors.
function_to_optimize: a function to optimize having the signature f(x, y).
"""
self.x = x
self.y = y
self.step_size = step_size
self.function = function_to_optimize
def score(self) -> int:
"""
Returns the output of the function called with current x and y coordinates.
>>> def test_function(x, y):
... return x + y
>>> SearchProblem(0, 0, 1, test_function).score() # 0 + 0 = 0
0
>>> SearchProblem(5, 7, 1, test_function).score() # 5 + 7 = 12
12
"""
return self.function(self.x, self.y)
def get_neighbors(self):
"""
Returns a list of coordinates of neighbors adjacent to the current coordinates.
Neighbors:
| 0 | 1 | 2 |
| 3 | _ | 4 |
| 5 | 6 | 7 |
"""
step_size = self.step_size
return [
SearchProblem(x, y, step_size, self.function)
for x, y in (
(self.x - step_size, self.y - step_size),
(self.x - step_size, self.y),
(self.x - step_size, self.y + step_size),
(self.x, self.y - step_size),
(self.x, self.y + step_size),
(self.x + step_size, self.y - step_size),
(self.x + step_size, self.y),
(self.x + step_size, self.y + step_size),
)
]
def __hash__(self):
"""
hash the string representation of the current search state.
"""
return hash(str(self))
def __eq__(self, obj):
"""
Check if the 2 objects are equal.
"""
if isinstance(obj, SearchProblem):
return hash(str(self)) == hash(str(obj))
return False
def __str__(self):
"""
string representation of the current search state.
>>> str(SearchProblem(0, 0, 1, None))
'x: 0 y: 0'
>>> str(SearchProblem(2, 5, 1, None))
'x: 2 y: 5'
"""
return f"x: {self.x} y: {self.y}"
def hill_climbing(
search_prob,
find_max: bool = True,
max_x: float = math.inf,
min_x: float = -math.inf,
max_y: float = math.inf,
min_y: float = -math.inf,
visualization: bool = False,
max_iter: int = 10000,
) -> SearchProblem:
"""
Implementation of the hill climbling algorithm.
We start with a given state, find all its neighbors,
move towards the neighbor which provides the maximum (or minimum) change.
We keep doing this until we are at a state where we do not have any
neighbors which can improve the solution.
Args:
search_prob: The search state at the start.
find_max: If True, the algorithm should find the maximum else the minimum.
max_x, min_x, max_y, min_y: the maximum and minimum bounds of x and y.
visualization: If True, a matplotlib graph is displayed.
max_iter: number of times to run the iteration.
Returns a search state having the maximum (or minimum) score.
"""
current_state = search_prob
scores = [] # list to store the current score at each iteration
iterations = 0
solution_found = False
visited = set()
while not solution_found and iterations < max_iter:
visited.add(current_state)
iterations += 1
current_score = current_state.score()
scores.append(current_score)
neighbors = current_state.get_neighbors()
max_change = -math.inf
min_change = math.inf
next_state = None # to hold the next best neighbor
for neighbor in neighbors:
if neighbor in visited:
continue # do not want to visit the same state again
if (
neighbor.x > max_x
or neighbor.x < min_x
or neighbor.y > max_y
or neighbor.y < min_y
):
continue # neighbor outside our bounds
change = neighbor.score() - current_score
if find_max: # finding max
# going to direction with greatest ascent
if change > max_change and change > 0:
max_change = change
next_state = neighbor
else: # finding min
# to direction with greatest descent
if change < min_change and change < 0:
min_change = change
next_state = neighbor
if next_state is not None:
# we found at least one neighbor which improved the current state
current_state = next_state
else:
# since we have no neighbor that improves the solution we stop the search
solution_found = True
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(iterations), scores)
plt.xlabel("Iterations")
plt.ylabel("Function values")
plt.show()
return current_state
if __name__ == "__main__":
import doctest
doctest.testmod()
def test_f1(x, y):
return (x**2) + (y**2)
# starting the problem with initial coordinates (3, 4)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(prob, find_max=False)
print(
"The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{local_min.score()}"
)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
def test_f2(x, y):
return (3 * x**2) - (6 * y)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(prob, find_max=True)
print(
"The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{local_min.score()}"
)
| # https://en.wikipedia.org/wiki/Hill_climbing
import math
class SearchProblem:
"""
An interface to define search problems.
The interface will be illustrated using the example of mathematical function.
"""
def __init__(self, x: int, y: int, step_size: int, function_to_optimize):
"""
The constructor of the search problem.
x: the x coordinate of the current search state.
y: the y coordinate of the current search state.
step_size: size of the step to take when looking for neighbors.
function_to_optimize: a function to optimize having the signature f(x, y).
"""
self.x = x
self.y = y
self.step_size = step_size
self.function = function_to_optimize
def score(self) -> int:
"""
Returns the output of the function called with current x and y coordinates.
>>> def test_function(x, y):
... return x + y
>>> SearchProblem(0, 0, 1, test_function).score() # 0 + 0 = 0
0
>>> SearchProblem(5, 7, 1, test_function).score() # 5 + 7 = 12
12
"""
return self.function(self.x, self.y)
def get_neighbors(self):
"""
Returns a list of coordinates of neighbors adjacent to the current coordinates.
Neighbors:
| 0 | 1 | 2 |
| 3 | _ | 4 |
| 5 | 6 | 7 |
"""
step_size = self.step_size
return [
SearchProblem(x, y, step_size, self.function)
for x, y in (
(self.x - step_size, self.y - step_size),
(self.x - step_size, self.y),
(self.x - step_size, self.y + step_size),
(self.x, self.y - step_size),
(self.x, self.y + step_size),
(self.x + step_size, self.y - step_size),
(self.x + step_size, self.y),
(self.x + step_size, self.y + step_size),
)
]
def __hash__(self):
"""
hash the string representation of the current search state.
"""
return hash(str(self))
def __eq__(self, obj):
"""
Check if the 2 objects are equal.
"""
if isinstance(obj, SearchProblem):
return hash(str(self)) == hash(str(obj))
return False
def __str__(self):
"""
string representation of the current search state.
>>> str(SearchProblem(0, 0, 1, None))
'x: 0 y: 0'
>>> str(SearchProblem(2, 5, 1, None))
'x: 2 y: 5'
"""
return f"x: {self.x} y: {self.y}"
def hill_climbing(
search_prob,
find_max: bool = True,
max_x: float = math.inf,
min_x: float = -math.inf,
max_y: float = math.inf,
min_y: float = -math.inf,
visualization: bool = False,
max_iter: int = 10000,
) -> SearchProblem:
"""
Implementation of the hill climbling algorithm.
We start with a given state, find all its neighbors,
move towards the neighbor which provides the maximum (or minimum) change.
We keep doing this until we are at a state where we do not have any
neighbors which can improve the solution.
Args:
search_prob: The search state at the start.
find_max: If True, the algorithm should find the maximum else the minimum.
max_x, min_x, max_y, min_y: the maximum and minimum bounds of x and y.
visualization: If True, a matplotlib graph is displayed.
max_iter: number of times to run the iteration.
Returns a search state having the maximum (or minimum) score.
"""
current_state = search_prob
scores = [] # list to store the current score at each iteration
iterations = 0
solution_found = False
visited = set()
while not solution_found and iterations < max_iter:
visited.add(current_state)
iterations += 1
current_score = current_state.score()
scores.append(current_score)
neighbors = current_state.get_neighbors()
max_change = -math.inf
min_change = math.inf
next_state = None # to hold the next best neighbor
for neighbor in neighbors:
if neighbor in visited:
continue # do not want to visit the same state again
if (
neighbor.x > max_x
or neighbor.x < min_x
or neighbor.y > max_y
or neighbor.y < min_y
):
continue # neighbor outside our bounds
change = neighbor.score() - current_score
if find_max: # finding max
# going to direction with greatest ascent
if change > max_change and change > 0:
max_change = change
next_state = neighbor
else: # finding min
# to direction with greatest descent
if change < min_change and change < 0:
min_change = change
next_state = neighbor
if next_state is not None:
# we found at least one neighbor which improved the current state
current_state = next_state
else:
# since we have no neighbor that improves the solution we stop the search
solution_found = True
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(iterations), scores)
plt.xlabel("Iterations")
plt.ylabel("Function values")
plt.show()
return current_state
if __name__ == "__main__":
import doctest
doctest.testmod()
def test_f1(x, y):
return (x**2) + (y**2)
# starting the problem with initial coordinates (3, 4)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(prob, find_max=False)
print(
"The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{local_min.score()}"
)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
def test_f2(x, y):
return (3 * x**2) - (6 * y)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(prob, find_max=True)
print(
"The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{local_min.score()}"
)
| -1 |
TheAlgorithms/Python | 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 mathematics, the Lucas–Lehmer test (LLT) is a primality test for Mersenne
numbers. https://en.wikipedia.org/wiki/Lucas%E2%80%93Lehmer_primality_test
A Mersenne number is a number that is one less than a power of two.
That is M_p = 2^p - 1
https://en.wikipedia.org/wiki/Mersenne_prime
The Lucas–Lehmer test is the primality test used by the
Great Internet Mersenne Prime Search (GIMPS) to locate large primes.
"""
# Primality test 2^p - 1
# Return true if 2^p - 1 is prime
def lucas_lehmer_test(p: int) -> bool:
"""
>>> lucas_lehmer_test(p=7)
True
>>> lucas_lehmer_test(p=11)
False
# M_11 = 2^11 - 1 = 2047 = 23 * 89
"""
if p < 2:
raise ValueError("p should not be less than 2!")
elif p == 2:
return True
s = 4
m = (1 << p) - 1
for _ in range(p - 2):
s = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(11))
| """
In mathematics, the Lucas–Lehmer test (LLT) is a primality test for Mersenne
numbers. https://en.wikipedia.org/wiki/Lucas%E2%80%93Lehmer_primality_test
A Mersenne number is a number that is one less than a power of two.
That is M_p = 2^p - 1
https://en.wikipedia.org/wiki/Mersenne_prime
The Lucas–Lehmer test is the primality test used by the
Great Internet Mersenne Prime Search (GIMPS) to locate large primes.
"""
# Primality test 2^p - 1
# Return true if 2^p - 1 is prime
def lucas_lehmer_test(p: int) -> bool:
"""
>>> lucas_lehmer_test(p=7)
True
>>> lucas_lehmer_test(p=11)
False
# M_11 = 2^11 - 1 = 2047 = 23 * 89
"""
if p < 2:
raise ValueError("p should not be less than 2!")
elif p == 2:
return True
s = 4
m = (1 << p) - 1
for _ in range(p - 2):
s = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(11))
| -1 |
TheAlgorithms/Python | 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}`.
| """
Horizontal Projectile Motion problem in physics.
This algorithm solves a specific problem in which
the motion starts from the ground as can be seen below:
(v = 0)
* *
* *
* *
* *
* *
* *
GROUND GROUND
For more info: https://en.wikipedia.org/wiki/Projectile_motion
"""
# Importing packages
from math import radians as angle_to_radians
from math import sin
# Acceleration Constant on Earth (unit m/s^2)
g = 9.80665
def check_args(init_velocity: float, angle: float) -> None:
"""
Check that the arguments are valid
"""
# Ensure valid instance
if not isinstance(init_velocity, (int, float)):
raise TypeError("Invalid velocity. Should be a positive number.")
if not isinstance(angle, (int, float)):
raise TypeError("Invalid angle. Range is 1-90 degrees.")
# Ensure valid angle
if angle > 90 or angle < 1:
raise ValueError("Invalid angle. Range is 1-90 degrees.")
# Ensure valid velocity
if init_velocity < 0:
raise ValueError("Invalid velocity. Should be a positive number.")
def horizontal_distance(init_velocity: float, angle: float) -> float:
"""
Returns the horizontal distance that the object cover
Formula:
v_0^2 * sin(2 * alpha)
---------------------
g
v_0 - initial velocity
alpha - angle
>>> horizontal_distance(30, 45)
91.77
>>> horizontal_distance(100, 78)
414.76
>>> horizontal_distance(-1, 20)
Traceback (most recent call last):
...
ValueError: Invalid velocity. Should be a positive number.
>>> horizontal_distance(30, -20)
Traceback (most recent call last):
...
ValueError: Invalid angle. Range is 1-90 degrees.
"""
check_args(init_velocity, angle)
radians = angle_to_radians(2 * angle)
return round(init_velocity**2 * sin(radians) / g, 2)
def max_height(init_velocity: float, angle: float) -> float:
"""
Returns the maximum height that the object reach
Formula:
v_0^2 * sin^2(alpha)
--------------------
2g
v_0 - initial velocity
alpha - angle
>>> max_height(30, 45)
22.94
>>> max_height(100, 78)
487.82
>>> max_height("a", 20)
Traceback (most recent call last):
...
TypeError: Invalid velocity. Should be a positive number.
>>> horizontal_distance(30, "b")
Traceback (most recent call last):
...
TypeError: Invalid angle. Range is 1-90 degrees.
"""
check_args(init_velocity, angle)
radians = angle_to_radians(angle)
return round(init_velocity**2 * sin(radians) ** 2 / (2 * g), 2)
def total_time(init_velocity: float, angle: float) -> float:
"""
Returns total time of the motion
Formula:
2 * v_0 * sin(alpha)
--------------------
g
v_0 - initial velocity
alpha - angle
>>> total_time(30, 45)
4.33
>>> total_time(100, 78)
19.95
>>> total_time(-10, 40)
Traceback (most recent call last):
...
ValueError: Invalid velocity. Should be a positive number.
>>> total_time(30, "b")
Traceback (most recent call last):
...
TypeError: Invalid angle. Range is 1-90 degrees.
"""
check_args(init_velocity, angle)
radians = angle_to_radians(angle)
return round(2 * init_velocity * sin(radians) / g, 2)
def test_motion() -> None:
"""
>>> test_motion()
"""
v0, angle = 25, 20
assert horizontal_distance(v0, angle) == 40.97
assert max_height(v0, angle) == 3.73
assert total_time(v0, angle) == 1.74
if __name__ == "__main__":
from doctest import testmod
testmod()
# Get input from user
init_vel = float(input("Initial Velocity: ").strip())
# Get input from user
angle = float(input("angle: ").strip())
# Print results
print()
print("Results: ")
print(f"Horizontal Distance: {str(horizontal_distance(init_vel, angle))} [m]")
print(f"Maximum Height: {str(max_height(init_vel, angle))} [m]")
print(f"Total Time: {str(total_time(init_vel, angle))} [s]")
| """
Horizontal Projectile Motion problem in physics.
This algorithm solves a specific problem in which
the motion starts from the ground as can be seen below:
(v = 0)
* *
* *
* *
* *
* *
* *
GROUND GROUND
For more info: https://en.wikipedia.org/wiki/Projectile_motion
"""
# Importing packages
from math import radians as angle_to_radians
from math import sin
# Acceleration Constant on Earth (unit m/s^2)
g = 9.80665
def check_args(init_velocity: float, angle: float) -> None:
"""
Check that the arguments are valid
"""
# Ensure valid instance
if not isinstance(init_velocity, (int, float)):
raise TypeError("Invalid velocity. Should be a positive number.")
if not isinstance(angle, (int, float)):
raise TypeError("Invalid angle. Range is 1-90 degrees.")
# Ensure valid angle
if angle > 90 or angle < 1:
raise ValueError("Invalid angle. Range is 1-90 degrees.")
# Ensure valid velocity
if init_velocity < 0:
raise ValueError("Invalid velocity. Should be a positive number.")
def horizontal_distance(init_velocity: float, angle: float) -> float:
"""
Returns the horizontal distance that the object cover
Formula:
v_0^2 * sin(2 * alpha)
---------------------
g
v_0 - initial velocity
alpha - angle
>>> horizontal_distance(30, 45)
91.77
>>> horizontal_distance(100, 78)
414.76
>>> horizontal_distance(-1, 20)
Traceback (most recent call last):
...
ValueError: Invalid velocity. Should be a positive number.
>>> horizontal_distance(30, -20)
Traceback (most recent call last):
...
ValueError: Invalid angle. Range is 1-90 degrees.
"""
check_args(init_velocity, angle)
radians = angle_to_radians(2 * angle)
return round(init_velocity**2 * sin(radians) / g, 2)
def max_height(init_velocity: float, angle: float) -> float:
"""
Returns the maximum height that the object reach
Formula:
v_0^2 * sin^2(alpha)
--------------------
2g
v_0 - initial velocity
alpha - angle
>>> max_height(30, 45)
22.94
>>> max_height(100, 78)
487.82
>>> max_height("a", 20)
Traceback (most recent call last):
...
TypeError: Invalid velocity. Should be a positive number.
>>> horizontal_distance(30, "b")
Traceback (most recent call last):
...
TypeError: Invalid angle. Range is 1-90 degrees.
"""
check_args(init_velocity, angle)
radians = angle_to_radians(angle)
return round(init_velocity**2 * sin(radians) ** 2 / (2 * g), 2)
def total_time(init_velocity: float, angle: float) -> float:
"""
Returns total time of the motion
Formula:
2 * v_0 * sin(alpha)
--------------------
g
v_0 - initial velocity
alpha - angle
>>> total_time(30, 45)
4.33
>>> total_time(100, 78)
19.95
>>> total_time(-10, 40)
Traceback (most recent call last):
...
ValueError: Invalid velocity. Should be a positive number.
>>> total_time(30, "b")
Traceback (most recent call last):
...
TypeError: Invalid angle. Range is 1-90 degrees.
"""
check_args(init_velocity, angle)
radians = angle_to_radians(angle)
return round(2 * init_velocity * sin(radians) / g, 2)
def test_motion() -> None:
"""
>>> test_motion()
"""
v0, angle = 25, 20
assert horizontal_distance(v0, angle) == 40.97
assert max_height(v0, angle) == 3.73
assert total_time(v0, angle) == 1.74
if __name__ == "__main__":
from doctest import testmod
testmod()
# Get input from user
init_vel = float(input("Initial Velocity: ").strip())
# Get input from user
angle = float(input("angle: ").strip())
# Print results
print()
print("Results: ")
print(f"Horizontal Distance: {str(horizontal_distance(init_vel, angle))} [m]")
print(f"Maximum Height: {str(max_height(init_vel, angle))} [m]")
print(f"Total Time: {str(total_time(init_vel, angle))} [s]")
| -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 double_sort(lst):
"""This sorting algorithm sorts an array using the principle of bubble sort,
but does it both from left to right and right to left.
Hence, it's called "Double sort"
:param collection: mutable ordered sequence of elements
:return: the same collection in ascending order
Examples:
>>> double_sort([-1 ,-2 ,-3 ,-4 ,-5 ,-6 ,-7])
[-7, -6, -5, -4, -3, -2, -1]
>>> double_sort([])
[]
>>> double_sort([-1 ,-2 ,-3 ,-4 ,-5 ,-6])
[-6, -5, -4, -3, -2, -1]
>>> double_sort([-3, 10, 16, -42, 29]) == sorted([-3, 10, 16, -42, 29])
True
"""
no_of_elements = len(lst)
for _ in range(
0, int(((no_of_elements - 1) / 2) + 1)
): # we don't need to traverse to end of list as
for j in range(0, no_of_elements - 1):
if (
lst[j + 1] < lst[j]
): # applying bubble sort algorithm from left to right (or forwards)
temp = lst[j + 1]
lst[j + 1] = lst[j]
lst[j] = temp
if (
lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
): # applying bubble sort algorithm from right to left (or backwards)
temp = lst[no_of_elements - 1 - j]
lst[no_of_elements - 1 - j] = lst[no_of_elements - 2 - j]
lst[no_of_elements - 2 - j] = temp
return lst
if __name__ == "__main__":
print("enter the list to be sorted")
lst = [int(x) for x in input().split()] # inputing elements of the list in one line
sorted_lst = double_sort(lst)
print("the sorted list is")
print(sorted_lst)
| def double_sort(lst):
"""This sorting algorithm sorts an array using the principle of bubble sort,
but does it both from left to right and right to left.
Hence, it's called "Double sort"
:param collection: mutable ordered sequence of elements
:return: the same collection in ascending order
Examples:
>>> double_sort([-1 ,-2 ,-3 ,-4 ,-5 ,-6 ,-7])
[-7, -6, -5, -4, -3, -2, -1]
>>> double_sort([])
[]
>>> double_sort([-1 ,-2 ,-3 ,-4 ,-5 ,-6])
[-6, -5, -4, -3, -2, -1]
>>> double_sort([-3, 10, 16, -42, 29]) == sorted([-3, 10, 16, -42, 29])
True
"""
no_of_elements = len(lst)
for _ in range(
0, int(((no_of_elements - 1) / 2) + 1)
): # we don't need to traverse to end of list as
for j in range(0, no_of_elements - 1):
if (
lst[j + 1] < lst[j]
): # applying bubble sort algorithm from left to right (or forwards)
temp = lst[j + 1]
lst[j + 1] = lst[j]
lst[j] = temp
if (
lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
): # applying bubble sort algorithm from right to left (or backwards)
temp = lst[no_of_elements - 1 - j]
lst[no_of_elements - 1 - j] = lst[no_of_elements - 2 - j]
lst[no_of_elements - 2 - j] = temp
return lst
if __name__ == "__main__":
print("enter the list to be sorted")
lst = [int(x) for x in input().split()] # inputing elements of the list in one line
sorted_lst = double_sort(lst)
print("the sorted list is")
print(sorted_lst)
| -1 |
TheAlgorithms/Python | 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
""" Here I implemented the scoring functions.
MAE, MSE, RMSE, RMSLE are included.
Those are used for calculating differences between
predicted values and actual values.
Metrics are slightly differentiated. Sometimes squared, rooted,
even log is used.
Using log and roots can be perceived as tools for penalizing big
errors. However, using appropriate metrics depends on the situations,
and types of data
"""
# Mean Absolute Error
def mae(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [1,2,3];predict = [1,4,3]
>>> np.around(mae(predict,actual),decimals = 2)
0.67
>>> actual = [1,1,1];predict = [1,1,1]
>>> mae(predict,actual)
0.0
"""
predict = np.array(predict)
actual = np.array(actual)
difference = abs(predict - actual)
score = difference.mean()
return score
# Mean Squared Error
def mse(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [1,2,3];predict = [1,4,3]
>>> np.around(mse(predict,actual),decimals = 2)
1.33
>>> actual = [1,1,1];predict = [1,1,1]
>>> mse(predict,actual)
0.0
"""
predict = np.array(predict)
actual = np.array(actual)
difference = predict - actual
square_diff = np.square(difference)
score = square_diff.mean()
return score
# Root Mean Squared Error
def rmse(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [1,2,3];predict = [1,4,3]
>>> np.around(rmse(predict,actual),decimals = 2)
1.15
>>> actual = [1,1,1];predict = [1,1,1]
>>> rmse(predict,actual)
0.0
"""
predict = np.array(predict)
actual = np.array(actual)
difference = predict - actual
square_diff = np.square(difference)
mean_square_diff = square_diff.mean()
score = np.sqrt(mean_square_diff)
return score
# Root Mean Square Logarithmic Error
def rmsle(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [10,10,30];predict = [10,2,30]
>>> np.around(rmsle(predict,actual),decimals = 2)
0.75
>>> actual = [1,1,1];predict = [1,1,1]
>>> rmsle(predict,actual)
0.0
"""
predict = np.array(predict)
actual = np.array(actual)
log_predict = np.log(predict + 1)
log_actual = np.log(actual + 1)
difference = log_predict - log_actual
square_diff = np.square(difference)
mean_square_diff = square_diff.mean()
score = np.sqrt(mean_square_diff)
return score
# Mean Bias Deviation
def mbd(predict, actual):
"""
This value is Negative, if the model underpredicts,
positive, if it overpredicts.
Example(rounded for precision):
Here the model overpredicts
>>> actual = [1,2,3];predict = [2,3,4]
>>> np.around(mbd(predict,actual),decimals = 2)
50.0
Here the model underpredicts
>>> actual = [1,2,3];predict = [0,1,1]
>>> np.around(mbd(predict,actual),decimals = 2)
-66.67
"""
predict = np.array(predict)
actual = np.array(actual)
difference = predict - actual
numerator = np.sum(difference) / len(predict)
denumerator = np.sum(actual) / len(predict)
# print(numerator, denumerator)
score = float(numerator) / denumerator * 100
return score
def manual_accuracy(predict, actual):
return np.mean(np.array(actual) == np.array(predict))
| import numpy as np
""" Here I implemented the scoring functions.
MAE, MSE, RMSE, RMSLE are included.
Those are used for calculating differences between
predicted values and actual values.
Metrics are slightly differentiated. Sometimes squared, rooted,
even log is used.
Using log and roots can be perceived as tools for penalizing big
errors. However, using appropriate metrics depends on the situations,
and types of data
"""
# Mean Absolute Error
def mae(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [1,2,3];predict = [1,4,3]
>>> np.around(mae(predict,actual),decimals = 2)
0.67
>>> actual = [1,1,1];predict = [1,1,1]
>>> mae(predict,actual)
0.0
"""
predict = np.array(predict)
actual = np.array(actual)
difference = abs(predict - actual)
score = difference.mean()
return score
# Mean Squared Error
def mse(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [1,2,3];predict = [1,4,3]
>>> np.around(mse(predict,actual),decimals = 2)
1.33
>>> actual = [1,1,1];predict = [1,1,1]
>>> mse(predict,actual)
0.0
"""
predict = np.array(predict)
actual = np.array(actual)
difference = predict - actual
square_diff = np.square(difference)
score = square_diff.mean()
return score
# Root Mean Squared Error
def rmse(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [1,2,3];predict = [1,4,3]
>>> np.around(rmse(predict,actual),decimals = 2)
1.15
>>> actual = [1,1,1];predict = [1,1,1]
>>> rmse(predict,actual)
0.0
"""
predict = np.array(predict)
actual = np.array(actual)
difference = predict - actual
square_diff = np.square(difference)
mean_square_diff = square_diff.mean()
score = np.sqrt(mean_square_diff)
return score
# Root Mean Square Logarithmic Error
def rmsle(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [10,10,30];predict = [10,2,30]
>>> np.around(rmsle(predict,actual),decimals = 2)
0.75
>>> actual = [1,1,1];predict = [1,1,1]
>>> rmsle(predict,actual)
0.0
"""
predict = np.array(predict)
actual = np.array(actual)
log_predict = np.log(predict + 1)
log_actual = np.log(actual + 1)
difference = log_predict - log_actual
square_diff = np.square(difference)
mean_square_diff = square_diff.mean()
score = np.sqrt(mean_square_diff)
return score
# Mean Bias Deviation
def mbd(predict, actual):
"""
This value is Negative, if the model underpredicts,
positive, if it overpredicts.
Example(rounded for precision):
Here the model overpredicts
>>> actual = [1,2,3];predict = [2,3,4]
>>> np.around(mbd(predict,actual),decimals = 2)
50.0
Here the model underpredicts
>>> actual = [1,2,3];predict = [0,1,1]
>>> np.around(mbd(predict,actual),decimals = 2)
-66.67
"""
predict = np.array(predict)
actual = np.array(actual)
difference = predict - actual
numerator = np.sum(difference) / len(predict)
denumerator = np.sum(actual) / len(predict)
# print(numerator, denumerator)
score = float(numerator) / denumerator * 100
return score
def manual_accuracy(predict, actual):
return np.mean(np.array(actual) == np.array(predict))
| -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 comment.
# Each line is a file pattern followed by one or more owners.
# More details are here: https://help.github.com/articles/about-codeowners/
# The '*' pattern is global owners.
# Order is important. The last matching pattern has the most precedence.
/.* @cclauss @dhruvmanila
# /arithmetic_analysis/
# /backtracking/
# /bit_manipulation/
# /blockchain/
# /boolean_algebra/
# /cellular_automata/
# /ciphers/ @cclauss # TODO: Uncomment this line after Hacktoberfest
# /compression/
# /computer_vision/
# /conversions/ @cclauss # TODO: Uncomment this line after Hacktoberfest
# /data_structures/ @cclauss # TODO: Uncomment this line after Hacktoberfest
# /digital_image_processing/
# /divide_and_conquer/
# /dynamic_programming/
# /file_transfer/
# /fuzzy_logic/
# /genetic_algorithm/
# /geodesy/
# /graphics/
# /graphs/
# /greedy_method/
# /hashes/
# /images/
# /linear_algebra/
# /machine_learning/
# /maths/
# /matrix/
# /networking_flow/
# /neural_network/
# /other/ @cclauss # TODO: Uncomment this line after Hacktoberfest
/project_euler/ @dhruvmanila
# /quantum/
# /scheduling/
# /scripts/
# /searches/
# /sorts/
# /strings/ @cclauss # TODO: Uncomment this line after Hacktoberfest
# /traversals/
/web_programming/ @cclauss
| # This is a comment.
# Each line is a file pattern followed by one or more owners.
# More details are here: https://help.github.com/articles/about-codeowners/
# The '*' pattern is global owners.
# Order is important. The last matching pattern has the most precedence.
/.* @cclauss @dhruvmanila
# /arithmetic_analysis/
# /backtracking/
# /bit_manipulation/
# /blockchain/
# /boolean_algebra/
# /cellular_automata/
# /ciphers/ @cclauss # TODO: Uncomment this line after Hacktoberfest
# /compression/
# /computer_vision/
# /conversions/ @cclauss # TODO: Uncomment this line after Hacktoberfest
# /data_structures/ @cclauss # TODO: Uncomment this line after Hacktoberfest
# /digital_image_processing/
# /divide_and_conquer/
# /dynamic_programming/
# /file_transfer/
# /fuzzy_logic/
# /genetic_algorithm/
# /geodesy/
# /graphics/
# /graphs/
# /greedy_method/
# /hashes/
# /images/
# /linear_algebra/
# /machine_learning/
# /maths/
# /matrix/
# /networking_flow/
# /neural_network/
# /other/ @cclauss # TODO: Uncomment this line after Hacktoberfest
/project_euler/ @dhruvmanila
# /quantum/
# /scheduling/
# /scripts/
# /searches/
# /sorts/
# /strings/ @cclauss # TODO: Uncomment this line after Hacktoberfest
# /traversals/
/web_programming/ @cclauss
| -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 csv
import requests
from bs4 import BeautifulSoup
def get_imdb_top_250_movies(url: str = "") -> dict[str, float]:
url = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
soup = BeautifulSoup(requests.get(url).text, "html.parser")
titles = soup.find_all("td", attrs="titleColumn")
ratings = soup.find_all("td", class_="ratingColumn imdbRating")
return {
title.a.text: float(rating.strong.text)
for title, rating in zip(titles, ratings)
}
def write_movies(filename: str = "IMDb_Top_250_Movies.csv") -> None:
movies = get_imdb_top_250_movies()
with open(filename, "w", newline="") as out_file:
writer = csv.writer(out_file)
writer.writerow(["Movie title", "IMDb rating"])
for title, rating in movies.items():
writer.writerow([title, rating])
if __name__ == "__main__":
write_movies()
| from __future__ import annotations
import csv
import requests
from bs4 import BeautifulSoup
def get_imdb_top_250_movies(url: str = "") -> dict[str, float]:
url = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
soup = BeautifulSoup(requests.get(url).text, "html.parser")
titles = soup.find_all("td", attrs="titleColumn")
ratings = soup.find_all("td", class_="ratingColumn imdbRating")
return {
title.a.text: float(rating.strong.text)
for title, rating in zip(titles, ratings)
}
def write_movies(filename: str = "IMDb_Top_250_Movies.csv") -> None:
movies = get_imdb_top_250_movies()
with open(filename, "w", newline="") as out_file:
writer = csv.writer(out_file)
writer.writerow(["Movie title", "IMDb rating"])
for title, rating in movies.items():
writer.writerow([title, rating])
if __name__ == "__main__":
write_movies()
| -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}`.
| -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}`.
| """
What is the greatest product of four adjacent numbers (horizontally,
vertically, or diagonally) in this 20x20 array?
08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08
49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00
81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65
52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91
22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80
24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50
32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70
67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21
24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72
21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95
78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92
16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57
86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58
19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40
04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66
88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69
04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36
20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16
20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54
01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48
"""
import os
def largest_product(grid):
n_columns = len(grid[0])
n_rows = len(grid)
largest = 0
lr_diag_product = 0
rl_diag_product = 0
# Check vertically, horizontally, diagonally at the same time (only works
# for nxn grid)
for i in range(n_columns):
for j in range(n_rows - 3):
vert_product = grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
horz_product = grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]
# Left-to-right diagonal (\) product
if i < n_columns - 3:
lr_diag_product = (
grid[i][j]
* grid[i + 1][j + 1]
* grid[i + 2][j + 2]
* grid[i + 3][j + 3]
)
# Right-to-left diagonal(/) product
if i > 2:
rl_diag_product = (
grid[i][j]
* grid[i - 1][j + 1]
* grid[i - 2][j + 2]
* grid[i - 3][j + 3]
)
max_product = max(
vert_product, horz_product, lr_diag_product, rl_diag_product
)
if max_product > largest:
largest = max_product
return largest
def solution():
"""Returns the greatest product of four adjacent numbers (horizontally,
vertically, or diagonally).
>>> solution()
70600674
"""
grid = []
with open(os.path.dirname(__file__) + "/grid.txt") as file:
for line in file:
grid.append(line.strip("\n").split(" "))
grid = [[int(i) for i in grid[j]] for j in range(len(grid))]
return largest_product(grid)
if __name__ == "__main__":
print(solution())
| """
What is the greatest product of four adjacent numbers (horizontally,
vertically, or diagonally) in this 20x20 array?
08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08
49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00
81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65
52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91
22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80
24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50
32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70
67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21
24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72
21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95
78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92
16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57
86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58
19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40
04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66
88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69
04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36
20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16
20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54
01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48
"""
import os
def largest_product(grid):
n_columns = len(grid[0])
n_rows = len(grid)
largest = 0
lr_diag_product = 0
rl_diag_product = 0
# Check vertically, horizontally, diagonally at the same time (only works
# for nxn grid)
for i in range(n_columns):
for j in range(n_rows - 3):
vert_product = grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
horz_product = grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]
# Left-to-right diagonal (\) product
if i < n_columns - 3:
lr_diag_product = (
grid[i][j]
* grid[i + 1][j + 1]
* grid[i + 2][j + 2]
* grid[i + 3][j + 3]
)
# Right-to-left diagonal(/) product
if i > 2:
rl_diag_product = (
grid[i][j]
* grid[i - 1][j + 1]
* grid[i - 2][j + 2]
* grid[i - 3][j + 3]
)
max_product = max(
vert_product, horz_product, lr_diag_product, rl_diag_product
)
if max_product > largest:
largest = max_product
return largest
def solution():
"""Returns the greatest product of four adjacent numbers (horizontally,
vertically, or diagonally).
>>> solution()
70600674
"""
grid = []
with open(os.path.dirname(__file__) + "/grid.txt") as file:
for line in file:
grid.append(line.strip("\n").split(" "))
grid = [[int(i) for i in grid[j]] for j in range(len(grid))]
return largest_product(grid)
if __name__ == "__main__":
print(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 qr_householder(a):
"""Return a QR-decomposition of the matrix A using Householder reflection.
The QR-decomposition decomposes the matrix A of shape (m, n) into an
orthogonal matrix Q of shape (m, m) and an upper triangular matrix R of
shape (m, n). Note that the matrix A does not have to be square. This
method of decomposing A uses the Householder reflection, which is
numerically stable and of complexity O(n^3).
https://en.wikipedia.org/wiki/QR_decomposition#Using_Householder_reflections
Arguments:
A -- a numpy.ndarray of shape (m, n)
Note: several optimizations can be made for numeric efficiency, but this is
intended to demonstrate how it would be represented in a mathematics
textbook. In cases where efficiency is particularly important, an optimized
version from BLAS should be used.
>>> A = np.array([[12, -51, 4], [6, 167, -68], [-4, 24, -41]], dtype=float)
>>> Q, R = qr_householder(A)
>>> # check that the decomposition is correct
>>> np.allclose(Q@R, A)
True
>>> # check that Q is orthogonal
>>> np.allclose([email protected], np.eye(A.shape[0]))
True
>>> np.allclose(Q.T@Q, np.eye(A.shape[0]))
True
>>> # check that R is upper triangular
>>> np.allclose(np.triu(R), R)
True
"""
m, n = a.shape
t = min(m, n)
q = np.eye(m)
r = a.copy()
for k in range(t - 1):
# select a column of modified matrix A':
x = r[k:, [k]]
# construct first basis vector
e1 = np.zeros_like(x)
e1[0] = 1.0
# determine scaling factor
alpha = np.linalg.norm(x)
# construct vector v for Householder reflection
v = x + np.sign(x[0]) * alpha * e1
v /= np.linalg.norm(v)
# construct the Householder matrix
q_k = np.eye(m - k) - 2.0 * v @ v.T
# pad with ones and zeros as necessary
q_k = np.block([[np.eye(k), np.zeros((k, m - k))], [np.zeros((m - k, k)), q_k]])
q = q @ q_k.T
r = q_k @ r
return q, r
if __name__ == "__main__":
import doctest
doctest.testmod()
| import numpy as np
def qr_householder(a):
"""Return a QR-decomposition of the matrix A using Householder reflection.
The QR-decomposition decomposes the matrix A of shape (m, n) into an
orthogonal matrix Q of shape (m, m) and an upper triangular matrix R of
shape (m, n). Note that the matrix A does not have to be square. This
method of decomposing A uses the Householder reflection, which is
numerically stable and of complexity O(n^3).
https://en.wikipedia.org/wiki/QR_decomposition#Using_Householder_reflections
Arguments:
A -- a numpy.ndarray of shape (m, n)
Note: several optimizations can be made for numeric efficiency, but this is
intended to demonstrate how it would be represented in a mathematics
textbook. In cases where efficiency is particularly important, an optimized
version from BLAS should be used.
>>> A = np.array([[12, -51, 4], [6, 167, -68], [-4, 24, -41]], dtype=float)
>>> Q, R = qr_householder(A)
>>> # check that the decomposition is correct
>>> np.allclose(Q@R, A)
True
>>> # check that Q is orthogonal
>>> np.allclose([email protected], np.eye(A.shape[0]))
True
>>> np.allclose(Q.T@Q, np.eye(A.shape[0]))
True
>>> # check that R is upper triangular
>>> np.allclose(np.triu(R), R)
True
"""
m, n = a.shape
t = min(m, n)
q = np.eye(m)
r = a.copy()
for k in range(t - 1):
# select a column of modified matrix A':
x = r[k:, [k]]
# construct first basis vector
e1 = np.zeros_like(x)
e1[0] = 1.0
# determine scaling factor
alpha = np.linalg.norm(x)
# construct vector v for Householder reflection
v = x + np.sign(x[0]) * alpha * e1
v /= np.linalg.norm(v)
# construct the Householder matrix
q_k = np.eye(m - k) - 2.0 * v @ v.T
# pad with ones and zeros as necessary
q_k = np.block([[np.eye(k), np.zeros((k, m - k))], [np.zeros((m - k, k)), q_k]])
q = q @ q_k.T
r = q_k @ r
return q, r
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
from .hash_table import HashTable
class QuadraticProbing(HashTable):
"""
Basic Hash Table example with open addressing using Quadratic Probing
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def _collision_resolution(self, key, data=None):
i = 1
new_key = self.hash_function(key + i * i)
while self.values[new_key] is not None and self.values[new_key] != key:
i += 1
new_key = (
self.hash_function(key + i * i)
if not self.balanced_factor() >= self.lim_charge
else None
)
if new_key is None:
break
return new_key
| #!/usr/bin/env python3
from .hash_table import HashTable
class QuadraticProbing(HashTable):
"""
Basic Hash Table example with open addressing using Quadratic Probing
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def _collision_resolution(self, key, data=None):
i = 1
new_key = self.hash_function(key + i * i)
while self.values[new_key] is not None and self.values[new_key] != key:
i += 1
new_key = (
self.hash_function(key + i * i)
if not self.balanced_factor() >= self.lim_charge
else None
)
if new_key is None:
break
return new_key
| -1 |
TheAlgorithms/Python | 7,949 | 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 : Mehdi ALAOUI
This is a pure Python implementation of Dynamic Programming solution to the longest
increasing subsequence of a given sequence.
The problem is :
Given an array, to find the longest and increasing sub-array in that given array and
return it.
Example: [10, 22, 9, 33, 21, 50, 41, 60, 80] as input will return
[10, 22, 33, 41, 60, 80] as output
"""
from __future__ import annotations
def longest_subsequence(array: list[int]) -> list[int]: # This function is recursive
"""
Some examples
>>> longest_subsequence([10, 22, 9, 33, 21, 50, 41, 60, 80])
[10, 22, 33, 41, 60, 80]
>>> longest_subsequence([4, 8, 7, 5, 1, 12, 2, 3, 9])
[1, 2, 3, 9]
>>> longest_subsequence([9, 8, 7, 6, 5, 7])
[8]
>>> longest_subsequence([1, 1, 1])
[1, 1, 1]
>>> longest_subsequence([])
[]
"""
array_length = len(array)
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
pivot = array[0]
is_found = False
i = 1
longest_subseq: list[int] = []
while not is_found and i < array_length:
if array[i] < pivot:
is_found = True
temp_array = [element for element in array[i:] if element >= array[i]]
temp_array = longest_subsequence(temp_array)
if len(temp_array) > len(longest_subseq):
longest_subseq = temp_array
else:
i += 1
temp_array = [element for element in array[1:] if element >= pivot]
temp_array = [pivot] + longest_subsequence(temp_array)
if len(temp_array) > len(longest_subseq):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Author : Mehdi ALAOUI
This is a pure Python implementation of Dynamic Programming solution to the longest
increasing subsequence of a given sequence.
The problem is :
Given an array, to find the longest and increasing sub-array in that given array and
return it.
Example: [10, 22, 9, 33, 21, 50, 41, 60, 80] as input will return
[10, 22, 33, 41, 60, 80] as output
"""
from __future__ import annotations
def longest_subsequence(array: list[int]) -> list[int]: # This function is recursive
"""
Some examples
>>> longest_subsequence([10, 22, 9, 33, 21, 50, 41, 60, 80])
[10, 22, 33, 41, 60, 80]
>>> longest_subsequence([4, 8, 7, 5, 1, 12, 2, 3, 9])
[1, 2, 3, 9]
>>> longest_subsequence([9, 8, 7, 6, 5, 7])
[8]
>>> longest_subsequence([1, 1, 1])
[1, 1, 1]
>>> longest_subsequence([])
[]
"""
array_length = len(array)
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
pivot = array[0]
is_found = False
i = 1
longest_subseq: list[int] = []
while not is_found and i < array_length:
if array[i] < pivot:
is_found = True
temp_array = [element for element in array[i:] if element >= array[i]]
temp_array = longest_subsequence(temp_array)
if len(temp_array) > len(longest_subseq):
longest_subseq = temp_array
else:
i += 1
temp_array = [element for element in array[1:] if element >= pivot]
temp_array = [pivot] + longest_subsequence(temp_array)
if len(temp_array) > len(longest_subseq):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 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 type of divide and conquer algorithm which divides the search space into
3 parts and finds the target value based on the property of the array or list
(usually monotonic property).
Time Complexity : O(log3 N)
Space Complexity : O(1)
"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
precision = 10
# This is the linear search that will occur after the search space has become smaller.
def lin_search(left: int, right: int, array: list[int], target: int) -> int:
"""Perform linear search in list. Returns -1 if element is not found.
Parameters
----------
left : int
left index bound.
right : int
right index bound.
array : List[int]
List of elements to be searched on
target : int
Element that is searched
Returns
-------
int
index of element that is looked for.
Examples
--------
>>> lin_search(0, 4, [4, 5, 6, 7], 7)
3
>>> lin_search(0, 3, [4, 5, 6, 7], 7)
-1
>>> lin_search(0, 2, [-18, 2], -18)
0
>>> lin_search(0, 1, [5], 5)
0
>>> lin_search(0, 3, ['a', 'c', 'd'], 'c')
1
>>> lin_search(0, 3, [.1, .4 , -.1], .1)
0
>>> lin_search(0, 3, [.1, .4 , -.1], -.1)
2
"""
for i in range(left, right):
if array[i] == target:
return i
return -1
def ite_ternary_search(array: list[int], target: int) -> int:
"""Iterative method of the ternary search algorithm.
>>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
>>> ite_ternary_search(test_list, 3)
-1
>>> ite_ternary_search(test_list, 13)
4
>>> ite_ternary_search([4, 5, 6, 7], 4)
0
>>> ite_ternary_search([4, 5, 6, 7], -10)
-1
>>> ite_ternary_search([-18, 2], -18)
0
>>> ite_ternary_search([5], 5)
0
>>> ite_ternary_search(['a', 'c', 'd'], 'c')
1
>>> ite_ternary_search(['a', 'c', 'd'], 'f')
-1
>>> ite_ternary_search([], 1)
-1
>>> ite_ternary_search([.1, .4 , -.1], .1)
0
"""
left = 0
right = len(array)
while left <= right:
if right - left < precision:
return lin_search(left, right, array, target)
one_third = (left + right) // 3 + 1
two_third = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
right = one_third - 1
elif array[two_third] < target:
left = two_third + 1
else:
left = one_third + 1
right = two_third - 1
else:
return -1
def rec_ternary_search(left: int, right: int, array: list[int], target: int) -> int:
"""Recursive method of the ternary search algorithm.
>>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
>>> rec_ternary_search(0, len(test_list), test_list, 3)
-1
>>> rec_ternary_search(4, len(test_list), test_list, 42)
8
>>> rec_ternary_search(0, 2, [4, 5, 6, 7], 4)
0
>>> rec_ternary_search(0, 3, [4, 5, 6, 7], -10)
-1
>>> rec_ternary_search(0, 1, [-18, 2], -18)
0
>>> rec_ternary_search(0, 1, [5], 5)
0
>>> rec_ternary_search(0, 2, ['a', 'c', 'd'], 'c')
1
>>> rec_ternary_search(0, 2, ['a', 'c', 'd'], 'f')
-1
>>> rec_ternary_search(0, 0, [], 1)
-1
>>> rec_ternary_search(0, 3, [.1, .4 , -.1], .1)
0
"""
if left < right:
if right - left < precision:
return lin_search(left, right, array, target)
one_third = (left + right) // 3 + 1
two_third = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(left, one_third - 1, array, target)
elif array[two_third] < target:
return rec_ternary_search(two_third + 1, right, array, target)
else:
return rec_ternary_search(one_third + 1, two_third - 1, array, target)
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
user_input = input("Enter numbers separated by comma:\n").strip()
collection = [int(item.strip()) for item in user_input.split(",")]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
target = int(input("Enter the number to be found in the list:\n").strip())
result1 = ite_ternary_search(collection, target)
result2 = rec_ternary_search(0, len(collection) - 1, collection, target)
if result2 != -1:
print(f"Iterative search: {target} found at positions: {result1}")
print(f"Recursive search: {target} found at positions: {result2}")
else:
print("Not found")
| """
This is a type of divide and conquer algorithm which divides the search space into
3 parts and finds the target value based on the property of the array or list
(usually monotonic property).
Time Complexity : O(log3 N)
Space Complexity : O(1)
"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
precision = 10
# This is the linear search that will occur after the search space has become smaller.
def lin_search(left: int, right: int, array: list[int], target: int) -> int:
"""Perform linear search in list. Returns -1 if element is not found.
Parameters
----------
left : int
left index bound.
right : int
right index bound.
array : List[int]
List of elements to be searched on
target : int
Element that is searched
Returns
-------
int
index of element that is looked for.
Examples
--------
>>> lin_search(0, 4, [4, 5, 6, 7], 7)
3
>>> lin_search(0, 3, [4, 5, 6, 7], 7)
-1
>>> lin_search(0, 2, [-18, 2], -18)
0
>>> lin_search(0, 1, [5], 5)
0
>>> lin_search(0, 3, ['a', 'c', 'd'], 'c')
1
>>> lin_search(0, 3, [.1, .4 , -.1], .1)
0
>>> lin_search(0, 3, [.1, .4 , -.1], -.1)
2
"""
for i in range(left, right):
if array[i] == target:
return i
return -1
def ite_ternary_search(array: list[int], target: int) -> int:
"""Iterative method of the ternary search algorithm.
>>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
>>> ite_ternary_search(test_list, 3)
-1
>>> ite_ternary_search(test_list, 13)
4
>>> ite_ternary_search([4, 5, 6, 7], 4)
0
>>> ite_ternary_search([4, 5, 6, 7], -10)
-1
>>> ite_ternary_search([-18, 2], -18)
0
>>> ite_ternary_search([5], 5)
0
>>> ite_ternary_search(['a', 'c', 'd'], 'c')
1
>>> ite_ternary_search(['a', 'c', 'd'], 'f')
-1
>>> ite_ternary_search([], 1)
-1
>>> ite_ternary_search([.1, .4 , -.1], .1)
0
"""
left = 0
right = len(array)
while left <= right:
if right - left < precision:
return lin_search(left, right, array, target)
one_third = (left + right) // 3 + 1
two_third = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
right = one_third - 1
elif array[two_third] < target:
left = two_third + 1
else:
left = one_third + 1
right = two_third - 1
else:
return -1
def rec_ternary_search(left: int, right: int, array: list[int], target: int) -> int:
"""Recursive method of the ternary search algorithm.
>>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
>>> rec_ternary_search(0, len(test_list), test_list, 3)
-1
>>> rec_ternary_search(4, len(test_list), test_list, 42)
8
>>> rec_ternary_search(0, 2, [4, 5, 6, 7], 4)
0
>>> rec_ternary_search(0, 3, [4, 5, 6, 7], -10)
-1
>>> rec_ternary_search(0, 1, [-18, 2], -18)
0
>>> rec_ternary_search(0, 1, [5], 5)
0
>>> rec_ternary_search(0, 2, ['a', 'c', 'd'], 'c')
1
>>> rec_ternary_search(0, 2, ['a', 'c', 'd'], 'f')
-1
>>> rec_ternary_search(0, 0, [], 1)
-1
>>> rec_ternary_search(0, 3, [.1, .4 , -.1], .1)
0
"""
if left < right:
if right - left < precision:
return lin_search(left, right, array, target)
one_third = (left + right) // 3 + 1
two_third = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(left, one_third - 1, array, target)
elif array[two_third] < target:
return rec_ternary_search(two_third + 1, right, array, target)
else:
return rec_ternary_search(one_third + 1, two_third - 1, array, target)
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
user_input = input("Enter numbers separated by comma:\n").strip()
collection = [int(item.strip()) for item in user_input.split(",")]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
target = int(input("Enter the number to be found in the list:\n").strip())
result1 = ite_ternary_search(collection, target)
result2 = rec_ternary_search(0, len(collection) - 1, collection, target)
if result2 != -1:
print(f"Iterative search: {target} found at positions: {result1}")
print(f"Recursive search: {target} found at positions: {result2}")
else:
print("Not found")
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
from collections.abc import Sequence
def compare_string(string1: str, string2: str) -> str:
"""
>>> compare_string('0010','0110')
'0_10'
>>> compare_string('0110','1101')
'X'
"""
list1 = list(string1)
list2 = list(string2)
count = 0
for i in range(len(list1)):
if list1[i] != list2[i]:
count += 1
list1[i] = "_"
if count > 1:
return "X"
else:
return "".join(list1)
def check(binary: list[str]) -> list[str]:
"""
>>> check(['0.00.01.5'])
['0.00.01.5']
"""
pi = []
while True:
check1 = ["$"] * len(binary)
temp = []
for i in range(len(binary)):
for j in range(i + 1, len(binary)):
k = compare_string(binary[i], binary[j])
if k != "X":
check1[i] = "*"
check1[j] = "*"
temp.append(k)
for i in range(len(binary)):
if check1[i] == "$":
pi.append(binary[i])
if len(temp) == 0:
return pi
binary = list(set(temp))
def decimal_to_binary(no_of_variable: int, minterms: Sequence[float]) -> list[str]:
"""
>>> decimal_to_binary(3,[1.5])
['0.00.01.5']
"""
temp = []
for minterm in minterms:
string = ""
for _ in range(no_of_variable):
string = str(minterm % 2) + string
minterm //= 2
temp.append(string)
return temp
def is_for_table(string1: str, string2: str, count: int) -> bool:
"""
>>> is_for_table('__1','011',2)
True
>>> is_for_table('01_','001',1)
False
"""
list1 = list(string1)
list2 = list(string2)
count_n = 0
for i in range(len(list1)):
if list1[i] != list2[i]:
count_n += 1
return count_n == count
def selection(chart: list[list[int]], prime_implicants: list[str]) -> list[str]:
"""
>>> selection([[1]],['0.00.01.5'])
['0.00.01.5']
>>> selection([[1]],['0.00.01.5'])
['0.00.01.5']
"""
temp = []
select = [0] * len(chart)
for i in range(len(chart[0])):
count = 0
rem = -1
for j in range(len(chart)):
if chart[j][i] == 1:
count += 1
rem = j
if count == 1:
select[rem] = 1
for i in range(len(select)):
if select[i] == 1:
for j in range(len(chart[0])):
if chart[i][j] == 1:
for k in range(len(chart)):
chart[k][j] = 0
temp.append(prime_implicants[i])
while True:
max_n = 0
rem = -1
count_n = 0
for i in range(len(chart)):
count_n = chart[i].count(1)
if count_n > max_n:
max_n = count_n
rem = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem])
for i in range(len(chart[0])):
if chart[rem][i] == 1:
for j in range(len(chart)):
chart[j][i] = 0
def prime_implicant_chart(
prime_implicants: list[str], binary: list[str]
) -> list[list[int]]:
"""
>>> prime_implicant_chart(['0.00.01.5'],['0.00.01.5'])
[[1]]
"""
chart = [[0 for x in range(len(binary))] for x in range(len(prime_implicants))]
for i in range(len(prime_implicants)):
count = prime_implicants[i].count("_")
for j in range(len(binary)):
if is_for_table(prime_implicants[i], binary[j], count):
chart[i][j] = 1
return chart
def main() -> None:
no_of_variable = int(input("Enter the no. of variables\n"))
minterms = [
float(x)
for x in input(
"Enter the decimal representation of Minterms 'Spaces Separated'\n"
).split()
]
binary = decimal_to_binary(no_of_variable, minterms)
prime_implicants = check(binary)
print("Prime Implicants are:")
print(prime_implicants)
chart = prime_implicant_chart(prime_implicants, binary)
essential_prime_implicants = selection(chart, prime_implicants)
print("Essential Prime Implicants are:")
print(essential_prime_implicants)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def compare_string(string1: str, string2: str) -> str | Literal[False]:
"""
>>> compare_string('0010','0110')
'0_10'
>>> compare_string('0110','1101')
False
"""
list1 = list(string1)
list2 = list(string2)
count = 0
for i in range(len(list1)):
if list1[i] != list2[i]:
count += 1
list1[i] = "_"
if count > 1:
return False
else:
return "".join(list1)
def check(binary: list[str]) -> list[str]:
"""
>>> check(['0.00.01.5'])
['0.00.01.5']
"""
pi = []
while True:
check1 = ["$"] * len(binary)
temp = []
for i in range(len(binary)):
for j in range(i + 1, len(binary)):
k = compare_string(binary[i], binary[j])
if k is False:
check1[i] = "*"
check1[j] = "*"
temp.append("X")
for i in range(len(binary)):
if check1[i] == "$":
pi.append(binary[i])
if len(temp) == 0:
return pi
binary = list(set(temp))
def decimal_to_binary(no_of_variable: int, minterms: Sequence[float]) -> list[str]:
"""
>>> decimal_to_binary(3,[1.5])
['0.00.01.5']
"""
temp = []
for minterm in minterms:
string = ""
for _ in range(no_of_variable):
string = str(minterm % 2) + string
minterm //= 2
temp.append(string)
return temp
def is_for_table(string1: str, string2: str, count: int) -> bool:
"""
>>> is_for_table('__1','011',2)
True
>>> is_for_table('01_','001',1)
False
"""
list1 = list(string1)
list2 = list(string2)
count_n = 0
for i in range(len(list1)):
if list1[i] != list2[i]:
count_n += 1
return count_n == count
def selection(chart: list[list[int]], prime_implicants: list[str]) -> list[str]:
"""
>>> selection([[1]],['0.00.01.5'])
['0.00.01.5']
>>> selection([[1]],['0.00.01.5'])
['0.00.01.5']
"""
temp = []
select = [0] * len(chart)
for i in range(len(chart[0])):
count = 0
rem = -1
for j in range(len(chart)):
if chart[j][i] == 1:
count += 1
rem = j
if count == 1:
select[rem] = 1
for i in range(len(select)):
if select[i] == 1:
for j in range(len(chart[0])):
if chart[i][j] == 1:
for k in range(len(chart)):
chart[k][j] = 0
temp.append(prime_implicants[i])
while True:
max_n = 0
rem = -1
count_n = 0
for i in range(len(chart)):
count_n = chart[i].count(1)
if count_n > max_n:
max_n = count_n
rem = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem])
for i in range(len(chart[0])):
if chart[rem][i] == 1:
for j in range(len(chart)):
chart[j][i] = 0
def prime_implicant_chart(
prime_implicants: list[str], binary: list[str]
) -> list[list[int]]:
"""
>>> prime_implicant_chart(['0.00.01.5'],['0.00.01.5'])
[[1]]
"""
chart = [[0 for x in range(len(binary))] for x in range(len(prime_implicants))]
for i in range(len(prime_implicants)):
count = prime_implicants[i].count("_")
for j in range(len(binary)):
if is_for_table(prime_implicants[i], binary[j], count):
chart[i][j] = 1
return chart
def main() -> None:
no_of_variable = int(input("Enter the no. of variables\n"))
minterms = [
float(x)
for x in input(
"Enter the decimal representation of Minterms 'Spaces Separated'\n"
).split()
]
binary = decimal_to_binary(no_of_variable, minterms)
prime_implicants = check(binary)
print("Prime Implicants are:")
print(prime_implicants)
chart = prime_implicant_chart(prime_implicants, binary)
essential_prime_implicants = selection(chart, prime_implicants)
print("Essential Prime Implicants are:")
print(essential_prime_implicants)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
import 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 "".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 | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import cv2
import numpy as np
"""
Harris Corner Detector
https://en.wikipedia.org/wiki/Harris_Corner_Detector
"""
class HarrisCorner:
def __init__(self, k: float, window_size: int):
"""
k : is an empirically determined constant in [0.04,0.06]
window_size : neighbourhoods considered
"""
if k in (0.04, 0.06):
self.k = k
self.window_size = window_size
else:
raise ValueError("invalid k value")
def __str__(self) -> str:
return f"Harris Corner detection with k : {self.k}"
def detect(self, img_path: str) -> tuple[cv2.Mat, list[list[int]]]:
"""
Returns the image with corners identified
img_path : path of the image
output : list of the corner positions, image
"""
img = cv2.imread(img_path, 0)
h, w = img.shape
corner_list: list[list[int]] = []
color_img = img.copy()
color_img = cv2.cvtColor(color_img, cv2.COLOR_GRAY2RGB)
dy, dx = np.gradient(img)
ixx = dx**2
iyy = dy**2
ixy = dx * dy
k = 0.04
offset = self.window_size // 2
for y in range(offset, h - offset):
for x in range(offset, w - offset):
wxx = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
wyy = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
wxy = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
det = (wxx * wyy) - (wxy**2)
trace = wxx + wyy
r = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r])
color_img.itemset((y, x, 0), 0)
color_img.itemset((y, x, 1), 0)
color_img.itemset((y, x, 2), 255)
return color_img, corner_list
if __name__ == "__main__":
edge_detect = HarrisCorner(0.04, 3)
color_img, _ = edge_detect.detect("path_to_image")
cv2.imwrite("detect.png", color_img)
| import cv2
import numpy as np
"""
Harris Corner Detector
https://en.wikipedia.org/wiki/Harris_Corner_Detector
"""
class HarrisCorner:
def __init__(self, k: float, window_size: int):
"""
k : is an empirically determined constant in [0.04,0.06]
window_size : neighbourhoods considered
"""
if k in (0.04, 0.06):
self.k = k
self.window_size = window_size
else:
raise ValueError("invalid k value")
def __str__(self) -> str:
return str(self.k)
def detect(self, img_path: str) -> tuple[cv2.Mat, list[list[int]]]:
"""
Returns the image with corners identified
img_path : path of the image
output : list of the corner positions, image
"""
img = cv2.imread(img_path, 0)
h, w = img.shape
corner_list: list[list[int]] = []
color_img = img.copy()
color_img = cv2.cvtColor(color_img, cv2.COLOR_GRAY2RGB)
dy, dx = np.gradient(img)
ixx = dx**2
iyy = dy**2
ixy = dx * dy
k = 0.04
offset = self.window_size // 2
for y in range(offset, h - offset):
for x in range(offset, w - offset):
wxx = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
wyy = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
wxy = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
det = (wxx * wyy) - (wxy**2)
trace = wxx + wyy
r = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r])
color_img.itemset((y, x, 0), 0)
color_img.itemset((y, x, 1), 0)
color_img.itemset((y, x, 2), 255)
return color_img, corner_list
if __name__ == "__main__":
edge_detect = HarrisCorner(0.04, 3)
color_img, _ = edge_detect.detect("path_to_image")
cv2.imwrite("detect.png", color_img)
| 1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Segment_tree creates a segment tree with a given array and function,
allowing queries to be done later in log(N) time
function takes 2 values and returns a same type value
"""
from collections.abc import Sequence
from queue import Queue
class SegmentTreeNode:
def __init__(self, start, end, val, left=None, right=None):
self.start = start
self.end = end
self.val = val
self.mid = (start + end) // 2
self.left = left
self.right = right
def __str__(self):
return f"val: {self.val}, start: {self.start}, end: {self.end}"
class SegmentTree:
"""
>>> import operator
>>> num_arr = SegmentTree([2, 1, 5, 3, 4], operator.add)
>>> for node in num_arr.traverse():
... print(node)
...
val: 15, start: 0, end: 4
val: 8, start: 0, end: 2
val: 7, start: 3, end: 4
val: 3, start: 0, end: 1
val: 5, start: 2, end: 2
val: 3, start: 3, end: 3
val: 4, start: 4, end: 4
val: 2, start: 0, end: 0
val: 1, start: 1, end: 1
>>>
>>> num_arr.update(1, 5)
>>> for node in num_arr.traverse():
... print(node)
...
val: 19, start: 0, end: 4
val: 12, start: 0, end: 2
val: 7, start: 3, end: 4
val: 7, start: 0, end: 1
val: 5, start: 2, end: 2
val: 3, start: 3, end: 3
val: 4, start: 4, end: 4
val: 2, start: 0, end: 0
val: 5, start: 1, end: 1
>>>
>>> num_arr.query_range(3, 4)
7
>>> num_arr.query_range(2, 2)
5
>>> num_arr.query_range(1, 3)
13
>>>
>>> max_arr = SegmentTree([2, 1, 5, 3, 4], max)
>>> for node in max_arr.traverse():
... print(node)
...
val: 5, start: 0, end: 4
val: 5, start: 0, end: 2
val: 4, start: 3, end: 4
val: 2, start: 0, end: 1
val: 5, start: 2, end: 2
val: 3, start: 3, end: 3
val: 4, start: 4, end: 4
val: 2, start: 0, end: 0
val: 1, start: 1, end: 1
>>>
>>> max_arr.update(1, 5)
>>> for node in max_arr.traverse():
... print(node)
...
val: 5, start: 0, end: 4
val: 5, start: 0, end: 2
val: 4, start: 3, end: 4
val: 5, start: 0, end: 1
val: 5, start: 2, end: 2
val: 3, start: 3, end: 3
val: 4, start: 4, end: 4
val: 2, start: 0, end: 0
val: 5, start: 1, end: 1
>>>
>>> max_arr.query_range(3, 4)
4
>>> max_arr.query_range(2, 2)
5
>>> max_arr.query_range(1, 3)
5
>>>
>>> min_arr = SegmentTree([2, 1, 5, 3, 4], min)
>>> for node in min_arr.traverse():
... print(node)
...
val: 1, start: 0, end: 4
val: 1, start: 0, end: 2
val: 3, start: 3, end: 4
val: 1, start: 0, end: 1
val: 5, start: 2, end: 2
val: 3, start: 3, end: 3
val: 4, start: 4, end: 4
val: 2, start: 0, end: 0
val: 1, start: 1, end: 1
>>>
>>> min_arr.update(1, 5)
>>> for node in min_arr.traverse():
... print(node)
...
val: 2, start: 0, end: 4
val: 2, start: 0, end: 2
val: 3, start: 3, end: 4
val: 2, start: 0, end: 1
val: 5, start: 2, end: 2
val: 3, start: 3, end: 3
val: 4, start: 4, end: 4
val: 2, start: 0, end: 0
val: 5, start: 1, end: 1
>>>
>>> min_arr.query_range(3, 4)
3
>>> min_arr.query_range(2, 2)
5
>>> min_arr.query_range(1, 3)
3
>>>
"""
def __init__(self, collection: Sequence, function):
self.collection = collection
self.fn = function
if self.collection:
self.root = self._build_tree(0, len(collection) - 1)
def update(self, i, val):
"""
Update an element in log(N) time
:param i: position to be update
:param val: new value
>>> import operator
>>> num_arr = SegmentTree([2, 1, 5, 3, 4], operator.add)
>>> num_arr.update(1, 5)
>>> num_arr.query_range(1, 3)
13
"""
self._update_tree(self.root, i, val)
def query_range(self, i, j):
"""
Get range query value in log(N) time
:param i: left element index
:param j: right element index
:return: element combined in the range [i, j]
>>> import operator
>>> num_arr = SegmentTree([2, 1, 5, 3, 4], operator.add)
>>> num_arr.update(1, 5)
>>> num_arr.query_range(3, 4)
7
>>> num_arr.query_range(2, 2)
5
>>> num_arr.query_range(1, 3)
13
>>>
"""
return self._query_range(self.root, i, j)
def _build_tree(self, start, end):
if start == end:
return SegmentTreeNode(start, end, self.collection[start])
mid = (start + end) // 2
left = self._build_tree(start, mid)
right = self._build_tree(mid + 1, end)
return SegmentTreeNode(start, end, self.fn(left.val, right.val), left, right)
def _update_tree(self, node, i, val):
if node.start == i and node.end == i:
node.val = val
return
if i <= node.mid:
self._update_tree(node.left, i, val)
else:
self._update_tree(node.right, i, val)
node.val = self.fn(node.left.val, node.right.val)
def _query_range(self, node, i, j):
if node.start == i and node.end == j:
return node.val
if i <= node.mid:
if j <= node.mid:
# range in left child tree
return self._query_range(node.left, i, j)
else:
# range in left child tree and right child tree
return self.fn(
self._query_range(node.left, i, node.mid),
self._query_range(node.right, node.mid + 1, j),
)
else:
# range in right child tree
return self._query_range(node.right, i, j)
def traverse(self):
if self.root is not None:
queue = Queue()
queue.put(self.root)
while not queue.empty():
node = queue.get()
yield node
if node.left is not None:
queue.put(node.left)
if node.right is not None:
queue.put(node.right)
if __name__ == "__main__":
import operator
for fn in [operator.add, max, min]:
print("*" * 50)
arr = SegmentTree([2, 1, 5, 3, 4], fn)
for node in arr.traverse():
print(node)
print()
arr.update(1, 5)
for node in arr.traverse():
print(node)
print()
print(arr.query_range(3, 4)) # 7
print(arr.query_range(2, 2)) # 5
print(arr.query_range(1, 3)) # 13
print()
| """
Segment_tree creates a segment tree with a given array and function,
allowing queries to be done later in log(N) time
function takes 2 values and returns a same type value
"""
from collections.abc import Sequence
from queue import Queue
class SegmentTreeNode:
def __init__(self, start, end, val, left=None, right=None):
self.start = start
self.end = end
self.val = val
self.mid = (start + end) // 2
self.left = left
self.right = right
def __repr__(self):
return f"SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"
class SegmentTree:
"""
>>> import operator
>>> num_arr = SegmentTree([2, 1, 5, 3, 4], operator.add)
>>> tuple(num_arr.traverse()) # doctest: +NORMALIZE_WHITESPACE
(SegmentTreeNode(start=0, end=4, val=15),
SegmentTreeNode(start=0, end=2, val=8),
SegmentTreeNode(start=3, end=4, val=7),
SegmentTreeNode(start=0, end=1, val=3),
SegmentTreeNode(start=2, end=2, val=5),
SegmentTreeNode(start=3, end=3, val=3),
SegmentTreeNode(start=4, end=4, val=4),
SegmentTreeNode(start=0, end=0, val=2),
SegmentTreeNode(start=1, end=1, val=1))
>>>
>>> num_arr.update(1, 5)
>>> tuple(num_arr.traverse()) # doctest: +NORMALIZE_WHITESPACE
(SegmentTreeNode(start=0, end=4, val=19),
SegmentTreeNode(start=0, end=2, val=12),
SegmentTreeNode(start=3, end=4, val=7),
SegmentTreeNode(start=0, end=1, val=7),
SegmentTreeNode(start=2, end=2, val=5),
SegmentTreeNode(start=3, end=3, val=3),
SegmentTreeNode(start=4, end=4, val=4),
SegmentTreeNode(start=0, end=0, val=2),
SegmentTreeNode(start=1, end=1, val=5))
>>>
>>> num_arr.query_range(3, 4)
7
>>> num_arr.query_range(2, 2)
5
>>> num_arr.query_range(1, 3)
13
>>>
>>> max_arr = SegmentTree([2, 1, 5, 3, 4], max)
>>> for node in max_arr.traverse():
... print(node)
...
SegmentTreeNode(start=0, end=4, val=5)
SegmentTreeNode(start=0, end=2, val=5)
SegmentTreeNode(start=3, end=4, val=4)
SegmentTreeNode(start=0, end=1, val=2)
SegmentTreeNode(start=2, end=2, val=5)
SegmentTreeNode(start=3, end=3, val=3)
SegmentTreeNode(start=4, end=4, val=4)
SegmentTreeNode(start=0, end=0, val=2)
SegmentTreeNode(start=1, end=1, val=1)
>>>
>>> max_arr.update(1, 5)
>>> for node in max_arr.traverse():
... print(node)
...
SegmentTreeNode(start=0, end=4, val=5)
SegmentTreeNode(start=0, end=2, val=5)
SegmentTreeNode(start=3, end=4, val=4)
SegmentTreeNode(start=0, end=1, val=5)
SegmentTreeNode(start=2, end=2, val=5)
SegmentTreeNode(start=3, end=3, val=3)
SegmentTreeNode(start=4, end=4, val=4)
SegmentTreeNode(start=0, end=0, val=2)
SegmentTreeNode(start=1, end=1, val=5)
>>>
>>> max_arr.query_range(3, 4)
4
>>> max_arr.query_range(2, 2)
5
>>> max_arr.query_range(1, 3)
5
>>>
>>> min_arr = SegmentTree([2, 1, 5, 3, 4], min)
>>> for node in min_arr.traverse():
... print(node)
...
SegmentTreeNode(start=0, end=4, val=1)
SegmentTreeNode(start=0, end=2, val=1)
SegmentTreeNode(start=3, end=4, val=3)
SegmentTreeNode(start=0, end=1, val=1)
SegmentTreeNode(start=2, end=2, val=5)
SegmentTreeNode(start=3, end=3, val=3)
SegmentTreeNode(start=4, end=4, val=4)
SegmentTreeNode(start=0, end=0, val=2)
SegmentTreeNode(start=1, end=1, val=1)
>>>
>>> min_arr.update(1, 5)
>>> for node in min_arr.traverse():
... print(node)
...
SegmentTreeNode(start=0, end=4, val=2)
SegmentTreeNode(start=0, end=2, val=2)
SegmentTreeNode(start=3, end=4, val=3)
SegmentTreeNode(start=0, end=1, val=2)
SegmentTreeNode(start=2, end=2, val=5)
SegmentTreeNode(start=3, end=3, val=3)
SegmentTreeNode(start=4, end=4, val=4)
SegmentTreeNode(start=0, end=0, val=2)
SegmentTreeNode(start=1, end=1, val=5)
>>>
>>> min_arr.query_range(3, 4)
3
>>> min_arr.query_range(2, 2)
5
>>> min_arr.query_range(1, 3)
3
>>>
"""
def __init__(self, collection: Sequence, function):
self.collection = collection
self.fn = function
if self.collection:
self.root = self._build_tree(0, len(collection) - 1)
def update(self, i, val):
"""
Update an element in log(N) time
:param i: position to be update
:param val: new value
>>> import operator
>>> num_arr = SegmentTree([2, 1, 5, 3, 4], operator.add)
>>> num_arr.update(1, 5)
>>> num_arr.query_range(1, 3)
13
"""
self._update_tree(self.root, i, val)
def query_range(self, i, j):
"""
Get range query value in log(N) time
:param i: left element index
:param j: right element index
:return: element combined in the range [i, j]
>>> import operator
>>> num_arr = SegmentTree([2, 1, 5, 3, 4], operator.add)
>>> num_arr.update(1, 5)
>>> num_arr.query_range(3, 4)
7
>>> num_arr.query_range(2, 2)
5
>>> num_arr.query_range(1, 3)
13
>>>
"""
return self._query_range(self.root, i, j)
def _build_tree(self, start, end):
if start == end:
return SegmentTreeNode(start, end, self.collection[start])
mid = (start + end) // 2
left = self._build_tree(start, mid)
right = self._build_tree(mid + 1, end)
return SegmentTreeNode(start, end, self.fn(left.val, right.val), left, right)
def _update_tree(self, node, i, val):
if node.start == i and node.end == i:
node.val = val
return
if i <= node.mid:
self._update_tree(node.left, i, val)
else:
self._update_tree(node.right, i, val)
node.val = self.fn(node.left.val, node.right.val)
def _query_range(self, node, i, j):
if node.start == i and node.end == j:
return node.val
if i <= node.mid:
if j <= node.mid:
# range in left child tree
return self._query_range(node.left, i, j)
else:
# range in left child tree and right child tree
return self.fn(
self._query_range(node.left, i, node.mid),
self._query_range(node.right, node.mid + 1, j),
)
else:
# range in right child tree
return self._query_range(node.right, i, j)
def traverse(self):
if self.root is not None:
queue = Queue()
queue.put(self.root)
while not queue.empty():
node = queue.get()
yield node
if node.left is not None:
queue.put(node.left)
if node.right is not None:
queue.put(node.right)
if __name__ == "__main__":
import operator
for fn in [operator.add, max, min]:
print("*" * 50)
arr = SegmentTree([2, 1, 5, 3, 4], fn)
for node in arr.traverse():
print(node)
print()
arr.update(1, 5)
for node in arr.traverse():
print(node)
print()
print(arr.query_range(3, 4)) # 7
print(arr.query_range(2, 2)) # 5
print(arr.query_range(1, 3)) # 13
print()
| 1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Wavelet tree is a data-structure designed to efficiently answer various range queries
for arrays. Wavelets trees are different from other binary trees in the sense that
the nodes are split based on the actual values of the elements and not on indices,
such as the with segment trees or fenwick trees. You can read more about them here:
1. https://users.dcc.uchile.cl/~jperez/papers/ioiconf16.pdf
2. https://www.youtube.com/watch?v=4aSv9PcecDw&t=811s
3. https://www.youtube.com/watch?v=CybAgVF-MMc&t=1178s
"""
from __future__ import annotations
test_array = [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
class Node:
def __init__(self, length: int) -> None:
self.minn: int = -1
self.maxx: int = -1
self.map_left: list[int] = [-1] * length
self.left: Node | None = None
self.right: Node | None = None
def __repr__(self) -> str:
"""
>>> node = Node(length=27)
>>> repr(node)
'min_value: -1, max_value: -1'
>>> repr(node) == str(node)
True
"""
return f"min_value: {self.minn}, max_value: {self.maxx}"
def build_tree(arr: list[int]) -> Node | None:
"""
Builds the tree for arr and returns the root
of the constructed tree
>>> build_tree(test_array)
min_value: 0, max_value: 9
"""
root = Node(len(arr))
root.minn, root.maxx = min(arr), max(arr)
# Leaf node case where the node contains only one unique value
if root.minn == root.maxx:
return root
"""
Take the mean of min and max element of arr as the pivot and
partition arr into left_arr and right_arr with all elements <= pivot in the
left_arr and the rest in right_arr, maintaining the order of the elements,
then recursively build trees for left_arr and right_arr
"""
pivot = (root.minn + root.maxx) // 2
left_arr: list[int] = []
right_arr: list[int] = []
for index, num in enumerate(arr):
if num <= pivot:
left_arr.append(num)
else:
right_arr.append(num)
root.map_left[index] = len(left_arr)
root.left = build_tree(left_arr)
root.right = build_tree(right_arr)
return root
def rank_till_index(node: Node | None, num: int, index: int) -> int:
"""
Returns the number of occurrences of num in interval [0, index] in the list
>>> root = build_tree(test_array)
>>> rank_till_index(root, 6, 6)
1
>>> rank_till_index(root, 2, 0)
1
>>> rank_till_index(root, 1, 10)
2
>>> rank_till_index(root, 17, 7)
0
>>> rank_till_index(root, 0, 9)
1
"""
if index < 0 or node is None:
return 0
# Leaf node cases
if node.minn == node.maxx:
return index + 1 if node.minn == num else 0
pivot = (node.minn + node.maxx) // 2
if num <= pivot:
# go the left subtree and map index to the left subtree
return rank_till_index(node.left, num, node.map_left[index] - 1)
else:
# go to the right subtree and map index to the right subtree
return rank_till_index(node.right, num, index - node.map_left[index])
def rank(node: Node | None, num: int, start: int, end: int) -> int:
"""
Returns the number of occurrences of num in interval [start, end] in the list
>>> root = build_tree(test_array)
>>> rank(root, 6, 3, 13)
2
>>> rank(root, 2, 0, 19)
4
>>> rank(root, 9, 2 ,2)
0
>>> rank(root, 0, 5, 10)
2
"""
if start > end:
return 0
rank_till_end = rank_till_index(node, num, end)
rank_before_start = rank_till_index(node, num, start - 1)
return rank_till_end - rank_before_start
def quantile(node: Node | None, index: int, start: int, end: int) -> int:
"""
Returns the index'th smallest element in interval [start, end] in the list
index is 0-indexed
>>> root = build_tree(test_array)
>>> quantile(root, 2, 2, 5)
5
>>> quantile(root, 5, 2, 13)
4
>>> quantile(root, 0, 6, 6)
8
>>> quantile(root, 4, 2, 5)
-1
"""
if index > (end - start) or start > end or node is None:
return -1
# Leaf node case
if node.minn == node.maxx:
return node.minn
# Number of elements in the left subtree in interval [start, end]
num_elements_in_left_tree = node.map_left[end] - (
node.map_left[start - 1] if start else 0
)
if num_elements_in_left_tree > index:
return quantile(
node.left,
index,
(node.map_left[start - 1] if start else 0),
node.map_left[end] - 1,
)
else:
return quantile(
node.right,
index - num_elements_in_left_tree,
start - (node.map_left[start - 1] if start else 0),
end - node.map_left[end],
)
def range_counting(
node: Node | None, start: int, end: int, start_num: int, end_num: int
) -> int:
"""
Returns the number of elements in range [start_num, end_num]
in interval [start, end] in the list
>>> root = build_tree(test_array)
>>> range_counting(root, 1, 10, 3, 7)
3
>>> range_counting(root, 2, 2, 1, 4)
1
>>> range_counting(root, 0, 19, 0, 100)
20
>>> range_counting(root, 1, 0, 1, 100)
0
>>> range_counting(root, 0, 17, 100, 1)
0
"""
if (
start > end
or node is None
or start_num > end_num
or node.minn > end_num
or node.maxx < start_num
):
return 0
if start_num <= node.minn and node.maxx <= end_num:
return end - start + 1
left = range_counting(
node.left,
(node.map_left[start - 1] if start else 0),
node.map_left[end] - 1,
start_num,
end_num,
)
right = range_counting(
node.right,
start - (node.map_left[start - 1] if start else 0),
end - node.map_left[end],
start_num,
end_num,
)
return left + right
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Wavelet tree is a data-structure designed to efficiently answer various range queries
for arrays. Wavelets trees are different from other binary trees in the sense that
the nodes are split based on the actual values of the elements and not on indices,
such as the with segment trees or fenwick trees. You can read more about them here:
1. https://users.dcc.uchile.cl/~jperez/papers/ioiconf16.pdf
2. https://www.youtube.com/watch?v=4aSv9PcecDw&t=811s
3. https://www.youtube.com/watch?v=CybAgVF-MMc&t=1178s
"""
from __future__ import annotations
test_array = [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
class Node:
def __init__(self, length: int) -> None:
self.minn: int = -1
self.maxx: int = -1
self.map_left: list[int] = [-1] * length
self.left: Node | None = None
self.right: Node | None = None
def __repr__(self) -> str:
"""
>>> node = Node(length=27)
>>> repr(node)
'Node(min_value=-1 max_value=-1)'
>>> repr(node) == str(node)
True
"""
return f"Node(min_value={self.minn} max_value={self.maxx})"
def build_tree(arr: list[int]) -> Node | None:
"""
Builds the tree for arr and returns the root
of the constructed tree
>>> build_tree(test_array)
Node(min_value=0 max_value=9)
"""
root = Node(len(arr))
root.minn, root.maxx = min(arr), max(arr)
# Leaf node case where the node contains only one unique value
if root.minn == root.maxx:
return root
"""
Take the mean of min and max element of arr as the pivot and
partition arr into left_arr and right_arr with all elements <= pivot in the
left_arr and the rest in right_arr, maintaining the order of the elements,
then recursively build trees for left_arr and right_arr
"""
pivot = (root.minn + root.maxx) // 2
left_arr: list[int] = []
right_arr: list[int] = []
for index, num in enumerate(arr):
if num <= pivot:
left_arr.append(num)
else:
right_arr.append(num)
root.map_left[index] = len(left_arr)
root.left = build_tree(left_arr)
root.right = build_tree(right_arr)
return root
def rank_till_index(node: Node | None, num: int, index: int) -> int:
"""
Returns the number of occurrences of num in interval [0, index] in the list
>>> root = build_tree(test_array)
>>> rank_till_index(root, 6, 6)
1
>>> rank_till_index(root, 2, 0)
1
>>> rank_till_index(root, 1, 10)
2
>>> rank_till_index(root, 17, 7)
0
>>> rank_till_index(root, 0, 9)
1
"""
if index < 0 or node is None:
return 0
# Leaf node cases
if node.minn == node.maxx:
return index + 1 if node.minn == num else 0
pivot = (node.minn + node.maxx) // 2
if num <= pivot:
# go the left subtree and map index to the left subtree
return rank_till_index(node.left, num, node.map_left[index] - 1)
else:
# go to the right subtree and map index to the right subtree
return rank_till_index(node.right, num, index - node.map_left[index])
def rank(node: Node | None, num: int, start: int, end: int) -> int:
"""
Returns the number of occurrences of num in interval [start, end] in the list
>>> root = build_tree(test_array)
>>> rank(root, 6, 3, 13)
2
>>> rank(root, 2, 0, 19)
4
>>> rank(root, 9, 2 ,2)
0
>>> rank(root, 0, 5, 10)
2
"""
if start > end:
return 0
rank_till_end = rank_till_index(node, num, end)
rank_before_start = rank_till_index(node, num, start - 1)
return rank_till_end - rank_before_start
def quantile(node: Node | None, index: int, start: int, end: int) -> int:
"""
Returns the index'th smallest element in interval [start, end] in the list
index is 0-indexed
>>> root = build_tree(test_array)
>>> quantile(root, 2, 2, 5)
5
>>> quantile(root, 5, 2, 13)
4
>>> quantile(root, 0, 6, 6)
8
>>> quantile(root, 4, 2, 5)
-1
"""
if index > (end - start) or start > end or node is None:
return -1
# Leaf node case
if node.minn == node.maxx:
return node.minn
# Number of elements in the left subtree in interval [start, end]
num_elements_in_left_tree = node.map_left[end] - (
node.map_left[start - 1] if start else 0
)
if num_elements_in_left_tree > index:
return quantile(
node.left,
index,
(node.map_left[start - 1] if start else 0),
node.map_left[end] - 1,
)
else:
return quantile(
node.right,
index - num_elements_in_left_tree,
start - (node.map_left[start - 1] if start else 0),
end - node.map_left[end],
)
def range_counting(
node: Node | None, start: int, end: int, start_num: int, end_num: int
) -> int:
"""
Returns the number of elements in range [start_num, end_num]
in interval [start, end] in the list
>>> root = build_tree(test_array)
>>> range_counting(root, 1, 10, 3, 7)
3
>>> range_counting(root, 2, 2, 1, 4)
1
>>> range_counting(root, 0, 19, 0, 100)
20
>>> range_counting(root, 1, 0, 1, 100)
0
>>> range_counting(root, 0, 17, 100, 1)
0
"""
if (
start > end
or node is None
or start_num > end_num
or node.minn > end_num
or node.maxx < start_num
):
return 0
if start_num <= node.minn and node.maxx <= end_num:
return end - start + 1
left = range_counting(
node.left,
(node.map_left[start - 1] if start else 0),
node.map_left[end] - 1,
start_num,
end_num,
)
right = range_counting(
node.right,
start - (node.map_left[start - 1] if start else 0),
end - node.map_left[end],
start_num,
end_num,
)
return left + right
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/Doubly_linked_list
"""
class Node:
def __init__(self, data):
self.data = data
self.previous = None
self.next = None
def __str__(self):
return f"{self.data}"
class DoublyLinkedList:
def __init__(self):
self.head = None
self.tail = None
def __iter__(self):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.insert_at_head('b')
>>> linked_list.insert_at_head('a')
>>> linked_list.insert_at_tail('c')
>>> tuple(linked_list)
('a', 'b', 'c')
"""
node = self.head
while node:
yield node.data
node = node.next
def __str__(self):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.insert_at_tail('a')
>>> linked_list.insert_at_tail('b')
>>> linked_list.insert_at_tail('c')
>>> str(linked_list)
'a->b->c'
"""
return "->".join([str(item) for item in self])
def __len__(self):
"""
>>> linked_list = DoublyLinkedList()
>>> for i in range(0, 5):
... linked_list.insert_at_nth(i, i + 1)
>>> len(linked_list) == 5
True
"""
return len(tuple(iter(self)))
def insert_at_head(self, data):
self.insert_at_nth(0, data)
def insert_at_tail(self, data):
self.insert_at_nth(len(self), data)
def insert_at_nth(self, index: int, data):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.insert_at_nth(-1, 666)
Traceback (most recent call last):
....
IndexError: list index out of range
>>> linked_list.insert_at_nth(1, 666)
Traceback (most recent call last):
....
IndexError: list index out of range
>>> linked_list.insert_at_nth(0, 2)
>>> linked_list.insert_at_nth(0, 1)
>>> linked_list.insert_at_nth(2, 4)
>>> linked_list.insert_at_nth(2, 3)
>>> str(linked_list)
'1->2->3->4'
>>> linked_list.insert_at_nth(5, 5)
Traceback (most recent call last):
....
IndexError: list index out of range
"""
if not 0 <= index <= len(self):
raise IndexError("list index out of range")
new_node = Node(data)
if self.head is None:
self.head = self.tail = new_node
elif index == 0:
self.head.previous = new_node
new_node.next = self.head
self.head = new_node
elif index == len(self):
self.tail.next = new_node
new_node.previous = self.tail
self.tail = new_node
else:
temp = self.head
for _ in range(0, index):
temp = temp.next
temp.previous.next = new_node
new_node.previous = temp.previous
new_node.next = temp
temp.previous = new_node
def delete_head(self):
return self.delete_at_nth(0)
def delete_tail(self):
return self.delete_at_nth(len(self) - 1)
def delete_at_nth(self, index: int):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.delete_at_nth(0)
Traceback (most recent call last):
....
IndexError: list index out of range
>>> for i in range(0, 5):
... linked_list.insert_at_nth(i, i + 1)
>>> linked_list.delete_at_nth(0) == 1
True
>>> linked_list.delete_at_nth(3) == 5
True
>>> linked_list.delete_at_nth(1) == 3
True
>>> str(linked_list)
'2->4'
>>> linked_list.delete_at_nth(2)
Traceback (most recent call last):
....
IndexError: list index out of range
"""
if not 0 <= index <= len(self) - 1:
raise IndexError("list index out of range")
delete_node = self.head # default first node
if len(self) == 1:
self.head = self.tail = None
elif index == 0:
self.head = self.head.next
self.head.previous = None
elif index == len(self) - 1:
delete_node = self.tail
self.tail = self.tail.previous
self.tail.next = None
else:
temp = self.head
for _ in range(0, index):
temp = temp.next
delete_node = temp
temp.next.previous = temp.previous
temp.previous.next = temp.next
return delete_node.data
def delete(self, data) -> str:
current = self.head
while current.data != data: # Find the position to delete
if current.next:
current = current.next
else: # We have reached the end an no value matches
return "No data matching given value"
if current == self.head:
self.delete_head()
elif current == self.tail:
self.delete_tail()
else: # Before: 1 <--> 2(current) <--> 3
current.previous.next = current.next # 1 --> 3
current.next.previous = current.previous # 1 <--> 3
return data
def is_empty(self):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.is_empty()
True
>>> linked_list.insert_at_tail(1)
>>> linked_list.is_empty()
False
"""
return len(self) == 0
def test_doubly_linked_list() -> None:
"""
>>> test_doubly_linked_list()
"""
linked_list = DoublyLinkedList()
assert linked_list.is_empty() is True
assert str(linked_list) == ""
try:
linked_list.delete_head()
raise AssertionError() # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError() # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10):
assert len(linked_list) == i
linked_list.insert_at_nth(i, i + 1)
assert str(linked_list) == "->".join(str(i) for i in range(1, 11))
linked_list.insert_at_head(0)
linked_list.insert_at_tail(11)
assert str(linked_list) == "->".join(str(i) for i in range(0, 12))
assert linked_list.delete_head() == 0
assert linked_list.delete_at_nth(9) == 10
assert linked_list.delete_tail() == 11
assert len(linked_list) == 9
assert str(linked_list) == "->".join(str(i) for i in range(1, 10))
if __name__ == "__main__":
from doctest import testmod
testmod()
| """
https://en.wikipedia.org/wiki/Doubly_linked_list
"""
class Node:
def __init__(self, data):
self.data = data
self.previous = None
self.next = None
def __str__(self):
return f"{self.data}"
class DoublyLinkedList:
def __init__(self):
self.head = None
self.tail = None
def __iter__(self):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.insert_at_head('b')
>>> linked_list.insert_at_head('a')
>>> linked_list.insert_at_tail('c')
>>> tuple(linked_list)
('a', 'b', 'c')
"""
node = self.head
while node:
yield node.data
node = node.next
def __str__(self):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.insert_at_tail('a')
>>> linked_list.insert_at_tail('b')
>>> linked_list.insert_at_tail('c')
>>> str(linked_list)
'a->b->c'
"""
return "->".join([str(item) for item in self])
def __len__(self):
"""
>>> linked_list = DoublyLinkedList()
>>> for i in range(0, 5):
... linked_list.insert_at_nth(i, i + 1)
>>> len(linked_list) == 5
True
"""
return len(tuple(iter(self)))
def insert_at_head(self, data):
self.insert_at_nth(0, data)
def insert_at_tail(self, data):
self.insert_at_nth(len(self), data)
def insert_at_nth(self, index: int, data):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.insert_at_nth(-1, 666)
Traceback (most recent call last):
....
IndexError: list index out of range
>>> linked_list.insert_at_nth(1, 666)
Traceback (most recent call last):
....
IndexError: list index out of range
>>> linked_list.insert_at_nth(0, 2)
>>> linked_list.insert_at_nth(0, 1)
>>> linked_list.insert_at_nth(2, 4)
>>> linked_list.insert_at_nth(2, 3)
>>> str(linked_list)
'1->2->3->4'
>>> linked_list.insert_at_nth(5, 5)
Traceback (most recent call last):
....
IndexError: list index out of range
"""
if not 0 <= index <= len(self):
raise IndexError("list index out of range")
new_node = Node(data)
if self.head is None:
self.head = self.tail = new_node
elif index == 0:
self.head.previous = new_node
new_node.next = self.head
self.head = new_node
elif index == len(self):
self.tail.next = new_node
new_node.previous = self.tail
self.tail = new_node
else:
temp = self.head
for _ in range(0, index):
temp = temp.next
temp.previous.next = new_node
new_node.previous = temp.previous
new_node.next = temp
temp.previous = new_node
def delete_head(self):
return self.delete_at_nth(0)
def delete_tail(self):
return self.delete_at_nth(len(self) - 1)
def delete_at_nth(self, index: int):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.delete_at_nth(0)
Traceback (most recent call last):
....
IndexError: list index out of range
>>> for i in range(0, 5):
... linked_list.insert_at_nth(i, i + 1)
>>> linked_list.delete_at_nth(0) == 1
True
>>> linked_list.delete_at_nth(3) == 5
True
>>> linked_list.delete_at_nth(1) == 3
True
>>> str(linked_list)
'2->4'
>>> linked_list.delete_at_nth(2)
Traceback (most recent call last):
....
IndexError: list index out of range
"""
if not 0 <= index <= len(self) - 1:
raise IndexError("list index out of range")
delete_node = self.head # default first node
if len(self) == 1:
self.head = self.tail = None
elif index == 0:
self.head = self.head.next
self.head.previous = None
elif index == len(self) - 1:
delete_node = self.tail
self.tail = self.tail.previous
self.tail.next = None
else:
temp = self.head
for _ in range(0, index):
temp = temp.next
delete_node = temp
temp.next.previous = temp.previous
temp.previous.next = temp.next
return delete_node.data
def delete(self, data) -> str:
current = self.head
while current.data != data: # Find the position to delete
if current.next:
current = current.next
else: # We have reached the end an no value matches
raise ValueError("No data matching given value")
if current == self.head:
self.delete_head()
elif current == self.tail:
self.delete_tail()
else: # Before: 1 <--> 2(current) <--> 3
current.previous.next = current.next # 1 --> 3
current.next.previous = current.previous # 1 <--> 3
return data
def is_empty(self):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.is_empty()
True
>>> linked_list.insert_at_tail(1)
>>> linked_list.is_empty()
False
"""
return len(self) == 0
def test_doubly_linked_list() -> None:
"""
>>> test_doubly_linked_list()
"""
linked_list = DoublyLinkedList()
assert linked_list.is_empty() is True
assert str(linked_list) == ""
try:
linked_list.delete_head()
raise AssertionError() # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError() # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10):
assert len(linked_list) == i
linked_list.insert_at_nth(i, i + 1)
assert str(linked_list) == "->".join(str(i) for i in range(1, 11))
linked_list.insert_at_head(0)
linked_list.insert_at_tail(11)
assert str(linked_list) == "->".join(str(i) for i in range(0, 12))
assert linked_list.delete_head() == 0
assert linked_list.delete_at_nth(9) == 10
assert linked_list.delete_tail() == 11
assert len(linked_list) == 9
assert str(linked_list) == "->".join(str(i) for i in range(1, 10))
if __name__ == "__main__":
from doctest import testmod
testmod()
| 1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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_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()
| """
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 f"[{', '.join(repr(val) for val in values_list)}]"
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Breath First Search (BFS) can be used when finding the shortest path
from a given source node to a target node in an unweighted graph.
"""
from __future__ import annotations
graph = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class Graph:
def __init__(self, graph: dict[str, list[str]], source_vertex: str) -> None:
"""
Graph is implemented as dictionary of adjacency lists. Also,
Source vertex have to be defined upon initialization.
"""
self.graph = graph
# mapping node to its parent in resulting breadth first tree
self.parent: dict[str, str | None] = {}
self.source_vertex = source_vertex
def breath_first_search(self) -> None:
"""
This function is a helper for running breath first search on this graph.
>>> g = Graph(graph, "G")
>>> g.breath_first_search()
>>> g.parent
{'G': None, 'C': 'G', 'A': 'C', 'F': 'C', 'B': 'A', 'E': 'A', 'D': 'B'}
"""
visited = {self.source_vertex}
self.parent[self.source_vertex] = None
queue = [self.source_vertex] # first in first out queue
while queue:
vertex = queue.pop(0)
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(adjacent_vertex)
self.parent[adjacent_vertex] = vertex
queue.append(adjacent_vertex)
def shortest_path(self, target_vertex: str) -> str:
"""
This shortest path function returns a string, describing the result:
1.) No path is found. The string is a human readable message to indicate this.
2.) The shortest path is found. The string is in the form
`v1(->v2->v3->...->vn)`, where v1 is the source vertex and vn is the target
vertex, if it exists separately.
>>> g = Graph(graph, "G")
>>> g.breath_first_search()
Case 1 - No path is found.
>>> g.shortest_path("Foo")
'No path from vertex:G to vertex:Foo'
Case 2 - The path is found.
>>> g.shortest_path("D")
'G->C->A->B->D'
>>> g.shortest_path("G")
'G'
"""
if target_vertex == self.source_vertex:
return self.source_vertex
target_vertex_parent = self.parent.get(target_vertex)
if target_vertex_parent is None:
return f"No path from vertex:{self.source_vertex} to vertex:{target_vertex}"
return self.shortest_path(target_vertex_parent) + f"->{target_vertex}"
if __name__ == "__main__":
g = Graph(graph, "G")
g.breath_first_search()
print(g.shortest_path("D"))
print(g.shortest_path("G"))
print(g.shortest_path("Foo"))
| """Breath First Search (BFS) can be used when finding the shortest path
from a given source node to a target node in an unweighted graph.
"""
from __future__ import annotations
graph = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class Graph:
def __init__(self, graph: dict[str, list[str]], source_vertex: str) -> None:
"""
Graph is implemented as dictionary of adjacency lists. Also,
Source vertex have to be defined upon initialization.
"""
self.graph = graph
# mapping node to its parent in resulting breadth first tree
self.parent: dict[str, str | None] = {}
self.source_vertex = source_vertex
def breath_first_search(self) -> None:
"""
This function is a helper for running breath first search on this graph.
>>> g = Graph(graph, "G")
>>> g.breath_first_search()
>>> g.parent
{'G': None, 'C': 'G', 'A': 'C', 'F': 'C', 'B': 'A', 'E': 'A', 'D': 'B'}
"""
visited = {self.source_vertex}
self.parent[self.source_vertex] = None
queue = [self.source_vertex] # first in first out queue
while queue:
vertex = queue.pop(0)
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(adjacent_vertex)
self.parent[adjacent_vertex] = vertex
queue.append(adjacent_vertex)
def shortest_path(self, target_vertex: str) -> str:
"""
This shortest path function returns a string, describing the result:
1.) No path is found. The string is a human readable message to indicate this.
2.) The shortest path is found. The string is in the form
`v1(->v2->v3->...->vn)`, where v1 is the source vertex and vn is the target
vertex, if it exists separately.
>>> g = Graph(graph, "G")
>>> g.breath_first_search()
Case 1 - No path is found.
>>> g.shortest_path("Foo")
Traceback (most recent call last):
...
ValueError: No path from vertex: G to vertex: Foo
Case 2 - The path is found.
>>> g.shortest_path("D")
'G->C->A->B->D'
>>> g.shortest_path("G")
'G'
"""
if target_vertex == self.source_vertex:
return self.source_vertex
target_vertex_parent = self.parent.get(target_vertex)
if target_vertex_parent is None:
raise ValueError(
f"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
)
return self.shortest_path(target_vertex_parent) + f"->{target_vertex}"
if __name__ == "__main__":
g = Graph(graph, "G")
g.breath_first_search()
print(g.shortest_path("D"))
print(g.shortest_path("G"))
print(g.shortest_path("Foo"))
| 1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Author: https://github.com/bhushan-borole
"""
"""
The input graph for the algorithm is:
A B C
A 0 1 1
B 0 0 1
C 1 0 0
"""
graph = [[0, 1, 1], [0, 0, 1], [1, 0, 0]]
class Node:
def __init__(self, name):
self.name = name
self.inbound = []
self.outbound = []
def add_inbound(self, node):
self.inbound.append(node)
def add_outbound(self, node):
self.outbound.append(node)
def __repr__(self):
return f"Node {self.name}: Inbound: {self.inbound} ; Outbound: {self.outbound}"
def page_rank(nodes, limit=3, d=0.85):
ranks = {}
for node in nodes:
ranks[node.name] = 1
outbounds = {}
for node in nodes:
outbounds[node.name] = len(node.outbound)
for i in range(limit):
print(f"======= Iteration {i + 1} =======")
for _, node in enumerate(nodes):
ranks[node.name] = (1 - d) + d * sum(
ranks[ib] / outbounds[ib] for ib in node.inbound
)
print(ranks)
def main():
names = list(input("Enter Names of the Nodes: ").split())
nodes = [Node(name) for name in names]
for ri, row in enumerate(graph):
for ci, col in enumerate(row):
if col == 1:
nodes[ci].add_inbound(names[ri])
nodes[ri].add_outbound(names[ci])
print("======= Nodes =======")
for node in nodes:
print(node)
page_rank(nodes)
if __name__ == "__main__":
main()
| """
Author: https://github.com/bhushan-borole
"""
"""
The input graph for the algorithm is:
A B C
A 0 1 1
B 0 0 1
C 1 0 0
"""
graph = [[0, 1, 1], [0, 0, 1], [1, 0, 0]]
class Node:
def __init__(self, name):
self.name = name
self.inbound = []
self.outbound = []
def add_inbound(self, node):
self.inbound.append(node)
def add_outbound(self, node):
self.outbound.append(node)
def __repr__(self):
return f"<node={self.name} inbound={self.inbound} outbound={self.outbound}>"
def page_rank(nodes, limit=3, d=0.85):
ranks = {}
for node in nodes:
ranks[node.name] = 1
outbounds = {}
for node in nodes:
outbounds[node.name] = len(node.outbound)
for i in range(limit):
print(f"======= Iteration {i + 1} =======")
for _, node in enumerate(nodes):
ranks[node.name] = (1 - d) + d * sum(
ranks[ib] / outbounds[ib] for ib in node.inbound
)
print(ranks)
def main():
names = list(input("Enter Names of the Nodes: ").split())
nodes = [Node(name) for name in names]
for ri, row in enumerate(graph):
for ci, col in enumerate(row):
if col == 1:
nodes[ci].add_inbound(names[ri])
nodes[ri].add_outbound(names[ci])
print("======= Nodes =======")
for node in nodes:
print(node)
page_rank(nodes)
if __name__ == "__main__":
main()
| 1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def points_to_polynomial(coordinates: list[list[int]]) -> str:
"""
coordinates is a two dimensional matrix: [[x, y], [x, y], ...]
number of points you want to use
>>> print(points_to_polynomial([]))
The program cannot work out a fitting polynomial.
>>> print(points_to_polynomial([[]]))
The program cannot work out a fitting polynomial.
>>> print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
f(x)=x^2*0.0+x^1*-0.0+x^0*0.0
>>> print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
f(x)=x^2*0.0+x^1*-0.0+x^0*1.0
>>> print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
f(x)=x^2*0.0+x^1*-0.0+x^0*3.0
>>> print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
f(x)=x^2*0.0+x^1*1.0+x^0*0.0
>>> print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
f(x)=x^2*1.0+x^1*-0.0+x^0*0.0
>>> print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
f(x)=x^2*1.0+x^1*-0.0+x^0*2.0
>>> print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
f(x)=x^2*-1.0+x^1*-0.0+x^0*-2.0
>>> print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))
f(x)=x^2*5.0+x^1*-18.0+x^0*18.0
"""
if len(coordinates) == 0 or not all(len(pair) == 2 for pair in coordinates):
return "The program cannot work out a fitting polynomial."
if len({tuple(pair) for pair in coordinates}) != len(coordinates):
return "The program cannot work out a fitting polynomial."
set_x = {x for x, _ in coordinates}
if len(set_x) == 1:
return f"x={coordinates[0][0]}"
if len(set_x) != len(coordinates):
return "The program cannot work out a fitting polynomial."
x = len(coordinates)
count_of_line = 0
matrix: list[list[float]] = []
# put the x and x to the power values in a matrix
while count_of_line < x:
count_in_line = 0
a = coordinates[count_of_line][0]
count_line: list[float] = []
while count_in_line < x:
count_line.append(a ** (x - (count_in_line + 1)))
count_in_line += 1
matrix.append(count_line)
count_of_line += 1
count_of_line = 0
# put the y values into a vector
vector: list[float] = []
while count_of_line < x:
vector.append(coordinates[count_of_line][1])
count_of_line += 1
count = 0
while count < x:
zahlen = 0
while zahlen < x:
if count == zahlen:
zahlen += 1
if zahlen == x:
break
bruch = matrix[zahlen][count] / matrix[count][count]
for counting_columns, item in enumerate(matrix[count]):
# manipulating all the values in the matrix
matrix[zahlen][counting_columns] -= item * bruch
# manipulating the values in the vector
vector[zahlen] -= vector[count] * bruch
zahlen += 1
count += 1
count = 0
# make solutions
solution: list[str] = []
while count < x:
solution.append(str(vector[count] / matrix[count][count]))
count += 1
count = 0
solved = "f(x)="
while count < x:
remove_e: list[str] = solution[count].split("E")
if len(remove_e) > 1:
solution[count] = f"{remove_e[0]}*10^{remove_e[1]}"
solved += f"x^{x - (count + 1)}*{solution[count]}"
if count + 1 != x:
solved += "+"
count += 1
return solved
if __name__ == "__main__":
print(points_to_polynomial([]))
print(points_to_polynomial([[]]))
print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))
| def points_to_polynomial(coordinates: list[list[int]]) -> str:
"""
coordinates is a two dimensional matrix: [[x, y], [x, y], ...]
number of points you want to use
>>> print(points_to_polynomial([]))
Traceback (most recent call last):
...
ValueError: The program cannot work out a fitting polynomial.
>>> print(points_to_polynomial([[]]))
Traceback (most recent call last):
...
ValueError: The program cannot work out a fitting polynomial.
>>> print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
f(x)=x^2*0.0+x^1*-0.0+x^0*0.0
>>> print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
f(x)=x^2*0.0+x^1*-0.0+x^0*1.0
>>> print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
f(x)=x^2*0.0+x^1*-0.0+x^0*3.0
>>> print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
f(x)=x^2*0.0+x^1*1.0+x^0*0.0
>>> print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
f(x)=x^2*1.0+x^1*-0.0+x^0*0.0
>>> print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
f(x)=x^2*1.0+x^1*-0.0+x^0*2.0
>>> print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
f(x)=x^2*-1.0+x^1*-0.0+x^0*-2.0
>>> print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))
f(x)=x^2*5.0+x^1*-18.0+x^0*18.0
"""
if len(coordinates) == 0 or not all(len(pair) == 2 for pair in coordinates):
raise ValueError("The program cannot work out a fitting polynomial.")
if len({tuple(pair) for pair in coordinates}) != len(coordinates):
raise ValueError("The program cannot work out a fitting polynomial.")
set_x = {x for x, _ in coordinates}
if len(set_x) == 1:
return f"x={coordinates[0][0]}"
if len(set_x) != len(coordinates):
raise ValueError("The program cannot work out a fitting polynomial.")
x = len(coordinates)
count_of_line = 0
matrix: list[list[float]] = []
# put the x and x to the power values in a matrix
while count_of_line < x:
count_in_line = 0
a = coordinates[count_of_line][0]
count_line: list[float] = []
while count_in_line < x:
count_line.append(a ** (x - (count_in_line + 1)))
count_in_line += 1
matrix.append(count_line)
count_of_line += 1
count_of_line = 0
# put the y values into a vector
vector: list[float] = []
while count_of_line < x:
vector.append(coordinates[count_of_line][1])
count_of_line += 1
count = 0
while count < x:
zahlen = 0
while zahlen < x:
if count == zahlen:
zahlen += 1
if zahlen == x:
break
bruch = matrix[zahlen][count] / matrix[count][count]
for counting_columns, item in enumerate(matrix[count]):
# manipulating all the values in the matrix
matrix[zahlen][counting_columns] -= item * bruch
# manipulating the values in the vector
vector[zahlen] -= vector[count] * bruch
zahlen += 1
count += 1
count = 0
# make solutions
solution: list[str] = []
while count < x:
solution.append(str(vector[count] / matrix[count][count]))
count += 1
count = 0
solved = "f(x)="
while count < x:
remove_e: list[str] = solution[count].split("E")
if len(remove_e) > 1:
solution[count] = f"{remove_e[0]}*10^{remove_e[1]}"
solved += f"x^{x - (count + 1)}*{solution[count]}"
if count + 1 != x:
solved += "+"
count += 1
return solved
if __name__ == "__main__":
print(points_to_polynomial([]))
print(points_to_polynomial([[]]))
print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))
| 1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
import 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 _str_(self):
return "Fair Dice"
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 | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # https://www.chilimath.com/lessons/advanced-algebra/cramers-rule-with-two-variables
# https://en.wikipedia.org/wiki/Cramer%27s_rule
def cramers_rule_2x2(equation1: list[int], equation2: list[int]) -> str:
"""
Solves the system of linear equation in 2 variables.
:param: equation1: list of 3 numbers
:param: equation2: list of 3 numbers
:return: String of result
input format : [a1, b1, d1], [a2, b2, d2]
determinant = [[a1, b1], [a2, b2]]
determinant_x = [[d1, b1], [d2, b2]]
determinant_y = [[a1, d1], [a2, d2]]
>>> cramers_rule_2x2([2, 3, 0], [5, 1, 0])
'Trivial solution. (Consistent system) x = 0 and y = 0'
>>> cramers_rule_2x2([0, 4, 50], [2, 0, 26])
'Non-Trivial Solution (Consistent system) x = 13.0, y = 12.5'
>>> cramers_rule_2x2([11, 2, 30], [1, 0, 4])
'Non-Trivial Solution (Consistent system) x = 4.0, y = -7.0'
>>> cramers_rule_2x2([4, 7, 1], [1, 2, 0])
'Non-Trivial Solution (Consistent system) x = 2.0, y = -1.0'
>>> cramers_rule_2x2([1, 2, 3], [2, 4, 6])
Traceback (most recent call last):
...
ValueError: Infinite solutions. (Consistent system)
>>> cramers_rule_2x2([1, 2, 3], [2, 4, 7])
Traceback (most recent call last):
...
ValueError: No solution. (Inconsistent system)
>>> cramers_rule_2x2([1, 2, 3], [11, 22])
Traceback (most recent call last):
...
ValueError: Please enter a valid equation.
>>> cramers_rule_2x2([0, 1, 6], [0, 0, 3])
Traceback (most recent call last):
...
ValueError: No solution. (Inconsistent system)
>>> cramers_rule_2x2([0, 0, 6], [0, 0, 3])
Traceback (most recent call last):
...
ValueError: Both a & b of two equations can't be zero.
>>> cramers_rule_2x2([1, 2, 3], [1, 2, 3])
Traceback (most recent call last):
...
ValueError: Infinite solutions. (Consistent system)
>>> cramers_rule_2x2([0, 4, 50], [0, 3, 99])
Traceback (most recent call last):
...
ValueError: No solution. (Inconsistent system)
"""
# Check if the input is valid
if not len(equation1) == len(equation2) == 3:
raise ValueError("Please enter a valid equation.")
if equation1[0] == equation1[1] == equation2[0] == equation2[1] == 0:
raise ValueError("Both a & b of two equations can't be zero.")
# Extract the coefficients
a1, b1, c1 = equation1
a2, b2, c2 = equation2
# Calculate the determinants of the matrices
determinant = a1 * b2 - a2 * b1
determinant_x = c1 * b2 - c2 * b1
determinant_y = a1 * c2 - a2 * c1
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("Infinite solutions. (Consistent system)")
else:
raise ValueError("No solution. (Inconsistent system)")
else:
if determinant_x == determinant_y == 0:
return "Trivial solution. (Consistent system) x = 0 and y = 0"
else:
x = determinant_x / determinant
y = determinant_y / determinant
return f"Non-Trivial Solution (Consistent system) x = {x}, y = {y}"
| # https://www.chilimath.com/lessons/advanced-algebra/cramers-rule-with-two-variables
# https://en.wikipedia.org/wiki/Cramer%27s_rule
def cramers_rule_2x2(equation1: list[int], equation2: list[int]) -> tuple[float, float]:
"""
Solves the system of linear equation in 2 variables.
:param: equation1: list of 3 numbers
:param: equation2: list of 3 numbers
:return: String of result
input format : [a1, b1, d1], [a2, b2, d2]
determinant = [[a1, b1], [a2, b2]]
determinant_x = [[d1, b1], [d2, b2]]
determinant_y = [[a1, d1], [a2, d2]]
>>> cramers_rule_2x2([2, 3, 0], [5, 1, 0])
(0.0, 0.0)
>>> cramers_rule_2x2([0, 4, 50], [2, 0, 26])
(13.0, 12.5)
>>> cramers_rule_2x2([11, 2, 30], [1, 0, 4])
(4.0, -7.0)
>>> cramers_rule_2x2([4, 7, 1], [1, 2, 0])
(2.0, -1.0)
>>> cramers_rule_2x2([1, 2, 3], [2, 4, 6])
Traceback (most recent call last):
...
ValueError: Infinite solutions. (Consistent system)
>>> cramers_rule_2x2([1, 2, 3], [2, 4, 7])
Traceback (most recent call last):
...
ValueError: No solution. (Inconsistent system)
>>> cramers_rule_2x2([1, 2, 3], [11, 22])
Traceback (most recent call last):
...
ValueError: Please enter a valid equation.
>>> cramers_rule_2x2([0, 1, 6], [0, 0, 3])
Traceback (most recent call last):
...
ValueError: No solution. (Inconsistent system)
>>> cramers_rule_2x2([0, 0, 6], [0, 0, 3])
Traceback (most recent call last):
...
ValueError: Both a & b of two equations can't be zero.
>>> cramers_rule_2x2([1, 2, 3], [1, 2, 3])
Traceback (most recent call last):
...
ValueError: Infinite solutions. (Consistent system)
>>> cramers_rule_2x2([0, 4, 50], [0, 3, 99])
Traceback (most recent call last):
...
ValueError: No solution. (Inconsistent system)
"""
# Check if the input is valid
if not len(equation1) == len(equation2) == 3:
raise ValueError("Please enter a valid equation.")
if equation1[0] == equation1[1] == equation2[0] == equation2[1] == 0:
raise ValueError("Both a & b of two equations can't be zero.")
# Extract the coefficients
a1, b1, c1 = equation1
a2, b2, c2 = equation2
# Calculate the determinants of the matrices
determinant = a1 * b2 - a2 * b1
determinant_x = c1 * b2 - c2 * b1
determinant_y = a1 * c2 - a2 * c1
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("Infinite solutions. (Consistent system)")
else:
raise ValueError("No solution. (Inconsistent system)")
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
x = determinant_x / determinant
y = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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 strong_password_detector(password: str, min_length: int = 8) -> str:
"""
>>> strong_password_detector('Hwea7$2!')
'This is a strong Password'
>>> strong_password_detector('Sh0r1')
'Your Password must be at least 8 characters long'
>>> strong_password_detector('Hello123')
'Password should contain UPPERCASE, lowercase, numbers, special characters'
>>> strong_password_detector('Hello1238udfhiaf038fajdvjjf!jaiuFhkqi1')
'This is a strong Password'
>>> strong_password_detector('0')
'Your Password must be at least 8 characters long'
"""
if len(password) < min_length:
return "Your Password must be at least 8 characters long"
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 "This is a strong Password"
else:
return (
"Password should contain UPPERCASE, lowercase, "
"numbers, special characters"
)
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 | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import re
def dna(dna: str) -> str:
"""
https://en.wikipedia.org/wiki/DNA
Returns the second side of a DNA strand
>>> dna("GCTA")
'CGAT'
>>> dna("ATGC")
'TACG'
>>> dna("CTGA")
'GACT'
>>> dna("GFGG")
'Invalid Strand'
"""
r = len(re.findall("[ATCG]", dna)) != len(dna)
val = dna.translate(dna.maketrans("ATCG", "TAGC"))
return "Invalid Strand" if r else val
if __name__ == "__main__":
__import__("doctest").testmod()
| import re
def dna(dna: str) -> str:
"""
https://en.wikipedia.org/wiki/DNA
Returns the second side of a DNA strand
>>> dna("GCTA")
'CGAT'
>>> dna("ATGC")
'TACG'
>>> dna("CTGA")
'GACT'
>>> dna("GFGG")
Traceback (most recent call last):
...
ValueError: Invalid Strand
"""
if len(re.findall("[ATCG]", dna)) != len(dna):
raise ValueError("Invalid Strand")
return dna.translate(dna.maketrans("ATCG", "TAGC"))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from datetime import datetime
import requests
def download_video(url: str) -> bytes:
base_url = "https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
video_url = requests.get(base_url + url).json()[0]["urls"][0]["src"]
return requests.get(video_url).content
if __name__ == "__main__":
url = input("Enter Video/IGTV url: ").strip()
file_name = f"{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"
with open(file_name, "wb") as fp:
fp.write(download_video(url))
print(f"Done. Video saved to disk as {file_name}.")
| from datetime import datetime
import requests
def download_video(url: str) -> bytes:
base_url = "https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
video_url = requests.get(base_url + url).json()[0]["urls"][0]["src"]
return requests.get(video_url).content
if __name__ == "__main__":
url = input("Enter Video/IGTV url: ").strip()
file_name = f"{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"
with open(file_name, "wb") as fp:
fp.write(download_video(url))
print(f"Done. Video saved to disk as {file_name}.")
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Number letter counts
Problem 17: https://projecteuler.net/problem=17
If the numbers 1 to 5 are written out in words: one, two, three, four, five,
then there are 3 + 3 + 5 + 4 + 4 = 19 letters used in total.
If all the numbers from 1 to 1000 (one thousand) inclusive were written out in
words, how many letters would be used?
NOTE: Do not count spaces or hyphens. For example, 342 (three hundred and
forty-two) contains 23 letters and 115 (one hundred and fifteen) contains 20
letters. The use of "and" when writing out numbers is in compliance withBritish
usage.
"""
def solution(n: int = 1000) -> int:
"""Returns the number of letters used to write all numbers from 1 to n.
where n is lower or equals to 1000.
>>> solution(1000)
21124
>>> solution(5)
19
"""
# number of letters in zero, one, two, ..., nineteen (0 for zero since it's
# never said aloud)
ones_counts = [0, 3, 3, 5, 4, 4, 3, 5, 5, 4, 3, 6, 6, 8, 8, 7, 7, 9, 8, 8]
# number of letters in twenty, thirty, ..., ninety (0 for numbers less than
# 20 due to inconsistency in teens)
tens_counts = [0, 0, 6, 6, 5, 5, 5, 7, 6, 6]
count = 0
for i in range(1, n + 1):
if i < 1000:
if i >= 100:
# add number of letters for "n hundred"
count += ones_counts[i // 100] + 7
if i % 100 != 0:
# add number of letters for "and" if number is not multiple
# of 100
count += 3
if 0 < i % 100 < 20:
# add number of letters for one, two, three, ..., nineteen
# (could be combined with below if not for inconsistency in
# teens)
count += ones_counts[i % 100]
else:
# add number of letters for twenty, twenty one, ..., ninety
# nine
count += ones_counts[i % 10]
count += tens_counts[(i % 100 - i % 10) // 10]
else:
count += ones_counts[i // 1000] + 8
return count
if __name__ == "__main__":
print(solution(int(input().strip())))
| """
Number letter counts
Problem 17: https://projecteuler.net/problem=17
If the numbers 1 to 5 are written out in words: one, two, three, four, five,
then there are 3 + 3 + 5 + 4 + 4 = 19 letters used in total.
If all the numbers from 1 to 1000 (one thousand) inclusive were written out in
words, how many letters would be used?
NOTE: Do not count spaces or hyphens. For example, 342 (three hundred and
forty-two) contains 23 letters and 115 (one hundred and fifteen) contains 20
letters. The use of "and" when writing out numbers is in compliance withBritish
usage.
"""
def solution(n: int = 1000) -> int:
"""Returns the number of letters used to write all numbers from 1 to n.
where n is lower or equals to 1000.
>>> solution(1000)
21124
>>> solution(5)
19
"""
# number of letters in zero, one, two, ..., nineteen (0 for zero since it's
# never said aloud)
ones_counts = [0, 3, 3, 5, 4, 4, 3, 5, 5, 4, 3, 6, 6, 8, 8, 7, 7, 9, 8, 8]
# number of letters in twenty, thirty, ..., ninety (0 for numbers less than
# 20 due to inconsistency in teens)
tens_counts = [0, 0, 6, 6, 5, 5, 5, 7, 6, 6]
count = 0
for i in range(1, n + 1):
if i < 1000:
if i >= 100:
# add number of letters for "n hundred"
count += ones_counts[i // 100] + 7
if i % 100 != 0:
# add number of letters for "and" if number is not multiple
# of 100
count += 3
if 0 < i % 100 < 20:
# add number of letters for one, two, three, ..., nineteen
# (could be combined with below if not for inconsistency in
# teens)
count += ones_counts[i % 100]
else:
# add number of letters for twenty, twenty one, ..., ninety
# nine
count += ones_counts[i % 10]
count += tens_counts[(i % 100 - i % 10) // 10]
else:
count += ones_counts[i // 1000] + 8
return count
if __name__ == "__main__":
print(solution(int(input().strip())))
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class Node:
def __init__(self, data: Any):
self.data: Any = data
self.next: Node | None = None
class CircularLinkedList:
def __init__(self):
self.head = None
self.tail = None
def __iter__(self) -> Iterator[Any]:
node = self.head
while self.head:
yield node.data
node = node.next
if node == self.head:
break
def __len__(self) -> int:
return len(tuple(iter(self)))
def __repr__(self):
return "->".join(str(item) for item in iter(self))
def insert_tail(self, data: Any) -> None:
self.insert_nth(len(self), data)
def insert_head(self, data: Any) -> None:
self.insert_nth(0, data)
def insert_nth(self, index: int, data: Any) -> None:
if index < 0 or index > len(self):
raise IndexError("list index out of range.")
new_node = Node(data)
if self.head is None:
new_node.next = new_node # first node points itself
self.tail = self.head = new_node
elif index == 0: # insert at head
new_node.next = self.head
self.head = self.tail.next = new_node
else:
temp = self.head
for _ in range(index - 1):
temp = temp.next
new_node.next = temp.next
temp.next = new_node
if index == len(self) - 1: # insert at tail
self.tail = new_node
def delete_front(self):
return self.delete_nth(0)
def delete_tail(self) -> Any:
return self.delete_nth(len(self) - 1)
def delete_nth(self, index: int = 0) -> Any:
if not 0 <= index < len(self):
raise IndexError("list index out of range.")
delete_node = self.head
if self.head == self.tail: # just one node
self.head = self.tail = None
elif index == 0: # delete head node
self.tail.next = self.tail.next.next
self.head = self.head.next
else:
temp = self.head
for _ in range(index - 1):
temp = temp.next
delete_node = temp.next
temp.next = temp.next.next
if index == len(self) - 1: # delete at tail
self.tail = temp
return delete_node.data
def is_empty(self) -> bool:
return len(self) == 0
def test_circular_linked_list() -> None:
"""
>>> test_circular_linked_list()
"""
circular_linked_list = CircularLinkedList()
assert len(circular_linked_list) == 0
assert circular_linked_list.is_empty() is True
assert str(circular_linked_list) == ""
try:
circular_linked_list.delete_front()
raise AssertionError() # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError() # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1)
raise AssertionError()
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0)
raise AssertionError()
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5):
assert len(circular_linked_list) == i
circular_linked_list.insert_nth(i, i + 1)
assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 6))
circular_linked_list.insert_tail(6)
assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 7))
circular_linked_list.insert_head(0)
assert str(circular_linked_list) == "->".join(str(i) for i in range(0, 7))
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 6))
assert circular_linked_list.delete_nth(2) == 3
circular_linked_list.insert_nth(2, 3)
assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 6))
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod()
| from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class Node:
def __init__(self, data: Any):
self.data: Any = data
self.next: Node | None = None
class CircularLinkedList:
def __init__(self):
self.head = None
self.tail = None
def __iter__(self) -> Iterator[Any]:
node = self.head
while self.head:
yield node.data
node = node.next
if node == self.head:
break
def __len__(self) -> int:
return len(tuple(iter(self)))
def __repr__(self):
return "->".join(str(item) for item in iter(self))
def insert_tail(self, data: Any) -> None:
self.insert_nth(len(self), data)
def insert_head(self, data: Any) -> None:
self.insert_nth(0, data)
def insert_nth(self, index: int, data: Any) -> None:
if index < 0 or index > len(self):
raise IndexError("list index out of range.")
new_node = Node(data)
if self.head is None:
new_node.next = new_node # first node points itself
self.tail = self.head = new_node
elif index == 0: # insert at head
new_node.next = self.head
self.head = self.tail.next = new_node
else:
temp = self.head
for _ in range(index - 1):
temp = temp.next
new_node.next = temp.next
temp.next = new_node
if index == len(self) - 1: # insert at tail
self.tail = new_node
def delete_front(self):
return self.delete_nth(0)
def delete_tail(self) -> Any:
return self.delete_nth(len(self) - 1)
def delete_nth(self, index: int = 0) -> Any:
if not 0 <= index < len(self):
raise IndexError("list index out of range.")
delete_node = self.head
if self.head == self.tail: # just one node
self.head = self.tail = None
elif index == 0: # delete head node
self.tail.next = self.tail.next.next
self.head = self.head.next
else:
temp = self.head
for _ in range(index - 1):
temp = temp.next
delete_node = temp.next
temp.next = temp.next.next
if index == len(self) - 1: # delete at tail
self.tail = temp
return delete_node.data
def is_empty(self) -> bool:
return len(self) == 0
def test_circular_linked_list() -> None:
"""
>>> test_circular_linked_list()
"""
circular_linked_list = CircularLinkedList()
assert len(circular_linked_list) == 0
assert circular_linked_list.is_empty() is True
assert str(circular_linked_list) == ""
try:
circular_linked_list.delete_front()
raise AssertionError() # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError() # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1)
raise AssertionError()
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0)
raise AssertionError()
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5):
assert len(circular_linked_list) == i
circular_linked_list.insert_nth(i, i + 1)
assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 6))
circular_linked_list.insert_tail(6)
assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 7))
circular_linked_list.insert_head(0)
assert str(circular_linked_list) == "->".join(str(i) for i in range(0, 7))
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 6))
assert circular_linked_list.delete_nth(2) == 3
circular_linked_list.insert_nth(2, 3)
assert str(circular_linked_list) == "->".join(str(i) for i in range(1, 6))
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import math
"""
Finding the intensity of light transmitted through a polariser using Malus Law
and by taking initial intensity and angle between polariser and axis as input
Description : Malus's law, which is named after Étienne-Louis Malus,
says that when a perfect polarizer is placed in a polarized
beam of light, the irradiance, I, of the light that passes
through is given by
I=I'cos²θ
where I' is the initial intensity and θ is the angle between the light's
initial polarization direction and the axis of the polarizer.
A beam of unpolarized light can be thought of as containing a
uniform mixture of linear polarizations at all possible angles.
Since the average value of cos²θ is 1/2, the transmission coefficient becomes
I/I' = 1/2
In practice, some light is lost in the polarizer and the actual transmission
will be somewhat lower than this, around 38% for Polaroid-type polarizers but
considerably higher (>49.9%) for some birefringent prism types.
If two polarizers are placed one after another (the second polarizer is
generally called an analyzer), the mutual angle between their polarizing axes
gives the value of θ in Malus's law. If the two axes are orthogonal, the
polarizers are crossed and in theory no light is transmitted, though again
practically speaking no polarizer is perfect and the transmission is not exactly
zero (for example, crossed Polaroid sheets appear slightly blue in colour because
their extinction ratio is better in the red). If a transparent object is placed
between the crossed polarizers, any polarization effects present in the sample
(such as birefringence) will be shown as an increase in transmission.
This effect is used in polarimetry to measure the optical activity of a sample.
Real polarizers are also not perfect blockers of the polarization orthogonal to
their polarization axis; the ratio of the transmission of the unwanted component
to the wanted component is called the extinction ratio, and varies from around
1:500 for Polaroid to about 1:106 for Glan–Taylor prism polarizers.
Reference : "https://en.wikipedia.org/wiki/Polarizer#Malus's_law_and_other_properties"
"""
def malus_law(initial_intensity: float, angle: float) -> float:
"""
>>> round(malus_law(10,45),2)
5.0
>>> round(malus_law(100,60),2)
25.0
>>> round(malus_law(50,150),2)
37.5
>>> round(malus_law(75,270),2)
0.0
>>> round(malus_law(10,-900),2)
Traceback (most recent call last):
...
ValueError: In Malus Law, the angle is in the range 0-360 degrees
>>> round(malus_law(10,900),2)
Traceback (most recent call last):
...
ValueError: In Malus Law, the angle is in the range 0-360 degrees
>>> round(malus_law(-100,900),2)
Traceback (most recent call last):
...
ValueError: The value of intensity cannot be negative
>>> round(malus_law(100,180),2)
100.0
>>> round(malus_law(100,360),2)
100.0
"""
if initial_intensity < 0:
raise ValueError("The value of intensity cannot be negative")
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise ValueError("In Malus Law, the angle is in the range 0-360 degrees")
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(angle)) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name="malus_law")
| import math
"""
Finding the intensity of light transmitted through a polariser using Malus Law
and by taking initial intensity and angle between polariser and axis as input
Description : Malus's law, which is named after Étienne-Louis Malus,
says that when a perfect polarizer is placed in a polarized
beam of light, the irradiance, I, of the light that passes
through is given by
I=I'cos²θ
where I' is the initial intensity and θ is the angle between the light's
initial polarization direction and the axis of the polarizer.
A beam of unpolarized light can be thought of as containing a
uniform mixture of linear polarizations at all possible angles.
Since the average value of cos²θ is 1/2, the transmission coefficient becomes
I/I' = 1/2
In practice, some light is lost in the polarizer and the actual transmission
will be somewhat lower than this, around 38% for Polaroid-type polarizers but
considerably higher (>49.9%) for some birefringent prism types.
If two polarizers are placed one after another (the second polarizer is
generally called an analyzer), the mutual angle between their polarizing axes
gives the value of θ in Malus's law. If the two axes are orthogonal, the
polarizers are crossed and in theory no light is transmitted, though again
practically speaking no polarizer is perfect and the transmission is not exactly
zero (for example, crossed Polaroid sheets appear slightly blue in colour because
their extinction ratio is better in the red). If a transparent object is placed
between the crossed polarizers, any polarization effects present in the sample
(such as birefringence) will be shown as an increase in transmission.
This effect is used in polarimetry to measure the optical activity of a sample.
Real polarizers are also not perfect blockers of the polarization orthogonal to
their polarization axis; the ratio of the transmission of the unwanted component
to the wanted component is called the extinction ratio, and varies from around
1:500 for Polaroid to about 1:106 for Glan–Taylor prism polarizers.
Reference : "https://en.wikipedia.org/wiki/Polarizer#Malus's_law_and_other_properties"
"""
def malus_law(initial_intensity: float, angle: float) -> float:
"""
>>> round(malus_law(10,45),2)
5.0
>>> round(malus_law(100,60),2)
25.0
>>> round(malus_law(50,150),2)
37.5
>>> round(malus_law(75,270),2)
0.0
>>> round(malus_law(10,-900),2)
Traceback (most recent call last):
...
ValueError: In Malus Law, the angle is in the range 0-360 degrees
>>> round(malus_law(10,900),2)
Traceback (most recent call last):
...
ValueError: In Malus Law, the angle is in the range 0-360 degrees
>>> round(malus_law(-100,900),2)
Traceback (most recent call last):
...
ValueError: The value of intensity cannot be negative
>>> round(malus_law(100,180),2)
100.0
>>> round(malus_law(100,360),2)
100.0
"""
if initial_intensity < 0:
raise ValueError("The value of intensity cannot be negative")
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise ValueError("In Malus Law, the angle is in the range 0-360 degrees")
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(angle)) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name="malus_law")
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem 20: https://projecteuler.net/problem=20
n! means n × (n − 1) × ... × 3 × 2 × 1
For example, 10! = 10 × 9 × ... × 3 × 2 × 1 = 3628800,
and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27.
Find the sum of the digits in the number 100!
"""
from math import factorial
def solution(num: int = 100) -> int:
"""Returns the sum of the digits in the factorial of num
>>> solution(100)
648
>>> solution(50)
216
>>> solution(10)
27
>>> solution(5)
3
>>> solution(3)
6
>>> solution(2)
2
>>> solution(1)
1
"""
return sum(int(x) for x in str(factorial(num)))
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| """
Problem 20: https://projecteuler.net/problem=20
n! means n × (n − 1) × ... × 3 × 2 × 1
For example, 10! = 10 × 9 × ... × 3 × 2 × 1 = 3628800,
and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27.
Find the sum of the digits in the number 100!
"""
from math import factorial
def solution(num: int = 100) -> int:
"""Returns the sum of the digits in the factorial of num
>>> solution(100)
648
>>> solution(50)
216
>>> solution(10)
27
>>> solution(5)
3
>>> solution(3)
6
>>> solution(2)
2
>>> solution(1)
1
"""
return sum(int(x) for x in str(factorial(num)))
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Uses Pythagoras theorem to calculate the distance between two points in space."""
import math
class Point:
def __init__(self, x, y, z):
self.x = x
self.y = y
self.z = z
def __repr__(self) -> str:
return f"Point({self.x}, {self.y}, {self.z})"
def distance(a: Point, b: Point) -> float:
return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2))
def test_distance() -> None:
"""
>>> point1 = Point(2, -1, 7)
>>> point2 = Point(1, -3, 5)
>>> print(f"Distance from {point1} to {point2} is {distance(point1, point2)}")
Distance from Point(2, -1, 7) to Point(1, -3, 5) is 3.0
"""
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| """Uses Pythagoras theorem to calculate the distance between two points in space."""
import math
class Point:
def __init__(self, x, y, z):
self.x = x
self.y = y
self.z = z
def __repr__(self) -> str:
return f"Point({self.x}, {self.y}, {self.z})"
def distance(a: Point, b: Point) -> float:
return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2))
def test_distance() -> None:
"""
>>> point1 = Point(2, -1, 7)
>>> point2 = Point(1, -3, 5)
>>> print(f"Distance from {point1} to {point2} is {distance(point1, point2)}")
Distance from Point(2, -1, 7) to Point(1, -3, 5) is 3.0
"""
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
from collections.abc import Iterable
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")
| 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 | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://cp-algorithms.com/string/z-function.html
Z-function or Z algorithm
Efficient algorithm for pattern occurrence in a string
Time Complexity: O(n) - where n is the length of the string
"""
def z_function(input_str: str) -> list[int]:
"""
For the given string this function computes value for each index,
which represents the maximal length substring starting from the index
and is the same as the prefix of the same size
e.x. for string 'abab' for second index value would be 2
For the value of the first element the algorithm always returns 0
>>> z_function("abracadabra")
[0, 0, 0, 1, 0, 1, 0, 4, 0, 0, 1]
>>> z_function("aaaa")
[0, 3, 2, 1]
>>> z_function("zxxzxxz")
[0, 0, 0, 4, 0, 0, 1]
"""
z_result = [0 for i in range(len(input_str))]
# initialize interval's left pointer and right pointer
left_pointer, right_pointer = 0, 0
for i in range(1, len(input_str)):
# case when current index is inside the interval
if i <= right_pointer:
min_edge = min(right_pointer - i + 1, z_result[i - left_pointer])
z_result[i] = min_edge
while go_next(i, z_result, input_str):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
left_pointer, right_pointer = i, i + z_result[i] - 1
return z_result
def go_next(i: int, z_result: list[int], s: str) -> bool:
"""
Check if we have to move forward to the next characters or not
"""
return i + z_result[i] < len(s) and s[z_result[i]] == s[i + z_result[i]]
def find_pattern(pattern: str, input_str: str) -> int:
"""
Example of using z-function for pattern occurrence
Given function returns the number of times 'pattern'
appears in 'input_str' as a substring
>>> find_pattern("abr", "abracadabra")
2
>>> find_pattern("a", "aaaa")
4
>>> find_pattern("xz", "zxxzxxz")
2
"""
answer = 0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
z_result = z_function(pattern + input_str)
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(pattern):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
https://cp-algorithms.com/string/z-function.html
Z-function or Z algorithm
Efficient algorithm for pattern occurrence in a string
Time Complexity: O(n) - where n is the length of the string
"""
def z_function(input_str: str) -> list[int]:
"""
For the given string this function computes value for each index,
which represents the maximal length substring starting from the index
and is the same as the prefix of the same size
e.x. for string 'abab' for second index value would be 2
For the value of the first element the algorithm always returns 0
>>> z_function("abracadabra")
[0, 0, 0, 1, 0, 1, 0, 4, 0, 0, 1]
>>> z_function("aaaa")
[0, 3, 2, 1]
>>> z_function("zxxzxxz")
[0, 0, 0, 4, 0, 0, 1]
"""
z_result = [0 for i in range(len(input_str))]
# initialize interval's left pointer and right pointer
left_pointer, right_pointer = 0, 0
for i in range(1, len(input_str)):
# case when current index is inside the interval
if i <= right_pointer:
min_edge = min(right_pointer - i + 1, z_result[i - left_pointer])
z_result[i] = min_edge
while go_next(i, z_result, input_str):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
left_pointer, right_pointer = i, i + z_result[i] - 1
return z_result
def go_next(i: int, z_result: list[int], s: str) -> bool:
"""
Check if we have to move forward to the next characters or not
"""
return i + z_result[i] < len(s) and s[z_result[i]] == s[i + z_result[i]]
def find_pattern(pattern: str, input_str: str) -> int:
"""
Example of using z-function for pattern occurrence
Given function returns the number of times 'pattern'
appears in 'input_str' as a substring
>>> find_pattern("abr", "abracadabra")
2
>>> find_pattern("a", "aaaa")
4
>>> find_pattern("xz", "zxxzxxz")
2
"""
answer = 0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
z_result = z_function(pattern + input_str)
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(pattern):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| B64_CHARSET = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def base64_encode(data: bytes) -> bytes:
"""Encodes data according to RFC4648.
The data is first transformed to binary and appended with binary digits so that its
length becomes a multiple of 6, then each 6 binary digits will match a character in
the B64_CHARSET string. The number of appended binary digits would later determine
how many "=" signs should be added, the padding.
For every 2 binary digits added, a "=" sign is added in the output.
We can add any binary digits to make it a multiple of 6, for instance, consider the
following example:
"AA" -> 0010100100101001 -> 001010 010010 1001
As can be seen above, 2 more binary digits should be added, so there's 4
possibilities here: 00, 01, 10 or 11.
That being said, Base64 encoding can be used in Steganography to hide data in these
appended digits.
>>> from base64 import b64encode
>>> a = b"This pull request is part of Hacktoberfest20!"
>>> b = b"https://tools.ietf.org/html/rfc4648"
>>> c = b"A"
>>> base64_encode(a) == b64encode(a)
True
>>> base64_encode(b) == b64encode(b)
True
>>> base64_encode(c) == b64encode(c)
True
>>> base64_encode("abc")
Traceback (most recent call last):
...
TypeError: a bytes-like object is required, not 'str'
"""
# Make sure the supplied data is a bytes-like object
if not isinstance(data, bytes):
raise TypeError(
f"a bytes-like object is required, not '{data.__class__.__name__}'"
)
binary_stream = "".join(bin(byte)[2:].zfill(8) for byte in data)
padding_needed = len(binary_stream) % 6 != 0
if padding_needed:
# The padding that will be added later
padding = b"=" * ((6 - len(binary_stream) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(binary_stream) % 6)
else:
padding = b""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6], 2)]
for index in range(0, len(binary_stream), 6)
).encode()
+ padding
)
def base64_decode(encoded_data: str) -> bytes:
"""Decodes data according to RFC4648.
This does the reverse operation of base64_encode.
We first transform the encoded data back to a binary stream, take off the
previously appended binary digits according to the padding, at this point we
would have a binary stream whose length is multiple of 8, the last step is
to convert every 8 bits to a byte.
>>> from base64 import b64decode
>>> a = "VGhpcyBwdWxsIHJlcXVlc3QgaXMgcGFydCBvZiBIYWNrdG9iZXJmZXN0MjAh"
>>> b = "aHR0cHM6Ly90b29scy5pZXRmLm9yZy9odG1sL3JmYzQ2NDg="
>>> c = "QQ=="
>>> base64_decode(a) == b64decode(a)
True
>>> base64_decode(b) == b64decode(b)
True
>>> base64_decode(c) == b64decode(c)
True
>>> base64_decode("abc")
Traceback (most recent call last):
...
AssertionError: Incorrect padding
"""
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(encoded_data, bytes) and not isinstance(encoded_data, str):
raise TypeError(
"argument should be a bytes-like object or ASCII string, not "
f"'{encoded_data.__class__.__name__}'"
)
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(encoded_data, bytes):
try:
encoded_data = encoded_data.decode("utf-8")
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters")
padding = encoded_data.count("=")
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding]
), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data
), "Invalid base64 character(s) found."
# Check the padding
assert len(encoded_data) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
encoded_data = encoded_data[:-padding]
binary_stream = "".join(
bin(B64_CHARSET.index(char))[2:].zfill(6) for char in encoded_data
)[: -padding * 2]
else:
binary_stream = "".join(
bin(B64_CHARSET.index(char))[2:].zfill(6) for char in encoded_data
)
data = [
int(binary_stream[index : index + 8], 2)
for index in range(0, len(binary_stream), 8)
]
return bytes(data)
if __name__ == "__main__":
import doctest
doctest.testmod()
| B64_CHARSET = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def base64_encode(data: bytes) -> bytes:
"""Encodes data according to RFC4648.
The data is first transformed to binary and appended with binary digits so that its
length becomes a multiple of 6, then each 6 binary digits will match a character in
the B64_CHARSET string. The number of appended binary digits would later determine
how many "=" signs should be added, the padding.
For every 2 binary digits added, a "=" sign is added in the output.
We can add any binary digits to make it a multiple of 6, for instance, consider the
following example:
"AA" -> 0010100100101001 -> 001010 010010 1001
As can be seen above, 2 more binary digits should be added, so there's 4
possibilities here: 00, 01, 10 or 11.
That being said, Base64 encoding can be used in Steganography to hide data in these
appended digits.
>>> from base64 import b64encode
>>> a = b"This pull request is part of Hacktoberfest20!"
>>> b = b"https://tools.ietf.org/html/rfc4648"
>>> c = b"A"
>>> base64_encode(a) == b64encode(a)
True
>>> base64_encode(b) == b64encode(b)
True
>>> base64_encode(c) == b64encode(c)
True
>>> base64_encode("abc")
Traceback (most recent call last):
...
TypeError: a bytes-like object is required, not 'str'
"""
# Make sure the supplied data is a bytes-like object
if not isinstance(data, bytes):
raise TypeError(
f"a bytes-like object is required, not '{data.__class__.__name__}'"
)
binary_stream = "".join(bin(byte)[2:].zfill(8) for byte in data)
padding_needed = len(binary_stream) % 6 != 0
if padding_needed:
# The padding that will be added later
padding = b"=" * ((6 - len(binary_stream) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(binary_stream) % 6)
else:
padding = b""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6], 2)]
for index in range(0, len(binary_stream), 6)
).encode()
+ padding
)
def base64_decode(encoded_data: str) -> bytes:
"""Decodes data according to RFC4648.
This does the reverse operation of base64_encode.
We first transform the encoded data back to a binary stream, take off the
previously appended binary digits according to the padding, at this point we
would have a binary stream whose length is multiple of 8, the last step is
to convert every 8 bits to a byte.
>>> from base64 import b64decode
>>> a = "VGhpcyBwdWxsIHJlcXVlc3QgaXMgcGFydCBvZiBIYWNrdG9iZXJmZXN0MjAh"
>>> b = "aHR0cHM6Ly90b29scy5pZXRmLm9yZy9odG1sL3JmYzQ2NDg="
>>> c = "QQ=="
>>> base64_decode(a) == b64decode(a)
True
>>> base64_decode(b) == b64decode(b)
True
>>> base64_decode(c) == b64decode(c)
True
>>> base64_decode("abc")
Traceback (most recent call last):
...
AssertionError: Incorrect padding
"""
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(encoded_data, bytes) and not isinstance(encoded_data, str):
raise TypeError(
"argument should be a bytes-like object or ASCII string, not "
f"'{encoded_data.__class__.__name__}'"
)
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(encoded_data, bytes):
try:
encoded_data = encoded_data.decode("utf-8")
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters")
padding = encoded_data.count("=")
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding]
), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data
), "Invalid base64 character(s) found."
# Check the padding
assert len(encoded_data) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
encoded_data = encoded_data[:-padding]
binary_stream = "".join(
bin(B64_CHARSET.index(char))[2:].zfill(6) for char in encoded_data
)[: -padding * 2]
else:
binary_stream = "".join(
bin(B64_CHARSET.index(char))[2:].zfill(6) for char in encoded_data
)
data = [
int(binary_stream[index : index + 8], 2)
for index in range(0, len(binary_stream), 8)
]
return bytes(data)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import cv2
import numpy as np
def get_neighbors_pixel(
image: np.ndarray, x_coordinate: int, y_coordinate: int, center: int
) -> int:
"""
Comparing local neighborhood pixel value with threshold value of centre pixel.
Exception is required when neighborhood value of a center pixel value is null.
i.e. values present at boundaries.
:param image: The image we're working with
:param x_coordinate: x-coordinate of the pixel
:param y_coordinate: The y coordinate of the pixel
:param center: center pixel value
:return: The value of the pixel is being returned.
"""
try:
return int(image[x_coordinate][y_coordinate] >= center)
except (IndexError, TypeError):
return 0
def local_binary_value(image: np.ndarray, x_coordinate: int, y_coordinate: int) -> int:
"""
It takes an image, an x and y coordinate, and returns the
decimal value of the local binary patternof the pixel
at that coordinate
:param image: the image to be processed
:param x_coordinate: x coordinate of the pixel
:param y_coordinate: the y coordinate of the pixel
:return: The decimal value of the binary value of the pixels
around the center pixel.
"""
center = image[x_coordinate][y_coordinate]
powers = [1, 2, 4, 8, 16, 32, 64, 128]
# skip get_neighbors_pixel if center is null
if center is None:
return 0
# Starting from the top right, assigning value to pixels clockwise
binary_values = [
get_neighbors_pixel(image, x_coordinate - 1, y_coordinate + 1, center),
get_neighbors_pixel(image, x_coordinate, y_coordinate + 1, center),
get_neighbors_pixel(image, x_coordinate - 1, y_coordinate, center),
get_neighbors_pixel(image, x_coordinate + 1, y_coordinate + 1, center),
get_neighbors_pixel(image, x_coordinate + 1, y_coordinate, center),
get_neighbors_pixel(image, x_coordinate + 1, y_coordinate - 1, center),
get_neighbors_pixel(image, x_coordinate, y_coordinate - 1, center),
get_neighbors_pixel(image, x_coordinate - 1, y_coordinate - 1, center),
]
# Converting the binary value to decimal.
return sum(
binary_value * power for binary_value, power in zip(binary_values, powers)
)
if __name__ == "__main__":
# Reading the image and converting it to grayscale.
image = cv2.imread(
"digital_image_processing/image_data/lena.jpg", cv2.IMREAD_GRAYSCALE
)
# Create a numpy array as the same height and width of read image
lbp_image = np.zeros((image.shape[0], image.shape[1]))
# Iterating through the image and calculating the
# local binary pattern value for each pixel.
for i in range(0, image.shape[0]):
for j in range(0, image.shape[1]):
lbp_image[i][j] = local_binary_value(image, i, j)
cv2.imshow("local binary pattern", lbp_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
| import cv2
import numpy as np
def get_neighbors_pixel(
image: np.ndarray, x_coordinate: int, y_coordinate: int, center: int
) -> int:
"""
Comparing local neighborhood pixel value with threshold value of centre pixel.
Exception is required when neighborhood value of a center pixel value is null.
i.e. values present at boundaries.
:param image: The image we're working with
:param x_coordinate: x-coordinate of the pixel
:param y_coordinate: The y coordinate of the pixel
:param center: center pixel value
:return: The value of the pixel is being returned.
"""
try:
return int(image[x_coordinate][y_coordinate] >= center)
except (IndexError, TypeError):
return 0
def local_binary_value(image: np.ndarray, x_coordinate: int, y_coordinate: int) -> int:
"""
It takes an image, an x and y coordinate, and returns the
decimal value of the local binary patternof the pixel
at that coordinate
:param image: the image to be processed
:param x_coordinate: x coordinate of the pixel
:param y_coordinate: the y coordinate of the pixel
:return: The decimal value of the binary value of the pixels
around the center pixel.
"""
center = image[x_coordinate][y_coordinate]
powers = [1, 2, 4, 8, 16, 32, 64, 128]
# skip get_neighbors_pixel if center is null
if center is None:
return 0
# Starting from the top right, assigning value to pixels clockwise
binary_values = [
get_neighbors_pixel(image, x_coordinate - 1, y_coordinate + 1, center),
get_neighbors_pixel(image, x_coordinate, y_coordinate + 1, center),
get_neighbors_pixel(image, x_coordinate - 1, y_coordinate, center),
get_neighbors_pixel(image, x_coordinate + 1, y_coordinate + 1, center),
get_neighbors_pixel(image, x_coordinate + 1, y_coordinate, center),
get_neighbors_pixel(image, x_coordinate + 1, y_coordinate - 1, center),
get_neighbors_pixel(image, x_coordinate, y_coordinate - 1, center),
get_neighbors_pixel(image, x_coordinate - 1, y_coordinate - 1, center),
]
# Converting the binary value to decimal.
return sum(
binary_value * power for binary_value, power in zip(binary_values, powers)
)
if __name__ == "__main__":
# Reading the image and converting it to grayscale.
image = cv2.imread(
"digital_image_processing/image_data/lena.jpg", cv2.IMREAD_GRAYSCALE
)
# Create a numpy array as the same height and width of read image
lbp_image = np.zeros((image.shape[0], image.shape[1]))
# Iterating through the image and calculating the
# local binary pattern value for each pixel.
for i in range(0, image.shape[0]):
for j in range(0, image.shape[1]):
lbp_image[i][j] = local_binary_value(image, i, j)
cv2.imshow("local binary pattern", lbp_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import math
from timeit import timeit
def num_digits(n: int) -> int:
"""
Find the number of digits in a number.
>>> num_digits(12345)
5
>>> num_digits(123)
3
>>> num_digits(0)
1
>>> num_digits(-1)
1
>>> num_digits(-123456)
6
"""
digits = 0
n = abs(n)
while True:
n = n // 10
digits += 1
if n == 0:
break
return digits
def num_digits_fast(n: int) -> int:
"""
Find the number of digits in a number.
abs() is used as logarithm for negative numbers is not defined.
>>> num_digits_fast(12345)
5
>>> num_digits_fast(123)
3
>>> num_digits_fast(0)
1
>>> num_digits_fast(-1)
1
>>> num_digits_fast(-123456)
6
"""
return 1 if n == 0 else math.floor(math.log(abs(n), 10) + 1)
def num_digits_faster(n: int) -> int:
"""
Find the number of digits in a number.
abs() is used for negative numbers
>>> num_digits_faster(12345)
5
>>> num_digits_faster(123)
3
>>> num_digits_faster(0)
1
>>> num_digits_faster(-1)
1
>>> num_digits_faster(-123456)
6
"""
return len(str(abs(n)))
def benchmark() -> None:
"""
Benchmark code for comparing 3 functions,
with 3 different length int values.
"""
print("\nFor small_num = ", small_num, ":")
print(
"> num_digits()",
"\t\tans =",
num_digits(small_num),
"\ttime =",
timeit("z.num_digits(z.small_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_fast()",
"\tans =",
num_digits_fast(small_num),
"\ttime =",
timeit("z.num_digits_fast(z.small_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_faster()",
"\tans =",
num_digits_faster(small_num),
"\ttime =",
timeit("z.num_digits_faster(z.small_num)", setup="import __main__ as z"),
"seconds",
)
print("\nFor medium_num = ", medium_num, ":")
print(
"> num_digits()",
"\t\tans =",
num_digits(medium_num),
"\ttime =",
timeit("z.num_digits(z.medium_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_fast()",
"\tans =",
num_digits_fast(medium_num),
"\ttime =",
timeit("z.num_digits_fast(z.medium_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_faster()",
"\tans =",
num_digits_faster(medium_num),
"\ttime =",
timeit("z.num_digits_faster(z.medium_num)", setup="import __main__ as z"),
"seconds",
)
print("\nFor large_num = ", large_num, ":")
print(
"> num_digits()",
"\t\tans =",
num_digits(large_num),
"\ttime =",
timeit("z.num_digits(z.large_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_fast()",
"\tans =",
num_digits_fast(large_num),
"\ttime =",
timeit("z.num_digits_fast(z.large_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_faster()",
"\tans =",
num_digits_faster(large_num),
"\ttime =",
timeit("z.num_digits_faster(z.large_num)", setup="import __main__ as z"),
"seconds",
)
if __name__ == "__main__":
small_num = 262144
medium_num = 1125899906842624
large_num = 1267650600228229401496703205376
benchmark()
import doctest
doctest.testmod()
| import math
from timeit import timeit
def num_digits(n: int) -> int:
"""
Find the number of digits in a number.
>>> num_digits(12345)
5
>>> num_digits(123)
3
>>> num_digits(0)
1
>>> num_digits(-1)
1
>>> num_digits(-123456)
6
"""
digits = 0
n = abs(n)
while True:
n = n // 10
digits += 1
if n == 0:
break
return digits
def num_digits_fast(n: int) -> int:
"""
Find the number of digits in a number.
abs() is used as logarithm for negative numbers is not defined.
>>> num_digits_fast(12345)
5
>>> num_digits_fast(123)
3
>>> num_digits_fast(0)
1
>>> num_digits_fast(-1)
1
>>> num_digits_fast(-123456)
6
"""
return 1 if n == 0 else math.floor(math.log(abs(n), 10) + 1)
def num_digits_faster(n: int) -> int:
"""
Find the number of digits in a number.
abs() is used for negative numbers
>>> num_digits_faster(12345)
5
>>> num_digits_faster(123)
3
>>> num_digits_faster(0)
1
>>> num_digits_faster(-1)
1
>>> num_digits_faster(-123456)
6
"""
return len(str(abs(n)))
def benchmark() -> None:
"""
Benchmark code for comparing 3 functions,
with 3 different length int values.
"""
print("\nFor small_num = ", small_num, ":")
print(
"> num_digits()",
"\t\tans =",
num_digits(small_num),
"\ttime =",
timeit("z.num_digits(z.small_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_fast()",
"\tans =",
num_digits_fast(small_num),
"\ttime =",
timeit("z.num_digits_fast(z.small_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_faster()",
"\tans =",
num_digits_faster(small_num),
"\ttime =",
timeit("z.num_digits_faster(z.small_num)", setup="import __main__ as z"),
"seconds",
)
print("\nFor medium_num = ", medium_num, ":")
print(
"> num_digits()",
"\t\tans =",
num_digits(medium_num),
"\ttime =",
timeit("z.num_digits(z.medium_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_fast()",
"\tans =",
num_digits_fast(medium_num),
"\ttime =",
timeit("z.num_digits_fast(z.medium_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_faster()",
"\tans =",
num_digits_faster(medium_num),
"\ttime =",
timeit("z.num_digits_faster(z.medium_num)", setup="import __main__ as z"),
"seconds",
)
print("\nFor large_num = ", large_num, ":")
print(
"> num_digits()",
"\t\tans =",
num_digits(large_num),
"\ttime =",
timeit("z.num_digits(z.large_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_fast()",
"\tans =",
num_digits_fast(large_num),
"\ttime =",
timeit("z.num_digits_fast(z.large_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_faster()",
"\tans =",
num_digits_faster(large_num),
"\ttime =",
timeit("z.num_digits_faster(z.large_num)", setup="import __main__ as z"),
"seconds",
)
if __name__ == "__main__":
small_num = 262144
medium_num = 1125899906842624
large_num = 1267650600228229401496703205376
benchmark()
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Prize Strings
Problem 191
A particular school offers cash rewards to children with good attendance and
punctuality. If they are absent for three consecutive days or late on more
than one occasion then they forfeit their prize.
During an n-day period a trinary string is formed for each child consisting
of L's (late), O's (on time), and A's (absent).
Although there are eighty-one trinary strings for a 4-day period that can be
formed, exactly forty-three strings would lead to a prize:
OOOO OOOA OOOL OOAO OOAA OOAL OOLO OOLA OAOO OAOA
OAOL OAAO OAAL OALO OALA OLOO OLOA OLAO OLAA AOOO
AOOA AOOL AOAO AOAA AOAL AOLO AOLA AAOO AAOA AAOL
AALO AALA ALOO ALOA ALAO ALAA LOOO LOOA LOAO LOAA
LAOO LAOA LAAO
How many "prize" strings exist over a 30-day period?
References:
- The original Project Euler project page:
https://projecteuler.net/problem=191
"""
cache: dict[tuple[int, int, int], int] = {}
def _calculate(days: int, absent: int, late: int) -> int:
"""
A small helper function for the recursion, mainly to have
a clean interface for the solution() function below.
It should get called with the number of days (corresponding
to the desired length of the 'prize strings'), and the
initial values for the number of consecutive absent days and
number of total late days.
>>> _calculate(days=4, absent=0, late=0)
43
>>> _calculate(days=30, absent=2, late=0)
0
>>> _calculate(days=30, absent=1, late=0)
98950096
"""
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
key = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
state_late = _calculate(days - 1, absent, late + 1)
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
state_absent = _calculate(days - 1, absent + 1, 0)
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
state_ontime = _calculate(days - 1, absent, 0)
prizestrings = state_late + state_absent + state_ontime
cache[key] = prizestrings
return prizestrings
def solution(days: int = 30) -> int:
"""
Returns the number of possible prize strings for a particular number
of days, using a simple recursive function with caching to speed it up.
>>> solution()
1918080160
>>> solution(4)
43
"""
return _calculate(days, absent=0, late=0)
if __name__ == "__main__":
print(solution())
| """
Prize Strings
Problem 191
A particular school offers cash rewards to children with good attendance and
punctuality. If they are absent for three consecutive days or late on more
than one occasion then they forfeit their prize.
During an n-day period a trinary string is formed for each child consisting
of L's (late), O's (on time), and A's (absent).
Although there are eighty-one trinary strings for a 4-day period that can be
formed, exactly forty-three strings would lead to a prize:
OOOO OOOA OOOL OOAO OOAA OOAL OOLO OOLA OAOO OAOA
OAOL OAAO OAAL OALO OALA OLOO OLOA OLAO OLAA AOOO
AOOA AOOL AOAO AOAA AOAL AOLO AOLA AAOO AAOA AAOL
AALO AALA ALOO ALOA ALAO ALAA LOOO LOOA LOAO LOAA
LAOO LAOA LAAO
How many "prize" strings exist over a 30-day period?
References:
- The original Project Euler project page:
https://projecteuler.net/problem=191
"""
cache: dict[tuple[int, int, int], int] = {}
def _calculate(days: int, absent: int, late: int) -> int:
"""
A small helper function for the recursion, mainly to have
a clean interface for the solution() function below.
It should get called with the number of days (corresponding
to the desired length of the 'prize strings'), and the
initial values for the number of consecutive absent days and
number of total late days.
>>> _calculate(days=4, absent=0, late=0)
43
>>> _calculate(days=30, absent=2, late=0)
0
>>> _calculate(days=30, absent=1, late=0)
98950096
"""
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
key = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
state_late = _calculate(days - 1, absent, late + 1)
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
state_absent = _calculate(days - 1, absent + 1, 0)
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
state_ontime = _calculate(days - 1, absent, 0)
prizestrings = state_late + state_absent + state_ontime
cache[key] = prizestrings
return prizestrings
def solution(days: int = 30) -> int:
"""
Returns the number of possible prize strings for a particular number
of days, using a simple recursive function with caching to speed it up.
>>> solution()
1918080160
>>> solution(4)
43
"""
return _calculate(days, absent=0, late=0)
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import shutil
import requests
def get_apod_data(api_key: str, download: bool = False, path: str = ".") -> dict:
"""
Get the APOD(Astronomical Picture of the day) data
Get your API Key from: https://api.nasa.gov/
"""
url = "https://api.nasa.gov/planetary/apod"
return requests.get(url, params={"api_key": api_key}).json()
def save_apod(api_key: str, path: str = ".") -> dict:
apod_data = get_apod_data(api_key)
img_url = apod_data["url"]
img_name = img_url.split("/")[-1]
response = requests.get(img_url, stream=True)
with open(f"{path}/{img_name}", "wb+") as img_file:
shutil.copyfileobj(response.raw, img_file)
del response
return apod_data
def get_archive_data(query: str) -> dict:
"""
Get the data of a particular query from NASA archives
"""
url = "https://images-api.nasa.gov/search"
return requests.get(url, params={"q": query}).json()
if __name__ == "__main__":
print(save_apod("YOUR API KEY"))
apollo_2011_items = get_archive_data("apollo 2011")["collection"]["items"]
print(apollo_2011_items[0]["data"][0]["description"])
| import shutil
import requests
def get_apod_data(api_key: str, download: bool = False, path: str = ".") -> dict:
"""
Get the APOD(Astronomical Picture of the day) data
Get your API Key from: https://api.nasa.gov/
"""
url = "https://api.nasa.gov/planetary/apod"
return requests.get(url, params={"api_key": api_key}).json()
def save_apod(api_key: str, path: str = ".") -> dict:
apod_data = get_apod_data(api_key)
img_url = apod_data["url"]
img_name = img_url.split("/")[-1]
response = requests.get(img_url, stream=True)
with open(f"{path}/{img_name}", "wb+") as img_file:
shutil.copyfileobj(response.raw, img_file)
del response
return apod_data
def get_archive_data(query: str) -> dict:
"""
Get the data of a particular query from NASA archives
"""
url = "https://images-api.nasa.gov/search"
return requests.get(url, params={"q": query}).json()
if __name__ == "__main__":
print(save_apod("YOUR API KEY"))
apollo_2011_items = get_archive_data("apollo 2011")["collection"]["items"]
print(apollo_2011_items[0]["data"][0]["description"])
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Based on "Skip Lists: A Probabilistic Alternative to Balanced Trees" by William Pugh
https://epaperpress.com/sortsearch/download/skiplist.pdf
"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
KT = TypeVar("KT")
VT = TypeVar("VT")
class Node(Generic[KT, VT]):
def __init__(self, key: KT | str = "root", value: VT | None = None):
self.key = key
self.value = value
self.forward: list[Node[KT, VT]] = []
def __repr__(self) -> str:
"""
:return: Visual representation of Node
>>> node = Node("Key", 2)
>>> repr(node)
'Node(Key: 2)'
"""
return f"Node({self.key}: {self.value})"
@property
def level(self) -> int:
"""
:return: Number of forward references
>>> node = Node("Key", 2)
>>> node.level
0
>>> node.forward.append(Node("Key2", 4))
>>> node.level
1
>>> node.forward.append(Node("Key3", 6))
>>> node.level
2
"""
return len(self.forward)
class SkipList(Generic[KT, VT]):
def __init__(self, p: float = 0.5, max_level: int = 16):
self.head: Node[KT, VT] = Node[KT, VT]()
self.level = 0
self.p = p
self.max_level = max_level
def __str__(self) -> str:
"""
:return: Visual representation of SkipList
>>> skip_list = SkipList()
>>> print(skip_list)
SkipList(level=0)
>>> skip_list.insert("Key1", "Value")
>>> print(skip_list) # doctest: +ELLIPSIS
SkipList(level=...
[root]--...
[Key1]--Key1...
None *...
>>> skip_list.insert("Key2", "OtherValue")
>>> print(skip_list) # doctest: +ELLIPSIS
SkipList(level=...
[root]--...
[Key1]--Key1...
[Key2]--Key2...
None *...
"""
items = list(self)
if len(items) == 0:
return f"SkipList(level={self.level})"
label_size = max((len(str(item)) for item in items), default=4)
label_size = max(label_size, 4) + 4
node = self.head
lines = []
forwards = node.forward.copy()
lines.append(f"[{node.key}]".ljust(label_size, "-") + "* " * len(forwards))
lines.append(" " * label_size + "| " * len(forwards))
while len(node.forward) != 0:
node = node.forward[0]
lines.append(
f"[{node.key}]".ljust(label_size, "-")
+ " ".join(str(n.key) if n.key == node.key else "|" for n in forwards)
)
lines.append(" " * label_size + "| " * len(forwards))
forwards[: node.level] = node.forward
lines.append("None".ljust(label_size) + "* " * len(forwards))
return f"SkipList(level={self.level})\n" + "\n".join(lines)
def __iter__(self):
node = self.head
while len(node.forward) != 0:
yield node.forward[0].key
node = node.forward[0]
def random_level(self) -> int:
"""
:return: Random level from [1, self.max_level] interval.
Higher values are less likely.
"""
level = 1
while random() < self.p and level < self.max_level:
level += 1
return level
def _locate_node(self, key) -> tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:
"""
:param key: Searched key,
:return: Tuple with searched node (or None if given key is not present)
and list of nodes that refer (if key is present) of should refer to
given node.
"""
# Nodes with refer or should refer to output node
update_vector = []
node = self.head
for i in reversed(range(self.level)):
# i < node.level - When node level is lesser than `i` decrement `i`.
# node.forward[i].key < key - Jumping to node with key value higher
# or equal to searched key would result
# in skipping searched key.
while i < node.level and node.forward[i].key < key:
node = node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(node)
update_vector.reverse() # Note that we were inserting values in reverse order.
# len(node.forward) != 0 - If current node doesn't contain any further
# references then searched key is not present.
# node.forward[0].key == key - Next node key should be equal to search key
# if key is present.
if len(node.forward) != 0 and node.forward[0].key == key:
return node.forward[0], update_vector
else:
return None, update_vector
def delete(self, key: KT):
"""
:param key: Key to remove from list.
>>> skip_list = SkipList()
>>> skip_list.insert(2, "Two")
>>> skip_list.insert(1, "One")
>>> skip_list.insert(3, "Three")
>>> list(skip_list)
[1, 2, 3]
>>> skip_list.delete(2)
>>> list(skip_list)
[1, 3]
"""
node, update_vector = self._locate_node(key)
if node is not None:
for i, update_node in enumerate(update_vector):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
update_node.forward[i] = node.forward[i]
else:
update_node.forward = update_node.forward[:i]
def insert(self, key: KT, value: VT):
"""
:param key: Key to insert.
:param value: Value associated with given key.
>>> skip_list = SkipList()
>>> skip_list.insert(2, "Two")
>>> skip_list.find(2)
'Two'
>>> list(skip_list)
[2]
"""
node, update_vector = self._locate_node(key)
if node is not None:
node.value = value
else:
level = self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for _ in range(self.level - 1, level):
update_vector.append(self.head)
self.level = level
new_node = Node(key, value)
for i, update_node in enumerate(update_vector[:level]):
# Change references to pass through new node.
if update_node.level > i:
new_node.forward.append(update_node.forward[i])
if update_node.level < i + 1:
update_node.forward.append(new_node)
else:
update_node.forward[i] = new_node
def find(self, key: VT) -> VT | None:
"""
:param key: Search key.
:return: Value associated with given key or None if given key is not present.
>>> skip_list = SkipList()
>>> skip_list.find(2)
>>> skip_list.insert(2, "Two")
>>> skip_list.find(2)
'Two'
>>> skip_list.insert(2, "Three")
>>> skip_list.find(2)
'Three'
"""
node, _ = self._locate_node(key)
if node is not None:
return node.value
return None
def test_insert():
skip_list = SkipList()
skip_list.insert("Key1", 3)
skip_list.insert("Key2", 12)
skip_list.insert("Key3", 41)
skip_list.insert("Key4", -19)
node = skip_list.head
all_values = {}
while node.level != 0:
node = node.forward[0]
all_values[node.key] = node.value
assert len(all_values) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 12
assert all_values["Key3"] == 41
assert all_values["Key4"] == -19
def test_insert_overrides_existing_value():
skip_list = SkipList()
skip_list.insert("Key1", 10)
skip_list.insert("Key1", 12)
skip_list.insert("Key5", 7)
skip_list.insert("Key7", 10)
skip_list.insert("Key10", 5)
skip_list.insert("Key7", 7)
skip_list.insert("Key5", 5)
skip_list.insert("Key10", 10)
node = skip_list.head
all_values = {}
while node.level != 0:
node = node.forward[0]
all_values[node.key] = node.value
if len(all_values) != 4:
print()
assert len(all_values) == 4
assert all_values["Key1"] == 12
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 10
def test_searching_empty_list_returns_none():
skip_list = SkipList()
assert skip_list.find("Some key") is None
def test_search():
skip_list = SkipList()
skip_list.insert("Key2", 20)
assert skip_list.find("Key2") == 20
skip_list.insert("Some Key", 10)
skip_list.insert("Key2", 8)
skip_list.insert("V", 13)
assert skip_list.find("Y") is None
assert skip_list.find("Key2") == 8
assert skip_list.find("Some Key") == 10
assert skip_list.find("V") == 13
def test_deleting_item_from_empty_list_do_nothing():
skip_list = SkipList()
skip_list.delete("Some key")
assert len(skip_list.head.forward) == 0
def test_deleted_items_are_not_founded_by_find_method():
skip_list = SkipList()
skip_list.insert("Key1", 12)
skip_list.insert("V", 13)
skip_list.insert("X", 14)
skip_list.insert("Key2", 15)
skip_list.delete("V")
skip_list.delete("Key2")
assert skip_list.find("V") is None
assert skip_list.find("Key2") is None
def test_delete_removes_only_given_key():
skip_list = SkipList()
skip_list.insert("Key1", 12)
skip_list.insert("V", 13)
skip_list.insert("X", 14)
skip_list.insert("Key2", 15)
skip_list.delete("V")
assert skip_list.find("V") is None
assert skip_list.find("X") == 14
assert skip_list.find("Key1") == 12
assert skip_list.find("Key2") == 15
skip_list.delete("X")
assert skip_list.find("V") is None
assert skip_list.find("X") is None
assert skip_list.find("Key1") == 12
assert skip_list.find("Key2") == 15
skip_list.delete("Key1")
assert skip_list.find("V") is None
assert skip_list.find("X") is None
assert skip_list.find("Key1") is None
assert skip_list.find("Key2") == 15
skip_list.delete("Key2")
assert skip_list.find("V") is None
assert skip_list.find("X") is None
assert skip_list.find("Key1") is None
assert skip_list.find("Key2") is None
def test_delete_doesnt_leave_dead_nodes():
skip_list = SkipList()
skip_list.insert("Key1", 12)
skip_list.insert("V", 13)
skip_list.insert("X", 142)
skip_list.insert("Key2", 15)
skip_list.delete("X")
def traverse_keys(node):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(forward_node)
assert len(set(traverse_keys(skip_list.head))) == 4
def test_iter_always_yields_sorted_values():
def is_sorted(lst):
for item, next_item in zip(lst, lst[1:]):
if next_item < item:
return False
return True
skip_list = SkipList()
for i in range(10):
skip_list.insert(i, i)
assert is_sorted(list(skip_list))
skip_list.delete(5)
skip_list.delete(8)
skip_list.delete(2)
assert is_sorted(list(skip_list))
skip_list.insert(-12, -12)
skip_list.insert(77, 77)
assert is_sorted(list(skip_list))
def pytests():
for _ in range(100):
# Repeat test 100 times due to the probabilistic nature of skip list
# random values == random bugs
test_insert()
test_insert_overrides_existing_value()
test_searching_empty_list_returns_none()
test_search()
test_deleting_item_from_empty_list_do_nothing()
test_deleted_items_are_not_founded_by_find_method()
test_delete_removes_only_given_key()
test_delete_doesnt_leave_dead_nodes()
test_iter_always_yields_sorted_values()
def main():
"""
>>> pytests()
"""
skip_list = SkipList()
skip_list.insert(2, "2")
skip_list.insert(4, "4")
skip_list.insert(6, "4")
skip_list.insert(4, "5")
skip_list.insert(8, "4")
skip_list.insert(9, "4")
skip_list.delete(4)
print(skip_list)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| """
Based on "Skip Lists: A Probabilistic Alternative to Balanced Trees" by William Pugh
https://epaperpress.com/sortsearch/download/skiplist.pdf
"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
KT = TypeVar("KT")
VT = TypeVar("VT")
class Node(Generic[KT, VT]):
def __init__(self, key: KT | str = "root", value: VT | None = None):
self.key = key
self.value = value
self.forward: list[Node[KT, VT]] = []
def __repr__(self) -> str:
"""
:return: Visual representation of Node
>>> node = Node("Key", 2)
>>> repr(node)
'Node(Key: 2)'
"""
return f"Node({self.key}: {self.value})"
@property
def level(self) -> int:
"""
:return: Number of forward references
>>> node = Node("Key", 2)
>>> node.level
0
>>> node.forward.append(Node("Key2", 4))
>>> node.level
1
>>> node.forward.append(Node("Key3", 6))
>>> node.level
2
"""
return len(self.forward)
class SkipList(Generic[KT, VT]):
def __init__(self, p: float = 0.5, max_level: int = 16):
self.head: Node[KT, VT] = Node[KT, VT]()
self.level = 0
self.p = p
self.max_level = max_level
def __str__(self) -> str:
"""
:return: Visual representation of SkipList
>>> skip_list = SkipList()
>>> print(skip_list)
SkipList(level=0)
>>> skip_list.insert("Key1", "Value")
>>> print(skip_list) # doctest: +ELLIPSIS
SkipList(level=...
[root]--...
[Key1]--Key1...
None *...
>>> skip_list.insert("Key2", "OtherValue")
>>> print(skip_list) # doctest: +ELLIPSIS
SkipList(level=...
[root]--...
[Key1]--Key1...
[Key2]--Key2...
None *...
"""
items = list(self)
if len(items) == 0:
return f"SkipList(level={self.level})"
label_size = max((len(str(item)) for item in items), default=4)
label_size = max(label_size, 4) + 4
node = self.head
lines = []
forwards = node.forward.copy()
lines.append(f"[{node.key}]".ljust(label_size, "-") + "* " * len(forwards))
lines.append(" " * label_size + "| " * len(forwards))
while len(node.forward) != 0:
node = node.forward[0]
lines.append(
f"[{node.key}]".ljust(label_size, "-")
+ " ".join(str(n.key) if n.key == node.key else "|" for n in forwards)
)
lines.append(" " * label_size + "| " * len(forwards))
forwards[: node.level] = node.forward
lines.append("None".ljust(label_size) + "* " * len(forwards))
return f"SkipList(level={self.level})\n" + "\n".join(lines)
def __iter__(self):
node = self.head
while len(node.forward) != 0:
yield node.forward[0].key
node = node.forward[0]
def random_level(self) -> int:
"""
:return: Random level from [1, self.max_level] interval.
Higher values are less likely.
"""
level = 1
while random() < self.p and level < self.max_level:
level += 1
return level
def _locate_node(self, key) -> tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:
"""
:param key: Searched key,
:return: Tuple with searched node (or None if given key is not present)
and list of nodes that refer (if key is present) of should refer to
given node.
"""
# Nodes with refer or should refer to output node
update_vector = []
node = self.head
for i in reversed(range(self.level)):
# i < node.level - When node level is lesser than `i` decrement `i`.
# node.forward[i].key < key - Jumping to node with key value higher
# or equal to searched key would result
# in skipping searched key.
while i < node.level and node.forward[i].key < key:
node = node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(node)
update_vector.reverse() # Note that we were inserting values in reverse order.
# len(node.forward) != 0 - If current node doesn't contain any further
# references then searched key is not present.
# node.forward[0].key == key - Next node key should be equal to search key
# if key is present.
if len(node.forward) != 0 and node.forward[0].key == key:
return node.forward[0], update_vector
else:
return None, update_vector
def delete(self, key: KT):
"""
:param key: Key to remove from list.
>>> skip_list = SkipList()
>>> skip_list.insert(2, "Two")
>>> skip_list.insert(1, "One")
>>> skip_list.insert(3, "Three")
>>> list(skip_list)
[1, 2, 3]
>>> skip_list.delete(2)
>>> list(skip_list)
[1, 3]
"""
node, update_vector = self._locate_node(key)
if node is not None:
for i, update_node in enumerate(update_vector):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
update_node.forward[i] = node.forward[i]
else:
update_node.forward = update_node.forward[:i]
def insert(self, key: KT, value: VT):
"""
:param key: Key to insert.
:param value: Value associated with given key.
>>> skip_list = SkipList()
>>> skip_list.insert(2, "Two")
>>> skip_list.find(2)
'Two'
>>> list(skip_list)
[2]
"""
node, update_vector = self._locate_node(key)
if node is not None:
node.value = value
else:
level = self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for _ in range(self.level - 1, level):
update_vector.append(self.head)
self.level = level
new_node = Node(key, value)
for i, update_node in enumerate(update_vector[:level]):
# Change references to pass through new node.
if update_node.level > i:
new_node.forward.append(update_node.forward[i])
if update_node.level < i + 1:
update_node.forward.append(new_node)
else:
update_node.forward[i] = new_node
def find(self, key: VT) -> VT | None:
"""
:param key: Search key.
:return: Value associated with given key or None if given key is not present.
>>> skip_list = SkipList()
>>> skip_list.find(2)
>>> skip_list.insert(2, "Two")
>>> skip_list.find(2)
'Two'
>>> skip_list.insert(2, "Three")
>>> skip_list.find(2)
'Three'
"""
node, _ = self._locate_node(key)
if node is not None:
return node.value
return None
def test_insert():
skip_list = SkipList()
skip_list.insert("Key1", 3)
skip_list.insert("Key2", 12)
skip_list.insert("Key3", 41)
skip_list.insert("Key4", -19)
node = skip_list.head
all_values = {}
while node.level != 0:
node = node.forward[0]
all_values[node.key] = node.value
assert len(all_values) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 12
assert all_values["Key3"] == 41
assert all_values["Key4"] == -19
def test_insert_overrides_existing_value():
skip_list = SkipList()
skip_list.insert("Key1", 10)
skip_list.insert("Key1", 12)
skip_list.insert("Key5", 7)
skip_list.insert("Key7", 10)
skip_list.insert("Key10", 5)
skip_list.insert("Key7", 7)
skip_list.insert("Key5", 5)
skip_list.insert("Key10", 10)
node = skip_list.head
all_values = {}
while node.level != 0:
node = node.forward[0]
all_values[node.key] = node.value
if len(all_values) != 4:
print()
assert len(all_values) == 4
assert all_values["Key1"] == 12
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 10
def test_searching_empty_list_returns_none():
skip_list = SkipList()
assert skip_list.find("Some key") is None
def test_search():
skip_list = SkipList()
skip_list.insert("Key2", 20)
assert skip_list.find("Key2") == 20
skip_list.insert("Some Key", 10)
skip_list.insert("Key2", 8)
skip_list.insert("V", 13)
assert skip_list.find("Y") is None
assert skip_list.find("Key2") == 8
assert skip_list.find("Some Key") == 10
assert skip_list.find("V") == 13
def test_deleting_item_from_empty_list_do_nothing():
skip_list = SkipList()
skip_list.delete("Some key")
assert len(skip_list.head.forward) == 0
def test_deleted_items_are_not_founded_by_find_method():
skip_list = SkipList()
skip_list.insert("Key1", 12)
skip_list.insert("V", 13)
skip_list.insert("X", 14)
skip_list.insert("Key2", 15)
skip_list.delete("V")
skip_list.delete("Key2")
assert skip_list.find("V") is None
assert skip_list.find("Key2") is None
def test_delete_removes_only_given_key():
skip_list = SkipList()
skip_list.insert("Key1", 12)
skip_list.insert("V", 13)
skip_list.insert("X", 14)
skip_list.insert("Key2", 15)
skip_list.delete("V")
assert skip_list.find("V") is None
assert skip_list.find("X") == 14
assert skip_list.find("Key1") == 12
assert skip_list.find("Key2") == 15
skip_list.delete("X")
assert skip_list.find("V") is None
assert skip_list.find("X") is None
assert skip_list.find("Key1") == 12
assert skip_list.find("Key2") == 15
skip_list.delete("Key1")
assert skip_list.find("V") is None
assert skip_list.find("X") is None
assert skip_list.find("Key1") is None
assert skip_list.find("Key2") == 15
skip_list.delete("Key2")
assert skip_list.find("V") is None
assert skip_list.find("X") is None
assert skip_list.find("Key1") is None
assert skip_list.find("Key2") is None
def test_delete_doesnt_leave_dead_nodes():
skip_list = SkipList()
skip_list.insert("Key1", 12)
skip_list.insert("V", 13)
skip_list.insert("X", 142)
skip_list.insert("Key2", 15)
skip_list.delete("X")
def traverse_keys(node):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(forward_node)
assert len(set(traverse_keys(skip_list.head))) == 4
def test_iter_always_yields_sorted_values():
def is_sorted(lst):
for item, next_item in zip(lst, lst[1:]):
if next_item < item:
return False
return True
skip_list = SkipList()
for i in range(10):
skip_list.insert(i, i)
assert is_sorted(list(skip_list))
skip_list.delete(5)
skip_list.delete(8)
skip_list.delete(2)
assert is_sorted(list(skip_list))
skip_list.insert(-12, -12)
skip_list.insert(77, 77)
assert is_sorted(list(skip_list))
def pytests():
for _ in range(100):
# Repeat test 100 times due to the probabilistic nature of skip list
# random values == random bugs
test_insert()
test_insert_overrides_existing_value()
test_searching_empty_list_returns_none()
test_search()
test_deleting_item_from_empty_list_do_nothing()
test_deleted_items_are_not_founded_by_find_method()
test_delete_removes_only_given_key()
test_delete_doesnt_leave_dead_nodes()
test_iter_always_yields_sorted_values()
def main():
"""
>>> pytests()
"""
skip_list = SkipList()
skip_list.insert(2, "2")
skip_list.insert(4, "4")
skip_list.insert(6, "4")
skip_list.insert(4, "5")
skip_list.insert(8, "4")
skip_list.insert(9, "4")
skip_list.delete(4)
print(skip_list)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 74: https://projecteuler.net/problem=74
The number 145 is well known for the property that the sum of the factorial of its
digits is equal to 145:
1! + 4! + 5! = 1 + 24 + 120 = 145
Perhaps less well known is 169, in that it produces the longest chain of numbers that
link back to 169; it turns out that there are only three such loops that exist:
169 → 363601 → 1454 → 169
871 → 45361 → 871
872 → 45362 → 872
It is not difficult to prove that EVERY starting number will eventually get stuck in
a loop. For example,
69 → 363600 → 1454 → 169 → 363601 (→ 1454)
78 → 45360 → 871 → 45361 (→ 871)
540 → 145 (→ 145)
Starting with 69 produces a chain of five non-repeating terms, but the longest
non-repeating chain with a starting number below one million is sixty terms.
How many chains, with a starting number below one million, contain exactly sixty
non-repeating terms?
"""
DIGIT_FACTORIALS = {
"0": 1,
"1": 1,
"2": 2,
"3": 6,
"4": 24,
"5": 120,
"6": 720,
"7": 5040,
"8": 40320,
"9": 362880,
}
CACHE_SUM_DIGIT_FACTORIALS = {145: 145}
CHAIN_LENGTH_CACHE = {
145: 0,
169: 3,
36301: 3,
1454: 3,
871: 2,
45361: 2,
872: 2,
}
def sum_digit_factorials(n: int) -> int:
"""
Return the sum of the factorial of the digits of n.
>>> sum_digit_factorials(145)
145
>>> sum_digit_factorials(45361)
871
>>> sum_digit_factorials(540)
145
"""
if n in CACHE_SUM_DIGIT_FACTORIALS:
return CACHE_SUM_DIGIT_FACTORIALS[n]
ret = sum(DIGIT_FACTORIALS[let] for let in str(n))
CACHE_SUM_DIGIT_FACTORIALS[n] = ret
return ret
def chain_length(n: int, previous: set = None) -> int:
"""
Calculate the length of the chain of non-repeating terms starting with n.
Previous is a set containing the previous member of the chain.
>>> chain_length(10101)
11
>>> chain_length(555)
20
>>> chain_length(178924)
39
"""
previous = previous or set()
if n in CHAIN_LENGTH_CACHE:
return CHAIN_LENGTH_CACHE[n]
next_number = sum_digit_factorials(n)
if next_number in previous:
CHAIN_LENGTH_CACHE[n] = 0
return 0
else:
previous.add(n)
ret = 1 + chain_length(next_number, previous)
CHAIN_LENGTH_CACHE[n] = ret
return ret
def solution(num_terms: int = 60, max_start: int = 1000000) -> int:
"""
Return the number of chains with a starting number below one million which
contain exactly n non-repeating terms.
>>> solution(10,1000)
28
"""
return sum(1 for i in range(1, max_start) if chain_length(i) == num_terms)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 74: https://projecteuler.net/problem=74
The number 145 is well known for the property that the sum of the factorial of its
digits is equal to 145:
1! + 4! + 5! = 1 + 24 + 120 = 145
Perhaps less well known is 169, in that it produces the longest chain of numbers that
link back to 169; it turns out that there are only three such loops that exist:
169 → 363601 → 1454 → 169
871 → 45361 → 871
872 → 45362 → 872
It is not difficult to prove that EVERY starting number will eventually get stuck in
a loop. For example,
69 → 363600 → 1454 → 169 → 363601 (→ 1454)
78 → 45360 → 871 → 45361 (→ 871)
540 → 145 (→ 145)
Starting with 69 produces a chain of five non-repeating terms, but the longest
non-repeating chain with a starting number below one million is sixty terms.
How many chains, with a starting number below one million, contain exactly sixty
non-repeating terms?
"""
DIGIT_FACTORIALS = {
"0": 1,
"1": 1,
"2": 2,
"3": 6,
"4": 24,
"5": 120,
"6": 720,
"7": 5040,
"8": 40320,
"9": 362880,
}
CACHE_SUM_DIGIT_FACTORIALS = {145: 145}
CHAIN_LENGTH_CACHE = {
145: 0,
169: 3,
36301: 3,
1454: 3,
871: 2,
45361: 2,
872: 2,
}
def sum_digit_factorials(n: int) -> int:
"""
Return the sum of the factorial of the digits of n.
>>> sum_digit_factorials(145)
145
>>> sum_digit_factorials(45361)
871
>>> sum_digit_factorials(540)
145
"""
if n in CACHE_SUM_DIGIT_FACTORIALS:
return CACHE_SUM_DIGIT_FACTORIALS[n]
ret = sum(DIGIT_FACTORIALS[let] for let in str(n))
CACHE_SUM_DIGIT_FACTORIALS[n] = ret
return ret
def chain_length(n: int, previous: set = None) -> int:
"""
Calculate the length of the chain of non-repeating terms starting with n.
Previous is a set containing the previous member of the chain.
>>> chain_length(10101)
11
>>> chain_length(555)
20
>>> chain_length(178924)
39
"""
previous = previous or set()
if n in CHAIN_LENGTH_CACHE:
return CHAIN_LENGTH_CACHE[n]
next_number = sum_digit_factorials(n)
if next_number in previous:
CHAIN_LENGTH_CACHE[n] = 0
return 0
else:
previous.add(n)
ret = 1 + chain_length(next_number, previous)
CHAIN_LENGTH_CACHE[n] = ret
return ret
def solution(num_terms: int = 60, max_start: int = 1000000) -> int:
"""
Return the number of chains with a starting number below one million which
contain exactly n non-repeating terms.
>>> solution(10,1000)
28
"""
return sum(1 for i in range(1, max_start) if chain_length(i) == num_terms)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def text_justification(word: str, max_width: int) -> list:
"""
Will format the string such that each line has exactly
(max_width) characters and is fully (left and right) justified,
and return the list of justified text.
example 1:
string = "This is an example of text justification."
max_width = 16
output = ['This is an',
'example of text',
'justification. ']
>>> text_justification("This is an example of text justification.", 16)
['This is an', 'example of text', 'justification. ']
example 2:
string = "Two roads diverged in a yellow wood"
max_width = 16
output = ['Two roads',
'diverged in a',
'yellow wood ']
>>> text_justification("Two roads diverged in a yellow wood", 16)
['Two roads', 'diverged in a', 'yellow wood ']
Time complexity: O(m*n)
Space complexity: O(m*n)
"""
# Converting string into list of strings split by a space
words = word.split()
def justify(line: list, width: int, max_width: int) -> str:
overall_spaces_count = max_width - width
words_count = len(line)
if len(line) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
spaces_to_insert_between_words = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
num_spaces_between_words_list = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
spaces_count_in_locations = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(spaces_count_in_locations):
num_spaces_between_words_list[i] += 1
aligned_words_list = []
for i in range(spaces_to_insert_between_words):
# add the word
aligned_words_list.append(line[i])
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * " ")
# just add the last word to the sentence
aligned_words_list.append(line[-1])
# join the aligned words list to form a justified line
return "".join(aligned_words_list)
answer = []
line: list[str] = []
width = 0
for word in words:
if width + len(word) + len(line) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(word)
width += len(word)
else:
# justify the line and add it to result
answer.append(justify(line, width, max_width))
# reset new line and new width
line, width = [word], len(word)
remaining_spaces = max_width - width - len(line)
answer.append(" ".join(line) + (remaining_spaces + 1) * " ")
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| def text_justification(word: str, max_width: int) -> list:
"""
Will format the string such that each line has exactly
(max_width) characters and is fully (left and right) justified,
and return the list of justified text.
example 1:
string = "This is an example of text justification."
max_width = 16
output = ['This is an',
'example of text',
'justification. ']
>>> text_justification("This is an example of text justification.", 16)
['This is an', 'example of text', 'justification. ']
example 2:
string = "Two roads diverged in a yellow wood"
max_width = 16
output = ['Two roads',
'diverged in a',
'yellow wood ']
>>> text_justification("Two roads diverged in a yellow wood", 16)
['Two roads', 'diverged in a', 'yellow wood ']
Time complexity: O(m*n)
Space complexity: O(m*n)
"""
# Converting string into list of strings split by a space
words = word.split()
def justify(line: list, width: int, max_width: int) -> str:
overall_spaces_count = max_width - width
words_count = len(line)
if len(line) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
spaces_to_insert_between_words = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
num_spaces_between_words_list = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
spaces_count_in_locations = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(spaces_count_in_locations):
num_spaces_between_words_list[i] += 1
aligned_words_list = []
for i in range(spaces_to_insert_between_words):
# add the word
aligned_words_list.append(line[i])
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * " ")
# just add the last word to the sentence
aligned_words_list.append(line[-1])
# join the aligned words list to form a justified line
return "".join(aligned_words_list)
answer = []
line: list[str] = []
width = 0
for word in words:
if width + len(word) + len(line) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(word)
width += len(word)
else:
# justify the line and add it to result
answer.append(justify(line, width, max_width))
# reset new line and new width
line, width = [word], len(word)
remaining_spaces = max_width - width - len(line)
answer.append(" ".join(line) + (remaining_spaces + 1) * " ")
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Convert a string of characters to a sequence of numbers
corresponding to the character's position in the alphabet.
https://www.dcode.fr/letter-number-cipher
http://bestcodes.weebly.com/a1z26.html
"""
from __future__ import annotations
def encode(plain: str) -> list[int]:
"""
>>> encode("myname")
[13, 25, 14, 1, 13, 5]
"""
return [ord(elem) - 96 for elem in plain]
def decode(encoded: list[int]) -> str:
"""
>>> decode([13, 25, 14, 1, 13, 5])
'myname'
"""
return "".join(chr(elem + 96) for elem in encoded)
def main() -> None:
encoded = encode(input("-> ").strip().lower())
print("Encoded: ", encoded)
print("Decoded:", decode(encoded))
if __name__ == "__main__":
main()
| """
Convert a string of characters to a sequence of numbers
corresponding to the character's position in the alphabet.
https://www.dcode.fr/letter-number-cipher
http://bestcodes.weebly.com/a1z26.html
"""
from __future__ import annotations
def encode(plain: str) -> list[int]:
"""
>>> encode("myname")
[13, 25, 14, 1, 13, 5]
"""
return [ord(elem) - 96 for elem in plain]
def decode(encoded: list[int]) -> str:
"""
>>> decode([13, 25, 14, 1, 13, 5])
'myname'
"""
return "".join(chr(elem + 96) for elem in encoded)
def main() -> None:
encoded = encode(input("-> ").strip().lower())
print("Encoded: ", encoded)
print("Decoded:", decode(encoded))
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
from __future__ import annotations
import json
import requests
from bs4 import BeautifulSoup
from fake_useragent import UserAgent
headers = {"UserAgent": UserAgent().random}
def extract_user_profile(script) -> dict:
"""
May raise json.decoder.JSONDecodeError
"""
data = script.contents[0]
info = json.loads(data[data.find('{"config"') : -1])
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class InstagramUser:
"""
Class Instagram crawl instagram user information
Usage: (doctest failing on GitHub Actions)
# >>> instagram_user = InstagramUser("github")
# >>> instagram_user.is_verified
True
# >>> instagram_user.biography
'Built for developers.'
"""
def __init__(self, username):
self.url = f"https://www.instagram.com/{username}/"
self.user_data = self.get_json()
def get_json(self) -> dict:
"""
Return a dict of user information
"""
html = requests.get(self.url, headers=headers).text
scripts = BeautifulSoup(html, "html.parser").find_all("script")
try:
return extract_user_profile(scripts[4])
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3])
def __repr__(self) -> str:
return f"{self.__class__.__name__}('{self.username}')"
def __str__(self) -> str:
return f"{self.fullname} ({self.username}) is {self.biography}"
@property
def username(self) -> str:
return self.user_data["username"]
@property
def fullname(self) -> str:
return self.user_data["full_name"]
@property
def biography(self) -> str:
return self.user_data["biography"]
@property
def email(self) -> str:
return self.user_data["business_email"]
@property
def website(self) -> str:
return self.user_data["external_url"]
@property
def number_of_followers(self) -> int:
return self.user_data["edge_followed_by"]["count"]
@property
def number_of_followings(self) -> int:
return self.user_data["edge_follow"]["count"]
@property
def number_of_posts(self) -> int:
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def profile_picture_url(self) -> str:
return self.user_data["profile_pic_url_hd"]
@property
def is_verified(self) -> bool:
return self.user_data["is_verified"]
@property
def is_private(self) -> bool:
return self.user_data["is_private"]
def test_instagram_user(username: str = "github") -> None:
"""
A self running doctest
>>> test_instagram_user()
"""
import os
if os.environ.get("CI"):
return None # test failing on GitHub Actions
instagram_user = InstagramUser(username)
assert instagram_user.user_data
assert isinstance(instagram_user.user_data, dict)
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 150
assert instagram_user.number_of_followers > 120000
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "[email protected]"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith("https://instagram.")
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
instagram_user = InstagramUser("github")
print(instagram_user)
print(f"{instagram_user.number_of_posts = }")
print(f"{instagram_user.number_of_followers = }")
print(f"{instagram_user.number_of_followings = }")
print(f"{instagram_user.email = }")
print(f"{instagram_user.website = }")
print(f"{instagram_user.profile_picture_url = }")
print(f"{instagram_user.is_verified = }")
print(f"{instagram_user.is_private = }")
| #!/usr/bin/env python3
from __future__ import annotations
import json
import requests
from bs4 import BeautifulSoup
from fake_useragent import UserAgent
headers = {"UserAgent": UserAgent().random}
def extract_user_profile(script) -> dict:
"""
May raise json.decoder.JSONDecodeError
"""
data = script.contents[0]
info = json.loads(data[data.find('{"config"') : -1])
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class InstagramUser:
"""
Class Instagram crawl instagram user information
Usage: (doctest failing on GitHub Actions)
# >>> instagram_user = InstagramUser("github")
# >>> instagram_user.is_verified
True
# >>> instagram_user.biography
'Built for developers.'
"""
def __init__(self, username):
self.url = f"https://www.instagram.com/{username}/"
self.user_data = self.get_json()
def get_json(self) -> dict:
"""
Return a dict of user information
"""
html = requests.get(self.url, headers=headers).text
scripts = BeautifulSoup(html, "html.parser").find_all("script")
try:
return extract_user_profile(scripts[4])
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3])
def __repr__(self) -> str:
return f"{self.__class__.__name__}('{self.username}')"
def __str__(self) -> str:
return f"{self.fullname} ({self.username}) is {self.biography}"
@property
def username(self) -> str:
return self.user_data["username"]
@property
def fullname(self) -> str:
return self.user_data["full_name"]
@property
def biography(self) -> str:
return self.user_data["biography"]
@property
def email(self) -> str:
return self.user_data["business_email"]
@property
def website(self) -> str:
return self.user_data["external_url"]
@property
def number_of_followers(self) -> int:
return self.user_data["edge_followed_by"]["count"]
@property
def number_of_followings(self) -> int:
return self.user_data["edge_follow"]["count"]
@property
def number_of_posts(self) -> int:
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def profile_picture_url(self) -> str:
return self.user_data["profile_pic_url_hd"]
@property
def is_verified(self) -> bool:
return self.user_data["is_verified"]
@property
def is_private(self) -> bool:
return self.user_data["is_private"]
def test_instagram_user(username: str = "github") -> None:
"""
A self running doctest
>>> test_instagram_user()
"""
import os
if os.environ.get("CI"):
return None # test failing on GitHub Actions
instagram_user = InstagramUser(username)
assert instagram_user.user_data
assert isinstance(instagram_user.user_data, dict)
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 150
assert instagram_user.number_of_followers > 120000
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "[email protected]"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith("https://instagram.")
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
instagram_user = InstagramUser("github")
print(instagram_user)
print(f"{instagram_user.number_of_posts = }")
print(f"{instagram_user.number_of_followers = }")
print(f"{instagram_user.number_of_followings = }")
print(f"{instagram_user.email = }")
print(f"{instagram_user.website = }")
print(f"{instagram_user.profile_picture_url = }")
print(f"{instagram_user.is_verified = }")
print(f"{instagram_user.is_private = }")
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Source: https://en.wikipedia.org/wiki/Odd%E2%80%93even_sort
This is a non-parallelized implementation of odd-even transposition sort.
Normally the swaps in each set happen simultaneously, without that the algorithm
is no better than bubble sort.
"""
def odd_even_transposition(arr: list) -> list:
"""
>>> odd_even_transposition([5, 4, 3, 2, 1])
[1, 2, 3, 4, 5]
>>> odd_even_transposition([13, 11, 18, 0, -1])
[-1, 0, 11, 13, 18]
>>> odd_even_transposition([-.1, 1.1, .1, -2.9])
[-2.9, -0.1, 0.1, 1.1]
"""
arr_size = len(arr)
for _ in range(arr_size):
for i in range(_ % 2, arr_size - 1, 2):
if arr[i + 1] < arr[i]:
arr[i], arr[i + 1] = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
arr = list(range(10, 0, -1))
print(f"Original: {arr}. Sorted: {odd_even_transposition(arr)}")
| """
Source: https://en.wikipedia.org/wiki/Odd%E2%80%93even_sort
This is a non-parallelized implementation of odd-even transposition sort.
Normally the swaps in each set happen simultaneously, without that the algorithm
is no better than bubble sort.
"""
def odd_even_transposition(arr: list) -> list:
"""
>>> odd_even_transposition([5, 4, 3, 2, 1])
[1, 2, 3, 4, 5]
>>> odd_even_transposition([13, 11, 18, 0, -1])
[-1, 0, 11, 13, 18]
>>> odd_even_transposition([-.1, 1.1, .1, -2.9])
[-2.9, -0.1, 0.1, 1.1]
"""
arr_size = len(arr)
for _ in range(arr_size):
for i in range(_ % 2, arr_size - 1, 2):
if arr[i + 1] < arr[i]:
arr[i], arr[i + 1] = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
arr = list(range(10, 0, -1))
print(f"Original: {arr}. Sorted: {odd_even_transposition(arr)}")
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def quick_sort(data: list) -> list:
"""
>>> for data in ([2, 1, 0], [2.2, 1.1, 0], "quick_sort"):
... quick_sort(data) == sorted(data)
True
True
True
"""
if len(data) <= 1:
return data
else:
return (
quick_sort([e for e in data[1:] if e <= data[0]])
+ [data[0]]
+ quick_sort([e for e in data[1:] if e > data[0]])
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| def quick_sort(data: list) -> list:
"""
>>> for data in ([2, 1, 0], [2.2, 1.1, 0], "quick_sort"):
... quick_sort(data) == sorted(data)
True
True
True
"""
if len(data) <= 1:
return data
else:
return (
quick_sort([e for e in data[1:] if e <= data[0]])
+ [data[0]]
+ quick_sort([e for e in data[1:] if e > data[0]])
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import numpy as np
from PIL import Image
def rgb2gray(rgb: np.array) -> np.array:
"""
Return gray image from rgb image
>>> rgb2gray(np.array([[[127, 255, 0]]]))
array([[187.6453]])
>>> rgb2gray(np.array([[[0, 0, 0]]]))
array([[0.]])
>>> rgb2gray(np.array([[[2, 4, 1]]]))
array([[3.0598]])
>>> rgb2gray(np.array([[[26, 255, 14], [5, 147, 20], [1, 200, 0]]]))
array([[159.0524, 90.0635, 117.6989]])
"""
r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
def gray2binary(gray: np.array) -> np.array:
"""
Return binary image from gray image
>>> gray2binary(np.array([[127, 255, 0]]))
array([[False, True, False]])
>>> gray2binary(np.array([[0]]))
array([[False]])
>>> gray2binary(np.array([[26.2409, 4.9315, 1.4729]]))
array([[False, False, False]])
>>> gray2binary(np.array([[26, 255, 14], [5, 147, 20], [1, 200, 0]]))
array([[False, True, False],
[False, True, False],
[False, True, False]])
"""
return (127 < gray) & (gray <= 255)
def erosion(image: np.array, kernel: np.array) -> np.array:
"""
Return eroded image
>>> erosion(np.array([[True, True, False]]), np.array([[0, 1, 0]]))
array([[False, False, False]])
>>> erosion(np.array([[True, False, False]]), np.array([[1, 1, 0]]))
array([[False, False, False]])
"""
output = np.zeros_like(image)
image_padded = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)
)
# Copy image to padded image
image_padded[kernel.shape[0] - 2 : -1 :, kernel.shape[1] - 2 : -1 :] = image
# Iterate over image & apply kernel
for x in range(image.shape[1]):
for y in range(image.shape[0]):
summation = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
output[y, x] = int(summation == 5)
return output
# kernel to be applied
structuring_element = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
if __name__ == "__main__":
# read original image
image = np.array(Image.open(r"..\image_data\lena.jpg"))
# Apply erosion operation to a binary image
output = erosion(gray2binary(rgb2gray(image)), structuring_element)
# Save the output image
pil_img = Image.fromarray(output).convert("RGB")
pil_img.save("result_erosion.png")
| import numpy as np
from PIL import Image
def rgb2gray(rgb: np.array) -> np.array:
"""
Return gray image from rgb image
>>> rgb2gray(np.array([[[127, 255, 0]]]))
array([[187.6453]])
>>> rgb2gray(np.array([[[0, 0, 0]]]))
array([[0.]])
>>> rgb2gray(np.array([[[2, 4, 1]]]))
array([[3.0598]])
>>> rgb2gray(np.array([[[26, 255, 14], [5, 147, 20], [1, 200, 0]]]))
array([[159.0524, 90.0635, 117.6989]])
"""
r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
def gray2binary(gray: np.array) -> np.array:
"""
Return binary image from gray image
>>> gray2binary(np.array([[127, 255, 0]]))
array([[False, True, False]])
>>> gray2binary(np.array([[0]]))
array([[False]])
>>> gray2binary(np.array([[26.2409, 4.9315, 1.4729]]))
array([[False, False, False]])
>>> gray2binary(np.array([[26, 255, 14], [5, 147, 20], [1, 200, 0]]))
array([[False, True, False],
[False, True, False],
[False, True, False]])
"""
return (127 < gray) & (gray <= 255)
def erosion(image: np.array, kernel: np.array) -> np.array:
"""
Return eroded image
>>> erosion(np.array([[True, True, False]]), np.array([[0, 1, 0]]))
array([[False, False, False]])
>>> erosion(np.array([[True, False, False]]), np.array([[1, 1, 0]]))
array([[False, False, False]])
"""
output = np.zeros_like(image)
image_padded = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)
)
# Copy image to padded image
image_padded[kernel.shape[0] - 2 : -1 :, kernel.shape[1] - 2 : -1 :] = image
# Iterate over image & apply kernel
for x in range(image.shape[1]):
for y in range(image.shape[0]):
summation = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
output[y, x] = int(summation == 5)
return output
# kernel to be applied
structuring_element = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
if __name__ == "__main__":
# read original image
image = np.array(Image.open(r"..\image_data\lena.jpg"))
# Apply erosion operation to a binary image
output = erosion(gray2binary(rgb2gray(image)), structuring_element)
# Save the output image
pil_img = Image.fromarray(output).convert("RGB")
pil_img.save("result_erosion.png")
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Source : https://computersciencewiki.org/index.php/Max-pooling_/_Pooling
# Importing the libraries
import numpy as np
from PIL import Image
# Maxpooling Function
def maxpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
"""
This function is used to perform maxpooling on the input array of 2D matrix(image)
Args:
arr: numpy array
size: size of pooling matrix
stride: the number of pixels shifts over the input matrix
Returns:
numpy array of maxpooled matrix
Sample Input Output:
>>> maxpooling([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]], 2, 2)
array([[ 6., 8.],
[14., 16.]])
>>> maxpooling([[147, 180, 122],[241, 76, 32],[126, 13, 157]], 2, 1)
array([[241., 180.],
[241., 157.]])
"""
arr = np.array(arr)
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix")
i = 0
j = 0
mat_i = 0
mat_j = 0
# compute the shape of the output matrix
maxpool_shape = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
updated_arr = np.zeros((maxpool_shape, maxpool_shape))
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
updated_arr[mat_i][mat_j] = np.max(arr[i : i + size, j : j + size])
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
j = 0
mat_j = 0
return updated_arr
# Averagepooling Function
def avgpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
"""
This function is used to perform avgpooling on the input array of 2D matrix(image)
Args:
arr: numpy array
size: size of pooling matrix
stride: the number of pixels shifts over the input matrix
Returns:
numpy array of avgpooled matrix
Sample Input Output:
>>> avgpooling([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]], 2, 2)
array([[ 3., 5.],
[11., 13.]])
>>> avgpooling([[147, 180, 122],[241, 76, 32],[126, 13, 157]], 2, 1)
array([[161., 102.],
[114., 69.]])
"""
arr = np.array(arr)
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix")
i = 0
j = 0
mat_i = 0
mat_j = 0
# compute the shape of the output matrix
avgpool_shape = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
updated_arr = np.zeros((avgpool_shape, avgpool_shape))
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
updated_arr[mat_i][mat_j] = int(np.average(arr[i : i + size, j : j + size]))
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
j = 0
mat_j = 0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name="avgpooling", verbose=True)
# Loading the image
image = Image.open("path_to_image")
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| # Source : https://computersciencewiki.org/index.php/Max-pooling_/_Pooling
# Importing the libraries
import numpy as np
from PIL import Image
# Maxpooling Function
def maxpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
"""
This function is used to perform maxpooling on the input array of 2D matrix(image)
Args:
arr: numpy array
size: size of pooling matrix
stride: the number of pixels shifts over the input matrix
Returns:
numpy array of maxpooled matrix
Sample Input Output:
>>> maxpooling([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]], 2, 2)
array([[ 6., 8.],
[14., 16.]])
>>> maxpooling([[147, 180, 122],[241, 76, 32],[126, 13, 157]], 2, 1)
array([[241., 180.],
[241., 157.]])
"""
arr = np.array(arr)
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix")
i = 0
j = 0
mat_i = 0
mat_j = 0
# compute the shape of the output matrix
maxpool_shape = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
updated_arr = np.zeros((maxpool_shape, maxpool_shape))
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
updated_arr[mat_i][mat_j] = np.max(arr[i : i + size, j : j + size])
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
j = 0
mat_j = 0
return updated_arr
# Averagepooling Function
def avgpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
"""
This function is used to perform avgpooling on the input array of 2D matrix(image)
Args:
arr: numpy array
size: size of pooling matrix
stride: the number of pixels shifts over the input matrix
Returns:
numpy array of avgpooled matrix
Sample Input Output:
>>> avgpooling([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]], 2, 2)
array([[ 3., 5.],
[11., 13.]])
>>> avgpooling([[147, 180, 122],[241, 76, 32],[126, 13, 157]], 2, 1)
array([[161., 102.],
[114., 69.]])
"""
arr = np.array(arr)
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix")
i = 0
j = 0
mat_i = 0
mat_j = 0
# compute the shape of the output matrix
avgpool_shape = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
updated_arr = np.zeros((avgpool_shape, avgpool_shape))
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
updated_arr[mat_i][mat_j] = int(np.average(arr[i : i + size, j : j + size]))
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
j = 0
mat_j = 0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name="avgpooling", verbose=True)
# Loading the image
image = Image.open("path_to_image")
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/Self-organizing_map
"""
import math
class SelfOrganizingMap:
def get_winner(self, weights: list[list[float]], sample: list[int]) -> int:
"""
Compute the winning vector by Euclidean distance
>>> SelfOrganizingMap().get_winner([[1, 2, 3], [4, 5, 6]], [1, 2, 3])
1
"""
d0 = 0.0
d1 = 0.0
for i in range(len(sample)):
d0 += math.pow((sample[i] - weights[0][i]), 2)
d1 += math.pow((sample[i] - weights[1][i]), 2)
return 0 if d0 > d1 else 1
return 0
def update(
self, weights: list[list[int | float]], sample: list[int], j: int, alpha: float
) -> list[list[int | float]]:
"""
Update the winning vector.
>>> SelfOrganizingMap().update([[1, 2, 3], [4, 5, 6]], [1, 2, 3], 1, 0.1)
[[1, 2, 3], [3.7, 4.7, 6]]
"""
for i in range(len(weights)):
weights[j][i] += alpha * (sample[i] - weights[j][i])
return weights
# Driver code
def main() -> None:
# Training Examples ( m, n )
training_samples = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
# weight initialization ( n, C )
weights = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
# training
self_organizing_map = SelfOrganizingMap()
epochs = 3
alpha = 0.5
for _ in range(epochs):
for j in range(len(training_samples)):
# training sample
sample = training_samples[j]
# Compute the winning vector
winner = self_organizing_map.get_winner(weights, sample)
# Update the winning vector
weights = self_organizing_map.update(weights, sample, winner, alpha)
# classify test sample
sample = [0, 0, 0, 1]
winner = self_organizing_map.get_winner(weights, sample)
# results
print(f"Clusters that the test sample belongs to : {winner}")
print(f"Weights that have been trained : {weights}")
# running the main() function
if __name__ == "__main__":
main()
| """
https://en.wikipedia.org/wiki/Self-organizing_map
"""
import math
class SelfOrganizingMap:
def get_winner(self, weights: list[list[float]], sample: list[int]) -> int:
"""
Compute the winning vector by Euclidean distance
>>> SelfOrganizingMap().get_winner([[1, 2, 3], [4, 5, 6]], [1, 2, 3])
1
"""
d0 = 0.0
d1 = 0.0
for i in range(len(sample)):
d0 += math.pow((sample[i] - weights[0][i]), 2)
d1 += math.pow((sample[i] - weights[1][i]), 2)
return 0 if d0 > d1 else 1
return 0
def update(
self, weights: list[list[int | float]], sample: list[int], j: int, alpha: float
) -> list[list[int | float]]:
"""
Update the winning vector.
>>> SelfOrganizingMap().update([[1, 2, 3], [4, 5, 6]], [1, 2, 3], 1, 0.1)
[[1, 2, 3], [3.7, 4.7, 6]]
"""
for i in range(len(weights)):
weights[j][i] += alpha * (sample[i] - weights[j][i])
return weights
# Driver code
def main() -> None:
# Training Examples ( m, n )
training_samples = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
# weight initialization ( n, C )
weights = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
# training
self_organizing_map = SelfOrganizingMap()
epochs = 3
alpha = 0.5
for _ in range(epochs):
for j in range(len(training_samples)):
# training sample
sample = training_samples[j]
# Compute the winning vector
winner = self_organizing_map.get_winner(weights, sample)
# Update the winning vector
weights = self_organizing_map.update(weights, sample, winner, alpha)
# classify test sample
sample = [0, 0, 0, 1]
winner = self_organizing_map.get_winner(weights, sample)
# results
print(f"Clusters that the test sample belongs to : {winner}")
print(f"Weights that have been trained : {weights}")
# running the main() function
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from xml.dom import NotFoundErr
import requests
from bs4 import BeautifulSoup, NavigableString
from fake_useragent import UserAgent
BASE_URL = "https://ww1.gogoanime2.org"
def search_scraper(anime_name: str) -> list:
"""[summary]
Take an url and
return list of anime after scraping the site.
>>> type(search_scraper("demon_slayer"))
<class 'list'>
Args:
anime_name (str): [Name of anime]
Raises:
e: [Raises exception on failure]
Returns:
[list]: [List of animes]
"""
# concat the name to form the search url.
search_url = f"{BASE_URL}/search/{anime_name}"
response = requests.get(
search_url, headers={"UserAgent": UserAgent().chrome}
) # request the url.
# Is the response ok?
response.raise_for_status()
# parse with soup.
soup = BeautifulSoup(response.text, "html.parser")
# get list of anime
anime_ul = soup.find("ul", {"class": "items"})
anime_li = anime_ul.children
# for each anime, insert to list. the name and url.
anime_list = []
for anime in anime_li:
if not isinstance(anime, NavigableString):
try:
anime_url, anime_title = (
anime.find("a")["href"],
anime.find("a")["title"],
)
anime_list.append(
{
"title": anime_title,
"url": anime_url,
}
)
except (NotFoundErr, KeyError):
pass
return anime_list
def search_anime_episode_list(episode_endpoint: str) -> list:
"""[summary]
Take an url and
return list of episodes after scraping the site
for an url.
>>> type(search_anime_episode_list("/anime/kimetsu-no-yaiba"))
<class 'list'>
Args:
episode_endpoint (str): [Endpoint of episode]
Raises:
e: [description]
Returns:
[list]: [List of episodes]
"""
request_url = f"{BASE_URL}{episode_endpoint}"
response = requests.get(url=request_url, headers={"UserAgent": UserAgent().chrome})
response.raise_for_status()
soup = BeautifulSoup(response.text, "html.parser")
# With this id. get the episode list.
episode_page_ul = soup.find("ul", {"id": "episode_related"})
episode_page_li = episode_page_ul.children
episode_list = []
for episode in episode_page_li:
try:
if not isinstance(episode, NavigableString):
episode_list.append(
{
"title": episode.find("div", {"class": "name"}).text.replace(
" ", ""
),
"url": episode.find("a")["href"],
}
)
except (KeyError, NotFoundErr):
pass
return episode_list
def get_anime_episode(episode_endpoint: str) -> list:
"""[summary]
Get click url and download url from episode url
>>> type(get_anime_episode("/watch/kimetsu-no-yaiba/1"))
<class 'list'>
Args:
episode_endpoint (str): [Endpoint of episode]
Raises:
e: [description]
Returns:
[list]: [List of download and watch url]
"""
episode_page_url = f"{BASE_URL}{episode_endpoint}"
response = requests.get(
url=episode_page_url, headers={"User-Agent": UserAgent().chrome}
)
response.raise_for_status()
soup = BeautifulSoup(response.text, "html.parser")
try:
episode_url = soup.find("iframe", {"id": "playerframe"})["src"]
download_url = episode_url.replace("/embed/", "/playlist/") + ".m3u8"
except (KeyError, NotFoundErr) as e:
raise e
return [f"{BASE_URL}{episode_url}", f"{BASE_URL}{download_url}"]
if __name__ == "__main__":
anime_name = input("Enter anime name: ").strip()
anime_list = search_scraper(anime_name)
print("\n")
if len(anime_list) == 0:
print("No anime found with this name")
else:
print(f"Found {len(anime_list)} results: ")
for (i, anime) in enumerate(anime_list):
anime_title = anime["title"]
print(f"{i+1}. {anime_title}")
anime_choice = int(input("\nPlease choose from the following list: ").strip())
chosen_anime = anime_list[anime_choice - 1]
print(f"You chose {chosen_anime['title']}. Searching for episodes...")
episode_list = search_anime_episode_list(chosen_anime["url"])
if len(episode_list) == 0:
print("No episode found for this anime")
else:
print(f"Found {len(episode_list)} results: ")
for (i, episode) in enumerate(episode_list):
print(f"{i+1}. {episode['title']}")
episode_choice = int(input("\nChoose an episode by serial no: ").strip())
chosen_episode = episode_list[episode_choice - 1]
print(f"You chose {chosen_episode['title']}. Searching...")
episode_url, download_url = get_anime_episode(chosen_episode["url"])
print(f"\nTo watch, ctrl+click on {episode_url}.")
print(f"To download, ctrl+click on {download_url}.")
| from xml.dom import NotFoundErr
import requests
from bs4 import BeautifulSoup, NavigableString
from fake_useragent import UserAgent
BASE_URL = "https://ww1.gogoanime2.org"
def search_scraper(anime_name: str) -> list:
"""[summary]
Take an url and
return list of anime after scraping the site.
>>> type(search_scraper("demon_slayer"))
<class 'list'>
Args:
anime_name (str): [Name of anime]
Raises:
e: [Raises exception on failure]
Returns:
[list]: [List of animes]
"""
# concat the name to form the search url.
search_url = f"{BASE_URL}/search/{anime_name}"
response = requests.get(
search_url, headers={"UserAgent": UserAgent().chrome}
) # request the url.
# Is the response ok?
response.raise_for_status()
# parse with soup.
soup = BeautifulSoup(response.text, "html.parser")
# get list of anime
anime_ul = soup.find("ul", {"class": "items"})
anime_li = anime_ul.children
# for each anime, insert to list. the name and url.
anime_list = []
for anime in anime_li:
if not isinstance(anime, NavigableString):
try:
anime_url, anime_title = (
anime.find("a")["href"],
anime.find("a")["title"],
)
anime_list.append(
{
"title": anime_title,
"url": anime_url,
}
)
except (NotFoundErr, KeyError):
pass
return anime_list
def search_anime_episode_list(episode_endpoint: str) -> list:
"""[summary]
Take an url and
return list of episodes after scraping the site
for an url.
>>> type(search_anime_episode_list("/anime/kimetsu-no-yaiba"))
<class 'list'>
Args:
episode_endpoint (str): [Endpoint of episode]
Raises:
e: [description]
Returns:
[list]: [List of episodes]
"""
request_url = f"{BASE_URL}{episode_endpoint}"
response = requests.get(url=request_url, headers={"UserAgent": UserAgent().chrome})
response.raise_for_status()
soup = BeautifulSoup(response.text, "html.parser")
# With this id. get the episode list.
episode_page_ul = soup.find("ul", {"id": "episode_related"})
episode_page_li = episode_page_ul.children
episode_list = []
for episode in episode_page_li:
try:
if not isinstance(episode, NavigableString):
episode_list.append(
{
"title": episode.find("div", {"class": "name"}).text.replace(
" ", ""
),
"url": episode.find("a")["href"],
}
)
except (KeyError, NotFoundErr):
pass
return episode_list
def get_anime_episode(episode_endpoint: str) -> list:
"""[summary]
Get click url and download url from episode url
>>> type(get_anime_episode("/watch/kimetsu-no-yaiba/1"))
<class 'list'>
Args:
episode_endpoint (str): [Endpoint of episode]
Raises:
e: [description]
Returns:
[list]: [List of download and watch url]
"""
episode_page_url = f"{BASE_URL}{episode_endpoint}"
response = requests.get(
url=episode_page_url, headers={"User-Agent": UserAgent().chrome}
)
response.raise_for_status()
soup = BeautifulSoup(response.text, "html.parser")
try:
episode_url = soup.find("iframe", {"id": "playerframe"})["src"]
download_url = episode_url.replace("/embed/", "/playlist/") + ".m3u8"
except (KeyError, NotFoundErr) as e:
raise e
return [f"{BASE_URL}{episode_url}", f"{BASE_URL}{download_url}"]
if __name__ == "__main__":
anime_name = input("Enter anime name: ").strip()
anime_list = search_scraper(anime_name)
print("\n")
if len(anime_list) == 0:
print("No anime found with this name")
else:
print(f"Found {len(anime_list)} results: ")
for (i, anime) in enumerate(anime_list):
anime_title = anime["title"]
print(f"{i+1}. {anime_title}")
anime_choice = int(input("\nPlease choose from the following list: ").strip())
chosen_anime = anime_list[anime_choice - 1]
print(f"You chose {chosen_anime['title']}. Searching for episodes...")
episode_list = search_anime_episode_list(chosen_anime["url"])
if len(episode_list) == 0:
print("No episode found for this anime")
else:
print(f"Found {len(episode_list)} results: ")
for (i, episode) in enumerate(episode_list):
print(f"{i+1}. {episode['title']}")
episode_choice = int(input("\nChoose an episode by serial no: ").strip())
chosen_episode = episode_list[episode_choice - 1]
print(f"You chose {chosen_episode['title']}. Searching...")
episode_url, download_url = get_anime_episode(chosen_episode["url"])
print(f"\nTo watch, ctrl+click on {episode_url}.")
print(f"To download, ctrl+click on {download_url}.")
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem Description:
Given a binary tree, return its mirror.
"""
def binary_tree_mirror_dict(binary_tree_mirror_dictionary: dict, root: int):
if not root or root not in binary_tree_mirror_dictionary:
return
left_child, right_child = binary_tree_mirror_dictionary[root][:2]
binary_tree_mirror_dictionary[root] = [right_child, left_child]
binary_tree_mirror_dict(binary_tree_mirror_dictionary, left_child)
binary_tree_mirror_dict(binary_tree_mirror_dictionary, right_child)
def binary_tree_mirror(binary_tree: dict, root: int = 1) -> dict:
"""
>>> binary_tree_mirror({ 1: [2,3], 2: [4,5], 3: [6,7], 7: [8,9]}, 1)
{1: [3, 2], 2: [5, 4], 3: [7, 6], 7: [9, 8]}
>>> binary_tree_mirror({ 1: [2,3], 2: [4,5], 3: [6,7], 4: [10,11]}, 1)
{1: [3, 2], 2: [5, 4], 3: [7, 6], 4: [11, 10]}
>>> binary_tree_mirror({ 1: [2,3], 2: [4,5], 3: [6,7], 4: [10,11]}, 5)
Traceback (most recent call last):
...
ValueError: root 5 is not present in the binary_tree
>>> binary_tree_mirror({}, 5)
Traceback (most recent call last):
...
ValueError: binary tree cannot be empty
"""
if not binary_tree:
raise ValueError("binary tree cannot be empty")
if root not in binary_tree:
raise ValueError(f"root {root} is not present in the binary_tree")
binary_tree_mirror_dictionary = dict(binary_tree)
binary_tree_mirror_dict(binary_tree_mirror_dictionary, root)
return binary_tree_mirror_dictionary
if __name__ == "__main__":
binary_tree = {1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
print(f"Binary tree: {binary_tree}")
binary_tree_mirror_dictionary = binary_tree_mirror(binary_tree, 5)
print(f"Binary tree mirror: {binary_tree_mirror_dictionary}")
| """
Problem Description:
Given a binary tree, return its mirror.
"""
def binary_tree_mirror_dict(binary_tree_mirror_dictionary: dict, root: int):
if not root or root not in binary_tree_mirror_dictionary:
return
left_child, right_child = binary_tree_mirror_dictionary[root][:2]
binary_tree_mirror_dictionary[root] = [right_child, left_child]
binary_tree_mirror_dict(binary_tree_mirror_dictionary, left_child)
binary_tree_mirror_dict(binary_tree_mirror_dictionary, right_child)
def binary_tree_mirror(binary_tree: dict, root: int = 1) -> dict:
"""
>>> binary_tree_mirror({ 1: [2,3], 2: [4,5], 3: [6,7], 7: [8,9]}, 1)
{1: [3, 2], 2: [5, 4], 3: [7, 6], 7: [9, 8]}
>>> binary_tree_mirror({ 1: [2,3], 2: [4,5], 3: [6,7], 4: [10,11]}, 1)
{1: [3, 2], 2: [5, 4], 3: [7, 6], 4: [11, 10]}
>>> binary_tree_mirror({ 1: [2,3], 2: [4,5], 3: [6,7], 4: [10,11]}, 5)
Traceback (most recent call last):
...
ValueError: root 5 is not present in the binary_tree
>>> binary_tree_mirror({}, 5)
Traceback (most recent call last):
...
ValueError: binary tree cannot be empty
"""
if not binary_tree:
raise ValueError("binary tree cannot be empty")
if root not in binary_tree:
raise ValueError(f"root {root} is not present in the binary_tree")
binary_tree_mirror_dictionary = dict(binary_tree)
binary_tree_mirror_dict(binary_tree_mirror_dictionary, root)
return binary_tree_mirror_dictionary
if __name__ == "__main__":
binary_tree = {1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
print(f"Binary tree: {binary_tree}")
binary_tree_mirror_dictionary = binary_tree_mirror(binary_tree, 5)
print(f"Binary tree mirror: {binary_tree_mirror_dictionary}")
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Approximates the area under the curve using the trapezoidal rule
"""
from __future__ import annotations
from collections.abc import Callable
def trapezoidal_area(
fnc: Callable[[int | float], int | float],
x_start: int | float,
x_end: int | float,
steps: int = 100,
) -> float:
"""
Treats curve as a collection of linear lines and sums the area of the
trapezium shape they form
:param fnc: a function which defines a curve
:param x_start: left end point to indicate the start of line segment
:param x_end: right end point to indicate end of line segment
:param steps: an accuracy gauge; more steps increases the accuracy
:return: a float representing the length of the curve
>>> def f(x):
... return 5
>>> f"{trapezoidal_area(f, 12.0, 14.0, 1000):.3f}"
'10.000'
>>> def f(x):
... return 9*x**2
>>> f"{trapezoidal_area(f, -4.0, 0, 10000):.4f}"
'192.0000'
>>> f"{trapezoidal_area(f, -4.0, 4.0, 10000):.4f}"
'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 + x**2
print("f(x) = x^3 + x^2")
print("The area between the curve, x = -5, x = 5 and the x axis is:")
i = 10
while i <= 100000:
print(f"with {i} steps: {trapezoidal_area(f, -5, 5, i)}")
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
>>> f"{trapezoidal_area(f, 12.0, 14.0, 1000):.3f}"
'10.000'
>>> def f(x):
... return 9*x**2
>>> f"{trapezoidal_area(f, -4.0, 0, 10000):.4f}"
'192.0000'
>>> f"{trapezoidal_area(f, -4.0, 4.0, 10000):.4f}"
'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 + x**2
print("f(x) = x^3 + x^2")
print("The area between the curve, x = -5, x = 5 and the x axis is:")
i = 10
while i <= 100000:
print(f"with {i} steps: {trapezoidal_area(f, -5, 5, i)}")
i *= 10
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from PIL import Image
"""
Mean thresholding algorithm for image processing
https://en.wikipedia.org/wiki/Thresholding_(image_processing)
"""
def mean_threshold(image: Image) -> Image:
"""
image: is a grayscale PIL image object
"""
height, width = image.size
mean = 0
pixels = image.load()
for i in range(width):
for j in range(height):
pixel = pixels[j, i]
mean += pixel
mean //= width * height
for j in range(width):
for i in range(height):
pixels[i, j] = 255 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
image = mean_threshold(Image.open("path_to_image").convert("L"))
image.save("output_image_path")
| from PIL import Image
"""
Mean thresholding algorithm for image processing
https://en.wikipedia.org/wiki/Thresholding_(image_processing)
"""
def mean_threshold(image: Image) -> Image:
"""
image: is a grayscale PIL image object
"""
height, width = image.size
mean = 0
pixels = image.load()
for i in range(width):
for j in range(height):
pixel = pixels[j, i]
mean += pixel
mean //= width * height
for j in range(width):
for i in range(height):
pixels[i, j] = 255 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
image = mean_threshold(Image.open("path_to_image").convert("L"))
image.save("output_image_path")
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def base16_encode(data: bytes) -> str:
"""
Encodes the given bytes into base16.
>>> base16_encode(b'Hello World!')
'48656C6C6F20576F726C6421'
>>> base16_encode(b'HELLO WORLD!')
'48454C4C4F20574F524C4421'
>>> base16_encode(b'')
''
"""
# Turn the data into a list of integers (where each integer is a byte),
# Then turn each byte into its hexadecimal representation, make sure
# it is uppercase, and then join everything together and return it.
return "".join([hex(byte)[2:].zfill(2).upper() for byte in list(data)])
def base16_decode(data: str) -> bytes:
"""
Decodes the given base16 encoded data into bytes.
>>> base16_decode('48656C6C6F20576F726C6421')
b'Hello World!'
>>> base16_decode('48454C4C4F20574F524C4421')
b'HELLO WORLD!'
>>> base16_decode('')
b''
>>> base16_decode('486')
Traceback (most recent call last):
...
ValueError: Base16 encoded data is invalid:
Data does not have an even number of hex digits.
>>> base16_decode('48656c6c6f20576f726c6421')
Traceback (most recent call last):
...
ValueError: Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.
>>> base16_decode('This is not base64 encoded data.')
Traceback (most recent call last):
...
ValueError: Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.
"""
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(data) % 2) != 0:
raise ValueError(
"""Base16 encoded data is invalid:
Data does not have an even number of hex digits."""
)
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(data) <= set("0123456789ABCDEF"):
raise ValueError(
"""Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters."""
)
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1], 16) for i in range(0, len(data), 2))
if __name__ == "__main__":
import doctest
doctest.testmod()
| def base16_encode(data: bytes) -> str:
"""
Encodes the given bytes into base16.
>>> base16_encode(b'Hello World!')
'48656C6C6F20576F726C6421'
>>> base16_encode(b'HELLO WORLD!')
'48454C4C4F20574F524C4421'
>>> base16_encode(b'')
''
"""
# Turn the data into a list of integers (where each integer is a byte),
# Then turn each byte into its hexadecimal representation, make sure
# it is uppercase, and then join everything together and return it.
return "".join([hex(byte)[2:].zfill(2).upper() for byte in list(data)])
def base16_decode(data: str) -> bytes:
"""
Decodes the given base16 encoded data into bytes.
>>> base16_decode('48656C6C6F20576F726C6421')
b'Hello World!'
>>> base16_decode('48454C4C4F20574F524C4421')
b'HELLO WORLD!'
>>> base16_decode('')
b''
>>> base16_decode('486')
Traceback (most recent call last):
...
ValueError: Base16 encoded data is invalid:
Data does not have an even number of hex digits.
>>> base16_decode('48656c6c6f20576f726c6421')
Traceback (most recent call last):
...
ValueError: Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.
>>> base16_decode('This is not base64 encoded data.')
Traceback (most recent call last):
...
ValueError: Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.
"""
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(data) % 2) != 0:
raise ValueError(
"""Base16 encoded data is invalid:
Data does not have an even number of hex digits."""
)
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(data) <= set("0123456789ABCDEF"):
raise ValueError(
"""Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters."""
)
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1], 16) for i in range(0, len(data), 2))
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/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 | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
"""
Build quantum teleportation circuit using three quantum bits
and 1 classical bit. The main idea is to send one qubit from
Alice to Bob using the entanglement properties. This experiment
run in IBM Q simulator with 1000 shots.
.
References:
https://en.wikipedia.org/wiki/Quantum_teleportation
https://qiskit.org/textbook/ch-algorithms/teleportation.html
"""
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def quantum_teleportation(
theta: float = np.pi / 2, phi: float = np.pi / 2, lam: float = np.pi / 2
) -> qiskit.result.counts.Counts:
"""
# >>> quantum_teleportation()
#{'00': 500, '11': 500} # ideally
# ┌─────────────────┐ ┌───┐
#qr_0: ┤ U(π/2,π/2,π/2) ├───────■──┤ H ├─■─────────
# └──────┬───┬──────┘ ┌─┴─┐└───┘ │
#qr_1: ───────┤ H ├─────────■──┤ X ├──────┼───■─────
# └───┘ ┌─┴─┐└───┘ │ ┌─┴─┐┌─┐
#qr_2: ───────────────────┤ X ├───────────■─┤ X ├┤M├
# └───┘ └───┘└╥┘
#cr: 1/═══════════════════════════════════════════╩═
Args:
theta (float): Single qubit rotation U Gate theta parameter. Default to np.pi/2
phi (float): Single qubit rotation U Gate phi parameter. Default to np.pi/2
lam (float): Single qubit rotation U Gate lam parameter. Default to np.pi/2
Returns:
qiskit.result.counts.Counts: Teleported qubit counts.
"""
qr = QuantumRegister(3, "qr") # Define the number of quantum bits
cr = ClassicalRegister(1, "cr") # Define the number of classical bits
quantum_circuit = QuantumCircuit(qr, cr) # Define the quantum circuit.
# Build the circuit
quantum_circuit.u(theta, phi, lam, 0) # Quantum State to teleport
quantum_circuit.h(1) # add hadamard gate
quantum_circuit.cx(
1, 2
) # add control gate with qubit 1 as control and 2 as target.
quantum_circuit.cx(0, 1)
quantum_circuit.h(0)
quantum_circuit.cz(0, 2) # add control z gate.
quantum_circuit.cx(1, 2)
quantum_circuit.measure([2], [0]) # measure the qubit.
# Simulate the circuit using qasm simulator
backend = Aer.get_backend("aer_simulator")
job = execute(quantum_circuit, backend, shots=1000)
return job.result().get_counts(quantum_circuit)
if __name__ == "__main__":
print(
"Total count for teleported state is: "
f"{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}"
)
| #!/usr/bin/env python3
"""
Build quantum teleportation circuit using three quantum bits
and 1 classical bit. The main idea is to send one qubit from
Alice to Bob using the entanglement properties. This experiment
run in IBM Q simulator with 1000 shots.
.
References:
https://en.wikipedia.org/wiki/Quantum_teleportation
https://qiskit.org/textbook/ch-algorithms/teleportation.html
"""
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def quantum_teleportation(
theta: float = np.pi / 2, phi: float = np.pi / 2, lam: float = np.pi / 2
) -> qiskit.result.counts.Counts:
"""
# >>> quantum_teleportation()
#{'00': 500, '11': 500} # ideally
# ┌─────────────────┐ ┌───┐
#qr_0: ┤ U(π/2,π/2,π/2) ├───────■──┤ H ├─■─────────
# └──────┬───┬──────┘ ┌─┴─┐└───┘ │
#qr_1: ───────┤ H ├─────────■──┤ X ├──────┼───■─────
# └───┘ ┌─┴─┐└───┘ │ ┌─┴─┐┌─┐
#qr_2: ───────────────────┤ X ├───────────■─┤ X ├┤M├
# └───┘ └───┘└╥┘
#cr: 1/═══════════════════════════════════════════╩═
Args:
theta (float): Single qubit rotation U Gate theta parameter. Default to np.pi/2
phi (float): Single qubit rotation U Gate phi parameter. Default to np.pi/2
lam (float): Single qubit rotation U Gate lam parameter. Default to np.pi/2
Returns:
qiskit.result.counts.Counts: Teleported qubit counts.
"""
qr = QuantumRegister(3, "qr") # Define the number of quantum bits
cr = ClassicalRegister(1, "cr") # Define the number of classical bits
quantum_circuit = QuantumCircuit(qr, cr) # Define the quantum circuit.
# Build the circuit
quantum_circuit.u(theta, phi, lam, 0) # Quantum State to teleport
quantum_circuit.h(1) # add hadamard gate
quantum_circuit.cx(
1, 2
) # add control gate with qubit 1 as control and 2 as target.
quantum_circuit.cx(0, 1)
quantum_circuit.h(0)
quantum_circuit.cz(0, 2) # add control z gate.
quantum_circuit.cx(1, 2)
quantum_circuit.measure([2], [0]) # measure the qubit.
# Simulate the circuit using qasm simulator
backend = Aer.get_backend("aer_simulator")
job = execute(quantum_circuit, backend, shots=1000)
return job.result().get_counts(quantum_circuit)
if __name__ == "__main__":
print(
"Total count for teleported state is: "
f"{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}"
)
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 135: https://projecteuler.net/problem=135
Given the positive integers, x, y, and z,
are consecutive terms of an arithmetic progression,
the least value of the positive integer, n,
for which the equation,
x2 − y2 − z2 = n, has exactly two solutions is n = 27:
342 − 272 − 202 = 122 − 92 − 62 = 27
It turns out that n = 1155 is the least value
which has exactly ten solutions.
How many values of n less than one million
have exactly ten distinct solutions?
Taking x,y,z of the form a+d,a,a-d respectively,
the given equation reduces to a*(4d-a)=n.
Calculating no of solutions for every n till 1 million by fixing a
,and n must be multiple of a.
Total no of steps=n*(1/1+1/2+1/3+1/4..+1/n)
,so roughly O(nlogn) time complexity.
"""
def solution(limit: int = 1000000) -> int:
"""
returns the values of n less than or equal to the limit
have exactly ten distinct solutions.
>>> solution(100)
0
>>> solution(10000)
45
>>> solution(50050)
292
"""
limit = limit + 1
frequency = [0] * limit
for first_term in range(1, limit):
for n in range(first_term, limit, first_term):
common_difference = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
count = sum(1 for x in frequency[1:limit] if x == 10)
return count
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 135: https://projecteuler.net/problem=135
Given the positive integers, x, y, and z,
are consecutive terms of an arithmetic progression,
the least value of the positive integer, n,
for which the equation,
x2 − y2 − z2 = n, has exactly two solutions is n = 27:
342 − 272 − 202 = 122 − 92 − 62 = 27
It turns out that n = 1155 is the least value
which has exactly ten solutions.
How many values of n less than one million
have exactly ten distinct solutions?
Taking x,y,z of the form a+d,a,a-d respectively,
the given equation reduces to a*(4d-a)=n.
Calculating no of solutions for every n till 1 million by fixing a
,and n must be multiple of a.
Total no of steps=n*(1/1+1/2+1/3+1/4..+1/n)
,so roughly O(nlogn) time complexity.
"""
def solution(limit: int = 1000000) -> int:
"""
returns the values of n less than or equal to the limit
have exactly ten distinct solutions.
>>> solution(100)
0
>>> solution(10000)
45
>>> solution(50050)
292
"""
limit = limit + 1
frequency = [0] * limit
for first_term in range(1, limit):
for n in range(first_term, limit, first_term):
common_difference = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
count = sum(1 for x in frequency[1:limit] if x == 10)
return count
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is a pure Python implementation of the merge-insertion sort algorithm
Source: https://en.wikipedia.org/wiki/Graham_scan
For doctests run following command:
python3 -m doctest -v graham_scan.py
"""
from __future__ import annotations
from collections import deque
from enum import Enum
from math import atan2, degrees
from sys import maxsize
# traversal from the lowest and the most left point in anti-clockwise direction
# if direction gets right, the previous point is not the convex hull.
class Direction(Enum):
left = 1
straight = 2
right = 3
def __repr__(self):
return f"{self.__class__.__name__}.{self.name}"
def angle_comparer(point: tuple[int, int], minx: int, miny: int) -> float:
"""Return the angle toward to point from (minx, miny)
:param point: The target point
minx: The starting point's x
miny: The starting point's y
:return: the angle
Examples:
>>> angle_comparer((1,1), 0, 0)
45.0
>>> angle_comparer((100,1), 10, 10)
-5.710593137499642
>>> angle_comparer((5,5), 2, 3)
33.690067525979785
"""
# sort the points accorgind to the angle from the lowest and the most left point
x, y = point
return degrees(atan2(y - miny, x - minx))
def check_direction(
starting: tuple[int, int], via: tuple[int, int], target: tuple[int, int]
) -> Direction:
"""Return the direction toward to the line from via to target from starting
:param starting: The starting point
via: The via point
target: The target point
:return: the Direction
Examples:
>>> check_direction((1,1), (2,2), (3,3))
Direction.straight
>>> check_direction((60,1), (-50,199), (30,2))
Direction.left
>>> check_direction((0,0), (5,5), (10,0))
Direction.right
"""
x0, y0 = starting
x1, y1 = via
x2, y2 = target
via_angle = degrees(atan2(y1 - y0, x1 - x0))
via_angle %= 360
target_angle = degrees(atan2(y2 - y0, x2 - x0))
target_angle %= 360
# t-
# \ \
# \ v
# \|
# s
# via_angle is always lower than target_angle, if direction is left.
# If they are same, it means they are on a same line of convex hull.
if target_angle > via_angle:
return Direction.left
elif target_angle == via_angle:
return Direction.straight
else:
return Direction.right
def graham_scan(points: list[tuple[int, int]]) -> list[tuple[int, int]]:
"""Pure implementation of graham scan algorithm in Python
:param points: The unique points on coordinates.
:return: The points on convex hell.
Examples:
>>> graham_scan([(9, 6), (3, 1), (0, 0), (5, 5), (5, 2), (7, 0), (3, 3), (1, 4)])
[(0, 0), (7, 0), (9, 6), (5, 5), (1, 4)]
>>> graham_scan([(0, 0), (1, 0), (1, 1), (0, 1)])
[(0, 0), (1, 0), (1, 1), (0, 1)]
>>> graham_scan([(0, 0), (1, 1), (2, 2), (3, 3), (-1, 2)])
[(0, 0), (1, 1), (2, 2), (3, 3), (-1, 2)]
>>> graham_scan([(-100, 20), (99, 3), (1, 10000001), (5133186, -25), (-66, -4)])
[(5133186, -25), (1, 10000001), (-100, 20), (-66, -4)]
"""
if len(points) <= 2:
# There is no convex hull
raise ValueError("graham_scan: argument must contain more than 3 points.")
if len(points) == 3:
return points
# find the lowest and the most left point
minidx = 0
miny, minx = maxsize, maxsize
for i, point in enumerate(points):
x = point[0]
y = point[1]
if y < miny:
miny = y
minx = x
minidx = i
if y == miny:
if x < minx:
minx = x
minidx = i
# remove the lowest and the most left point from points for preparing for sort
points.pop(minidx)
sorted_points = sorted(points, key=lambda point: angle_comparer(point, minx, miny))
# This insert actually costs complexity,
# and you should instead add (minx, miny) into stack later.
# I'm using insert just for easy understanding.
sorted_points.insert(0, (minx, miny))
stack: deque[tuple[int, int]] = deque()
stack.append(sorted_points[0])
stack.append(sorted_points[1])
stack.append(sorted_points[2])
# In any ways, the first 3 points line are towards left.
# Because we sort them the angle from minx, miny.
current_direction = Direction.left
for i in range(3, len(sorted_points)):
while True:
starting = stack[-2]
via = stack[-1]
target = sorted_points[i]
next_direction = check_direction(starting, via, target)
if next_direction == Direction.left:
current_direction = Direction.left
break
if next_direction == Direction.straight:
if current_direction == Direction.left:
# We keep current_direction as left.
# Because if the straight line keeps as straight,
# we want to know if this straight line is towards left.
break
elif current_direction == Direction.right:
# If the straight line is towards right,
# every previous points on those straigh line is not convex hull.
stack.pop()
if next_direction == Direction.right:
stack.pop()
stack.append(sorted_points[i])
return list(stack)
| """
This is a pure Python implementation of the merge-insertion sort algorithm
Source: https://en.wikipedia.org/wiki/Graham_scan
For doctests run following command:
python3 -m doctest -v graham_scan.py
"""
from __future__ import annotations
from collections import deque
from enum import Enum
from math import atan2, degrees
from sys import maxsize
# traversal from the lowest and the most left point in anti-clockwise direction
# if direction gets right, the previous point is not the convex hull.
class Direction(Enum):
left = 1
straight = 2
right = 3
def __repr__(self):
return f"{self.__class__.__name__}.{self.name}"
def angle_comparer(point: tuple[int, int], minx: int, miny: int) -> float:
"""Return the angle toward to point from (minx, miny)
:param point: The target point
minx: The starting point's x
miny: The starting point's y
:return: the angle
Examples:
>>> angle_comparer((1,1), 0, 0)
45.0
>>> angle_comparer((100,1), 10, 10)
-5.710593137499642
>>> angle_comparer((5,5), 2, 3)
33.690067525979785
"""
# sort the points accorgind to the angle from the lowest and the most left point
x, y = point
return degrees(atan2(y - miny, x - minx))
def check_direction(
starting: tuple[int, int], via: tuple[int, int], target: tuple[int, int]
) -> Direction:
"""Return the direction toward to the line from via to target from starting
:param starting: The starting point
via: The via point
target: The target point
:return: the Direction
Examples:
>>> check_direction((1,1), (2,2), (3,3))
Direction.straight
>>> check_direction((60,1), (-50,199), (30,2))
Direction.left
>>> check_direction((0,0), (5,5), (10,0))
Direction.right
"""
x0, y0 = starting
x1, y1 = via
x2, y2 = target
via_angle = degrees(atan2(y1 - y0, x1 - x0))
via_angle %= 360
target_angle = degrees(atan2(y2 - y0, x2 - x0))
target_angle %= 360
# t-
# \ \
# \ v
# \|
# s
# via_angle is always lower than target_angle, if direction is left.
# If they are same, it means they are on a same line of convex hull.
if target_angle > via_angle:
return Direction.left
elif target_angle == via_angle:
return Direction.straight
else:
return Direction.right
def graham_scan(points: list[tuple[int, int]]) -> list[tuple[int, int]]:
"""Pure implementation of graham scan algorithm in Python
:param points: The unique points on coordinates.
:return: The points on convex hell.
Examples:
>>> graham_scan([(9, 6), (3, 1), (0, 0), (5, 5), (5, 2), (7, 0), (3, 3), (1, 4)])
[(0, 0), (7, 0), (9, 6), (5, 5), (1, 4)]
>>> graham_scan([(0, 0), (1, 0), (1, 1), (0, 1)])
[(0, 0), (1, 0), (1, 1), (0, 1)]
>>> graham_scan([(0, 0), (1, 1), (2, 2), (3, 3), (-1, 2)])
[(0, 0), (1, 1), (2, 2), (3, 3), (-1, 2)]
>>> graham_scan([(-100, 20), (99, 3), (1, 10000001), (5133186, -25), (-66, -4)])
[(5133186, -25), (1, 10000001), (-100, 20), (-66, -4)]
"""
if len(points) <= 2:
# There is no convex hull
raise ValueError("graham_scan: argument must contain more than 3 points.")
if len(points) == 3:
return points
# find the lowest and the most left point
minidx = 0
miny, minx = maxsize, maxsize
for i, point in enumerate(points):
x = point[0]
y = point[1]
if y < miny:
miny = y
minx = x
minidx = i
if y == miny:
if x < minx:
minx = x
minidx = i
# remove the lowest and the most left point from points for preparing for sort
points.pop(minidx)
sorted_points = sorted(points, key=lambda point: angle_comparer(point, minx, miny))
# This insert actually costs complexity,
# and you should instead add (minx, miny) into stack later.
# I'm using insert just for easy understanding.
sorted_points.insert(0, (minx, miny))
stack: deque[tuple[int, int]] = deque()
stack.append(sorted_points[0])
stack.append(sorted_points[1])
stack.append(sorted_points[2])
# In any ways, the first 3 points line are towards left.
# Because we sort them the angle from minx, miny.
current_direction = Direction.left
for i in range(3, len(sorted_points)):
while True:
starting = stack[-2]
via = stack[-1]
target = sorted_points[i]
next_direction = check_direction(starting, via, target)
if next_direction == Direction.left:
current_direction = Direction.left
break
if next_direction == Direction.straight:
if current_direction == Direction.left:
# We keep current_direction as left.
# Because if the straight line keeps as straight,
# we want to know if this straight line is towards left.
break
elif current_direction == Direction.right:
# If the straight line is towards right,
# every previous points on those straigh line is not convex hull.
stack.pop()
if next_direction == Direction.right:
stack.pop()
stack.append(sorted_points[i])
return list(stack)
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/Infix_notation
https://en.wikipedia.org/wiki/Reverse_Polish_notation
https://en.wikipedia.org/wiki/Shunting-yard_algorithm
"""
from .balanced_parentheses import balanced_parentheses
from .stack import Stack
def precedence(char: str) -> int:
"""
Return integer value representing an operator's precedence, or
order of operation.
https://en.wikipedia.org/wiki/Order_of_operations
"""
return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(char, -1)
def infix_to_postfix(expression_str: str) -> str:
"""
>>> infix_to_postfix("(1*(2+3)+4))")
Traceback (most recent call last):
...
ValueError: Mismatched parentheses
>>> infix_to_postfix("")
''
>>> infix_to_postfix("3+2")
'3 2 +'
>>> infix_to_postfix("(3+4)*5-6")
'3 4 + 5 * 6 -'
>>> infix_to_postfix("(1+2)*3/4-5")
'1 2 + 3 * 4 / 5 -'
>>> infix_to_postfix("a+b*c+(d*e+f)*g")
'a b c * + d e * f + g * +'
>>> infix_to_postfix("x^y/(5*z)+2")
'x y ^ 5 z * / 2 +'
"""
if not balanced_parentheses(expression_str):
raise ValueError("Mismatched parentheses")
stack: Stack[str] = Stack()
postfix = []
for char in expression_str:
if char.isalpha() or char.isdigit():
postfix.append(char)
elif char == "(":
stack.push(char)
elif char == ")":
while not stack.is_empty() and stack.peek() != "(":
postfix.append(stack.pop())
stack.pop()
else:
while not stack.is_empty() and precedence(char) <= precedence(stack.peek()):
postfix.append(stack.pop())
stack.push(char)
while not stack.is_empty():
postfix.append(stack.pop())
return " ".join(postfix)
if __name__ == "__main__":
from doctest import testmod
testmod()
expression = "a+b*(c^d-e)^(f+g*h)-i"
print("Infix to Postfix Notation demonstration:\n")
print("Infix notation: " + expression)
print("Postfix notation: " + infix_to_postfix(expression))
| """
https://en.wikipedia.org/wiki/Infix_notation
https://en.wikipedia.org/wiki/Reverse_Polish_notation
https://en.wikipedia.org/wiki/Shunting-yard_algorithm
"""
from .balanced_parentheses import balanced_parentheses
from .stack import Stack
def precedence(char: str) -> int:
"""
Return integer value representing an operator's precedence, or
order of operation.
https://en.wikipedia.org/wiki/Order_of_operations
"""
return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(char, -1)
def infix_to_postfix(expression_str: str) -> str:
"""
>>> infix_to_postfix("(1*(2+3)+4))")
Traceback (most recent call last):
...
ValueError: Mismatched parentheses
>>> infix_to_postfix("")
''
>>> infix_to_postfix("3+2")
'3 2 +'
>>> infix_to_postfix("(3+4)*5-6")
'3 4 + 5 * 6 -'
>>> infix_to_postfix("(1+2)*3/4-5")
'1 2 + 3 * 4 / 5 -'
>>> infix_to_postfix("a+b*c+(d*e+f)*g")
'a b c * + d e * f + g * +'
>>> infix_to_postfix("x^y/(5*z)+2")
'x y ^ 5 z * / 2 +'
"""
if not balanced_parentheses(expression_str):
raise ValueError("Mismatched parentheses")
stack: Stack[str] = Stack()
postfix = []
for char in expression_str:
if char.isalpha() or char.isdigit():
postfix.append(char)
elif char == "(":
stack.push(char)
elif char == ")":
while not stack.is_empty() and stack.peek() != "(":
postfix.append(stack.pop())
stack.pop()
else:
while not stack.is_empty() and precedence(char) <= precedence(stack.peek()):
postfix.append(stack.pop())
stack.push(char)
while not stack.is_empty():
postfix.append(stack.pop())
return " ".join(postfix)
if __name__ == "__main__":
from doctest import testmod
testmod()
expression = "a+b*(c^d-e)^(f+g*h)-i"
print("Infix to Postfix Notation demonstration:\n")
print("Infix notation: " + expression)
print("Postfix notation: " + infix_to_postfix(expression))
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/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 | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Arithmetic mean
Reference: https://en.wikipedia.org/wiki/Arithmetic_mean
Arithmetic series
Reference: https://en.wikipedia.org/wiki/Arithmetic_series
(The URL above will redirect you to arithmetic progression)
"""
def is_arithmetic_series(series: list) -> bool:
"""
checking whether the input series is arithmetic series or not
>>> is_arithmetic_series([2, 4, 6])
True
>>> is_arithmetic_series([3, 6, 12, 24])
False
>>> is_arithmetic_series([1, 2, 3])
True
>>> is_arithmetic_series(4)
Traceback (most recent call last):
...
ValueError: Input series is not valid, valid series - [2, 4, 6]
>>> is_arithmetic_series([])
Traceback (most recent call last):
...
ValueError: Input list must be a non empty list
"""
if not isinstance(series, list):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]")
if len(series) == 0:
raise ValueError("Input list must be a non empty list")
if len(series) == 1:
return True
common_diff = series[1] - series[0]
for index in range(len(series) - 1):
if series[index + 1] - series[index] != common_diff:
return False
return True
def arithmetic_mean(series: list) -> float:
"""
return the arithmetic mean of series
>>> arithmetic_mean([2, 4, 6])
4.0
>>> arithmetic_mean([3, 6, 9, 12])
7.5
>>> arithmetic_mean(4)
Traceback (most recent call last):
...
ValueError: Input series is not valid, valid series - [2, 4, 6]
>>> arithmetic_mean([4, 8, 1])
4.333333333333333
>>> arithmetic_mean([1, 2, 3])
2.0
>>> arithmetic_mean([])
Traceback (most recent call last):
...
ValueError: Input list must be a non empty list
"""
if not isinstance(series, list):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]")
if len(series) == 0:
raise ValueError("Input list must be a non empty list")
answer = 0
for val in series:
answer += val
return answer / len(series)
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Arithmetic mean
Reference: https://en.wikipedia.org/wiki/Arithmetic_mean
Arithmetic series
Reference: https://en.wikipedia.org/wiki/Arithmetic_series
(The URL above will redirect you to arithmetic progression)
"""
def is_arithmetic_series(series: list) -> bool:
"""
checking whether the input series is arithmetic series or not
>>> is_arithmetic_series([2, 4, 6])
True
>>> is_arithmetic_series([3, 6, 12, 24])
False
>>> is_arithmetic_series([1, 2, 3])
True
>>> is_arithmetic_series(4)
Traceback (most recent call last):
...
ValueError: Input series is not valid, valid series - [2, 4, 6]
>>> is_arithmetic_series([])
Traceback (most recent call last):
...
ValueError: Input list must be a non empty list
"""
if not isinstance(series, list):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]")
if len(series) == 0:
raise ValueError("Input list must be a non empty list")
if len(series) == 1:
return True
common_diff = series[1] - series[0]
for index in range(len(series) - 1):
if series[index + 1] - series[index] != common_diff:
return False
return True
def arithmetic_mean(series: list) -> float:
"""
return the arithmetic mean of series
>>> arithmetic_mean([2, 4, 6])
4.0
>>> arithmetic_mean([3, 6, 9, 12])
7.5
>>> arithmetic_mean(4)
Traceback (most recent call last):
...
ValueError: Input series is not valid, valid series - [2, 4, 6]
>>> arithmetic_mean([4, 8, 1])
4.333333333333333
>>> arithmetic_mean([1, 2, 3])
2.0
>>> arithmetic_mean([])
Traceback (most recent call last):
...
ValueError: Input list must be a non empty list
"""
if not isinstance(series, list):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]")
if len(series) == 0:
raise ValueError("Input list must be a non empty list")
answer = 0
for val in series:
answer += val
return answer / len(series)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def snake_to_camel_case(input_str: str, use_pascal: bool = False) -> str:
"""
Transforms a snake_case given string to camelCase (or PascalCase if indicated)
(defaults to not use Pascal)
>>> snake_to_camel_case("some_random_string")
'someRandomString'
>>> snake_to_camel_case("some_random_string", use_pascal=True)
'SomeRandomString'
>>> snake_to_camel_case("some_random_string_with_numbers_123")
'someRandomStringWithNumbers123'
>>> snake_to_camel_case("some_random_string_with_numbers_123", use_pascal=True)
'SomeRandomStringWithNumbers123'
>>> snake_to_camel_case(123)
Traceback (most recent call last):
...
ValueError: Expected string as input, found <class 'int'>
>>> snake_to_camel_case("some_string", use_pascal="True")
Traceback (most recent call last):
...
ValueError: Expected boolean as use_pascal parameter, found <class 'str'>
"""
if not isinstance(input_str, str):
raise ValueError(f"Expected string as input, found {type(input_str)}")
if not isinstance(use_pascal, bool):
raise ValueError(
f"Expected boolean as use_pascal parameter, found {type(use_pascal)}"
)
words = input_str.split("_")
start_index = 0 if use_pascal else 1
words_to_capitalize = words[start_index:]
capitalized_words = [word[0].upper() + word[1:] for word in words_to_capitalize]
initial_word = "" if use_pascal else words[0]
return "".join([initial_word] + capitalized_words)
if __name__ == "__main__":
from doctest import testmod
testmod()
| def snake_to_camel_case(input_str: str, use_pascal: bool = False) -> str:
"""
Transforms a snake_case given string to camelCase (or PascalCase if indicated)
(defaults to not use Pascal)
>>> snake_to_camel_case("some_random_string")
'someRandomString'
>>> snake_to_camel_case("some_random_string", use_pascal=True)
'SomeRandomString'
>>> snake_to_camel_case("some_random_string_with_numbers_123")
'someRandomStringWithNumbers123'
>>> snake_to_camel_case("some_random_string_with_numbers_123", use_pascal=True)
'SomeRandomStringWithNumbers123'
>>> snake_to_camel_case(123)
Traceback (most recent call last):
...
ValueError: Expected string as input, found <class 'int'>
>>> snake_to_camel_case("some_string", use_pascal="True")
Traceback (most recent call last):
...
ValueError: Expected boolean as use_pascal parameter, found <class 'str'>
"""
if not isinstance(input_str, str):
raise ValueError(f"Expected string as input, found {type(input_str)}")
if not isinstance(use_pascal, bool):
raise ValueError(
f"Expected boolean as use_pascal parameter, found {type(use_pascal)}"
)
words = input_str.split("_")
start_index = 0 if use_pascal else 1
words_to_capitalize = words[start_index:]
capitalized_words = [word[0].upper() + word[1:] for word in words_to_capitalize]
initial_word = "" if use_pascal else words[0]
return "".join([initial_word] + capitalized_words)
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
The RGB color model is an additive color model in which red, green, and blue light
are added together in various ways to reproduce a broad array of colors. The name
of the model comes from the initials of the three additive primary colors, red,
green, and blue. Meanwhile, the HSV representation models how colors appear under
light. In it, colors are represented using three components: hue, saturation and
(brightness-)value. This file provides functions for converting colors from one
representation to the other.
(description adapted from https://en.wikipedia.org/wiki/RGB_color_model and
https://en.wikipedia.org/wiki/HSL_and_HSV).
"""
def hsv_to_rgb(hue: float, saturation: float, value: float) -> list[int]:
"""
Conversion from the HSV-representation to the RGB-representation.
Expected RGB-values taken from
https://www.rapidtables.com/convert/color/hsv-to-rgb.html
>>> hsv_to_rgb(0, 0, 0)
[0, 0, 0]
>>> hsv_to_rgb(0, 0, 1)
[255, 255, 255]
>>> hsv_to_rgb(0, 1, 1)
[255, 0, 0]
>>> hsv_to_rgb(60, 1, 1)
[255, 255, 0]
>>> hsv_to_rgb(120, 1, 1)
[0, 255, 0]
>>> hsv_to_rgb(240, 1, 1)
[0, 0, 255]
>>> hsv_to_rgb(300, 1, 1)
[255, 0, 255]
>>> hsv_to_rgb(180, 0.5, 0.5)
[64, 128, 128]
>>> hsv_to_rgb(234, 0.14, 0.88)
[193, 196, 224]
>>> hsv_to_rgb(330, 0.75, 0.5)
[128, 32, 80]
"""
if hue < 0 or hue > 360:
raise Exception("hue should be between 0 and 360")
if saturation < 0 or saturation > 1:
raise Exception("saturation should be between 0 and 1")
if value < 0 or value > 1:
raise Exception("value should be between 0 and 1")
chroma = value * saturation
hue_section = hue / 60
second_largest_component = chroma * (1 - abs(hue_section % 2 - 1))
match_value = value - chroma
if hue_section >= 0 and hue_section <= 1:
red = round(255 * (chroma + match_value))
green = round(255 * (second_largest_component + match_value))
blue = round(255 * (match_value))
elif hue_section > 1 and hue_section <= 2:
red = round(255 * (second_largest_component + match_value))
green = round(255 * (chroma + match_value))
blue = round(255 * (match_value))
elif hue_section > 2 and hue_section <= 3:
red = round(255 * (match_value))
green = round(255 * (chroma + match_value))
blue = round(255 * (second_largest_component + match_value))
elif hue_section > 3 and hue_section <= 4:
red = round(255 * (match_value))
green = round(255 * (second_largest_component + match_value))
blue = round(255 * (chroma + match_value))
elif hue_section > 4 and hue_section <= 5:
red = round(255 * (second_largest_component + match_value))
green = round(255 * (match_value))
blue = round(255 * (chroma + match_value))
else:
red = round(255 * (chroma + match_value))
green = round(255 * (match_value))
blue = round(255 * (second_largest_component + match_value))
return [red, green, blue]
def rgb_to_hsv(red: int, green: int, blue: int) -> list[float]:
"""
Conversion from the RGB-representation to the HSV-representation.
The tested values are the reverse values from the hsv_to_rgb-doctests.
Function "approximately_equal_hsv" is needed because of small deviations due to
rounding for the RGB-values.
>>> approximately_equal_hsv(rgb_to_hsv(0, 0, 0), [0, 0, 0])
True
>>> approximately_equal_hsv(rgb_to_hsv(255, 255, 255), [0, 0, 1])
True
>>> approximately_equal_hsv(rgb_to_hsv(255, 0, 0), [0, 1, 1])
True
>>> approximately_equal_hsv(rgb_to_hsv(255, 255, 0), [60, 1, 1])
True
>>> approximately_equal_hsv(rgb_to_hsv(0, 255, 0), [120, 1, 1])
True
>>> approximately_equal_hsv(rgb_to_hsv(0, 0, 255), [240, 1, 1])
True
>>> approximately_equal_hsv(rgb_to_hsv(255, 0, 255), [300, 1, 1])
True
>>> approximately_equal_hsv(rgb_to_hsv(64, 128, 128), [180, 0.5, 0.5])
True
>>> approximately_equal_hsv(rgb_to_hsv(193, 196, 224), [234, 0.14, 0.88])
True
>>> approximately_equal_hsv(rgb_to_hsv(128, 32, 80), [330, 0.75, 0.5])
True
"""
if red < 0 or red > 255:
raise Exception("red should be between 0 and 255")
if green < 0 or green > 255:
raise Exception("green should be between 0 and 255")
if blue < 0 or blue > 255:
raise Exception("blue should be between 0 and 255")
float_red = red / 255
float_green = green / 255
float_blue = blue / 255
value = max(max(float_red, float_green), float_blue)
chroma = value - min(min(float_red, float_green), float_blue)
saturation = 0 if value == 0 else chroma / value
if chroma == 0:
hue = 0.0
elif value == float_red:
hue = 60 * (0 + (float_green - float_blue) / chroma)
elif value == float_green:
hue = 60 * (2 + (float_blue - float_red) / chroma)
else:
hue = 60 * (4 + (float_red - float_green) / chroma)
hue = (hue + 360) % 360
return [hue, saturation, value]
def approximately_equal_hsv(hsv_1: list[float], hsv_2: list[float]) -> bool:
"""
Utility-function to check that two hsv-colors are approximately equal
>>> approximately_equal_hsv([0, 0, 0], [0, 0, 0])
True
>>> approximately_equal_hsv([180, 0.5, 0.3], [179.9999, 0.500001, 0.30001])
True
>>> approximately_equal_hsv([0, 0, 0], [1, 0, 0])
False
>>> approximately_equal_hsv([180, 0.5, 0.3], [179.9999, 0.6, 0.30001])
False
"""
check_hue = abs(hsv_1[0] - hsv_2[0]) < 0.2
check_saturation = abs(hsv_1[1] - hsv_2[1]) < 0.002
check_value = abs(hsv_1[2] - hsv_2[2]) < 0.002
return check_hue and check_saturation and check_value
| """
The RGB color model is an additive color model in which red, green, and blue light
are added together in various ways to reproduce a broad array of colors. The name
of the model comes from the initials of the three additive primary colors, red,
green, and blue. Meanwhile, the HSV representation models how colors appear under
light. In it, colors are represented using three components: hue, saturation and
(brightness-)value. This file provides functions for converting colors from one
representation to the other.
(description adapted from https://en.wikipedia.org/wiki/RGB_color_model and
https://en.wikipedia.org/wiki/HSL_and_HSV).
"""
def hsv_to_rgb(hue: float, saturation: float, value: float) -> list[int]:
"""
Conversion from the HSV-representation to the RGB-representation.
Expected RGB-values taken from
https://www.rapidtables.com/convert/color/hsv-to-rgb.html
>>> hsv_to_rgb(0, 0, 0)
[0, 0, 0]
>>> hsv_to_rgb(0, 0, 1)
[255, 255, 255]
>>> hsv_to_rgb(0, 1, 1)
[255, 0, 0]
>>> hsv_to_rgb(60, 1, 1)
[255, 255, 0]
>>> hsv_to_rgb(120, 1, 1)
[0, 255, 0]
>>> hsv_to_rgb(240, 1, 1)
[0, 0, 255]
>>> hsv_to_rgb(300, 1, 1)
[255, 0, 255]
>>> hsv_to_rgb(180, 0.5, 0.5)
[64, 128, 128]
>>> hsv_to_rgb(234, 0.14, 0.88)
[193, 196, 224]
>>> hsv_to_rgb(330, 0.75, 0.5)
[128, 32, 80]
"""
if hue < 0 or hue > 360:
raise Exception("hue should be between 0 and 360")
if saturation < 0 or saturation > 1:
raise Exception("saturation should be between 0 and 1")
if value < 0 or value > 1:
raise Exception("value should be between 0 and 1")
chroma = value * saturation
hue_section = hue / 60
second_largest_component = chroma * (1 - abs(hue_section % 2 - 1))
match_value = value - chroma
if hue_section >= 0 and hue_section <= 1:
red = round(255 * (chroma + match_value))
green = round(255 * (second_largest_component + match_value))
blue = round(255 * (match_value))
elif hue_section > 1 and hue_section <= 2:
red = round(255 * (second_largest_component + match_value))
green = round(255 * (chroma + match_value))
blue = round(255 * (match_value))
elif hue_section > 2 and hue_section <= 3:
red = round(255 * (match_value))
green = round(255 * (chroma + match_value))
blue = round(255 * (second_largest_component + match_value))
elif hue_section > 3 and hue_section <= 4:
red = round(255 * (match_value))
green = round(255 * (second_largest_component + match_value))
blue = round(255 * (chroma + match_value))
elif hue_section > 4 and hue_section <= 5:
red = round(255 * (second_largest_component + match_value))
green = round(255 * (match_value))
blue = round(255 * (chroma + match_value))
else:
red = round(255 * (chroma + match_value))
green = round(255 * (match_value))
blue = round(255 * (second_largest_component + match_value))
return [red, green, blue]
def rgb_to_hsv(red: int, green: int, blue: int) -> list[float]:
"""
Conversion from the RGB-representation to the HSV-representation.
The tested values are the reverse values from the hsv_to_rgb-doctests.
Function "approximately_equal_hsv" is needed because of small deviations due to
rounding for the RGB-values.
>>> approximately_equal_hsv(rgb_to_hsv(0, 0, 0), [0, 0, 0])
True
>>> approximately_equal_hsv(rgb_to_hsv(255, 255, 255), [0, 0, 1])
True
>>> approximately_equal_hsv(rgb_to_hsv(255, 0, 0), [0, 1, 1])
True
>>> approximately_equal_hsv(rgb_to_hsv(255, 255, 0), [60, 1, 1])
True
>>> approximately_equal_hsv(rgb_to_hsv(0, 255, 0), [120, 1, 1])
True
>>> approximately_equal_hsv(rgb_to_hsv(0, 0, 255), [240, 1, 1])
True
>>> approximately_equal_hsv(rgb_to_hsv(255, 0, 255), [300, 1, 1])
True
>>> approximately_equal_hsv(rgb_to_hsv(64, 128, 128), [180, 0.5, 0.5])
True
>>> approximately_equal_hsv(rgb_to_hsv(193, 196, 224), [234, 0.14, 0.88])
True
>>> approximately_equal_hsv(rgb_to_hsv(128, 32, 80), [330, 0.75, 0.5])
True
"""
if red < 0 or red > 255:
raise Exception("red should be between 0 and 255")
if green < 0 or green > 255:
raise Exception("green should be between 0 and 255")
if blue < 0 or blue > 255:
raise Exception("blue should be between 0 and 255")
float_red = red / 255
float_green = green / 255
float_blue = blue / 255
value = max(max(float_red, float_green), float_blue)
chroma = value - min(min(float_red, float_green), float_blue)
saturation = 0 if value == 0 else chroma / value
if chroma == 0:
hue = 0.0
elif value == float_red:
hue = 60 * (0 + (float_green - float_blue) / chroma)
elif value == float_green:
hue = 60 * (2 + (float_blue - float_red) / chroma)
else:
hue = 60 * (4 + (float_red - float_green) / chroma)
hue = (hue + 360) % 360
return [hue, saturation, value]
def approximately_equal_hsv(hsv_1: list[float], hsv_2: list[float]) -> bool:
"""
Utility-function to check that two hsv-colors are approximately equal
>>> approximately_equal_hsv([0, 0, 0], [0, 0, 0])
True
>>> approximately_equal_hsv([180, 0.5, 0.3], [179.9999, 0.500001, 0.30001])
True
>>> approximately_equal_hsv([0, 0, 0], [1, 0, 0])
False
>>> approximately_equal_hsv([180, 0.5, 0.3], [179.9999, 0.6, 0.30001])
False
"""
check_hue = abs(hsv_1[0] - hsv_2[0]) < 0.2
check_saturation = abs(hsv_1[1] - hsv_2[1]) < 0.002
check_value = abs(hsv_1[2] - hsv_2[2]) < 0.002
return check_hue and check_saturation and check_value
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] 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 | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 75: https://projecteuler.net/problem=75
It turns out that 12 cm is the smallest length of wire that can be bent to form an
integer sided right angle triangle in exactly one way, but there are many more examples.
12 cm: (3,4,5)
24 cm: (6,8,10)
30 cm: (5,12,13)
36 cm: (9,12,15)
40 cm: (8,15,17)
48 cm: (12,16,20)
In contrast, some lengths of wire, like 20 cm, cannot be bent to form an integer sided
right angle triangle, and other lengths allow more than one solution to be found; for
example, using 120 cm it is possible to form exactly three different integer sided
right angle triangles.
120 cm: (30,40,50), (20,48,52), (24,45,51)
Given that L is the length of the wire, for how many values of L ≤ 1,500,000 can
exactly one integer sided right angle triangle be formed?
Solution: we generate all pythagorean triples using Euclid's formula and
keep track of the frequencies of the perimeters.
Reference: https://en.wikipedia.org/wiki/Pythagorean_triple#Generating_a_triple
"""
from collections import defaultdict
from math import gcd
from typing import DefaultDict
def solution(limit: int = 1500000) -> int:
"""
Return the number of values of L <= limit such that a wire of length L can be
formmed into an integer sided right angle triangle in exactly one way.
>>> solution(50)
6
>>> solution(1000)
112
>>> solution(50000)
5502
"""
frequencies: DefaultDict = defaultdict(int)
euclid_m = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1, euclid_m, 2):
if gcd(euclid_m, euclid_n) > 1:
continue
primitive_perimeter = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(primitive_perimeter, limit + 1, primitive_perimeter):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 75: https://projecteuler.net/problem=75
It turns out that 12 cm is the smallest length of wire that can be bent to form an
integer sided right angle triangle in exactly one way, but there are many more examples.
12 cm: (3,4,5)
24 cm: (6,8,10)
30 cm: (5,12,13)
36 cm: (9,12,15)
40 cm: (8,15,17)
48 cm: (12,16,20)
In contrast, some lengths of wire, like 20 cm, cannot be bent to form an integer sided
right angle triangle, and other lengths allow more than one solution to be found; for
example, using 120 cm it is possible to form exactly three different integer sided
right angle triangles.
120 cm: (30,40,50), (20,48,52), (24,45,51)
Given that L is the length of the wire, for how many values of L ≤ 1,500,000 can
exactly one integer sided right angle triangle be formed?
Solution: we generate all pythagorean triples using Euclid's formula and
keep track of the frequencies of the perimeters.
Reference: https://en.wikipedia.org/wiki/Pythagorean_triple#Generating_a_triple
"""
from collections import defaultdict
from math import gcd
from typing import DefaultDict
def solution(limit: int = 1500000) -> int:
"""
Return the number of values of L <= limit such that a wire of length L can be
formmed into an integer sided right angle triangle in exactly one way.
>>> solution(50)
6
>>> solution(1000)
112
>>> solution(50000)
5502
"""
frequencies: DefaultDict = defaultdict(int)
euclid_m = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1, euclid_m, 2):
if gcd(euclid_m, euclid_n) > 1:
continue
primitive_perimeter = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(primitive_perimeter, limit + 1, primitive_perimeter):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python
#
# Sort large text files in a minimum amount of memory
#
import argparse
import os
class FileSplitter:
BLOCK_FILENAME_FORMAT = "block_{0}.dat"
def __init__(self, filename):
self.filename = filename
self.block_filenames = []
def write_block(self, data, block_number):
filename = self.BLOCK_FILENAME_FORMAT.format(block_number)
with open(filename, "w") as file:
file.write(data)
self.block_filenames.append(filename)
def get_block_filenames(self):
return self.block_filenames
def split(self, block_size, sort_key=None):
i = 0
with open(self.filename) as file:
while True:
lines = file.readlines(block_size)
if lines == []:
break
if sort_key is None:
lines.sort()
else:
lines.sort(key=sort_key)
self.write_block("".join(lines), i)
i += 1
def cleanup(self):
map(os.remove, self.block_filenames)
class NWayMerge:
def select(self, choices):
min_index = -1
min_str = None
for i in range(len(choices)):
if min_str is None or choices[i] < min_str:
min_index = i
return min_index
class FilesArray:
def __init__(self, files):
self.files = files
self.empty = set()
self.num_buffers = len(files)
self.buffers = {i: None for i in range(self.num_buffers)}
def get_dict(self):
return {
i: self.buffers[i] for i in range(self.num_buffers) if i not in self.empty
}
def refresh(self):
for i in range(self.num_buffers):
if self.buffers[i] is None and i not in self.empty:
self.buffers[i] = self.files[i].readline()
if self.buffers[i] == "":
self.empty.add(i)
self.files[i].close()
if len(self.empty) == self.num_buffers:
return False
return True
def unshift(self, index):
value = self.buffers[index]
self.buffers[index] = None
return value
class FileMerger:
def __init__(self, merge_strategy):
self.merge_strategy = merge_strategy
def merge(self, filenames, outfilename, buffer_size):
buffers = FilesArray(self.get_file_handles(filenames, buffer_size))
with open(outfilename, "w", buffer_size) as outfile:
while buffers.refresh():
min_index = self.merge_strategy.select(buffers.get_dict())
outfile.write(buffers.unshift(min_index))
def get_file_handles(self, filenames, buffer_size):
files = {}
for i in range(len(filenames)):
files[i] = open(filenames[i], "r", buffer_size)
return files
class ExternalSort:
def __init__(self, block_size):
self.block_size = block_size
def sort(self, filename, sort_key=None):
num_blocks = self.get_number_blocks(filename, self.block_size)
splitter = FileSplitter(filename)
splitter.split(self.block_size, sort_key)
merger = FileMerger(NWayMerge())
buffer_size = self.block_size / (num_blocks + 1)
merger.merge(splitter.get_block_filenames(), filename + ".out", buffer_size)
splitter.cleanup()
def get_number_blocks(self, filename, block_size):
return (os.stat(filename).st_size / block_size) + 1
def parse_memory(string):
if string[-1].lower() == "k":
return int(string[:-1]) * 1024
elif string[-1].lower() == "m":
return int(string[:-1]) * 1024 * 1024
elif string[-1].lower() == "g":
return int(string[:-1]) * 1024 * 1024 * 1024
else:
return int(string)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"-m", "--mem", help="amount of memory to use for sorting", default="100M"
)
parser.add_argument(
"filename", metavar="<filename>", nargs=1, help="name of file to sort"
)
args = parser.parse_args()
sorter = ExternalSort(parse_memory(args.mem))
sorter.sort(args.filename[0])
if __name__ == "__main__":
main()
| #!/usr/bin/env python
#
# Sort large text files in a minimum amount of memory
#
import argparse
import os
class FileSplitter:
BLOCK_FILENAME_FORMAT = "block_{0}.dat"
def __init__(self, filename):
self.filename = filename
self.block_filenames = []
def write_block(self, data, block_number):
filename = self.BLOCK_FILENAME_FORMAT.format(block_number)
with open(filename, "w") as file:
file.write(data)
self.block_filenames.append(filename)
def get_block_filenames(self):
return self.block_filenames
def split(self, block_size, sort_key=None):
i = 0
with open(self.filename) as file:
while True:
lines = file.readlines(block_size)
if lines == []:
break
if sort_key is None:
lines.sort()
else:
lines.sort(key=sort_key)
self.write_block("".join(lines), i)
i += 1
def cleanup(self):
map(os.remove, self.block_filenames)
class NWayMerge:
def select(self, choices):
min_index = -1
min_str = None
for i in range(len(choices)):
if min_str is None or choices[i] < min_str:
min_index = i
return min_index
class FilesArray:
def __init__(self, files):
self.files = files
self.empty = set()
self.num_buffers = len(files)
self.buffers = {i: None for i in range(self.num_buffers)}
def get_dict(self):
return {
i: self.buffers[i] for i in range(self.num_buffers) if i not in self.empty
}
def refresh(self):
for i in range(self.num_buffers):
if self.buffers[i] is None and i not in self.empty:
self.buffers[i] = self.files[i].readline()
if self.buffers[i] == "":
self.empty.add(i)
self.files[i].close()
if len(self.empty) == self.num_buffers:
return False
return True
def unshift(self, index):
value = self.buffers[index]
self.buffers[index] = None
return value
class FileMerger:
def __init__(self, merge_strategy):
self.merge_strategy = merge_strategy
def merge(self, filenames, outfilename, buffer_size):
buffers = FilesArray(self.get_file_handles(filenames, buffer_size))
with open(outfilename, "w", buffer_size) as outfile:
while buffers.refresh():
min_index = self.merge_strategy.select(buffers.get_dict())
outfile.write(buffers.unshift(min_index))
def get_file_handles(self, filenames, buffer_size):
files = {}
for i in range(len(filenames)):
files[i] = open(filenames[i], "r", buffer_size)
return files
class ExternalSort:
def __init__(self, block_size):
self.block_size = block_size
def sort(self, filename, sort_key=None):
num_blocks = self.get_number_blocks(filename, self.block_size)
splitter = FileSplitter(filename)
splitter.split(self.block_size, sort_key)
merger = FileMerger(NWayMerge())
buffer_size = self.block_size / (num_blocks + 1)
merger.merge(splitter.get_block_filenames(), filename + ".out", buffer_size)
splitter.cleanup()
def get_number_blocks(self, filename, block_size):
return (os.stat(filename).st_size / block_size) + 1
def parse_memory(string):
if string[-1].lower() == "k":
return int(string[:-1]) * 1024
elif string[-1].lower() == "m":
return int(string[:-1]) * 1024 * 1024
elif string[-1].lower() == "g":
return int(string[:-1]) * 1024 * 1024 * 1024
else:
return int(string)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"-m", "--mem", help="amount of memory to use for sorting", default="100M"
)
parser.add_argument(
"filename", metavar="<filename>", nargs=1, help="name of file to sort"
)
args = parser.parse_args()
sorter = ExternalSort(parse_memory(args.mem))
sorter.sort(args.filename[0])
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Output:
Enter a Postfix Equation (space separated) = 5 6 9 * +
Symbol | Action | Stack
-----------------------------------
5 | push(5) | 5
6 | push(6) | 5,6
9 | push(9) | 5,6,9
| pop(9) | 5,6
| pop(6) | 5
* | push(6*9) | 5,54
| pop(54) | 5
| pop(5) |
+ | push(5+54) | 59
Result = 59
"""
import operator as op
def solve(post_fix):
stack = []
div = lambda x, y: int(x / y) # noqa: E731 integer division operation
opr = {
"^": op.pow,
"*": op.mul,
"/": div,
"+": op.add,
"-": op.sub,
} # operators & their respective operation
# print table header
print("Symbol".center(8), "Action".center(12), "Stack", sep=" | ")
print("-" * (30 + len(post_fix)))
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(x) # append x to stack
# output in tabular format
print(x.rjust(8), ("push(" + x + ")").ljust(12), ",".join(stack), sep=" | ")
else:
b = stack.pop() # pop stack
# output in tabular format
print("".rjust(8), ("pop(" + b + ")").ljust(12), ",".join(stack), sep=" | ")
a = stack.pop() # pop stack
# output in tabular format
print("".rjust(8), ("pop(" + a + ")").ljust(12), ",".join(stack), sep=" | ")
stack.append(
str(opr[x](int(a), int(b)))
) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8),
("push(" + a + x + b + ")").ljust(12),
",".join(stack),
sep=" | ",
)
return int(stack[0])
if __name__ == "__main__":
Postfix = input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
print("\n\tResult = ", solve(Postfix))
| """
Output:
Enter a Postfix Equation (space separated) = 5 6 9 * +
Symbol | Action | Stack
-----------------------------------
5 | push(5) | 5
6 | push(6) | 5,6
9 | push(9) | 5,6,9
| pop(9) | 5,6
| pop(6) | 5
* | push(6*9) | 5,54
| pop(54) | 5
| pop(5) |
+ | push(5+54) | 59
Result = 59
"""
import operator as op
def solve(post_fix):
stack = []
div = lambda x, y: int(x / y) # noqa: E731 integer division operation
opr = {
"^": op.pow,
"*": op.mul,
"/": div,
"+": op.add,
"-": op.sub,
} # operators & their respective operation
# print table header
print("Symbol".center(8), "Action".center(12), "Stack", sep=" | ")
print("-" * (30 + len(post_fix)))
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(x) # append x to stack
# output in tabular format
print(x.rjust(8), ("push(" + x + ")").ljust(12), ",".join(stack), sep=" | ")
else:
b = stack.pop() # pop stack
# output in tabular format
print("".rjust(8), ("pop(" + b + ")").ljust(12), ",".join(stack), sep=" | ")
a = stack.pop() # pop stack
# output in tabular format
print("".rjust(8), ("pop(" + a + ")").ljust(12), ",".join(stack), sep=" | ")
stack.append(
str(opr[x](int(a), int(b)))
) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8),
("push(" + a + x + b + ")").ljust(12),
",".join(stack),
sep=" | ",
)
return int(stack[0])
if __name__ == "__main__":
Postfix = input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
print("\n\tResult = ", solve(Postfix))
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Simple multithreaded algorithm to show how the 4 phases of a genetic algorithm works
(Evaluation, Selection, Crossover and Mutation)
https://en.wikipedia.org/wiki/Genetic_algorithm
Author: D4rkia
"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
N_POPULATION = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
N_SELECTED = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
MUTATION_PROBABILITY = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def basic(target: str, genes: list[str], debug: bool = True) -> tuple[int, int, str]:
"""
Verify that the target contains no genes besides the ones inside genes variable.
>>> from string import ascii_lowercase
>>> basic("doctest", ascii_lowercase, debug=False)[2]
'doctest'
>>> genes = list(ascii_lowercase)
>>> genes.remove("e")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e'] is not in genes list, evolution cannot converge
>>> genes.remove("s")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e', 's'] is not in genes list, evolution cannot converge
>>> genes.remove("t")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e', 's', 't'] is not in genes list, evolution cannot converge
"""
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
raise ValueError(f"{N_POPULATION} must be bigger than {N_SELECTED}")
# Verify that the target contains no genes besides the ones inside genes variable.
not_in_genes_list = sorted({c for c in target if c not in genes})
if not_in_genes_list:
raise ValueError(
f"{not_in_genes_list} is not in genes list, evolution cannot converge"
)
# Generate random starting population.
population = []
for _ in range(N_POPULATION):
population.append("".join([random.choice(genes) for i in range(len(target))]))
# Just some logs to know what the algorithms is doing.
generation, total_population = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(population)
# Random population created. Now it's time to evaluate.
def evaluate(item: str, main_target: str = target) -> tuple[str, float]:
"""
Evaluate how similar the item is with the target by just
counting each char in the right position
>>> evaluate("Helxo Worlx", Hello World)
["Helxo Worlx", 9]
"""
score = len(
[g for position, g in enumerate(item) if g == main_target[position]]
)
return (item, float(score))
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
population_score = [evaluate(item) for item in population]
# Check if there is a matching evolution.
population_score = sorted(population_score, key=lambda x: x[1], reverse=True)
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
f"\nGeneration: {generation}"
f"\nTotal Population:{total_population}"
f"\nBest score: {population_score[0][1]}"
f"\nBest string: {population_score[0][0]}"
)
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
population_best = population[: int(N_POPULATION / 3)]
population.clear()
population.extend(population_best)
# Normalize population score to be between 0 and 1.
population_score = [
(item, score / len(target)) for item, score in population_score
]
# Select, crossover and mutate a new population.
def select(parent_1: tuple[str, float]) -> list[str]:
"""Select the second parent and generate new population"""
pop = []
# Generate more children proportionally to the fitness score.
child_n = int(parent_1[1] * 100) + 1
child_n = 10 if child_n >= 10 else child_n
for _ in range(child_n):
parent_2 = population_score[ # noqa: B023
random.randint(0, N_SELECTED)
][0]
child_1, child_2 = crossover(parent_1[0], parent_2)
# Append new string to the population list.
pop.append(mutate(child_1))
pop.append(mutate(child_2))
return pop
def crossover(parent_1: str, parent_2: str) -> tuple[str, str]:
"""Slice and combine two string at a random point."""
random_slice = random.randint(0, len(parent_1) - 1)
child_1 = parent_1[:random_slice] + parent_2[random_slice:]
child_2 = parent_2[:random_slice] + parent_1[random_slice:]
return (child_1, child_2)
def mutate(child: str) -> str:
"""Mutate a random gene of a child with another one from the list."""
child_list = list(child)
if random.uniform(0, 1) < MUTATION_PROBABILITY:
child_list[random.randint(0, len(child)) - 1] = random.choice(genes)
return "".join(child_list)
# This is selection
for i in range(N_SELECTED):
population.extend(select(population_score[int(i)]))
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(population) > N_POPULATION:
break
if __name__ == "__main__":
target_str = (
"This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
)
genes_list = list(
" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
"nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
)
generation, population, target = basic(target_str, genes_list)
print(
f"\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"
)
| """
Simple multithreaded algorithm to show how the 4 phases of a genetic algorithm works
(Evaluation, Selection, Crossover and Mutation)
https://en.wikipedia.org/wiki/Genetic_algorithm
Author: D4rkia
"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
N_POPULATION = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
N_SELECTED = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
MUTATION_PROBABILITY = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def basic(target: str, genes: list[str], debug: bool = True) -> tuple[int, int, str]:
"""
Verify that the target contains no genes besides the ones inside genes variable.
>>> from string import ascii_lowercase
>>> basic("doctest", ascii_lowercase, debug=False)[2]
'doctest'
>>> genes = list(ascii_lowercase)
>>> genes.remove("e")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e'] is not in genes list, evolution cannot converge
>>> genes.remove("s")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e', 's'] is not in genes list, evolution cannot converge
>>> genes.remove("t")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e', 's', 't'] is not in genes list, evolution cannot converge
"""
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
raise ValueError(f"{N_POPULATION} must be bigger than {N_SELECTED}")
# Verify that the target contains no genes besides the ones inside genes variable.
not_in_genes_list = sorted({c for c in target if c not in genes})
if not_in_genes_list:
raise ValueError(
f"{not_in_genes_list} is not in genes list, evolution cannot converge"
)
# Generate random starting population.
population = []
for _ in range(N_POPULATION):
population.append("".join([random.choice(genes) for i in range(len(target))]))
# Just some logs to know what the algorithms is doing.
generation, total_population = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(population)
# Random population created. Now it's time to evaluate.
def evaluate(item: str, main_target: str = target) -> tuple[str, float]:
"""
Evaluate how similar the item is with the target by just
counting each char in the right position
>>> evaluate("Helxo Worlx", Hello World)
["Helxo Worlx", 9]
"""
score = len(
[g for position, g in enumerate(item) if g == main_target[position]]
)
return (item, float(score))
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
population_score = [evaluate(item) for item in population]
# Check if there is a matching evolution.
population_score = sorted(population_score, key=lambda x: x[1], reverse=True)
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
f"\nGeneration: {generation}"
f"\nTotal Population:{total_population}"
f"\nBest score: {population_score[0][1]}"
f"\nBest string: {population_score[0][0]}"
)
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
population_best = population[: int(N_POPULATION / 3)]
population.clear()
population.extend(population_best)
# Normalize population score to be between 0 and 1.
population_score = [
(item, score / len(target)) for item, score in population_score
]
# Select, crossover and mutate a new population.
def select(parent_1: tuple[str, float]) -> list[str]:
"""Select the second parent and generate new population"""
pop = []
# Generate more children proportionally to the fitness score.
child_n = int(parent_1[1] * 100) + 1
child_n = 10 if child_n >= 10 else child_n
for _ in range(child_n):
parent_2 = population_score[ # noqa: B023
random.randint(0, N_SELECTED)
][0]
child_1, child_2 = crossover(parent_1[0], parent_2)
# Append new string to the population list.
pop.append(mutate(child_1))
pop.append(mutate(child_2))
return pop
def crossover(parent_1: str, parent_2: str) -> tuple[str, str]:
"""Slice and combine two string at a random point."""
random_slice = random.randint(0, len(parent_1) - 1)
child_1 = parent_1[:random_slice] + parent_2[random_slice:]
child_2 = parent_2[:random_slice] + parent_1[random_slice:]
return (child_1, child_2)
def mutate(child: str) -> str:
"""Mutate a random gene of a child with another one from the list."""
child_list = list(child)
if random.uniform(0, 1) < MUTATION_PROBABILITY:
child_list[random.randint(0, len(child)) - 1] = random.choice(genes)
return "".join(child_list)
# This is selection
for i in range(N_SELECTED):
population.extend(select(population_score[int(i)]))
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(population) > N_POPULATION:
break
if __name__ == "__main__":
target_str = (
"This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
)
genes_list = list(
" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
"nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
)
generation, population, target = basic(target_str, genes_list)
print(
f"\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"
)
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """ Multiple image resizing techniques """
import numpy as np
from cv2 import destroyAllWindows, imread, imshow, waitKey
class NearestNeighbour:
"""
Simplest and fastest version of image resizing.
Source: https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
"""
def __init__(self, img, dst_width: int, dst_height: int):
if dst_width < 0 or dst_height < 0:
raise ValueError("Destination width/height should be > 0")
self.img = img
self.src_w = img.shape[1]
self.src_h = img.shape[0]
self.dst_w = dst_width
self.dst_h = dst_height
self.ratio_x = self.src_w / self.dst_w
self.ratio_y = self.src_h / self.dst_h
self.output = self.output_img = (
np.ones((self.dst_h, self.dst_w, 3), np.uint8) * 255
)
def process(self):
for i in range(self.dst_h):
for j in range(self.dst_w):
self.output[i][j] = self.img[self.get_y(i)][self.get_x(j)]
def get_x(self, x: int) -> int:
"""
Get parent X coordinate for destination X
:param x: Destination X coordinate
:return: Parent X coordinate based on `x ratio`
>>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg",
... 1), 100, 100)
>>> nn.ratio_x = 0.5
>>> nn.get_x(4)
2
"""
return int(self.ratio_x * x)
def get_y(self, y: int) -> int:
"""
Get parent Y coordinate for destination Y
:param y: Destination X coordinate
:return: Parent X coordinate based on `y ratio`
>>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg",
... 1), 100, 100)
>>> nn.ratio_y = 0.5
>>> nn.get_y(4)
2
"""
return int(self.ratio_y * y)
if __name__ == "__main__":
dst_w, dst_h = 800, 600
im = imread("image_data/lena.jpg", 1)
n = NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
f"Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}", n.output
)
waitKey(0)
destroyAllWindows()
| """ Multiple image resizing techniques """
import numpy as np
from cv2 import destroyAllWindows, imread, imshow, waitKey
class NearestNeighbour:
"""
Simplest and fastest version of image resizing.
Source: https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
"""
def __init__(self, img, dst_width: int, dst_height: int):
if dst_width < 0 or dst_height < 0:
raise ValueError("Destination width/height should be > 0")
self.img = img
self.src_w = img.shape[1]
self.src_h = img.shape[0]
self.dst_w = dst_width
self.dst_h = dst_height
self.ratio_x = self.src_w / self.dst_w
self.ratio_y = self.src_h / self.dst_h
self.output = self.output_img = (
np.ones((self.dst_h, self.dst_w, 3), np.uint8) * 255
)
def process(self):
for i in range(self.dst_h):
for j in range(self.dst_w):
self.output[i][j] = self.img[self.get_y(i)][self.get_x(j)]
def get_x(self, x: int) -> int:
"""
Get parent X coordinate for destination X
:param x: Destination X coordinate
:return: Parent X coordinate based on `x ratio`
>>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg",
... 1), 100, 100)
>>> nn.ratio_x = 0.5
>>> nn.get_x(4)
2
"""
return int(self.ratio_x * x)
def get_y(self, y: int) -> int:
"""
Get parent Y coordinate for destination Y
:param y: Destination X coordinate
:return: Parent X coordinate based on `y ratio`
>>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg",
... 1), 100, 100)
>>> nn.ratio_y = 0.5
>>> nn.get_y(4)
2
"""
return int(self.ratio_y * y)
if __name__ == "__main__":
dst_w, dst_h = 800, 600
im = imread("image_data/lena.jpg", 1)
n = NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
f"Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}", n.output
)
waitKey(0)
destroyAllWindows()
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
import requests
valid_terms = set(
"""approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)
def get_subreddit_data(
subreddit: str, limit: int = 1, age: str = "new", wanted_data: list | None = None
) -> dict:
"""
subreddit : Subreddit to query
limit : Number of posts to fetch
age : ["new", "top", "hot"]
wanted_data : Get only the required data in the list
"""
wanted_data = wanted_data or []
if invalid_search_terms := ", ".join(sorted(set(wanted_data) - valid_terms)):
raise ValueError(f"Invalid search term: {invalid_search_terms}")
response = requests.get(
f"https://reddit.com/r/{subreddit}/{age}.json?limit={limit}",
headers={"User-agent": "A random string"},
)
if response.status_code == 429:
raise requests.HTTPError
data = response.json()
if not wanted_data:
return {id_: data["data"]["children"][id_] for id_ in range(limit)}
data_dict = {}
for id_ in range(limit):
data_dict[id_] = {
item: data["data"]["children"][id_]["data"][item] for item in wanted_data
}
return data_dict
if __name__ == "__main__":
# If you get Error 429, that means you are rate limited.Try after some time
print(get_subreddit_data("learnpython", wanted_data=["title", "url", "selftext"]))
| from __future__ import annotations
import requests
valid_terms = set(
"""approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)
def get_subreddit_data(
subreddit: str, limit: int = 1, age: str = "new", wanted_data: list | None = None
) -> dict:
"""
subreddit : Subreddit to query
limit : Number of posts to fetch
age : ["new", "top", "hot"]
wanted_data : Get only the required data in the list
"""
wanted_data = wanted_data or []
if invalid_search_terms := ", ".join(sorted(set(wanted_data) - valid_terms)):
raise ValueError(f"Invalid search term: {invalid_search_terms}")
response = requests.get(
f"https://reddit.com/r/{subreddit}/{age}.json?limit={limit}",
headers={"User-agent": "A random string"},
)
if response.status_code == 429:
raise requests.HTTPError
data = response.json()
if not wanted_data:
return {id_: data["data"]["children"][id_] for id_ in range(limit)}
data_dict = {}
for id_ in range(limit):
data_dict[id_] = {
item: data["data"]["children"][id_]["data"][item] for item in wanted_data
}
return data_dict
if __name__ == "__main__":
# If you get Error 429, that means you are rate limited.Try after some time
print(get_subreddit_data("learnpython", wanted_data=["title", "url", "selftext"]))
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Algorithms to determine if a string is palindrome
test_data = {
"MALAYALAM": True,
"String": False,
"rotor": True,
"level": True,
"A": True,
"BB": True,
"ABC": False,
"amanaplanacanalpanama": True, # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())
def is_palindrome(s: str) -> bool:
"""
Return True if s is a palindrome otherwise return False.
>>> all(is_palindrome(key) is value for key, value in test_data.items())
True
"""
start_i = 0
end_i = len(s) - 1
while start_i < end_i:
if s[start_i] == s[end_i]:
start_i += 1
end_i -= 1
else:
return False
return True
def is_palindrome_recursive(s: str) -> bool:
"""
Return True if s is a palindrome otherwise return False.
>>> all(is_palindrome_recursive(key) is value for key, value in test_data.items())
True
"""
if len(s) <= 1:
return True
if s[0] == s[len(s) - 1]:
return is_palindrome_recursive(s[1:-1])
else:
return False
def is_palindrome_slice(s: str) -> bool:
"""
Return True if s is a palindrome otherwise return False.
>>> all(is_palindrome_slice(key) is value for key, value in test_data.items())
True
"""
return s == s[::-1]
if __name__ == "__main__":
for key, value in test_data.items():
assert is_palindrome(key) is is_palindrome_recursive(key)
assert is_palindrome(key) is is_palindrome_slice(key)
print(f"{key:21} {value}")
print("a man a plan a canal panama")
| # Algorithms to determine if a string is palindrome
test_data = {
"MALAYALAM": True,
"String": False,
"rotor": True,
"level": True,
"A": True,
"BB": True,
"ABC": False,
"amanaplanacanalpanama": True, # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())
def is_palindrome(s: str) -> bool:
"""
Return True if s is a palindrome otherwise return False.
>>> all(is_palindrome(key) is value for key, value in test_data.items())
True
"""
start_i = 0
end_i = len(s) - 1
while start_i < end_i:
if s[start_i] == s[end_i]:
start_i += 1
end_i -= 1
else:
return False
return True
def is_palindrome_recursive(s: str) -> bool:
"""
Return True if s is a palindrome otherwise return False.
>>> all(is_palindrome_recursive(key) is value for key, value in test_data.items())
True
"""
if len(s) <= 1:
return True
if s[0] == s[len(s) - 1]:
return is_palindrome_recursive(s[1:-1])
else:
return False
def is_palindrome_slice(s: str) -> bool:
"""
Return True if s is a palindrome otherwise return False.
>>> all(is_palindrome_slice(key) is value for key, value in test_data.items())
True
"""
return s == s[::-1]
if __name__ == "__main__":
for key, value in test_data.items():
assert is_palindrome(key) is is_palindrome_recursive(key)
assert is_palindrome(key) is is_palindrome_slice(key)
print(f"{key:21} {value}")
print("a man a plan a canal panama")
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Python implementation of a sort algorithm.
Best Case Scenario : O(n)
Worst Case Scenario : O(n^2) because native Python functions:min, max and remove are
already O(n)
"""
def merge_sort(collection):
"""Pure implementation of the fastest merge sort algorithm in Python
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: a collection ordered by ascending
Examples:
>>> merge_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> merge_sort([])
[]
>>> merge_sort([-2, -5, -45])
[-45, -5, -2]
"""
start, end = [], []
while len(collection) > 1:
min_one, max_one = min(collection), max(collection)
start.append(min_one)
end.append(max_one)
collection.remove(min_one)
collection.remove(max_one)
end.reverse()
return start + collection + end
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(*merge_sort(unsorted), sep=",")
| """
Python implementation of a sort algorithm.
Best Case Scenario : O(n)
Worst Case Scenario : O(n^2) because native Python functions:min, max and remove are
already O(n)
"""
def merge_sort(collection):
"""Pure implementation of the fastest merge sort algorithm in Python
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: a collection ordered by ascending
Examples:
>>> merge_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> merge_sort([])
[]
>>> merge_sort([-2, -5, -45])
[-45, -5, -2]
"""
start, end = [], []
while len(collection) > 1:
min_one, max_one = min(collection), max(collection)
start.append(min_one)
end.append(max_one)
collection.remove(min_one)
collection.remove(max_one)
end.reverse()
return start + collection + end
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(*merge_sort(unsorted), sep=",")
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #
| #
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Question:
Given a binary matrix mat of size n * m, find out the maximum size square
sub-matrix with all 1s.
---
Example 1:
Input:
n = 2, m = 2
mat = [[1, 1],
[1, 1]]
Output:
2
Explanation: The maximum size of the square
sub-matrix is 2. The matrix itself is the
maximum sized sub-matrix in this case.
---
Example 2
Input:
n = 2, m = 2
mat = [[0, 0],
[0, 0]]
Output: 0
Explanation: There is no 1 in the matrix.
Approach:
We initialize another matrix (dp) with the same dimensions
as the original one initialized with all 0’s.
dp_array(i,j) represents the side length of the maximum square whose
bottom right corner is the cell with index (i,j) in the original matrix.
Starting from index (0,0), for every 1 found in the original matrix,
we update the value of the current element as
dp_array(i,j)=dp_array(dp(i−1,j),dp_array(i−1,j−1),dp_array(i,j−1)) + 1.
"""
def largest_square_area_in_matrix_top_down_approch(
rows: int, cols: int, mat: list[list[int]]
) -> int:
"""
Function updates the largest_square_area[0], if recursive call found
square with maximum area.
We aren't using dp_array here, so the time complexity would be exponential.
>>> largest_square_area_in_matrix_top_down_approch(2, 2, [[1,1], [1,1]])
2
>>> largest_square_area_in_matrix_top_down_approch(2, 2, [[0,0], [0,0]])
0
"""
def update_area_of_max_square(row: int, col: int) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
right = update_area_of_max_square(row, col + 1)
diagonal = update_area_of_max_square(row + 1, col + 1)
down = update_area_of_max_square(row + 1, col)
if mat[row][col]:
sub_problem_sol = 1 + min([right, diagonal, down])
largest_square_area[0] = max(largest_square_area[0], sub_problem_sol)
return sub_problem_sol
else:
return 0
largest_square_area = [0]
update_area_of_max_square(0, 0)
return largest_square_area[0]
def largest_square_area_in_matrix_top_down_approch_with_dp(
rows: int, cols: int, mat: list[list[int]]
) -> int:
"""
Function updates the largest_square_area[0], if recursive call found
square with maximum area.
We are using dp_array here, so the time complexity would be O(N^2).
>>> largest_square_area_in_matrix_top_down_approch_with_dp(2, 2, [[1,1], [1,1]])
2
>>> largest_square_area_in_matrix_top_down_approch_with_dp(2, 2, [[0,0], [0,0]])
0
"""
def update_area_of_max_square_using_dp_array(
row: int, col: int, dp_array: list[list[int]]
) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
right = update_area_of_max_square_using_dp_array(row, col + 1, dp_array)
diagonal = update_area_of_max_square_using_dp_array(row + 1, col + 1, dp_array)
down = update_area_of_max_square_using_dp_array(row + 1, col, dp_array)
if mat[row][col]:
sub_problem_sol = 1 + min([right, diagonal, down])
largest_square_area[0] = max(largest_square_area[0], sub_problem_sol)
dp_array[row][col] = sub_problem_sol
return sub_problem_sol
else:
return 0
largest_square_area = [0]
dp_array = [[-1] * cols for _ in range(rows)]
update_area_of_max_square_using_dp_array(0, 0, dp_array)
return largest_square_area[0]
def largest_square_area_in_matrix_bottom_up(
rows: int, cols: int, mat: list[list[int]]
) -> int:
"""
Function updates the largest_square_area, using bottom up approach.
>>> largest_square_area_in_matrix_bottom_up(2, 2, [[1,1], [1,1]])
2
>>> largest_square_area_in_matrix_bottom_up(2, 2, [[0,0], [0,0]])
0
"""
dp_array = [[0] * (cols + 1) for _ in range(rows + 1)]
largest_square_area = 0
for row in range(rows - 1, -1, -1):
for col in range(cols - 1, -1, -1):
right = dp_array[row][col + 1]
diagonal = dp_array[row + 1][col + 1]
bottom = dp_array[row + 1][col]
if mat[row][col] == 1:
dp_array[row][col] = 1 + min(right, diagonal, bottom)
largest_square_area = max(dp_array[row][col], largest_square_area)
else:
dp_array[row][col] = 0
return largest_square_area
def largest_square_area_in_matrix_bottom_up_space_optimization(
rows: int, cols: int, mat: list[list[int]]
) -> int:
"""
Function updates the largest_square_area, using bottom up
approach. with space optimization.
>>> largest_square_area_in_matrix_bottom_up_space_optimization(2, 2, [[1,1], [1,1]])
2
>>> largest_square_area_in_matrix_bottom_up_space_optimization(2, 2, [[0,0], [0,0]])
0
"""
current_row = [0] * (cols + 1)
next_row = [0] * (cols + 1)
largest_square_area = 0
for row in range(rows - 1, -1, -1):
for col in range(cols - 1, -1, -1):
right = current_row[col + 1]
diagonal = next_row[col + 1]
bottom = next_row[col]
if mat[row][col] == 1:
current_row[col] = 1 + min(right, diagonal, bottom)
largest_square_area = max(current_row[col], largest_square_area)
else:
current_row[col] = 0
next_row = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| """
Question:
Given a binary matrix mat of size n * m, find out the maximum size square
sub-matrix with all 1s.
---
Example 1:
Input:
n = 2, m = 2
mat = [[1, 1],
[1, 1]]
Output:
2
Explanation: The maximum size of the square
sub-matrix is 2. The matrix itself is the
maximum sized sub-matrix in this case.
---
Example 2
Input:
n = 2, m = 2
mat = [[0, 0],
[0, 0]]
Output: 0
Explanation: There is no 1 in the matrix.
Approach:
We initialize another matrix (dp) with the same dimensions
as the original one initialized with all 0’s.
dp_array(i,j) represents the side length of the maximum square whose
bottom right corner is the cell with index (i,j) in the original matrix.
Starting from index (0,0), for every 1 found in the original matrix,
we update the value of the current element as
dp_array(i,j)=dp_array(dp(i−1,j),dp_array(i−1,j−1),dp_array(i,j−1)) + 1.
"""
def largest_square_area_in_matrix_top_down_approch(
rows: int, cols: int, mat: list[list[int]]
) -> int:
"""
Function updates the largest_square_area[0], if recursive call found
square with maximum area.
We aren't using dp_array here, so the time complexity would be exponential.
>>> largest_square_area_in_matrix_top_down_approch(2, 2, [[1,1], [1,1]])
2
>>> largest_square_area_in_matrix_top_down_approch(2, 2, [[0,0], [0,0]])
0
"""
def update_area_of_max_square(row: int, col: int) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
right = update_area_of_max_square(row, col + 1)
diagonal = update_area_of_max_square(row + 1, col + 1)
down = update_area_of_max_square(row + 1, col)
if mat[row][col]:
sub_problem_sol = 1 + min([right, diagonal, down])
largest_square_area[0] = max(largest_square_area[0], sub_problem_sol)
return sub_problem_sol
else:
return 0
largest_square_area = [0]
update_area_of_max_square(0, 0)
return largest_square_area[0]
def largest_square_area_in_matrix_top_down_approch_with_dp(
rows: int, cols: int, mat: list[list[int]]
) -> int:
"""
Function updates the largest_square_area[0], if recursive call found
square with maximum area.
We are using dp_array here, so the time complexity would be O(N^2).
>>> largest_square_area_in_matrix_top_down_approch_with_dp(2, 2, [[1,1], [1,1]])
2
>>> largest_square_area_in_matrix_top_down_approch_with_dp(2, 2, [[0,0], [0,0]])
0
"""
def update_area_of_max_square_using_dp_array(
row: int, col: int, dp_array: list[list[int]]
) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
right = update_area_of_max_square_using_dp_array(row, col + 1, dp_array)
diagonal = update_area_of_max_square_using_dp_array(row + 1, col + 1, dp_array)
down = update_area_of_max_square_using_dp_array(row + 1, col, dp_array)
if mat[row][col]:
sub_problem_sol = 1 + min([right, diagonal, down])
largest_square_area[0] = max(largest_square_area[0], sub_problem_sol)
dp_array[row][col] = sub_problem_sol
return sub_problem_sol
else:
return 0
largest_square_area = [0]
dp_array = [[-1] * cols for _ in range(rows)]
update_area_of_max_square_using_dp_array(0, 0, dp_array)
return largest_square_area[0]
def largest_square_area_in_matrix_bottom_up(
rows: int, cols: int, mat: list[list[int]]
) -> int:
"""
Function updates the largest_square_area, using bottom up approach.
>>> largest_square_area_in_matrix_bottom_up(2, 2, [[1,1], [1,1]])
2
>>> largest_square_area_in_matrix_bottom_up(2, 2, [[0,0], [0,0]])
0
"""
dp_array = [[0] * (cols + 1) for _ in range(rows + 1)]
largest_square_area = 0
for row in range(rows - 1, -1, -1):
for col in range(cols - 1, -1, -1):
right = dp_array[row][col + 1]
diagonal = dp_array[row + 1][col + 1]
bottom = dp_array[row + 1][col]
if mat[row][col] == 1:
dp_array[row][col] = 1 + min(right, diagonal, bottom)
largest_square_area = max(dp_array[row][col], largest_square_area)
else:
dp_array[row][col] = 0
return largest_square_area
def largest_square_area_in_matrix_bottom_up_space_optimization(
rows: int, cols: int, mat: list[list[int]]
) -> int:
"""
Function updates the largest_square_area, using bottom up
approach. with space optimization.
>>> largest_square_area_in_matrix_bottom_up_space_optimization(2, 2, [[1,1], [1,1]])
2
>>> largest_square_area_in_matrix_bottom_up_space_optimization(2, 2, [[0,0], [0,0]])
0
"""
current_row = [0] * (cols + 1)
next_row = [0] * (cols + 1)
largest_square_area = 0
for row in range(rows - 1, -1, -1):
for col in range(cols - 1, -1, -1):
right = current_row[col + 1]
diagonal = next_row[col + 1]
bottom = next_row[col]
if mat[row][col] == 1:
current_row[col] = 1 + min(right, diagonal, bottom)
largest_square_area = max(current_row[col], largest_square_area)
else:
current_row[col] = 0
next_row = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| -1 |
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,945 | Raise error not string | ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | "2022-11-02T16:49:17Z" | "2022-11-06T14:54:44Z" | 51708530b6a46a5e53d12e750521a11c6bf5c986 | daa1c7529ac6491338adb81622d5041a4ba1f446 | Raise error not string. ### Describe your change:
Replaces string errors with raising exceptions
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from collections.abc import Sequence
def 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 |
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