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TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 10: https://projecteuler.net/problem=10
Summation of primes
The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17.
Find the sum of all the primes below two million.
References:
- https://en.wikipedia.org/wiki/Prime_number
- https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes
"""
def solution(n: int = 2000000) -> int:
"""
Returns the sum of all the primes below n using Sieve of Eratosthenes:
The sieve of Eratosthenes is one of the most efficient ways to find all primes
smaller than n when n is smaller than 10 million. Only for positive numbers.
>>> solution(1000)
76127
>>> solution(5000)
1548136
>>> solution(10000)
5736396
>>> solution(7)
10
>>> solution(7.1) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
>>> solution(-7) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
IndexError: list assignment index out of range
>>> solution("seven") # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: can only concatenate str (not "int") to str
"""
primality_list = [0 for i in range(n + 1)]
primality_list[0] = 1
primality_list[1] = 1
for i in range(2, int(n ** 0.5) + 1):
if primality_list[i] == 0:
for j in range(i * i, n + 1, i):
primality_list[j] = 1
sum_of_primes = 0
for i in range(n):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 10: https://projecteuler.net/problem=10
Summation of primes
The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17.
Find the sum of all the primes below two million.
References:
- https://en.wikipedia.org/wiki/Prime_number
- https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes
"""
def solution(n: int = 2000000) -> int:
"""
Returns the sum of all the primes below n using Sieve of Eratosthenes:
The sieve of Eratosthenes is one of the most efficient ways to find all primes
smaller than n when n is smaller than 10 million. Only for positive numbers.
>>> solution(1000)
76127
>>> solution(5000)
1548136
>>> solution(10000)
5736396
>>> solution(7)
10
>>> solution(7.1) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
>>> solution(-7) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
IndexError: list assignment index out of range
>>> solution("seven") # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: can only concatenate str (not "int") to str
"""
primality_list = [0 for i in range(n + 1)]
primality_list[0] = 1
primality_list[1] = 1
for i in range(2, int(n ** 0.5) + 1):
if primality_list[i] == 0:
for j in range(i * i, n + 1, i):
primality_list[j] = 1
sum_of_primes = 0
for i in range(n):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # https://en.wikipedia.org/wiki/Simulated_annealing
import math
import random
from .hill_climbing import SearchProblem
def simulated_annealing(
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,
start_temperate: float = 100,
rate_of_decrease: float = 0.01,
threshold_temp: float = 1,
) -> SearchProblem:
"""
Implementation of the simulated annealing algorithm. We start with a given state,
find all its neighbors. Pick a random neighbor, if that neighbor improves the
solution, we move in that direction, if that neighbor does not improve the solution,
we generate a random real number between 0 and 1, if the number is within a certain
range (calculated using temperature) we move in that direction, else we pick
another neighbor randomly and repeat the process.
Args:
search_prob: The search state at the start.
find_max: If True, the algorithm should find the minimum 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.
start_temperate: the initial temperate of the system when the program starts.
rate_of_decrease: the rate at which the temperate decreases in each iteration.
threshold_temp: the threshold temperature below which we end the search
Returns a search state having the maximum (or minimum) score.
"""
search_end = False
current_state = search_prob
current_temp = start_temperate
scores = []
iterations = 0
best_state = None
while not search_end:
current_score = current_state.score()
if best_state is None or current_score > best_state.score():
best_state = current_state
scores.append(current_score)
iterations += 1
next_state = None
neighbors = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
index = random.randint(0, len(neighbors) - 1) # picking a random neighbor
picked_neighbor = neighbors.pop(index)
change = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
change = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
next_state = picked_neighbor
else:
probability = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
next_state = picked_neighbor
current_temp = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
search_end = True
else:
current_state = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(iterations), scores)
plt.xlabel("Iterations")
plt.ylabel("Function values")
plt.show()
return best_state
if __name__ == "__main__":
def test_f1(x, y):
return (x ** 2) + (y ** 2)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(
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()}"
)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(
prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The maximum 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 = simulated_annealing(prob, find_max=False, visualization=True)
print(
"The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
f"{local_min.score()}"
)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(prob, find_max=True, visualization=True)
print(
"The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
f"{local_min.score()}"
)
| # https://en.wikipedia.org/wiki/Simulated_annealing
import math
import random
from .hill_climbing import SearchProblem
def simulated_annealing(
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,
start_temperate: float = 100,
rate_of_decrease: float = 0.01,
threshold_temp: float = 1,
) -> SearchProblem:
"""
Implementation of the simulated annealing algorithm. We start with a given state,
find all its neighbors. Pick a random neighbor, if that neighbor improves the
solution, we move in that direction, if that neighbor does not improve the solution,
we generate a random real number between 0 and 1, if the number is within a certain
range (calculated using temperature) we move in that direction, else we pick
another neighbor randomly and repeat the process.
Args:
search_prob: The search state at the start.
find_max: If True, the algorithm should find the minimum 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.
start_temperate: the initial temperate of the system when the program starts.
rate_of_decrease: the rate at which the temperate decreases in each iteration.
threshold_temp: the threshold temperature below which we end the search
Returns a search state having the maximum (or minimum) score.
"""
search_end = False
current_state = search_prob
current_temp = start_temperate
scores = []
iterations = 0
best_state = None
while not search_end:
current_score = current_state.score()
if best_state is None or current_score > best_state.score():
best_state = current_state
scores.append(current_score)
iterations += 1
next_state = None
neighbors = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
index = random.randint(0, len(neighbors) - 1) # picking a random neighbor
picked_neighbor = neighbors.pop(index)
change = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
change = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
next_state = picked_neighbor
else:
probability = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
next_state = picked_neighbor
current_temp = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
search_end = True
else:
current_state = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(iterations), scores)
plt.xlabel("Iterations")
plt.ylabel("Function values")
plt.show()
return best_state
if __name__ == "__main__":
def test_f1(x, y):
return (x ** 2) + (y ** 2)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(
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()}"
)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(
prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The maximum 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 = simulated_annealing(prob, find_max=False, visualization=True)
print(
"The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
f"{local_min.score()}"
)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(prob, find_max=True, visualization=True)
print(
"The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
f"{local_min.score()}"
)
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Counting Summations
Problem 76: https://projecteuler.net/problem=76
It is possible to write five as a sum in exactly six different ways:
4 + 1
3 + 2
3 + 1 + 1
2 + 2 + 1
2 + 1 + 1 + 1
1 + 1 + 1 + 1 + 1
How many different ways can one hundred be written as a sum of at least two
positive integers?
"""
def solution(m: int = 100) -> int:
"""
Returns the number of different ways the number m can be written as a
sum of at least two positive integers.
>>> solution(100)
190569291
>>> solution(50)
204225
>>> solution(30)
5603
>>> solution(10)
41
>>> solution(5)
6
>>> solution(3)
2
>>> solution(2)
1
>>> solution(1)
0
"""
memo = [[0 for _ in range(m)] for _ in range(m + 1)]
for i in range(m + 1):
memo[i][0] = 1
for n in range(m + 1):
for k in range(1, m):
memo[n][k] += memo[n][k - 1]
if n > k:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1] - 1
if __name__ == "__main__":
print(solution(int(input("Enter a number: ").strip())))
| """
Counting Summations
Problem 76: https://projecteuler.net/problem=76
It is possible to write five as a sum in exactly six different ways:
4 + 1
3 + 2
3 + 1 + 1
2 + 2 + 1
2 + 1 + 1 + 1
1 + 1 + 1 + 1 + 1
How many different ways can one hundred be written as a sum of at least two
positive integers?
"""
def solution(m: int = 100) -> int:
"""
Returns the number of different ways the number m can be written as a
sum of at least two positive integers.
>>> solution(100)
190569291
>>> solution(50)
204225
>>> solution(30)
5603
>>> solution(10)
41
>>> solution(5)
6
>>> solution(3)
2
>>> solution(2)
1
>>> solution(1)
0
"""
memo = [[0 for _ in range(m)] for _ in range(m + 1)]
for i in range(m + 1):
memo[i][0] = 1
for n in range(m + 1):
for k in range(1, m):
memo[n][k] += memo[n][k - 1]
if n > k:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1] - 1
if __name__ == "__main__":
print(solution(int(input("Enter a number: ").strip())))
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #
| #
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def mixed_keyword(key: str = "college", pt: str = "UNIVERSITY") -> str:
"""
For key:hello
H E L O
A B C D
F G I J
K M N P
Q R S T
U V W X
Y Z
and map vertically
>>> mixed_keyword("college", "UNIVERSITY") # doctest: +NORMALIZE_WHITESPACE
{'A': 'C', 'B': 'A', 'C': 'I', 'D': 'P', 'E': 'U', 'F': 'Z', 'G': 'O', 'H': 'B',
'I': 'J', 'J': 'Q', 'K': 'V', 'L': 'L', 'M': 'D', 'N': 'K', 'O': 'R', 'P': 'W',
'Q': 'E', 'R': 'F', 'S': 'M', 'T': 'S', 'U': 'X', 'V': 'G', 'W': 'H', 'X': 'N',
'Y': 'T', 'Z': 'Y'}
'XKJGUFMJST'
"""
key = key.upper()
pt = pt.upper()
temp = []
for i in key:
if i not in temp:
temp.append(i)
len_temp = len(temp)
# print(temp)
alpha = []
modalpha = []
for j in range(65, 91):
t = chr(j)
alpha.append(t)
if t not in temp:
temp.append(t)
# print(temp)
r = int(26 / 4)
# print(r)
k = 0
for _ in range(r):
s = []
for j in range(len_temp):
s.append(temp[k])
if not (k < 25):
break
k += 1
modalpha.append(s)
# print(modalpha)
d = {}
j = 0
k = 0
for j in range(len_temp):
for m in modalpha:
if not (len(m) - 1 >= j):
break
d[alpha[k]] = m[j]
if not k < 25:
break
k += 1
print(d)
cypher = ""
for i in pt:
cypher += d[i]
return cypher
print(mixed_keyword("college", "UNIVERSITY"))
| def mixed_keyword(key: str = "college", pt: str = "UNIVERSITY") -> str:
"""
For key:hello
H E L O
A B C D
F G I J
K M N P
Q R S T
U V W X
Y Z
and map vertically
>>> mixed_keyword("college", "UNIVERSITY") # doctest: +NORMALIZE_WHITESPACE
{'A': 'C', 'B': 'A', 'C': 'I', 'D': 'P', 'E': 'U', 'F': 'Z', 'G': 'O', 'H': 'B',
'I': 'J', 'J': 'Q', 'K': 'V', 'L': 'L', 'M': 'D', 'N': 'K', 'O': 'R', 'P': 'W',
'Q': 'E', 'R': 'F', 'S': 'M', 'T': 'S', 'U': 'X', 'V': 'G', 'W': 'H', 'X': 'N',
'Y': 'T', 'Z': 'Y'}
'XKJGUFMJST'
"""
key = key.upper()
pt = pt.upper()
temp = []
for i in key:
if i not in temp:
temp.append(i)
len_temp = len(temp)
# print(temp)
alpha = []
modalpha = []
for j in range(65, 91):
t = chr(j)
alpha.append(t)
if t not in temp:
temp.append(t)
# print(temp)
r = int(26 / 4)
# print(r)
k = 0
for _ in range(r):
s = []
for j in range(len_temp):
s.append(temp[k])
if not (k < 25):
break
k += 1
modalpha.append(s)
# print(modalpha)
d = {}
j = 0
k = 0
for j in range(len_temp):
for m in modalpha:
if not (len(m) - 1 >= j):
break
d[alpha[k]] = m[j]
if not k < 25:
break
k += 1
print(d)
cypher = ""
for i in pt:
cypher += d[i]
return cypher
print(mixed_keyword("college", "UNIVERSITY"))
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Pure Python implementation of a binary search algorithm.
For doctests run following command:
python3 -m doctest -v simple_binary_search.py
For manual testing run:
python3 simple_binary_search.py
"""
from __future__ import annotations
def binary_search(a_list: list[int], item: int) -> bool:
"""
>>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
>>> print(binary_search(test_list, 3))
False
>>> print(binary_search(test_list, 13))
True
>>> print(binary_search([4, 4, 5, 6, 7], 4))
True
>>> print(binary_search([4, 4, 5, 6, 7], -10))
False
>>> print(binary_search([-18, 2], -18))
True
>>> print(binary_search([5], 5))
True
>>> print(binary_search(['a', 'c', 'd'], 'c'))
True
>>> print(binary_search(['a', 'c', 'd'], 'f'))
False
>>> print(binary_search([], 1))
False
>>> print(binary_search([-.1, .1 , .8], .1))
True
>>> binary_search(range(-5000, 5000, 10), 80)
True
>>> binary_search(range(-5000, 5000, 10), 1255)
False
>>> binary_search(range(0, 10000, 5), 2)
False
"""
if len(a_list) == 0:
return False
midpoint = len(a_list) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint], item)
else:
return binary_search(a_list[midpoint + 1 :], item)
if __name__ == "__main__":
user_input = input("Enter numbers separated by comma:\n").strip()
sequence = [int(item.strip()) for item in user_input.split(",")]
target = int(input("Enter the number to be found in the list:\n").strip())
not_str = "" if binary_search(sequence, target) else "not "
print(f"{target} was {not_str}found in {sequence}")
| """
Pure Python implementation of a binary search algorithm.
For doctests run following command:
python3 -m doctest -v simple_binary_search.py
For manual testing run:
python3 simple_binary_search.py
"""
from __future__ import annotations
def binary_search(a_list: list[int], item: int) -> bool:
"""
>>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
>>> print(binary_search(test_list, 3))
False
>>> print(binary_search(test_list, 13))
True
>>> print(binary_search([4, 4, 5, 6, 7], 4))
True
>>> print(binary_search([4, 4, 5, 6, 7], -10))
False
>>> print(binary_search([-18, 2], -18))
True
>>> print(binary_search([5], 5))
True
>>> print(binary_search(['a', 'c', 'd'], 'c'))
True
>>> print(binary_search(['a', 'c', 'd'], 'f'))
False
>>> print(binary_search([], 1))
False
>>> print(binary_search([-.1, .1 , .8], .1))
True
>>> binary_search(range(-5000, 5000, 10), 80)
True
>>> binary_search(range(-5000, 5000, 10), 1255)
False
>>> binary_search(range(0, 10000, 5), 2)
False
"""
if len(a_list) == 0:
return False
midpoint = len(a_list) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint], item)
else:
return binary_search(a_list[midpoint + 1 :], item)
if __name__ == "__main__":
user_input = input("Enter numbers separated by comma:\n").strip()
sequence = [int(item.strip()) for item in user_input.split(",")]
target = int(input("Enter the number to be found in the list:\n").strip())
not_str = "" if binary_search(sequence, target) else "not "
print(f"{target} was {not_str}found in {sequence}")
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """ Luhn Algorithm """
from __future__ import annotations
def is_luhn(string: str) -> bool:
"""
Perform Luhn validation on an input string
Algorithm:
* Double every other digit starting from 2nd last digit.
* Subtract 9 if number is greater than 9.
* Sum the numbers
*
>>> test_cases = (79927398710, 79927398711, 79927398712, 79927398713,
... 79927398714, 79927398715, 79927398716, 79927398717, 79927398718,
... 79927398719)
>>> [is_luhn(str(test_case)) for test_case in test_cases]
[False, False, False, True, False, False, False, False, False, False]
"""
check_digit: int
_vector: list[str] = list(string)
__vector, check_digit = _vector[:-1], int(_vector[-1])
vector: list[int] = [int(digit) for digit in __vector]
vector.reverse()
for i, digit in enumerate(vector):
if i & 1 == 0:
doubled: int = digit * 2
if doubled > 9:
doubled -= 9
check_digit += doubled
else:
check_digit += digit
return check_digit % 10 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
assert is_luhn("79927398713")
assert not is_luhn("79927398714")
| """ Luhn Algorithm """
from __future__ import annotations
def is_luhn(string: str) -> bool:
"""
Perform Luhn validation on an input string
Algorithm:
* Double every other digit starting from 2nd last digit.
* Subtract 9 if number is greater than 9.
* Sum the numbers
*
>>> test_cases = (79927398710, 79927398711, 79927398712, 79927398713,
... 79927398714, 79927398715, 79927398716, 79927398717, 79927398718,
... 79927398719)
>>> [is_luhn(str(test_case)) for test_case in test_cases]
[False, False, False, True, False, False, False, False, False, False]
"""
check_digit: int
_vector: list[str] = list(string)
__vector, check_digit = _vector[:-1], int(_vector[-1])
vector: list[int] = [int(digit) for digit in __vector]
vector.reverse()
for i, digit in enumerate(vector):
if i & 1 == 0:
doubled: int = digit * 2
if doubled > 9:
doubled -= 9
check_digit += doubled
else:
check_digit += digit
return check_digit % 10 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
assert is_luhn("79927398713")
assert not is_luhn("79927398714")
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from sklearn.neural_network import MLPClassifier
X = [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
y = [0, 1, 0, 0]
clf = MLPClassifier(
solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)
clf.fit(X, y)
test = [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
Y = clf.predict(test)
def wrapper(Y):
"""
>>> wrapper(Y)
[0, 0, 1]
"""
return list(Y)
if __name__ == "__main__":
import doctest
doctest.testmod()
| from sklearn.neural_network import MLPClassifier
X = [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
y = [0, 1, 0, 0]
clf = MLPClassifier(
solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)
clf.fit(X, y)
test = [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
Y = clf.predict(test)
def wrapper(Y):
"""
>>> wrapper(Y)
[0, 0, 1]
"""
return list(Y)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Champernowne's constant
Problem 40
An irrational decimal fraction is created by concatenating the positive
integers:
0.123456789101112131415161718192021...
It can be seen that the 12th digit of the fractional part is 1.
If dn represents the nth digit of the fractional part, find the value of the
following expression.
d1 × d10 × d100 × d1000 × d10000 × d100000 × d1000000
"""
def solution():
"""Returns
>>> solution()
210
"""
constant = []
i = 1
while len(constant) < 1e6:
constant.append(str(i))
i += 1
constant = "".join(constant)
return (
int(constant[0])
* int(constant[9])
* int(constant[99])
* int(constant[999])
* int(constant[9999])
* int(constant[99999])
* int(constant[999999])
)
if __name__ == "__main__":
print(solution())
| """
Champernowne's constant
Problem 40
An irrational decimal fraction is created by concatenating the positive
integers:
0.123456789101112131415161718192021...
It can be seen that the 12th digit of the fractional part is 1.
If dn represents the nth digit of the fractional part, find the value of the
following expression.
d1 × d10 × d100 × d1000 × d10000 × d100000 × d1000000
"""
def solution():
"""Returns
>>> solution()
210
"""
constant = []
i = 1
while len(constant) < 1e6:
constant.append(str(i))
i += 1
constant = "".join(constant)
return (
int(constant[0])
* int(constant[9])
* int(constant[99])
* int(constant[999])
* int(constant[9999])
* int(constant[99999])
* int(constant[999999])
)
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """ https://en.wikipedia.org/wiki/Atbash """
import string
def atbash_slow(sequence: str) -> str:
"""
>>> atbash_slow("ABCDEFG")
'ZYXWVUT'
>>> atbash_slow("aW;;123BX")
'zD;;123YC'
"""
output = ""
for i in sequence:
extract = ord(i)
if 65 <= extract <= 90:
output += chr(155 - extract)
elif 97 <= extract <= 122:
output += chr(219 - extract)
else:
output += i
return output
def atbash(sequence: str) -> str:
"""
>>> atbash("ABCDEFG")
'ZYXWVUT'
>>> atbash("aW;;123BX")
'zD;;123YC'
"""
letters = string.ascii_letters
letters_reversed = string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
return "".join(
letters_reversed[letters.index(c)] if c in letters else c for c in sequence
)
def benchmark() -> None:
"""Let's benchmark them side-by-side..."""
from timeit import timeit
print("Running performance benchmarks...")
print(
"> atbash_slow()",
timeit(
"atbash_slow(printable)",
setup="from string import printable ; from __main__ import atbash_slow",
),
"seconds",
)
print(
"> atbash()",
timeit(
"atbash(printable)",
setup="from string import printable ; from __main__ import atbash",
),
"seconds",
)
if __name__ == "__main__":
for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
print(f"{example} encrypted in atbash: {atbash(example)}")
benchmark()
| """ https://en.wikipedia.org/wiki/Atbash """
import string
def atbash_slow(sequence: str) -> str:
"""
>>> atbash_slow("ABCDEFG")
'ZYXWVUT'
>>> atbash_slow("aW;;123BX")
'zD;;123YC'
"""
output = ""
for i in sequence:
extract = ord(i)
if 65 <= extract <= 90:
output += chr(155 - extract)
elif 97 <= extract <= 122:
output += chr(219 - extract)
else:
output += i
return output
def atbash(sequence: str) -> str:
"""
>>> atbash("ABCDEFG")
'ZYXWVUT'
>>> atbash("aW;;123BX")
'zD;;123YC'
"""
letters = string.ascii_letters
letters_reversed = string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
return "".join(
letters_reversed[letters.index(c)] if c in letters else c for c in sequence
)
def benchmark() -> None:
"""Let's benchmark them side-by-side..."""
from timeit import timeit
print("Running performance benchmarks...")
print(
"> atbash_slow()",
timeit(
"atbash_slow(printable)",
setup="from string import printable ; from __main__ import atbash_slow",
),
"seconds",
)
print(
"> atbash()",
timeit(
"atbash(printable)",
setup="from string import printable ; from __main__ import atbash",
),
"seconds",
)
if __name__ == "__main__":
for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
print(f"{example} encrypted in atbash: {atbash(example)}")
benchmark()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Fast Polynomial Multiplication using radix-2 fast Fourier Transform.
"""
import mpmath # for roots of unity
import numpy as np
class FFT:
"""
Fast Polynomial Multiplication using radix-2 fast Fourier Transform.
Reference:
https://en.wikipedia.org/wiki/Cooley%E2%80%93Tukey_FFT_algorithm#The_radix-2_DIT_case
For polynomials of degree m and n the algorithms has complexity
O(n*logn + m*logm)
The main part of the algorithm is split in two parts:
1) __DFT: We compute the discrete fourier transform (DFT) of A and B using a
bottom-up dynamic approach -
2) __multiply: Once we obtain the DFT of A*B, we can similarly
invert it to obtain A*B
The class FFT takes two polynomials A and B with complex coefficients as arguments;
The two polynomials should be represented as a sequence of coefficients starting
from the free term. Thus, for instance x + 2*x^3 could be represented as
[0,1,0,2] or (0,1,0,2). The constructor adds some zeros at the end so that the
polynomials have the same length which is a power of 2 at least the length of
their product.
Example:
Create two polynomials as sequences
>>> A = [0, 1, 0, 2] # x+2x^3
>>> B = (2, 3, 4, 0) # 2+3x+4x^2
Create an FFT object with them
>>> x = FFT(A, B)
Print product
>>> print(x.product) # 2x + 3x^2 + 8x^3 + 4x^4 + 6x^5
[(-0+0j), (2+0j), (3+0j), (8+0j), (6+0j), (8+0j)]
__str__ test
>>> print(x)
A = 0*x^0 + 1*x^1 + 2*x^0 + 3*x^2
B = 0*x^2 + 1*x^3 + 2*x^4
A*B = 0*x^(-0+0j) + 1*x^(2+0j) + 2*x^(3+0j) + 3*x^(8+0j) + 4*x^(6+0j) + 5*x^(8+0j)
"""
def __init__(self, polyA=None, polyB=None):
# Input as list
self.polyA = list(polyA or [0])[:]
self.polyB = list(polyB or [0])[:]
# Remove leading zero coefficients
while self.polyA[-1] == 0:
self.polyA.pop()
self.len_A = len(self.polyA)
while self.polyB[-1] == 0:
self.polyB.pop()
self.len_B = len(self.polyB)
# Add 0 to make lengths equal a power of 2
self.C_max_length = int(
2 ** np.ceil(np.log2(len(self.polyA) + len(self.polyB) - 1))
)
while len(self.polyA) < self.C_max_length:
self.polyA.append(0)
while len(self.polyB) < self.C_max_length:
self.polyB.append(0)
# A complex root used for the fourier transform
self.root = complex(mpmath.root(x=1, n=self.C_max_length, k=1))
# The product
self.product = self.__multiply()
# Discrete fourier transform of A and B
def __DFT(self, which):
if which == "A":
dft = [[x] for x in self.polyA]
else:
dft = [[x] for x in self.polyB]
# Corner case
if len(dft) <= 1:
return dft[0]
#
next_ncol = self.C_max_length // 2
while next_ncol > 0:
new_dft = [[] for i in range(next_ncol)]
root = self.root ** next_ncol
# First half of next step
current_root = 1
for j in range(self.C_max_length // (next_ncol * 2)):
for i in range(next_ncol):
new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j])
current_root *= root
# Second half of next step
current_root = 1
for j in range(self.C_max_length // (next_ncol * 2)):
for i in range(next_ncol):
new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j])
current_root *= root
# Update
dft = new_dft
next_ncol = next_ncol // 2
return dft[0]
# multiply the DFTs of A and B and find A*B
def __multiply(self):
dftA = self.__DFT("A")
dftB = self.__DFT("B")
inverseC = [[dftA[i] * dftB[i] for i in range(self.C_max_length)]]
del dftA
del dftB
# Corner Case
if len(inverseC[0]) <= 1:
return inverseC[0]
# Inverse DFT
next_ncol = 2
while next_ncol <= self.C_max_length:
new_inverseC = [[] for i in range(next_ncol)]
root = self.root ** (next_ncol // 2)
current_root = 1
# First half of next step
for j in range(self.C_max_length // next_ncol):
for i in range(next_ncol // 2):
# Even positions
new_inverseC[i].append(
(
inverseC[i][j]
+ inverseC[i][j + self.C_max_length // next_ncol]
)
/ 2
)
# Odd positions
new_inverseC[i + next_ncol // 2].append(
(
inverseC[i][j]
- inverseC[i][j + self.C_max_length // next_ncol]
)
/ (2 * current_root)
)
current_root *= root
# Update
inverseC = new_inverseC
next_ncol *= 2
# Unpack
inverseC = [round(x[0].real, 8) + round(x[0].imag, 8) * 1j for x in inverseC]
# Remove leading 0's
while inverseC[-1] == 0:
inverseC.pop()
return inverseC
# Overwrite __str__ for print(); Shows A, B and A*B
def __str__(self):
A = "A = " + " + ".join(
f"{coef}*x^{i}" for coef, i in enumerate(self.polyA[: self.len_A])
)
B = "B = " + " + ".join(
f"{coef}*x^{i}" for coef, i in enumerate(self.polyB[: self.len_B])
)
C = "A*B = " + " + ".join(
f"{coef}*x^{i}" for coef, i in enumerate(self.product)
)
return "\n".join((A, B, C))
# Unit tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Fast Polynomial Multiplication using radix-2 fast Fourier Transform.
"""
import mpmath # for roots of unity
import numpy as np
class FFT:
"""
Fast Polynomial Multiplication using radix-2 fast Fourier Transform.
Reference:
https://en.wikipedia.org/wiki/Cooley%E2%80%93Tukey_FFT_algorithm#The_radix-2_DIT_case
For polynomials of degree m and n the algorithms has complexity
O(n*logn + m*logm)
The main part of the algorithm is split in two parts:
1) __DFT: We compute the discrete fourier transform (DFT) of A and B using a
bottom-up dynamic approach -
2) __multiply: Once we obtain the DFT of A*B, we can similarly
invert it to obtain A*B
The class FFT takes two polynomials A and B with complex coefficients as arguments;
The two polynomials should be represented as a sequence of coefficients starting
from the free term. Thus, for instance x + 2*x^3 could be represented as
[0,1,0,2] or (0,1,0,2). The constructor adds some zeros at the end so that the
polynomials have the same length which is a power of 2 at least the length of
their product.
Example:
Create two polynomials as sequences
>>> A = [0, 1, 0, 2] # x+2x^3
>>> B = (2, 3, 4, 0) # 2+3x+4x^2
Create an FFT object with them
>>> x = FFT(A, B)
Print product
>>> print(x.product) # 2x + 3x^2 + 8x^3 + 4x^4 + 6x^5
[(-0+0j), (2+0j), (3+0j), (8+0j), (6+0j), (8+0j)]
__str__ test
>>> print(x)
A = 0*x^0 + 1*x^1 + 2*x^0 + 3*x^2
B = 0*x^2 + 1*x^3 + 2*x^4
A*B = 0*x^(-0+0j) + 1*x^(2+0j) + 2*x^(3+0j) + 3*x^(8+0j) + 4*x^(6+0j) + 5*x^(8+0j)
"""
def __init__(self, polyA=None, polyB=None):
# Input as list
self.polyA = list(polyA or [0])[:]
self.polyB = list(polyB or [0])[:]
# Remove leading zero coefficients
while self.polyA[-1] == 0:
self.polyA.pop()
self.len_A = len(self.polyA)
while self.polyB[-1] == 0:
self.polyB.pop()
self.len_B = len(self.polyB)
# Add 0 to make lengths equal a power of 2
self.C_max_length = int(
2 ** np.ceil(np.log2(len(self.polyA) + len(self.polyB) - 1))
)
while len(self.polyA) < self.C_max_length:
self.polyA.append(0)
while len(self.polyB) < self.C_max_length:
self.polyB.append(0)
# A complex root used for the fourier transform
self.root = complex(mpmath.root(x=1, n=self.C_max_length, k=1))
# The product
self.product = self.__multiply()
# Discrete fourier transform of A and B
def __DFT(self, which):
if which == "A":
dft = [[x] for x in self.polyA]
else:
dft = [[x] for x in self.polyB]
# Corner case
if len(dft) <= 1:
return dft[0]
#
next_ncol = self.C_max_length // 2
while next_ncol > 0:
new_dft = [[] for i in range(next_ncol)]
root = self.root ** next_ncol
# First half of next step
current_root = 1
for j in range(self.C_max_length // (next_ncol * 2)):
for i in range(next_ncol):
new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j])
current_root *= root
# Second half of next step
current_root = 1
for j in range(self.C_max_length // (next_ncol * 2)):
for i in range(next_ncol):
new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j])
current_root *= root
# Update
dft = new_dft
next_ncol = next_ncol // 2
return dft[0]
# multiply the DFTs of A and B and find A*B
def __multiply(self):
dftA = self.__DFT("A")
dftB = self.__DFT("B")
inverseC = [[dftA[i] * dftB[i] for i in range(self.C_max_length)]]
del dftA
del dftB
# Corner Case
if len(inverseC[0]) <= 1:
return inverseC[0]
# Inverse DFT
next_ncol = 2
while next_ncol <= self.C_max_length:
new_inverseC = [[] for i in range(next_ncol)]
root = self.root ** (next_ncol // 2)
current_root = 1
# First half of next step
for j in range(self.C_max_length // next_ncol):
for i in range(next_ncol // 2):
# Even positions
new_inverseC[i].append(
(
inverseC[i][j]
+ inverseC[i][j + self.C_max_length // next_ncol]
)
/ 2
)
# Odd positions
new_inverseC[i + next_ncol // 2].append(
(
inverseC[i][j]
- inverseC[i][j + self.C_max_length // next_ncol]
)
/ (2 * current_root)
)
current_root *= root
# Update
inverseC = new_inverseC
next_ncol *= 2
# Unpack
inverseC = [round(x[0].real, 8) + round(x[0].imag, 8) * 1j for x in inverseC]
# Remove leading 0's
while inverseC[-1] == 0:
inverseC.pop()
return inverseC
# Overwrite __str__ for print(); Shows A, B and A*B
def __str__(self):
A = "A = " + " + ".join(
f"{coef}*x^{i}" for coef, i in enumerate(self.polyA[: self.len_A])
)
B = "B = " + " + ".join(
f"{coef}*x^{i}" for coef, i in enumerate(self.polyB[: self.len_B])
)
C = "A*B = " + " + ".join(
f"{coef}*x^{i}" for coef, i in enumerate(self.product)
)
return "\n".join((A, B, C))
# Unit tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
"""
Illustrate how to implement bucket sort algorithm.
Author: OMKAR PATHAK
This program will illustrate how to implement bucket sort algorithm
Wikipedia says: Bucket sort, or bin sort, is a sorting algorithm that works
by distributing the elements of an array into a number of buckets.
Each bucket is then sorted individually, either using a different sorting
algorithm, or by recursively applying the bucket sorting algorithm. It is a
distribution sort, and is a cousin of radix sort in the most to least
significant digit flavour.
Bucket sort is a generalization of pigeonhole sort. Bucket sort can be
implemented with comparisons and therefore can also be considered a
comparison sort algorithm. The computational complexity estimates involve the
number of buckets.
Time Complexity of Solution:
Worst case scenario occurs when all the elements are placed in a single bucket.
The overall performance would then be dominated by the algorithm used to sort each
bucket. In this case, O(n log n), because of TimSort
Average Case O(n + (n^2)/k + k), where k is the number of buckets
If k = O(n), time complexity is O(n)
Source: https://en.wikipedia.org/wiki/Bucket_sort
"""
from __future__ import annotations
def bucket_sort(my_list: list) -> list:
"""
>>> data = [-1, 2, -5, 0]
>>> bucket_sort(data) == sorted(data)
True
>>> data = [9, 8, 7, 6, -12]
>>> bucket_sort(data) == sorted(data)
True
>>> data = [.4, 1.2, .1, .2, -.9]
>>> bucket_sort(data) == sorted(data)
True
>>> bucket_sort([]) == sorted([])
True
>>> import random
>>> collection = random.sample(range(-50, 50), 50)
>>> bucket_sort(collection) == sorted(collection)
True
"""
if len(my_list) == 0:
return []
min_value, max_value = min(my_list), max(my_list)
bucket_count = int(max_value - min_value) + 1
buckets: list[list] = [[] for _ in range(bucket_count)]
for i in range(len(my_list)):
buckets[(int(my_list[i] - min_value) // bucket_count)].append(my_list[i])
return [v for bucket in buckets for v in sorted(bucket)]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]
| #!/usr/bin/env python3
"""
Illustrate how to implement bucket sort algorithm.
Author: OMKAR PATHAK
This program will illustrate how to implement bucket sort algorithm
Wikipedia says: Bucket sort, or bin sort, is a sorting algorithm that works
by distributing the elements of an array into a number of buckets.
Each bucket is then sorted individually, either using a different sorting
algorithm, or by recursively applying the bucket sorting algorithm. It is a
distribution sort, and is a cousin of radix sort in the most to least
significant digit flavour.
Bucket sort is a generalization of pigeonhole sort. Bucket sort can be
implemented with comparisons and therefore can also be considered a
comparison sort algorithm. The computational complexity estimates involve the
number of buckets.
Time Complexity of Solution:
Worst case scenario occurs when all the elements are placed in a single bucket.
The overall performance would then be dominated by the algorithm used to sort each
bucket. In this case, O(n log n), because of TimSort
Average Case O(n + (n^2)/k + k), where k is the number of buckets
If k = O(n), time complexity is O(n)
Source: https://en.wikipedia.org/wiki/Bucket_sort
"""
from __future__ import annotations
def bucket_sort(my_list: list) -> list:
"""
>>> data = [-1, 2, -5, 0]
>>> bucket_sort(data) == sorted(data)
True
>>> data = [9, 8, 7, 6, -12]
>>> bucket_sort(data) == sorted(data)
True
>>> data = [.4, 1.2, .1, .2, -.9]
>>> bucket_sort(data) == sorted(data)
True
>>> bucket_sort([]) == sorted([])
True
>>> import random
>>> collection = random.sample(range(-50, 50), 50)
>>> bucket_sort(collection) == sorted(collection)
True
"""
if len(my_list) == 0:
return []
min_value, max_value = min(my_list), max(my_list)
bucket_count = int(max_value - min_value) + 1
buckets: list[list] = [[] for _ in range(bucket_count)]
for i in range(len(my_list)):
buckets[(int(my_list[i] - min_value) // bucket_count)].append(my_list[i])
return [v for bucket in buckets for v in sorted(bucket)]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """README, Author - Jigyasa Gandhi(mailto:[email protected])
Requirements:
- scikit-fuzzy
- numpy
- matplotlib
Python:
- 3.5
"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
X = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
abc1 = [0, 25, 50]
abc2 = [25, 50, 75]
young = fuzz.membership.trimf(X, abc1)
middle_aged = fuzz.membership.trimf(X, abc2)
# Compute the different operations using inbuilt functions.
one = np.ones(75)
zero = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
union = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
intersection = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
complement_a = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
difference = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
alg_sum = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
alg_product = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
bdd_sum = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
bdd_difference = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title("Young")
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title("Middle aged")
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title("union")
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title("intersection")
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title("complement_a")
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title("difference a/b")
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title("alg_sum")
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title("alg_product")
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title("bdd_sum")
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title("bdd_difference")
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| """README, Author - Jigyasa Gandhi(mailto:[email protected])
Requirements:
- scikit-fuzzy
- numpy
- matplotlib
Python:
- 3.5
"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
X = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
abc1 = [0, 25, 50]
abc2 = [25, 50, 75]
young = fuzz.membership.trimf(X, abc1)
middle_aged = fuzz.membership.trimf(X, abc2)
# Compute the different operations using inbuilt functions.
one = np.ones(75)
zero = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
union = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
intersection = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
complement_a = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
difference = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
alg_sum = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
alg_product = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
bdd_sum = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
bdd_difference = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title("Young")
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title("Middle aged")
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title("union")
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title("intersection")
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title("complement_a")
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title("difference a/b")
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title("alg_sum")
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title("alg_product")
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title("bdd_sum")
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title("bdd_difference")
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def get_set_bits_count(number: int) -> int:
"""
Count the number of set bits in a 32 bit integer
>>> get_set_bits_count(25)
3
>>> get_set_bits_count(37)
3
>>> get_set_bits_count(21)
3
>>> get_set_bits_count(58)
4
>>> get_set_bits_count(0)
0
>>> get_set_bits_count(256)
1
>>> get_set_bits_count(-1)
Traceback (most recent call last):
...
ValueError: the value of input must be positive
"""
if number < 0:
raise ValueError("the value of input must be positive")
result = 0
while number:
if number % 2 == 1:
result += 1
number = number >> 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| def get_set_bits_count(number: int) -> int:
"""
Count the number of set bits in a 32 bit integer
>>> get_set_bits_count(25)
3
>>> get_set_bits_count(37)
3
>>> get_set_bits_count(21)
3
>>> get_set_bits_count(58)
4
>>> get_set_bits_count(0)
0
>>> get_set_bits_count(256)
1
>>> get_set_bits_count(-1)
Traceback (most recent call last):
...
ValueError: the value of input must be positive
"""
if number < 0:
raise ValueError("the value of input must be positive")
result = 0
while number:
if number % 2 == 1:
result += 1
number = number >> 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
from collections import deque
class Automaton:
def __init__(self, keywords: list[str]):
self.adlist: list[dict] = list()
self.adlist.append(
{"value": "", "next_states": [], "fail_state": 0, "output": []}
)
for keyword in keywords:
self.add_keyword(keyword)
self.set_fail_transitions()
def find_next_state(self, current_state: int, char: str) -> int | None:
for state in self.adlist[current_state]["next_states"]:
if char == self.adlist[state]["value"]:
return state
return None
def add_keyword(self, keyword: str) -> None:
current_state = 0
for character in keyword:
next_state = self.find_next_state(current_state, character)
if next_state is None:
self.adlist.append(
{
"value": character,
"next_states": [],
"fail_state": 0,
"output": [],
}
)
self.adlist[current_state]["next_states"].append(len(self.adlist) - 1)
current_state = len(self.adlist) - 1
else:
current_state = next_state
self.adlist[current_state]["output"].append(keyword)
def set_fail_transitions(self) -> None:
q: deque = deque()
for node in self.adlist[0]["next_states"]:
q.append(node)
self.adlist[node]["fail_state"] = 0
while q:
r = q.popleft()
for child in self.adlist[r]["next_states"]:
q.append(child)
state = self.adlist[r]["fail_state"]
while (
self.find_next_state(state, self.adlist[child]["value"]) is None
and state != 0
):
state = self.adlist[state]["fail_state"]
self.adlist[child]["fail_state"] = self.find_next_state(
state, self.adlist[child]["value"]
)
if self.adlist[child]["fail_state"] is None:
self.adlist[child]["fail_state"] = 0
self.adlist[child]["output"] = (
self.adlist[child]["output"]
+ self.adlist[self.adlist[child]["fail_state"]]["output"]
)
def search_in(self, string: str) -> dict[str, list[int]]:
"""
>>> A = Automaton(["what", "hat", "ver", "er"])
>>> A.search_in("whatever, err ... , wherever")
{'what': [0], 'hat': [1], 'ver': [5, 25], 'er': [6, 10, 22, 26]}
"""
result: dict = (
dict()
) # returns a dict with keywords and list of its occurrences
current_state = 0
for i in range(len(string)):
while (
self.find_next_state(current_state, string[i]) is None
and current_state != 0
):
current_state = self.adlist[current_state]["fail_state"]
next_state = self.find_next_state(current_state, string[i])
if next_state is None:
current_state = 0
else:
current_state = next_state
for key in self.adlist[current_state]["output"]:
if not (key in result):
result[key] = []
result[key].append(i - len(key) + 1)
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| from __future__ import annotations
from collections import deque
class Automaton:
def __init__(self, keywords: list[str]):
self.adlist: list[dict] = list()
self.adlist.append(
{"value": "", "next_states": [], "fail_state": 0, "output": []}
)
for keyword in keywords:
self.add_keyword(keyword)
self.set_fail_transitions()
def find_next_state(self, current_state: int, char: str) -> int | None:
for state in self.adlist[current_state]["next_states"]:
if char == self.adlist[state]["value"]:
return state
return None
def add_keyword(self, keyword: str) -> None:
current_state = 0
for character in keyword:
next_state = self.find_next_state(current_state, character)
if next_state is None:
self.adlist.append(
{
"value": character,
"next_states": [],
"fail_state": 0,
"output": [],
}
)
self.adlist[current_state]["next_states"].append(len(self.adlist) - 1)
current_state = len(self.adlist) - 1
else:
current_state = next_state
self.adlist[current_state]["output"].append(keyword)
def set_fail_transitions(self) -> None:
q: deque = deque()
for node in self.adlist[0]["next_states"]:
q.append(node)
self.adlist[node]["fail_state"] = 0
while q:
r = q.popleft()
for child in self.adlist[r]["next_states"]:
q.append(child)
state = self.adlist[r]["fail_state"]
while (
self.find_next_state(state, self.adlist[child]["value"]) is None
and state != 0
):
state = self.adlist[state]["fail_state"]
self.adlist[child]["fail_state"] = self.find_next_state(
state, self.adlist[child]["value"]
)
if self.adlist[child]["fail_state"] is None:
self.adlist[child]["fail_state"] = 0
self.adlist[child]["output"] = (
self.adlist[child]["output"]
+ self.adlist[self.adlist[child]["fail_state"]]["output"]
)
def search_in(self, string: str) -> dict[str, list[int]]:
"""
>>> A = Automaton(["what", "hat", "ver", "er"])
>>> A.search_in("whatever, err ... , wherever")
{'what': [0], 'hat': [1], 'ver': [5, 25], 'er': [6, 10, 22, 26]}
"""
result: dict = (
dict()
) # returns a dict with keywords and list of its occurrences
current_state = 0
for i in range(len(string)):
while (
self.find_next_state(current_state, string[i]) is None
and current_state != 0
):
current_state = self.adlist[current_state]["fail_state"]
next_state = self.find_next_state(current_state, string[i])
if next_state is None:
current_state = 0
else:
current_state = next_state
for key in self.adlist[current_state]["output"]:
if not (key in result):
result[key] = []
result[key].append(i - len(key) + 1)
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
References:
- http://neuralnetworksanddeeplearning.com/chap2.html (Backpropagation)
- https://en.wikipedia.org/wiki/Sigmoid_function (Sigmoid activation function)
- https://en.wikipedia.org/wiki/Feedforward_neural_network (Feedforward)
"""
import numpy
class TwoHiddenLayerNeuralNetwork:
def __init__(self, input_array: numpy.ndarray, output_array: numpy.ndarray) -> None:
"""
This function initializes the TwoHiddenLayerNeuralNetwork class with random
weights for every layer and initializes predicted output with zeroes.
input_array : input values for training the neural network (i.e training data) .
output_array : expected output values of the given inputs.
"""
# Input values provided for training the model.
self.input_array = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
# number of nodes in the next layer.
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
self.input_layer_and_first_hidden_layer_weights = numpy.random.rand(
self.input_array.shape[1], 4
)
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
self.first_hidden_layer_and_second_hidden_layer_weights = numpy.random.rand(
4, 3
)
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
self.second_hidden_layer_and_output_layer_weights = numpy.random.rand(3, 1)
# Real output values provided.
self.output_array = output_array
# Predicted output values by the neural network.
# Predicted_output array initially consists of zeroes.
self.predicted_output = numpy.zeros(output_array.shape)
def feedforward(self) -> numpy.ndarray:
"""
The information moves in only one direction i.e. forward from the input nodes,
through the two hidden nodes and to the output nodes.
There are no cycles or loops in the network.
Return layer_between_second_hidden_layer_and_output
(i.e the last layer of the neural network).
>>> input_val = numpy.array(([0, 0, 0], [0, 0, 0], [0, 0, 0]), dtype=float)
>>> output_val = numpy.array(([0], [0], [0]), dtype=float)
>>> nn = TwoHiddenLayerNeuralNetwork(input_val, output_val)
>>> res = nn.feedforward()
>>> array_sum = numpy.sum(res)
>>> numpy.isnan(array_sum)
False
"""
# Layer_between_input_and_first_hidden_layer is the layer connecting the
# input nodes with the first hidden layer nodes.
self.layer_between_input_and_first_hidden_layer = sigmoid(
numpy.dot(self.input_array, self.input_layer_and_first_hidden_layer_weights)
)
# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
# connecting the first hidden set of nodes with the second hidden set of nodes.
self.layer_between_first_hidden_layer_and_second_hidden_layer = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer,
self.first_hidden_layer_and_second_hidden_layer_weights,
)
)
# layer_between_second_hidden_layer_and_output is the layer connecting
# second hidden layer with the output node.
self.layer_between_second_hidden_layer_and_output = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer,
self.second_hidden_layer_and_output_layer_weights,
)
)
return self.layer_between_second_hidden_layer_and_output
def back_propagation(self) -> None:
"""
Function for fine-tuning the weights of the neural net based on the
error rate obtained in the previous epoch (i.e., iteration).
Updation is done using derivative of sogmoid activation function.
>>> input_val = numpy.array(([0, 0, 0], [0, 0, 0], [0, 0, 0]), dtype=float)
>>> output_val = numpy.array(([0], [0], [0]), dtype=float)
>>> nn = TwoHiddenLayerNeuralNetwork(input_val, output_val)
>>> res = nn.feedforward()
>>> nn.back_propagation()
>>> updated_weights = nn.second_hidden_layer_and_output_layer_weights
>>> (res == updated_weights).all()
False
"""
updated_second_hidden_layer_and_output_layer_weights = numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer.T,
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output),
)
updated_first_hidden_layer_and_second_hidden_layer_weights = numpy.dot(
self.layer_between_input_and_first_hidden_layer.T,
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output),
self.second_hidden_layer_and_output_layer_weights.T,
)
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer
),
)
updated_input_layer_and_first_hidden_layer_weights = numpy.dot(
self.input_array.T,
numpy.dot(
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output),
self.second_hidden_layer_and_output_layer_weights.T,
)
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer
),
self.first_hidden_layer_and_second_hidden_layer_weights.T,
)
* sigmoid_derivative(self.layer_between_input_and_first_hidden_layer),
)
self.input_layer_and_first_hidden_layer_weights += (
updated_input_layer_and_first_hidden_layer_weights
)
self.first_hidden_layer_and_second_hidden_layer_weights += (
updated_first_hidden_layer_and_second_hidden_layer_weights
)
self.second_hidden_layer_and_output_layer_weights += (
updated_second_hidden_layer_and_output_layer_weights
)
def train(self, output: numpy.ndarray, iterations: int, give_loss: bool) -> None:
"""
Performs the feedforwarding and back propagation process for the
given number of iterations.
Every iteration will update the weights of neural network.
output : real output values,required for calculating loss.
iterations : number of times the weights are to be updated.
give_loss : boolean value, If True then prints loss for each iteration,
If False then nothing is printed
>>> input_val = numpy.array(([0, 0, 0], [0, 1, 0], [0, 0, 1]), dtype=float)
>>> output_val = numpy.array(([0], [1], [1]), dtype=float)
>>> nn = TwoHiddenLayerNeuralNetwork(input_val, output_val)
>>> first_iteration_weights = nn.feedforward()
>>> nn.back_propagation()
>>> updated_weights = nn.second_hidden_layer_and_output_layer_weights
>>> (first_iteration_weights == updated_weights).all()
False
"""
for iteration in range(1, iterations + 1):
self.output = self.feedforward()
self.back_propagation()
if give_loss:
loss = numpy.mean(numpy.square(output - self.feedforward()))
print(f"Iteration {iteration} Loss: {loss}")
def predict(self, input: numpy.ndarray) -> int:
"""
Predict's the output for the given input values using
the trained neural network.
The output value given by the model ranges in-between 0 and 1.
The predict function returns 1 if the model value is greater
than the threshold value else returns 0,
as the real output values are in binary.
>>> input_val = numpy.array(([0, 0, 0], [0, 1, 0], [0, 0, 1]), dtype=float)
>>> output_val = numpy.array(([0], [1], [1]), dtype=float)
>>> nn = TwoHiddenLayerNeuralNetwork(input_val, output_val)
>>> nn.train(output_val, 1000, False)
>>> nn.predict([0,1,0]) in (0, 1)
True
"""
# Input values for which the predictions are to be made.
self.array = input
self.layer_between_input_and_first_hidden_layer = sigmoid(
numpy.dot(self.array, self.input_layer_and_first_hidden_layer_weights)
)
self.layer_between_first_hidden_layer_and_second_hidden_layer = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer,
self.first_hidden_layer_and_second_hidden_layer_weights,
)
)
self.layer_between_second_hidden_layer_and_output = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer,
self.second_hidden_layer_and_output_layer_weights,
)
)
return int(self.layer_between_second_hidden_layer_and_output > 0.6)
def sigmoid(value: numpy.ndarray) -> numpy.ndarray:
"""
Applies sigmoid activation function.
return normalized values
>>> sigmoid(numpy.array(([1, 0, 2], [1, 0, 0]), dtype=numpy.float64))
array([[0.73105858, 0.5 , 0.88079708],
[0.73105858, 0.5 , 0.5 ]])
"""
return 1 / (1 + numpy.exp(-value))
def sigmoid_derivative(value: numpy.ndarray) -> numpy.ndarray:
"""
Provides the derivative value of the sigmoid function.
returns derivative of the sigmoid value
>>> sigmoid_derivative(numpy.array(([1, 0, 2], [1, 0, 0]), dtype=numpy.float64))
array([[ 0., 0., -2.],
[ 0., 0., 0.]])
"""
return (value) * (1 - (value))
def example() -> int:
"""
Example for "how to use the neural network class and use the
respected methods for the desired output".
Calls the TwoHiddenLayerNeuralNetwork class and
provides the fixed input output values to the model.
Model is trained for a fixed amount of iterations then the predict method is called.
In this example the output is divided into 2 classes i.e. binary classification,
the two classes are represented by '0' and '1'.
>>> example() in (0, 1)
True
"""
# Input values.
input = numpy.array(
(
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
),
dtype=numpy.float64,
)
# True output values for the given input values.
output = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]), dtype=numpy.float64)
# Calling neural network class.
neural_network = TwoHiddenLayerNeuralNetwork(input_array=input, output_array=output)
# Calling training function.
# Set give_loss to True if you want to see loss in every iteration.
neural_network.train(output=output, iterations=10, give_loss=False)
return neural_network.predict(numpy.array(([1, 1, 1]), dtype=numpy.float64))
if __name__ == "__main__":
example()
| """
References:
- http://neuralnetworksanddeeplearning.com/chap2.html (Backpropagation)
- https://en.wikipedia.org/wiki/Sigmoid_function (Sigmoid activation function)
- https://en.wikipedia.org/wiki/Feedforward_neural_network (Feedforward)
"""
import numpy
class TwoHiddenLayerNeuralNetwork:
def __init__(self, input_array: numpy.ndarray, output_array: numpy.ndarray) -> None:
"""
This function initializes the TwoHiddenLayerNeuralNetwork class with random
weights for every layer and initializes predicted output with zeroes.
input_array : input values for training the neural network (i.e training data) .
output_array : expected output values of the given inputs.
"""
# Input values provided for training the model.
self.input_array = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
# number of nodes in the next layer.
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
self.input_layer_and_first_hidden_layer_weights = numpy.random.rand(
self.input_array.shape[1], 4
)
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
self.first_hidden_layer_and_second_hidden_layer_weights = numpy.random.rand(
4, 3
)
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
self.second_hidden_layer_and_output_layer_weights = numpy.random.rand(3, 1)
# Real output values provided.
self.output_array = output_array
# Predicted output values by the neural network.
# Predicted_output array initially consists of zeroes.
self.predicted_output = numpy.zeros(output_array.shape)
def feedforward(self) -> numpy.ndarray:
"""
The information moves in only one direction i.e. forward from the input nodes,
through the two hidden nodes and to the output nodes.
There are no cycles or loops in the network.
Return layer_between_second_hidden_layer_and_output
(i.e the last layer of the neural network).
>>> input_val = numpy.array(([0, 0, 0], [0, 0, 0], [0, 0, 0]), dtype=float)
>>> output_val = numpy.array(([0], [0], [0]), dtype=float)
>>> nn = TwoHiddenLayerNeuralNetwork(input_val, output_val)
>>> res = nn.feedforward()
>>> array_sum = numpy.sum(res)
>>> numpy.isnan(array_sum)
False
"""
# Layer_between_input_and_first_hidden_layer is the layer connecting the
# input nodes with the first hidden layer nodes.
self.layer_between_input_and_first_hidden_layer = sigmoid(
numpy.dot(self.input_array, self.input_layer_and_first_hidden_layer_weights)
)
# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
# connecting the first hidden set of nodes with the second hidden set of nodes.
self.layer_between_first_hidden_layer_and_second_hidden_layer = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer,
self.first_hidden_layer_and_second_hidden_layer_weights,
)
)
# layer_between_second_hidden_layer_and_output is the layer connecting
# second hidden layer with the output node.
self.layer_between_second_hidden_layer_and_output = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer,
self.second_hidden_layer_and_output_layer_weights,
)
)
return self.layer_between_second_hidden_layer_and_output
def back_propagation(self) -> None:
"""
Function for fine-tuning the weights of the neural net based on the
error rate obtained in the previous epoch (i.e., iteration).
Updation is done using derivative of sogmoid activation function.
>>> input_val = numpy.array(([0, 0, 0], [0, 0, 0], [0, 0, 0]), dtype=float)
>>> output_val = numpy.array(([0], [0], [0]), dtype=float)
>>> nn = TwoHiddenLayerNeuralNetwork(input_val, output_val)
>>> res = nn.feedforward()
>>> nn.back_propagation()
>>> updated_weights = nn.second_hidden_layer_and_output_layer_weights
>>> (res == updated_weights).all()
False
"""
updated_second_hidden_layer_and_output_layer_weights = numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer.T,
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output),
)
updated_first_hidden_layer_and_second_hidden_layer_weights = numpy.dot(
self.layer_between_input_and_first_hidden_layer.T,
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output),
self.second_hidden_layer_and_output_layer_weights.T,
)
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer
),
)
updated_input_layer_and_first_hidden_layer_weights = numpy.dot(
self.input_array.T,
numpy.dot(
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output),
self.second_hidden_layer_and_output_layer_weights.T,
)
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer
),
self.first_hidden_layer_and_second_hidden_layer_weights.T,
)
* sigmoid_derivative(self.layer_between_input_and_first_hidden_layer),
)
self.input_layer_and_first_hidden_layer_weights += (
updated_input_layer_and_first_hidden_layer_weights
)
self.first_hidden_layer_and_second_hidden_layer_weights += (
updated_first_hidden_layer_and_second_hidden_layer_weights
)
self.second_hidden_layer_and_output_layer_weights += (
updated_second_hidden_layer_and_output_layer_weights
)
def train(self, output: numpy.ndarray, iterations: int, give_loss: bool) -> None:
"""
Performs the feedforwarding and back propagation process for the
given number of iterations.
Every iteration will update the weights of neural network.
output : real output values,required for calculating loss.
iterations : number of times the weights are to be updated.
give_loss : boolean value, If True then prints loss for each iteration,
If False then nothing is printed
>>> input_val = numpy.array(([0, 0, 0], [0, 1, 0], [0, 0, 1]), dtype=float)
>>> output_val = numpy.array(([0], [1], [1]), dtype=float)
>>> nn = TwoHiddenLayerNeuralNetwork(input_val, output_val)
>>> first_iteration_weights = nn.feedforward()
>>> nn.back_propagation()
>>> updated_weights = nn.second_hidden_layer_and_output_layer_weights
>>> (first_iteration_weights == updated_weights).all()
False
"""
for iteration in range(1, iterations + 1):
self.output = self.feedforward()
self.back_propagation()
if give_loss:
loss = numpy.mean(numpy.square(output - self.feedforward()))
print(f"Iteration {iteration} Loss: {loss}")
def predict(self, input: numpy.ndarray) -> int:
"""
Predict's the output for the given input values using
the trained neural network.
The output value given by the model ranges in-between 0 and 1.
The predict function returns 1 if the model value is greater
than the threshold value else returns 0,
as the real output values are in binary.
>>> input_val = numpy.array(([0, 0, 0], [0, 1, 0], [0, 0, 1]), dtype=float)
>>> output_val = numpy.array(([0], [1], [1]), dtype=float)
>>> nn = TwoHiddenLayerNeuralNetwork(input_val, output_val)
>>> nn.train(output_val, 1000, False)
>>> nn.predict([0,1,0]) in (0, 1)
True
"""
# Input values for which the predictions are to be made.
self.array = input
self.layer_between_input_and_first_hidden_layer = sigmoid(
numpy.dot(self.array, self.input_layer_and_first_hidden_layer_weights)
)
self.layer_between_first_hidden_layer_and_second_hidden_layer = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer,
self.first_hidden_layer_and_second_hidden_layer_weights,
)
)
self.layer_between_second_hidden_layer_and_output = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer,
self.second_hidden_layer_and_output_layer_weights,
)
)
return int(self.layer_between_second_hidden_layer_and_output > 0.6)
def sigmoid(value: numpy.ndarray) -> numpy.ndarray:
"""
Applies sigmoid activation function.
return normalized values
>>> sigmoid(numpy.array(([1, 0, 2], [1, 0, 0]), dtype=numpy.float64))
array([[0.73105858, 0.5 , 0.88079708],
[0.73105858, 0.5 , 0.5 ]])
"""
return 1 / (1 + numpy.exp(-value))
def sigmoid_derivative(value: numpy.ndarray) -> numpy.ndarray:
"""
Provides the derivative value of the sigmoid function.
returns derivative of the sigmoid value
>>> sigmoid_derivative(numpy.array(([1, 0, 2], [1, 0, 0]), dtype=numpy.float64))
array([[ 0., 0., -2.],
[ 0., 0., 0.]])
"""
return (value) * (1 - (value))
def example() -> int:
"""
Example for "how to use the neural network class and use the
respected methods for the desired output".
Calls the TwoHiddenLayerNeuralNetwork class and
provides the fixed input output values to the model.
Model is trained for a fixed amount of iterations then the predict method is called.
In this example the output is divided into 2 classes i.e. binary classification,
the two classes are represented by '0' and '1'.
>>> example() in (0, 1)
True
"""
# Input values.
input = numpy.array(
(
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
),
dtype=numpy.float64,
)
# True output values for the given input values.
output = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]), dtype=numpy.float64)
# Calling neural network class.
neural_network = TwoHiddenLayerNeuralNetwork(input_array=input, output_array=output)
# Calling training function.
# Set give_loss to True if you want to see loss in every iteration.
neural_network.train(output=output, iterations=10, give_loss=False)
return neural_network.predict(numpy.array(([1, 1, 1]), dtype=numpy.float64))
if __name__ == "__main__":
example()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Find the area of various geometric shapes
"""
from math import pi, sqrt
def surface_area_cube(side_length: float) -> float:
"""
Calculate the Surface Area of a Cube.
>>> surface_area_cube(1)
6
>>> surface_area_cube(3)
54
>>> surface_area_cube(-1)
Traceback (most recent call last):
...
ValueError: surface_area_cube() only accepts non-negative values
"""
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length ** 2
def surface_area_sphere(radius: float) -> float:
"""
Calculate the Surface Area of a Sphere.
Wikipedia reference: https://en.wikipedia.org/wiki/Sphere
Formula: 4 * pi * r^2
>>> surface_area_sphere(5)
314.1592653589793
>>> surface_area_sphere(1)
12.566370614359172
>>> surface_area_sphere(-1)
Traceback (most recent call last):
...
ValueError: surface_area_sphere() only accepts non-negative values
"""
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values")
return 4 * pi * radius ** 2
def area_rectangle(length: float, width: float) -> float:
"""
Calculate the area of a rectangle.
>>> area_rectangle(10, 20)
200
>>> area_rectangle(-1, -2)
Traceback (most recent call last):
...
ValueError: area_rectangle() only accepts non-negative values
>>> area_rectangle(1, -2)
Traceback (most recent call last):
...
ValueError: area_rectangle() only accepts non-negative values
>>> area_rectangle(-1, 2)
Traceback (most recent call last):
...
ValueError: area_rectangle() only accepts non-negative values
"""
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values")
return length * width
def area_square(side_length: float) -> float:
"""
Calculate the area of a square.
>>> area_square(10)
100
>>> area_square(-1)
Traceback (most recent call last):
...
ValueError: area_square() only accepts non-negative values
"""
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values")
return side_length ** 2
def area_triangle(base: float, height: float) -> float:
"""
Calculate the area of a triangle given the base and height.
>>> area_triangle(10, 10)
50.0
>>> area_triangle(-1, -2)
Traceback (most recent call last):
...
ValueError: area_triangle() only accepts non-negative values
>>> area_triangle(1, -2)
Traceback (most recent call last):
...
ValueError: area_triangle() only accepts non-negative values
>>> area_triangle(-1, 2)
Traceback (most recent call last):
...
ValueError: area_triangle() only accepts non-negative values
"""
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values")
return (base * height) / 2
def area_triangle_three_sides(side1: float, side2: float, side3: float) -> float:
"""
Calculate area of triangle when the length of 3 sides are known.
This function uses Heron's formula: https://en.wikipedia.org/wiki/Heron%27s_formula
>>> area_triangle_three_sides(5, 12, 13)
30.0
>>> area_triangle_three_sides(10, 11, 12)
51.521233486786784
>>> area_triangle_three_sides(-1, -2, -1)
Traceback (most recent call last):
...
ValueError: area_triangle_three_sides() only accepts non-negative values
>>> area_triangle_three_sides(1, -2, 1)
Traceback (most recent call last):
...
ValueError: area_triangle_three_sides() only accepts non-negative values
"""
if side1 < 0 or side2 < 0 or side3 < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values")
elif side1 + side2 < side3 or side1 + side3 < side2 or side2 + side3 < side1:
raise ValueError("Given three sides do not form a triangle")
semi_perimeter = (side1 + side2 + side3) / 2
area = sqrt(
semi_perimeter
* (semi_perimeter - side1)
* (semi_perimeter - side2)
* (semi_perimeter - side3)
)
return area
def area_parallelogram(base: float, height: float) -> float:
"""
Calculate the area of a parallelogram.
>>> area_parallelogram(10, 20)
200
>>> area_parallelogram(-1, -2)
Traceback (most recent call last):
...
ValueError: area_parallelogram() only accepts non-negative values
>>> area_parallelogram(1, -2)
Traceback (most recent call last):
...
ValueError: area_parallelogram() only accepts non-negative values
>>> area_parallelogram(-1, 2)
Traceback (most recent call last):
...
ValueError: area_parallelogram() only accepts non-negative values
"""
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values")
return base * height
def area_trapezium(base1: float, base2: float, height: float) -> float:
"""
Calculate the area of a trapezium.
>>> area_trapezium(10, 20, 30)
450.0
>>> area_trapezium(-1, -2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(-1, 2, 3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(1, -2, 3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(1, 2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(-1, -2, 3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(1, -2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(-1, 2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
"""
if base1 < 0 or base2 < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values")
return 1 / 2 * (base1 + base2) * height
def area_circle(radius: float) -> float:
"""
Calculate the area of a circle.
>>> area_circle(20)
1256.6370614359173
>>> area_circle(-1)
Traceback (most recent call last):
...
ValueError: area_circle() only accepts non-negative values
"""
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values")
return pi * radius ** 2
def area_ellipse(radius_x: float, radius_y: float) -> float:
"""
Calculate the area of a ellipse.
>>> area_ellipse(10, 10)
314.1592653589793
>>> area_ellipse(10, 20)
628.3185307179587
>>> area_ellipse(-10, 20)
Traceback (most recent call last):
...
ValueError: area_ellipse() only accepts non-negative values
>>> area_ellipse(10, -20)
Traceback (most recent call last):
...
ValueError: area_ellipse() only accepts non-negative values
>>> area_ellipse(-10, -20)
Traceback (most recent call last):
...
ValueError: area_ellipse() only accepts non-negative values
"""
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values")
return pi * radius_x * radius_y
def area_rhombus(diagonal_1: float, diagonal_2: float) -> float:
"""
Calculate the area of a rhombus.
>>> area_rhombus(10, 20)
100.0
>>> area_rhombus(-1, -2)
Traceback (most recent call last):
...
ValueError: area_rhombus() only accepts non-negative values
>>> area_rhombus(1, -2)
Traceback (most recent call last):
...
ValueError: area_rhombus() only accepts non-negative values
>>> area_rhombus(-1, 2)
Traceback (most recent call last):
...
ValueError: area_rhombus() only accepts non-negative values
"""
if diagonal_1 < 0 or diagonal_2 < 0:
raise ValueError("area_rhombus() only accepts non-negative values")
return 1 / 2 * diagonal_1 * diagonal_2
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print("\nSurface Areas of various geometric shapes: \n")
print(f"Cube: {surface_area_cube(20) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
| """
Find the area of various geometric shapes
"""
from math import pi, sqrt
def surface_area_cube(side_length: float) -> float:
"""
Calculate the Surface Area of a Cube.
>>> surface_area_cube(1)
6
>>> surface_area_cube(3)
54
>>> surface_area_cube(-1)
Traceback (most recent call last):
...
ValueError: surface_area_cube() only accepts non-negative values
"""
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length ** 2
def surface_area_sphere(radius: float) -> float:
"""
Calculate the Surface Area of a Sphere.
Wikipedia reference: https://en.wikipedia.org/wiki/Sphere
Formula: 4 * pi * r^2
>>> surface_area_sphere(5)
314.1592653589793
>>> surface_area_sphere(1)
12.566370614359172
>>> surface_area_sphere(-1)
Traceback (most recent call last):
...
ValueError: surface_area_sphere() only accepts non-negative values
"""
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values")
return 4 * pi * radius ** 2
def area_rectangle(length: float, width: float) -> float:
"""
Calculate the area of a rectangle.
>>> area_rectangle(10, 20)
200
>>> area_rectangle(-1, -2)
Traceback (most recent call last):
...
ValueError: area_rectangle() only accepts non-negative values
>>> area_rectangle(1, -2)
Traceback (most recent call last):
...
ValueError: area_rectangle() only accepts non-negative values
>>> area_rectangle(-1, 2)
Traceback (most recent call last):
...
ValueError: area_rectangle() only accepts non-negative values
"""
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values")
return length * width
def area_square(side_length: float) -> float:
"""
Calculate the area of a square.
>>> area_square(10)
100
>>> area_square(-1)
Traceback (most recent call last):
...
ValueError: area_square() only accepts non-negative values
"""
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values")
return side_length ** 2
def area_triangle(base: float, height: float) -> float:
"""
Calculate the area of a triangle given the base and height.
>>> area_triangle(10, 10)
50.0
>>> area_triangle(-1, -2)
Traceback (most recent call last):
...
ValueError: area_triangle() only accepts non-negative values
>>> area_triangle(1, -2)
Traceback (most recent call last):
...
ValueError: area_triangle() only accepts non-negative values
>>> area_triangle(-1, 2)
Traceback (most recent call last):
...
ValueError: area_triangle() only accepts non-negative values
"""
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values")
return (base * height) / 2
def area_triangle_three_sides(side1: float, side2: float, side3: float) -> float:
"""
Calculate area of triangle when the length of 3 sides are known.
This function uses Heron's formula: https://en.wikipedia.org/wiki/Heron%27s_formula
>>> area_triangle_three_sides(5, 12, 13)
30.0
>>> area_triangle_three_sides(10, 11, 12)
51.521233486786784
>>> area_triangle_three_sides(-1, -2, -1)
Traceback (most recent call last):
...
ValueError: area_triangle_three_sides() only accepts non-negative values
>>> area_triangle_three_sides(1, -2, 1)
Traceback (most recent call last):
...
ValueError: area_triangle_three_sides() only accepts non-negative values
"""
if side1 < 0 or side2 < 0 or side3 < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values")
elif side1 + side2 < side3 or side1 + side3 < side2 or side2 + side3 < side1:
raise ValueError("Given three sides do not form a triangle")
semi_perimeter = (side1 + side2 + side3) / 2
area = sqrt(
semi_perimeter
* (semi_perimeter - side1)
* (semi_perimeter - side2)
* (semi_perimeter - side3)
)
return area
def area_parallelogram(base: float, height: float) -> float:
"""
Calculate the area of a parallelogram.
>>> area_parallelogram(10, 20)
200
>>> area_parallelogram(-1, -2)
Traceback (most recent call last):
...
ValueError: area_parallelogram() only accepts non-negative values
>>> area_parallelogram(1, -2)
Traceback (most recent call last):
...
ValueError: area_parallelogram() only accepts non-negative values
>>> area_parallelogram(-1, 2)
Traceback (most recent call last):
...
ValueError: area_parallelogram() only accepts non-negative values
"""
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values")
return base * height
def area_trapezium(base1: float, base2: float, height: float) -> float:
"""
Calculate the area of a trapezium.
>>> area_trapezium(10, 20, 30)
450.0
>>> area_trapezium(-1, -2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(-1, 2, 3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(1, -2, 3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(1, 2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(-1, -2, 3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(1, -2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
>>> area_trapezium(-1, 2, -3)
Traceback (most recent call last):
...
ValueError: area_trapezium() only accepts non-negative values
"""
if base1 < 0 or base2 < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values")
return 1 / 2 * (base1 + base2) * height
def area_circle(radius: float) -> float:
"""
Calculate the area of a circle.
>>> area_circle(20)
1256.6370614359173
>>> area_circle(-1)
Traceback (most recent call last):
...
ValueError: area_circle() only accepts non-negative values
"""
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values")
return pi * radius ** 2
def area_ellipse(radius_x: float, radius_y: float) -> float:
"""
Calculate the area of a ellipse.
>>> area_ellipse(10, 10)
314.1592653589793
>>> area_ellipse(10, 20)
628.3185307179587
>>> area_ellipse(-10, 20)
Traceback (most recent call last):
...
ValueError: area_ellipse() only accepts non-negative values
>>> area_ellipse(10, -20)
Traceback (most recent call last):
...
ValueError: area_ellipse() only accepts non-negative values
>>> area_ellipse(-10, -20)
Traceback (most recent call last):
...
ValueError: area_ellipse() only accepts non-negative values
"""
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values")
return pi * radius_x * radius_y
def area_rhombus(diagonal_1: float, diagonal_2: float) -> float:
"""
Calculate the area of a rhombus.
>>> area_rhombus(10, 20)
100.0
>>> area_rhombus(-1, -2)
Traceback (most recent call last):
...
ValueError: area_rhombus() only accepts non-negative values
>>> area_rhombus(1, -2)
Traceback (most recent call last):
...
ValueError: area_rhombus() only accepts non-negative values
>>> area_rhombus(-1, 2)
Traceback (most recent call last):
...
ValueError: area_rhombus() only accepts non-negative values
"""
if diagonal_1 < 0 or diagonal_2 < 0:
raise ValueError("area_rhombus() only accepts non-negative values")
return 1 / 2 * diagonal_1 * diagonal_2
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print("\nSurface Areas of various geometric shapes: \n")
print(f"Cube: {surface_area_cube(20) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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 | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Hey, we are going to find an exciting number called Catalan number which is use to find
the number of possible binary search trees from tree of a given number of nodes.
We will use the formula: t(n) = SUMMATION(i = 1 to n)t(i-1)t(n-i)
Further details at Wikipedia: https://en.wikipedia.org/wiki/Catalan_number
"""
"""
Our Contribution:
Basically we Create the 2 function:
1. catalan_number(node_count: int) -> int
Returns the number of possible binary search trees for n nodes.
2. binary_tree_count(node_count: int) -> int
Returns the number of possible binary trees for n nodes.
"""
def binomial_coefficient(n: int, k: int) -> int:
"""
Since Here we Find the Binomial Coefficient:
https://en.wikipedia.org/wiki/Binomial_coefficient
C(n,k) = n! / k!(n-k)!
:param n: 2 times of Number of nodes
:param k: Number of nodes
:return: Integer Value
>>> binomial_coefficient(4, 2)
6
"""
result = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
k = n - k
# Calculate C(n,k)
for i in range(k):
result *= n - i
result //= i + 1
return result
def catalan_number(node_count: int) -> int:
"""
We can find Catalan number many ways but here we use Binomial Coefficient because it
does the job in O(n)
return the Catalan number of n using 2nCn/(n+1).
:param n: number of nodes
:return: Catalan number of n nodes
>>> catalan_number(5)
42
>>> catalan_number(6)
132
"""
return binomial_coefficient(2 * node_count, node_count) // (node_count + 1)
def factorial(n: int) -> int:
"""
Return the factorial of a number.
:param n: Number to find the Factorial of.
:return: Factorial of n.
>>> import math
>>> all(factorial(i) == math.factorial(i) for i in range(10))
True
>>> factorial(-5) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: factorial() not defined for negative values
"""
if n < 0:
raise ValueError("factorial() not defined for negative values")
result = 1
for i in range(1, n + 1):
result *= i
return result
def binary_tree_count(node_count: int) -> int:
"""
Return the number of possible of binary trees.
:param n: number of nodes
:return: Number of possible binary trees
>>> binary_tree_count(5)
5040
>>> binary_tree_count(6)
95040
"""
return catalan_number(node_count) * factorial(node_count)
if __name__ == "__main__":
node_count = int(input("Enter the number of nodes: ").strip() or 0)
if node_count <= 0:
raise ValueError("We need some nodes to work with.")
print(
f"Given {node_count} nodes, there are {binary_tree_count(node_count)} "
f"binary trees and {catalan_number(node_count)} binary search trees."
)
| """
Hey, we are going to find an exciting number called Catalan number which is use to find
the number of possible binary search trees from tree of a given number of nodes.
We will use the formula: t(n) = SUMMATION(i = 1 to n)t(i-1)t(n-i)
Further details at Wikipedia: https://en.wikipedia.org/wiki/Catalan_number
"""
"""
Our Contribution:
Basically we Create the 2 function:
1. catalan_number(node_count: int) -> int
Returns the number of possible binary search trees for n nodes.
2. binary_tree_count(node_count: int) -> int
Returns the number of possible binary trees for n nodes.
"""
def binomial_coefficient(n: int, k: int) -> int:
"""
Since Here we Find the Binomial Coefficient:
https://en.wikipedia.org/wiki/Binomial_coefficient
C(n,k) = n! / k!(n-k)!
:param n: 2 times of Number of nodes
:param k: Number of nodes
:return: Integer Value
>>> binomial_coefficient(4, 2)
6
"""
result = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
k = n - k
# Calculate C(n,k)
for i in range(k):
result *= n - i
result //= i + 1
return result
def catalan_number(node_count: int) -> int:
"""
We can find Catalan number many ways but here we use Binomial Coefficient because it
does the job in O(n)
return the Catalan number of n using 2nCn/(n+1).
:param n: number of nodes
:return: Catalan number of n nodes
>>> catalan_number(5)
42
>>> catalan_number(6)
132
"""
return binomial_coefficient(2 * node_count, node_count) // (node_count + 1)
def factorial(n: int) -> int:
"""
Return the factorial of a number.
:param n: Number to find the Factorial of.
:return: Factorial of n.
>>> import math
>>> all(factorial(i) == math.factorial(i) for i in range(10))
True
>>> factorial(-5) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: factorial() not defined for negative values
"""
if n < 0:
raise ValueError("factorial() not defined for negative values")
result = 1
for i in range(1, n + 1):
result *= i
return result
def binary_tree_count(node_count: int) -> int:
"""
Return the number of possible of binary trees.
:param n: number of nodes
:return: Number of possible binary trees
>>> binary_tree_count(5)
5040
>>> binary_tree_count(6)
95040
"""
return catalan_number(node_count) * factorial(node_count)
if __name__ == "__main__":
node_count = int(input("Enter the number of nodes: ").strip() or 0)
if node_count <= 0:
raise ValueError("We need some nodes to work with.")
print(
f"Given {node_count} nodes, there are {binary_tree_count(node_count)} "
f"binary trees and {catalan_number(node_count)} binary search trees."
)
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/Burrows%E2%80%93Wheeler_transform
The Burrows–Wheeler transform (BWT, also called block-sorting compression)
rearranges a character string into runs of similar characters. This is useful
for compression, since it tends to be easy to compress a string that has runs
of repeated characters by techniques such as move-to-front transform and
run-length encoding. More importantly, the transformation is reversible,
without needing to store any additional data except the position of the first
original character. The BWT is thus a "free" method of improving the efficiency
of text compression algorithms, costing only some extra computation.
"""
from __future__ import annotations
def all_rotations(s: str) -> list[str]:
"""
:param s: The string that will be rotated len(s) times.
:return: A list with the rotations.
:raises TypeError: If s is not an instance of str.
Examples:
>>> all_rotations("^BANANA|") # doctest: +NORMALIZE_WHITESPACE
['^BANANA|', 'BANANA|^', 'ANANA|^B', 'NANA|^BA', 'ANA|^BAN', 'NA|^BANA',
'A|^BANAN', '|^BANANA']
>>> all_rotations("a_asa_da_casa") # doctest: +NORMALIZE_WHITESPACE
['a_asa_da_casa', '_asa_da_casaa', 'asa_da_casaa_', 'sa_da_casaa_a',
'a_da_casaa_as', '_da_casaa_asa', 'da_casaa_asa_', 'a_casaa_asa_d',
'_casaa_asa_da', 'casaa_asa_da_', 'asaa_asa_da_c', 'saa_asa_da_ca',
'aa_asa_da_cas']
>>> all_rotations("panamabanana") # doctest: +NORMALIZE_WHITESPACE
['panamabanana', 'anamabananap', 'namabananapa', 'amabananapan',
'mabananapana', 'abananapanam', 'bananapanama', 'ananapanamab',
'nanapanamaba', 'anapanamaban', 'napanamabana', 'apanamabanan']
>>> all_rotations(5)
Traceback (most recent call last):
...
TypeError: The parameter s type must be str.
"""
if not isinstance(s, str):
raise TypeError("The parameter s type must be str.")
return [s[i:] + s[:i] for i in range(len(s))]
def bwt_transform(s: str) -> dict:
"""
:param s: The string that will be used at bwt algorithm
:return: the string composed of the last char of each row of the ordered
rotations and the index of the original string at ordered rotations list
:raises TypeError: If the s parameter type is not str
:raises ValueError: If the s parameter is empty
Examples:
>>> bwt_transform("^BANANA")
{'bwt_string': 'BNN^AAA', 'idx_original_string': 6}
>>> bwt_transform("a_asa_da_casa")
{'bwt_string': 'aaaadss_c__aa', 'idx_original_string': 3}
>>> bwt_transform("panamabanana")
{'bwt_string': 'mnpbnnaaaaaa', 'idx_original_string': 11}
>>> bwt_transform(4)
Traceback (most recent call last):
...
TypeError: The parameter s type must be str.
>>> bwt_transform('')
Traceback (most recent call last):
...
ValueError: The parameter s must not be empty.
"""
if not isinstance(s, str):
raise TypeError("The parameter s type must be str.")
if not s:
raise ValueError("The parameter s must not be empty.")
rotations = all_rotations(s)
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
return {
"bwt_string": "".join([word[-1] for word in rotations]),
"idx_original_string": rotations.index(s),
}
def reverse_bwt(bwt_string: str, idx_original_string: int) -> str:
"""
:param bwt_string: The string returned from bwt algorithm execution
:param idx_original_string: A 0-based index of the string that was used to
generate bwt_string at ordered rotations list
:return: The string used to generate bwt_string when bwt was executed
:raises TypeError: If the bwt_string parameter type is not str
:raises ValueError: If the bwt_string parameter is empty
:raises TypeError: If the idx_original_string type is not int or if not
possible to cast it to int
:raises ValueError: If the idx_original_string value is lower than 0 or
greater than len(bwt_string) - 1
>>> reverse_bwt("BNN^AAA", 6)
'^BANANA'
>>> reverse_bwt("aaaadss_c__aa", 3)
'a_asa_da_casa'
>>> reverse_bwt("mnpbnnaaaaaa", 11)
'panamabanana'
>>> reverse_bwt(4, 11)
Traceback (most recent call last):
...
TypeError: The parameter bwt_string type must be str.
>>> reverse_bwt("", 11)
Traceback (most recent call last):
...
ValueError: The parameter bwt_string must not be empty.
>>> reverse_bwt("mnpbnnaaaaaa", "asd") # doctest: +NORMALIZE_WHITESPACE
Traceback (most recent call last):
...
TypeError: The parameter idx_original_string type must be int or passive
of cast to int.
>>> reverse_bwt("mnpbnnaaaaaa", -1)
Traceback (most recent call last):
...
ValueError: The parameter idx_original_string must not be lower than 0.
>>> reverse_bwt("mnpbnnaaaaaa", 12) # doctest: +NORMALIZE_WHITESPACE
Traceback (most recent call last):
...
ValueError: The parameter idx_original_string must be lower than
len(bwt_string).
>>> reverse_bwt("mnpbnnaaaaaa", 11.0)
'panamabanana'
>>> reverse_bwt("mnpbnnaaaaaa", 11.4)
'panamabanana'
"""
if not isinstance(bwt_string, str):
raise TypeError("The parameter bwt_string type must be str.")
if not bwt_string:
raise ValueError("The parameter bwt_string must not be empty.")
try:
idx_original_string = int(idx_original_string)
except ValueError:
raise TypeError(
"The parameter idx_original_string type must be int or passive"
" of cast to int."
)
if idx_original_string < 0:
raise ValueError("The parameter idx_original_string must not be lower than 0.")
if idx_original_string >= len(bwt_string):
raise ValueError(
"The parameter idx_original_string must be lower than" " len(bwt_string)."
)
ordered_rotations = [""] * len(bwt_string)
for x in range(len(bwt_string)):
for i in range(len(bwt_string)):
ordered_rotations[i] = bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
entry_msg = "Provide a string that I will generate its BWT transform: "
s = input(entry_msg).strip()
result = bwt_transform(s)
print(
f"Burrows Wheeler transform for string '{s}' results "
f"in '{result['bwt_string']}'"
)
original_string = reverse_bwt(result["bwt_string"], result["idx_original_string"])
print(
f"Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' "
f"we get original string '{original_string}'"
)
| """
https://en.wikipedia.org/wiki/Burrows%E2%80%93Wheeler_transform
The Burrows–Wheeler transform (BWT, also called block-sorting compression)
rearranges a character string into runs of similar characters. This is useful
for compression, since it tends to be easy to compress a string that has runs
of repeated characters by techniques such as move-to-front transform and
run-length encoding. More importantly, the transformation is reversible,
without needing to store any additional data except the position of the first
original character. The BWT is thus a "free" method of improving the efficiency
of text compression algorithms, costing only some extra computation.
"""
from __future__ import annotations
def all_rotations(s: str) -> list[str]:
"""
:param s: The string that will be rotated len(s) times.
:return: A list with the rotations.
:raises TypeError: If s is not an instance of str.
Examples:
>>> all_rotations("^BANANA|") # doctest: +NORMALIZE_WHITESPACE
['^BANANA|', 'BANANA|^', 'ANANA|^B', 'NANA|^BA', 'ANA|^BAN', 'NA|^BANA',
'A|^BANAN', '|^BANANA']
>>> all_rotations("a_asa_da_casa") # doctest: +NORMALIZE_WHITESPACE
['a_asa_da_casa', '_asa_da_casaa', 'asa_da_casaa_', 'sa_da_casaa_a',
'a_da_casaa_as', '_da_casaa_asa', 'da_casaa_asa_', 'a_casaa_asa_d',
'_casaa_asa_da', 'casaa_asa_da_', 'asaa_asa_da_c', 'saa_asa_da_ca',
'aa_asa_da_cas']
>>> all_rotations("panamabanana") # doctest: +NORMALIZE_WHITESPACE
['panamabanana', 'anamabananap', 'namabananapa', 'amabananapan',
'mabananapana', 'abananapanam', 'bananapanama', 'ananapanamab',
'nanapanamaba', 'anapanamaban', 'napanamabana', 'apanamabanan']
>>> all_rotations(5)
Traceback (most recent call last):
...
TypeError: The parameter s type must be str.
"""
if not isinstance(s, str):
raise TypeError("The parameter s type must be str.")
return [s[i:] + s[:i] for i in range(len(s))]
def bwt_transform(s: str) -> dict:
"""
:param s: The string that will be used at bwt algorithm
:return: the string composed of the last char of each row of the ordered
rotations and the index of the original string at ordered rotations list
:raises TypeError: If the s parameter type is not str
:raises ValueError: If the s parameter is empty
Examples:
>>> bwt_transform("^BANANA")
{'bwt_string': 'BNN^AAA', 'idx_original_string': 6}
>>> bwt_transform("a_asa_da_casa")
{'bwt_string': 'aaaadss_c__aa', 'idx_original_string': 3}
>>> bwt_transform("panamabanana")
{'bwt_string': 'mnpbnnaaaaaa', 'idx_original_string': 11}
>>> bwt_transform(4)
Traceback (most recent call last):
...
TypeError: The parameter s type must be str.
>>> bwt_transform('')
Traceback (most recent call last):
...
ValueError: The parameter s must not be empty.
"""
if not isinstance(s, str):
raise TypeError("The parameter s type must be str.")
if not s:
raise ValueError("The parameter s must not be empty.")
rotations = all_rotations(s)
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
return {
"bwt_string": "".join([word[-1] for word in rotations]),
"idx_original_string": rotations.index(s),
}
def reverse_bwt(bwt_string: str, idx_original_string: int) -> str:
"""
:param bwt_string: The string returned from bwt algorithm execution
:param idx_original_string: A 0-based index of the string that was used to
generate bwt_string at ordered rotations list
:return: The string used to generate bwt_string when bwt was executed
:raises TypeError: If the bwt_string parameter type is not str
:raises ValueError: If the bwt_string parameter is empty
:raises TypeError: If the idx_original_string type is not int or if not
possible to cast it to int
:raises ValueError: If the idx_original_string value is lower than 0 or
greater than len(bwt_string) - 1
>>> reverse_bwt("BNN^AAA", 6)
'^BANANA'
>>> reverse_bwt("aaaadss_c__aa", 3)
'a_asa_da_casa'
>>> reverse_bwt("mnpbnnaaaaaa", 11)
'panamabanana'
>>> reverse_bwt(4, 11)
Traceback (most recent call last):
...
TypeError: The parameter bwt_string type must be str.
>>> reverse_bwt("", 11)
Traceback (most recent call last):
...
ValueError: The parameter bwt_string must not be empty.
>>> reverse_bwt("mnpbnnaaaaaa", "asd") # doctest: +NORMALIZE_WHITESPACE
Traceback (most recent call last):
...
TypeError: The parameter idx_original_string type must be int or passive
of cast to int.
>>> reverse_bwt("mnpbnnaaaaaa", -1)
Traceback (most recent call last):
...
ValueError: The parameter idx_original_string must not be lower than 0.
>>> reverse_bwt("mnpbnnaaaaaa", 12) # doctest: +NORMALIZE_WHITESPACE
Traceback (most recent call last):
...
ValueError: The parameter idx_original_string must be lower than
len(bwt_string).
>>> reverse_bwt("mnpbnnaaaaaa", 11.0)
'panamabanana'
>>> reverse_bwt("mnpbnnaaaaaa", 11.4)
'panamabanana'
"""
if not isinstance(bwt_string, str):
raise TypeError("The parameter bwt_string type must be str.")
if not bwt_string:
raise ValueError("The parameter bwt_string must not be empty.")
try:
idx_original_string = int(idx_original_string)
except ValueError:
raise TypeError(
"The parameter idx_original_string type must be int or passive"
" of cast to int."
)
if idx_original_string < 0:
raise ValueError("The parameter idx_original_string must not be lower than 0.")
if idx_original_string >= len(bwt_string):
raise ValueError(
"The parameter idx_original_string must be lower than" " len(bwt_string)."
)
ordered_rotations = [""] * len(bwt_string)
for x in range(len(bwt_string)):
for i in range(len(bwt_string)):
ordered_rotations[i] = bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
entry_msg = "Provide a string that I will generate its BWT transform: "
s = input(entry_msg).strip()
result = bwt_transform(s)
print(
f"Burrows Wheeler transform for string '{s}' results "
f"in '{result['bwt_string']}'"
)
original_string = reverse_bwt(result["bwt_string"], result["idx_original_string"])
print(
f"Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' "
f"we get original string '{original_string}'"
)
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # https://en.wikipedia.org/wiki/Charge_carrier_density
# https://www.pveducation.org/pvcdrom/pn-junctions/equilibrium-carrier-concentration
# http://www.ece.utep.edu/courses/ee3329/ee3329/Studyguide/ToC/Fundamentals/Carriers/concentrations.html
from __future__ import annotations
def carrier_concentration(
electron_conc: float,
hole_conc: float,
intrinsic_conc: float,
) -> tuple:
"""
This function can calculate any one of the three -
1. Electron Concentration
2, Hole Concentration
3. Intrinsic Concentration
given the other two.
Examples -
>>> carrier_concentration(electron_conc=25, hole_conc=100, intrinsic_conc=0)
('intrinsic_conc', 50.0)
>>> carrier_concentration(electron_conc=0, hole_conc=1600, intrinsic_conc=200)
('electron_conc', 25.0)
>>> carrier_concentration(electron_conc=1000, hole_conc=0, intrinsic_conc=1200)
('hole_conc', 1440.0)
>>> carrier_concentration(electron_conc=1000, hole_conc=400, intrinsic_conc=1200)
Traceback (most recent call last):
File "<stdin>", line 37, in <module>
ValueError: You cannot supply more or less than 2 values
>>> carrier_concentration(electron_conc=-1000, hole_conc=0, intrinsic_conc=1200)
Traceback (most recent call last):
File "<stdin>", line 40, in <module>
ValueError: Electron concentration cannot be negative in a semiconductor
>>> carrier_concentration(electron_conc=0, hole_conc=-400, intrinsic_conc=1200)
Traceback (most recent call last):
File "<stdin>", line 44, in <module>
ValueError: Hole concentration cannot be negative in a semiconductor
>>> carrier_concentration(electron_conc=0, hole_conc=400, intrinsic_conc=-1200)
Traceback (most recent call last):
File "<stdin>", line 48, in <module>
ValueError: Intrinsic concentration cannot be negative in a semiconductor
"""
if (electron_conc, hole_conc, intrinsic_conc).count(0) != 1:
raise ValueError("You cannot supply more or less than 2 values")
elif electron_conc < 0:
raise ValueError("Electron concentration cannot be negative in a semiconductor")
elif hole_conc < 0:
raise ValueError("Hole concentration cannot be negative in a semiconductor")
elif intrinsic_conc < 0:
raise ValueError(
"Intrinsic concentration cannot be negative in a semiconductor"
)
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc ** 2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc ** 2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| # https://en.wikipedia.org/wiki/Charge_carrier_density
# https://www.pveducation.org/pvcdrom/pn-junctions/equilibrium-carrier-concentration
# http://www.ece.utep.edu/courses/ee3329/ee3329/Studyguide/ToC/Fundamentals/Carriers/concentrations.html
from __future__ import annotations
def carrier_concentration(
electron_conc: float,
hole_conc: float,
intrinsic_conc: float,
) -> tuple:
"""
This function can calculate any one of the three -
1. Electron Concentration
2, Hole Concentration
3. Intrinsic Concentration
given the other two.
Examples -
>>> carrier_concentration(electron_conc=25, hole_conc=100, intrinsic_conc=0)
('intrinsic_conc', 50.0)
>>> carrier_concentration(electron_conc=0, hole_conc=1600, intrinsic_conc=200)
('electron_conc', 25.0)
>>> carrier_concentration(electron_conc=1000, hole_conc=0, intrinsic_conc=1200)
('hole_conc', 1440.0)
>>> carrier_concentration(electron_conc=1000, hole_conc=400, intrinsic_conc=1200)
Traceback (most recent call last):
File "<stdin>", line 37, in <module>
ValueError: You cannot supply more or less than 2 values
>>> carrier_concentration(electron_conc=-1000, hole_conc=0, intrinsic_conc=1200)
Traceback (most recent call last):
File "<stdin>", line 40, in <module>
ValueError: Electron concentration cannot be negative in a semiconductor
>>> carrier_concentration(electron_conc=0, hole_conc=-400, intrinsic_conc=1200)
Traceback (most recent call last):
File "<stdin>", line 44, in <module>
ValueError: Hole concentration cannot be negative in a semiconductor
>>> carrier_concentration(electron_conc=0, hole_conc=400, intrinsic_conc=-1200)
Traceback (most recent call last):
File "<stdin>", line 48, in <module>
ValueError: Intrinsic concentration cannot be negative in a semiconductor
"""
if (electron_conc, hole_conc, intrinsic_conc).count(0) != 1:
raise ValueError("You cannot supply more or less than 2 values")
elif electron_conc < 0:
raise ValueError("Electron concentration cannot be negative in a semiconductor")
elif hole_conc < 0:
raise ValueError("Hole concentration cannot be negative in a semiconductor")
elif intrinsic_conc < 0:
raise ValueError(
"Intrinsic concentration cannot be negative in a semiconductor"
)
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc ** 2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc ** 2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from collections import deque
from math import floor
from random import random
from time import time
# the default weight is 1 if not assigned but all the implementation is weighted
class DirectedGraph:
def __init__(self):
self.graph = {}
# adding vertices and edges
# adding the weight is optional
# handles repetition
def add_pair(self, u, v, w=1):
if self.graph.get(u):
if self.graph[u].count([w, v]) == 0:
self.graph[u].append([w, v])
else:
self.graph[u] = [[w, v]]
if not self.graph.get(v):
self.graph[v] = []
def all_nodes(self):
return list(self.graph)
# handles if the input does not exist
def remove_pair(self, u, v):
if self.graph.get(u):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(_)
# if no destination is meant the default value is -1
def dfs(self, s=-2, d=-1):
if s == d:
return []
stack = []
visited = []
if s == -2:
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
ss = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if visited.count(node[1]) < 1:
if node[1] == d:
visited.append(d)
return visited
else:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return visited
# c is the count of nodes you want and if you leave it or pass -1 to the function
# the count will be random from 10 to 10000
def fill_graph_randomly(self, c=-1):
if c == -1:
c = floor(random() * 10000) + 10
for i in range(c):
# every vertex has max 100 edges
for _ in range(floor(random() * 102) + 1):
n = floor(random() * c) + 1
if n != i:
self.add_pair(i, n, 1)
def bfs(self, s=-2):
d = deque()
visited = []
if s == -2:
s = list(self.graph)[0]
d.append(s)
visited.append(s)
while d:
s = d.popleft()
if len(self.graph[s]) != 0:
for node in self.graph[s]:
if visited.count(node[1]) < 1:
d.append(node[1])
visited.append(node[1])
return visited
def in_degree(self, u):
count = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def out_degree(self, u):
return len(self.graph[u])
def topological_sort(self, s=-2):
stack = []
visited = []
if s == -2:
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
ss = s
sorted_nodes = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop())
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return sorted_nodes
def cycle_nodes(self):
stack = []
visited = []
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if (
visited.count(node[1]) > 0
and node[1] != parent
and indirect_parents.count(node[1]) > 0
and not on_the_way_back
):
len_stack = len(stack) - 1
while True and len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1])
break
else:
anticipating_nodes.add(stack[len_stack])
len_stack -= 1
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return list(anticipating_nodes)
def has_cycle(self):
stack = []
visited = []
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if (
visited.count(node[1]) > 0
and node[1] != parent
and indirect_parents.count(node[1]) > 0
and not on_the_way_back
):
len_stack_minus_one = len(stack) - 1
while True and len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1])
break
else:
return True
# TODO:The following code is unreachable.
anticipating_nodes.add(stack[len_stack_minus_one])
len_stack_minus_one -= 1
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return False
def dfs_time(self, s=-2, e=-1):
begin = time()
self.dfs(s, e)
end = time()
return end - begin
def bfs_time(self, s=-2):
begin = time()
self.bfs(s)
end = time()
return end - begin
class Graph:
def __init__(self):
self.graph = {}
# adding vertices and edges
# adding the weight is optional
# handles repetition
def add_pair(self, u, v, w=1):
# check if the u exists
if self.graph.get(u):
# if there already is a edge
if self.graph[u].count([w, v]) == 0:
self.graph[u].append([w, v])
else:
# if u does not exist
self.graph[u] = [[w, v]]
# add the other way
if self.graph.get(v):
# if there already is a edge
if self.graph[v].count([w, u]) == 0:
self.graph[v].append([w, u])
else:
# if u does not exist
self.graph[v] = [[w, u]]
# handles if the input does not exist
def remove_pair(self, u, v):
if self.graph.get(u):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(_)
# the other way round
if self.graph.get(v):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(_)
# if no destination is meant the default value is -1
def dfs(self, s=-2, d=-1):
if s == d:
return []
stack = []
visited = []
if s == -2:
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
ss = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if visited.count(node[1]) < 1:
if node[1] == d:
visited.append(d)
return visited
else:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return visited
# c is the count of nodes you want and if you leave it or pass -1 to the function
# the count will be random from 10 to 10000
def fill_graph_randomly(self, c=-1):
if c == -1:
c = floor(random() * 10000) + 10
for i in range(c):
# every vertex has max 100 edges
for _ in range(floor(random() * 102) + 1):
n = floor(random() * c) + 1
if n != i:
self.add_pair(i, n, 1)
def bfs(self, s=-2):
d = deque()
visited = []
if s == -2:
s = list(self.graph)[0]
d.append(s)
visited.append(s)
while d:
s = d.popleft()
if len(self.graph[s]) != 0:
for node in self.graph[s]:
if visited.count(node[1]) < 1:
d.append(node[1])
visited.append(node[1])
return visited
def degree(self, u):
return len(self.graph[u])
def cycle_nodes(self):
stack = []
visited = []
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if (
visited.count(node[1]) > 0
and node[1] != parent
and indirect_parents.count(node[1]) > 0
and not on_the_way_back
):
len_stack = len(stack) - 1
while True and len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1])
break
else:
anticipating_nodes.add(stack[len_stack])
len_stack -= 1
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return list(anticipating_nodes)
def has_cycle(self):
stack = []
visited = []
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if (
visited.count(node[1]) > 0
and node[1] != parent
and indirect_parents.count(node[1]) > 0
and not on_the_way_back
):
len_stack_minus_one = len(stack) - 1
while True and len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1])
break
else:
return True
# TODO: the following code is unreachable
# is this meant to be called in the else ?
anticipating_nodes.add(stack[len_stack_minus_one])
len_stack_minus_one -= 1
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return False
def all_nodes(self):
return list(self.graph)
def dfs_time(self, s=-2, e=-1):
begin = time()
self.dfs(s, e)
end = time()
return end - begin
def bfs_time(self, s=-2):
begin = time()
self.bfs(s)
end = time()
return end - begin
| from collections import deque
from math import floor
from random import random
from time import time
# the default weight is 1 if not assigned but all the implementation is weighted
class DirectedGraph:
def __init__(self):
self.graph = {}
# adding vertices and edges
# adding the weight is optional
# handles repetition
def add_pair(self, u, v, w=1):
if self.graph.get(u):
if self.graph[u].count([w, v]) == 0:
self.graph[u].append([w, v])
else:
self.graph[u] = [[w, v]]
if not self.graph.get(v):
self.graph[v] = []
def all_nodes(self):
return list(self.graph)
# handles if the input does not exist
def remove_pair(self, u, v):
if self.graph.get(u):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(_)
# if no destination is meant the default value is -1
def dfs(self, s=-2, d=-1):
if s == d:
return []
stack = []
visited = []
if s == -2:
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
ss = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if visited.count(node[1]) < 1:
if node[1] == d:
visited.append(d)
return visited
else:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return visited
# c is the count of nodes you want and if you leave it or pass -1 to the function
# the count will be random from 10 to 10000
def fill_graph_randomly(self, c=-1):
if c == -1:
c = floor(random() * 10000) + 10
for i in range(c):
# every vertex has max 100 edges
for _ in range(floor(random() * 102) + 1):
n = floor(random() * c) + 1
if n != i:
self.add_pair(i, n, 1)
def bfs(self, s=-2):
d = deque()
visited = []
if s == -2:
s = list(self.graph)[0]
d.append(s)
visited.append(s)
while d:
s = d.popleft()
if len(self.graph[s]) != 0:
for node in self.graph[s]:
if visited.count(node[1]) < 1:
d.append(node[1])
visited.append(node[1])
return visited
def in_degree(self, u):
count = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def out_degree(self, u):
return len(self.graph[u])
def topological_sort(self, s=-2):
stack = []
visited = []
if s == -2:
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
ss = s
sorted_nodes = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop())
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return sorted_nodes
def cycle_nodes(self):
stack = []
visited = []
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if (
visited.count(node[1]) > 0
and node[1] != parent
and indirect_parents.count(node[1]) > 0
and not on_the_way_back
):
len_stack = len(stack) - 1
while True and len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1])
break
else:
anticipating_nodes.add(stack[len_stack])
len_stack -= 1
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return list(anticipating_nodes)
def has_cycle(self):
stack = []
visited = []
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if (
visited.count(node[1]) > 0
and node[1] != parent
and indirect_parents.count(node[1]) > 0
and not on_the_way_back
):
len_stack_minus_one = len(stack) - 1
while True and len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1])
break
else:
return True
# TODO:The following code is unreachable.
anticipating_nodes.add(stack[len_stack_minus_one])
len_stack_minus_one -= 1
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return False
def dfs_time(self, s=-2, e=-1):
begin = time()
self.dfs(s, e)
end = time()
return end - begin
def bfs_time(self, s=-2):
begin = time()
self.bfs(s)
end = time()
return end - begin
class Graph:
def __init__(self):
self.graph = {}
# adding vertices and edges
# adding the weight is optional
# handles repetition
def add_pair(self, u, v, w=1):
# check if the u exists
if self.graph.get(u):
# if there already is a edge
if self.graph[u].count([w, v]) == 0:
self.graph[u].append([w, v])
else:
# if u does not exist
self.graph[u] = [[w, v]]
# add the other way
if self.graph.get(v):
# if there already is a edge
if self.graph[v].count([w, u]) == 0:
self.graph[v].append([w, u])
else:
# if u does not exist
self.graph[v] = [[w, u]]
# handles if the input does not exist
def remove_pair(self, u, v):
if self.graph.get(u):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(_)
# the other way round
if self.graph.get(v):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(_)
# if no destination is meant the default value is -1
def dfs(self, s=-2, d=-1):
if s == d:
return []
stack = []
visited = []
if s == -2:
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
ss = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if visited.count(node[1]) < 1:
if node[1] == d:
visited.append(d)
return visited
else:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return visited
# c is the count of nodes you want and if you leave it or pass -1 to the function
# the count will be random from 10 to 10000
def fill_graph_randomly(self, c=-1):
if c == -1:
c = floor(random() * 10000) + 10
for i in range(c):
# every vertex has max 100 edges
for _ in range(floor(random() * 102) + 1):
n = floor(random() * c) + 1
if n != i:
self.add_pair(i, n, 1)
def bfs(self, s=-2):
d = deque()
visited = []
if s == -2:
s = list(self.graph)[0]
d.append(s)
visited.append(s)
while d:
s = d.popleft()
if len(self.graph[s]) != 0:
for node in self.graph[s]:
if visited.count(node[1]) < 1:
d.append(node[1])
visited.append(node[1])
return visited
def degree(self, u):
return len(self.graph[u])
def cycle_nodes(self):
stack = []
visited = []
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if (
visited.count(node[1]) > 0
and node[1] != parent
and indirect_parents.count(node[1]) > 0
and not on_the_way_back
):
len_stack = len(stack) - 1
while True and len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1])
break
else:
anticipating_nodes.add(stack[len_stack])
len_stack -= 1
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return list(anticipating_nodes)
def has_cycle(self):
stack = []
visited = []
s = list(self.graph)[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for node in self.graph[s]:
if (
visited.count(node[1]) > 0
and node[1] != parent
and indirect_parents.count(node[1]) > 0
and not on_the_way_back
):
len_stack_minus_one = len(stack) - 1
while True and len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1])
break
else:
return True
# TODO: the following code is unreachable
# is this meant to be called in the else ?
anticipating_nodes.add(stack[len_stack_minus_one])
len_stack_minus_one -= 1
if visited.count(node[1]) < 1:
stack.append(node[1])
visited.append(node[1])
ss = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return False
def all_nodes(self):
return list(self.graph)
def dfs_time(self, s=-2, e=-1):
begin = time()
self.dfs(s, e)
end = time()
return end - begin
def bfs_time(self, s=-2):
begin = time()
self.bfs(s)
end = time()
return end - begin
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
A pure Python implementation of the quick sort algorithm
For doctests run following command:
python3 -m doctest -v quick_sort.py
For manual testing run:
python3 quick_sort.py
"""
from __future__ import annotations
def quick_sort(collection: list) -> list:
"""A pure Python implementation of quick sort algorithm
:param collection: a mutable collection of comparable items
:return: the same collection ordered by ascending
Examples:
>>> quick_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> quick_sort([])
[]
>>> quick_sort([-2, 5, 0, -45])
[-45, -2, 0, 5]
"""
if len(collection) < 2:
return collection
pivot = collection.pop() # Use the last element as the first pivot
greater: list[int] = [] # All elements greater than pivot
lesser: list[int] = [] # All elements less than or equal to pivot
for element in collection:
(greater if element > pivot else lesser).append(element)
return quick_sort(lesser) + [pivot] + quick_sort(greater)
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(quick_sort(unsorted))
| """
A pure Python implementation of the quick sort algorithm
For doctests run following command:
python3 -m doctest -v quick_sort.py
For manual testing run:
python3 quick_sort.py
"""
from __future__ import annotations
def quick_sort(collection: list) -> list:
"""A pure Python implementation of quick sort algorithm
:param collection: a mutable collection of comparable items
:return: the same collection ordered by ascending
Examples:
>>> quick_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> quick_sort([])
[]
>>> quick_sort([-2, 5, 0, -45])
[-45, -2, 0, 5]
"""
if len(collection) < 2:
return collection
pivot = collection.pop() # Use the last element as the first pivot
greater: list[int] = [] # All elements greater than pivot
lesser: list[int] = [] # All elements less than or equal to pivot
for element in collection:
(greater if element > pivot else lesser).append(element)
return quick_sort(lesser) + [pivot] + quick_sort(greater)
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(quick_sort(unsorted))
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 1: https://projecteuler.net/problem=1
Multiples of 3 and 5
If we list all the natural numbers below 10 that are multiples of 3 or 5,
we get 3, 5, 6 and 9. The sum of these multiples is 23.
Find the sum of all the multiples of 3 or 5 below 1000.
"""
def solution(n: int = 1000) -> int:
"""
Returns the sum of all the multiples of 3 or 5 below n.
>>> solution(3)
0
>>> solution(4)
3
>>> solution(10)
23
>>> solution(600)
83700
>>> solution(-7)
0
"""
return sum(e for e in range(3, n) if e % 3 == 0 or e % 5 == 0)
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
>>> solution(-7)
0
"""
return sum(e for e in range(3, n) if e % 3 == 0 or e % 5 == 0)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Calculates the nth number in Sylvester's sequence
Source:
https://en.wikipedia.org/wiki/Sylvester%27s_sequence
"""
def sylvester(number: int) -> int:
"""
:param number: nth number to calculate in the sequence
:return: the nth number in Sylvester's sequence
>>> sylvester(8)
113423713055421844361000443
>>> sylvester(-1)
Traceback (most recent call last):
...
ValueError: The input value of [n=-1] has to be > 0
>>> sylvester(8.0)
Traceback (most recent call last):
...
AssertionError: The input value of [n=8.0] is not an integer
"""
assert isinstance(number, int), f"The input value of [n={number}] is not an integer"
if number == 1:
return 2
elif number < 1:
raise ValueError(f"The input value of [n={number}] has to be > 0")
else:
num = sylvester(number - 1)
lower = num - 1
upper = num
return lower * upper + 1
if __name__ == "__main__":
print(f"The 8th number in Sylvester's sequence: {sylvester(8)}")
| """
Calculates the nth number in Sylvester's sequence
Source:
https://en.wikipedia.org/wiki/Sylvester%27s_sequence
"""
def sylvester(number: int) -> int:
"""
:param number: nth number to calculate in the sequence
:return: the nth number in Sylvester's sequence
>>> sylvester(8)
113423713055421844361000443
>>> sylvester(-1)
Traceback (most recent call last):
...
ValueError: The input value of [n=-1] has to be > 0
>>> sylvester(8.0)
Traceback (most recent call last):
...
AssertionError: The input value of [n=8.0] is not an integer
"""
assert isinstance(number, int), f"The input value of [n={number}] is not an integer"
if number == 1:
return 2
elif number < 1:
raise ValueError(f"The input value of [n={number}] has to be > 0")
else:
num = sylvester(number - 1)
lower = num - 1
upper = num
return lower * upper + 1
if __name__ == "__main__":
print(f"The 8th number in Sylvester's sequence: {sylvester(8)}")
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import random
import sys
from . import cryptomath_module as cryptomath
SYMBOLS = (
r""" !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`"""
r"""abcdefghijklmnopqrstuvwxyz{|}~"""
)
def check_keys(keyA: int, keyB: int, mode: str) -> None:
if mode == "encrypt":
if keyA == 1:
sys.exit(
"The affine cipher becomes weak when key "
"A is set to 1. Choose different key"
)
if keyB == 0:
sys.exit(
"The affine cipher becomes weak when key "
"B is set to 0. Choose different key"
)
if keyA < 0 or keyB < 0 or keyB > len(SYMBOLS) - 1:
sys.exit(
"Key A must be greater than 0 and key B must "
f"be between 0 and {len(SYMBOLS) - 1}."
)
if cryptomath.gcd(keyA, len(SYMBOLS)) != 1:
sys.exit(
f"Key A {keyA} and the symbol set size {len(SYMBOLS)} "
"are not relatively prime. Choose a different key."
)
def encrypt_message(key: int, message: str) -> str:
"""
>>> encrypt_message(4545, 'The affine cipher is a type of monoalphabetic '
... 'substitution cipher.')
'VL}p MM{I}p~{HL}Gp{vp pFsH}pxMpyxIx JHL O}F{~pvuOvF{FuF{xIp~{HL}Gi'
"""
keyA, keyB = divmod(key, len(SYMBOLS))
check_keys(keyA, keyB, "encrypt")
cipherText = ""
for symbol in message:
if symbol in SYMBOLS:
symIndex = SYMBOLS.find(symbol)
cipherText += SYMBOLS[(symIndex * keyA + keyB) % len(SYMBOLS)]
else:
cipherText += symbol
return cipherText
def decrypt_message(key: int, message: str) -> str:
"""
>>> decrypt_message(4545, 'VL}p MM{I}p~{HL}Gp{vp pFsH}pxMpyxIx JHL O}F{~pvuOvF{FuF'
... '{xIp~{HL}Gi')
'The affine cipher is a type of monoalphabetic substitution cipher.'
"""
keyA, keyB = divmod(key, len(SYMBOLS))
check_keys(keyA, keyB, "decrypt")
plainText = ""
modInverseOfkeyA = cryptomath.find_mod_inverse(keyA, len(SYMBOLS))
for symbol in message:
if symbol in SYMBOLS:
symIndex = SYMBOLS.find(symbol)
plainText += SYMBOLS[(symIndex - keyB) * modInverseOfkeyA % len(SYMBOLS)]
else:
plainText += symbol
return plainText
def get_random_key() -> int:
while True:
keyA = random.randint(2, len(SYMBOLS))
keyB = random.randint(2, len(SYMBOLS))
if cryptomath.gcd(keyA, len(SYMBOLS)) == 1 and keyB % len(SYMBOLS) != 0:
return keyA * len(SYMBOLS) + keyB
def main() -> None:
"""
>>> key = get_random_key()
>>> msg = "This is a test!"
>>> decrypt_message(key, encrypt_message(key, msg)) == msg
True
"""
message = input("Enter message: ").strip()
key = int(input("Enter key [2000 - 9000]: ").strip())
mode = input("Encrypt/Decrypt [E/D]: ").strip().lower()
if mode.startswith("e"):
mode = "encrypt"
translated = encrypt_message(key, message)
elif mode.startswith("d"):
mode = "decrypt"
translated = decrypt_message(key, message)
print(f"\n{mode.title()}ed text: \n{translated}")
if __name__ == "__main__":
import doctest
doctest.testmod()
# main()
| import random
import sys
from . import cryptomath_module as cryptomath
SYMBOLS = (
r""" !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`"""
r"""abcdefghijklmnopqrstuvwxyz{|}~"""
)
def check_keys(keyA: int, keyB: int, mode: str) -> None:
if mode == "encrypt":
if keyA == 1:
sys.exit(
"The affine cipher becomes weak when key "
"A is set to 1. Choose different key"
)
if keyB == 0:
sys.exit(
"The affine cipher becomes weak when key "
"B is set to 0. Choose different key"
)
if keyA < 0 or keyB < 0 or keyB > len(SYMBOLS) - 1:
sys.exit(
"Key A must be greater than 0 and key B must "
f"be between 0 and {len(SYMBOLS) - 1}."
)
if cryptomath.gcd(keyA, len(SYMBOLS)) != 1:
sys.exit(
f"Key A {keyA} and the symbol set size {len(SYMBOLS)} "
"are not relatively prime. Choose a different key."
)
def encrypt_message(key: int, message: str) -> str:
"""
>>> encrypt_message(4545, 'The affine cipher is a type of monoalphabetic '
... 'substitution cipher.')
'VL}p MM{I}p~{HL}Gp{vp pFsH}pxMpyxIx JHL O}F{~pvuOvF{FuF{xIp~{HL}Gi'
"""
keyA, keyB = divmod(key, len(SYMBOLS))
check_keys(keyA, keyB, "encrypt")
cipherText = ""
for symbol in message:
if symbol in SYMBOLS:
symIndex = SYMBOLS.find(symbol)
cipherText += SYMBOLS[(symIndex * keyA + keyB) % len(SYMBOLS)]
else:
cipherText += symbol
return cipherText
def decrypt_message(key: int, message: str) -> str:
"""
>>> decrypt_message(4545, 'VL}p MM{I}p~{HL}Gp{vp pFsH}pxMpyxIx JHL O}F{~pvuOvF{FuF'
... '{xIp~{HL}Gi')
'The affine cipher is a type of monoalphabetic substitution cipher.'
"""
keyA, keyB = divmod(key, len(SYMBOLS))
check_keys(keyA, keyB, "decrypt")
plainText = ""
modInverseOfkeyA = cryptomath.find_mod_inverse(keyA, len(SYMBOLS))
for symbol in message:
if symbol in SYMBOLS:
symIndex = SYMBOLS.find(symbol)
plainText += SYMBOLS[(symIndex - keyB) * modInverseOfkeyA % len(SYMBOLS)]
else:
plainText += symbol
return plainText
def get_random_key() -> int:
while True:
keyA = random.randint(2, len(SYMBOLS))
keyB = random.randint(2, len(SYMBOLS))
if cryptomath.gcd(keyA, len(SYMBOLS)) == 1 and keyB % len(SYMBOLS) != 0:
return keyA * len(SYMBOLS) + keyB
def main() -> None:
"""
>>> key = get_random_key()
>>> msg = "This is a test!"
>>> decrypt_message(key, encrypt_message(key, msg)) == msg
True
"""
message = input("Enter message: ").strip()
key = int(input("Enter key [2000 - 9000]: ").strip())
mode = input("Encrypt/Decrypt [E/D]: ").strip().lower()
if mode.startswith("e"):
mode = "encrypt"
translated = encrypt_message(key, message)
elif mode.startswith("d"):
mode = "decrypt"
translated = decrypt_message(key, message)
print(f"\n{mode.title()}ed text: \n{translated}")
if __name__ == "__main__":
import doctest
doctest.testmod()
# main()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
The algorithm finds distance between closest pair of points
in the given n points.
Approach used -> Divide and conquer
The points are sorted based on Xco-ords and
then based on Yco-ords separately.
And by applying divide and conquer approach,
minimum distance is obtained recursively.
>> Closest points can lie on different sides of partition.
This case handled by forming a strip of points
whose Xco-ords distance is less than closest_pair_dis
from mid-point's Xco-ords. Points sorted based on Yco-ords
are used in this step to reduce sorting time.
Closest pair distance is found in the strip of points. (closest_in_strip)
min(closest_pair_dis, closest_in_strip) would be the final answer.
Time complexity: O(n * log n)
"""
def euclidean_distance_sqr(point1, point2):
"""
>>> euclidean_distance_sqr([1,2],[2,4])
5
"""
return (point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2
def column_based_sort(array, column=0):
"""
>>> column_based_sort([(5, 1), (4, 2), (3, 0)], 1)
[(3, 0), (5, 1), (4, 2)]
"""
return sorted(array, key=lambda x: x[column])
def dis_between_closest_pair(points, points_counts, min_dis=float("inf")):
"""
brute force approach to find distance between closest pair points
Parameters :
points, points_count, min_dis (list(tuple(int, int)), int, int)
Returns :
min_dis (float): distance between closest pair of points
>>> dis_between_closest_pair([[1,2],[2,4],[5,7],[8,9],[11,0]],5)
5
"""
for i in range(points_counts - 1):
for j in range(i + 1, points_counts):
current_dis = euclidean_distance_sqr(points[i], points[j])
if current_dis < min_dis:
min_dis = current_dis
return min_dis
def dis_between_closest_in_strip(points, points_counts, min_dis=float("inf")):
"""
closest pair of points in strip
Parameters :
points, points_count, min_dis (list(tuple(int, int)), int, int)
Returns :
min_dis (float): distance btw closest pair of points in the strip (< min_dis)
>>> dis_between_closest_in_strip([[1,2],[2,4],[5,7],[8,9],[11,0]],5)
85
"""
for i in range(min(6, points_counts - 1), points_counts):
for j in range(max(0, i - 6), i):
current_dis = euclidean_distance_sqr(points[i], points[j])
if current_dis < min_dis:
min_dis = current_dis
return min_dis
def closest_pair_of_points_sqr(points_sorted_on_x, points_sorted_on_y, points_counts):
"""divide and conquer approach
Parameters :
points, points_count (list(tuple(int, int)), int)
Returns :
(float): distance btw closest pair of points
>>> closest_pair_of_points_sqr([(1, 2), (3, 4)], [(5, 6), (7, 8)], 2)
8
"""
# base case
if points_counts <= 3:
return dis_between_closest_pair(points_sorted_on_x, points_counts)
# recursion
mid = points_counts // 2
closest_in_left = closest_pair_of_points_sqr(
points_sorted_on_x, points_sorted_on_y[:mid], mid
)
closest_in_right = closest_pair_of_points_sqr(
points_sorted_on_y, points_sorted_on_y[mid:], points_counts - mid
)
closest_pair_dis = min(closest_in_left, closest_in_right)
"""
cross_strip contains the points, whose Xcoords are at a
distance(< closest_pair_dis) from mid's Xcoord
"""
cross_strip = []
for point in points_sorted_on_x:
if abs(point[0] - points_sorted_on_x[mid][0]) < closest_pair_dis:
cross_strip.append(point)
closest_in_strip = dis_between_closest_in_strip(
cross_strip, len(cross_strip), closest_pair_dis
)
return min(closest_pair_dis, closest_in_strip)
def closest_pair_of_points(points, points_counts):
"""
>>> closest_pair_of_points([(2, 3), (12, 30)], len([(2, 3), (12, 30)]))
28.792360097775937
"""
points_sorted_on_x = column_based_sort(points, column=0)
points_sorted_on_y = column_based_sort(points, column=1)
return (
closest_pair_of_points_sqr(
points_sorted_on_x, points_sorted_on_y, points_counts
)
) ** 0.5
if __name__ == "__main__":
points = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
print("Distance:", closest_pair_of_points(points, len(points)))
| """
The algorithm finds distance between closest pair of points
in the given n points.
Approach used -> Divide and conquer
The points are sorted based on Xco-ords and
then based on Yco-ords separately.
And by applying divide and conquer approach,
minimum distance is obtained recursively.
>> Closest points can lie on different sides of partition.
This case handled by forming a strip of points
whose Xco-ords distance is less than closest_pair_dis
from mid-point's Xco-ords. Points sorted based on Yco-ords
are used in this step to reduce sorting time.
Closest pair distance is found in the strip of points. (closest_in_strip)
min(closest_pair_dis, closest_in_strip) would be the final answer.
Time complexity: O(n * log n)
"""
def euclidean_distance_sqr(point1, point2):
"""
>>> euclidean_distance_sqr([1,2],[2,4])
5
"""
return (point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2
def column_based_sort(array, column=0):
"""
>>> column_based_sort([(5, 1), (4, 2), (3, 0)], 1)
[(3, 0), (5, 1), (4, 2)]
"""
return sorted(array, key=lambda x: x[column])
def dis_between_closest_pair(points, points_counts, min_dis=float("inf")):
"""
brute force approach to find distance between closest pair points
Parameters :
points, points_count, min_dis (list(tuple(int, int)), int, int)
Returns :
min_dis (float): distance between closest pair of points
>>> dis_between_closest_pair([[1,2],[2,4],[5,7],[8,9],[11,0]],5)
5
"""
for i in range(points_counts - 1):
for j in range(i + 1, points_counts):
current_dis = euclidean_distance_sqr(points[i], points[j])
if current_dis < min_dis:
min_dis = current_dis
return min_dis
def dis_between_closest_in_strip(points, points_counts, min_dis=float("inf")):
"""
closest pair of points in strip
Parameters :
points, points_count, min_dis (list(tuple(int, int)), int, int)
Returns :
min_dis (float): distance btw closest pair of points in the strip (< min_dis)
>>> dis_between_closest_in_strip([[1,2],[2,4],[5,7],[8,9],[11,0]],5)
85
"""
for i in range(min(6, points_counts - 1), points_counts):
for j in range(max(0, i - 6), i):
current_dis = euclidean_distance_sqr(points[i], points[j])
if current_dis < min_dis:
min_dis = current_dis
return min_dis
def closest_pair_of_points_sqr(points_sorted_on_x, points_sorted_on_y, points_counts):
"""divide and conquer approach
Parameters :
points, points_count (list(tuple(int, int)), int)
Returns :
(float): distance btw closest pair of points
>>> closest_pair_of_points_sqr([(1, 2), (3, 4)], [(5, 6), (7, 8)], 2)
8
"""
# base case
if points_counts <= 3:
return dis_between_closest_pair(points_sorted_on_x, points_counts)
# recursion
mid = points_counts // 2
closest_in_left = closest_pair_of_points_sqr(
points_sorted_on_x, points_sorted_on_y[:mid], mid
)
closest_in_right = closest_pair_of_points_sqr(
points_sorted_on_y, points_sorted_on_y[mid:], points_counts - mid
)
closest_pair_dis = min(closest_in_left, closest_in_right)
"""
cross_strip contains the points, whose Xcoords are at a
distance(< closest_pair_dis) from mid's Xcoord
"""
cross_strip = []
for point in points_sorted_on_x:
if abs(point[0] - points_sorted_on_x[mid][0]) < closest_pair_dis:
cross_strip.append(point)
closest_in_strip = dis_between_closest_in_strip(
cross_strip, len(cross_strip), closest_pair_dis
)
return min(closest_pair_dis, closest_in_strip)
def closest_pair_of_points(points, points_counts):
"""
>>> closest_pair_of_points([(2, 3), (12, 30)], len([(2, 3), (12, 30)]))
28.792360097775937
"""
points_sorted_on_x = column_based_sort(points, column=0)
points_sorted_on_y = column_based_sort(points, column=1)
return (
closest_pair_of_points_sqr(
points_sorted_on_x, points_sorted_on_y, points_counts
)
) ** 0.5
if __name__ == "__main__":
points = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
print("Distance:", closest_pair_of_points(points, len(points)))
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from math import pi
def radians(degree: float) -> float:
"""
Coverts the given angle from degrees to radians
https://en.wikipedia.org/wiki/Radian
>>> radians(180)
3.141592653589793
>>> radians(92)
1.6057029118347832
>>> radians(274)
4.782202150464463
>>> radians(109.82)
1.9167205845401725
>>> from math import radians as math_radians
>>> all(abs(radians(i)-math_radians(i)) <= 0.00000001 for i in range(-2, 361))
True
"""
return degree / (180 / pi)
if __name__ == "__main__":
from doctest import testmod
testmod()
| from math import pi
def radians(degree: float) -> float:
"""
Coverts the given angle from degrees to radians
https://en.wikipedia.org/wiki/Radian
>>> radians(180)
3.141592653589793
>>> radians(92)
1.6057029118347832
>>> radians(274)
4.782202150464463
>>> radians(109.82)
1.9167205845401725
>>> from math import radians as math_radians
>>> all(abs(radians(i)-math_radians(i)) <= 0.00000001 for i in range(-2, 361))
True
"""
return degree / (180 / pi)
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import sys
import webbrowser
import requests
from bs4 import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
url = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
res = requests.get(url, headers={"UserAgent": UserAgent().random})
# res.raise_for_status()
with open("project1a.html", "wb") as out_file: # only for knowing the class
for data in res.iter_content(10000):
out_file.write(data)
soup = BeautifulSoup(res.text, "html.parser")
links = list(soup.select(".eZt8xd"))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get("href"))
else:
webbrowser.open(f"http://google.com{link.get('href')}")
| import sys
import webbrowser
import requests
from bs4 import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
url = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
res = requests.get(url, headers={"UserAgent": UserAgent().random})
# res.raise_for_status()
with open("project1a.html", "wb") as out_file: # only for knowing the class
for data in res.iter_content(10000):
out_file.write(data)
soup = BeautifulSoup(res.text, "html.parser")
links = list(soup.select(".eZt8xd"))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get("href"))
else:
webbrowser.open(f"http://google.com{link.get('href')}")
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import sys
class Letter:
def __init__(self, letter, freq):
self.letter = letter
self.freq = freq
self.bitstring = {}
def __repr__(self):
return f"{self.letter}:{self.freq}"
class TreeNode:
def __init__(self, freq, left, right):
self.freq = freq
self.left = left
self.right = right
def parse_file(file_path):
"""
Read the file and build a dict of all letters and their
frequencies, then convert the dict into a list of Letters.
"""
chars = {}
with open(file_path) as f:
while True:
c = f.read(1)
if not c:
break
chars[c] = chars[c] + 1 if c in chars.keys() else 1
return sorted((Letter(c, f) for c, f in chars.items()), key=lambda l: l.freq)
def build_tree(letters):
"""
Run through the list of Letters and build the min heap
for the Huffman Tree.
"""
while len(letters) > 1:
left = letters.pop(0)
right = letters.pop(0)
total_freq = left.freq + right.freq
node = TreeNode(total_freq, left, right)
letters.append(node)
letters.sort(key=lambda l: l.freq)
return letters[0]
def traverse_tree(root, bitstring):
"""
Recursively traverse the Huffman Tree to set each
Letter's bitstring dictionary, and return the list of Letters
"""
if type(root) is Letter:
root.bitstring[root.letter] = bitstring
return [root]
letters = []
letters += traverse_tree(root.left, bitstring + "0")
letters += traverse_tree(root.right, bitstring + "1")
return letters
def huffman(file_path):
"""
Parse the file, build the tree, then run through the file
again, using the letters dictionary to find and print out the
bitstring for each letter.
"""
letters_list = parse_file(file_path)
root = build_tree(letters_list)
letters = {
k: v for letter in traverse_tree(root, "") for k, v in letter.bitstring.items()
}
print(f"Huffman Coding of {file_path}: ")
with open(file_path) as f:
while True:
c = f.read(1)
if not c:
break
print(letters[c], end=" ")
print()
if __name__ == "__main__":
# pass the file path to the huffman function
huffman(sys.argv[1])
| import sys
class Letter:
def __init__(self, letter, freq):
self.letter = letter
self.freq = freq
self.bitstring = {}
def __repr__(self):
return f"{self.letter}:{self.freq}"
class TreeNode:
def __init__(self, freq, left, right):
self.freq = freq
self.left = left
self.right = right
def parse_file(file_path):
"""
Read the file and build a dict of all letters and their
frequencies, then convert the dict into a list of Letters.
"""
chars = {}
with open(file_path) as f:
while True:
c = f.read(1)
if not c:
break
chars[c] = chars[c] + 1 if c in chars.keys() else 1
return sorted((Letter(c, f) for c, f in chars.items()), key=lambda l: l.freq)
def build_tree(letters):
"""
Run through the list of Letters and build the min heap
for the Huffman Tree.
"""
while len(letters) > 1:
left = letters.pop(0)
right = letters.pop(0)
total_freq = left.freq + right.freq
node = TreeNode(total_freq, left, right)
letters.append(node)
letters.sort(key=lambda l: l.freq)
return letters[0]
def traverse_tree(root, bitstring):
"""
Recursively traverse the Huffman Tree to set each
Letter's bitstring dictionary, and return the list of Letters
"""
if type(root) is Letter:
root.bitstring[root.letter] = bitstring
return [root]
letters = []
letters += traverse_tree(root.left, bitstring + "0")
letters += traverse_tree(root.right, bitstring + "1")
return letters
def huffman(file_path):
"""
Parse the file, build the tree, then run through the file
again, using the letters dictionary to find and print out the
bitstring for each letter.
"""
letters_list = parse_file(file_path)
root = build_tree(letters_list)
letters = {
k: v for letter in traverse_tree(root, "") for k, v in letter.bitstring.items()
}
print(f"Huffman Coding of {file_path}: ")
with open(file_path) as f:
while True:
c = f.read(1)
if not c:
break
print(letters[c], end=" ")
print()
if __name__ == "__main__":
# pass the file path to the huffman function
huffman(sys.argv[1])
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem 123: https://projecteuler.net/problem=123
Name: Prime square remainders
Let pn be the nth prime: 2, 3, 5, 7, 11, ..., and
let r be the remainder when (pn−1)^n + (pn+1)^n is divided by pn^2.
For example, when n = 3, p3 = 5, and 43 + 63 = 280 ≡ 5 mod 25.
The least value of n for which the remainder first exceeds 10^9 is 7037.
Find the least value of n for which the remainder first exceeds 10^10.
Solution:
n=1: (p-1) + (p+1) = 2p
n=2: (p-1)^2 + (p+1)^2
= p^2 + 1 - 2p + p^2 + 1 + 2p (Using (p+b)^2 = (p^2 + b^2 + 2pb),
(p-b)^2 = (p^2 + b^2 - 2pb) and b = 1)
= 2p^2 + 2
n=3: (p-1)^3 + (p+1)^3 (Similarly using (p+b)^3 & (p-b)^3 formula and so on)
= 2p^3 + 6p
n=4: 2p^4 + 12p^2 + 2
n=5: 2p^5 + 20p^3 + 10p
As you could see, when the expression is divided by p^2.
Except for the last term, the rest will result in the remainder 0.
n=1: 2p
n=2: 2
n=3: 6p
n=4: 2
n=5: 10p
So it could be simplified as,
r = 2pn when n is odd
r = 2 when n is even.
"""
from __future__ import annotations
from typing import Generator
def sieve() -> Generator[int, None, None]:
"""
Returns a prime number generator using sieve method.
>>> type(sieve())
<class 'generator'>
>>> primes = sieve()
>>> next(primes)
2
>>> next(primes)
3
>>> next(primes)
5
>>> next(primes)
7
>>> next(primes)
11
>>> next(primes)
13
"""
factor_map: dict[int, int] = {}
prime = 2
while True:
factor = factor_map.pop(prime, None)
if factor:
x = factor + prime
while x in factor_map:
x += factor
factor_map[x] = factor
else:
factor_map[prime * prime] = prime
yield prime
prime += 1
def solution(limit: float = 1e10) -> int:
"""
Returns the least value of n for which the remainder first exceeds 10^10.
>>> solution(1e8)
2371
>>> solution(1e9)
7037
"""
primes = sieve()
n = 1
while True:
prime = next(primes)
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(primes)
n += 2
if __name__ == "__main__":
print(solution())
| """
Problem 123: https://projecteuler.net/problem=123
Name: Prime square remainders
Let pn be the nth prime: 2, 3, 5, 7, 11, ..., and
let r be the remainder when (pn−1)^n + (pn+1)^n is divided by pn^2.
For example, when n = 3, p3 = 5, and 43 + 63 = 280 ≡ 5 mod 25.
The least value of n for which the remainder first exceeds 10^9 is 7037.
Find the least value of n for which the remainder first exceeds 10^10.
Solution:
n=1: (p-1) + (p+1) = 2p
n=2: (p-1)^2 + (p+1)^2
= p^2 + 1 - 2p + p^2 + 1 + 2p (Using (p+b)^2 = (p^2 + b^2 + 2pb),
(p-b)^2 = (p^2 + b^2 - 2pb) and b = 1)
= 2p^2 + 2
n=3: (p-1)^3 + (p+1)^3 (Similarly using (p+b)^3 & (p-b)^3 formula and so on)
= 2p^3 + 6p
n=4: 2p^4 + 12p^2 + 2
n=5: 2p^5 + 20p^3 + 10p
As you could see, when the expression is divided by p^2.
Except for the last term, the rest will result in the remainder 0.
n=1: 2p
n=2: 2
n=3: 6p
n=4: 2
n=5: 10p
So it could be simplified as,
r = 2pn when n is odd
r = 2 when n is even.
"""
from __future__ import annotations
from typing import Generator
def sieve() -> Generator[int, None, None]:
"""
Returns a prime number generator using sieve method.
>>> type(sieve())
<class 'generator'>
>>> primes = sieve()
>>> next(primes)
2
>>> next(primes)
3
>>> next(primes)
5
>>> next(primes)
7
>>> next(primes)
11
>>> next(primes)
13
"""
factor_map: dict[int, int] = {}
prime = 2
while True:
factor = factor_map.pop(prime, None)
if factor:
x = factor + prime
while x in factor_map:
x += factor
factor_map[x] = factor
else:
factor_map[prime * prime] = prime
yield prime
prime += 1
def solution(limit: float = 1e10) -> int:
"""
Returns the least value of n for which the remainder first exceeds 10^10.
>>> solution(1e8)
2371
>>> solution(1e9)
7037
"""
primes = sieve()
n = 1
while True:
prime = next(primes)
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(primes)
n += 2
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from bisect import bisect
from itertools import accumulate
def fracKnapsack(vl, wt, W, n):
"""
>>> fracKnapsack([60, 100, 120], [10, 20, 30], 50, 3)
240.0
"""
r = list(sorted(zip(vl, wt), key=lambda x: x[0] / x[1], reverse=True))
vl, wt = [i[0] for i in r], [i[1] for i in r]
acc = list(accumulate(wt))
k = bisect(acc, W)
return (
0
if k == 0
else sum(vl[:k]) + (W - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k])
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| from bisect import bisect
from itertools import accumulate
def fracKnapsack(vl, wt, W, n):
"""
>>> fracKnapsack([60, 100, 120], [10, 20, 30], 50, 3)
240.0
"""
r = list(sorted(zip(vl, wt), key=lambda x: x[0] / x[1], reverse=True))
vl, wt = [i[0] for i in r], [i[1] for i in r]
acc = list(accumulate(wt))
k = bisect(acc, W)
return (
0
if k == 0
else sum(vl[:k]) + (W - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k])
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
def solve_maze(maze: list[list[int]]) -> bool:
"""
This method solves the "rat in maze" problem.
In this problem we have some n by n matrix, a start point and an end point.
We want to go from the start to the end. In this matrix zeroes represent walls
and ones paths we can use.
Parameters :
maze(2D matrix) : maze
Returns:
Return: True if the maze has a solution or False if it does not.
>>> maze = [[0, 1, 0, 1, 1],
... [0, 0, 0, 0, 0],
... [1, 0, 1, 0, 1],
... [0, 0, 1, 0, 0],
... [1, 0, 0, 1, 0]]
>>> solve_maze(maze)
[1, 0, 0, 0, 0]
[1, 1, 1, 1, 0]
[0, 0, 0, 1, 0]
[0, 0, 0, 1, 1]
[0, 0, 0, 0, 1]
True
>>> maze = [[0, 1, 0, 1, 1],
... [0, 0, 0, 0, 0],
... [0, 0, 0, 0, 1],
... [0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0]]
>>> solve_maze(maze)
[1, 0, 0, 0, 0]
[1, 0, 0, 0, 0]
[1, 0, 0, 0, 0]
[1, 0, 0, 0, 0]
[1, 1, 1, 1, 1]
True
>>> maze = [[0, 0, 0],
... [0, 1, 0],
... [1, 0, 0]]
>>> solve_maze(maze)
[1, 1, 1]
[0, 0, 1]
[0, 0, 1]
True
>>> maze = [[0, 1, 0],
... [0, 1, 0],
... [1, 0, 0]]
>>> solve_maze(maze)
No solution exists!
False
>>> maze = [[0, 1],
... [1, 0]]
>>> solve_maze(maze)
No solution exists!
False
"""
size = len(maze)
# We need to create solution object to save path.
solutions = [[0 for _ in range(size)] for _ in range(size)]
solved = run_maze(maze, 0, 0, solutions)
if solved:
print("\n".join(str(row) for row in solutions))
else:
print("No solution exists!")
return solved
def run_maze(maze: list[list[int]], i: int, j: int, solutions: list[list[int]]) -> bool:
"""
This method is recursive starting from (i, j) and going in one of four directions:
up, down, left, right.
If a path is found to destination it returns True otherwise it returns False.
Parameters:
maze(2D matrix) : maze
i, j : coordinates of matrix
solutions(2D matrix) : solutions
Returns:
Boolean if path is found True, Otherwise False.
"""
size = len(maze)
# Final check point.
if i == j == (size - 1):
solutions[i][j] = 1
return True
lower_flag = (not (i < 0)) and (not (j < 0)) # Check lower bounds
upper_flag = (i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
block_flag = (not (solutions[i][j])) and (not (maze[i][j]))
if block_flag:
# check visited
solutions[i][j] = 1
# check for directions
if (
run_maze(maze, i + 1, j, solutions)
or run_maze(maze, i, j + 1, solutions)
or run_maze(maze, i - 1, j, solutions)
or run_maze(maze, i, j - 1, solutions)
):
return True
solutions[i][j] = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| from __future__ import annotations
def solve_maze(maze: list[list[int]]) -> bool:
"""
This method solves the "rat in maze" problem.
In this problem we have some n by n matrix, a start point and an end point.
We want to go from the start to the end. In this matrix zeroes represent walls
and ones paths we can use.
Parameters :
maze(2D matrix) : maze
Returns:
Return: True if the maze has a solution or False if it does not.
>>> maze = [[0, 1, 0, 1, 1],
... [0, 0, 0, 0, 0],
... [1, 0, 1, 0, 1],
... [0, 0, 1, 0, 0],
... [1, 0, 0, 1, 0]]
>>> solve_maze(maze)
[1, 0, 0, 0, 0]
[1, 1, 1, 1, 0]
[0, 0, 0, 1, 0]
[0, 0, 0, 1, 1]
[0, 0, 0, 0, 1]
True
>>> maze = [[0, 1, 0, 1, 1],
... [0, 0, 0, 0, 0],
... [0, 0, 0, 0, 1],
... [0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0]]
>>> solve_maze(maze)
[1, 0, 0, 0, 0]
[1, 0, 0, 0, 0]
[1, 0, 0, 0, 0]
[1, 0, 0, 0, 0]
[1, 1, 1, 1, 1]
True
>>> maze = [[0, 0, 0],
... [0, 1, 0],
... [1, 0, 0]]
>>> solve_maze(maze)
[1, 1, 1]
[0, 0, 1]
[0, 0, 1]
True
>>> maze = [[0, 1, 0],
... [0, 1, 0],
... [1, 0, 0]]
>>> solve_maze(maze)
No solution exists!
False
>>> maze = [[0, 1],
... [1, 0]]
>>> solve_maze(maze)
No solution exists!
False
"""
size = len(maze)
# We need to create solution object to save path.
solutions = [[0 for _ in range(size)] for _ in range(size)]
solved = run_maze(maze, 0, 0, solutions)
if solved:
print("\n".join(str(row) for row in solutions))
else:
print("No solution exists!")
return solved
def run_maze(maze: list[list[int]], i: int, j: int, solutions: list[list[int]]) -> bool:
"""
This method is recursive starting from (i, j) and going in one of four directions:
up, down, left, right.
If a path is found to destination it returns True otherwise it returns False.
Parameters:
maze(2D matrix) : maze
i, j : coordinates of matrix
solutions(2D matrix) : solutions
Returns:
Boolean if path is found True, Otherwise False.
"""
size = len(maze)
# Final check point.
if i == j == (size - 1):
solutions[i][j] = 1
return True
lower_flag = (not (i < 0)) and (not (j < 0)) # Check lower bounds
upper_flag = (i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
block_flag = (not (solutions[i][j])) and (not (maze[i][j]))
if block_flag:
# check visited
solutions[i][j] = 1
# check for directions
if (
run_maze(maze, i + 1, j, solutions)
or run_maze(maze, i, j + 1, solutions)
or run_maze(maze, i - 1, j, solutions)
or run_maze(maze, i, j - 1, solutions)
):
return True
solutions[i][j] = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Adler-32 is a checksum algorithm which was invented by Mark Adler in 1995.
Compared to a cyclic redundancy check of the same length, it trades reliability for
speed (preferring the latter).
Adler-32 is more reliable than Fletcher-16, and slightly less reliable than
Fletcher-32.[2]
source: https://en.wikipedia.org/wiki/Adler-32
"""
def adler32(plain_text: str) -> int:
"""
Function implements adler-32 hash.
Iterates and evaluates a new value for each character
>>> adler32('Algorithms')
363791387
>>> adler32('go adler em all')
708642122
"""
MOD_ADLER = 65521
a = 1
b = 0
for plain_chr in plain_text:
a = (a + ord(plain_chr)) % MOD_ADLER
b = (b + a) % MOD_ADLER
return (b << 16) | a
| """
Adler-32 is a checksum algorithm which was invented by Mark Adler in 1995.
Compared to a cyclic redundancy check of the same length, it trades reliability for
speed (preferring the latter).
Adler-32 is more reliable than Fletcher-16, and slightly less reliable than
Fletcher-32.[2]
source: https://en.wikipedia.org/wiki/Adler-32
"""
def adler32(plain_text: str) -> int:
"""
Function implements adler-32 hash.
Iterates and evaluates a new value for each character
>>> adler32('Algorithms')
363791387
>>> adler32('go adler em all')
708642122
"""
MOD_ADLER = 65521
a = 1
b = 0
for plain_chr in plain_text:
a = (a + ord(plain_chr)) % MOD_ADLER
b = (b + a) % MOD_ADLER
return (b << 16) | a
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
"""
Build a simple bare-minimum quantum circuit that starts with a single
qubit (by default, in state 0), runs the experiment 1000 times, and
finally prints the total count of the states finally observed.
Qiskit Docs: https://qiskit.org/documentation/getting_started.html
"""
import qiskit as q
def single_qubit_measure(qubits: int, classical_bits: int) -> q.result.counts.Counts:
"""
>>> single_qubit_measure(1, 1)
{'0': 1000}
"""
# Use Aer's qasm_simulator
simulator = q.Aer.get_backend("qasm_simulator")
# Create a Quantum Circuit acting on the q register
circuit = q.QuantumCircuit(qubits, classical_bits)
# Map the quantum measurement to the classical bits
circuit.measure([0], [0])
# Execute the circuit on the qasm simulator
job = q.execute(circuit, simulator, shots=1000)
# Return the histogram data of the results of the experiment.
return job.result().get_counts(circuit)
if __name__ == "__main__":
print(f"Total count for various states are: {single_qubit_measure(1, 1)}")
| #!/usr/bin/env python3
"""
Build a simple bare-minimum quantum circuit that starts with a single
qubit (by default, in state 0), runs the experiment 1000 times, and
finally prints the total count of the states finally observed.
Qiskit Docs: https://qiskit.org/documentation/getting_started.html
"""
import qiskit as q
def single_qubit_measure(qubits: int, classical_bits: int) -> q.result.counts.Counts:
"""
>>> single_qubit_measure(1, 1)
{'0': 1000}
"""
# Use Aer's qasm_simulator
simulator = q.Aer.get_backend("qasm_simulator")
# Create a Quantum Circuit acting on the q register
circuit = q.QuantumCircuit(qubits, classical_bits)
# Map the quantum measurement to the classical bits
circuit.measure([0], [0])
# Execute the circuit on the qasm simulator
job = q.execute(circuit, simulator, shots=1000)
# Return the histogram data of the results of the experiment.
return job.result().get_counts(circuit)
if __name__ == "__main__":
print(f"Total count for various states are: {single_qubit_measure(1, 1)}")
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from typing import Optional
class Node:
def __init__(self, data: int) -> None:
self.data = data
self.next = None
class LinkedList:
def __init__(self):
self.head = None
def push(self, new_data: int) -> int:
new_node = Node(new_data)
new_node.next = self.head
self.head = new_node
return self.head.data
def middle_element(self) -> Optional[int]:
"""
>>> link = LinkedList()
>>> link.middle_element()
No element found.
>>> link.push(5)
5
>>> link.push(6)
6
>>> link.push(8)
8
>>> link.push(8)
8
>>> link.push(10)
10
>>> link.push(12)
12
>>> link.push(17)
17
>>> link.push(7)
7
>>> link.push(3)
3
>>> link.push(20)
20
>>> link.push(-20)
-20
>>> link.middle_element()
12
>>>
"""
slow_pointer = self.head
fast_pointer = self.head
if self.head:
while fast_pointer and fast_pointer.next:
fast_pointer = fast_pointer.next.next
slow_pointer = slow_pointer.next
return slow_pointer.data
else:
print("No element found.")
return None
if __name__ == "__main__":
link = LinkedList()
for i in range(int(input().strip())):
data = int(input().strip())
link.push(data)
print(link.middle_element())
| from typing import Optional
class Node:
def __init__(self, data: int) -> None:
self.data = data
self.next = None
class LinkedList:
def __init__(self):
self.head = None
def push(self, new_data: int) -> int:
new_node = Node(new_data)
new_node.next = self.head
self.head = new_node
return self.head.data
def middle_element(self) -> Optional[int]:
"""
>>> link = LinkedList()
>>> link.middle_element()
No element found.
>>> link.push(5)
5
>>> link.push(6)
6
>>> link.push(8)
8
>>> link.push(8)
8
>>> link.push(10)
10
>>> link.push(12)
12
>>> link.push(17)
17
>>> link.push(7)
7
>>> link.push(3)
3
>>> link.push(20)
20
>>> link.push(-20)
-20
>>> link.middle_element()
12
>>>
"""
slow_pointer = self.head
fast_pointer = self.head
if self.head:
while fast_pointer and fast_pointer.next:
fast_pointer = fast_pointer.next.next
slow_pointer = slow_pointer.next
return slow_pointer.data
else:
print("No element found.")
return None
if __name__ == "__main__":
link = LinkedList()
for i in range(int(input().strip())):
data = int(input().strip())
link.push(data)
print(link.middle_element())
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| 9caf4784aada17dc75348f77cc8c356df503c0f3 https://github.com/TheAlgorithms/Python
| 9caf4784aada17dc75348f77cc8c356df503c0f3 https://github.com/TheAlgorithms/Python
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
def median_of_two_arrays(nums1: list[float], nums2: list[float]) -> float:
"""
>>> median_of_two_arrays([1, 2], [3])
2
>>> median_of_two_arrays([0, -1.1], [2.5, 1])
0.5
>>> median_of_two_arrays([], [2.5, 1])
1.75
>>> median_of_two_arrays([], [0])
0
>>> median_of_two_arrays([], [])
Traceback (most recent call last):
...
IndexError: list index out of range
"""
all_numbers = sorted(nums1 + nums2)
div, mod = divmod(len(all_numbers), 2)
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
array_1 = [float(x) for x in input("Enter the elements of first array: ").split()]
array_2 = [float(x) for x in input("Enter the elements of second array: ").split()]
print(f"The median of two arrays is: {median_of_two_arrays(array_1, array_2)}")
| from __future__ import annotations
def median_of_two_arrays(nums1: list[float], nums2: list[float]) -> float:
"""
>>> median_of_two_arrays([1, 2], [3])
2
>>> median_of_two_arrays([0, -1.1], [2.5, 1])
0.5
>>> median_of_two_arrays([], [2.5, 1])
1.75
>>> median_of_two_arrays([], [0])
0
>>> median_of_two_arrays([], [])
Traceback (most recent call last):
...
IndexError: list index out of range
"""
all_numbers = sorted(nums1 + nums2)
div, mod = divmod(len(all_numbers), 2)
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
array_1 = [float(x) for x in input("Enter the elements of first array: ").split()]
array_2 = [float(x) for x in input("Enter the elements of second array: ").split()]
print(f"The median of two arrays is: {median_of_two_arrays(array_1, array_2)}")
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
def find_min(nums: list[int | float]) -> int | float:
"""
Find Minimum Number in a List
:param nums: contains elements
:return: min number in list
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_min(nums) == min(nums)
True
True
True
True
>>> find_min([0, 1, 2, 3, 4, 5, -3, 24, -56])
-56
>>> find_min([])
Traceback (most recent call last):
...
ValueError: find_min() arg is an empty sequence
"""
if len(nums) == 0:
raise ValueError("find_min() arg is an empty sequence")
min_num = nums[0]
for num in nums:
if min_num > num:
min_num = num
return min_num
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| from __future__ import annotations
def find_min(nums: list[int | float]) -> int | float:
"""
Find Minimum Number in a List
:param nums: contains elements
:return: min number in list
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_min(nums) == min(nums)
True
True
True
True
>>> find_min([0, 1, 2, 3, 4, 5, -3, 24, -56])
-56
>>> find_min([])
Traceback (most recent call last):
...
ValueError: find_min() arg is an empty sequence
"""
if len(nums) == 0:
raise ValueError("find_min() arg is an empty sequence")
min_num = nums[0]
for num in nums:
if min_num > num:
min_num = num
return min_num
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # https://en.wikipedia.org/wiki/Trifid_cipher
from __future__ import annotations
def __encryptPart(messagePart: str, character2Number: dict[str, str]) -> str:
one, two, three = "", "", ""
tmp = []
for character in messagePart:
tmp.append(character2Number[character])
for each in tmp:
one += each[0]
two += each[1]
three += each[2]
return one + two + three
def __decryptPart(
messagePart: str, character2Number: dict[str, str]
) -> tuple[str, str, str]:
tmp, thisPart = "", ""
result = []
for character in messagePart:
thisPart += character2Number[character]
for digit in thisPart:
tmp += digit
if len(tmp) == len(messagePart):
result.append(tmp)
tmp = ""
return result[0], result[1], result[2]
def __prepare(
message: str, alphabet: str
) -> tuple[str, str, dict[str, str], dict[str, str]]:
# Validate message and alphabet, set to upper and remove spaces
alphabet = alphabet.replace(" ", "").upper()
message = message.replace(" ", "").upper()
# Check length and characters
if len(alphabet) != 27:
raise KeyError("Length of alphabet has to be 27.")
for each in message:
if each not in alphabet:
raise ValueError("Each message character has to be included in alphabet!")
# Generate dictionares
numbers = (
"111",
"112",
"113",
"121",
"122",
"123",
"131",
"132",
"133",
"211",
"212",
"213",
"221",
"222",
"223",
"231",
"232",
"233",
"311",
"312",
"313",
"321",
"322",
"323",
"331",
"332",
"333",
)
character2Number = {}
number2Character = {}
for letter, number in zip(alphabet, numbers):
character2Number[letter] = number
number2Character[number] = letter
return message, alphabet, character2Number, number2Character
def encryptMessage(
message: str, alphabet: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period: int = 5
) -> str:
message, alphabet, character2Number, number2Character = __prepare(message, alphabet)
encrypted, encrypted_numeric = "", ""
for i in range(0, len(message) + 1, period):
encrypted_numeric += __encryptPart(message[i : i + period], character2Number)
for i in range(0, len(encrypted_numeric), 3):
encrypted += number2Character[encrypted_numeric[i : i + 3]]
return encrypted
def decryptMessage(
message: str, alphabet: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period: int = 5
) -> str:
message, alphabet, character2Number, number2Character = __prepare(message, alphabet)
decrypted_numeric = []
decrypted = ""
for i in range(0, len(message) + 1, period):
a, b, c = __decryptPart(message[i : i + period], character2Number)
for j in range(0, len(a)):
decrypted_numeric.append(a[j] + b[j] + c[j])
for each in decrypted_numeric:
decrypted += number2Character[each]
return decrypted
if __name__ == "__main__":
msg = "DEFEND THE EAST WALL OF THE CASTLE."
encrypted = encryptMessage(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
decrypted = decryptMessage(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
print(f"Encrypted: {encrypted}\nDecrypted: {decrypted}")
| # https://en.wikipedia.org/wiki/Trifid_cipher
from __future__ import annotations
def __encryptPart(messagePart: str, character2Number: dict[str, str]) -> str:
one, two, three = "", "", ""
tmp = []
for character in messagePart:
tmp.append(character2Number[character])
for each in tmp:
one += each[0]
two += each[1]
three += each[2]
return one + two + three
def __decryptPart(
messagePart: str, character2Number: dict[str, str]
) -> tuple[str, str, str]:
tmp, thisPart = "", ""
result = []
for character in messagePart:
thisPart += character2Number[character]
for digit in thisPart:
tmp += digit
if len(tmp) == len(messagePart):
result.append(tmp)
tmp = ""
return result[0], result[1], result[2]
def __prepare(
message: str, alphabet: str
) -> tuple[str, str, dict[str, str], dict[str, str]]:
# Validate message and alphabet, set to upper and remove spaces
alphabet = alphabet.replace(" ", "").upper()
message = message.replace(" ", "").upper()
# Check length and characters
if len(alphabet) != 27:
raise KeyError("Length of alphabet has to be 27.")
for each in message:
if each not in alphabet:
raise ValueError("Each message character has to be included in alphabet!")
# Generate dictionares
numbers = (
"111",
"112",
"113",
"121",
"122",
"123",
"131",
"132",
"133",
"211",
"212",
"213",
"221",
"222",
"223",
"231",
"232",
"233",
"311",
"312",
"313",
"321",
"322",
"323",
"331",
"332",
"333",
)
character2Number = {}
number2Character = {}
for letter, number in zip(alphabet, numbers):
character2Number[letter] = number
number2Character[number] = letter
return message, alphabet, character2Number, number2Character
def encryptMessage(
message: str, alphabet: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period: int = 5
) -> str:
message, alphabet, character2Number, number2Character = __prepare(message, alphabet)
encrypted, encrypted_numeric = "", ""
for i in range(0, len(message) + 1, period):
encrypted_numeric += __encryptPart(message[i : i + period], character2Number)
for i in range(0, len(encrypted_numeric), 3):
encrypted += number2Character[encrypted_numeric[i : i + 3]]
return encrypted
def decryptMessage(
message: str, alphabet: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period: int = 5
) -> str:
message, alphabet, character2Number, number2Character = __prepare(message, alphabet)
decrypted_numeric = []
decrypted = ""
for i in range(0, len(message) + 1, period):
a, b, c = __decryptPart(message[i : i + period], character2Number)
for j in range(0, len(a)):
decrypted_numeric.append(a[j] + b[j] + c[j])
for each in decrypted_numeric:
decrypted += number2Character[each]
return decrypted
if __name__ == "__main__":
msg = "DEFEND THE EAST WALL OF THE CASTLE."
encrypted = encryptMessage(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
decrypted = decryptMessage(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
print(f"Encrypted: {encrypted}\nDecrypted: {decrypted}")
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Conway's Game of Life implemented in Python.
https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life
"""
from __future__ import annotations
from PIL import Image
# Define glider example
GLIDER = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
]
# Define blinker example
BLINKER = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def new_generation(cells: list[list[int]]) -> list[list[int]]:
"""
Generates the next generation for a given state of Conway's Game of Life.
>>> new_generation(BLINKER)
[[0, 0, 0], [1, 1, 1], [0, 0, 0]]
"""
next_generation = []
for i in range(len(cells)):
next_generation_row = []
for j in range(len(cells[i])):
# Get the number of live neighbours
neighbour_count = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i]) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i]) - 1:
neighbour_count += cells[i][j + 1]
if i < len(cells) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(cells) - 1:
neighbour_count += cells[i + 1][j]
if i < len(cells) - 1 and j < len(cells[i]) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
alive = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1)
else:
next_generation_row.append(0)
next_generation.append(next_generation_row)
return next_generation
def generate_images(cells: list[list[int]], frames) -> list[Image.Image]:
"""
Generates a list of images of subsequent Game of Life states.
"""
images = []
for _ in range(frames):
# Create output image
img = Image.new("RGB", (len(cells[0]), len(cells)))
pixels = img.load()
# Save cells to image
for x in range(len(cells)):
for y in range(len(cells[0])):
colour = 255 - cells[y][x] * 255
pixels[x, y] = (colour, colour, colour)
# Save image
images.append(img)
cells = new_generation(cells)
return images
if __name__ == "__main__":
images = generate_images(GLIDER, 16)
images[0].save("out.gif", save_all=True, append_images=images[1:])
| """
Conway's Game of Life implemented in Python.
https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life
"""
from __future__ import annotations
from PIL import Image
# Define glider example
GLIDER = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
]
# Define blinker example
BLINKER = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def new_generation(cells: list[list[int]]) -> list[list[int]]:
"""
Generates the next generation for a given state of Conway's Game of Life.
>>> new_generation(BLINKER)
[[0, 0, 0], [1, 1, 1], [0, 0, 0]]
"""
next_generation = []
for i in range(len(cells)):
next_generation_row = []
for j in range(len(cells[i])):
# Get the number of live neighbours
neighbour_count = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i]) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i]) - 1:
neighbour_count += cells[i][j + 1]
if i < len(cells) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(cells) - 1:
neighbour_count += cells[i + 1][j]
if i < len(cells) - 1 and j < len(cells[i]) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
alive = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1)
else:
next_generation_row.append(0)
next_generation.append(next_generation_row)
return next_generation
def generate_images(cells: list[list[int]], frames) -> list[Image.Image]:
"""
Generates a list of images of subsequent Game of Life states.
"""
images = []
for _ in range(frames):
# Create output image
img = Image.new("RGB", (len(cells[0]), len(cells)))
pixels = img.load()
# Save cells to image
for x in range(len(cells)):
for y in range(len(cells[0])):
colour = 255 - cells[y][x] * 255
pixels[x, y] = (colour, colour, colour)
# Save image
images.append(img)
cells = new_generation(cells)
return images
if __name__ == "__main__":
images = generate_images(GLIDER, 16)
images[0].save("out.gif", save_all=True, append_images=images[1:])
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Implementation of iterative merge sort in Python
Author: Aman Gupta
For doctests run following command:
python3 -m doctest -v iterative_merge_sort.py
For manual testing run:
python3 iterative_merge_sort.py
"""
from __future__ import annotations
def merge(input_list: list, low: int, mid: int, high: int) -> list:
"""
sorting left-half and right-half individually
then merging them into result
"""
result = []
left, right = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((left if left[0] <= right[0] else right).pop(0))
input_list[low : high + 1] = result + left + right
return input_list
# iteration over the unsorted list
def iter_merge_sort(input_list: list) -> list:
"""
Return a sorted copy of the input list
>>> iter_merge_sort([5, 9, 8, 7, 1, 2, 7])
[1, 2, 5, 7, 7, 8, 9]
>>> iter_merge_sort([6])
[6]
>>> iter_merge_sort([])
[]
>>> iter_merge_sort([-2, -9, -1, -4])
[-9, -4, -2, -1]
>>> iter_merge_sort([1.1, 1, 0.0, -1, -1.1])
[-1.1, -1, 0.0, 1, 1.1]
>>> iter_merge_sort(['c', 'b', 'a'])
['a', 'b', 'c']
>>> iter_merge_sort('cba')
['a', 'b', 'c']
"""
if len(input_list) <= 1:
return input_list
input_list = list(input_list)
# iteration for two-way merging
p = 2
while p < len(input_list):
# getting low, high and middle value for merge-sort of single list
for i in range(0, len(input_list), p):
low = i
high = i + p - 1
mid = (low + high + 1) // 2
input_list = merge(input_list, low, mid, high)
# final merge of last two parts
if p * 2 >= len(input_list):
mid = i
input_list = merge(input_list, 0, mid, len(input_list) - 1)
p *= 2
return input_list
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item.strip()) for item in user_input.split(",")]
print(iter_merge_sort(unsorted))
| """
Implementation of iterative merge sort in Python
Author: Aman Gupta
For doctests run following command:
python3 -m doctest -v iterative_merge_sort.py
For manual testing run:
python3 iterative_merge_sort.py
"""
from __future__ import annotations
def merge(input_list: list, low: int, mid: int, high: int) -> list:
"""
sorting left-half and right-half individually
then merging them into result
"""
result = []
left, right = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((left if left[0] <= right[0] else right).pop(0))
input_list[low : high + 1] = result + left + right
return input_list
# iteration over the unsorted list
def iter_merge_sort(input_list: list) -> list:
"""
Return a sorted copy of the input list
>>> iter_merge_sort([5, 9, 8, 7, 1, 2, 7])
[1, 2, 5, 7, 7, 8, 9]
>>> iter_merge_sort([6])
[6]
>>> iter_merge_sort([])
[]
>>> iter_merge_sort([-2, -9, -1, -4])
[-9, -4, -2, -1]
>>> iter_merge_sort([1.1, 1, 0.0, -1, -1.1])
[-1.1, -1, 0.0, 1, 1.1]
>>> iter_merge_sort(['c', 'b', 'a'])
['a', 'b', 'c']
>>> iter_merge_sort('cba')
['a', 'b', 'c']
"""
if len(input_list) <= 1:
return input_list
input_list = list(input_list)
# iteration for two-way merging
p = 2
while p < len(input_list):
# getting low, high and middle value for merge-sort of single list
for i in range(0, len(input_list), p):
low = i
high = i + p - 1
mid = (low + high + 1) // 2
input_list = merge(input_list, low, mid, high)
# final merge of last two parts
if p * 2 >= len(input_list):
mid = i
input_list = merge(input_list, 0, mid, len(input_list) - 1)
p *= 2
return input_list
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item.strip()) for item in user_input.split(",")]
print(iter_merge_sort(unsorted))
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| * text=auto
| * text=auto
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #
| #
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
A Hamiltonian cycle (Hamiltonian circuit) is a graph cycle
through a graph that visits each node exactly once.
Determining whether such paths and cycles exist in graphs
is the 'Hamiltonian path problem', which is NP-complete.
Wikipedia: https://en.wikipedia.org/wiki/Hamiltonian_path
"""
def valid_connection(
graph: list[list[int]], next_ver: int, curr_ind: int, path: list[int]
) -> bool:
"""
Checks whether it is possible to add next into path by validating 2 statements
1. There should be path between current and next vertex
2. Next vertex should not be in path
If both validations succeed we return True, saying that it is possible to connect
this vertices, otherwise we return False
Case 1:Use exact graph as in main function, with initialized values
>>> graph = [[0, 1, 0, 1, 0],
... [1, 0, 1, 1, 1],
... [0, 1, 0, 0, 1],
... [1, 1, 0, 0, 1],
... [0, 1, 1, 1, 0]]
>>> path = [0, -1, -1, -1, -1, 0]
>>> curr_ind = 1
>>> next_ver = 1
>>> valid_connection(graph, next_ver, curr_ind, path)
True
Case 2: Same graph, but trying to connect to node that is already in path
>>> path = [0, 1, 2, 4, -1, 0]
>>> curr_ind = 4
>>> next_ver = 1
>>> valid_connection(graph, next_ver, curr_ind, path)
False
"""
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path)
def util_hamilton_cycle(graph: list[list[int]], path: list[int], curr_ind: int) -> bool:
"""
Pseudo-Code
Base Case:
1. Check if we visited all of vertices
1.1 If last visited vertex has path to starting vertex return True either
return False
Recursive Step:
2. Iterate over each vertex
Check if next vertex is valid for transiting from current vertex
2.1 Remember next vertex as next transition
2.2 Do recursive call and check if going to this vertex solves problem
2.3 If next vertex leads to solution return True
2.4 Else backtrack, delete remembered vertex
Case 1: Use exact graph as in main function, with initialized values
>>> graph = [[0, 1, 0, 1, 0],
... [1, 0, 1, 1, 1],
... [0, 1, 0, 0, 1],
... [1, 1, 0, 0, 1],
... [0, 1, 1, 1, 0]]
>>> path = [0, -1, -1, -1, -1, 0]
>>> curr_ind = 1
>>> util_hamilton_cycle(graph, path, curr_ind)
True
>>> print(path)
[0, 1, 2, 4, 3, 0]
Case 2: Use exact graph as in previous case, but in the properties taken from
middle of calculation
>>> graph = [[0, 1, 0, 1, 0],
... [1, 0, 1, 1, 1],
... [0, 1, 0, 0, 1],
... [1, 1, 0, 0, 1],
... [0, 1, 1, 1, 0]]
>>> path = [0, 1, 2, -1, -1, 0]
>>> curr_ind = 3
>>> util_hamilton_cycle(graph, path, curr_ind)
True
>>> print(path)
[0, 1, 2, 4, 3, 0]
"""
# Base Case
if curr_ind == len(graph):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next in range(0, len(graph)):
if valid_connection(graph, next, curr_ind, path):
# Insert current vertex into path as next transition
path[curr_ind] = next
# Validate created path
if util_hamilton_cycle(graph, path, curr_ind + 1):
return True
# Backtrack
path[curr_ind] = -1
return False
def hamilton_cycle(graph: list[list[int]], start_index: int = 0) -> list[int]:
r"""
Wrapper function to call subroutine called util_hamilton_cycle,
which will either return array of vertices indicating hamiltonian cycle
or an empty list indicating that hamiltonian cycle was not found.
Case 1:
Following graph consists of 5 edges.
If we look closely, we can see that there are multiple Hamiltonian cycles.
For example one result is when we iterate like:
(0)->(1)->(2)->(4)->(3)->(0)
(0)---(1)---(2)
| / \ |
| / \ |
| / \ |
|/ \|
(3)---------(4)
>>> graph = [[0, 1, 0, 1, 0],
... [1, 0, 1, 1, 1],
... [0, 1, 0, 0, 1],
... [1, 1, 0, 0, 1],
... [0, 1, 1, 1, 0]]
>>> hamilton_cycle(graph)
[0, 1, 2, 4, 3, 0]
Case 2:
Same Graph as it was in Case 1, changed starting index from default to 3
(0)---(1)---(2)
| / \ |
| / \ |
| / \ |
|/ \|
(3)---------(4)
>>> graph = [[0, 1, 0, 1, 0],
... [1, 0, 1, 1, 1],
... [0, 1, 0, 0, 1],
... [1, 1, 0, 0, 1],
... [0, 1, 1, 1, 0]]
>>> hamilton_cycle(graph, 3)
[3, 0, 1, 2, 4, 3]
Case 3:
Following Graph is exactly what it was before, but edge 3-4 is removed.
Result is that there is no Hamiltonian Cycle anymore.
(0)---(1)---(2)
| / \ |
| / \ |
| / \ |
|/ \|
(3) (4)
>>> graph = [[0, 1, 0, 1, 0],
... [1, 0, 1, 1, 1],
... [0, 1, 0, 0, 1],
... [1, 1, 0, 0, 0],
... [0, 1, 1, 0, 0]]
>>> hamilton_cycle(graph,4)
[]
"""
# Initialize path with -1, indicating that we have not visited them yet
path = [-1] * (len(graph) + 1)
# initialize start and end of path with starting index
path[0] = path[-1] = start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(graph, path, 1) else []
| """
A Hamiltonian cycle (Hamiltonian circuit) is a graph cycle
through a graph that visits each node exactly once.
Determining whether such paths and cycles exist in graphs
is the 'Hamiltonian path problem', which is NP-complete.
Wikipedia: https://en.wikipedia.org/wiki/Hamiltonian_path
"""
def valid_connection(
graph: list[list[int]], next_ver: int, curr_ind: int, path: list[int]
) -> bool:
"""
Checks whether it is possible to add next into path by validating 2 statements
1. There should be path between current and next vertex
2. Next vertex should not be in path
If both validations succeed we return True, saying that it is possible to connect
this vertices, otherwise we return False
Case 1:Use exact graph as in main function, with initialized values
>>> graph = [[0, 1, 0, 1, 0],
... [1, 0, 1, 1, 1],
... [0, 1, 0, 0, 1],
... [1, 1, 0, 0, 1],
... [0, 1, 1, 1, 0]]
>>> path = [0, -1, -1, -1, -1, 0]
>>> curr_ind = 1
>>> next_ver = 1
>>> valid_connection(graph, next_ver, curr_ind, path)
True
Case 2: Same graph, but trying to connect to node that is already in path
>>> path = [0, 1, 2, 4, -1, 0]
>>> curr_ind = 4
>>> next_ver = 1
>>> valid_connection(graph, next_ver, curr_ind, path)
False
"""
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path)
def util_hamilton_cycle(graph: list[list[int]], path: list[int], curr_ind: int) -> bool:
"""
Pseudo-Code
Base Case:
1. Check if we visited all of vertices
1.1 If last visited vertex has path to starting vertex return True either
return False
Recursive Step:
2. Iterate over each vertex
Check if next vertex is valid for transiting from current vertex
2.1 Remember next vertex as next transition
2.2 Do recursive call and check if going to this vertex solves problem
2.3 If next vertex leads to solution return True
2.4 Else backtrack, delete remembered vertex
Case 1: Use exact graph as in main function, with initialized values
>>> graph = [[0, 1, 0, 1, 0],
... [1, 0, 1, 1, 1],
... [0, 1, 0, 0, 1],
... [1, 1, 0, 0, 1],
... [0, 1, 1, 1, 0]]
>>> path = [0, -1, -1, -1, -1, 0]
>>> curr_ind = 1
>>> util_hamilton_cycle(graph, path, curr_ind)
True
>>> print(path)
[0, 1, 2, 4, 3, 0]
Case 2: Use exact graph as in previous case, but in the properties taken from
middle of calculation
>>> graph = [[0, 1, 0, 1, 0],
... [1, 0, 1, 1, 1],
... [0, 1, 0, 0, 1],
... [1, 1, 0, 0, 1],
... [0, 1, 1, 1, 0]]
>>> path = [0, 1, 2, -1, -1, 0]
>>> curr_ind = 3
>>> util_hamilton_cycle(graph, path, curr_ind)
True
>>> print(path)
[0, 1, 2, 4, 3, 0]
"""
# Base Case
if curr_ind == len(graph):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next in range(0, len(graph)):
if valid_connection(graph, next, curr_ind, path):
# Insert current vertex into path as next transition
path[curr_ind] = next
# Validate created path
if util_hamilton_cycle(graph, path, curr_ind + 1):
return True
# Backtrack
path[curr_ind] = -1
return False
def hamilton_cycle(graph: list[list[int]], start_index: int = 0) -> list[int]:
r"""
Wrapper function to call subroutine called util_hamilton_cycle,
which will either return array of vertices indicating hamiltonian cycle
or an empty list indicating that hamiltonian cycle was not found.
Case 1:
Following graph consists of 5 edges.
If we look closely, we can see that there are multiple Hamiltonian cycles.
For example one result is when we iterate like:
(0)->(1)->(2)->(4)->(3)->(0)
(0)---(1)---(2)
| / \ |
| / \ |
| / \ |
|/ \|
(3)---------(4)
>>> graph = [[0, 1, 0, 1, 0],
... [1, 0, 1, 1, 1],
... [0, 1, 0, 0, 1],
... [1, 1, 0, 0, 1],
... [0, 1, 1, 1, 0]]
>>> hamilton_cycle(graph)
[0, 1, 2, 4, 3, 0]
Case 2:
Same Graph as it was in Case 1, changed starting index from default to 3
(0)---(1)---(2)
| / \ |
| / \ |
| / \ |
|/ \|
(3)---------(4)
>>> graph = [[0, 1, 0, 1, 0],
... [1, 0, 1, 1, 1],
... [0, 1, 0, 0, 1],
... [1, 1, 0, 0, 1],
... [0, 1, 1, 1, 0]]
>>> hamilton_cycle(graph, 3)
[3, 0, 1, 2, 4, 3]
Case 3:
Following Graph is exactly what it was before, but edge 3-4 is removed.
Result is that there is no Hamiltonian Cycle anymore.
(0)---(1)---(2)
| / \ |
| / \ |
| / \ |
|/ \|
(3) (4)
>>> graph = [[0, 1, 0, 1, 0],
... [1, 0, 1, 1, 1],
... [0, 1, 0, 0, 1],
... [1, 1, 0, 0, 0],
... [0, 1, 1, 0, 0]]
>>> hamilton_cycle(graph,4)
[]
"""
# Initialize path with -1, indicating that we have not visited them yet
path = [-1] * (len(graph) + 1)
# initialize start and end of path with starting index
path[0] = path[-1] = start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(graph, path, 1) else []
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
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2S 4C AD 7H JC 5C 2D 6D 4H 3D
7S JS 2C 4H 8C AD QD 9C 3S TD
JD TS 4C 6H 9H 7D QD 6D 3C AS
AS 7C 4C 6S 5D 5S 5C JS QC 4S
KD 6S 9S 7C 3C 5S 7D JH QD JS
4S 7S JH 2C 8S 5D 7H 3D QH AD
TD 6H 2H 8D 4H 2D 7C AD KH 5D
TS 3S 5H 2C QD AH 2S 5C KH TD
KC 4D 8C 5D AS 6C 2H 2S 9H 7C
KD JS QC TS QS KH JH 2C 5D AD
3S 5H KC 6C 9H 3H 2H AD 7D 7S
7S JS JH KD 8S 7D 2S 9H 7C 2H
9H 2D 8D QC 6S AD AS 8H 5H 6C
2S 7H 6C 6D 7D 8C 5D 9D JC 3C
7C 9C 7H JD 2H KD 3S KH AD 4S
QH AS 9H 4D JD KS KD TS KH 5H
4C 8H 5S 3S 3D 7D TD AD 7S KC
JS 8S 5S JC 8H TH 9C 4D 5D KC
7C 5S 9C QD 2C QH JS 5H 8D KH
TD 2S KS 3D AD KC 7S TC 3C 5D
4C 2S AD QS 6C 9S QD TH QH 5C
8C AD QS 2D 2S KC JD KS 6C JC
8D 4D JS 2H 5D QD 7S 7D QH TS
6S 7H 3S 8C 8S 9D QS 8H 6C 9S
4S TC 2S 5C QD 4D QS 6D TH 6S
3S 5C 9D 6H 8D 4C 7D TC 7C TD
AH 6S AS 7H 5S KD 3H 5H AC 4C
8D 8S AH KS QS 2C AD 6H 7D 5D
6H 9H 9S 2H QS 8S 9C 5D 2D KD
TS QC 5S JH 7D 7S TH 9S 9H AC
7H 3H 6S KC 4D 6D 5C 4S QD TS
TD 2S 7C QD 3H JH 9D 4H 7S 7H
KS 3D 4H 5H TC 2S AS 2D 6D 7D
8H 3C 7H TD 3H AD KC TH 9C KH
TC 4C 2C 9S 9D 9C 5C 2H JD 3C
3H AC TS 5D AD 8D 6H QC 6S 8C
2S TS 3S JD 7H 8S QH 4C 5S 8D
AC 4S 6C 3C KH 3D 7C 2D 8S 2H
4H 6C 8S TH 2H 4S 8H 9S 3H 7S
7C 4C 9C 2C 5C AS 5D KD 4D QH
9H 4H TS AS 7D 8D 5D 9S 8C 2H
QC KD AC AD 2H 7S AS 3S 2D 9S
2H QC 8H TC 6D QD QS 5D KH 3C
TH JD QS 4C 2S 5S AD 7H 3S AS
7H JS 3D 6C 3S 6D AS 9S AC QS
9C TS AS 8C TC 8S 6H 9D 8D 6C
4D JD 9C KC 7C 6D KS 3S 8C AS
3H 6S TC 8D TS 3S KC 9S 7C AS
8C QC 4H 4S 8S 6C 3S TC AH AC
4D 7D 5C AS 2H 6S TS QC AD TC
QD QC 8S 4S TH 3D AH TS JH 4H
5C 2D 9S 2C 3H 3C 9D QD QH 7D
KC 9H 6C KD 7S 3C 4D AS TC 2D
3D JS 4D 9D KS 7D TH QC 3H 3C
8D 5S 2H 9D 3H 8C 4C 4H 3C TH
JC TH 4S 6S JD 2D 4D 6C 3D 4C
TS 3S 2D 4H AC 2C 6S 2H JH 6H
TD 8S AD TC AH AC JH 9S 6S 7S
6C KC 4S JD 8D 9H 5S 7H QH AH
KD 8D TS JH 5C 5H 3H AD AS JS
2D 4H 3D 6C 8C 7S AD 5D 5C 8S
TD 5D 7S 9C 4S 5H 6C 8C 4C 8S
JS QH 9C AS 5C QS JC 3D QC 7C
JC 9C KH JH QS QC 2C TS 3D AD
5D JH AC 5C 9S TS 4C JD 8C KS
KC AS 2D KH 9H 2C 5S 4D 3D 6H
TH AH 2D 8S JC 3D 8C QH 7S 3S
8H QD 4H JC AS KH KS 3C 9S 6D
9S QH 7D 9C 4S AC 7H KH 4D KD
AH AD TH 6D 9C 9S KD KS QH 4H
QD 6H 9C 7C QS 6D 6S 9D 5S JH
AH 8D 5H QD 2H JC KS 4H KH 5S
5C 2S JS 8D 9C 8C 3D AS KC AH
JD 9S 2H QS 8H 5S 8C TH 5C 4C
QC QS 8C 2S 2C 3S 9C 4C KS KH
2D 5D 8S AH AD TD 2C JS KS 8C
TC 5S 5H 8H QC 9H 6H JD 4H 9S
3C JH 4H 9H AH 4S 2H 4C 8D AC
8S TH 4D 7D 6D QD QS 7S TC 7C
KH 6D 2D JD 5H JS QD JH 4H 4S
9C 7S JH 4S 3S TS QC 8C TC 4H
QH 9D 4D JH QS 3S 2C 7C 6C 2D
4H 9S JD 5C 5H AH 9D TS 2D 4C
KS JH TS 5D 2D AH JS 7H AS 8D
JS AH 8C AD KS 5S 8H 2C 6C TH
2H 5D AD AC KS 3D 8H TS 6H QC
6D 4H TS 9C 5H JS JH 6S JD 4C
JH QH 4H 2C 6D 3C 5D 4C QS KC
6H 4H 6C 7H 6S 2S 8S KH QC 8C
3H 3D 5D KS 4H TD AD 3S 4D TS
5S 7C 8S 7D 2C KS 7S 6C 8C JS
5D 2H 3S 7C 5C QD 5H 6D 9C 9H
JS 2S KD 9S 8D TD TS AC 8C 9D
5H QD 2S AC 8C 9H KS 7C 4S 3C
KH AS 3H 8S 9C JS QS 4S AD 4D
AS 2S TD AD 4D 9H JC 4C 5H QS
5D 7C 4H TC 2D 6C JS 4S KC 3S
4C 2C 5D AC 9H 3D JD 8S QS QH
2C 8S 6H 3C QH 6D TC KD AC AH
QC 6C 3S QS 4S AC 8D 5C AD KH
5S 4C AC KH AS QC 2C 5C 8D 9C
8H JD 3C KH 8D 5C 9C QD QH 9D
7H TS 2C 8C 4S TD JC 9C 5H QH
JS 4S 2C 7C TH 6C AS KS 7S JD
JH 7C 9H 7H TC 5H 3D 6D 5D 4D
2C QD JH 2H 9D 5S 3D TD AD KS
JD QH 3S 4D TH 7D 6S QS KS 4H
TC KS 5S 8D 8H AD 2S 2D 4C JH
5S JH TC 3S 2D QS 9D 4C KD 9S
AC KH 3H AS 9D KC 9H QD 6C 6S
9H 7S 3D 5C 7D KC TD 8H 4H 6S
3C 7H 8H TC QD 4D 7S 6S QH 6C
6D AD 4C QD 6C 5D 7D 9D KS TS
JH 2H JD 9S 7S TS KH 8D 5D 8H
2D 9S 4C 7D 9D 5H QD 6D AC 6S
7S 6D JC QD JH 4C 6S QS 2H 7D
8C TD JH KD 2H 5C QS 2C JS 7S
TC 5H 4H JH QD 3S 5S 5D 8S KH
KS KH 7C 2C 5D JH 6S 9C 6D JC
5H AH JD 9C JS KC 2H 6H 4D 5S
AS 3C TH QC 6H 9C 8S 8C TD 7C
KC 2C QD 9C KH 4D 7S 3C TS 9H
9C QC 2S TS 8C TD 9S QD 3S 3C
4D 9D TH JH AH 6S 2S JD QH JS
QD 9H 6C KD 7D 7H 5D 6S 8H AH
8H 3C 4S 2H 5H QS QH 7S 4H AC
QS 3C 7S 9S 4H 3S AH KS 9D 7C
AD 5S 6S 2H 2D 5H TC 4S 3C 8C
QH TS 6S 4D JS KS JH AS 8S 6D
2C 8S 2S TD 5H AS TC TS 6C KC
KC TS 8H 2H 3H 7C 4C 5S TH TD
KD AD KH 7H 7S 5D 5H 5S 2D 9C
AD 9S 3D 7S 8C QC 7C 9C KD KS
3C QC 9S 8C 4D 5C AS QD 6C 2C
2H KC 8S JD 7S AC 8D 5C 2S 4D
9D QH 3D 2S TC 3S KS 3C 9H TD
KD 6S AC 2C 7H 5H 3S 6C 6H 8C
QH TC 8S 6S KH TH 4H 5D TS 4D
8C JS 4H 6H 2C 2H 7D AC QD 3D
QS KC 6S 2D 5S 4H TD 3H JH 4C
7S 5H 7H 8H KH 6H QS TH KD 7D
5H AD KD 7C KH 5S TD 6D 3C 6C
8C 9C 5H JD 7C KC KH 7H 2H 3S
7S 4H AD 4D 8S QS TH 3D 7H 5S
8D TC KS KD 9S 6D AD JD 5C 2S
7H 8H 6C QD 2H 6H 9D TC 9S 7C
8D 6D 4C 7C 6C 3C TH KH JS JH
5S 3S 8S JS 9H AS AD 8H 7S KD
JH 7C 2C KC 5H AS AD 9C 9S JS
AD AC 2C 6S QD 7C 3H TH KS KD
9D JD 4H 8H 4C KH 7S TS 8C KC
3S 5S 2H 7S 6H 7D KS 5C 6D AD
5S 8C 9H QS 7H 7S 2H 6C 7D TD
QS 5S TD AC 9D KC 3D TC 2D 4D
TD 2H 7D JD QD 4C 7H 5D KC 3D
4C 3H 8S KD QH 5S QC 9H TC 5H
9C QD TH 5H TS 5C 9H AH QH 2C
4D 6S 3C AC 6C 3D 2C 2H TD TH
AC 9C 5D QC 4D AD 8D 6D 8C KC
AD 3C 4H AC 8D 8H 7S 9S TD JC
4H 9H QH JS 2D TH TD TC KD KS
5S 6S 9S 8D TH AS KH 5H 5C 8S
JD 2S 9S 6S 5S 8S 5D 7S 7H 9D
5D 8C 4C 9D AD TS 2C 7D KD TC
8S QS 4D KC 5C 8D 4S KH JD KD
AS 5C AD QH 7D 2H 9S 7H 7C TC
2S 8S JD KH 7S 6C 6D AD 5D QC
9H 6H 3S 8C 8H AH TC 4H JS TD
2C TS 4D 7H 2D QC 9C 5D TH 7C
6C 8H QC 5D TS JH 5C 5H 9H 4S
2D QC 7H AS JS 8S 2H 4C 4H 8D
JS 6S AC KD 3D 3C 4S 7H TH KC
QH KH 6S QS 5S 4H 3C QD 3S 3H
7H AS KH 8C 4H 9C 5S 3D 6S TS
9C 7C 3H 5S QD 2C 3D AD AC 5H
JH TD 2D 4C TS 3H KH AD 3S 7S
AS 4C 5H 4D 6S KD JC 3C 6H 2D
3H 6S 8C 2D TH 4S AH QH AD 5H
7C 2S 9H 7H KC 5C 6D 5S 3H JC
3C TC 9C 4H QD TD JH 6D 9H 5S
7C 6S 5C 5D 6C 4S 7H 9H 6H AH
AD 2H 7D KC 2C 4C 2S 9S 7H 3S
TH 4C 8S 6S 3S AD KS AS JH TD
5C TD 4S 4D AD 6S 5D TC 9C 7D
8H 3S 4D 4S 5S 6H 5C AC 3H 3D
9H 3C AC 4S QS 8S 9D QH 5H 4D
JC 6C 5H TS AC 9C JD 8C 7C QD
8S 8H 9C JD 2D QC QH 6H 3C 8D
KS JS 2H 6H 5H QH QS 3H 7C 6D
TC 3H 4S 7H QC 2H 3S 8C JS KH
AH 8H 5S 4C 9H JD 3H 7S JC AC
3C 2D 4C 5S 6C 4S QS 3S JD 3D
5H 2D TC AH KS 6D 7H AD 8C 6H
6C 7S 3C JD 7C 8H KS KH AH 6D
AH 7D 3H 8H 8S 7H QS 5H 9D 2D
JD AC 4H 7S 8S 9S KS AS 9D QH
7S 2C 8S 5S JH QS JC AH KD 4C
AH 2S 9H 4H 8D TS TD 6H QH JD
4H JC 3H QS 6D 7S 9C 8S 9D 8D
5H TD 4S 9S 4C 8C 8D 7H 3H 3D
QS KH 3S 2C 2S 3C 7S TD 4S QD
7C TD 4D 5S KH AC AS 7H 4C 6C
2S 5H 6D JD 9H QS 8S 2C 2H TD
2S TS 6H 9H 7S 4H JC 4C 5D 5S
2C 5H 7D 4H 3S QH JC JS 6D 8H
4C QH 7C QD 3S AD TH 8S 5S TS
9H TC 2S TD JC 7D 3S 3D TH QH
7D 4C 8S 5C JH 8H 6S 3S KC 3H
JC 3H KH TC QH TH 6H 2C AC 5H
QS 2H 9D 2C AS 6S 6C 2S 8C 8S
9H 7D QC TH 4H KD QS AC 7S 3C
4D JH 6S 5S 8H KS 9S QC 3S AS
JD 2D 6S 7S TC 9H KC 3H 7D KD
2H KH 7C 4D 4S 3H JS QD 7D KC
4C JC AS 9D 3C JS 6C 8H QD 4D
AH JS 3S 6C 4C 3D JH 6D 9C 9H
9H 2D 8C 7H 5S KS 6H 9C 2S TC
6C 8C AD 7H 6H 3D KH AS 5D TH
KS 8C 3S TS 8S 4D 5S 9S 6C 4H
9H 4S 4H 5C 7D KC 2D 2H 9D JH
5C JS TC 9D 9H 5H 7S KH JC 6S
7C 9H 8H 4D JC KH JD 2H TD TC
8H 6C 2H 2C KH 6H 9D QS QH 5H
AC 7D 2S 3D QD JC 2D 8D JD JH
2H JC 2D 7H 2C 3C 8D KD TD 4H
3S 4H 6D 8D TS 3H TD 3D 6H TH
JH JC 3S AC QH 9H 7H 8S QC 2C
7H TD QS 4S 8S 9C 2S 5D 4D 2H
3D TS 3H 2S QC 8H 6H KC JC KS
5D JD 7D TC 8C 6C 9S 3D 8D AC
8H 6H JH 6C 5D 8D 8S 4H AD 2C
9D 4H 2D 2C 3S TS AS TC 3C 5D
4D TH 5H KS QS 6C 4S 2H 3D AD
5C KC 6H 2C 5S 3C 4D 2D 9H 9S
JD 4C 3H TH QH 9H 5S AH 8S AC
7D 9S 6S 2H TD 9C 4H 8H QS 4C
3C 6H 5D 4H 8C 9C KC 6S QD QS
3S 9H KD TC 2D JS 8C 6S 4H 4S
2S 4C 8S QS 6H KH 3H TH 8C 5D
2C KH 5S 3S 7S 7H 6C 9D QD 8D
8H KS AC 2D KH TS 6C JS KC 7H
9C KS 5C TD QC AH 6C 5H 9S 7C
5D 4D 3H 4H 6S 7C 7S AH QD TD
2H 7D QC 6S TC TS AH 7S 9D 3H
TH 5H QD 9S KS 7S 7C 6H 8C TD
TH 2D 4D QC 5C 7D JD AH 9C 4H
4H 3H AH 8D 6H QC QH 9H 2H 2C
2D AD 4C TS 6H 7S TH 4H QS TD
3C KD 2H 3H QS JD TC QC 5D 8H
KS JC QD TH 9S KD 8D 8C 2D 9C
3C QD KD 6D 4D 8D AH AD QC 8S
8H 3S 9D 2S 3H KS 6H 4C 7C KC
TH 9S 5C 3D 7D 6H AC 7S 4D 2C
5C 3D JD 4D 2D 6D 5H 9H 4C KH
AS 7H TD 6C 2H 3D QD KS 4C 4S
JC 3C AC 7C JD JS 8H 9S QC 5D
JD 6S 5S 2H AS 8C 7D 5H JH 3D
8D TC 5S 9S 8S 3H JC 5H 7S AS
5C TD 3D 7D 4H 8D 7H 4D 5D JS
QS 9C KS TD 2S 8S 5C 2H 4H AS
TH 7S 4H 7D 3H JD KD 5D 2S KC
JD 7H 4S 8H 4C JS 6H QH 5S 4H
2C QS 8C 5S 3H QC 2S 6C QD AD
8C 3D JD TC 4H 2H AD 5S AC 2S
5D 2C JS 2D AD 9D 3D 4C 4S JH
8D 5H 5D 6H 7S 4D KS 9D TD JD
3D 6D 9C 2S AS 7D 5S 5C 8H JD
7C 8S 3S 6S 5H JD TC AD 7H 7S
2S 9D TS 4D AC 8D 6C QD JD 3H
9S KH 2C 3C AC 3D 5H 6H 8D 5D
KS 3D 2D 6S AS 4C 2S 7C 7H KH
AC 2H 3S JC 5C QH 4D 2D 5H 7S
TS AS JD 8C 6H JC 8S 5S 2C 5D
7S QH 7H 6C QC 8H 2D 7C JD 2S
2C QD 2S 2H JC 9C 5D 2D JD JH
7C 5C 9C 8S 7D 6D 8D 6C 9S JH
2C AD 6S 5H 3S KS 7S 9D KH 4C
7H 6C 2C 5C TH 9D 8D 3S QC AH
5S KC 6H TC 5H 8S TH 6D 3C AH
9C KD 4H AD TD 9S 4S 7D 6H 5D
7H 5C 5H 6D AS 4C KD KH 4H 9D
3C 2S 5C 6C JD QS 2H 9D 7D 3H
AC 2S 6S 7S JS QD 5C QS 6H AD
5H TH QC 7H TC 3S 7C 6D KC 3D
4H 3D QC 9S 8H 2C 3S JC KS 5C
4S 6S 2C 6H 8S 3S 3D 9H 3H JS
4S 8C 4D 2D 8H 9H 7D 9D AH TS
9S 2C 9H 4C 8D AS 7D 3D 6D 5S
6S 4C 7H 8C 3H 5H JC AH 9D 9C
2S 7C 5S JD 8C 3S 3D 4D 7D 6S
3C KC 4S 5D 7D 3D JD 7H 3H 4H
9C 9H 4H 4D TH 6D QD 8S 9S 7S
2H AC 8S 4S AD 8C 2C AH 7D TC
TS 9H 3C AD KS TC 3D 8C 8H JD
QC 8D 2C 3C 7D 7C JD 9H 9C 6C
AH 6S JS JH 5D AS QC 2C JD TD
9H KD 2H 5D 2D 3S 7D TC AH TS
TD 8H AS 5D AH QC AC 6S TC 5H
KS 4S 7H 4D 8D 9C TC 2H 6H 3H
3H KD 4S QD QH 3D 8H 8C TD 7S
8S JD TC AH JS QS 2D KH KS 4D
3C AD JC KD JS KH 4S TH 9H 2C
QC 5S JS 9S KS AS 7C QD 2S JD
KC 5S QS 3S 2D AC 5D 9H 8H KS
6H 9C TC AD 2C 6D 5S JD 6C 7C
QS KH TD QD 2C 3H 8S 2S QC AH
9D 9H JH TC QH 3C 2S JS 5C 7H
6C 3S 3D 2S 4S QD 2D TH 5D 2C
2D 6H 6D 2S JC QH AS 7H 4H KH
5H 6S KS AD TC TS 7C AC 4S 4H
AD 3C 4H QS 8C 9D KS 2H 2D 4D
4S 9D 6C 6D 9C AC 8D 3H 7H KD
JC AH 6C TS JD 6D AD 3S 5D QD
JC JH JD 3S 7S 8S JS QC 3H 4S
JD TH 5C 2C AD JS 7H 9S 2H 7S
8D 3S JH 4D QC AS JD 2C KC 6H
2C AC 5H KD 5S 7H QD JH AH 2D
JC QH 8D 8S TC 5H 5C AH 8C 6C
3H JS 8S QD JH 3C 4H 6D 5C 3S
6D 4S 4C AH 5H 5S 3H JD 7C 8D
8H AH 2H 3H JS 3C 7D QC 4H KD
6S 2H KD 5H 8H 2D 3C 8S 7S QD
2S 7S KC QC AH TC QS 6D 4C 8D
5S 9H 2C 3S QD 7S 6C 2H 7C 9D
3C 6C 5C 5S JD JC KS 3S 5D TS
7C KS 6S 5S 2S 2D TC 2H 5H QS
AS 7H 6S TS 5H 9S 9D 3C KD 2H
4S JS QS 3S 4H 7C 2S AC 6S 9D
8C JH 2H 5H 7C 5D QH QS KH QC
3S TD 3H 7C KC 8D 5H 8S KH 8C
4H KH JD TS 3C 7H AS QC JS 5S
AH 9D 2C 8D 4D 2D 6H 6C KC 6S
2S 6H 9D 3S 7H 4D KH 8H KD 3D
9C TC AC JH KH 4D JD 5H TD 3S
7S 4H 9D AS 4C 7D QS 9S 2S KH
3S 8D 8S KS 8C JC 5C KH 2H 5D
8S QH 2C 4D KC JS QC 9D AC 6H
8S 8C 7C JS JD 6S 4C 9C AC 4S
QH 5D 2C 7D JC 8S 2D JS JH 4C
JS 4C 7S TS JH KC KH 5H QD 4S
QD 8C 8D 2D 6S TD 9D AC QH 5S
QH QC JS 3D 3C 5C 4H KH 8S 7H
7C 2C 5S JC 8S 3H QC 5D 2H KC
5S 8D KD 6H 4H QD QH 6D AH 3D
7S KS 6C 2S 4D AC QS 5H TS JD
7C 2D TC 5D QS AC JS QC 6C KC
2C KS 4D 3H TS 8S AD 4H 7S 9S
QD 9H QH 5H 4H 4D KH 3S JC AD
4D AC KC 8D 6D 4C 2D KH 2C JD
2C 9H 2D AH 3H 6D 9C 7D TC KS
8C 3H KD 7C 5C 2S 4S 5H AS AH
TH JD 4H KD 3H TC 5C 3S AC KH
6D 7H AH 7S QC 6H 2D TD JD AS
JH 5D 7H TC 9S 7D JC AS 5S KH
2H 8C AD TH 6H QD KD 9H 6S 6C
QH KC 9D 4D 3S JS JH 4H 2C 9H
TC 7H KH 4H JC 7D 9S 3H QS 7S
AD 7D JH 6C 7H 4H 3S 3H 4D QH
JD 2H 5C AS 6C QC 4D 3C TC JH
AC JD 3H 6H 4C JC AD 7D 7H 9H
4H TC TS 2C 8C 6S KS 2H JD 9S
4C 3H QS QC 9S 9H 6D KC 9D 9C
5C AD 8C 2C QH TH QD JC 8D 8H
QC 2C 2S QD 9C 4D 3S 8D JH QS
9D 3S 2C 7S 7C JC TD 3C TC 9H
3C TS 8H 5C 4C 2C 6S 8D 7C 4H
KS 7H 2H TC 4H 2C 3S AS AH QS
8C 2D 2H 2C 4S 4C 6S 7D 5S 3S
TH QC 5D TD 3C QS KD KC KS AS
4D AH KD 9H KS 5C 4C 6H JC 7S
KC 4H 5C QS TC 2H JC 9S AH QH
4S 9H 3H 5H 3C QD 2H QC JH 8H
5D AS 7H 2C 3D JH 6H 4C 6S 7D
9C JD 9H AH JS 8S QH 3H KS 8H
3S AC QC TS 4D AD 3D AH 8S 9H
7H 3H QS 9C 9S 5H JH JS AH AC
8D 3C JD 2H AC 9C 7H 5S 4D 8H
7C JH 9H 6C JS 9S 7H 8C 9D 4H
2D AS 9S 6H 4D JS JH 9H AD QD
6H 7S JH KH AH 7H TD 5S 6S 2C
8H JH 6S 5H 5S 9D TC 4C QC 9S
7D 2C KD 3H 5H AS QD 7H JS 4D
TS QH 6C 8H TH 5H 3C 3H 9C 9D
AD KH JS 5D 3H AS AC 9S 5C KC
2C KH 8C JC QS 6D AH 2D KC TC
9D 3H 2S 7C 4D 6D KH KS 8D 7D
9H 2S TC JH AC QC 3H 5S 3S 8H
3S AS KD 8H 4C 3H 7C JH QH TS
7S 6D 7H 9D JH 4C 3D 3S 6C AS
4S 2H 2C 4C 8S 5H KC 8C QC QD
3H 3S 6C QS QC 2D 6S 5D 2C 9D
2H 8D JH 2S 3H 2D 6C 5C 7S AD
9H JS 5D QH 8S TS 2H 7S 6S AD
6D QC 9S 7H 5H 5C 7D KC JD 4H
QC 5S 9H 9C 4D 6S KS 2S 4C 7C
9H 7C 4H 8D 3S 6H 5C 8H JS 7S
2D 6H JS TD 4H 4D JC TH 5H KC
AC 7C 8D TH 3H 9S 2D 4C KC 4D
KD QS 9C 7S 3D KS AD TS 4C 4H
QH 9C 8H 2S 7D KS 7H 5D KD 4C
9C 2S 2H JC 6S 6C TC QC JH 5C
7S AC 8H KC 8S 6H QS JC 3D 6S
JS 2D JH 8C 4S 6H 8H 6D 5D AD
6H 7D 2S 4H 9H 7C AS AC 8H 5S
3C JS 4S 6D 5H 2S QH 6S 9C 2C
3D 5S 6S 9S 4C QS 8D QD 8S TC
9C 3D AH 9H 5S 2C 7D AD JC 3S
7H TC AS 3C 6S 6D 7S KH KC 9H
3S TC 8H 6S 5H JH 8C 7D AC 2S
QD 9D 9C 3S JC 8C KS 8H 5D 4D
JS AH JD 6D 9D 8C 9H 9S 8H 3H
2D 6S 4C 4D 8S AD 4S TC AH 9H
TS AC QC TH KC 6D 4H 7S 8C 2H
3C QD JS 9D 5S JC AH 2H TS 9H
3H 4D QH 5D 9C 5H 7D 4S JC 3S
8S TH 3H 7C 2H JD JS TS AC 8D
9C 2H TD KC JD 2S 8C 5S AD 2C
3D KD 7C 5H 4D QH QD TC 6H 7D
7H 2C KC 5S KD 6H AH QC 7S QH
6H 5C AC 5H 2C 9C 2D 7C TD 2S
4D 9D AH 3D 7C JD 4H 8C 4C KS
TH 3C JS QH 8H 4C AS 3D QS QC
4D 7S 5H JH 6D 7D 6H JS KH 3C
QD 8S 7D 2H 2C 7C JC 2S 5H 8C
QH 8S 9D TC 2H AD 7C 8D QD 6S
3S 7C AD 9H 2H 9S JD TS 4C 2D
3S AS 4H QC 2C 8H 8S 7S TD TC
JH TH TD 3S 4D 4H 5S 5D QS 2C
8C QD QH TC 6D 4S 9S 9D 4H QC
8C JS 9D 6H JD 3H AD 6S TD QC
KC 8S 3D 7C TD 7D 8D 9H 4S 3S
6C 4S 3D 9D KD TC KC KS AC 5S
7C 6S QH 3D JS KD 6H 6D 2D 8C
JD 2S 5S 4H 8S AC 2D 6S TS 5C
5H 8C 5S 3C 4S 3D 7C 8D AS 3H
AS TS 7C 3H AD 7D JC QS 6C 6H
3S 9S 4C AC QH 5H 5D 9H TS 4H
6C 5C 7H 7S TD AD JD 5S 2H 2S
7D 6C KC 3S JD 8D 8S TS QS KH
8S QS 8D 6C TH AC AH 2C 8H 9S
7H TD KH QH 8S 3D 4D AH JD AS
TS 3D 2H JC 2S JH KH 6C QC JS
KC TH 2D 6H 7S 2S TC 8C 9D QS
3C 9D 6S KH 8H 6D 5D TH 2C 2H
6H TC 7D AD 4D 8S TS 9H TD 7S
JS 6D JD JC 2H AC 6C 3D KH 8D
KH JD 9S 5D 4H 4C 3H 7S QS 5C
4H JD 5D 3S 3C 4D KH QH QS 7S
JD TS 8S QD AH 4C 6H 3S 5S 2C
QS 3D JD AS 8D TH 7C 6S QC KS
7S 2H 8C QC 7H AC 6D 2D TH KH
5S 6C 7H KH 7D AH 8C 5C 7S 3D
3C KD AD 7D 6C 4D KS 2D 8C 4S
7C 8D 5S 2D 2S AH AD 2C 9D TD
3C AD 4S KS JH 7C 5C 8C 9C TH
AS TD 4D 7C JD 8C QH 3C 5H 9S
3H 9C 8S 9S 6S QD KS AH 5H JH
QC 9C 5S 4H 2H TD 7D AS 8C 9D
8C 2C 9D KD TC 7S 3D KH QC 3C
4D AS 4C QS 5S 9D 6S JD QH KS
6D AH 6C 4C 5H TS 9H 7D 3D 5S
QS JD 7C 8D 9C AC 3S 6S 6C KH
8H JH 5D 9S 6D AS 6S 3S QC 7H
QD AD 5C JH 2H AH 4H AS KC 2C
JH 9C 2C 6H 2D JS 5D 9H KC 6D
7D 9D KD TH 3H AS 6S QC 6H AD
JD 4H 7D KC 3H JS 3C TH 3D QS
4C 3H 8C QD 5H 6H AS 8H AD JD
TH 8S KD 5D QC 7D JS 5S 5H TS
7D KC 9D QS 3H 3C 6D TS 7S AH
7C 4H 7H AH QC AC 4D 5D 6D TH
3C 4H 2S KD 8H 5H JH TC 6C JD
4S 8C 3D 4H JS TD 7S JH QS KD
7C QC KD 4D 7H 6S AD TD TC KH
5H 9H KC 3H 4D 3D AD 6S QD 6H
TH 7C 6H TS QH 5S 2C KC TD 6S
7C 4D 5S JD JH 7D AC KD KH 4H
7D 6C 8D 8H 5C JH 8S QD TH JD
8D 7D 6C 7C 9D KD AS 5C QH JH
9S 2C 8C 3C 4C KS JH 2D 8D 4H
7S 6C JH KH 8H 3H 9D 2D AH 6D
4D TC 9C 8D 7H TD KS TH KD 3C
JD 9H 8D QD AS KD 9D 2C 2S 9C
8D 3H 5C 7H KS 5H QH 2D 8C 9H
2D TH 6D QD 6C KC 3H 3S AD 4C
4H 3H JS 9D 3C TC 5H QH QC JC
3D 5C 6H 3S 3C JC 5S 7S 2S QH
AC 5C 8C 4D 5D 4H 2S QD 3C 3H
2C TD AH 9C KD JS 6S QD 4C QC
QS 8C 3S 4H TC JS 3H 7C JC AD
5H 4D 9C KS JC TD 9S TS 8S 9H
QD TS 7D AS AC 2C TD 6H 8H AH
6S AD 8C 4S 9H 8D 9D KH 8S 3C
QS 4D 2D 7S KH JS JC AD 4C 3C
QS 9S 7H KC TD TH 5H JS AC JH
6D AC 2S QS 7C AS KS 6S KH 5S
6D 8H KH 3C QS 2H 5C 9C 9D 6C
JS 2C 4C 6H 7D JC AC QD TD 3H
4H QC 8H JD 4C KD KS 5C KC 7S
6D 2D 3H 2S QD 5S 7H AS TH 6S
AS 6D 8D 2C 8S TD 8H QD JC AH
9C 9H 2D TD QH 2H 5C TC 3D 8H
KC 8S 3D KH 2S TS TC 6S 4D JH
9H 9D QS AC KC 6H 5D 4D 8D AH
9S 5C QS 4H 7C 7D 2H 8S AD JS
3D AC 9S AS 2C 2D 2H 3H JC KH
7H QH KH JD TC KS 5S 8H 4C 8D
2H 7H 3S 2S 5H QS 3C AS 9H KD
AD 3D JD 6H 5S 9C 6D AC 9S 3S
3D 5D 9C 2D AC 4S 2S AD 6C 6S
QC 4C 2D 3H 6S KC QH QD 2H JH
QC 3C 8S 4D 9S 2H 5C 8H QS QD
6D KD 6S 7H 3S KH 2H 5C JC 6C
3S 9S TC 6S 8H 2D AD 7S 8S TS
3C 6H 9C 3H 5C JC 8H QH TD QD
3C JS QD 5D TD 2C KH 9H TH AS
9S TC JD 3D 5C 5H AD QH 9H KC
TC 7H 4H 8H 3H TD 6S AC 7C 2S
QS 9D 5D 3C JC KS 4D 6C JH 2S
9S 6S 3C 7H TS 4C KD 6D 3D 9C
2D 9H AH AC 7H 2S JH 3S 7C QC
QD 9H 3C 2H AC AS 8S KD 8C KH
2D 7S TD TH 6D JD 8D 4D 2H 5S
8S QH KD JD QS JH 4D KC 5H 3S
3C KH QC 6D 8H 3S AH 7D TD 2D
5S 9H QH 4S 6S 6C 6D TS TH 7S
6C 4C 6D QS JS 9C TS 3H 8D 8S
JS 5C 7S AS 2C AH 2H AD 5S TC
KD 6C 9C 9D TS 2S JC 4H 2C QD
QS 9H TC 3H KC KS 4H 3C AD TH
KH 9C 2H KD 9D TC 7S KC JH 2D
7C 3S KC AS 8C 5D 9C 9S QH 3H
2D 8C TD 4C 2H QC 5D TC 2C 7D
KS 4D 6C QH TD KH 5D 7C AD 8D
2S 9S 8S 4C 8C 3D 6H QD 7C 7H
6C 8S QH 5H TS 5C 3C 4S 2S 2H
8S 6S 2H JC 3S 3H 9D 8C 2S 7H
QC 2C 8H 9C AC JD 4C 4H 6S 3S
3H 3S 7D 4C 9S 5H 8H JC 3D TC
QH 2S 2D 9S KD QD 9H AD 6D 9C
8D 2D KS 9S JC 4C JD KC 4S TH
KH TS 6D 4D 5C KD 5H AS 9H AD
QD JS 7C 6D 5D 5C TH 5H QH QS
9D QH KH 5H JH 4C 4D TC TH 6C
KH AS TS 9D KD 9C 7S 4D 8H 5S
KH AS 2S 7D 9D 4C TS TH AH 7C
KS 4D AC 8S 9S 8D TH QH 9D 5C
5D 5C 8C QS TC 4C 3D 3S 2C 8D
9D KS 2D 3C KC 4S 8C KH 6C JC
8H AH 6H 7D 7S QD 3C 4C 6C KC
3H 2C QH 8H AS 7D 4C 8C 4H KC
QD 5S 4H 2C TD AH JH QH 4C 8S
3H QS 5S JS 8H 2S 9H 9C 3S 2C
6H TS 7S JC QD AC TD KC 5S 3H
QH AS QS 7D JC KC 2C 4C 5C 5S
QH 3D AS JS 4H 8D 7H JC 2S 9C
5D 4D 2S 4S 9D 9C 2D QS 8H 7H
6D 7H 3H JS TS AC 2D JH 7C 8S
JH 5H KC 3C TC 5S 9H 4C 8H 9D
8S KC 5H 9H AD KS 9D KH 8D AH
JC 2H 9H KS 6S 3H QC 5H AH 9C
5C KH 5S AD 6C JC 9H QC 9C TD
5S 5D JC QH 2D KS 8H QS 2H TS
JH 5H 5S AH 7H 3C 8S AS TD KH
6H 3D JD 2C 4C KC 7S AH 6C JH
4C KS 9D AD 7S KC 7D 8H 3S 9C
7H 5C 5H 3C 8H QC 3D KH 6D JC
2D 4H 5D 7D QC AD AH 9H QH 8H
KD 8C JS 9D 3S 3C 2H 5D 6D 2S
8S 6S TS 3C 6H 8D 5S 3H TD 6C
KS 3D JH 9C 7C 9S QS 5S 4H 6H
7S 6S TH 4S KC KD 3S JC JH KS
7C 3C 2S 6D QH 2C 7S 5H 8H AH
KC 8D QD 6D KH 5C 7H 9D 3D 9C
6H 2D 8S JS 9S 2S 6D KC 7C TC
KD 9C JH 7H KC 8S 2S 7S 3D 6H
4H 9H 2D 4C 8H 7H 5S 8S 2H 8D
AD 7C 3C 7S 5S 4D 9H 3D JC KH
5D AS 7D 6D 9C JC 4C QH QS KH
KD JD 7D 3D QS QC 8S 6D JS QD
6S 8C 5S QH TH 9H AS AC 2C JD
QC KS QH 7S 3C 4C 5C KC 5D AH
6C 4H 9D AH 2C 3H KD 3D TS 5C
TD 8S QS AS JS 3H KD AC 4H KS
7D 5D TS 9H 4H 4C 9C 2H 8C QC
2C 7D 9H 4D KS 4C QH AD KD JS
QD AD AH KH 9D JS 9H JC KD JD
8S 3C 4S TS 7S 4D 5C 2S 6H 7C
JS 7S 5C KD 6D QH 8S TD 2H 6S
QH 6C TC 6H TD 4C 9D 2H QC 8H
3D TS 4D 2H 6H 6S 2C 7H 8S 6C
9H 9D JD JH 3S AH 2C 6S 3H 8S
2C QS 8C 5S 3H 2S 7D 3C AD 4S
5C QC QH AS TS 4S 6S 4C 5H JS
JH 5C TD 4C 6H JS KD KH QS 4H
TC KH JC 4D 9H 9D 8D KC 3C 8H
2H TC 8S AD 9S 4H TS 7H 2C 5C
4H 2S 6C 5S KS AH 9C 7C 8H KD
TS QH TD QS 3C JH AH 2C 8D 7D
5D KC 3H 5S AC 4S 7H QS 4C 2H
3D 7D QC KH JH 6D 6C TD TH KD
5S 8D TH 6C 9D 7D KH 8C 9S 6D
JD QS 7S QC 2S QH JC 4S KS 8D
7S 5S 9S JD KD 9C JC AD 2D 7C
4S 5H AH JH 9C 5D TD 7C 2D 6S
KC 6C 7H 6S 9C QD 5S 4H KS TD
6S 8D KS 2D TH TD 9H JD TS 3S
KH JS 4H 5D 9D TC TD QC JD TS
QS QD AC AD 4C 6S 2D AS 3H KC
4C 7C 3C TD QS 9C KC AS 8D AD
KC 7H QC 6D 8H 6S 5S AH 7S 8C
3S AD 9H JC 6D JD AS KH 6S JH
AD 3D TS KS 7H JH 2D JS QD AC
9C JD 7C 6D TC 6H 6C JC 3D 3S
QC KC 3S JC KD 2C 8D AH QS TS
AS KD 3D JD 8H 7C 8C 5C QD 6C
| 8C TS KC 9H 4S 7D 2S 5D 3S AC
5C AD 5D AC 9C 7C 5H 8D TD KS
3H 7H 6S KC JS QH TD JC 2D 8S
TH 8H 5C QS TC 9H 4D JC KS JS
7C 5H KC QH JD AS KH 4C AD 4S
5H KS 9C 7D 9H 8D 3S 5D 5C AH
6H 4H 5C 3H 2H 3S QH 5S 6S AS
TD 8C 4H 7C TC KC 4C 3H 7S KS
7C 9C 6D KD 3H 4C QS QC AC KH
JC 6S 5H 2H 2D KD 9D 7C AS JS
AD QH TH 9D 8H TS 6D 3S AS AC
2H 4S 5C 5S TC KC JD 6C TS 3C
QD AS 6H JS 2C 3D 9H KC 4H 8S
KD 8S 9S 7C 2S 3S 6D 6S 4H KC
3C 8C 2D 7D 4D 9S 4S QH 4H JD
8C KC 7S TC 2D TS 8H QD AC 5C
3D KH QD 6C 6S AD AS 8H 2H QS
6S 8D 4C 8S 6C QH TC 6D 7D 9D
2S 8D 8C 4C TS 9S 9D 9C AC 3D
3C QS 2S 4H JH 3D 2D TD 8S 9H
5H QS 8S 6D 3C 8C JD AS 7H 7D
6H TD 9D AS JH 6C QC 9S KD JC
AH 8S QS 4D TH AC TS 3C 3D 5C
5S 4D JS 3D 8H 6C TS 3S AD 8C
6D 7C 5D 5H 3S 5C JC 2H 5S 3D
5H 6H 2S KS 3D 5D JD 7H JS 8H
KH 4H AS JS QS QC TC 6D 7C KS
3D QS TS 2H JS 4D AS 9S JC KD
QD 5H 4D 5D KH 7H 3D JS KD 4H
2C 9H 6H 5C 9D 6C JC 2D TH 9S
7D 6D AS QD JH 4D JS 7C QS 5C
3H KH QD AD 8C 8H 3S TH 9D 5S
AH 9S 4D 9D 8S 4H JS 3C TC 8D
2C KS 5H QD 3S TS 9H AH AD 8S
5C 7H 5D KD 9H 4D 3D 2D KS AD
KS KC 9S 6D 2C QH 9D 9H TS TC
9C 6H 5D QH 4D AD 6D QC JS KH
9S 3H 9D JD 5C 4D 9H AS TC QH
2C 6D JC 9C 3C AD 9S KH 9D 7D
KC 9C 7C JC JS KD 3H AS 3C 7D
QD KH QS 2C 3S 8S 8H 9H 9C JC
QH 8D 3C KC 4C 4H 6D AD 9H 9D
3S KS QS 7H KH 7D 5H 5D JD AD
2H 2C 6H TH TC 7D 8D 4H 8C AS
4S 2H AC QC 3S 6D TH 4D 4C KH
4D TC KS AS 7C 3C 6D 2D 9H 6C
8C TD 5D QS 2C 7H 4C 9C 3H 9H
5H JH TS 7S TD 6H AD QD 8H 8S
5S AD 9C 8C 7C 8D 5H 9D 8S 2S
4H KH KS 9S 2S KC 5S AD 4S 7D
QS 9C QD 6H JS 5D AC 8D 2S AS
KH AC JC 3S 9D 9S 3C 9C 5S JS
AD 3C 3D KS 3S 5C 9C 8C TS 4S
JH 8D 5D 6H KD QS QD 3D 6C KC
8S JD 6C 3S 8C TC QC 3C QH JS
KC JC 8H 2S 9H 9C JH 8S 8C 9S
8S 2H QH 4D QC 9D KC AS TH 3C
8S 6H TH 7C 2H 6S 3C 3H AS 7S
QH 5S JS 4H 5H TS 8H AH AC JC
9D 8H 2S 4S TC JC 3C 7H 3H 5C
3D AD 3C 3S 4C QC AS 5D TH 8C
6S 9D 4C JS KH AH TS JD 8H AD
4C 6S 9D 7S AC 4D 3D 3S TC JD
AD 7H 6H 4H JH KC TD TS 7D 6S
8H JH TC 3S 8D 8C 9S 2C 5C 4D
2C 9D KC QH TH QS JC 9C 4H TS
QS 3C QD 8H KH 4H 8D TD 8S AC
7C 3C TH 5S 8H 8C 9C JD TC KD
QC TC JD TS 8C 3H 6H KD 7C TD
JH QS KS 9C 6D 6S AS 9H KH 6H
2H 4D AH 2D JH 6H TD 5D 4H JD
KD 8C 9S JH QD JS 2C QS 5C 7C
4S TC 7H 8D 2S 6H 7S 9C 7C KC
8C 5D 7H 4S TD QC 8S JS 4H KS
AD 8S JH 6D TD KD 7C 6C 2D 7D
JC 6H 6S JS 4H QH 9H AH 4C 3C
6H 5H AS 7C 7S 3D KH KC 5D 5C
JC 3D TD AS 4D 6D 6S QH JD KS
8C 7S 8S QH 2S JD 5C 7H AH QD
8S 3C 6H 6C 2C 8D TD 7D 4C 4D
5D QH KH 7C 2S 7H JS 6D QC QD
AD 6C 6S 7D TH 6H 2H 8H KH 4H
KS JS KD 5D 2D KH 7D 9C 8C 3D
9C 6D QD 3C KS 3S 7S AH JD 2D
AH QH AS JC 8S 8H 4C KC TH 7D
JC 5H TD 7C 5D KD 4C AD 8H JS
KC 2H AC AH 7D JH KH 5D 7S 6D
9S 5S 9C 6H 8S TD JD 9H 6C AC
7D 8S 6D TS KD 7H AC 5S 7C 5D
AH QC JC 4C TC 8C 2H TS 2C 7D
KD KC 6S 3D 7D 2S 8S 3H 5S 5C
8S 5D 8H 4C 6H KC 3H 7C 5S KD
JH 8C 3D 3C 6C KC TD 7H 7C 4C
JC KC 6H TS QS TD KS 8H 8C 9S
6C 5S 9C QH 7D AH KS KC 9S 2C
4D 4S 8H TD 9C 3S 7D 9D AS TH
6S 7D 3C 6H 5D KD 2C 5C 9D 9C
2H KC 3D AD 3H QD QS 8D JC 4S
8C 3H 9C 7C AD 5D JC 9D JS AS
5D 9H 5C 7H 6S 6C QC JC QD 9S
JC QS JH 2C 6S 9C QC 3D 4S TC
4H 5S 8D 3D 4D 2S KC 2H JS 2C
TD 3S TH KD 4D 7H JH JS KS AC
7S 8C 9S 2D 8S 7D 5C AD 9D AS
8C 7H 2S 6C TH 3H 4C 3S 8H AC
KD 5H JC 8H JD 2D 4H TD JH 5C
3D AS QH KS 7H JD 8S 5S 6D 5H
9S 6S TC QS JC 5C 5D 9C TH 8C
5H 3S JH 9H 2S 2C 6S 7S AS KS
8C QD JC QS TC QC 4H AC KH 6C
TC 5H 7D JH 4H 2H 8D JC KS 4D
5S 9C KH KD 9H 5C TS 3D 7D 2D
5H AS TC 4D 8C 2C TS 9D 3H 8D
6H 8D 2D 9H JD 6C 4S 5H 5S 6D
AD 9C JC 7D 6H 9S 6D JS 9H 3C
AD JH TC QS 4C 5D 9S 7C 9C AH
KD 6H 2H TH 8S QD KS 9D 9H AS
4H 8H 8D 5H 6C AH 5S AS AD 8S
QS 5D 4S 2H TD KS 5H AC 3H JC
9C 7D QD KD AC 6D 5H QH 6H 5S
KC AH QH 2H 7D QS 3H KS 7S JD
6C 8S 3H 6D KS QD 5D 5C 8H TC
9H 4D 4S 6S 9D KH QC 4H 6C JD
TD 2D QH 4S 6H JH KD 3C QD 8C
4S 6H 7C QD 9D AS AH 6S AD 3C
2C KC TH 6H 8D AH 5C 6D 8S 5D
TD TS 7C AD JC QD 9H 3C KC 7H
5D 4D 5S 8H 4H 7D 3H JD KD 2D
JH TD 6H QS 4S KD 5C 8S 7D 8H
AC 3D AS 8C TD 7H KH 5D 6C JD
9D KS 7C 6D QH TC JD KD AS KC
JH 8S 5S 7S 7D AS 2D 3D AD 2H
2H 5D AS 3C QD KC 6H 9H 9S 2C
9D 5D TH 4C JH 3H 8D TC 8H 9H
6H KD 2C TD 2H 6C 9D 2D JS 8C
KD 7S 3C 7C AS QH TS AD 8C 2S
QS 8H 6C JS 4C 9S QC AD TD TS
2H 7C TS TC 8C 3C 9H 2D 6D JC
TC 2H 8D JH KS 6D 3H TD TH 8H
9D TD 9H QC 5D 6C 8H 8C KC TS
2H 8C 3D AH 4D TH TC 7D 8H KC
TS 5C 2D 8C 6S KH AH 5H 6H KC
5S 5D AH TC 4C JD 8D 6H 8C 6C
KC QD 3D 8H 2D JC 9H 4H AD 2S
TD 6S 7D JS KD 4H QS 2S 3S 8C
4C 9H JH TS 3S 4H QC 5S 9S 9C
2C KD 9H JS 9S 3H JC TS 5D AC
AS 2H 5D AD 5H JC 7S TD JS 4C
2D 4S 8H 3D 7D 2C AD KD 9C TS
7H QD JH 5H JS AC 3D TH 4C 8H
6D KH KC QD 5C AD 7C 2D 4H AC
3D 9D TC 8S QD 2C JC 4H JD AH
6C TD 5S TC 8S AH 2C 5D AS AC
TH 7S 3D AS 6C 4C 7H 7D 4H AH
5C 2H KS 6H 7S 4H 5H 3D 3C 7H
3C 9S AC 7S QH 2H 3D 6S 3S 3H
2D 3H AS 2C 6H TC JS 6S 9C 6C
QH KD QD 6D AC 6H KH 2C TS 8C
8H 7D 3S 9H 5D 3H 4S QC 9S 5H
2D 9D 7H 6H 3C 8S 5H 4D 3S 4S
KD 9S 4S TC 7S QC 3S 8S 2H 7H
TC 3D 8C 3H 6C 2H 6H KS KD 4D
KC 3D 9S 3H JS 4S 8H 2D 6C 8S
6H QS 6C TC QD 9H 7D 7C 5H 4D
TD 9D 8D 6S 6C TC 5D TS JS 8H
4H KC JD 9H TC 2C 6S 5H 8H AS
JS 9C 5C 6S 9D JD 8H KC 4C 6D
4D 8D 8S 6C 7C 6H 7H 8H 5C KC
TC 3D JC 6D KS 9S 6H 7S 9C 2C
6C 3S KD 5H TS 7D 9H 9S 6H KH
3D QD 4C 6H TS AC 3S 5C 2H KD
4C AS JS 9S 7C TS 7H 9H JC KS
4H 8C JD 3H 6H AD 9S 4S 5S KS
4C 2C 7D 3D AS 9C 2S QS KC 6C
8S 5H 3D 2S AC 9D 6S 3S 4D TD
QD TH 7S TS 3D AC 7H 6C 5D QC
TC QD AD 9C QS 5C 8D KD 3D 3C
9D 8H AS 3S 7C 8S JD 2D 8D KC
4C TH AC QH JS 8D 7D 7S 9C KH
9D 8D 4C JH 2C 2S QD KD TS 4H
4D 6D 5D 2D JH 3S 8S 3H TC KH
AD 4D 2C QS 8C KD JH JD AH 5C
5C 6C 5H 2H JH 4H KS 7C TC 3H
3C 4C QC 5D JH 9C QD KH 8D TC
3H 9C JS 7H QH AS 7C 9H 5H JC
2D 5S QD 4S 3C KC 6S 6C 5C 4C
5D KH 2D TS 8S 9C AS 9S 7C 4C
7C AH 8C 8D 5S KD QH QS JH 2C
8C 9D AH 2H AC QC 5S 8H 7H 2C
QD 9H 5S QS QC 9C 5H JC TH 4H
6C 6S 3H 5H 3S 6H KS 8D AC 7S
AC QH 7H 8C 4S KC 6C 3D 3S TC
9D 3D JS TH AC 5H 3H 8S 3S TC
QD KH JS KS 9S QC 8D AH 3C AC
5H 6C KH 3S 9S JH 2D QD AS 8C
6C 4D 7S 7H 5S JC 6S 9H 4H JH
AH 5S 6H 9S AD 3S TH 2H 9D 8C
4C 8D 9H 7C QC AD 4S 9C KC 5S
9D 6H 4D TC 4C JH 2S 5D 3S AS
2H 6C 7C KH 5C AD QS TH JD 8S
3S 4S 7S AH AS KC JS 2S AD TH
JS KC 2S 7D 8C 5C 9C TS 5H 9D
7S 9S 4D TD JH JS KH 6H 5D 2C
JD JS JC TH 2D 3D QD 8C AC 5H
7S KH 5S 9D 5D TD 4S 6H 3C 2D
4S 5D AC 8D 4D 7C AD AS AH 9C
6S TH TS KS 2C QC AH AS 3C 4S
2H 8C 3S JC 5C 7C 3H 3C KH JH
7S 3H JC 5S 6H 4C 2S 4D KC 7H
4D 7C 4H 9S 8S 6S AD TC 6C JC
KH QS 3S TC 4C 8H 8S AC 3C TS
QD QS TH 3C TS 7H 7D AH TD JC
TD JD QC 4D 9S 7S TS AD 7D AC
AH 7H 4S 6D 7C 2H 9D KS JC TD
7C AH JD 4H 6D QS TS 2H 2C 5C
TC KC 8C 9S 4C JS 3C JC 6S AH
AS 7D QC 3D 5S JC JD 9D TD KH
TH 3C 2S 6H AH AC 5H 5C 7S 8H
QC 2D AC QD 2S 3S JD QS 6S 8H
KC 4H 3C 9D JS 6H 3S 8S AS 8C
7H KC 7D JD 2H JC QH 5S 3H QS
9H TD 3S 8H 7S AC 5C 6C AH 7C
8D 9H AH JD TD QS 7D 3S 9C 8S
AH QH 3C JD KC 4S 5S 5D TD KS
9H 7H 6S JH TH 4C 7C AD 5C 2D
7C KD 5S TC 9D 6S 6C 5D 2S TH
KC 9H 8D 5H 7H 4H QC 3D 7C AS
6S 8S QC TD 4S 5C TH QS QD 2S
8S 5H TH QC 9H 6S KC 7D 7C 5C
7H KD AH 4D KH 5C 4S 2D KC QH
6S 2C TD JC AS 4D 6C 8C 4H 5S
JC TC JD 5S 6S 8D AS 9D AD 3S
6D 6H 5D 5S TC 3D 7D QS 9D QD
4S 6C 8S 3S 7S AD KS 2D 7D 7C
KC QH JC AC QD 5D 8D QS 7H 7D
JS AH 8S 5H 3D TD 3H 4S 6C JH
4S QS 7D AS 9H JS KS 6D TC 5C
2D 5C 6H TC 4D QH 3D 9H 8S 6C
6D 7H TC TH 5S JD 5C 9C KS KD
8D TD QH 6S 4S 6C 8S KC 5C TC
5S 3D KS AC 4S 7D QD 4C TH 2S
TS 8H 9S 6S 7S QH 3C AH 7H 8C
4C 8C TS JS QC 3D 7D 5D 7S JH
8S 7S 9D QC AC 7C 6D 2H JH KC
JS KD 3C 6S 4S 7C AH QC KS 5H
KS 6S 4H JD QS TC 8H KC 6H AS
KH 7C TC 6S TD JC 5C 7D AH 3S
3H 4C 4H TC TH 6S 7H 6D 9C QH
7D 5H 4S 8C JS 4D 3D 8S QH KC
3H 6S AD 7H 3S QC 8S 4S 7S JS
3S JD KH TH 6H QS 9C 6C 2D QD
4S QH 4D 5H KC 7D 6D 8D TH 5S
TD AD 6S 7H KD KH 9H 5S KC JC
3H QC AS TS 4S QD KS 9C 7S KC
TS 6S QC 6C TH TC 9D 5C 5D KD
JS 3S 4H KD 4C QD 6D 9S JC 9D
8S JS 6D 4H JH 6H 6S 6C KS KH
AC 7D 5D TC 9S KH 6S QD 6H AS
AS 7H 6D QH 8D TH 2S KH 5C 5H
4C 7C 3D QC TC 4S KH 8C 2D JS
6H 5D 7S 5H 9C 9H JH 8S TH 7H
AS JS 2S QD KH 8H 4S AC 8D 8S
3H 4C TD KD 8C JC 5C QS 2D JD
TS 7D 5D 6C 2C QS 2H 3C AH KS
4S 7C 9C 7D JH 6C 5C 8H 9D QD
2S TD 7S 6D 9C 9S QS KH QH 5C
JC 6S 9C QH JH 8D 7S JS KH 2H
8D 5H TH KC 4D 4S 3S 6S 3D QS
2D JD 4C TD 7C 6D TH 7S JC AH
QS 7S 4C TH 9D TS AD 4D 3H 6H
2D 3H 7D JD 3D AS 2S 9C QC 8S
4H 9H 9C 2C 7S JH KD 5C 5D 6H
TC 9H 8H JC 3C 9S 8D KS AD KC
TS 5H JD QS QH QC 8D 5D KH AH
5D AS 8S 6S 4C AH QC QD TH 7H
3H 4H 7D 6S 4S 9H AS 8H JS 9D
JD 8C 2C 9D 7D 5H 5S 9S JC KD
KD 9C 4S QD AH 7C AD 9D AC TD
6S 4H 4S 9C 8D KS TC 9D JH 7C
5S JC 5H 4S QH AC 2C JS 2S 9S
8C 5H AS QD AD 5C 7D 8S QC TD
JC 4C 8D 5C KH QS 4D 6H 2H 2C
TH 4S 2D KC 3H QD AC 7H AD 9D
KH QD AS 8H TH KC 8D 7S QH 8C
JC 6C 7D 8C KH AD QS 2H 6S 2D
JC KH 2D 7D JS QC 5H 4C 5D AD
TS 3S AD 4S TD 2D TH 6S 9H JH
9H 2D QS 2C 4S 3D KH AS AC 9D
KH 6S 8H 4S KD 7D 9D TS QD QC
JH 5H AH KS AS AD JC QC 5S KH
5D 7D 6D KS KD 3D 7C 4D JD 3S
AC JS 8D 5H 9C 3H 4H 4D TS 2C
6H KS KH 9D 7C 2S 6S 8S 2H 3D
6H AC JS 7S 3S TD 8H 3H 4H TH
9H TC QC KC 5C KS 6H 4H AC 8S
TC 7D QH 4S JC TS 6D 6C AC KH
QH 7D 7C JH QS QD TH 3H 5D KS
3D 5S 8D JS 4C 2C KS 7H 9C 4H
5H 8S 4H TD 2C 3S QD QC 3H KC
QC JS KD 9C AD 5S 9D 7D 7H TS
8C JC KH 7C 7S 6C TS 2C QD TH
5S 9D TH 3C 7S QH 8S 9C 2H 5H
5D 9H 6H 2S JS KH 3H 7C 2H 5S
JD 5D 5S 2C TC 2S 6S 6C 3C 8S
4D KH 8H 4H 2D KS 3H 5C 2S 9H
3S 2D TD 7H 8S 6H JD KC 9C 8D
6S QD JH 7C 9H 5H 8S 8H TH TD
QS 7S TD 7D TS JC KD 7C 3C 2C
3C JD 8S 4H 2D 2S TD AS 4D AC
AH KS 6C 4C 4S 7D 8C 9H 6H AS
5S 3C 9S 2C QS KD 4D 4S AC 5D
2D TS 2C JS KH QH 5D 8C AS KC
KD 3H 6C TH 8S 7S KH 6H 9S AC
6H 7S 6C QS AH 2S 2H 4H 5D 5H
5H JC QD 2C 2S JD AS QC 6S 7D
6C TC AS KD 8H 9D 2C 7D JH 9S
2H 4C 6C AH 8S TD 3H TH 7C TS
KD 4S TS 6C QH 8D 9D 9C AH 7D
6D JS 5C QD QC 9C 5D 8C 2H KD
3C QH JH AD 6S AH KC 8S 6D 6H
3D 7C 4C 7S 5S 3S 6S 5H JC 3C
QH 7C 5H 3C 3S 8C TS 4C KD 9C
QD 3S 7S 5H 7H QH JC 7C 8C KD
3C KD KH 2S 4C TS AC 6S 2C 7C
2C KH 3C 4C 6H 4D 5H 5S 7S QD
4D 7C 8S QD TS 9D KS 6H KD 3C
QS 4D TS 7S 4C 3H QD 8D 9S TC
TS QH AC 6S 3C 9H 9D QS 8S 6H
3S 7S 5D 4S JS 2D 6C QH 6S TH
4C 4H AS JS 5D 3D TS 9C AC 8S
6S 9C 7C 3S 5C QS AD AS 6H 3C
9S 8C 7H 3H 6S 7C AS 9H JD KH
3D 3H 7S 4D 6C 7C AC 2H 9C TH
4H 5S 3H AC TC TH 9C 9H 9S 8D
8D 9H 5H 4D 6C 2H QD 6S 5D 3S
4C 5C JD QS 4D 3H TH AC QH 8C
QC 5S 3C 7H AD 4C KS 4H JD 6D
QS AH 3H KS 9H 2S JS JH 5H 2H
2H 5S TH 6S TS 3S KS 3C 5H JS
2D 9S 7H 3D KC JH 6D 7D JS TD
AC JS 8H 2C 8C JH JC 2D TH 7S
5D 9S 8H 2H 3D TC AH JC KD 9C
9D QD JC 2H 6D KH TS 9S QH TH
2C 8D 4S JD 5H 3H TH TC 9C KC
AS 3D 9H 7D 4D TH KH 2H 7S 3H
4H 7S KS 2S JS TS 8S 2H QD 8D
5S 6H JH KS 8H 2S QC AC 6S 3S
JC AS AD QS 8H 6C KH 4C 4D QD
2S 3D TS TD 9S KS 6S QS 5C 8D
3C 6D 4S QC KC JH QD TH KH AD
9H AH 4D KS 2S 8D JH JC 7C QS
2D 6C TH 3C 8H QD QH 2S 3S KS
6H 5D 9S 4C TS TD JS QD 9D JD
5H 8H KH 8S KS 7C TD AD 4S KD
2C 7C JC 5S AS 6C 7D 8S 5H 9C
6S QD 9S TS KH QS 5S QH 3C KC
7D 3H 3C KD 5C AS JH 7H 6H JD
9D 5C 9H KC 8H KS 4S AD 4D 2S
3S JD QD 8D 2S 7C 5S 6S 5H TS
6D 9S KC TD 3S 6H QD JD 5C 8D
5H 9D TS KD 8D 6H TD QC 4C 7D
6D 4S JD 9D AH 9S AS TD 9H QD
2D 5S 2H 9C 6H 9S TD QC 7D TC
3S 2H KS TS 2C 9C 8S JS 9D 7D
3C KC 6D 5D 6C 6H 8S AS 7S QS
JH 9S 2H 8D 4C 8H 9H AD TH KH
QC AS 2S JS 5C 6H KD 3H 7H 2C
QD 8H 2S 8D 3S 6D AH 2C TC 5C
JD JS TS 8S 3H 5D TD KC JC 6H
6S QS TC 3H 5D AH JC 7C 7D 4H
7C 5D 8H 9C 2H 9H JH KH 5S 2C
9C 7H 6S TH 3S QC QD 4C AC JD
2H 5D 9S 7D KC 3S QS 2D AS KH
2S 4S 2H 7D 5C TD TH QH 9S 4D
6D 3S TS 6H 4H KS 9D 8H 5S 2D
9H KS 4H 3S 5C 5D KH 6H 6S JS
KC AS 8C 4C JC KH QC TH QD AH
6S KH 9S 2C 5H TC 3C 7H JC 4D
JD 4S 6S 5S 8D 7H 7S 4D 4C 2H
7H 9H 5D KH 9C 7C TS TC 7S 5H
4C 8D QC TS 4S 9H 3D AD JS 7C
8C QS 5C 5D 3H JS AH KC 4S 9D
TS JD 8S QS TH JH KH 2D QD JS
JD QC 5D 6S 9H 3S 2C 8H 9S TS
2S 4C AD 7H JC 5C 2D 6D 4H 3D
7S JS 2C 4H 8C AD QD 9C 3S TD
JD TS 4C 6H 9H 7D QD 6D 3C AS
AS 7C 4C 6S 5D 5S 5C JS QC 4S
KD 6S 9S 7C 3C 5S 7D JH QD JS
4S 7S JH 2C 8S 5D 7H 3D QH AD
TD 6H 2H 8D 4H 2D 7C AD KH 5D
TS 3S 5H 2C QD AH 2S 5C KH TD
KC 4D 8C 5D AS 6C 2H 2S 9H 7C
KD JS QC TS QS KH JH 2C 5D AD
3S 5H KC 6C 9H 3H 2H AD 7D 7S
7S JS JH KD 8S 7D 2S 9H 7C 2H
9H 2D 8D QC 6S AD AS 8H 5H 6C
2S 7H 6C 6D 7D 8C 5D 9D JC 3C
7C 9C 7H JD 2H KD 3S KH AD 4S
QH AS 9H 4D JD KS KD TS KH 5H
4C 8H 5S 3S 3D 7D TD AD 7S KC
JS 8S 5S JC 8H TH 9C 4D 5D KC
7C 5S 9C QD 2C QH JS 5H 8D KH
TD 2S KS 3D AD KC 7S TC 3C 5D
4C 2S AD QS 6C 9S QD TH QH 5C
8C AD QS 2D 2S KC JD KS 6C JC
8D 4D JS 2H 5D QD 7S 7D QH TS
6S 7H 3S 8C 8S 9D QS 8H 6C 9S
4S TC 2S 5C QD 4D QS 6D TH 6S
3S 5C 9D 6H 8D 4C 7D TC 7C TD
AH 6S AS 7H 5S KD 3H 5H AC 4C
8D 8S AH KS QS 2C AD 6H 7D 5D
6H 9H 9S 2H QS 8S 9C 5D 2D KD
TS QC 5S JH 7D 7S TH 9S 9H AC
7H 3H 6S KC 4D 6D 5C 4S QD TS
TD 2S 7C QD 3H JH 9D 4H 7S 7H
KS 3D 4H 5H TC 2S AS 2D 6D 7D
8H 3C 7H TD 3H AD KC TH 9C KH
TC 4C 2C 9S 9D 9C 5C 2H JD 3C
3H AC TS 5D AD 8D 6H QC 6S 8C
2S TS 3S JD 7H 8S QH 4C 5S 8D
AC 4S 6C 3C KH 3D 7C 2D 8S 2H
4H 6C 8S TH 2H 4S 8H 9S 3H 7S
7C 4C 9C 2C 5C AS 5D KD 4D QH
9H 4H TS AS 7D 8D 5D 9S 8C 2H
QC KD AC AD 2H 7S AS 3S 2D 9S
2H QC 8H TC 6D QD QS 5D KH 3C
TH JD QS 4C 2S 5S AD 7H 3S AS
7H JS 3D 6C 3S 6D AS 9S AC QS
9C TS AS 8C TC 8S 6H 9D 8D 6C
4D JD 9C KC 7C 6D KS 3S 8C AS
3H 6S TC 8D TS 3S KC 9S 7C AS
8C QC 4H 4S 8S 6C 3S TC AH AC
4D 7D 5C AS 2H 6S TS QC AD TC
QD QC 8S 4S TH 3D AH TS JH 4H
5C 2D 9S 2C 3H 3C 9D QD QH 7D
KC 9H 6C KD 7S 3C 4D AS TC 2D
3D JS 4D 9D KS 7D TH QC 3H 3C
8D 5S 2H 9D 3H 8C 4C 4H 3C TH
JC TH 4S 6S JD 2D 4D 6C 3D 4C
TS 3S 2D 4H AC 2C 6S 2H JH 6H
TD 8S AD TC AH AC JH 9S 6S 7S
6C KC 4S JD 8D 9H 5S 7H QH AH
KD 8D TS JH 5C 5H 3H AD AS JS
2D 4H 3D 6C 8C 7S AD 5D 5C 8S
TD 5D 7S 9C 4S 5H 6C 8C 4C 8S
JS QH 9C AS 5C QS JC 3D QC 7C
JC 9C KH JH QS QC 2C TS 3D AD
5D JH AC 5C 9S TS 4C JD 8C KS
KC AS 2D KH 9H 2C 5S 4D 3D 6H
TH AH 2D 8S JC 3D 8C QH 7S 3S
8H QD 4H JC AS KH KS 3C 9S 6D
9S QH 7D 9C 4S AC 7H KH 4D KD
AH AD TH 6D 9C 9S KD KS QH 4H
QD 6H 9C 7C QS 6D 6S 9D 5S JH
AH 8D 5H QD 2H JC KS 4H KH 5S
5C 2S JS 8D 9C 8C 3D AS KC AH
JD 9S 2H QS 8H 5S 8C TH 5C 4C
QC QS 8C 2S 2C 3S 9C 4C KS KH
2D 5D 8S AH AD TD 2C JS KS 8C
TC 5S 5H 8H QC 9H 6H JD 4H 9S
3C JH 4H 9H AH 4S 2H 4C 8D AC
8S TH 4D 7D 6D QD QS 7S TC 7C
KH 6D 2D JD 5H JS QD JH 4H 4S
9C 7S JH 4S 3S TS QC 8C TC 4H
QH 9D 4D JH QS 3S 2C 7C 6C 2D
4H 9S JD 5C 5H AH 9D TS 2D 4C
KS JH TS 5D 2D AH JS 7H AS 8D
JS AH 8C AD KS 5S 8H 2C 6C TH
2H 5D AD AC KS 3D 8H TS 6H QC
6D 4H TS 9C 5H JS JH 6S JD 4C
JH QH 4H 2C 6D 3C 5D 4C QS KC
6H 4H 6C 7H 6S 2S 8S KH QC 8C
3H 3D 5D KS 4H TD AD 3S 4D TS
5S 7C 8S 7D 2C KS 7S 6C 8C JS
5D 2H 3S 7C 5C QD 5H 6D 9C 9H
JS 2S KD 9S 8D TD TS AC 8C 9D
5H QD 2S AC 8C 9H KS 7C 4S 3C
KH AS 3H 8S 9C JS QS 4S AD 4D
AS 2S TD AD 4D 9H JC 4C 5H QS
5D 7C 4H TC 2D 6C JS 4S KC 3S
4C 2C 5D AC 9H 3D JD 8S QS QH
2C 8S 6H 3C QH 6D TC KD AC AH
QC 6C 3S QS 4S AC 8D 5C AD KH
5S 4C AC KH AS QC 2C 5C 8D 9C
8H JD 3C KH 8D 5C 9C QD QH 9D
7H TS 2C 8C 4S TD JC 9C 5H QH
JS 4S 2C 7C TH 6C AS KS 7S JD
JH 7C 9H 7H TC 5H 3D 6D 5D 4D
2C QD JH 2H 9D 5S 3D TD AD KS
JD QH 3S 4D TH 7D 6S QS KS 4H
TC KS 5S 8D 8H AD 2S 2D 4C JH
5S JH TC 3S 2D QS 9D 4C KD 9S
AC KH 3H AS 9D KC 9H QD 6C 6S
9H 7S 3D 5C 7D KC TD 8H 4H 6S
3C 7H 8H TC QD 4D 7S 6S QH 6C
6D AD 4C QD 6C 5D 7D 9D KS TS
JH 2H JD 9S 7S TS KH 8D 5D 8H
2D 9S 4C 7D 9D 5H QD 6D AC 6S
7S 6D JC QD JH 4C 6S QS 2H 7D
8C TD JH KD 2H 5C QS 2C JS 7S
TC 5H 4H JH QD 3S 5S 5D 8S KH
KS KH 7C 2C 5D JH 6S 9C 6D JC
5H AH JD 9C JS KC 2H 6H 4D 5S
AS 3C TH QC 6H 9C 8S 8C TD 7C
KC 2C QD 9C KH 4D 7S 3C TS 9H
9C QC 2S TS 8C TD 9S QD 3S 3C
4D 9D TH JH AH 6S 2S JD QH JS
QD 9H 6C KD 7D 7H 5D 6S 8H AH
8H 3C 4S 2H 5H QS QH 7S 4H AC
QS 3C 7S 9S 4H 3S AH KS 9D 7C
AD 5S 6S 2H 2D 5H TC 4S 3C 8C
QH TS 6S 4D JS KS JH AS 8S 6D
2C 8S 2S TD 5H AS TC TS 6C KC
KC TS 8H 2H 3H 7C 4C 5S TH TD
KD AD KH 7H 7S 5D 5H 5S 2D 9C
AD 9S 3D 7S 8C QC 7C 9C KD KS
3C QC 9S 8C 4D 5C AS QD 6C 2C
2H KC 8S JD 7S AC 8D 5C 2S 4D
9D QH 3D 2S TC 3S KS 3C 9H TD
KD 6S AC 2C 7H 5H 3S 6C 6H 8C
QH TC 8S 6S KH TH 4H 5D TS 4D
8C JS 4H 6H 2C 2H 7D AC QD 3D
QS KC 6S 2D 5S 4H TD 3H JH 4C
7S 5H 7H 8H KH 6H QS TH KD 7D
5H AD KD 7C KH 5S TD 6D 3C 6C
8C 9C 5H JD 7C KC KH 7H 2H 3S
7S 4H AD 4D 8S QS TH 3D 7H 5S
8D TC KS KD 9S 6D AD JD 5C 2S
7H 8H 6C QD 2H 6H 9D TC 9S 7C
8D 6D 4C 7C 6C 3C TH KH JS JH
5S 3S 8S JS 9H AS AD 8H 7S KD
JH 7C 2C KC 5H AS AD 9C 9S JS
AD AC 2C 6S QD 7C 3H TH KS KD
9D JD 4H 8H 4C KH 7S TS 8C KC
3S 5S 2H 7S 6H 7D KS 5C 6D AD
5S 8C 9H QS 7H 7S 2H 6C 7D TD
QS 5S TD AC 9D KC 3D TC 2D 4D
TD 2H 7D JD QD 4C 7H 5D KC 3D
4C 3H 8S KD QH 5S QC 9H TC 5H
9C QD TH 5H TS 5C 9H AH QH 2C
4D 6S 3C AC 6C 3D 2C 2H TD TH
AC 9C 5D QC 4D AD 8D 6D 8C KC
AD 3C 4H AC 8D 8H 7S 9S TD JC
4H 9H QH JS 2D TH TD TC KD KS
5S 6S 9S 8D TH AS KH 5H 5C 8S
JD 2S 9S 6S 5S 8S 5D 7S 7H 9D
5D 8C 4C 9D AD TS 2C 7D KD TC
8S QS 4D KC 5C 8D 4S KH JD KD
AS 5C AD QH 7D 2H 9S 7H 7C TC
2S 8S JD KH 7S 6C 6D AD 5D QC
9H 6H 3S 8C 8H AH TC 4H JS TD
2C TS 4D 7H 2D QC 9C 5D TH 7C
6C 8H QC 5D TS JH 5C 5H 9H 4S
2D QC 7H AS JS 8S 2H 4C 4H 8D
JS 6S AC KD 3D 3C 4S 7H TH KC
QH KH 6S QS 5S 4H 3C QD 3S 3H
7H AS KH 8C 4H 9C 5S 3D 6S TS
9C 7C 3H 5S QD 2C 3D AD AC 5H
JH TD 2D 4C TS 3H KH AD 3S 7S
AS 4C 5H 4D 6S KD JC 3C 6H 2D
3H 6S 8C 2D TH 4S AH QH AD 5H
7C 2S 9H 7H KC 5C 6D 5S 3H JC
3C TC 9C 4H QD TD JH 6D 9H 5S
7C 6S 5C 5D 6C 4S 7H 9H 6H AH
AD 2H 7D KC 2C 4C 2S 9S 7H 3S
TH 4C 8S 6S 3S AD KS AS JH TD
5C TD 4S 4D AD 6S 5D TC 9C 7D
8H 3S 4D 4S 5S 6H 5C AC 3H 3D
9H 3C AC 4S QS 8S 9D QH 5H 4D
JC 6C 5H TS AC 9C JD 8C 7C QD
8S 8H 9C JD 2D QC QH 6H 3C 8D
KS JS 2H 6H 5H QH QS 3H 7C 6D
TC 3H 4S 7H QC 2H 3S 8C JS KH
AH 8H 5S 4C 9H JD 3H 7S JC AC
3C 2D 4C 5S 6C 4S QS 3S JD 3D
5H 2D TC AH KS 6D 7H AD 8C 6H
6C 7S 3C JD 7C 8H KS KH AH 6D
AH 7D 3H 8H 8S 7H QS 5H 9D 2D
JD AC 4H 7S 8S 9S KS AS 9D QH
7S 2C 8S 5S JH QS JC AH KD 4C
AH 2S 9H 4H 8D TS TD 6H QH JD
4H JC 3H QS 6D 7S 9C 8S 9D 8D
5H TD 4S 9S 4C 8C 8D 7H 3H 3D
QS KH 3S 2C 2S 3C 7S TD 4S QD
7C TD 4D 5S KH AC AS 7H 4C 6C
2S 5H 6D JD 9H QS 8S 2C 2H TD
2S TS 6H 9H 7S 4H JC 4C 5D 5S
2C 5H 7D 4H 3S QH JC JS 6D 8H
4C QH 7C QD 3S AD TH 8S 5S TS
9H TC 2S TD JC 7D 3S 3D TH QH
7D 4C 8S 5C JH 8H 6S 3S KC 3H
JC 3H KH TC QH TH 6H 2C AC 5H
QS 2H 9D 2C AS 6S 6C 2S 8C 8S
9H 7D QC TH 4H KD QS AC 7S 3C
4D JH 6S 5S 8H KS 9S QC 3S AS
JD 2D 6S 7S TC 9H KC 3H 7D KD
2H KH 7C 4D 4S 3H JS QD 7D KC
4C JC AS 9D 3C JS 6C 8H QD 4D
AH JS 3S 6C 4C 3D JH 6D 9C 9H
9H 2D 8C 7H 5S KS 6H 9C 2S TC
6C 8C AD 7H 6H 3D KH AS 5D TH
KS 8C 3S TS 8S 4D 5S 9S 6C 4H
9H 4S 4H 5C 7D KC 2D 2H 9D JH
5C JS TC 9D 9H 5H 7S KH JC 6S
7C 9H 8H 4D JC KH JD 2H TD TC
8H 6C 2H 2C KH 6H 9D QS QH 5H
AC 7D 2S 3D QD JC 2D 8D JD JH
2H JC 2D 7H 2C 3C 8D KD TD 4H
3S 4H 6D 8D TS 3H TD 3D 6H TH
JH JC 3S AC QH 9H 7H 8S QC 2C
7H TD QS 4S 8S 9C 2S 5D 4D 2H
3D TS 3H 2S QC 8H 6H KC JC KS
5D JD 7D TC 8C 6C 9S 3D 8D AC
8H 6H JH 6C 5D 8D 8S 4H AD 2C
9D 4H 2D 2C 3S TS AS TC 3C 5D
4D TH 5H KS QS 6C 4S 2H 3D AD
5C KC 6H 2C 5S 3C 4D 2D 9H 9S
JD 4C 3H TH QH 9H 5S AH 8S AC
7D 9S 6S 2H TD 9C 4H 8H QS 4C
3C 6H 5D 4H 8C 9C KC 6S QD QS
3S 9H KD TC 2D JS 8C 6S 4H 4S
2S 4C 8S QS 6H KH 3H TH 8C 5D
2C KH 5S 3S 7S 7H 6C 9D QD 8D
8H KS AC 2D KH TS 6C JS KC 7H
9C KS 5C TD QC AH 6C 5H 9S 7C
5D 4D 3H 4H 6S 7C 7S AH QD TD
2H 7D QC 6S TC TS AH 7S 9D 3H
TH 5H QD 9S KS 7S 7C 6H 8C TD
TH 2D 4D QC 5C 7D JD AH 9C 4H
4H 3H AH 8D 6H QC QH 9H 2H 2C
2D AD 4C TS 6H 7S TH 4H QS TD
3C KD 2H 3H QS JD TC QC 5D 8H
KS JC QD TH 9S KD 8D 8C 2D 9C
3C QD KD 6D 4D 8D AH AD QC 8S
8H 3S 9D 2S 3H KS 6H 4C 7C KC
TH 9S 5C 3D 7D 6H AC 7S 4D 2C
5C 3D JD 4D 2D 6D 5H 9H 4C KH
AS 7H TD 6C 2H 3D QD KS 4C 4S
JC 3C AC 7C JD JS 8H 9S QC 5D
JD 6S 5S 2H AS 8C 7D 5H JH 3D
8D TC 5S 9S 8S 3H JC 5H 7S AS
5C TD 3D 7D 4H 8D 7H 4D 5D JS
QS 9C KS TD 2S 8S 5C 2H 4H AS
TH 7S 4H 7D 3H JD KD 5D 2S KC
JD 7H 4S 8H 4C JS 6H QH 5S 4H
2C QS 8C 5S 3H QC 2S 6C QD AD
8C 3D JD TC 4H 2H AD 5S AC 2S
5D 2C JS 2D AD 9D 3D 4C 4S JH
8D 5H 5D 6H 7S 4D KS 9D TD JD
3D 6D 9C 2S AS 7D 5S 5C 8H JD
7C 8S 3S 6S 5H JD TC AD 7H 7S
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| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem 43: https://projecteuler.net/problem=43
The number, 1406357289, is a 0 to 9 pandigital number because it is made up of
each of the digits 0 to 9 in some order, but it also has a rather interesting
sub-string divisibility property.
Let d1 be the 1st digit, d2 be the 2nd digit, and so on. In this way, we note
the following:
d2d3d4=406 is divisible by 2
d3d4d5=063 is divisible by 3
d4d5d6=635 is divisible by 5
d5d6d7=357 is divisible by 7
d6d7d8=572 is divisible by 11
d7d8d9=728 is divisible by 13
d8d9d10=289 is divisible by 17
Find the sum of all 0 to 9 pandigital numbers with this property.
"""
from itertools import permutations
def is_substring_divisible(num: tuple) -> bool:
"""
Returns True if the pandigital number passes
all the divisibility tests.
>>> is_substring_divisible((0, 1, 2, 4, 6, 5, 7, 3, 8, 9))
False
>>> is_substring_divisible((5, 1, 2, 4, 6, 0, 7, 8, 3, 9))
False
>>> is_substring_divisible((1, 4, 0, 6, 3, 5, 7, 2, 8, 9))
True
"""
tests = [2, 3, 5, 7, 11, 13, 17]
for i, test in enumerate(tests):
if (num[i + 1] * 100 + num[i + 2] * 10 + num[i + 3]) % test != 0:
return False
return True
def solution(n: int = 10) -> int:
"""
Returns the sum of all pandigital numbers which pass the
divisiility tests.
>>> solution(10)
16695334890
"""
list_nums = [
int("".join(map(str, num)))
for num in permutations(range(n))
if is_substring_divisible(num)
]
return sum(list_nums)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Problem 43: https://projecteuler.net/problem=43
The number, 1406357289, is a 0 to 9 pandigital number because it is made up of
each of the digits 0 to 9 in some order, but it also has a rather interesting
sub-string divisibility property.
Let d1 be the 1st digit, d2 be the 2nd digit, and so on. In this way, we note
the following:
d2d3d4=406 is divisible by 2
d3d4d5=063 is divisible by 3
d4d5d6=635 is divisible by 5
d5d6d7=357 is divisible by 7
d6d7d8=572 is divisible by 11
d7d8d9=728 is divisible by 13
d8d9d10=289 is divisible by 17
Find the sum of all 0 to 9 pandigital numbers with this property.
"""
from itertools import permutations
def is_substring_divisible(num: tuple) -> bool:
"""
Returns True if the pandigital number passes
all the divisibility tests.
>>> is_substring_divisible((0, 1, 2, 4, 6, 5, 7, 3, 8, 9))
False
>>> is_substring_divisible((5, 1, 2, 4, 6, 0, 7, 8, 3, 9))
False
>>> is_substring_divisible((1, 4, 0, 6, 3, 5, 7, 2, 8, 9))
True
"""
tests = [2, 3, 5, 7, 11, 13, 17]
for i, test in enumerate(tests):
if (num[i + 1] * 100 + num[i + 2] * 10 + num[i + 3]) % test != 0:
return False
return True
def solution(n: int = 10) -> int:
"""
Returns the sum of all pandigital numbers which pass the
divisiility tests.
>>> solution(10)
16695334890
"""
list_nums = [
int("".join(map(str, num)))
for num in permutations(range(n))
if is_substring_divisible(num)
]
return sum(list_nums)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Extended Euclidean Algorithm.
Finds 2 numbers a and b such that it satisfies
the equation am + bn = gcd(m, n) (a.k.a Bezout's Identity)
https://en.wikipedia.org/wiki/Extended_Euclidean_algorithm
"""
# @Author: S. Sharma <silentcat>
# @Date: 2019-02-25T12:08:53-06:00
# @Email: [email protected]
# @Last modified by: pikulet
# @Last modified time: 2020-10-02
from __future__ import annotations
import sys
def extended_euclidean_algorithm(a: int, b: int) -> tuple[int, int]:
"""
Extended Euclidean Algorithm.
Finds 2 numbers a and b such that it satisfies
the equation am + bn = gcd(m, n) (a.k.a Bezout's Identity)
>>> extended_euclidean_algorithm(1, 24)
(1, 0)
>>> extended_euclidean_algorithm(8, 14)
(2, -1)
>>> extended_euclidean_algorithm(240, 46)
(-9, 47)
>>> extended_euclidean_algorithm(1, -4)
(1, 0)
>>> extended_euclidean_algorithm(-2, -4)
(-1, 0)
>>> extended_euclidean_algorithm(0, -4)
(0, -1)
>>> extended_euclidean_algorithm(2, 0)
(1, 0)
"""
# base cases
if abs(a) == 1:
return a, 0
elif abs(b) == 1:
return 0, b
old_remainder, remainder = a, b
old_coeff_a, coeff_a = 1, 0
old_coeff_b, coeff_b = 0, 1
while remainder != 0:
quotient = old_remainder // remainder
old_remainder, remainder = remainder, old_remainder - quotient * remainder
old_coeff_a, coeff_a = coeff_a, old_coeff_a - quotient * coeff_a
old_coeff_b, coeff_b = coeff_b, old_coeff_b - quotient * coeff_b
# sign correction for negative numbers
if a < 0:
old_coeff_a = -old_coeff_a
if b < 0:
old_coeff_b = -old_coeff_b
return old_coeff_a, old_coeff_b
def main():
"""Call Extended Euclidean Algorithm."""
if len(sys.argv) < 3:
print("2 integer arguments required")
exit(1)
a = int(sys.argv[1])
b = int(sys.argv[2])
print(extended_euclidean_algorithm(a, b))
if __name__ == "__main__":
main()
| """
Extended Euclidean Algorithm.
Finds 2 numbers a and b such that it satisfies
the equation am + bn = gcd(m, n) (a.k.a Bezout's Identity)
https://en.wikipedia.org/wiki/Extended_Euclidean_algorithm
"""
# @Author: S. Sharma <silentcat>
# @Date: 2019-02-25T12:08:53-06:00
# @Email: [email protected]
# @Last modified by: pikulet
# @Last modified time: 2020-10-02
from __future__ import annotations
import sys
def extended_euclidean_algorithm(a: int, b: int) -> tuple[int, int]:
"""
Extended Euclidean Algorithm.
Finds 2 numbers a and b such that it satisfies
the equation am + bn = gcd(m, n) (a.k.a Bezout's Identity)
>>> extended_euclidean_algorithm(1, 24)
(1, 0)
>>> extended_euclidean_algorithm(8, 14)
(2, -1)
>>> extended_euclidean_algorithm(240, 46)
(-9, 47)
>>> extended_euclidean_algorithm(1, -4)
(1, 0)
>>> extended_euclidean_algorithm(-2, -4)
(-1, 0)
>>> extended_euclidean_algorithm(0, -4)
(0, -1)
>>> extended_euclidean_algorithm(2, 0)
(1, 0)
"""
# base cases
if abs(a) == 1:
return a, 0
elif abs(b) == 1:
return 0, b
old_remainder, remainder = a, b
old_coeff_a, coeff_a = 1, 0
old_coeff_b, coeff_b = 0, 1
while remainder != 0:
quotient = old_remainder // remainder
old_remainder, remainder = remainder, old_remainder - quotient * remainder
old_coeff_a, coeff_a = coeff_a, old_coeff_a - quotient * coeff_a
old_coeff_b, coeff_b = coeff_b, old_coeff_b - quotient * coeff_b
# sign correction for negative numbers
if a < 0:
old_coeff_a = -old_coeff_a
if b < 0:
old_coeff_b = -old_coeff_b
return old_coeff_a, old_coeff_b
def main():
"""Call Extended Euclidean Algorithm."""
if len(sys.argv) < 3:
print("2 integer arguments required")
exit(1)
a = int(sys.argv[1])
b = int(sys.argv[2])
print(extended_euclidean_algorithm(a, b))
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Graph Coloring also called "m coloring problem"
consists of coloring a given graph with at most m colors
such that no adjacent vertices are assigned the same color
Wikipedia: https://en.wikipedia.org/wiki/Graph_coloring
"""
def valid_coloring(
neighbours: list[int], colored_vertices: list[int], color: int
) -> bool:
"""
For each neighbour check if the coloring constraint is satisfied
If any of the neighbours fail the constraint return False
If all neighbours validate the constraint return True
>>> neighbours = [0,1,0,1,0]
>>> colored_vertices = [0, 2, 1, 2, 0]
>>> color = 1
>>> valid_coloring(neighbours, colored_vertices, color)
True
>>> color = 2
>>> valid_coloring(neighbours, colored_vertices, color)
False
"""
# Does any neighbour not satisfy the constraints
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(neighbours)
)
def util_color(
graph: list[list[int]], max_colors: int, colored_vertices: list[int], index: int
) -> bool:
"""
Pseudo-Code
Base Case:
1. Check if coloring is complete
1.1 If complete return True (meaning that we successfully colored the graph)
Recursive Step:
2. Iterates over each color:
Check if the current coloring is valid:
2.1. Color given vertex
2.2. Do recursive call, check if this coloring leads to a solution
2.4. if current coloring leads to a solution return
2.5. Uncolor given vertex
>>> graph = [[0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1],
... [0, 1, 0, 1, 0],
... [0, 1, 1, 0, 0],
... [0, 1, 0, 0, 0]]
>>> max_colors = 3
>>> colored_vertices = [0, 1, 0, 0, 0]
>>> index = 3
>>> util_color(graph, max_colors, colored_vertices, index)
True
>>> max_colors = 2
>>> util_color(graph, max_colors, colored_vertices, index)
False
"""
# Base Case
if index == len(graph):
return True
# Recursive Step
for i in range(max_colors):
if valid_coloring(graph[index], colored_vertices, i):
# Color current vertex
colored_vertices[index] = i
# Validate coloring
if util_color(graph, max_colors, colored_vertices, index + 1):
return True
# Backtrack
colored_vertices[index] = -1
return False
def color(graph: list[list[int]], max_colors: int) -> list[int]:
"""
Wrapper function to call subroutine called util_color
which will either return True or False.
If True is returned colored_vertices list is filled with correct colorings
>>> graph = [[0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1],
... [0, 1, 0, 1, 0],
... [0, 1, 1, 0, 0],
... [0, 1, 0, 0, 0]]
>>> max_colors = 3
>>> color(graph, max_colors)
[0, 1, 0, 2, 0]
>>> max_colors = 2
>>> color(graph, max_colors)
[]
"""
colored_vertices = [-1] * len(graph)
if util_color(graph, max_colors, colored_vertices, 0):
return colored_vertices
return []
| """
Graph Coloring also called "m coloring problem"
consists of coloring a given graph with at most m colors
such that no adjacent vertices are assigned the same color
Wikipedia: https://en.wikipedia.org/wiki/Graph_coloring
"""
def valid_coloring(
neighbours: list[int], colored_vertices: list[int], color: int
) -> bool:
"""
For each neighbour check if the coloring constraint is satisfied
If any of the neighbours fail the constraint return False
If all neighbours validate the constraint return True
>>> neighbours = [0,1,0,1,0]
>>> colored_vertices = [0, 2, 1, 2, 0]
>>> color = 1
>>> valid_coloring(neighbours, colored_vertices, color)
True
>>> color = 2
>>> valid_coloring(neighbours, colored_vertices, color)
False
"""
# Does any neighbour not satisfy the constraints
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(neighbours)
)
def util_color(
graph: list[list[int]], max_colors: int, colored_vertices: list[int], index: int
) -> bool:
"""
Pseudo-Code
Base Case:
1. Check if coloring is complete
1.1 If complete return True (meaning that we successfully colored the graph)
Recursive Step:
2. Iterates over each color:
Check if the current coloring is valid:
2.1. Color given vertex
2.2. Do recursive call, check if this coloring leads to a solution
2.4. if current coloring leads to a solution return
2.5. Uncolor given vertex
>>> graph = [[0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1],
... [0, 1, 0, 1, 0],
... [0, 1, 1, 0, 0],
... [0, 1, 0, 0, 0]]
>>> max_colors = 3
>>> colored_vertices = [0, 1, 0, 0, 0]
>>> index = 3
>>> util_color(graph, max_colors, colored_vertices, index)
True
>>> max_colors = 2
>>> util_color(graph, max_colors, colored_vertices, index)
False
"""
# Base Case
if index == len(graph):
return True
# Recursive Step
for i in range(max_colors):
if valid_coloring(graph[index], colored_vertices, i):
# Color current vertex
colored_vertices[index] = i
# Validate coloring
if util_color(graph, max_colors, colored_vertices, index + 1):
return True
# Backtrack
colored_vertices[index] = -1
return False
def color(graph: list[list[int]], max_colors: int) -> list[int]:
"""
Wrapper function to call subroutine called util_color
which will either return True or False.
If True is returned colored_vertices list is filled with correct colorings
>>> graph = [[0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1],
... [0, 1, 0, 1, 0],
... [0, 1, 1, 0, 0],
... [0, 1, 0, 0, 0]]
>>> max_colors = 3
>>> color(graph, max_colors)
[0, 1, 0, 2, 0]
>>> max_colors = 2
>>> color(graph, max_colors)
[]
"""
colored_vertices = [-1] * len(graph)
if util_color(graph, max_colors, colored_vertices, 0):
return colored_vertices
return []
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
What is the greatest product of four adjacent numbers (horizontally,
vertically, or diagonally) in this 20x20 array?
08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08
49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00
81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65
52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91
22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80
24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50
32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70
67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21
24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72
21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95
78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92
16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57
86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58
19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40
04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66
88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69
04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36
20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16
20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54
01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48
"""
import os
def solution():
"""Returns the greatest product of four adjacent numbers (horizontally,
vertically, or diagonally).
>>> solution()
70600674
"""
with open(os.path.dirname(__file__) + "/grid.txt") as f:
l = [] # noqa: E741
for i in range(20):
l.append([int(x) for x in f.readline().split()])
maximum = 0
# right
for i in range(20):
for j in range(17):
temp = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
if temp > maximum:
maximum = temp
# down
for i in range(17):
for j in range(20):
temp = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
if temp > maximum:
maximum = temp
# diagonal 1
for i in range(17):
for j in range(17):
temp = l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
if temp > maximum:
maximum = temp
# diagonal 2
for i in range(17):
for j in range(3, 20):
temp = l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
if temp > maximum:
maximum = temp
return maximum
if __name__ == "__main__":
print(solution())
| """
What is the greatest product of four adjacent numbers (horizontally,
vertically, or diagonally) in this 20x20 array?
08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08
49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00
81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65
52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91
22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80
24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50
32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70
67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21
24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72
21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95
78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92
16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57
86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58
19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40
04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66
88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69
04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36
20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16
20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54
01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48
"""
import os
def solution():
"""Returns the greatest product of four adjacent numbers (horizontally,
vertically, or diagonally).
>>> solution()
70600674
"""
with open(os.path.dirname(__file__) + "/grid.txt") as f:
l = [] # noqa: E741
for i in range(20):
l.append([int(x) for x in f.readline().split()])
maximum = 0
# right
for i in range(20):
for j in range(17):
temp = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
if temp > maximum:
maximum = temp
# down
for i in range(17):
for j in range(20):
temp = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
if temp > maximum:
maximum = temp
# diagonal 1
for i in range(17):
for j in range(17):
temp = l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
if temp > maximum:
maximum = temp
# diagonal 2
for i in range(17):
for j in range(3, 20):
temp = l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
if temp > maximum:
maximum = temp
return maximum
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def main() -> None:
message = input("Enter message: ")
key = input("Enter key [alphanumeric]: ")
mode = input("Encrypt/Decrypt [e/d]: ")
if mode.lower().startswith("e"):
mode = "encrypt"
translated = encryptMessage(key, message)
elif mode.lower().startswith("d"):
mode = "decrypt"
translated = decryptMessage(key, message)
print("\n%sed message:" % mode.title())
print(translated)
def encryptMessage(key: str, message: str) -> str:
"""
>>> encryptMessage('HDarji', 'This is Harshil Darji from Dharmaj.')
'Akij ra Odrjqqs Gaisq muod Mphumrs.'
"""
return translateMessage(key, message, "encrypt")
def decryptMessage(key: str, message: str) -> str:
"""
>>> decryptMessage('HDarji', 'Akij ra Odrjqqs Gaisq muod Mphumrs.')
'This is Harshil Darji from Dharmaj.'
"""
return translateMessage(key, message, "decrypt")
def translateMessage(key: str, message: str, mode: str) -> str:
translated = []
keyIndex = 0
key = key.upper()
for symbol in message:
num = LETTERS.find(symbol.upper())
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[keyIndex])
elif mode == "decrypt":
num -= LETTERS.find(key[keyIndex])
num %= len(LETTERS)
if symbol.isupper():
translated.append(LETTERS[num])
elif symbol.islower():
translated.append(LETTERS[num].lower())
keyIndex += 1
if keyIndex == len(key):
keyIndex = 0
else:
translated.append(symbol)
return "".join(translated)
if __name__ == "__main__":
main()
| LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def main() -> None:
message = input("Enter message: ")
key = input("Enter key [alphanumeric]: ")
mode = input("Encrypt/Decrypt [e/d]: ")
if mode.lower().startswith("e"):
mode = "encrypt"
translated = encryptMessage(key, message)
elif mode.lower().startswith("d"):
mode = "decrypt"
translated = decryptMessage(key, message)
print("\n%sed message:" % mode.title())
print(translated)
def encryptMessage(key: str, message: str) -> str:
"""
>>> encryptMessage('HDarji', 'This is Harshil Darji from Dharmaj.')
'Akij ra Odrjqqs Gaisq muod Mphumrs.'
"""
return translateMessage(key, message, "encrypt")
def decryptMessage(key: str, message: str) -> str:
"""
>>> decryptMessage('HDarji', 'Akij ra Odrjqqs Gaisq muod Mphumrs.')
'This is Harshil Darji from Dharmaj.'
"""
return translateMessage(key, message, "decrypt")
def translateMessage(key: str, message: str, mode: str) -> str:
translated = []
keyIndex = 0
key = key.upper()
for symbol in message:
num = LETTERS.find(symbol.upper())
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[keyIndex])
elif mode == "decrypt":
num -= LETTERS.find(key[keyIndex])
num %= len(LETTERS)
if symbol.isupper():
translated.append(LETTERS[num])
elif symbol.islower():
translated.append(LETTERS[num].lower())
keyIndex += 1
if keyIndex == len(key):
keyIndex = 0
else:
translated.append(symbol)
return "".join(translated)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Eulerian Path is a path in graph that visits every edge exactly once.
# Eulerian Circuit is an Eulerian Path which starts and ends on the same
# vertex.
# time complexity is O(V+E)
# space complexity is O(VE)
# using dfs for finding eulerian path traversal
def dfs(u, graph, visited_edge, path=None):
path = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
visited_edge[u][v], visited_edge[v][u] = True, True
path = dfs(v, graph, visited_edge, path)
return path
# for checking in graph has euler path or circuit
def check_circuit_or_path(graph, max_node):
odd_degree_nodes = 0
odd_node = -1
for i in range(max_node):
if i not in graph.keys():
continue
if len(graph[i]) % 2 == 1:
odd_degree_nodes += 1
odd_node = i
if odd_degree_nodes == 0:
return 1, odd_node
if odd_degree_nodes == 2:
return 2, odd_node
return 3, odd_node
def check_euler(graph, max_node):
visited_edge = [[False for _ in range(max_node + 1)] for _ in range(max_node + 1)]
check, odd_node = check_circuit_or_path(graph, max_node)
if check == 3:
print("graph is not Eulerian")
print("no path")
return
start_node = 1
if check == 2:
start_node = odd_node
print("graph has a Euler path")
if check == 1:
print("graph has a Euler cycle")
path = dfs(start_node, graph, visited_edge)
print(path)
def main():
G1 = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
G2 = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
G3 = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
G4 = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
G5 = {
1: [],
2: []
# all degree is zero
}
max_node = 10
check_euler(G1, max_node)
check_euler(G2, max_node)
check_euler(G3, max_node)
check_euler(G4, max_node)
check_euler(G5, max_node)
if __name__ == "__main__":
main()
| # Eulerian Path is a path in graph that visits every edge exactly once.
# Eulerian Circuit is an Eulerian Path which starts and ends on the same
# vertex.
# time complexity is O(V+E)
# space complexity is O(VE)
# using dfs for finding eulerian path traversal
def dfs(u, graph, visited_edge, path=None):
path = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
visited_edge[u][v], visited_edge[v][u] = True, True
path = dfs(v, graph, visited_edge, path)
return path
# for checking in graph has euler path or circuit
def check_circuit_or_path(graph, max_node):
odd_degree_nodes = 0
odd_node = -1
for i in range(max_node):
if i not in graph.keys():
continue
if len(graph[i]) % 2 == 1:
odd_degree_nodes += 1
odd_node = i
if odd_degree_nodes == 0:
return 1, odd_node
if odd_degree_nodes == 2:
return 2, odd_node
return 3, odd_node
def check_euler(graph, max_node):
visited_edge = [[False for _ in range(max_node + 1)] for _ in range(max_node + 1)]
check, odd_node = check_circuit_or_path(graph, max_node)
if check == 3:
print("graph is not Eulerian")
print("no path")
return
start_node = 1
if check == 2:
start_node = odd_node
print("graph has a Euler path")
if check == 1:
print("graph has a Euler cycle")
path = dfs(start_node, graph, visited_edge)
print(path)
def main():
G1 = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
G2 = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
G3 = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
G4 = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
G5 = {
1: [],
2: []
# all degree is zero
}
max_node = 10
check_euler(G1, max_node)
check_euler(G2, max_node)
check_euler(G3, max_node)
check_euler(G4, max_node)
check_euler(G5, max_node)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
def stable_matching(
donor_pref: list[list[int]], recipient_pref: list[list[int]]
) -> list[int]:
"""
Finds the stable match in any bipartite graph, i.e a pairing where no 2 objects
prefer each other over their partner. The function accepts the preferences of
oegan donors and recipients (where both are assigned numbers from 0 to n-1) and
returns a list where the index position corresponds to the donor and value at the
index is the organ recipient.
To better understand the algorithm, see also:
https://github.com/akashvshroff/Gale_Shapley_Stable_Matching (README).
https://www.youtube.com/watch?v=Qcv1IqHWAzg&t=13s (Numberphile YouTube).
>>> donor_pref = [[0, 1, 3, 2], [0, 2, 3, 1], [1, 0, 2, 3], [0, 3, 1, 2]]
>>> recipient_pref = [[3, 1, 2, 0], [3, 1, 0, 2], [0, 3, 1, 2], [1, 0, 3, 2]]
>>> print(stable_matching(donor_pref, recipient_pref))
[1, 2, 3, 0]
"""
assert len(donor_pref) == len(recipient_pref)
n = len(donor_pref)
unmatched_donors = list(range(n))
donor_record = [-1] * n # who the donor has donated to
rec_record = [-1] * n # who the recipient has received from
num_donations = [0] * n
while unmatched_donors:
donor = unmatched_donors[0]
donor_preference = donor_pref[donor]
recipient = donor_preference[num_donations[donor]]
num_donations[donor] += 1
rec_preference = recipient_pref[recipient]
prev_donor = rec_record[recipient]
if prev_donor != -1:
if rec_preference.index(prev_donor) > rec_preference.index(donor):
rec_record[recipient] = donor
donor_record[donor] = recipient
unmatched_donors.append(prev_donor)
unmatched_donors.remove(donor)
else:
rec_record[recipient] = donor
donor_record[donor] = recipient
unmatched_donors.remove(donor)
return donor_record
| from __future__ import annotations
def stable_matching(
donor_pref: list[list[int]], recipient_pref: list[list[int]]
) -> list[int]:
"""
Finds the stable match in any bipartite graph, i.e a pairing where no 2 objects
prefer each other over their partner. The function accepts the preferences of
oegan donors and recipients (where both are assigned numbers from 0 to n-1) and
returns a list where the index position corresponds to the donor and value at the
index is the organ recipient.
To better understand the algorithm, see also:
https://github.com/akashvshroff/Gale_Shapley_Stable_Matching (README).
https://www.youtube.com/watch?v=Qcv1IqHWAzg&t=13s (Numberphile YouTube).
>>> donor_pref = [[0, 1, 3, 2], [0, 2, 3, 1], [1, 0, 2, 3], [0, 3, 1, 2]]
>>> recipient_pref = [[3, 1, 2, 0], [3, 1, 0, 2], [0, 3, 1, 2], [1, 0, 3, 2]]
>>> print(stable_matching(donor_pref, recipient_pref))
[1, 2, 3, 0]
"""
assert len(donor_pref) == len(recipient_pref)
n = len(donor_pref)
unmatched_donors = list(range(n))
donor_record = [-1] * n # who the donor has donated to
rec_record = [-1] * n # who the recipient has received from
num_donations = [0] * n
while unmatched_donors:
donor = unmatched_donors[0]
donor_preference = donor_pref[donor]
recipient = donor_preference[num_donations[donor]]
num_donations[donor] += 1
rec_preference = recipient_pref[recipient]
prev_donor = rec_record[recipient]
if prev_donor != -1:
if rec_preference.index(prev_donor) > rec_preference.index(donor):
rec_record[recipient] = donor
donor_record[donor] = recipient
unmatched_donors.append(prev_donor)
unmatched_donors.remove(donor)
else:
rec_record[recipient] = donor
donor_record[donor] = recipient
unmatched_donors.remove(donor)
return donor_record
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import math
def perfect_square(num: int) -> bool:
"""
Check if a number is perfect square number or not
:param num: the number to be checked
:return: True if number is square number, otherwise False
>>> perfect_square(9)
True
>>> perfect_square(16)
True
>>> perfect_square(1)
True
>>> perfect_square(0)
True
>>> perfect_square(10)
False
"""
return math.sqrt(num) * math.sqrt(num) == num
def perfect_square_binary_search(n: int) -> bool:
"""
Check if a number is perfect square using binary search.
Time complexity : O(Log(n))
Space complexity: O(1)
>>> perfect_square_binary_search(9)
True
>>> perfect_square_binary_search(16)
True
>>> perfect_square_binary_search(1)
True
>>> perfect_square_binary_search(0)
True
>>> perfect_square_binary_search(10)
False
>>> perfect_square_binary_search(-1)
False
>>> perfect_square_binary_search(1.1)
False
>>> perfect_square_binary_search("a")
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'str'
>>> perfect_square_binary_search(None)
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'NoneType'
>>> perfect_square_binary_search([])
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'list'
"""
left = 0
right = n
while left <= right:
mid = (left + right) // 2
if mid ** 2 == n:
return True
elif mid ** 2 > n:
right = mid - 1
else:
left = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| import math
def perfect_square(num: int) -> bool:
"""
Check if a number is perfect square number or not
:param num: the number to be checked
:return: True if number is square number, otherwise False
>>> perfect_square(9)
True
>>> perfect_square(16)
True
>>> perfect_square(1)
True
>>> perfect_square(0)
True
>>> perfect_square(10)
False
"""
return math.sqrt(num) * math.sqrt(num) == num
def perfect_square_binary_search(n: int) -> bool:
"""
Check if a number is perfect square using binary search.
Time complexity : O(Log(n))
Space complexity: O(1)
>>> perfect_square_binary_search(9)
True
>>> perfect_square_binary_search(16)
True
>>> perfect_square_binary_search(1)
True
>>> perfect_square_binary_search(0)
True
>>> perfect_square_binary_search(10)
False
>>> perfect_square_binary_search(-1)
False
>>> perfect_square_binary_search(1.1)
False
>>> perfect_square_binary_search("a")
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'str'
>>> perfect_square_binary_search(None)
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'NoneType'
>>> perfect_square_binary_search([])
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'list'
"""
left = 0
right = n
while left <= right:
mid = (left + right) // 2
if mid ** 2 == n:
return True
elif mid ** 2 > n:
right = mid - 1
else:
left = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
ARITHMETIC MEAN : https://en.wikipedia.org/wiki/Arithmetic_mean
"""
def is_arithmetic_series(series: list) -> bool:
"""
checking whether the input series is arithmetic series or not
>>> is_arithmetic_series([2, 4, 6])
True
>>> is_arithmetic_series([3, 6, 12, 24])
False
>>> is_arithmetic_series([1, 2, 3])
True
"""
if len(series) == 1:
return True
common_diff = series[1] - series[0]
for index in range(len(series) - 1):
if series[index + 1] - series[index] != common_diff:
return False
return True
def arithmetic_mean(series: list) -> float:
"""
return the arithmetic mean of series
>>> arithmetic_mean([2, 4, 6])
4.0
>>> arithmetic_mean([3, 6, 9, 12])
7.5
>>> arithmetic_mean(4)
Traceback (most recent call last):
...
ValueError: Input series is not valid, valid series - [2, 4, 6]
>>> arithmetic_mean([4, 8, 1])
Traceback (most recent call last):
...
ValueError: Input list is not an arithmetic series
>>> arithmetic_mean([1, 2, 3])
2.0
>>> arithmetic_mean([])
Traceback (most recent call last):
...
ValueError: Input list must be a non empty list
"""
if not isinstance(series, list):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]")
if len(series) == 0:
raise ValueError("Input list must be a non empty list")
if not is_arithmetic_series(series):
raise ValueError("Input list is not an arithmetic series")
answer = 0
for val in series:
answer += val
return answer / len(series)
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
ARITHMETIC MEAN : https://en.wikipedia.org/wiki/Arithmetic_mean
"""
def is_arithmetic_series(series: list) -> bool:
"""
checking whether the input series is arithmetic series or not
>>> is_arithmetic_series([2, 4, 6])
True
>>> is_arithmetic_series([3, 6, 12, 24])
False
>>> is_arithmetic_series([1, 2, 3])
True
"""
if len(series) == 1:
return True
common_diff = series[1] - series[0]
for index in range(len(series) - 1):
if series[index + 1] - series[index] != common_diff:
return False
return True
def arithmetic_mean(series: list) -> float:
"""
return the arithmetic mean of series
>>> arithmetic_mean([2, 4, 6])
4.0
>>> arithmetic_mean([3, 6, 9, 12])
7.5
>>> arithmetic_mean(4)
Traceback (most recent call last):
...
ValueError: Input series is not valid, valid series - [2, 4, 6]
>>> arithmetic_mean([4, 8, 1])
Traceback (most recent call last):
...
ValueError: Input list is not an arithmetic series
>>> arithmetic_mean([1, 2, 3])
2.0
>>> arithmetic_mean([])
Traceback (most recent call last):
...
ValueError: Input list must be a non empty list
"""
if not isinstance(series, list):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]")
if len(series) == 0:
raise ValueError("Input list must be a non empty list")
if not is_arithmetic_series(series):
raise ValueError("Input list is not an arithmetic series")
answer = 0
for val in series:
answer += val
return answer / len(series)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # https://en.wikipedia.org/wiki/B%C3%A9zier_curve
# https://www.tutorialspoint.com/computer_graphics/computer_graphics_curves.htm
from __future__ import annotations
from scipy.special import comb # type: ignore
class BezierCurve:
"""
Bezier curve is a weighted sum of a set of control points.
Generate Bezier curves from a given set of control points.
This implementation works only for 2d coordinates in the xy plane.
"""
def __init__(self, list_of_points: list[tuple[float, float]]):
"""
list_of_points: Control points in the xy plane on which to interpolate. These
points control the behavior (shape) of the Bezier curve.
"""
self.list_of_points = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
self.degree = len(list_of_points) - 1
def basis_function(self, t: float) -> list[float]:
"""
The basis function determines the weight of each control point at time t.
t: time value between 0 and 1 inclusive at which to evaluate the basis of
the curve.
returns the x, y values of basis function at time t
>>> curve = BezierCurve([(1,1), (1,2)])
>>> curve.basis_function(0)
[1.0, 0.0]
>>> curve.basis_function(1)
[0.0, 1.0]
"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
output_values: list[float] = []
for i in range(len(self.list_of_points)):
# basis function for each i
output_values.append(
comb(self.degree, i) * ((1 - t) ** (self.degree - i)) * (t ** i)
)
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(output_values), 5) == 1
return output_values
def bezier_curve_function(self, t: float) -> tuple[float, float]:
"""
The function to produce the values of the Bezier curve at time t.
t: the value of time t at which to evaluate the Bezier function
Returns the x, y coordinates of the Bezier curve at time t.
The first point in the curve is when t = 0.
The last point in the curve is when t = 1.
>>> curve = BezierCurve([(1,1), (1,2)])
>>> curve.bezier_curve_function(0)
(1.0, 1.0)
>>> curve.bezier_curve_function(1)
(1.0, 2.0)
"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
basis_function = self.basis_function(t)
x = 0.0
y = 0.0
for i in range(len(self.list_of_points)):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def plot_curve(self, step_size: float = 0.01):
"""
Plots the Bezier curve using matplotlib plotting capabilities.
step_size: defines the step(s) at which to evaluate the Bezier curve.
The smaller the step size, the finer the curve produced.
"""
from matplotlib import pyplot as plt # type: ignore
to_plot_x: list[float] = [] # x coordinates of points to plot
to_plot_y: list[float] = [] # y coordinates of points to plot
t = 0.0
while t <= 1:
value = self.bezier_curve_function(t)
to_plot_x.append(value[0])
to_plot_y.append(value[1])
t += step_size
x = [i[0] for i in self.list_of_points]
y = [i[1] for i in self.list_of_points]
plt.plot(
to_plot_x,
to_plot_y,
color="blue",
label="Curve of Degree " + str(self.degree),
)
plt.scatter(x, y, color="red", label="Control Points")
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| # https://en.wikipedia.org/wiki/B%C3%A9zier_curve
# https://www.tutorialspoint.com/computer_graphics/computer_graphics_curves.htm
from __future__ import annotations
from scipy.special import comb # type: ignore
class BezierCurve:
"""
Bezier curve is a weighted sum of a set of control points.
Generate Bezier curves from a given set of control points.
This implementation works only for 2d coordinates in the xy plane.
"""
def __init__(self, list_of_points: list[tuple[float, float]]):
"""
list_of_points: Control points in the xy plane on which to interpolate. These
points control the behavior (shape) of the Bezier curve.
"""
self.list_of_points = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
self.degree = len(list_of_points) - 1
def basis_function(self, t: float) -> list[float]:
"""
The basis function determines the weight of each control point at time t.
t: time value between 0 and 1 inclusive at which to evaluate the basis of
the curve.
returns the x, y values of basis function at time t
>>> curve = BezierCurve([(1,1), (1,2)])
>>> curve.basis_function(0)
[1.0, 0.0]
>>> curve.basis_function(1)
[0.0, 1.0]
"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
output_values: list[float] = []
for i in range(len(self.list_of_points)):
# basis function for each i
output_values.append(
comb(self.degree, i) * ((1 - t) ** (self.degree - i)) * (t ** i)
)
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(output_values), 5) == 1
return output_values
def bezier_curve_function(self, t: float) -> tuple[float, float]:
"""
The function to produce the values of the Bezier curve at time t.
t: the value of time t at which to evaluate the Bezier function
Returns the x, y coordinates of the Bezier curve at time t.
The first point in the curve is when t = 0.
The last point in the curve is when t = 1.
>>> curve = BezierCurve([(1,1), (1,2)])
>>> curve.bezier_curve_function(0)
(1.0, 1.0)
>>> curve.bezier_curve_function(1)
(1.0, 2.0)
"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
basis_function = self.basis_function(t)
x = 0.0
y = 0.0
for i in range(len(self.list_of_points)):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def plot_curve(self, step_size: float = 0.01):
"""
Plots the Bezier curve using matplotlib plotting capabilities.
step_size: defines the step(s) at which to evaluate the Bezier curve.
The smaller the step size, the finer the curve produced.
"""
from matplotlib import pyplot as plt # type: ignore
to_plot_x: list[float] = [] # x coordinates of points to plot
to_plot_y: list[float] = [] # y coordinates of points to plot
t = 0.0
while t <= 1:
value = self.bezier_curve_function(t)
to_plot_x.append(value[0])
to_plot_y.append(value[1])
t += step_size
x = [i[0] for i in self.list_of_points]
y = [i[1] for i in self.list_of_points]
plt.plot(
to_plot_x,
to_plot_y,
color="blue",
label="Curve of Degree " + str(self.degree),
)
plt.scatter(x, y, color="red", label="Control Points")
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| B64_CHARSET = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def base64_encode(data: bytes) -> bytes:
"""Encodes data according to RFC4648.
The data is first transformed to binary and appended with binary digits so that its
length becomes a multiple of 6, then each 6 binary digits will match a character in
the B64_CHARSET string. The number of appended binary digits would later determine
how many "=" sign 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 "=" sign 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 | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Prim's (also known as Jarník's) algorithm is a greedy algorithm that finds a minimum
spanning tree for a weighted undirected graph. This means it finds a subset of the
edges that forms a tree that includes every vertex, where the total weight of all the
edges in the tree is minimized. The algorithm operates by building this tree one vertex
at a time, from an arbitrary starting vertex, at each step adding the cheapest possible
connection from the tree to another vertex.
"""
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
T = TypeVar("T")
def get_parent_position(position: int) -> int:
"""
heap helper function get the position of the parent of the current node
>>> get_parent_position(1)
0
>>> get_parent_position(2)
0
"""
return (position - 1) // 2
def get_child_left_position(position: int) -> int:
"""
heap helper function get the position of the left child of the current node
>>> get_child_left_position(0)
1
"""
return (2 * position) + 1
def get_child_right_position(position: int) -> int:
"""
heap helper function get the position of the right child of the current node
>>> get_child_right_position(0)
2
"""
return (2 * position) + 2
class MinPriorityQueue(Generic[T]):
"""
Minimum Priority Queue Class
Functions:
is_empty: function to check if the priority queue is empty
push: function to add an element with given priority to the queue
extract_min: function to remove and return the element with lowest weight (highest
priority)
update_key: function to update the weight of the given key
_bubble_up: helper function to place a node at the proper position (upward
movement)
_bubble_down: helper function to place a node at the proper position (downward
movement)
_swap_nodes: helper function to swap the nodes at the given positions
>>> queue = MinPriorityQueue()
>>> queue.push(1, 1000)
>>> queue.push(2, 100)
>>> queue.push(3, 4000)
>>> queue.push(4, 3000)
>>> print(queue.extract_min())
2
>>> queue.update_key(4, 50)
>>> print(queue.extract_min())
4
>>> print(queue.extract_min())
1
>>> print(queue.extract_min())
3
"""
def __init__(self) -> None:
self.heap: list[tuple[T, int]] = []
self.position_map: dict[T, int] = {}
self.elements: int = 0
def __len__(self) -> int:
return self.elements
def __repr__(self) -> str:
return str(self.heap)
def is_empty(self) -> bool:
# Check if the priority queue is empty
return self.elements == 0
def push(self, elem: T, weight: int) -> None:
# Add an element with given priority to the queue
self.heap.append((elem, weight))
self.position_map[elem] = self.elements
self.elements += 1
self._bubble_up(elem)
def extract_min(self) -> T:
# Remove and return the element with lowest weight (highest priority)
if self.elements > 1:
self._swap_nodes(0, self.elements - 1)
elem, _ = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
bubble_down_elem, _ = self.heap[0]
self._bubble_down(bubble_down_elem)
return elem
def update_key(self, elem: T, weight: int) -> None:
# Update the weight of the given key
position = self.position_map[elem]
self.heap[position] = (elem, weight)
if position > 0:
parent_position = get_parent_position(position)
_, parent_weight = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(elem)
else:
self._bubble_down(elem)
else:
self._bubble_down(elem)
def _bubble_up(self, elem: T) -> None:
# Place a node at the proper position (upward movement) [to be used internally
# only]
curr_pos = self.position_map[elem]
if curr_pos == 0:
return
parent_position = get_parent_position(curr_pos)
_, weight = self.heap[curr_pos]
_, parent_weight = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(parent_position, curr_pos)
return self._bubble_up(elem)
return
def _bubble_down(self, elem: T) -> None:
# Place a node at the proper position (downward movement) [to be used
# internally only]
curr_pos = self.position_map[elem]
_, weight = self.heap[curr_pos]
child_left_position = get_child_left_position(curr_pos)
child_right_position = get_child_right_position(curr_pos)
if child_left_position < self.elements and child_right_position < self.elements:
_, child_left_weight = self.heap[child_left_position]
_, child_right_weight = self.heap[child_right_position]
if child_right_weight < child_left_weight:
if child_right_weight < weight:
self._swap_nodes(child_right_position, curr_pos)
return self._bubble_down(elem)
if child_left_position < self.elements:
_, child_left_weight = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(child_left_position, curr_pos)
return self._bubble_down(elem)
else:
return
if child_right_position < self.elements:
_, child_right_weight = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(child_right_position, curr_pos)
return self._bubble_down(elem)
else:
return
def _swap_nodes(self, node1_pos: int, node2_pos: int) -> None:
# Swap the nodes at the given positions
node1_elem = self.heap[node1_pos][0]
node2_elem = self.heap[node2_pos][0]
self.heap[node1_pos], self.heap[node2_pos] = (
self.heap[node2_pos],
self.heap[node1_pos],
)
self.position_map[node1_elem] = node2_pos
self.position_map[node2_elem] = node1_pos
class GraphUndirectedWeighted(Generic[T]):
"""
Graph Undirected Weighted Class
Functions:
add_node: function to add a node in the graph
add_edge: function to add an edge between 2 nodes in the graph
"""
def __init__(self) -> None:
self.connections: dict[T, dict[T, int]] = {}
self.nodes: int = 0
def __repr__(self) -> str:
return str(self.connections)
def __len__(self) -> int:
return self.nodes
def add_node(self, node: T) -> None:
# Add a node in the graph if it is not in the graph
if node not in self.connections:
self.connections[node] = {}
self.nodes += 1
def add_edge(self, node1: T, node2: T, weight: int) -> None:
# Add an edge between 2 nodes in the graph
self.add_node(node1)
self.add_node(node2)
self.connections[node1][node2] = weight
self.connections[node2][node1] = weight
def prims_algo(
graph: GraphUndirectedWeighted[T],
) -> tuple[dict[T, int], dict[T, T | None]]:
"""
>>> graph = GraphUndirectedWeighted()
>>> graph.add_edge("a", "b", 3)
>>> graph.add_edge("b", "c", 10)
>>> graph.add_edge("c", "d", 5)
>>> graph.add_edge("a", "c", 15)
>>> graph.add_edge("b", "d", 100)
>>> dist, parent = prims_algo(graph)
>>> abs(dist["a"] - dist["b"])
3
>>> abs(dist["d"] - dist["b"])
15
>>> abs(dist["a"] - dist["c"])
13
"""
# prim's algorithm for minimum spanning tree
dist: dict[T, int] = {node: maxsize for node in graph.connections}
parent: dict[T, T | None] = {node: None for node in graph.connections}
priority_queue: MinPriorityQueue[T] = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(node, weight)
if priority_queue.is_empty():
return dist, parent
# initialization
node = priority_queue.extract_min()
dist[node] = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
dist[neighbour] = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(neighbour, dist[neighbour])
parent[neighbour] = node
# running prim's algorithm
while not priority_queue.is_empty():
node = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
dist[neighbour] = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(neighbour, dist[neighbour])
parent[neighbour] = node
return dist, parent
| """
Prim's (also known as Jarník's) algorithm is a greedy algorithm that finds a minimum
spanning tree for a weighted undirected graph. This means it finds a subset of the
edges that forms a tree that includes every vertex, where the total weight of all the
edges in the tree is minimized. The algorithm operates by building this tree one vertex
at a time, from an arbitrary starting vertex, at each step adding the cheapest possible
connection from the tree to another vertex.
"""
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
T = TypeVar("T")
def get_parent_position(position: int) -> int:
"""
heap helper function get the position of the parent of the current node
>>> get_parent_position(1)
0
>>> get_parent_position(2)
0
"""
return (position - 1) // 2
def get_child_left_position(position: int) -> int:
"""
heap helper function get the position of the left child of the current node
>>> get_child_left_position(0)
1
"""
return (2 * position) + 1
def get_child_right_position(position: int) -> int:
"""
heap helper function get the position of the right child of the current node
>>> get_child_right_position(0)
2
"""
return (2 * position) + 2
class MinPriorityQueue(Generic[T]):
"""
Minimum Priority Queue Class
Functions:
is_empty: function to check if the priority queue is empty
push: function to add an element with given priority to the queue
extract_min: function to remove and return the element with lowest weight (highest
priority)
update_key: function to update the weight of the given key
_bubble_up: helper function to place a node at the proper position (upward
movement)
_bubble_down: helper function to place a node at the proper position (downward
movement)
_swap_nodes: helper function to swap the nodes at the given positions
>>> queue = MinPriorityQueue()
>>> queue.push(1, 1000)
>>> queue.push(2, 100)
>>> queue.push(3, 4000)
>>> queue.push(4, 3000)
>>> print(queue.extract_min())
2
>>> queue.update_key(4, 50)
>>> print(queue.extract_min())
4
>>> print(queue.extract_min())
1
>>> print(queue.extract_min())
3
"""
def __init__(self) -> None:
self.heap: list[tuple[T, int]] = []
self.position_map: dict[T, int] = {}
self.elements: int = 0
def __len__(self) -> int:
return self.elements
def __repr__(self) -> str:
return str(self.heap)
def is_empty(self) -> bool:
# Check if the priority queue is empty
return self.elements == 0
def push(self, elem: T, weight: int) -> None:
# Add an element with given priority to the queue
self.heap.append((elem, weight))
self.position_map[elem] = self.elements
self.elements += 1
self._bubble_up(elem)
def extract_min(self) -> T:
# Remove and return the element with lowest weight (highest priority)
if self.elements > 1:
self._swap_nodes(0, self.elements - 1)
elem, _ = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
bubble_down_elem, _ = self.heap[0]
self._bubble_down(bubble_down_elem)
return elem
def update_key(self, elem: T, weight: int) -> None:
# Update the weight of the given key
position = self.position_map[elem]
self.heap[position] = (elem, weight)
if position > 0:
parent_position = get_parent_position(position)
_, parent_weight = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(elem)
else:
self._bubble_down(elem)
else:
self._bubble_down(elem)
def _bubble_up(self, elem: T) -> None:
# Place a node at the proper position (upward movement) [to be used internally
# only]
curr_pos = self.position_map[elem]
if curr_pos == 0:
return
parent_position = get_parent_position(curr_pos)
_, weight = self.heap[curr_pos]
_, parent_weight = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(parent_position, curr_pos)
return self._bubble_up(elem)
return
def _bubble_down(self, elem: T) -> None:
# Place a node at the proper position (downward movement) [to be used
# internally only]
curr_pos = self.position_map[elem]
_, weight = self.heap[curr_pos]
child_left_position = get_child_left_position(curr_pos)
child_right_position = get_child_right_position(curr_pos)
if child_left_position < self.elements and child_right_position < self.elements:
_, child_left_weight = self.heap[child_left_position]
_, child_right_weight = self.heap[child_right_position]
if child_right_weight < child_left_weight:
if child_right_weight < weight:
self._swap_nodes(child_right_position, curr_pos)
return self._bubble_down(elem)
if child_left_position < self.elements:
_, child_left_weight = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(child_left_position, curr_pos)
return self._bubble_down(elem)
else:
return
if child_right_position < self.elements:
_, child_right_weight = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(child_right_position, curr_pos)
return self._bubble_down(elem)
else:
return
def _swap_nodes(self, node1_pos: int, node2_pos: int) -> None:
# Swap the nodes at the given positions
node1_elem = self.heap[node1_pos][0]
node2_elem = self.heap[node2_pos][0]
self.heap[node1_pos], self.heap[node2_pos] = (
self.heap[node2_pos],
self.heap[node1_pos],
)
self.position_map[node1_elem] = node2_pos
self.position_map[node2_elem] = node1_pos
class GraphUndirectedWeighted(Generic[T]):
"""
Graph Undirected Weighted Class
Functions:
add_node: function to add a node in the graph
add_edge: function to add an edge between 2 nodes in the graph
"""
def __init__(self) -> None:
self.connections: dict[T, dict[T, int]] = {}
self.nodes: int = 0
def __repr__(self) -> str:
return str(self.connections)
def __len__(self) -> int:
return self.nodes
def add_node(self, node: T) -> None:
# Add a node in the graph if it is not in the graph
if node not in self.connections:
self.connections[node] = {}
self.nodes += 1
def add_edge(self, node1: T, node2: T, weight: int) -> None:
# Add an edge between 2 nodes in the graph
self.add_node(node1)
self.add_node(node2)
self.connections[node1][node2] = weight
self.connections[node2][node1] = weight
def prims_algo(
graph: GraphUndirectedWeighted[T],
) -> tuple[dict[T, int], dict[T, T | None]]:
"""
>>> graph = GraphUndirectedWeighted()
>>> graph.add_edge("a", "b", 3)
>>> graph.add_edge("b", "c", 10)
>>> graph.add_edge("c", "d", 5)
>>> graph.add_edge("a", "c", 15)
>>> graph.add_edge("b", "d", 100)
>>> dist, parent = prims_algo(graph)
>>> abs(dist["a"] - dist["b"])
3
>>> abs(dist["d"] - dist["b"])
15
>>> abs(dist["a"] - dist["c"])
13
"""
# prim's algorithm for minimum spanning tree
dist: dict[T, int] = {node: maxsize for node in graph.connections}
parent: dict[T, T | None] = {node: None for node in graph.connections}
priority_queue: MinPriorityQueue[T] = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(node, weight)
if priority_queue.is_empty():
return dist, parent
# initialization
node = priority_queue.extract_min()
dist[node] = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
dist[neighbour] = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(neighbour, dist[neighbour])
parent[neighbour] = node
# running prim's algorithm
while not priority_queue.is_empty():
node = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
dist[neighbour] = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(neighbour, dist[neighbour])
parent[neighbour] = node
return dist, parent
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """ A naive recursive implementation of 0-1 Knapsack Problem
https://en.wikipedia.org/wiki/Knapsack_problem
"""
from __future__ import annotations
def knapsack(capacity: int, weights: list[int], values: list[int], counter: int) -> int:
"""
Returns the maximum value that can be put in a knapsack of a capacity cap,
whereby each weight w has a specific value val.
>>> cap = 50
>>> val = [60, 100, 120]
>>> w = [10, 20, 30]
>>> c = len(val)
>>> knapsack(cap, w, val, c)
220
The result is 220 cause the values of 100 and 120 got the weight of 50
which is the limit of the capacity.
"""
# Base Case
if counter == 0 or capacity == 0:
return 0
# If weight of the nth item is more than Knapsack of capacity,
# then this item cannot be included in the optimal solution,
# else return the maximum of two cases:
# (1) nth item included
# (2) not included
if weights[counter - 1] > capacity:
return knapsack(capacity, weights, values, counter - 1)
else:
left_capacity = capacity - weights[counter - 1]
new_value_included = values[counter - 1] + knapsack(
left_capacity, weights, values, counter - 1
)
without_new_value = knapsack(capacity, weights, values, counter - 1)
return max(new_value_included, without_new_value)
if __name__ == "__main__":
import doctest
doctest.testmod()
| """ A naive recursive implementation of 0-1 Knapsack Problem
https://en.wikipedia.org/wiki/Knapsack_problem
"""
from __future__ import annotations
def knapsack(capacity: int, weights: list[int], values: list[int], counter: int) -> int:
"""
Returns the maximum value that can be put in a knapsack of a capacity cap,
whereby each weight w has a specific value val.
>>> cap = 50
>>> val = [60, 100, 120]
>>> w = [10, 20, 30]
>>> c = len(val)
>>> knapsack(cap, w, val, c)
220
The result is 220 cause the values of 100 and 120 got the weight of 50
which is the limit of the capacity.
"""
# Base Case
if counter == 0 or capacity == 0:
return 0
# If weight of the nth item is more than Knapsack of capacity,
# then this item cannot be included in the optimal solution,
# else return the maximum of two cases:
# (1) nth item included
# (2) not included
if weights[counter - 1] > capacity:
return knapsack(capacity, weights, values, counter - 1)
else:
left_capacity = capacity - weights[counter - 1]
new_value_included = values[counter - 1] + knapsack(
left_capacity, weights, values, counter - 1
)
without_new_value = knapsack(capacity, weights, values, counter - 1)
return max(new_value_included, without_new_value)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Segmented Sieve."""
import math
def sieve(n):
"""Segmented Sieve."""
in_prime = []
start = 2
end = int(math.sqrt(n)) # Size of every segment
temp = [True] * (end + 1)
prime = []
while start <= end:
if temp[start] is True:
in_prime.append(start)
for i in range(start * start, end + 1, start):
if temp[i] is True:
temp[i] = False
start += 1
prime += in_prime
low = end + 1
high = low + end - 1
if high > n:
high = n
while low <= n:
temp = [True] * (high - low + 1)
for each in in_prime:
t = math.floor(low / each) * each
if t < low:
t += each
for j in range(t, high + 1, each):
temp[j - low] = False
for j in range(len(temp)):
if temp[j] is True:
prime.append(j + low)
low = high + 1
high = low + end - 1
if high > n:
high = n
return prime
print(sieve(10 ** 6))
| """Segmented Sieve."""
import math
def sieve(n):
"""Segmented Sieve."""
in_prime = []
start = 2
end = int(math.sqrt(n)) # Size of every segment
temp = [True] * (end + 1)
prime = []
while start <= end:
if temp[start] is True:
in_prime.append(start)
for i in range(start * start, end + 1, start):
if temp[i] is True:
temp[i] = False
start += 1
prime += in_prime
low = end + 1
high = low + end - 1
if high > n:
high = n
while low <= n:
temp = [True] * (high - low + 1)
for each in in_prime:
t = math.floor(low / each) * each
if t < low:
t += each
for j in range(t, high + 1, each):
temp[j - low] = False
for j in range(len(temp)):
if temp[j] is True:
prime.append(j + low)
low = high + 1
high = low + end - 1
if high > n:
high = n
return prime
print(sieve(10 ** 6))
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Combinatoric selections
Problem 47
The first two consecutive numbers to have two distinct prime factors are:
14 = 2 × 7
15 = 3 × 5
The first three consecutive numbers to have three distinct prime factors are:
644 = 2² × 7 × 23
645 = 3 × 5 × 43
646 = 2 × 17 × 19.
Find the first four consecutive integers to have four distinct prime factors each.
What is the first of these numbers?
"""
from functools import lru_cache
def unique_prime_factors(n: int) -> set:
"""
Find unique prime factors of an integer.
Tests include sorting because only the set really matters,
not the order in which it is produced.
>>> sorted(set(unique_prime_factors(14)))
[2, 7]
>>> sorted(set(unique_prime_factors(644)))
[2, 7, 23]
>>> sorted(set(unique_prime_factors(646)))
[2, 17, 19]
"""
i = 2
factors = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(i)
if n > 1:
factors.add(n)
return factors
@lru_cache
def upf_len(num: int) -> int:
"""
Memoize upf() length results for a given value.
>>> upf_len(14)
2
"""
return len(unique_prime_factors(num))
def equality(iterable: list) -> bool:
"""
Check equality of ALL elements in an interable.
>>> equality([1, 2, 3, 4])
False
>>> equality([2, 2, 2, 2])
True
>>> equality([1, 2, 3, 2, 1])
False
"""
return len(set(iterable)) in (0, 1)
def run(n: int) -> list:
"""
Runs core process to find problem solution.
>>> run(3)
[644, 645, 646]
"""
# Incrementor variable for our group list comprehension.
# This serves as the first number in each list of values
# to test.
base = 2
while True:
# Increment each value of a generated range
group = [base + i for i in range(n)]
# Run elements through out unique_prime_factors function
# Append our target number to the end.
checker = [upf_len(x) for x in group]
checker.append(n)
# If all numbers in the list are equal, return the group variable.
if equality(checker):
return group
# Increment our base variable by 1
base += 1
def solution(n: int = 4) -> int:
"""Return the first value of the first four consecutive integers to have four
distinct prime factors each.
>>> solution()
134043
"""
results = run(n)
return results[0] if len(results) else None
if __name__ == "__main__":
print(solution())
| """
Combinatoric selections
Problem 47
The first two consecutive numbers to have two distinct prime factors are:
14 = 2 × 7
15 = 3 × 5
The first three consecutive numbers to have three distinct prime factors are:
644 = 2² × 7 × 23
645 = 3 × 5 × 43
646 = 2 × 17 × 19.
Find the first four consecutive integers to have four distinct prime factors each.
What is the first of these numbers?
"""
from functools import lru_cache
def unique_prime_factors(n: int) -> set:
"""
Find unique prime factors of an integer.
Tests include sorting because only the set really matters,
not the order in which it is produced.
>>> sorted(set(unique_prime_factors(14)))
[2, 7]
>>> sorted(set(unique_prime_factors(644)))
[2, 7, 23]
>>> sorted(set(unique_prime_factors(646)))
[2, 17, 19]
"""
i = 2
factors = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(i)
if n > 1:
factors.add(n)
return factors
@lru_cache
def upf_len(num: int) -> int:
"""
Memoize upf() length results for a given value.
>>> upf_len(14)
2
"""
return len(unique_prime_factors(num))
def equality(iterable: list) -> bool:
"""
Check equality of ALL elements in an interable.
>>> equality([1, 2, 3, 4])
False
>>> equality([2, 2, 2, 2])
True
>>> equality([1, 2, 3, 2, 1])
False
"""
return len(set(iterable)) in (0, 1)
def run(n: int) -> list:
"""
Runs core process to find problem solution.
>>> run(3)
[644, 645, 646]
"""
# Incrementor variable for our group list comprehension.
# This serves as the first number in each list of values
# to test.
base = 2
while True:
# Increment each value of a generated range
group = [base + i for i in range(n)]
# Run elements through out unique_prime_factors function
# Append our target number to the end.
checker = [upf_len(x) for x in group]
checker.append(n)
# If all numbers in the list are equal, return the group variable.
if equality(checker):
return group
# Increment our base variable by 1
base += 1
def solution(n: int = 4) -> int:
"""Return the first value of the first four consecutive integers to have four
distinct prime factors each.
>>> solution()
134043
"""
results = run(n)
return results[0] if len(results) else None
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Just to check
"""
def add(a, b):
"""
>>> add(2, 2)
4
>>> add(2, -2)
0
"""
return a + b
if __name__ == "__main__":
a = 5
b = 6
print(f"The sum of {a} + {b} is {add(a, b)}")
| """
Just to check
"""
def add(a, b):
"""
>>> add(2, 2)
4
>>> add(2, -2)
0
"""
return a + b
if __name__ == "__main__":
a = 5
b = 6
print(f"The sum of {a} + {b} is {add(a, b)}")
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| [mypy]
ignore_missing_imports = True
; FIXME: #4052 fix mypy errors in the exclude directories and remove them below
exclude = (data_structures|dynamic_programming|graphs|maths|matrix|other|project_euler|searches|strings*)/$
| [mypy]
ignore_missing_imports = True
; FIXME: #4052 fix mypy errors in the exclude directories and remove them below
exclude = (data_structures|dynamic_programming|graphs|maths|matrix|other|project_euler|searches|strings*)/$
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Author : Turfa Auliarachman
Date : October 12, 2016
This is a pure Python implementation of Dynamic Programming solution to the edit
distance problem.
The problem is :
Given two strings A and B. Find the minimum number of operations to string B such that
A = B. The permitted operations are removal, insertion, and substitution.
"""
class EditDistance:
"""
Use :
solver = EditDistance()
editDistanceResult = solver.solve(firstString, secondString)
"""
def __init__(self):
self.__prepare__()
def __prepare__(self, N=0, M=0):
self.dp = [[-1 for y in range(0, M)] for x in range(0, N)]
def __solveDP(self, x, y):
if x == -1:
return y + 1
elif y == -1:
return x + 1
elif self.dp[x][y] > -1:
return self.dp[x][y]
else:
if self.A[x] == self.B[y]:
self.dp[x][y] = self.__solveDP(x - 1, y - 1)
else:
self.dp[x][y] = 1 + min(
self.__solveDP(x, y - 1),
self.__solveDP(x - 1, y),
self.__solveDP(x - 1, y - 1),
)
return self.dp[x][y]
def solve(self, A, B):
if isinstance(A, bytes):
A = A.decode("ascii")
if isinstance(B, bytes):
B = B.decode("ascii")
self.A = str(A)
self.B = str(B)
self.__prepare__(len(A), len(B))
return self.__solveDP(len(A) - 1, len(B) - 1)
def min_distance_bottom_up(word1: str, word2: str) -> int:
"""
>>> min_distance_bottom_up("intention", "execution")
5
>>> min_distance_bottom_up("intention", "")
9
>>> min_distance_bottom_up("", "")
0
"""
m = len(word1)
n = len(word2)
dp = [[0 for _ in range(n + 1)] for _ in range(m + 1)]
for i in range(m + 1):
for j in range(n + 1):
if i == 0: # first string is empty
dp[i][j] = j
elif j == 0: # second string is empty
dp[i][j] = i
elif (
word1[i - 1] == word2[j - 1]
): # last character of both substing is equal
dp[i][j] = dp[i - 1][j - 1]
else:
insert = dp[i][j - 1]
delete = dp[i - 1][j]
replace = dp[i - 1][j - 1]
dp[i][j] = 1 + min(insert, delete, replace)
return dp[m][n]
if __name__ == "__main__":
solver = EditDistance()
print("****************** Testing Edit Distance DP Algorithm ******************")
print()
S1 = input("Enter the first string: ").strip()
S2 = input("Enter the second string: ").strip()
print()
print("The minimum Edit Distance is: %d" % (solver.solve(S1, S2)))
print("The minimum Edit Distance is: %d" % (min_distance_bottom_up(S1, S2)))
print()
print("*************** End of Testing Edit Distance DP Algorithm ***************")
| """
Author : Turfa Auliarachman
Date : October 12, 2016
This is a pure Python implementation of Dynamic Programming solution to the edit
distance problem.
The problem is :
Given two strings A and B. Find the minimum number of operations to string B such that
A = B. The permitted operations are removal, insertion, and substitution.
"""
class EditDistance:
"""
Use :
solver = EditDistance()
editDistanceResult = solver.solve(firstString, secondString)
"""
def __init__(self):
self.__prepare__()
def __prepare__(self, N=0, M=0):
self.dp = [[-1 for y in range(0, M)] for x in range(0, N)]
def __solveDP(self, x, y):
if x == -1:
return y + 1
elif y == -1:
return x + 1
elif self.dp[x][y] > -1:
return self.dp[x][y]
else:
if self.A[x] == self.B[y]:
self.dp[x][y] = self.__solveDP(x - 1, y - 1)
else:
self.dp[x][y] = 1 + min(
self.__solveDP(x, y - 1),
self.__solveDP(x - 1, y),
self.__solveDP(x - 1, y - 1),
)
return self.dp[x][y]
def solve(self, A, B):
if isinstance(A, bytes):
A = A.decode("ascii")
if isinstance(B, bytes):
B = B.decode("ascii")
self.A = str(A)
self.B = str(B)
self.__prepare__(len(A), len(B))
return self.__solveDP(len(A) - 1, len(B) - 1)
def min_distance_bottom_up(word1: str, word2: str) -> int:
"""
>>> min_distance_bottom_up("intention", "execution")
5
>>> min_distance_bottom_up("intention", "")
9
>>> min_distance_bottom_up("", "")
0
"""
m = len(word1)
n = len(word2)
dp = [[0 for _ in range(n + 1)] for _ in range(m + 1)]
for i in range(m + 1):
for j in range(n + 1):
if i == 0: # first string is empty
dp[i][j] = j
elif j == 0: # second string is empty
dp[i][j] = i
elif (
word1[i - 1] == word2[j - 1]
): # last character of both substing is equal
dp[i][j] = dp[i - 1][j - 1]
else:
insert = dp[i][j - 1]
delete = dp[i - 1][j]
replace = dp[i - 1][j - 1]
dp[i][j] = 1 + min(insert, delete, replace)
return dp[m][n]
if __name__ == "__main__":
solver = EditDistance()
print("****************** Testing Edit Distance DP Algorithm ******************")
print()
S1 = input("Enter the first string: ").strip()
S2 = input("Enter the second string: ").strip()
print()
print("The minimum Edit Distance is: %d" % (solver.solve(S1, S2)))
print("The minimum Edit Distance is: %d" % (min_distance_bottom_up(S1, S2)))
print()
print("*************** End of Testing Edit Distance DP Algorithm ***************")
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is a pure Python implementation of the quick sort algorithm
For doctests run following command:
python -m doctest -v radix_sort.py
or
python3 -m doctest -v radix_sort.py
For manual testing run:
python radix_sort.py
"""
from __future__ import annotations
def radix_sort(list_of_ints: list[int]) -> list[int]:
"""
Examples:
>>> radix_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> radix_sort(list(range(15))) == sorted(range(15))
True
>>> radix_sort(list(range(14,-1,-1))) == sorted(range(15))
True
>>> radix_sort([1,100,10,1000]) == sorted([1,100,10,1000])
True
"""
RADIX = 10
placement = 1
max_digit = max(list_of_ints)
while placement <= max_digit:
# declare and initialize empty buckets
buckets: list[list] = [list() for _ in range(RADIX)]
# split list_of_ints between the buckets
for i in list_of_ints:
tmp = int((i / placement) % RADIX)
buckets[tmp].append(i)
# put each buckets' contents into list_of_ints
a = 0
for b in range(RADIX):
for i in buckets[b]:
list_of_ints[a] = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
This is a pure Python implementation of the quick sort algorithm
For doctests run following command:
python -m doctest -v radix_sort.py
or
python3 -m doctest -v radix_sort.py
For manual testing run:
python radix_sort.py
"""
from __future__ import annotations
def radix_sort(list_of_ints: list[int]) -> list[int]:
"""
Examples:
>>> radix_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> radix_sort(list(range(15))) == sorted(range(15))
True
>>> radix_sort(list(range(14,-1,-1))) == sorted(range(15))
True
>>> radix_sort([1,100,10,1000]) == sorted([1,100,10,1000])
True
"""
RADIX = 10
placement = 1
max_digit = max(list_of_ints)
while placement <= max_digit:
# declare and initialize empty buckets
buckets: list[list] = [list() for _ in range(RADIX)]
# split list_of_ints between the buckets
for i in list_of_ints:
tmp = int((i / placement) % RADIX)
buckets[tmp].append(i)
# put each buckets' contents into list_of_ints
a = 0
for b in range(RADIX):
for i in buckets[b]:
list_of_ints[a] = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
"""
This program calculates the nth Fibonacci number in O(log(n)).
It's possible to calculate F(1_000_000) in less than a second.
"""
from __future__ import annotations
import sys
def fibonacci(n: int) -> int:
"""
return F(n)
>>> [fibonacci(i) for i in range(13)]
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144]
"""
if n < 0:
raise ValueError("Negative arguments are not supported")
return _fib(n)[0]
# returns (F(n), F(n-1))
def _fib(n: int) -> tuple[int, int]:
if n == 0: # (F(0), F(1))
return (0, 1)
# F(2n) = F(n)[2F(n+1) − F(n)]
# F(2n+1) = F(n+1)^2+F(n)^2
a, b = _fib(n // 2)
c = a * (b * 2 - a)
d = a * a + b * b
return (d, c + d) if n % 2 else (c, d)
if __name__ == "__main__":
n = int(sys.argv[1])
print(f"fibonacci({n}) is {fibonacci(n)}")
| #!/usr/bin/env python3
"""
This program calculates the nth Fibonacci number in O(log(n)).
It's possible to calculate F(1_000_000) in less than a second.
"""
from __future__ import annotations
import sys
def fibonacci(n: int) -> int:
"""
return F(n)
>>> [fibonacci(i) for i in range(13)]
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144]
"""
if n < 0:
raise ValueError("Negative arguments are not supported")
return _fib(n)[0]
# returns (F(n), F(n-1))
def _fib(n: int) -> tuple[int, int]:
if n == 0: # (F(0), F(1))
return (0, 1)
# F(2n) = F(n)[2F(n+1) − F(n)]
# F(2n+1) = F(n+1)^2+F(n)^2
a, b = _fib(n // 2)
c = a * (b * 2 - a)
d = a * a + b * b
return (d, c + d) if n % 2 else (c, d)
if __name__ == "__main__":
n = int(sys.argv[1])
print(f"fibonacci({n}) is {fibonacci(n)}")
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/Best-first_search#Greedy_BFS
"""
from __future__ import annotations
Path = list[tuple[int, int]]
grid = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
delta = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right
class Node:
"""
>>> k = Node(0, 0, 4, 5, 0, None)
>>> k.calculate_heuristic()
9
>>> n = Node(1, 4, 3, 4, 2, None)
>>> n.calculate_heuristic()
2
>>> l = [k, n]
>>> n == l[0]
False
>>> l.sort()
>>> n == l[0]
True
"""
def __init__(
self,
pos_x: int,
pos_y: int,
goal_x: int,
goal_y: int,
g_cost: float,
parent: Node | None,
):
self.pos_x = pos_x
self.pos_y = pos_y
self.pos = (pos_y, pos_x)
self.goal_x = goal_x
self.goal_y = goal_y
self.g_cost = g_cost
self.parent = parent
self.f_cost = self.calculate_heuristic()
def calculate_heuristic(self) -> float:
"""
The heuristic here is the Manhattan Distance
Could elaborate to offer more than one choice
"""
dy = abs(self.pos_x - self.goal_x)
dx = abs(self.pos_y - self.goal_y)
return dx + dy
def __lt__(self, other) -> bool:
return self.f_cost < other.f_cost
class GreedyBestFirst:
"""
>>> gbf = GreedyBestFirst((0, 0), (len(grid) - 1, len(grid[0]) - 1))
>>> [x.pos for x in gbf.get_successors(gbf.start)]
[(1, 0), (0, 1)]
>>> (gbf.start.pos_y + delta[3][0], gbf.start.pos_x + delta[3][1])
(0, 1)
>>> (gbf.start.pos_y + delta[2][0], gbf.start.pos_x + delta[2][1])
(1, 0)
>>> gbf.retrace_path(gbf.start)
[(0, 0)]
>>> gbf.search() # doctest: +NORMALIZE_WHITESPACE
[(0, 0), (1, 0), (2, 0), (3, 0), (3, 1), (4, 1), (5, 1), (6, 1),
(6, 2), (6, 3), (5, 3), (5, 4), (5, 5), (6, 5), (6, 6)]
"""
def __init__(self, start: tuple[int, int], goal: tuple[int, int]):
self.start = Node(start[1], start[0], goal[1], goal[0], 0, None)
self.target = Node(goal[1], goal[0], goal[1], goal[0], 99999, None)
self.open_nodes = [self.start]
self.closed_nodes: list[Node] = []
self.reached = False
def search(self) -> Path | None:
"""
Search for the path,
if a path is not found, only the starting position is returned
"""
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
current_node = self.open_nodes.pop(0)
if current_node.pos == self.target.pos:
self.reached = True
return self.retrace_path(current_node)
self.closed_nodes.append(current_node)
successors = self.get_successors(current_node)
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(child_node)
else:
# retrieve the best current path
better_node = self.open_nodes.pop(self.open_nodes.index(child_node))
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(child_node)
else:
self.open_nodes.append(better_node)
if not self.reached:
return [self.start.pos]
return None
def get_successors(self, parent: Node) -> list[Node]:
"""
Returns a list of successors (both in the grid and free spaces)
"""
successors = []
for action in delta:
pos_x = parent.pos_x + action[1]
pos_y = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(grid) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
pos_x,
pos_y,
self.target.pos_y,
self.target.pos_x,
parent.g_cost + 1,
parent,
)
)
return successors
def retrace_path(self, node: Node | None) -> Path:
"""
Retrace the path from parents to parents until start node
"""
current_node = node
path = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x))
current_node = current_node.parent
path.reverse()
return path
if __name__ == "__main__":
init = (0, 0)
goal = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
print("------")
greedy_bf = GreedyBestFirst(init, goal)
path = greedy_bf.search()
if path:
for pos_x, pos_y in path:
grid[pos_x][pos_y] = 2
for elem in grid:
print(elem)
| """
https://en.wikipedia.org/wiki/Best-first_search#Greedy_BFS
"""
from __future__ import annotations
Path = list[tuple[int, int]]
grid = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
delta = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right
class Node:
"""
>>> k = Node(0, 0, 4, 5, 0, None)
>>> k.calculate_heuristic()
9
>>> n = Node(1, 4, 3, 4, 2, None)
>>> n.calculate_heuristic()
2
>>> l = [k, n]
>>> n == l[0]
False
>>> l.sort()
>>> n == l[0]
True
"""
def __init__(
self,
pos_x: int,
pos_y: int,
goal_x: int,
goal_y: int,
g_cost: float,
parent: Node | None,
):
self.pos_x = pos_x
self.pos_y = pos_y
self.pos = (pos_y, pos_x)
self.goal_x = goal_x
self.goal_y = goal_y
self.g_cost = g_cost
self.parent = parent
self.f_cost = self.calculate_heuristic()
def calculate_heuristic(self) -> float:
"""
The heuristic here is the Manhattan Distance
Could elaborate to offer more than one choice
"""
dy = abs(self.pos_x - self.goal_x)
dx = abs(self.pos_y - self.goal_y)
return dx + dy
def __lt__(self, other) -> bool:
return self.f_cost < other.f_cost
class GreedyBestFirst:
"""
>>> gbf = GreedyBestFirst((0, 0), (len(grid) - 1, len(grid[0]) - 1))
>>> [x.pos for x in gbf.get_successors(gbf.start)]
[(1, 0), (0, 1)]
>>> (gbf.start.pos_y + delta[3][0], gbf.start.pos_x + delta[3][1])
(0, 1)
>>> (gbf.start.pos_y + delta[2][0], gbf.start.pos_x + delta[2][1])
(1, 0)
>>> gbf.retrace_path(gbf.start)
[(0, 0)]
>>> gbf.search() # doctest: +NORMALIZE_WHITESPACE
[(0, 0), (1, 0), (2, 0), (3, 0), (3, 1), (4, 1), (5, 1), (6, 1),
(6, 2), (6, 3), (5, 3), (5, 4), (5, 5), (6, 5), (6, 6)]
"""
def __init__(self, start: tuple[int, int], goal: tuple[int, int]):
self.start = Node(start[1], start[0], goal[1], goal[0], 0, None)
self.target = Node(goal[1], goal[0], goal[1], goal[0], 99999, None)
self.open_nodes = [self.start]
self.closed_nodes: list[Node] = []
self.reached = False
def search(self) -> Path | None:
"""
Search for the path,
if a path is not found, only the starting position is returned
"""
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
current_node = self.open_nodes.pop(0)
if current_node.pos == self.target.pos:
self.reached = True
return self.retrace_path(current_node)
self.closed_nodes.append(current_node)
successors = self.get_successors(current_node)
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(child_node)
else:
# retrieve the best current path
better_node = self.open_nodes.pop(self.open_nodes.index(child_node))
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(child_node)
else:
self.open_nodes.append(better_node)
if not self.reached:
return [self.start.pos]
return None
def get_successors(self, parent: Node) -> list[Node]:
"""
Returns a list of successors (both in the grid and free spaces)
"""
successors = []
for action in delta:
pos_x = parent.pos_x + action[1]
pos_y = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(grid) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
pos_x,
pos_y,
self.target.pos_y,
self.target.pos_x,
parent.g_cost + 1,
parent,
)
)
return successors
def retrace_path(self, node: Node | None) -> Path:
"""
Retrace the path from parents to parents until start node
"""
current_node = node
path = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x))
current_node = current_node.parent
path.reverse()
return path
if __name__ == "__main__":
init = (0, 0)
goal = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
print("------")
greedy_bf = GreedyBestFirst(init, goal)
path = greedy_bf.search()
if path:
for pos_x, pos_y in path:
grid[pos_x][pos_y] = 2
for elem in grid:
print(elem)
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def bubble_sort(list_data: list, length: int = 0) -> list:
"""
It is similar is bubble sort but recursive.
:param list_data: mutable ordered sequence of elements
:param length: length of list data
:return: the same list in ascending order
>>> bubble_sort([0, 5, 2, 3, 2], 5)
[0, 2, 2, 3, 5]
>>> bubble_sort([], 0)
[]
>>> bubble_sort([-2, -45, -5], 3)
[-45, -5, -2]
>>> bubble_sort([-23, 0, 6, -4, 34], 5)
[-23, -4, 0, 6, 34]
>>> bubble_sort([-23, 0, 6, -4, 34], 5) == sorted([-23, 0, 6, -4, 34])
True
>>> bubble_sort(['z','a','y','b','x','c'], 6)
['a', 'b', 'c', 'x', 'y', 'z']
>>> bubble_sort([1.1, 3.3, 5.5, 7.7, 2.2, 4.4, 6.6])
[1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7]
"""
length = length or len(list_data)
swapped = False
for i in range(length - 1):
if list_data[i] > list_data[i + 1]:
list_data[i], list_data[i + 1] = list_data[i + 1], list_data[i]
swapped = True
return list_data if not swapped else bubble_sort(list_data, length - 1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| def bubble_sort(list_data: list, length: int = 0) -> list:
"""
It is similar is bubble sort but recursive.
:param list_data: mutable ordered sequence of elements
:param length: length of list data
:return: the same list in ascending order
>>> bubble_sort([0, 5, 2, 3, 2], 5)
[0, 2, 2, 3, 5]
>>> bubble_sort([], 0)
[]
>>> bubble_sort([-2, -45, -5], 3)
[-45, -5, -2]
>>> bubble_sort([-23, 0, 6, -4, 34], 5)
[-23, -4, 0, 6, 34]
>>> bubble_sort([-23, 0, 6, -4, 34], 5) == sorted([-23, 0, 6, -4, 34])
True
>>> bubble_sort(['z','a','y','b','x','c'], 6)
['a', 'b', 'c', 'x', 'y', 'z']
>>> bubble_sort([1.1, 3.3, 5.5, 7.7, 2.2, 4.4, 6.6])
[1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7]
"""
length = length or len(list_data)
swapped = False
for i in range(length - 1):
if list_data[i] > list_data[i + 1]:
list_data[i], list_data[i + 1] = list_data[i + 1], list_data[i]
swapped = True
return list_data if not swapped else bubble_sort(list_data, length - 1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Normalization Wikipedia: https://en.wikipedia.org/wiki/Normalization
Normalization is the process of converting numerical data to a standard range of values.
This range is typically between [0, 1] or [-1, 1]. The equation for normalization is
x_norm = (x - x_min)/(x_max - x_min) where x_norm is the normalized value, x is the
value, x_min is the minimum value within the column or list of data, and x_max is the
maximum value within the column or list of data. Normalization is used to speed up the
training of data and put all of the data on a similar scale. This is useful because
variance in the range of values of a dataset can heavily impact optimization
(particularly Gradient Descent).
Standardization Wikipedia: https://en.wikipedia.org/wiki/Standardization
Standardization is the process of converting numerical data to a normally distributed
range of values. This range will have a mean of 0 and standard deviation of 1. This is
also known as z-score normalization. The equation for standardization is
x_std = (x - mu)/(sigma) where mu is the mean of the column or list of values and sigma
is the standard deviation of the column or list of values.
Choosing between Normalization & Standardization is more of an art of a science, but it
is often recommended to run experiments with both to see which performs better.
Additionally, a few rules of thumb are:
1. gaussian (normal) distributions work better with standardization
2. non-gaussian (non-normal) distributions work better with normalization
3. If a column or list of values has extreme values / outliers, use standardization
"""
from statistics import mean, stdev
def normalization(data: list, ndigits: int = 3) -> list:
"""
Returns a normalized list of values
@params: data, a list of values to normalize
@returns: a list of normalized values (rounded to ndigits decimal places)
@examples:
>>> normalization([2, 7, 10, 20, 30, 50])
[0.0, 0.104, 0.167, 0.375, 0.583, 1.0]
>>> normalization([5, 10, 15, 20, 25])
[0.0, 0.25, 0.5, 0.75, 1.0]
"""
# variables for calculation
x_min = min(data)
x_max = max(data)
# normalize data
return [round((x - x_min) / (x_max - x_min), ndigits) for x in data]
def standardization(data: list, ndigits: int = 3) -> list:
"""
Returns a standardized list of values
@params: data, a list of values to standardize
@returns: a list of standardized values (rounded to ndigits decimal places)
@examples:
>>> standardization([2, 7, 10, 20, 30, 50])
[-0.999, -0.719, -0.551, 0.009, 0.57, 1.69]
>>> standardization([5, 10, 15, 20, 25])
[-1.265, -0.632, 0.0, 0.632, 1.265]
"""
# variables for calculation
mu = mean(data)
sigma = stdev(data)
# standardize data
return [round((x - mu) / (sigma), ndigits) for x in data]
| """
Normalization Wikipedia: https://en.wikipedia.org/wiki/Normalization
Normalization is the process of converting numerical data to a standard range of values.
This range is typically between [0, 1] or [-1, 1]. The equation for normalization is
x_norm = (x - x_min)/(x_max - x_min) where x_norm is the normalized value, x is the
value, x_min is the minimum value within the column or list of data, and x_max is the
maximum value within the column or list of data. Normalization is used to speed up the
training of data and put all of the data on a similar scale. This is useful because
variance in the range of values of a dataset can heavily impact optimization
(particularly Gradient Descent).
Standardization Wikipedia: https://en.wikipedia.org/wiki/Standardization
Standardization is the process of converting numerical data to a normally distributed
range of values. This range will have a mean of 0 and standard deviation of 1. This is
also known as z-score normalization. The equation for standardization is
x_std = (x - mu)/(sigma) where mu is the mean of the column or list of values and sigma
is the standard deviation of the column or list of values.
Choosing between Normalization & Standardization is more of an art of a science, but it
is often recommended to run experiments with both to see which performs better.
Additionally, a few rules of thumb are:
1. gaussian (normal) distributions work better with standardization
2. non-gaussian (non-normal) distributions work better with normalization
3. If a column or list of values has extreme values / outliers, use standardization
"""
from statistics import mean, stdev
def normalization(data: list, ndigits: int = 3) -> list:
"""
Returns a normalized list of values
@params: data, a list of values to normalize
@returns: a list of normalized values (rounded to ndigits decimal places)
@examples:
>>> normalization([2, 7, 10, 20, 30, 50])
[0.0, 0.104, 0.167, 0.375, 0.583, 1.0]
>>> normalization([5, 10, 15, 20, 25])
[0.0, 0.25, 0.5, 0.75, 1.0]
"""
# variables for calculation
x_min = min(data)
x_max = max(data)
# normalize data
return [round((x - x_min) / (x_max - x_min), ndigits) for x in data]
def standardization(data: list, ndigits: int = 3) -> list:
"""
Returns a standardized list of values
@params: data, a list of values to standardize
@returns: a list of standardized values (rounded to ndigits decimal places)
@examples:
>>> standardization([2, 7, 10, 20, 30, 50])
[-0.999, -0.719, -0.551, 0.009, 0.57, 1.69]
>>> standardization([5, 10, 15, 20, 25])
[-1.265, -0.632, 0.0, 0.632, 1.265]
"""
# variables for calculation
mu = mean(data)
sigma = stdev(data)
# standardize data
return [round((x - mu) / (sigma), ndigits) for x in data]
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def isSumSubset(arr, arrLen, requiredSum):
"""
>>> isSumSubset([2, 4, 6, 8], 4, 5)
False
>>> isSumSubset([2, 4, 6, 8], 4, 14)
True
"""
# a subset value says 1 if that subset sum can be formed else 0
# initially no subsets can be formed hence False/0
subset = [[False for i in range(requiredSum + 1)] for i in range(arrLen + 1)]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arrLen + 1):
subset[i][0] = True
# sum is not zero and set is empty then false
for i in range(1, requiredSum + 1):
subset[0][i] = False
for i in range(1, arrLen + 1):
for j in range(1, requiredSum + 1):
if arr[i - 1] > j:
subset[i][j] = subset[i - 1][j]
if arr[i - 1] <= j:
subset[i][j] = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
# uncomment to print the subset
# for i in range(arrLen+1):
# print(subset[i])
print(subset[arrLen][requiredSum])
if __name__ == "__main__":
import doctest
doctest.testmod()
| def isSumSubset(arr, arrLen, requiredSum):
"""
>>> isSumSubset([2, 4, 6, 8], 4, 5)
False
>>> isSumSubset([2, 4, 6, 8], 4, 14)
True
"""
# a subset value says 1 if that subset sum can be formed else 0
# initially no subsets can be formed hence False/0
subset = [[False for i in range(requiredSum + 1)] for i in range(arrLen + 1)]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arrLen + 1):
subset[i][0] = True
# sum is not zero and set is empty then false
for i in range(1, requiredSum + 1):
subset[0][i] = False
for i in range(1, arrLen + 1):
for j in range(1, requiredSum + 1):
if arr[i - 1] > j:
subset[i][j] = subset[i - 1][j]
if arr[i - 1] <= j:
subset[i][j] = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
# uncomment to print the subset
# for i in range(arrLen+1):
# print(subset[i])
print(subset[arrLen][requiredSum])
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://cp-algorithms.com/string/prefix-function.html
Prefix function Knuth–Morris–Pratt algorithm
Different algorithm than Knuth-Morris-Pratt pattern finding
E.x. Finding longest prefix which is also suffix
Time Complexity: O(n) - where n is the length of the string
"""
def prefix_function(input_string: str) -> list:
"""
For the given string this function computes value for each index(i),
which represents the longest coincidence of prefix and suffix
for given substring (input_str[0...i])
For the value of the first element the algorithm always returns 0
>>> prefix_function("aabcdaabc")
[0, 1, 0, 0, 0, 1, 2, 3, 4]
>>> prefix_function("asdasdad")
[0, 0, 0, 1, 2, 3, 4, 0]
"""
# list for the result values
prefix_result = [0] * len(input_string)
for i in range(1, len(input_string)):
# use last results for better performance - dynamic programming
j = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
j = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
prefix_result[i] = j
return prefix_result
def longest_prefix(input_str: str) -> int:
"""
Prefix-function use case
Finding longest prefix which is suffix as well
>>> longest_prefix("aabcdaabc")
4
>>> longest_prefix("asdasdad")
4
>>> longest_prefix("abcab")
2
"""
# just returning maximum value of the array gives us answer
return max(prefix_function(input_str))
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
https://cp-algorithms.com/string/prefix-function.html
Prefix function Knuth–Morris–Pratt algorithm
Different algorithm than Knuth-Morris-Pratt pattern finding
E.x. Finding longest prefix which is also suffix
Time Complexity: O(n) - where n is the length of the string
"""
def prefix_function(input_string: str) -> list:
"""
For the given string this function computes value for each index(i),
which represents the longest coincidence of prefix and suffix
for given substring (input_str[0...i])
For the value of the first element the algorithm always returns 0
>>> prefix_function("aabcdaabc")
[0, 1, 0, 0, 0, 1, 2, 3, 4]
>>> prefix_function("asdasdad")
[0, 0, 0, 1, 2, 3, 4, 0]
"""
# list for the result values
prefix_result = [0] * len(input_string)
for i in range(1, len(input_string)):
# use last results for better performance - dynamic programming
j = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
j = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
prefix_result[i] = j
return prefix_result
def longest_prefix(input_str: str) -> int:
"""
Prefix-function use case
Finding longest prefix which is suffix as well
>>> longest_prefix("aabcdaabc")
4
>>> longest_prefix("asdasdad")
4
>>> longest_prefix("abcab")
2
"""
# just returning maximum value of the array gives us answer
return max(prefix_function(input_str))
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/Shellsort#Pseudocode
"""
def shell_sort(collection):
"""Pure implementation of shell sort algorithm in Python
:param collection: Some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
>>> shell_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> shell_sort([])
[]
>>> shell_sort([-2, -5, -45])
[-45, -5, -2]
"""
# Marcin Ciura's gap sequence
gaps = [701, 301, 132, 57, 23, 10, 4, 1]
for gap in gaps:
for i in range(gap, len(collection)):
insert_value = collection[i]
j = i
while j >= gap and collection[j - gap] > insert_value:
collection[j] = collection[j - gap]
j -= gap
if j != i:
collection[j] = insert_value
return collection
if __name__ == "__main__":
from doctest import testmod
testmod()
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(shell_sort(unsorted))
| """
https://en.wikipedia.org/wiki/Shellsort#Pseudocode
"""
def shell_sort(collection):
"""Pure implementation of shell sort algorithm in Python
:param collection: Some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
>>> shell_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> shell_sort([])
[]
>>> shell_sort([-2, -5, -45])
[-45, -5, -2]
"""
# Marcin Ciura's gap sequence
gaps = [701, 301, 132, 57, 23, 10, 4, 1]
for gap in gaps:
for i in range(gap, len(collection)):
insert_value = collection[i]
j = i
while j >= gap and collection[j - gap] > insert_value:
collection[j] = collection[j - gap]
j -= gap
if j != i:
collection[j] = insert_value
return collection
if __name__ == "__main__":
from doctest import testmod
testmod()
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(shell_sort(unsorted))
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #
| #
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def double_sort(lst):
"""this sorting algorithm sorts an array using the principle of bubble sort,
but does it both from left to right and right to left,
hence i decided to call it "double sort"
:param collection: mutable ordered sequence of elements
:return: the same collection in ascending order
Examples:
>>> double_sort([-1 ,-2 ,-3 ,-4 ,-5 ,-6 ,-7])
[-7, -6, -5, -4, -3, -2, -1]
>>> double_sort([])
[]
>>> double_sort([-1 ,-2 ,-3 ,-4 ,-5 ,-6])
[-6, -5, -4, -3, -2, -1]
>>> double_sort([-3, 10, 16, -42, 29]) == sorted([-3, 10, 16, -42, 29])
True
"""
no_of_elements = len(lst)
for i in range(
0, int(((no_of_elements - 1) / 2) + 1)
): # we don't need to traverse to end of list as
for j in range(0, no_of_elements - 1):
if (
lst[j + 1] < lst[j]
): # applying bubble sort algorithm from left to right (or forwards)
temp = lst[j + 1]
lst[j + 1] = lst[j]
lst[j] = temp
if (
lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
): # applying bubble sort algorithm from right to left (or backwards)
temp = lst[no_of_elements - 1 - j]
lst[no_of_elements - 1 - j] = lst[no_of_elements - 2 - j]
lst[no_of_elements - 2 - j] = temp
return lst
if __name__ == "__main__":
print("enter the list to be sorted")
lst = [int(x) for x in input().split()] # inputing elements of the list in one line
sorted_lst = double_sort(lst)
print("the sorted list is")
print(sorted_lst)
| def double_sort(lst):
"""this sorting algorithm sorts an array using the principle of bubble sort,
but does it both from left to right and right to left,
hence i decided to call it "double sort"
:param collection: mutable ordered sequence of elements
:return: the same collection in ascending order
Examples:
>>> double_sort([-1 ,-2 ,-3 ,-4 ,-5 ,-6 ,-7])
[-7, -6, -5, -4, -3, -2, -1]
>>> double_sort([])
[]
>>> double_sort([-1 ,-2 ,-3 ,-4 ,-5 ,-6])
[-6, -5, -4, -3, -2, -1]
>>> double_sort([-3, 10, 16, -42, 29]) == sorted([-3, 10, 16, -42, 29])
True
"""
no_of_elements = len(lst)
for i in range(
0, int(((no_of_elements - 1) / 2) + 1)
): # we don't need to traverse to end of list as
for j in range(0, no_of_elements - 1):
if (
lst[j + 1] < lst[j]
): # applying bubble sort algorithm from left to right (or forwards)
temp = lst[j + 1]
lst[j + 1] = lst[j]
lst[j] = temp
if (
lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
): # applying bubble sort algorithm from right to left (or backwards)
temp = lst[no_of_elements - 1 - j]
lst[no_of_elements - 1 - j] = lst[no_of_elements - 2 - j]
lst[no_of_elements - 2 - j] = temp
return lst
if __name__ == "__main__":
print("enter the list to be sorted")
lst = [int(x) for x in input().split()] # inputing elements of the list in one line
sorted_lst = double_sort(lst)
print("the sorted list is")
print(sorted_lst)
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Self Powers
Problem 48
The series, 1^1 + 2^2 + 3^3 + ... + 10^10 = 10405071317.
Find the last ten digits of the series, 1^1 + 2^2 + 3^3 + ... + 1000^1000.
"""
def solution():
"""
Returns the last 10 digits of the series, 1^1 + 2^2 + 3^3 + ... + 1000^1000.
>>> solution()
'9110846700'
"""
total = 0
for i in range(1, 1001):
total += i ** i
return str(total)[-10:]
if __name__ == "__main__":
print(solution())
| """
Self Powers
Problem 48
The series, 1^1 + 2^2 + 3^3 + ... + 10^10 = 10405071317.
Find the last ten digits of the series, 1^1 + 2^2 + 3^3 + ... + 1000^1000.
"""
def solution():
"""
Returns the last 10 digits of the series, 1^1 + 2^2 + 3^3 + ... + 1000^1000.
>>> solution()
'9110846700'
"""
total = 0
for i in range(1, 1001):
total += i ** i
return str(total)[-10:]
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/python
""" Author: OMKAR PATHAK """
class Graph:
def __init__(self):
self.vertex = {}
# for printing the Graph vertices
def print_graph(self) -> None:
print(self.vertex)
for i in self.vertex:
print(i, " -> ", " -> ".join([str(j) for j in self.vertex[i]]))
# for adding the edge between two vertices
def add_edge(self, from_vertex: int, to_vertex: int) -> None:
# check if vertex is already present,
if from_vertex in self.vertex:
self.vertex[from_vertex].append(to_vertex)
else:
# else make a new vertex
self.vertex[from_vertex] = [to_vertex]
def dfs(self) -> None:
# visited array for storing already visited nodes
visited = [False] * len(self.vertex)
# call the recursive helper function
for i in range(len(self.vertex)):
if not visited[i]:
self.dfs_recursive(i, visited)
def dfs_recursive(self, start_vertex: int, visited: list) -> None:
# mark start vertex as visited
visited[start_vertex] = True
print(start_vertex, end=" ")
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(i, visited)
if __name__ == "__main__":
g = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print("DFS:")
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| #!/usr/bin/python
""" Author: OMKAR PATHAK """
class Graph:
def __init__(self):
self.vertex = {}
# for printing the Graph vertices
def print_graph(self) -> None:
print(self.vertex)
for i in self.vertex:
print(i, " -> ", " -> ".join([str(j) for j in self.vertex[i]]))
# for adding the edge between two vertices
def add_edge(self, from_vertex: int, to_vertex: int) -> None:
# check if vertex is already present,
if from_vertex in self.vertex:
self.vertex[from_vertex].append(to_vertex)
else:
# else make a new vertex
self.vertex[from_vertex] = [to_vertex]
def dfs(self) -> None:
# visited array for storing already visited nodes
visited = [False] * len(self.vertex)
# call the recursive helper function
for i in range(len(self.vertex)):
if not visited[i]:
self.dfs_recursive(i, visited)
def dfs_recursive(self, start_vertex: int, visited: list) -> None:
# mark start vertex as visited
visited[start_vertex] = True
print(start_vertex, end=" ")
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(i, visited)
if __name__ == "__main__":
g = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print("DFS:")
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import cv2
import numpy as np
from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter
PI = 180
def gen_gaussian_kernel(k_size, sigma):
center = k_size // 2
x, y = np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
g = (
1
/ (2 * np.pi * sigma)
* np.exp(-(np.square(x) + np.square(y)) / (2 * np.square(sigma)))
)
return g
def canny(image, threshold_low=15, threshold_high=30, weak=128, strong=255):
image_row, image_col = image.shape[0], image.shape[1]
# gaussian_filter
gaussian_out = img_convolve(image, gen_gaussian_kernel(9, sigma=1.4))
# get the gradient and degree by sobel_filter
sobel_grad, sobel_theta = sobel_filter(gaussian_out)
gradient_direction = np.rad2deg(sobel_theta)
gradient_direction += PI
dst = np.zeros((image_row, image_col))
"""
Non-maximum suppression. If the edge strength of the current pixel is the largest
compared to the other pixels in the mask with the same direction, the value will be
preserved. Otherwise, the value will be suppressed.
"""
for row in range(1, image_row - 1):
for col in range(1, image_col - 1):
direction = gradient_direction[row, col]
if (
0 <= direction < 22.5
or 15 * PI / 8 <= direction <= 2 * PI
or 7 * PI / 8 <= direction <= 9 * PI / 8
):
W = sobel_grad[row, col - 1]
E = sobel_grad[row, col + 1]
if sobel_grad[row, col] >= W and sobel_grad[row, col] >= E:
dst[row, col] = sobel_grad[row, col]
elif (PI / 8 <= direction < 3 * PI / 8) or (
9 * PI / 8 <= direction < 11 * PI / 8
):
SW = sobel_grad[row + 1, col - 1]
NE = sobel_grad[row - 1, col + 1]
if sobel_grad[row, col] >= SW and sobel_grad[row, col] >= NE:
dst[row, col] = sobel_grad[row, col]
elif (3 * PI / 8 <= direction < 5 * PI / 8) or (
11 * PI / 8 <= direction < 13 * PI / 8
):
N = sobel_grad[row - 1, col]
S = sobel_grad[row + 1, col]
if sobel_grad[row, col] >= N and sobel_grad[row, col] >= S:
dst[row, col] = sobel_grad[row, col]
elif (5 * PI / 8 <= direction < 7 * PI / 8) or (
13 * PI / 8 <= direction < 15 * PI / 8
):
NW = sobel_grad[row - 1, col - 1]
SE = sobel_grad[row + 1, col + 1]
if sobel_grad[row, col] >= NW and sobel_grad[row, col] >= SE:
dst[row, col] = sobel_grad[row, col]
"""
High-Low threshold detection. If an edge pixel’s gradient value is higher
than the high threshold value, it is marked as a strong edge pixel. If an
edge pixel’s gradient value is smaller than the high threshold value and
larger than the low threshold value, it is marked as a weak edge pixel. If
an edge pixel's value is smaller than the low threshold value, it will be
suppressed.
"""
if dst[row, col] >= threshold_high:
dst[row, col] = strong
elif dst[row, col] <= threshold_low:
dst[row, col] = 0
else:
dst[row, col] = weak
"""
Edge tracking. Usually a weak edge pixel caused from true edges will be connected
to a strong edge pixel while noise responses are unconnected. As long as there is
one strong edge pixel that is involved in its 8-connected neighborhood, that weak
edge point can be identified as one that should be preserved.
"""
for row in range(1, image_row):
for col in range(1, image_col):
if dst[row, col] == weak:
if 255 in (
dst[row, col + 1],
dst[row, col - 1],
dst[row - 1, col],
dst[row + 1, col],
dst[row - 1, col - 1],
dst[row + 1, col - 1],
dst[row - 1, col + 1],
dst[row + 1, col + 1],
):
dst[row, col] = strong
else:
dst[row, col] = 0
return dst
if __name__ == "__main__":
# read original image in gray mode
lena = cv2.imread(r"../image_data/lena.jpg", 0)
# canny edge detection
canny_dst = canny(lena)
cv2.imshow("canny", canny_dst)
cv2.waitKey(0)
| import cv2
import numpy as np
from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter
PI = 180
def gen_gaussian_kernel(k_size, sigma):
center = k_size // 2
x, y = np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
g = (
1
/ (2 * np.pi * sigma)
* np.exp(-(np.square(x) + np.square(y)) / (2 * np.square(sigma)))
)
return g
def canny(image, threshold_low=15, threshold_high=30, weak=128, strong=255):
image_row, image_col = image.shape[0], image.shape[1]
# gaussian_filter
gaussian_out = img_convolve(image, gen_gaussian_kernel(9, sigma=1.4))
# get the gradient and degree by sobel_filter
sobel_grad, sobel_theta = sobel_filter(gaussian_out)
gradient_direction = np.rad2deg(sobel_theta)
gradient_direction += PI
dst = np.zeros((image_row, image_col))
"""
Non-maximum suppression. If the edge strength of the current pixel is the largest
compared to the other pixels in the mask with the same direction, the value will be
preserved. Otherwise, the value will be suppressed.
"""
for row in range(1, image_row - 1):
for col in range(1, image_col - 1):
direction = gradient_direction[row, col]
if (
0 <= direction < 22.5
or 15 * PI / 8 <= direction <= 2 * PI
or 7 * PI / 8 <= direction <= 9 * PI / 8
):
W = sobel_grad[row, col - 1]
E = sobel_grad[row, col + 1]
if sobel_grad[row, col] >= W and sobel_grad[row, col] >= E:
dst[row, col] = sobel_grad[row, col]
elif (PI / 8 <= direction < 3 * PI / 8) or (
9 * PI / 8 <= direction < 11 * PI / 8
):
SW = sobel_grad[row + 1, col - 1]
NE = sobel_grad[row - 1, col + 1]
if sobel_grad[row, col] >= SW and sobel_grad[row, col] >= NE:
dst[row, col] = sobel_grad[row, col]
elif (3 * PI / 8 <= direction < 5 * PI / 8) or (
11 * PI / 8 <= direction < 13 * PI / 8
):
N = sobel_grad[row - 1, col]
S = sobel_grad[row + 1, col]
if sobel_grad[row, col] >= N and sobel_grad[row, col] >= S:
dst[row, col] = sobel_grad[row, col]
elif (5 * PI / 8 <= direction < 7 * PI / 8) or (
13 * PI / 8 <= direction < 15 * PI / 8
):
NW = sobel_grad[row - 1, col - 1]
SE = sobel_grad[row + 1, col + 1]
if sobel_grad[row, col] >= NW and sobel_grad[row, col] >= SE:
dst[row, col] = sobel_grad[row, col]
"""
High-Low threshold detection. If an edge pixel’s gradient value is higher
than the high threshold value, it is marked as a strong edge pixel. If an
edge pixel’s gradient value is smaller than the high threshold value and
larger than the low threshold value, it is marked as a weak edge pixel. If
an edge pixel's value is smaller than the low threshold value, it will be
suppressed.
"""
if dst[row, col] >= threshold_high:
dst[row, col] = strong
elif dst[row, col] <= threshold_low:
dst[row, col] = 0
else:
dst[row, col] = weak
"""
Edge tracking. Usually a weak edge pixel caused from true edges will be connected
to a strong edge pixel while noise responses are unconnected. As long as there is
one strong edge pixel that is involved in its 8-connected neighborhood, that weak
edge point can be identified as one that should be preserved.
"""
for row in range(1, image_row):
for col in range(1, image_col):
if dst[row, col] == weak:
if 255 in (
dst[row, col + 1],
dst[row, col - 1],
dst[row - 1, col],
dst[row + 1, col],
dst[row - 1, col - 1],
dst[row + 1, col - 1],
dst[row - 1, col + 1],
dst[row + 1, col + 1],
):
dst[row, col] = strong
else:
dst[row, col] = 0
return dst
if __name__ == "__main__":
# read original image in gray mode
lena = cv2.imread(r"../image_data/lena.jpg", 0)
# canny edge detection
canny_dst = canny(lena)
cv2.imshow("canny", canny_dst)
cv2.waitKey(0)
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/Doubly_linked_list
"""
class Node:
def __init__(self, data):
self.data = data
self.previous = None
self.next = None
def __str__(self):
return f"{self.data}"
class DoublyLinkedList:
def __init__(self):
self.head = None
self.tail = None
def __iter__(self):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.insert_at_head('b')
>>> linked_list.insert_at_head('a')
>>> linked_list.insert_at_tail('c')
>>> tuple(linked_list)
('a', 'b', 'c')
"""
node = self.head
while node:
yield node.data
node = node.next
def __str__(self):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.insert_at_tail('a')
>>> linked_list.insert_at_tail('b')
>>> linked_list.insert_at_tail('c')
>>> str(linked_list)
'a->b->c'
"""
return "->".join([str(item) for item in self])
def __len__(self):
"""
>>> linked_list = DoublyLinkedList()
>>> for i in range(0, 5):
... linked_list.insert_at_nth(i, i + 1)
>>> len(linked_list) == 5
True
"""
return len(tuple(iter(self)))
def insert_at_head(self, data):
self.insert_at_nth(0, data)
def insert_at_tail(self, data):
self.insert_at_nth(len(self), data)
def insert_at_nth(self, index: int, data):
"""
>>> linked_list = DoublyLinkedList()
>>> linked_list.insert_at_nth(-1, 666)
Traceback (most recent call last):
....
IndexError: list index out of range
>>> linked_list.insert_at_nth(1, 666)
Traceback (most recent call last):
....
IndexError: list index out of range
>>> linked_list.insert_at_nth(0, 2)
>>> linked_list.insert_at_nth(0, 1)
>>> linked_list.insert_at_nth(2, 4)
>>> linked_list.insert_at_nth(2, 3)
>>> str(linked_list)
'1->2->3->4'
>>> linked_list.insert_at_nth(5, 5)
Traceback (most recent call last):
....
IndexError: list index out of range
"""
if not 0 <= index <= len(self):
raise IndexError("list index out of range")
new_node = Node(data)
if self.head is None:
self.head = self.tail = new_node
elif index == 0:
self.head.previous = new_node
new_node.next = self.head
self.head = new_node
elif index == len(self):
self.tail.next = new_node
new_node.previous = self.tail
self.tail = new_node
else:
temp = self.head
for i 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 i 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()
assert False # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
assert False # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10):
assert len(linked_list) == i
linked_list.insert_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 i 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 i 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()
assert False # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
assert False # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10):
assert len(linked_list) == i
linked_list.insert_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 | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
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TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Reference: https://en.wikipedia.org/wiki/Gaussian_function
"""
from numpy import exp, pi, sqrt
def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int:
"""
>>> gaussian(1)
0.24197072451914337
>>> gaussian(24)
3.342714441794458e-126
>>> gaussian(1, 4, 2)
0.06475879783294587
>>> gaussian(1, 5, 3)
0.05467002489199788
Supports NumPy Arrays
Use numpy.meshgrid with this to generate gaussian blur on images.
>>> import numpy as np
>>> x = np.arange(15)
>>> gaussian(x)
array([3.98942280e-01, 2.41970725e-01, 5.39909665e-02, 4.43184841e-03,
1.33830226e-04, 1.48671951e-06, 6.07588285e-09, 9.13472041e-12,
5.05227108e-15, 1.02797736e-18, 7.69459863e-23, 2.11881925e-27,
2.14638374e-32, 7.99882776e-38, 1.09660656e-43])
>>> gaussian(15)
5.530709549844416e-50
>>> gaussian([1,2, 'string'])
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for -: 'list' and 'float'
>>> gaussian('hello world')
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for -: 'str' and 'float'
>>> gaussian(10**234) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
OverflowError: (34, 'Result too large')
>>> gaussian(10**-326)
0.3989422804014327
>>> gaussian(2523, mu=234234, sigma=3425)
0.0
"""
return 1 / sqrt(2 * pi * sigma ** 2) * exp(-((x - mu) ** 2) / (2 * sigma ** 2))
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Reference: https://en.wikipedia.org/wiki/Gaussian_function
"""
from numpy import exp, pi, sqrt
def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int:
"""
>>> gaussian(1)
0.24197072451914337
>>> gaussian(24)
3.342714441794458e-126
>>> gaussian(1, 4, 2)
0.06475879783294587
>>> gaussian(1, 5, 3)
0.05467002489199788
Supports NumPy Arrays
Use numpy.meshgrid with this to generate gaussian blur on images.
>>> import numpy as np
>>> x = np.arange(15)
>>> gaussian(x)
array([3.98942280e-01, 2.41970725e-01, 5.39909665e-02, 4.43184841e-03,
1.33830226e-04, 1.48671951e-06, 6.07588285e-09, 9.13472041e-12,
5.05227108e-15, 1.02797736e-18, 7.69459863e-23, 2.11881925e-27,
2.14638374e-32, 7.99882776e-38, 1.09660656e-43])
>>> gaussian(15)
5.530709549844416e-50
>>> gaussian([1,2, 'string'])
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for -: 'list' and 'float'
>>> gaussian('hello world')
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for -: 'str' and 'float'
>>> gaussian(10**234) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
OverflowError: (34, 'Result too large')
>>> gaussian(10**-326)
0.3989422804014327
>>> gaussian(2523, mu=234234, sigma=3425)
0.0
"""
return 1 / sqrt(2 * pi * sigma ** 2) * exp(-((x - mu) ** 2) / (2 * sigma ** 2))
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Gnome Sort Algorithm (A.K.A. Stupid Sort)
This algorithm iterates over a list comparing an element with the previous one.
If order is not respected, it swaps element backward until order is respected with
previous element. It resumes the initial iteration from element new position.
For doctests run following command:
python3 -m doctest -v gnome_sort.py
For manual testing run:
python3 gnome_sort.py
"""
def gnome_sort(lst: list) -> list:
"""
Pure implementation of the gnome sort algorithm in Python
Take some mutable ordered collection with heterogeneous comparable items inside as
arguments, return the same collection ordered by ascending.
Examples:
>>> gnome_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> gnome_sort([])
[]
>>> gnome_sort([-2, -5, -45])
[-45, -5, -2]
>>> "".join(gnome_sort(list(set("Gnomes are stupid!"))))
' !Gadeimnoprstu'
"""
if len(lst) <= 1:
return lst
i = 1
while i < len(lst):
if lst[i - 1] <= lst[i]:
i += 1
else:
lst[i - 1], lst[i] = lst[i], lst[i - 1]
i -= 1
if i == 0:
i = 1
return lst
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(gnome_sort(unsorted))
| """
Gnome Sort Algorithm (A.K.A. Stupid Sort)
This algorithm iterates over a list comparing an element with the previous one.
If order is not respected, it swaps element backward until order is respected with
previous element. It resumes the initial iteration from element new position.
For doctests run following command:
python3 -m doctest -v gnome_sort.py
For manual testing run:
python3 gnome_sort.py
"""
def gnome_sort(lst: list) -> list:
"""
Pure implementation of the gnome sort algorithm in Python
Take some mutable ordered collection with heterogeneous comparable items inside as
arguments, return the same collection ordered by ascending.
Examples:
>>> gnome_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> gnome_sort([])
[]
>>> gnome_sort([-2, -5, -45])
[-45, -5, -2]
>>> "".join(gnome_sort(list(set("Gnomes are stupid!"))))
' !Gadeimnoprstu'
"""
if len(lst) <= 1:
return lst
i = 1
while i < len(lst):
if lst[i - 1] <= lst[i]:
i += 1
else:
lst[i - 1], lst[i] = lst[i], lst[i - 1]
i -= 1
if i == 0:
i = 1
return lst
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(gnome_sort(unsorted))
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 6: https://projecteuler.net/problem=6
Sum square difference
The sum of the squares of the first ten natural numbers is,
1^2 + 2^2 + ... + 10^2 = 385
The square of the sum of the first ten natural numbers is,
(1 + 2 + ... + 10)^2 = 55^2 = 3025
Hence the difference between the sum of the squares of the first ten
natural numbers and the square of the sum is 3025 - 385 = 2640.
Find the difference between the sum of the squares of the first one
hundred natural numbers and the square of the sum.
"""
def solution(n: int = 100) -> int:
"""
Returns the difference between the sum of the squares of the first n
natural numbers and the square of the sum.
>>> solution(10)
2640
>>> solution(15)
13160
>>> solution(20)
41230
>>> solution(50)
1582700
"""
sum_cubes = (n * (n + 1) // 2) ** 2
sum_squares = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 6: https://projecteuler.net/problem=6
Sum square difference
The sum of the squares of the first ten natural numbers is,
1^2 + 2^2 + ... + 10^2 = 385
The square of the sum of the first ten natural numbers is,
(1 + 2 + ... + 10)^2 = 55^2 = 3025
Hence the difference between the sum of the squares of the first ten
natural numbers and the square of the sum is 3025 - 385 = 2640.
Find the difference between the sum of the squares of the first one
hundred natural numbers and the square of the sum.
"""
def solution(n: int = 100) -> int:
"""
Returns the difference between the sum of the squares of the first n
natural numbers and the square of the sum.
>>> solution(10)
2640
>>> solution(15)
13160
>>> solution(20)
41230
>>> solution(50)
1582700
"""
sum_cubes = (n * (n + 1) // 2) ** 2
sum_squares = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
"""
Provide the current worldwide COVID-19 statistics.
This data is being scrapped from 'https://www.worldometers.info/coronavirus/'.
"""
import requests
from bs4 import BeautifulSoup
def world_covid19_stats(url: str = "https://www.worldometers.info/coronavirus") -> dict:
"""
Return a dict of current worldwide COVID-19 statistics
"""
soup = BeautifulSoup(requests.get(url).text, "html.parser")
keys = soup.findAll("h1")
values = soup.findAll("div", {"class": "maincounter-number"})
keys += soup.findAll("span", {"class": "panel-title"})
values += soup.findAll("div", {"class": "number-table-main"})
return {key.text.strip(): value.text.strip() for key, value in zip(keys, values)}
if __name__ == "__main__":
print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
for key, value in world_covid19_stats().items():
print(f"{key}\n{value}\n")
| #!/usr/bin/env python3
"""
Provide the current worldwide COVID-19 statistics.
This data is being scrapped from 'https://www.worldometers.info/coronavirus/'.
"""
import requests
from bs4 import BeautifulSoup
def world_covid19_stats(url: str = "https://www.worldometers.info/coronavirus") -> dict:
"""
Return a dict of current worldwide COVID-19 statistics
"""
soup = BeautifulSoup(requests.get(url).text, "html.parser")
keys = soup.findAll("h1")
values = soup.findAll("div", {"class": "maincounter-number"})
keys += soup.findAll("span", {"class": "panel-title"})
values += soup.findAll("div", {"class": "number-table-main"})
return {key.text.strip(): value.text.strip() for key, value in zip(keys, values)}
if __name__ == "__main__":
print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
for key, value in world_covid19_stats().items():
print(f"{key}\n{value}\n")
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v3.4.0
hooks:
- id: check-executables-have-shebangs
- id: check-yaml
- id: end-of-file-fixer
types: [python]
- id: trailing-whitespace
exclude: |
(?x)^(
data_structures/heap/binomial_heap.py
)$
- id: requirements-txt-fixer
- repo: https://github.com/psf/black
rev: 21.4b0
hooks:
- id: black
- repo: https://github.com/PyCQA/isort
rev: 5.8.0
hooks:
- id: isort
args:
- --profile=black
- repo: https://gitlab.com/pycqa/flake8
rev: 3.9.1
hooks:
- id: flake8
args:
- --ignore=E203,W503
- --max-complexity=25
- --max-line-length=88
# FIXME: fix mypy errors and then uncomment this
# - repo: https://github.com/pre-commit/mirrors-mypy
# rev: v0.782
# hooks:
# - id: mypy
# args:
# - --ignore-missing-imports
- repo: https://github.com/codespell-project/codespell
rev: v2.0.0
hooks:
- id: codespell
args:
- --ignore-words-list=ans,crate,fo,followings,hist,iff,mater,secant,som,tim
- --skip="./.*,./strings/dictionary.txt,./strings/words.txt,./project_euler/problem_022/p022_names.txt"
- --quiet-level=2
exclude: |
(?x)^(
strings/dictionary.txt |
strings/words.txt |
project_euler/problem_022/p022_names.txt
)$
- repo: local
hooks:
- id: validate-filenames
name: Validate filenames
entry: ./scripts/validate_filenames.py
language: script
pass_filenames: false
| repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v3.4.0
hooks:
- id: check-executables-have-shebangs
- id: check-yaml
- id: end-of-file-fixer
types: [python]
- id: trailing-whitespace
exclude: |
(?x)^(
data_structures/heap/binomial_heap.py
)$
- id: requirements-txt-fixer
- repo: https://github.com/psf/black
rev: 21.4b0
hooks:
- id: black
- repo: https://github.com/PyCQA/isort
rev: 5.8.0
hooks:
- id: isort
args:
- --profile=black
- repo: https://gitlab.com/pycqa/flake8
rev: 3.9.1
hooks:
- id: flake8
args:
- --ignore=E203,W503
- --max-complexity=25
- --max-line-length=88
# FIXME: fix mypy errors and then uncomment this
# - repo: https://github.com/pre-commit/mirrors-mypy
# rev: v0.782
# hooks:
# - id: mypy
# args:
# - --ignore-missing-imports
- repo: https://github.com/codespell-project/codespell
rev: v2.0.0
hooks:
- id: codespell
args:
- --ignore-words-list=ans,crate,fo,followings,hist,iff,mater,secant,som,tim
- --skip="./.*,./strings/dictionary.txt,./strings/words.txt,./project_euler/problem_022/p022_names.txt"
- --quiet-level=2
exclude: |
(?x)^(
strings/dictionary.txt |
strings/words.txt |
project_euler/problem_022/p022_names.txt
)$
- repo: local
hooks:
- id: validate-filenames
name: Validate filenames
entry: ./scripts/validate_filenames.py
language: script
pass_filenames: false
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| class Node:
def __init__(self, data):
self.data = data
self.next = None
def __repr__(self):
return f"Node({self.data})"
class LinkedList:
def __init__(self):
self.head = None
def __iter__(self):
node = self.head
while node:
yield node.data
node = node.next
def __len__(self) -> int:
"""
Return length of linked list i.e. number of nodes
>>> linked_list = LinkedList()
>>> len(linked_list)
0
>>> linked_list.insert_tail("head")
>>> len(linked_list)
1
>>> linked_list.insert_head("head")
>>> len(linked_list)
2
>>> _ = linked_list.delete_tail()
>>> len(linked_list)
1
>>> _ = linked_list.delete_head()
>>> len(linked_list)
0
"""
return len(tuple(iter(self)))
def __repr__(self):
"""
String representation/visualization of a Linked Lists
"""
return "->".join([str(item) for item in self])
def __getitem__(self, index):
"""
Indexing Support. Used to get a node at particular position
>>> linked_list = LinkedList()
>>> for i in range(0, 10):
... linked_list.insert_nth(i, i)
>>> all(str(linked_list[i]) == str(i) for i in range(0, 10))
True
>>> linked_list[-10]
Traceback (most recent call last):
...
ValueError: list index out of range.
>>> linked_list[len(linked_list)]
Traceback (most recent call last):
...
ValueError: list index out of range.
"""
if not 0 <= index < len(self):
raise ValueError("list index out of range.")
for i, node in enumerate(self):
if i == index:
return node
# Used to change the data of a particular node
def __setitem__(self, index, data):
"""
>>> linked_list = LinkedList()
>>> for i in range(0, 10):
... linked_list.insert_nth(i, i)
>>> linked_list[0] = 666
>>> linked_list[0]
666
>>> linked_list[5] = -666
>>> linked_list[5]
-666
>>> linked_list[-10] = 666
Traceback (most recent call last):
...
ValueError: list index out of range.
>>> linked_list[len(linked_list)] = 666
Traceback (most recent call last):
...
ValueError: list index out of range.
"""
if not 0 <= index < len(self):
raise ValueError("list index out of range.")
current = self.head
for i in range(index):
current = current.next
current.data = data
def insert_tail(self, data) -> None:
self.insert_nth(len(self), data)
def insert_head(self, data) -> None:
self.insert_nth(0, data)
def insert_nth(self, index: int, data) -> None:
if not 0 <= index <= len(self):
raise IndexError("list index out of range")
new_node = Node(data)
if self.head is None:
self.head = new_node
elif index == 0:
new_node.next = self.head # link new_node to head
self.head = new_node
else:
temp = self.head
for _ in range(index - 1):
temp = temp.next
new_node.next = temp.next
temp.next = new_node
def print_list(self) -> None: # print every node data
print(self)
def delete_head(self):
return self.delete_nth(0)
def delete_tail(self): # delete from tail
return self.delete_nth(len(self) - 1)
def delete_nth(self, index: int = 0):
if not 0 <= index <= len(self) - 1: # test if index is valid
raise IndexError("list index out of range")
delete_node = self.head # default first node
if index == 0:
self.head = self.head.next
else:
temp = self.head
for _ in range(index - 1):
temp = temp.next
delete_node = temp.next
temp.next = temp.next.next
return delete_node.data
def is_empty(self) -> bool:
return self.head is None
def reverse(self):
prev = None
current = self.head
while current:
# Store the current node's next node.
next_node = current.next
# Make the current node's next point backwards
current.next = prev
# Make the previous node be the current node
prev = current
# Make the current node the next node (to progress iteration)
current = next_node
# Return prev in order to put the head at the end
self.head = prev
def test_singly_linked_list() -> None:
"""
>>> test_singly_linked_list()
"""
linked_list = LinkedList()
assert linked_list.is_empty() is True
assert str(linked_list) == ""
try:
linked_list.delete_head()
assert False # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
assert False # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10):
assert len(linked_list) == i
linked_list.insert_nth(i, i + 1)
assert str(linked_list) == "->".join(str(i) for i in range(1, 11))
linked_list.insert_head(0)
linked_list.insert_tail(11)
assert str(linked_list) == "->".join(str(i) for i in range(0, 12))
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9) == 10
assert linked_list.delete_tail() == 11
assert len(linked_list) == 9
assert str(linked_list) == "->".join(str(i) for i in range(1, 10))
assert all(linked_list[i] == i + 1 for i in range(0, 9)) is True
for i in range(0, 9):
linked_list[i] = -i
assert all(linked_list[i] == -i for i in range(0, 9)) is True
def main():
from doctest import testmod
testmod()
linked_list = LinkedList()
linked_list.insert_head(input("Inserting 1st at head ").strip())
linked_list.insert_head(input("Inserting 2nd at head ").strip())
print("\nPrint list:")
linked_list.print_list()
linked_list.insert_tail(input("\nInserting 1st at tail ").strip())
linked_list.insert_tail(input("Inserting 2nd at tail ").strip())
print("\nPrint list:")
linked_list.print_list()
print("\nDelete head")
linked_list.delete_head()
print("Delete tail")
linked_list.delete_tail()
print("\nPrint list:")
linked_list.print_list()
print("\nReverse linked list")
linked_list.reverse()
print("\nPrint list:")
linked_list.print_list()
print("\nString representation of linked list:")
print(linked_list)
print("\nReading/changing Node data using indexing:")
print(f"Element at Position 1: {linked_list[1]}")
linked_list[1] = input("Enter New Value: ").strip()
print("New list:")
print(linked_list)
print(f"length of linked_list is : {len(linked_list)}")
if __name__ == "__main__":
main()
| class Node:
def __init__(self, data):
self.data = data
self.next = None
def __repr__(self):
return f"Node({self.data})"
class LinkedList:
def __init__(self):
self.head = None
def __iter__(self):
node = self.head
while node:
yield node.data
node = node.next
def __len__(self) -> int:
"""
Return length of linked list i.e. number of nodes
>>> linked_list = LinkedList()
>>> len(linked_list)
0
>>> linked_list.insert_tail("head")
>>> len(linked_list)
1
>>> linked_list.insert_head("head")
>>> len(linked_list)
2
>>> _ = linked_list.delete_tail()
>>> len(linked_list)
1
>>> _ = linked_list.delete_head()
>>> len(linked_list)
0
"""
return len(tuple(iter(self)))
def __repr__(self):
"""
String representation/visualization of a Linked Lists
"""
return "->".join([str(item) for item in self])
def __getitem__(self, index):
"""
Indexing Support. Used to get a node at particular position
>>> linked_list = LinkedList()
>>> for i in range(0, 10):
... linked_list.insert_nth(i, i)
>>> all(str(linked_list[i]) == str(i) for i in range(0, 10))
True
>>> linked_list[-10]
Traceback (most recent call last):
...
ValueError: list index out of range.
>>> linked_list[len(linked_list)]
Traceback (most recent call last):
...
ValueError: list index out of range.
"""
if not 0 <= index < len(self):
raise ValueError("list index out of range.")
for i, node in enumerate(self):
if i == index:
return node
# Used to change the data of a particular node
def __setitem__(self, index, data):
"""
>>> linked_list = LinkedList()
>>> for i in range(0, 10):
... linked_list.insert_nth(i, i)
>>> linked_list[0] = 666
>>> linked_list[0]
666
>>> linked_list[5] = -666
>>> linked_list[5]
-666
>>> linked_list[-10] = 666
Traceback (most recent call last):
...
ValueError: list index out of range.
>>> linked_list[len(linked_list)] = 666
Traceback (most recent call last):
...
ValueError: list index out of range.
"""
if not 0 <= index < len(self):
raise ValueError("list index out of range.")
current = self.head
for i in range(index):
current = current.next
current.data = data
def insert_tail(self, data) -> None:
self.insert_nth(len(self), data)
def insert_head(self, data) -> None:
self.insert_nth(0, data)
def insert_nth(self, index: int, data) -> None:
if not 0 <= index <= len(self):
raise IndexError("list index out of range")
new_node = Node(data)
if self.head is None:
self.head = new_node
elif index == 0:
new_node.next = self.head # link new_node to head
self.head = new_node
else:
temp = self.head
for _ in range(index - 1):
temp = temp.next
new_node.next = temp.next
temp.next = new_node
def print_list(self) -> None: # print every node data
print(self)
def delete_head(self):
return self.delete_nth(0)
def delete_tail(self): # delete from tail
return self.delete_nth(len(self) - 1)
def delete_nth(self, index: int = 0):
if not 0 <= index <= len(self) - 1: # test if index is valid
raise IndexError("list index out of range")
delete_node = self.head # default first node
if index == 0:
self.head = self.head.next
else:
temp = self.head
for _ in range(index - 1):
temp = temp.next
delete_node = temp.next
temp.next = temp.next.next
return delete_node.data
def is_empty(self) -> bool:
return self.head is None
def reverse(self):
prev = None
current = self.head
while current:
# Store the current node's next node.
next_node = current.next
# Make the current node's next point backwards
current.next = prev
# Make the previous node be the current node
prev = current
# Make the current node the next node (to progress iteration)
current = next_node
# Return prev in order to put the head at the end
self.head = prev
def test_singly_linked_list() -> None:
"""
>>> test_singly_linked_list()
"""
linked_list = LinkedList()
assert linked_list.is_empty() is True
assert str(linked_list) == ""
try:
linked_list.delete_head()
assert False # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
assert False # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10):
assert len(linked_list) == i
linked_list.insert_nth(i, i + 1)
assert str(linked_list) == "->".join(str(i) for i in range(1, 11))
linked_list.insert_head(0)
linked_list.insert_tail(11)
assert str(linked_list) == "->".join(str(i) for i in range(0, 12))
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9) == 10
assert linked_list.delete_tail() == 11
assert len(linked_list) == 9
assert str(linked_list) == "->".join(str(i) for i in range(1, 10))
assert all(linked_list[i] == i + 1 for i in range(0, 9)) is True
for i in range(0, 9):
linked_list[i] = -i
assert all(linked_list[i] == -i for i in range(0, 9)) is True
def main():
from doctest import testmod
testmod()
linked_list = LinkedList()
linked_list.insert_head(input("Inserting 1st at head ").strip())
linked_list.insert_head(input("Inserting 2nd at head ").strip())
print("\nPrint list:")
linked_list.print_list()
linked_list.insert_tail(input("\nInserting 1st at tail ").strip())
linked_list.insert_tail(input("Inserting 2nd at tail ").strip())
print("\nPrint list:")
linked_list.print_list()
print("\nDelete head")
linked_list.delete_head()
print("Delete tail")
linked_list.delete_tail()
print("\nPrint list:")
linked_list.print_list()
print("\nReverse linked list")
linked_list.reverse()
print("\nPrint list:")
linked_list.print_list()
print("\nString representation of linked list:")
print(linked_list)
print("\nReading/changing Node data using indexing:")
print(f"Element at Position 1: {linked_list[1]}")
linked_list[1] = input("Enter New Value: ").strip()
print("New list:")
print(linked_list)
print(f"length of linked_list is : {len(linked_list)}")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Linear Discriminant Analysis
Assumptions About Data :
1. The input variables has a gaussian distribution.
2. The variance calculated for each input variables by class grouping is the
same.
3. The mix of classes in your training set is representative of the problem.
Learning The Model :
The LDA model requires the estimation of statistics from the training data :
1. Mean of each input value for each class.
2. Probability of an instance belong to each class.
3. Covariance for the input data for each class
Calculate the class means :
mean(x) = 1/n ( for i = 1 to i = n --> sum(xi))
Calculate the class probabilities :
P(y = 0) = count(y = 0) / (count(y = 0) + count(y = 1))
P(y = 1) = count(y = 1) / (count(y = 0) + count(y = 1))
Calculate the variance :
We can calculate the variance for dataset in two steps :
1. Calculate the squared difference for each input variable from the
group mean.
2. Calculate the mean of the squared difference.
------------------------------------------------
Squared_Difference = (x - mean(k)) ** 2
Variance = (1 / (count(x) - count(classes))) *
(for i = 1 to i = n --> sum(Squared_Difference(xi)))
Making Predictions :
discriminant(x) = x * (mean / variance) -
((mean ** 2) / (2 * variance)) + Ln(probability)
---------------------------------------------------------------------------
After calculating the discriminant value for each class, the class with the
largest discriminant value is taken as the prediction.
Author: @EverLookNeverSee
"""
from math import log
from os import name, system
from random import gauss, seed
from typing import Callable, TypeVar
# Make a training dataset drawn from a gaussian distribution
def gaussian_distribution(mean: float, std_dev: float, instance_count: int) -> list:
"""
Generate gaussian distribution instances based-on given mean and standard deviation
:param mean: mean value of class
:param std_dev: value of standard deviation entered by usr or default value of it
:param instance_count: instance number of class
:return: a list containing generated values based-on given mean, std_dev and
instance_count
>>> gaussian_distribution(5.0, 1.0, 20) # doctest: +NORMALIZE_WHITESPACE
[6.288184753155463, 6.4494456086997705, 5.066335808938262, 4.235456349028368,
3.9078267848958586, 5.031334516831717, 3.977896829989127, 3.56317055489747,
5.199311976483754, 5.133374604658605, 5.546468300338232, 4.086029056264687,
5.005005283626573, 4.935258239627312, 3.494170998739258, 5.537997178661033,
5.320711100998849, 7.3891120432406865, 5.202969177309964, 4.855297691835079]
"""
seed(1)
return [gauss(mean, std_dev) for _ in range(instance_count)]
# Make corresponding Y flags to detecting classes
def y_generator(class_count: int, instance_count: list) -> list:
"""
Generate y values for corresponding classes
:param class_count: Number of classes(data groupings) in dataset
:param instance_count: number of instances in class
:return: corresponding values for data groupings in dataset
>>> y_generator(1, [10])
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
>>> y_generator(2, [5, 10])
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
>>> y_generator(4, [10, 5, 15, 20]) # doctest: +NORMALIZE_WHITESPACE
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
"""
return [k for k in range(class_count) for _ in range(instance_count[k])]
# Calculate the class means
def calculate_mean(instance_count: int, items: list) -> float:
"""
Calculate given class mean
:param instance_count: Number of instances in class
:param items: items that related to specific class(data grouping)
:return: calculated actual mean of considered class
>>> items = gaussian_distribution(5.0, 1.0, 20)
>>> calculate_mean(len(items), items)
5.011267842911003
"""
# the sum of all items divided by number of instances
return sum(items) / instance_count
# Calculate the class probabilities
def calculate_probabilities(instance_count: int, total_count: int) -> float:
"""
Calculate the probability that a given instance will belong to which class
:param instance_count: number of instances in class
:param total_count: the number of all instances
:return: value of probability for considered class
>>> calculate_probabilities(20, 60)
0.3333333333333333
>>> calculate_probabilities(30, 100)
0.3
"""
# number of instances in specific class divided by number of all instances
return instance_count / total_count
# Calculate the variance
def calculate_variance(items: list, means: list, total_count: int) -> float:
"""
Calculate the variance
:param items: a list containing all items(gaussian distribution of all classes)
:param means: a list containing real mean values of each class
:param total_count: the number of all instances
:return: calculated variance for considered dataset
>>> items = gaussian_distribution(5.0, 1.0, 20)
>>> means = [5.011267842911003]
>>> total_count = 20
>>> calculate_variance([items], means, total_count)
0.9618530973487491
"""
squared_diff = [] # An empty list to store all squared differences
# iterate over number of elements in items
for i in range(len(items)):
# for loop iterates over number of elements in inner layer of items
for j in range(len(items[i])):
# appending squared differences to 'squared_diff' list
squared_diff.append((items[i][j] - means[i]) ** 2)
# one divided by (the number of all instances - number of classes) multiplied by
# sum of all squared differences
n_classes = len(means) # Number of classes in dataset
return 1 / (total_count - n_classes) * sum(squared_diff)
# Making predictions
def predict_y_values(
x_items: list, means: list, variance: float, probabilities: list
) -> list:
"""This function predicts new indexes(groups for our data)
:param x_items: a list containing all items(gaussian distribution of all classes)
:param means: a list containing real mean values of each class
:param variance: calculated value of variance by calculate_variance function
:param probabilities: a list containing all probabilities of classes
:return: a list containing predicted Y values
>>> x_items = [[6.288184753155463, 6.4494456086997705, 5.066335808938262,
... 4.235456349028368, 3.9078267848958586, 5.031334516831717,
... 3.977896829989127, 3.56317055489747, 5.199311976483754,
... 5.133374604658605, 5.546468300338232, 4.086029056264687,
... 5.005005283626573, 4.935258239627312, 3.494170998739258,
... 5.537997178661033, 5.320711100998849, 7.3891120432406865,
... 5.202969177309964, 4.855297691835079], [11.288184753155463,
... 11.44944560869977, 10.066335808938263, 9.235456349028368,
... 8.907826784895859, 10.031334516831716, 8.977896829989128,
... 8.56317055489747, 10.199311976483754, 10.133374604658606,
... 10.546468300338232, 9.086029056264687, 10.005005283626572,
... 9.935258239627313, 8.494170998739259, 10.537997178661033,
... 10.320711100998848, 12.389112043240686, 10.202969177309964,
... 9.85529769183508], [16.288184753155463, 16.449445608699772,
... 15.066335808938263, 14.235456349028368, 13.907826784895859,
... 15.031334516831716, 13.977896829989128, 13.56317055489747,
... 15.199311976483754, 15.133374604658606, 15.546468300338232,
... 14.086029056264687, 15.005005283626572, 14.935258239627313,
... 13.494170998739259, 15.537997178661033, 15.320711100998848,
... 17.389112043240686, 15.202969177309964, 14.85529769183508]]
>>> means = [5.011267842911003, 10.011267842911003, 15.011267842911002]
>>> variance = 0.9618530973487494
>>> probabilities = [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]
>>> predict_y_values(x_items, means, variance,
... probabilities) # doctest: +NORMALIZE_WHITESPACE
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2]
"""
# An empty list to store generated discriminant values of all items in dataset for
# each class
results = []
# for loop iterates over number of elements in list
for i in range(len(x_items)):
# for loop iterates over number of inner items of each element
for j in range(len(x_items[i])):
temp = [] # to store all discriminant values of each item as a list
# for loop iterates over number of classes we have in our dataset
for k in range(len(x_items)):
# appending values of discriminants for each class to 'temp' list
temp.append(
x_items[i][j] * (means[k] / variance)
- (means[k] ** 2 / (2 * variance))
+ log(probabilities[k])
)
# appending discriminant values of each item to 'results' list
results.append(temp)
return [result.index(max(result)) for result in results]
# Calculating Accuracy
def accuracy(actual_y: list, predicted_y: list) -> float:
"""
Calculate the value of accuracy based-on predictions
:param actual_y:a list containing initial Y values generated by 'y_generator'
function
:param predicted_y: a list containing predicted Y values generated by
'predict_y_values' function
:return: percentage of accuracy
>>> actual_y = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
... 1, 1 ,1 ,1 ,1 ,1 ,1]
>>> predicted_y = [0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0,
... 0, 0, 1, 1, 1, 0, 1, 1, 1]
>>> accuracy(actual_y, predicted_y)
50.0
>>> actual_y = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
... 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
>>> predicted_y = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
... 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
>>> accuracy(actual_y, predicted_y)
100.0
"""
# iterate over one element of each list at a time (zip mode)
# prediction is correct if actual Y value equals to predicted Y value
correct = sum(1 for i, j in zip(actual_y, predicted_y) if i == j)
# percentage of accuracy equals to number of correct predictions divided by number
# of all data and multiplied by 100
return (correct / len(actual_y)) * 100
num = TypeVar("num")
def valid_input(
input_type: Callable[[object], num], # Usually float or int
input_msg: str,
err_msg: str,
condition: Callable[[num], bool] = lambda x: True,
default: str = None,
) -> num:
"""
Ask for user value and validate that it fulfill a condition.
:input_type: user input expected type of value
:input_msg: message to show user in the screen
:err_msg: message to show in the screen in case of error
:condition: function that represents the condition that user input is valid.
:default: Default value in case the user does not type anything
:return: user's input
"""
while True:
try:
user_input = input_type(input(input_msg).strip() or default)
if condition(user_input):
return user_input
else:
print(f"{user_input}: {err_msg}")
continue
except ValueError:
print(
f"{user_input}: Incorrect input type, expected {input_type.__name__!r}"
)
# Main Function
def main():
"""This function starts execution phase"""
while True:
print(" Linear Discriminant Analysis ".center(50, "*"))
print("*" * 50, "\n")
print("First of all we should specify the number of classes that")
print("we want to generate as training dataset")
# Trying to get number of classes
n_classes = valid_input(
input_type=int,
condition=lambda x: x > 0,
input_msg="Enter the number of classes (Data Groupings): ",
err_msg="Number of classes should be positive!",
)
print("-" * 100)
# Trying to get the value of standard deviation
std_dev = valid_input(
input_type=float,
condition=lambda x: x >= 0,
input_msg=(
"Enter the value of standard deviation"
"(Default value is 1.0 for all classes): "
),
err_msg="Standard deviation should not be negative!",
default="1.0",
)
print("-" * 100)
# Trying to get number of instances in classes and theirs means to generate
# dataset
counts = [] # An empty list to store instance counts of classes in dataset
for i in range(n_classes):
user_count = valid_input(
input_type=int,
condition=lambda x: x > 0,
input_msg=(f"Enter The number of instances for class_{i+1}: "),
err_msg="Number of instances should be positive!",
)
counts.append(user_count)
print("-" * 100)
# An empty list to store values of user-entered means of classes
user_means = []
for a in range(n_classes):
user_mean = valid_input(
input_type=float,
input_msg=(f"Enter the value of mean for class_{a+1}: "),
err_msg="This is an invalid value.",
)
user_means.append(user_mean)
print("-" * 100)
print("Standard deviation: ", std_dev)
# print out the number of instances in classes in separated line
for i, count in enumerate(counts, 1):
print(f"Number of instances in class_{i} is: {count}")
print("-" * 100)
# print out mean values of classes separated line
for i, user_mean in enumerate(user_means, 1):
print(f"Mean of class_{i} is: {user_mean}")
print("-" * 100)
# Generating training dataset drawn from gaussian distribution
x = [
gaussian_distribution(user_means[j], std_dev, counts[j])
for j in range(n_classes)
]
print("Generated Normal Distribution: \n", x)
print("-" * 100)
# Generating Ys to detecting corresponding classes
y = y_generator(n_classes, counts)
print("Generated Corresponding Ys: \n", y)
print("-" * 100)
# Calculating the value of actual mean for each class
actual_means = [calculate_mean(counts[k], x[k]) for k in range(n_classes)]
# for loop iterates over number of elements in 'actual_means' list and print
# out them in separated line
for i, actual_mean in enumerate(actual_means, 1):
print(f"Actual(Real) mean of class_{i} is: {actual_mean}")
print("-" * 100)
# Calculating the value of probabilities for each class
probabilities = [
calculate_probabilities(counts[i], sum(counts)) for i in range(n_classes)
]
# for loop iterates over number of elements in 'probabilities' list and print
# out them in separated line
for i, probability in enumerate(probabilities, 1):
print(f"Probability of class_{i} is: {probability}")
print("-" * 100)
# Calculating the values of variance for each class
variance = calculate_variance(x, actual_means, sum(counts))
print("Variance: ", variance)
print("-" * 100)
# Predicting Y values
# storing predicted Y values in 'pre_indexes' variable
pre_indexes = predict_y_values(x, actual_means, variance, probabilities)
print("-" * 100)
# Calculating Accuracy of the model
print(f"Accuracy: {accuracy(y, pre_indexes)}")
print("-" * 100)
print(" DONE ".center(100, "+"))
if input("Press any key to restart or 'q' for quit: ").strip().lower() == "q":
print("\n" + "GoodBye!".center(100, "-") + "\n")
break
system("cls" if name == "nt" else "clear")
if __name__ == "__main__":
main()
| """
Linear Discriminant Analysis
Assumptions About Data :
1. The input variables has a gaussian distribution.
2. The variance calculated for each input variables by class grouping is the
same.
3. The mix of classes in your training set is representative of the problem.
Learning The Model :
The LDA model requires the estimation of statistics from the training data :
1. Mean of each input value for each class.
2. Probability of an instance belong to each class.
3. Covariance for the input data for each class
Calculate the class means :
mean(x) = 1/n ( for i = 1 to i = n --> sum(xi))
Calculate the class probabilities :
P(y = 0) = count(y = 0) / (count(y = 0) + count(y = 1))
P(y = 1) = count(y = 1) / (count(y = 0) + count(y = 1))
Calculate the variance :
We can calculate the variance for dataset in two steps :
1. Calculate the squared difference for each input variable from the
group mean.
2. Calculate the mean of the squared difference.
------------------------------------------------
Squared_Difference = (x - mean(k)) ** 2
Variance = (1 / (count(x) - count(classes))) *
(for i = 1 to i = n --> sum(Squared_Difference(xi)))
Making Predictions :
discriminant(x) = x * (mean / variance) -
((mean ** 2) / (2 * variance)) + Ln(probability)
---------------------------------------------------------------------------
After calculating the discriminant value for each class, the class with the
largest discriminant value is taken as the prediction.
Author: @EverLookNeverSee
"""
from math import log
from os import name, system
from random import gauss, seed
from typing import Callable, TypeVar
# Make a training dataset drawn from a gaussian distribution
def gaussian_distribution(mean: float, std_dev: float, instance_count: int) -> list:
"""
Generate gaussian distribution instances based-on given mean and standard deviation
:param mean: mean value of class
:param std_dev: value of standard deviation entered by usr or default value of it
:param instance_count: instance number of class
:return: a list containing generated values based-on given mean, std_dev and
instance_count
>>> gaussian_distribution(5.0, 1.0, 20) # doctest: +NORMALIZE_WHITESPACE
[6.288184753155463, 6.4494456086997705, 5.066335808938262, 4.235456349028368,
3.9078267848958586, 5.031334516831717, 3.977896829989127, 3.56317055489747,
5.199311976483754, 5.133374604658605, 5.546468300338232, 4.086029056264687,
5.005005283626573, 4.935258239627312, 3.494170998739258, 5.537997178661033,
5.320711100998849, 7.3891120432406865, 5.202969177309964, 4.855297691835079]
"""
seed(1)
return [gauss(mean, std_dev) for _ in range(instance_count)]
# Make corresponding Y flags to detecting classes
def y_generator(class_count: int, instance_count: list) -> list:
"""
Generate y values for corresponding classes
:param class_count: Number of classes(data groupings) in dataset
:param instance_count: number of instances in class
:return: corresponding values for data groupings in dataset
>>> y_generator(1, [10])
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
>>> y_generator(2, [5, 10])
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
>>> y_generator(4, [10, 5, 15, 20]) # doctest: +NORMALIZE_WHITESPACE
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
"""
return [k for k in range(class_count) for _ in range(instance_count[k])]
# Calculate the class means
def calculate_mean(instance_count: int, items: list) -> float:
"""
Calculate given class mean
:param instance_count: Number of instances in class
:param items: items that related to specific class(data grouping)
:return: calculated actual mean of considered class
>>> items = gaussian_distribution(5.0, 1.0, 20)
>>> calculate_mean(len(items), items)
5.011267842911003
"""
# the sum of all items divided by number of instances
return sum(items) / instance_count
# Calculate the class probabilities
def calculate_probabilities(instance_count: int, total_count: int) -> float:
"""
Calculate the probability that a given instance will belong to which class
:param instance_count: number of instances in class
:param total_count: the number of all instances
:return: value of probability for considered class
>>> calculate_probabilities(20, 60)
0.3333333333333333
>>> calculate_probabilities(30, 100)
0.3
"""
# number of instances in specific class divided by number of all instances
return instance_count / total_count
# Calculate the variance
def calculate_variance(items: list, means: list, total_count: int) -> float:
"""
Calculate the variance
:param items: a list containing all items(gaussian distribution of all classes)
:param means: a list containing real mean values of each class
:param total_count: the number of all instances
:return: calculated variance for considered dataset
>>> items = gaussian_distribution(5.0, 1.0, 20)
>>> means = [5.011267842911003]
>>> total_count = 20
>>> calculate_variance([items], means, total_count)
0.9618530973487491
"""
squared_diff = [] # An empty list to store all squared differences
# iterate over number of elements in items
for i in range(len(items)):
# for loop iterates over number of elements in inner layer of items
for j in range(len(items[i])):
# appending squared differences to 'squared_diff' list
squared_diff.append((items[i][j] - means[i]) ** 2)
# one divided by (the number of all instances - number of classes) multiplied by
# sum of all squared differences
n_classes = len(means) # Number of classes in dataset
return 1 / (total_count - n_classes) * sum(squared_diff)
# Making predictions
def predict_y_values(
x_items: list, means: list, variance: float, probabilities: list
) -> list:
"""This function predicts new indexes(groups for our data)
:param x_items: a list containing all items(gaussian distribution of all classes)
:param means: a list containing real mean values of each class
:param variance: calculated value of variance by calculate_variance function
:param probabilities: a list containing all probabilities of classes
:return: a list containing predicted Y values
>>> x_items = [[6.288184753155463, 6.4494456086997705, 5.066335808938262,
... 4.235456349028368, 3.9078267848958586, 5.031334516831717,
... 3.977896829989127, 3.56317055489747, 5.199311976483754,
... 5.133374604658605, 5.546468300338232, 4.086029056264687,
... 5.005005283626573, 4.935258239627312, 3.494170998739258,
... 5.537997178661033, 5.320711100998849, 7.3891120432406865,
... 5.202969177309964, 4.855297691835079], [11.288184753155463,
... 11.44944560869977, 10.066335808938263, 9.235456349028368,
... 8.907826784895859, 10.031334516831716, 8.977896829989128,
... 8.56317055489747, 10.199311976483754, 10.133374604658606,
... 10.546468300338232, 9.086029056264687, 10.005005283626572,
... 9.935258239627313, 8.494170998739259, 10.537997178661033,
... 10.320711100998848, 12.389112043240686, 10.202969177309964,
... 9.85529769183508], [16.288184753155463, 16.449445608699772,
... 15.066335808938263, 14.235456349028368, 13.907826784895859,
... 15.031334516831716, 13.977896829989128, 13.56317055489747,
... 15.199311976483754, 15.133374604658606, 15.546468300338232,
... 14.086029056264687, 15.005005283626572, 14.935258239627313,
... 13.494170998739259, 15.537997178661033, 15.320711100998848,
... 17.389112043240686, 15.202969177309964, 14.85529769183508]]
>>> means = [5.011267842911003, 10.011267842911003, 15.011267842911002]
>>> variance = 0.9618530973487494
>>> probabilities = [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]
>>> predict_y_values(x_items, means, variance,
... probabilities) # doctest: +NORMALIZE_WHITESPACE
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2]
"""
# An empty list to store generated discriminant values of all items in dataset for
# each class
results = []
# for loop iterates over number of elements in list
for i in range(len(x_items)):
# for loop iterates over number of inner items of each element
for j in range(len(x_items[i])):
temp = [] # to store all discriminant values of each item as a list
# for loop iterates over number of classes we have in our dataset
for k in range(len(x_items)):
# appending values of discriminants for each class to 'temp' list
temp.append(
x_items[i][j] * (means[k] / variance)
- (means[k] ** 2 / (2 * variance))
+ log(probabilities[k])
)
# appending discriminant values of each item to 'results' list
results.append(temp)
return [result.index(max(result)) for result in results]
# Calculating Accuracy
def accuracy(actual_y: list, predicted_y: list) -> float:
"""
Calculate the value of accuracy based-on predictions
:param actual_y:a list containing initial Y values generated by 'y_generator'
function
:param predicted_y: a list containing predicted Y values generated by
'predict_y_values' function
:return: percentage of accuracy
>>> actual_y = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
... 1, 1 ,1 ,1 ,1 ,1 ,1]
>>> predicted_y = [0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0,
... 0, 0, 1, 1, 1, 0, 1, 1, 1]
>>> accuracy(actual_y, predicted_y)
50.0
>>> actual_y = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
... 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
>>> predicted_y = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
... 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
>>> accuracy(actual_y, predicted_y)
100.0
"""
# iterate over one element of each list at a time (zip mode)
# prediction is correct if actual Y value equals to predicted Y value
correct = sum(1 for i, j in zip(actual_y, predicted_y) if i == j)
# percentage of accuracy equals to number of correct predictions divided by number
# of all data and multiplied by 100
return (correct / len(actual_y)) * 100
num = TypeVar("num")
def valid_input(
input_type: Callable[[object], num], # Usually float or int
input_msg: str,
err_msg: str,
condition: Callable[[num], bool] = lambda x: True,
default: str = None,
) -> num:
"""
Ask for user value and validate that it fulfill a condition.
:input_type: user input expected type of value
:input_msg: message to show user in the screen
:err_msg: message to show in the screen in case of error
:condition: function that represents the condition that user input is valid.
:default: Default value in case the user does not type anything
:return: user's input
"""
while True:
try:
user_input = input_type(input(input_msg).strip() or default)
if condition(user_input):
return user_input
else:
print(f"{user_input}: {err_msg}")
continue
except ValueError:
print(
f"{user_input}: Incorrect input type, expected {input_type.__name__!r}"
)
# Main Function
def main():
"""This function starts execution phase"""
while True:
print(" Linear Discriminant Analysis ".center(50, "*"))
print("*" * 50, "\n")
print("First of all we should specify the number of classes that")
print("we want to generate as training dataset")
# Trying to get number of classes
n_classes = valid_input(
input_type=int,
condition=lambda x: x > 0,
input_msg="Enter the number of classes (Data Groupings): ",
err_msg="Number of classes should be positive!",
)
print("-" * 100)
# Trying to get the value of standard deviation
std_dev = valid_input(
input_type=float,
condition=lambda x: x >= 0,
input_msg=(
"Enter the value of standard deviation"
"(Default value is 1.0 for all classes): "
),
err_msg="Standard deviation should not be negative!",
default="1.0",
)
print("-" * 100)
# Trying to get number of instances in classes and theirs means to generate
# dataset
counts = [] # An empty list to store instance counts of classes in dataset
for i in range(n_classes):
user_count = valid_input(
input_type=int,
condition=lambda x: x > 0,
input_msg=(f"Enter The number of instances for class_{i+1}: "),
err_msg="Number of instances should be positive!",
)
counts.append(user_count)
print("-" * 100)
# An empty list to store values of user-entered means of classes
user_means = []
for a in range(n_classes):
user_mean = valid_input(
input_type=float,
input_msg=(f"Enter the value of mean for class_{a+1}: "),
err_msg="This is an invalid value.",
)
user_means.append(user_mean)
print("-" * 100)
print("Standard deviation: ", std_dev)
# print out the number of instances in classes in separated line
for i, count in enumerate(counts, 1):
print(f"Number of instances in class_{i} is: {count}")
print("-" * 100)
# print out mean values of classes separated line
for i, user_mean in enumerate(user_means, 1):
print(f"Mean of class_{i} is: {user_mean}")
print("-" * 100)
# Generating training dataset drawn from gaussian distribution
x = [
gaussian_distribution(user_means[j], std_dev, counts[j])
for j in range(n_classes)
]
print("Generated Normal Distribution: \n", x)
print("-" * 100)
# Generating Ys to detecting corresponding classes
y = y_generator(n_classes, counts)
print("Generated Corresponding Ys: \n", y)
print("-" * 100)
# Calculating the value of actual mean for each class
actual_means = [calculate_mean(counts[k], x[k]) for k in range(n_classes)]
# for loop iterates over number of elements in 'actual_means' list and print
# out them in separated line
for i, actual_mean in enumerate(actual_means, 1):
print(f"Actual(Real) mean of class_{i} is: {actual_mean}")
print("-" * 100)
# Calculating the value of probabilities for each class
probabilities = [
calculate_probabilities(counts[i], sum(counts)) for i in range(n_classes)
]
# for loop iterates over number of elements in 'probabilities' list and print
# out them in separated line
for i, probability in enumerate(probabilities, 1):
print(f"Probability of class_{i} is: {probability}")
print("-" * 100)
# Calculating the values of variance for each class
variance = calculate_variance(x, actual_means, sum(counts))
print("Variance: ", variance)
print("-" * 100)
# Predicting Y values
# storing predicted Y values in 'pre_indexes' variable
pre_indexes = predict_y_values(x, actual_means, variance, probabilities)
print("-" * 100)
# Calculating Accuracy of the model
print(f"Accuracy: {accuracy(y, pre_indexes)}")
print("-" * 100)
print(" DONE ".center(100, "+"))
if input("Press any key to restart or 'q' for quit: ").strip().lower() == "q":
print("\n" + "GoodBye!".center(100, "-") + "\n")
break
system("cls" if name == "nt" else "clear")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Python program for generating diamond pattern in Python 3.7+
# Function to print upper half of diamond (pyramid)
def floyd(n):
"""
Parameters:
n : size of pattern
"""
for i in range(0, n):
for j in range(0, n - i - 1): # printing spaces
print(" ", end="")
for k in range(0, i + 1): # printing stars
print("* ", end="")
print()
# Function to print lower half of diamond (pyramid)
def reverse_floyd(n):
"""
Parameters:
n : size of pattern
"""
for i in range(n, 0, -1):
for j in range(i, 0, -1): # printing stars
print("* ", end="")
print()
for k in range(n - i + 1, 0, -1): # printing spaces
print(" ", end="")
# Function to print complete diamond pattern of "*"
def pretty_print(n):
"""
Parameters:
n : size of pattern
"""
if n <= 0:
print(" ... .... nothing printing :(")
return
floyd(n) # upper half
reverse_floyd(n) # lower half
if __name__ == "__main__":
print(r"| /\ | |- | |- |--| |\ /| |-")
print(r"|/ \| |- |_ |_ |__| | \/ | |_")
K = 1
while K:
user_number = int(input("enter the number and , and see the magic : "))
print()
pretty_print(user_number)
K = int(input("press 0 to exit... and 1 to continue..."))
print("Good Bye...")
| # Python program for generating diamond pattern in Python 3.7+
# Function to print upper half of diamond (pyramid)
def floyd(n):
"""
Parameters:
n : size of pattern
"""
for i in range(0, n):
for j in range(0, n - i - 1): # printing spaces
print(" ", end="")
for k in range(0, i + 1): # printing stars
print("* ", end="")
print()
# Function to print lower half of diamond (pyramid)
def reverse_floyd(n):
"""
Parameters:
n : size of pattern
"""
for i in range(n, 0, -1):
for j in range(i, 0, -1): # printing stars
print("* ", end="")
print()
for k in range(n - i + 1, 0, -1): # printing spaces
print(" ", end="")
# Function to print complete diamond pattern of "*"
def pretty_print(n):
"""
Parameters:
n : size of pattern
"""
if n <= 0:
print(" ... .... nothing printing :(")
return
floyd(n) # upper half
reverse_floyd(n) # lower half
if __name__ == "__main__":
print(r"| /\ | |- | |- |--| |\ /| |-")
print(r"|/ \| |- |_ |_ |__| | \/ | |_")
K = 1
while K:
user_number = int(input("enter the number and , and see the magic : "))
print()
pretty_print(user_number)
K = int(input("press 0 to exit... and 1 to continue..."))
print("Good Bye...")
| -1 |
TheAlgorithms/Python | 4,747 | [mypy] fix type annotations for all Project Euler problems | ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| pikulet | "2021-09-10T06:11:28Z" | "2021-10-11T16:33:45Z" | e311b02e704891b2f31a1e2c8fe2df77a032b09b | bcfca67faa120cc4cb42775af302d6d52d3a3f1e | [mypy] fix type annotations for all Project Euler problems. ### **Describe your change:**
Fixes #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 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 solution(n: str = N) -> int:
"""
Find the thirteen adjacent digits in the 1000-digit number n that have
the greatest product and returns it.
>>> solution("13978431290823798458352374")
609638400
>>> solution("13978431295823798458352374")
2612736000
>>> solution("1397843129582379841238352374")
209018880
"""
largest_product = -sys.maxsize - 1
for i in range(len(n) - 12):
product = 1
for j in range(13):
product *= int(n[i + j])
if product > largest_product:
largest_product = product
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 solution(n: str = N) -> int:
"""
Find the thirteen adjacent digits in the 1000-digit number n that have
the greatest product and returns it.
>>> solution("13978431290823798458352374")
609638400
>>> solution("13978431295823798458352374")
2612736000
>>> solution("1397843129582379841238352374")
209018880
"""
largest_product = -sys.maxsize - 1
for i in range(len(n) - 12):
product = 1
for j in range(13):
product *= int(n[i + j])
if product > largest_product:
largest_product = product
return largest_product
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
Subsets and Splits