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Upgrade to Python 3.13 (#11588)

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Christian Clauss 2024-09-30 23:01:15 +02:00 committed by GitHub
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35 changed files with 135 additions and 131 deletions
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6
.github/workflows/build.yml vendored
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@ -12,7 +12,7 @@ jobs:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: 3.12
python-version: 3.13
allow-prereleases: true
- uses: actions/cache@v4
with:
@ -26,6 +26,10 @@ jobs:
# TODO: #8818 Re-enable quantum tests
run: pytest
--ignore=quantum/q_fourier_transform.py
--ignore=computer_vision/cnn_classification.py
--ignore=dynamic_programming/k_means_clustering_tensorflow.py
--ignore=machine_learning/lstm/lstm_prediction.py
--ignore=neural_network/input_data.py
--ignore=project_euler/
--ignore=scripts/validate_solutions.py
--cov-report=term-missing:skip-covered

1
DIRECTORY.md
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@ -1343,7 +1343,6 @@
* [Get Ip Geolocation](web_programming/get_ip_geolocation.py)
* [Get Top Billionaires](web_programming/get_top_billionaires.py)
* [Get Top Hn Posts](web_programming/get_top_hn_posts.py)
* [Get User Tweets](web_programming/get_user_tweets.py)
* [Giphy](web_programming/giphy.py)
* [Instagram Crawler](web_programming/instagram_crawler.py)
* [Instagram Pic](web_programming/instagram_pic.py)

8
computer_vision/haralick_descriptors.py
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@ -19,7 +19,7 @@ def root_mean_square_error(original: np.ndarray, reference: np.ndarray) -> float
>>> root_mean_square_error(np.array([1, 2, 3]), np.array([6, 4, 2]))
3.1622776601683795
"""
return np.sqrt(((original - reference) ** 2).mean())
return float(np.sqrt(((original - reference) ** 2).mean()))
def normalize_image(
@ -273,7 +273,7 @@ def haralick_descriptors(matrix: np.ndarray) -> list[float]:
>>> morphological = opening_filter(binary)
>>> mask_1 = binary_mask(gray, morphological)[0]
>>> concurrency = matrix_concurrency(mask_1, (0, 1))
>>> haralick_descriptors(concurrency)
>>> [float(f) for f in haralick_descriptors(concurrency)]
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
"""
# Function np.indices could be used for bigger input types,
@ -335,7 +335,7 @@ def get_descriptors(
return np.concatenate(descriptors, axis=None)
def euclidean(point_1: np.ndarray, point_2: np.ndarray) -> np.float32:
def euclidean(point_1: np.ndarray, point_2: np.ndarray) -> float:
"""
Simple method for calculating the euclidean distance between two points,
with type np.ndarray.
@ -346,7 +346,7 @@ def euclidean(point_1: np.ndarray, point_2: np.ndarray) -> np.float32:
>>> euclidean(a, b)
3.3166247903554
"""
return np.sqrt(np.sum(np.square(point_1 - point_2)))
return float(np.sqrt(np.sum(np.square(point_1 - point_2))))
def get_distances(descriptors: np.ndarray, base: int) -> list[tuple[int, float]]:

6
data_structures/heap/binomial_heap.py
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@ -73,7 +73,7 @@ class BinomialHeap:
30
Deleting - delete() test
>>> [first_heap.delete_min() for _ in range(20)]
>>> [int(first_heap.delete_min()) for _ in range(20)]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
Create a new Heap
@ -118,7 +118,7 @@ class BinomialHeap:
values in merged heap; (merge is inplace)
>>> results = []
>>> while not first_heap.is_empty():
... results.append(first_heap.delete_min())
... results.append(int(first_heap.delete_min()))
>>> results
[17, 20, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 34]
"""
@ -354,7 +354,7 @@ class BinomialHeap:
# Merge heaps
self.merge_heaps(new_heap)
return min_value
return int(min_value)
def pre_order(self):
"""

6
electronics/circular_convolution.py
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@ -39,7 +39,7 @@ class CircularConvolution:
Usage:
>>> convolution = CircularConvolution()
>>> convolution.circular_convolution()
[10, 10, 6, 14]
[10.0, 10.0, 6.0, 14.0]
>>> convolution.first_signal = [0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6]
>>> convolution.second_signal = [0.1, 0.3, 0.5, 0.7, 0.9, 1.1, 1.3, 1.5]
@ -54,7 +54,7 @@ class CircularConvolution:
>>> convolution.first_signal = [1, -1, 2, 3, -1]
>>> convolution.second_signal = [1, 2, 3]
>>> convolution.circular_convolution()
[8, -2, 3, 4, 11]
[8.0, -2.0, 3.0, 4.0, 11.0]
"""
@ -91,7 +91,7 @@ class CircularConvolution:
final_signal = np.matmul(np.transpose(matrix), np.transpose(self.first_signal))
# rounding-off to two decimal places
return [round(i, 2) for i in final_signal]
return [float(round(i, 2)) for i in final_signal]
if __name__ == "__main__":

18
fractals/julia_sets.py
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@ -40,11 +40,11 @@ nb_pixels = 666
def eval_exponential(c_parameter: complex, z_values: np.ndarray) -> np.ndarray:
"""
Evaluate $e^z + c$.
>>> eval_exponential(0, 0)
>>> float(eval_exponential(0, 0))
1.0
>>> abs(eval_exponential(1, np.pi*1.j)) < 1e-15
>>> bool(abs(eval_exponential(1, np.pi*1.j)) < 1e-15)
True
>>> abs(eval_exponential(1.j, 0)-1-1.j) < 1e-15
>>> bool(abs(eval_exponential(1.j, 0)-1-1.j) < 1e-15)
True
"""
return np.exp(z_values) + c_parameter
@ -98,20 +98,20 @@ def iterate_function(
>>> iterate_function(eval_quadratic_polynomial, 0, 3, np.array([0,1,2])).shape
(3,)
>>> np.round(iterate_function(eval_quadratic_polynomial,
>>> complex(np.round(iterate_function(eval_quadratic_polynomial,
... 0,
... 3,
... np.array([0,1,2]))[0])
... np.array([0,1,2]))[0]))
0j
>>> np.round(iterate_function(eval_quadratic_polynomial,
>>> complex(np.round(iterate_function(eval_quadratic_polynomial,
... 0,
... 3,
... np.array([0,1,2]))[1])
... np.array([0,1,2]))[1]))
(1+0j)
>>> np.round(iterate_function(eval_quadratic_polynomial,
>>> complex(np.round(iterate_function(eval_quadratic_polynomial,
... 0,
... 3,
... np.array([0,1,2]))[2])
... np.array([0,1,2]))[2]))
(256+0j)
"""

8
graphics/bezier_curve.py
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@ -30,9 +30,9 @@ class BezierCurve:
returns the x, y values of basis function at time t
>>> curve = BezierCurve([(1,1), (1,2)])
>>> curve.basis_function(0)
>>> [float(x) for x in curve.basis_function(0)]
[1.0, 0.0]
>>> curve.basis_function(1)
>>> [float(x) for x in curve.basis_function(1)]
[0.0, 1.0]
"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
@ -55,9 +55,9 @@ class BezierCurve:
The last point in the curve is when t = 1.
>>> curve = BezierCurve([(1,1), (1,2)])
>>> curve.bezier_curve_function(0)
>>> tuple(float(x) for x in curve.bezier_curve_function(0))
(1.0, 1.0)
>>> curve.bezier_curve_function(1)
>>> tuple(float(x) for x in curve.bezier_curve_function(1))
(1.0, 2.0)
"""

2
graphs/dijkstra_binary_grid.py
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@ -69,7 +69,7 @@ def dijkstra(
x, y = predecessors[x, y]
path.append(source) # add the source manually
path.reverse()
return matrix[destination], path
return float(matrix[destination]), path
for i in range(len(dx)):
nx, ny = x + dx[i], y + dy[i]

2
linear_algebra/src/power_iteration.py
View File

@ -78,7 +78,7 @@ def power_iteration(
if is_complex:
lambda_ = np.real(lambda_)
return lambda_, vector
return float(lambda_), vector
def test_power_iteration() -> None:

32
linear_programming/simplex.py
View File

@ -107,8 +107,8 @@ class Tableau:
def find_pivot(self) -> tuple[Any, Any]:
"""Finds the pivot row and column.
>>> Tableau(np.array([[-2,1,0,0,0], [3,1,1,0,6], [1,2,0,1,7.]]),
... 2, 0).find_pivot()
>>> tuple(int(x) for x in Tableau(np.array([[-2,1,0,0,0], [3,1,1,0,6],
... [1,2,0,1,7.]]), 2, 0).find_pivot())
(1, 0)
"""
objective = self.objectives[-1]
@ -215,8 +215,8 @@ class Tableau:
Max: x1 + x2
ST: x1 + 3x2 <= 4
3x1 + x2 <= 4
>>> Tableau(np.array([[-1,-1,0,0,0],[1,3,1,0,4],[3,1,0,1,4.]]),
... 2, 0).run_simplex()
>>> {key: float(value) for key, value in Tableau(np.array([[-1,-1,0,0,0],
... [1,3,1,0,4],[3,1,0,1,4.]]), 2, 0).run_simplex().items()}
{'P': 2.0, 'x1': 1.0, 'x2': 1.0}
# Standard linear program with 3 variables:
@ -224,21 +224,21 @@ class Tableau:
ST: 2x1 + x2 + x3 ≤ 2
x1 + 2x2 + 3x3 ≤ 5
2x1 + 2x2 + x3 ≤ 6
>>> Tableau(np.array([
>>> {key: float(value) for key, value in Tableau(np.array([
... [-3,-1,-3,0,0,0,0],
... [2,1,1,1,0,0,2],
... [1,2,3,0,1,0,5],
... [2,2,1,0,0,1,6.]
... ]),3,0).run_simplex() # doctest: +ELLIPSIS
... ]),3,0).run_simplex().items()} # doctest: +ELLIPSIS
{'P': 5.4, 'x1': 0.199..., 'x3': 1.6}
# Optimal tableau input:
>>> Tableau(np.array([
>>> {key: float(value) for key, value in Tableau(np.array([
... [0, 0, 0.25, 0.25, 2],
... [0, 1, 0.375, -0.125, 1],
... [1, 0, -0.125, 0.375, 1]
... ]), 2, 0).run_simplex()
... ]), 2, 0).run_simplex().items()}
{'P': 2.0, 'x1': 1.0, 'x2': 1.0}
# Non-standard: >= constraints
@ -246,25 +246,25 @@ class Tableau:
ST: x1 + x2 + x3 <= 40
2x1 + x2 - x3 >= 10
- x2 + x3 >= 10
>>> Tableau(np.array([
>>> {key: float(value) for key, value in Tableau(np.array([
... [2, 0, 0, 0, -1, -1, 0, 0, 20],
... [-2, -3, -1, 0, 0, 0, 0, 0, 0],
... [1, 1, 1, 1, 0, 0, 0, 0, 40],
... [2, 1, -1, 0, -1, 0, 1, 0, 10],
... [0, -1, 1, 0, 0, -1, 0, 1, 10.]
... ]), 3, 2).run_simplex()
... ]), 3, 2).run_simplex().items()}
{'P': 70.0, 'x1': 10.0, 'x2': 10.0, 'x3': 20.0}
# Non standard: minimisation and equalities
Min: x1 + x2
ST: 2x1 + x2 = 12
6x1 + 5x2 = 40
>>> Tableau(np.array([
>>> {key: float(value) for key, value in Tableau(np.array([
... [8, 6, 0, 0, 52],
... [1, 1, 0, 0, 0],
... [2, 1, 1, 0, 12],
... [6, 5, 0, 1, 40.],
... ]), 2, 2).run_simplex()
... ]), 2, 2).run_simplex().items()}
{'P': 7.0, 'x1': 5.0, 'x2': 2.0}
@ -275,7 +275,7 @@ class Tableau:
2x1 + 4x2 <= 48
x1 + x2 >= 10
x1 >= 2
>>> Tableau(np.array([
>>> {key: float(value) for key, value in Tableau(np.array([
... [2, 1, 0, 0, 0, -1, -1, 0, 0, 12.0],
... [-8, -6, 0, 0, 0, 0, 0, 0, 0, 0.0],
... [1, 3, 1, 0, 0, 0, 0, 0, 0, 33.0],
@ -283,7 +283,7 @@ class Tableau:
... [2, 4, 0, 0, 1, 0, 0, 0, 0, 48.0],
... [1, 1, 0, 0, 0, -1, 0, 1, 0, 10.0],
... [1, 0, 0, 0, 0, 0, -1, 0, 1, 2.0]
... ]), 2, 2).run_simplex() # doctest: +ELLIPSIS
... ]), 2, 2).run_simplex().items()} # doctest: +ELLIPSIS
{'P': 132.0, 'x1': 12.000... 'x2': 5.999...}
"""
# Stop simplex algorithm from cycling.
@ -307,11 +307,11 @@ class Tableau:
def interpret_tableau(self) -> dict[str, float]:
"""Given the final tableau, add the corresponding values of the basic
decision variables to the `output_dict`
>>> Tableau(np.array([
>>> {key: float(value) for key, value in Tableau(np.array([
... [0,0,0.875,0.375,5],
... [0,1,0.375,-0.125,1],
... [1,0,-0.125,0.375,1]
... ]),2, 0).interpret_tableau()
... ]),2, 0).interpret_tableau().items()}
{'P': 5.0, 'x1': 1.0, 'x2': 1.0}
"""
# P = RHS of final tableau

8
machine_learning/decision_tree.py
View File

@ -26,15 +26,15 @@ class DecisionTree:
>>> tester = DecisionTree()
>>> test_labels = np.array([1,2,3,4,5,6,7,8,9,10])
>>> test_prediction = float(6)
>>> tester.mean_squared_error(test_labels, test_prediction) == (
>>> bool(tester.mean_squared_error(test_labels, test_prediction) == (
... TestDecisionTree.helper_mean_squared_error_test(test_labels,
... test_prediction))
... test_prediction)))
True
>>> test_labels = np.array([1,2,3])
>>> test_prediction = float(2)
>>> tester.mean_squared_error(test_labels, test_prediction) == (
>>> bool(tester.mean_squared_error(test_labels, test_prediction) == (
... TestDecisionTree.helper_mean_squared_error_test(test_labels,
... test_prediction))
... test_prediction)))
True
"""
if labels.ndim != 1:

8
machine_learning/forecasting/run.py
View File

@ -28,7 +28,7 @@ def linear_regression_prediction(
input : training data (date, total_user, total_event) in list of float
output : list of total user prediction in float
>>> n = linear_regression_prediction([2,3,4,5], [5,3,4,6], [3,1,2,4], [2,1], [2,2])
>>> abs(n - 5.0) < 1e-6 # Checking precision because of floating point errors
>>> bool(abs(n - 5.0) < 1e-6) # Checking precision because of floating point errors
True
"""
x = np.array([[1, item, train_mtch[i]] for i, item in enumerate(train_dt)])
@ -56,7 +56,7 @@ def sarimax_predictor(train_user: list, train_match: list, test_match: list) ->
)
model_fit = model.fit(disp=False, maxiter=600, method="nm")
result = model_fit.predict(1, len(test_match), exog=[test_match])
return result[0]
return float(result[0])
def support_vector_regressor(x_train: list, x_test: list, train_user: list) -> float:
@ -75,7 +75,7 @@ def support_vector_regressor(x_train: list, x_test: list, train_user: list) -> f
regressor = SVR(kernel="rbf", C=1, gamma=0.1, epsilon=0.1)
regressor.fit(x_train, train_user)
y_pred = regressor.predict(x_test)
return y_pred[0]
return float(y_pred[0])
def interquartile_range_checker(train_user: list) -> float:
@ -92,7 +92,7 @@ def interquartile_range_checker(train_user: list) -> float:
q3 = np.percentile(train_user, 75)
iqr = q3 - q1
low_lim = q1 - (iqr * 0.1)
return low_lim
return float(low_lim)
def data_safety_checker(list_vote: list, actual_result: float) -> bool:

2
machine_learning/k_nearest_neighbours.py
View File

@ -42,7 +42,7 @@ class KNN:
>>> KNN._euclidean_distance(np.array([1, 2, 3]), np.array([1, 8, 11]))
10.0
"""
return np.linalg.norm(a - b)
return float(np.linalg.norm(a - b))
def classify(self, pred_point: np.ndarray[float], k: int = 5) -> str:
"""

4
machine_learning/logistic_regression.py
View File

@ -45,7 +45,7 @@ def sigmoid_function(z: float | np.ndarray) -> float | np.ndarray:
@returns: returns value in the range 0 to 1
Examples:
>>> sigmoid_function(4)
>>> float(sigmoid_function(4))
0.9820137900379085
>>> sigmoid_function(np.array([-3, 3]))
array([0.04742587, 0.95257413])
@ -100,7 +100,7 @@ def cost_function(h: np.ndarray, y: np.ndarray) -> float:
References:
- https://en.wikipedia.org/wiki/Logistic_regression
"""
return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean()
return float((-y * np.log(h) - (1 - y) * np.log(1 - h)).mean())
def log_likelihood(x, y, weights):

40
machine_learning/loss_functions.py
View File

@ -22,7 +22,7 @@ def binary_cross_entropy(
>>> true_labels = np.array([0, 1, 1, 0, 1])
>>> predicted_probs = np.array([0.2, 0.7, 0.9, 0.3, 0.8])
>>> binary_cross_entropy(true_labels, predicted_probs)
>>> float(binary_cross_entropy(true_labels, predicted_probs))
0.2529995012327421
>>> true_labels = np.array([0, 1, 1, 0, 1])
>>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
@ -68,7 +68,7 @@ def binary_focal_cross_entropy(
>>> true_labels = np.array([0, 1, 1, 0, 1])
>>> predicted_probs = np.array([0.2, 0.7, 0.9, 0.3, 0.8])
>>> binary_focal_cross_entropy(true_labels, predicted_probs)
>>> float(binary_focal_cross_entropy(true_labels, predicted_probs))
0.008257977659239775
>>> true_labels = np.array([0, 1, 1, 0, 1])
>>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
@ -108,7 +108,7 @@ def categorical_cross_entropy(
>>> true_labels = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
>>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1], [0.0, 0.1, 0.9]])
>>> categorical_cross_entropy(true_labels, pred_probs)
>>> float(categorical_cross_entropy(true_labels, pred_probs))
0.567395975254385
>>> true_labels = np.array([[1, 0], [0, 1]])
>>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
@ -179,13 +179,13 @@ def categorical_focal_cross_entropy(
>>> true_labels = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
>>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1], [0.0, 0.1, 0.9]])
>>> alpha = np.array([0.6, 0.2, 0.7])
>>> categorical_focal_cross_entropy(true_labels, pred_probs, alpha)
>>> float(categorical_focal_cross_entropy(true_labels, pred_probs, alpha))
0.0025966118981496423
>>> true_labels = np.array([[0, 1, 0], [0, 0, 1]])
>>> pred_probs = np.array([[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
>>> alpha = np.array([0.25, 0.25, 0.25])
>>> categorical_focal_cross_entropy(true_labels, pred_probs, alpha)
>>> float(categorical_focal_cross_entropy(true_labels, pred_probs, alpha))
0.23315276982014324
>>> true_labels = np.array([[1, 0], [0, 1]])
@ -265,7 +265,7 @@ def hinge_loss(y_true: np.ndarray, y_pred: np.ndarray) -> float:
>>> true_labels = np.array([-1, 1, 1, -1, 1])
>>> pred = np.array([-4, -0.3, 0.7, 5, 10])
>>> hinge_loss(true_labels, pred)
>>> float(hinge_loss(true_labels, pred))
1.52
>>> true_labels = np.array([-1, 1, 1, -1, 1, 1])
>>> pred = np.array([-4, -0.3, 0.7, 5, 10])
@ -309,11 +309,11 @@ def huber_loss(y_true: np.ndarray, y_pred: np.ndarray, delta: float) -> float:
>>> true_values = np.array([0.9, 10.0, 2.0, 1.0, 5.2])
>>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
>>> np.isclose(huber_loss(true_values, predicted_values, 1.0), 2.102)
>>> bool(np.isclose(huber_loss(true_values, predicted_values, 1.0), 2.102))
True
>>> true_labels = np.array([11.0, 21.0, 3.32, 4.0, 5.0])
>>> predicted_probs = np.array([8.3, 20.8, 2.9, 11.2, 5.0])
>>> np.isclose(huber_loss(true_labels, predicted_probs, 1.0), 1.80164)
>>> bool(np.isclose(huber_loss(true_labels, predicted_probs, 1.0), 1.80164))
True
>>> true_labels = np.array([11.0, 21.0, 3.32, 4.0])
>>> predicted_probs = np.array([8.3, 20.8, 2.9, 11.2, 5.0])
@ -347,7 +347,7 @@ def mean_squared_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
>>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
>>> np.isclose(mean_squared_error(true_values, predicted_values), 0.028)
>>> bool(np.isclose(mean_squared_error(true_values, predicted_values), 0.028))
True
>>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
@ -381,11 +381,11 @@ def mean_absolute_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
>>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
>>> np.isclose(mean_absolute_error(true_values, predicted_values), 0.16)
>>> bool(np.isclose(mean_absolute_error(true_values, predicted_values), 0.16))
True
>>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
>>> np.isclose(mean_absolute_error(true_values, predicted_values), 2.16)
>>> bool(np.isclose(mean_absolute_error(true_values, predicted_values), 2.16))
False
>>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> predicted_probs = np.array([0.3, 0.8, 0.9, 5.2])
@ -420,7 +420,7 @@ def mean_squared_logarithmic_error(y_true: np.ndarray, y_pred: np.ndarray) -> fl
>>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
>>> mean_squared_logarithmic_error(true_values, predicted_values)
>>> float(mean_squared_logarithmic_error(true_values, predicted_values))
0.0030860877925181344
>>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
@ -459,17 +459,17 @@ def mean_absolute_percentage_error(
Examples:
>>> y_true = np.array([10, 20, 30, 40])
>>> y_pred = np.array([12, 18, 33, 45])
>>> mean_absolute_percentage_error(y_true, y_pred)
>>> float(mean_absolute_percentage_error(y_true, y_pred))
0.13125
>>> y_true = np.array([1, 2, 3, 4])
>>> y_pred = np.array([2, 3, 4, 5])
>>> mean_absolute_percentage_error(y_true, y_pred)
>>> float(mean_absolute_percentage_error(y_true, y_pred))
0.5208333333333333
>>> y_true = np.array([34, 37, 44, 47, 48, 48, 46, 43, 32, 27, 26, 24])
>>> y_pred = np.array([37, 40, 46, 44, 46, 50, 45, 44, 34, 30, 22, 23])
>>> mean_absolute_percentage_error(y_true, y_pred)
>>> float(mean_absolute_percentage_error(y_true, y_pred))
0.064671076436071
"""
if len(y_true) != len(y_pred):
@ -511,7 +511,7 @@ def perplexity_loss(
... [[0.03, 0.26, 0.21, 0.18, 0.30],
... [0.28, 0.10, 0.33, 0.15, 0.12]]]
... )
>>> perplexity_loss(y_true, y_pred)
>>> float(perplexity_loss(y_true, y_pred))
5.0247347775367945
>>> y_true = np.array([[1, 4], [2, 3]])
>>> y_pred = np.array(
@ -600,17 +600,17 @@ def smooth_l1_loss(y_true: np.ndarray, y_pred: np.ndarray, beta: float = 1.0) ->
>>> y_true = np.array([3, 5, 2, 7])
>>> y_pred = np.array([2.9, 4.8, 2.1, 7.2])
>>> smooth_l1_loss(y_true, y_pred, 1.0)
>>> float(smooth_l1_loss(y_true, y_pred, 1.0))
0.012500000000000022
>>> y_true = np.array([2, 4, 6])
>>> y_pred = np.array([1, 5, 7])
>>> smooth_l1_loss(y_true, y_pred, 1.0)
>>> float(smooth_l1_loss(y_true, y_pred, 1.0))
0.5
>>> y_true = np.array([1, 3, 5, 7])
>>> y_pred = np.array([1, 3, 5, 7])
>>> smooth_l1_loss(y_true, y_pred, 1.0)
>>> float(smooth_l1_loss(y_true, y_pred, 1.0))
0.0
>>> y_true = np.array([1, 3, 5])
@ -647,7 +647,7 @@ def kullback_leibler_divergence(y_true: np.ndarray, y_pred: np.ndarray) -> float
>>> true_labels = np.array([0.2, 0.3, 0.5])
>>> predicted_probs = np.array([0.3, 0.3, 0.4])
>>> kullback_leibler_divergence(true_labels, predicted_probs)
>>> float(kullback_leibler_divergence(true_labels, predicted_probs))
0.030478754035472025
>>> true_labels = np.array([0.2, 0.3, 0.5])
>>> predicted_probs = np.array([0.3, 0.3, 0.4, 0.5])

13
machine_learning/mfcc.py
View File

@ -162,9 +162,9 @@ def normalize(audio: np.ndarray) -> np.ndarray:
Examples:
>>> audio = np.array([1, 2, 3, 4, 5])
>>> normalized_audio = normalize(audio)
>>> np.max(normalized_audio)
>>> float(np.max(normalized_audio))
1.0
>>> np.min(normalized_audio)
>>> float(np.min(normalized_audio))
0.2
"""
# Divide the entire audio signal by the maximum absolute value
@ -229,7 +229,8 @@ def calculate_fft(audio_windowed: np.ndarray, ftt_size: int = 1024) -> np.ndarra
Examples:
>>> audio_windowed = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
>>> audio_fft = calculate_fft(audio_windowed, ftt_size=4)
>>> np.allclose(audio_fft[0], np.array([6.0+0.j, -1.5+0.8660254j, -1.5-0.8660254j]))
>>> bool(np.allclose(audio_fft[0], np.array([6.0+0.j, -1.5+0.8660254j,
... -1.5-0.8660254j])))
True
"""
# Transpose the audio data to have time in rows and channels in columns
@ -281,7 +282,7 @@ def freq_to_mel(freq: float) -> float:
The frequency in mel scale.
Examples:
>>> round(freq_to_mel(1000), 2)
>>> float(round(freq_to_mel(1000), 2))
999.99
"""
# Use the formula to convert frequency to the mel scale
@ -321,7 +322,7 @@ def mel_spaced_filterbank(
Mel-spaced filter bank.
Examples:
>>> round(mel_spaced_filterbank(8000, 10, 1024)[0][1], 10)
>>> float(round(mel_spaced_filterbank(8000, 10, 1024)[0][1], 10))
0.0004603981
"""
freq_min = 0
@ -438,7 +439,7 @@ def discrete_cosine_transform(dct_filter_num: int, filter_num: int) -> np.ndarra
The DCT basis matrix.
Examples:
>>> round(discrete_cosine_transform(3, 5)[0][0], 5)
>>> float(round(discrete_cosine_transform(3, 5)[0][0], 5))
0.44721
"""
basis = np.empty((dct_filter_num, filter_num))

2
machine_learning/multilayer_perceptron_classifier.py
View File

@ -17,7 +17,7 @@ Y = clf.predict(test)
def wrapper(y):
"""
>>> wrapper(Y)
>>> [int(x) for x in wrapper(Y)]
[0, 0, 1]
"""
return list(y)

22
machine_learning/scoring_functions.py
View File

@ -20,11 +20,11 @@ def mae(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [1,2,3];predict = [1,4,3]
>>> np.around(mae(predict,actual),decimals = 2)
>>> float(np.around(mae(predict,actual),decimals = 2))
0.67
>>> actual = [1,1,1];predict = [1,1,1]
>>> mae(predict,actual)
>>> float(mae(predict,actual))
0.0
"""
predict = np.array(predict)
@ -41,11 +41,11 @@ def mse(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [1,2,3];predict = [1,4,3]
>>> np.around(mse(predict,actual),decimals = 2)
>>> float(np.around(mse(predict,actual),decimals = 2))
1.33
>>> actual = [1,1,1];predict = [1,1,1]
>>> mse(predict,actual)
>>> float(mse(predict,actual))
0.0
"""
predict = np.array(predict)
@ -63,11 +63,11 @@ def rmse(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [1,2,3];predict = [1,4,3]
>>> np.around(rmse(predict,actual),decimals = 2)
>>> float(np.around(rmse(predict,actual),decimals = 2))
1.15
>>> actual = [1,1,1];predict = [1,1,1]
>>> rmse(predict,actual)
>>> float(rmse(predict,actual))
0.0
"""
predict = np.array(predict)
@ -84,12 +84,10 @@ def rmse(predict, actual):
def rmsle(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [10,10,30];predict = [10,2,30]
>>> np.around(rmsle(predict,actual),decimals = 2)
>>> float(np.around(rmsle(predict=[10, 2, 30], actual=[10, 10, 30]), decimals=2))
0.75
>>> actual = [1,1,1];predict = [1,1,1]
>>> rmsle(predict,actual)
>>> float(rmsle(predict=[1, 1, 1], actual=[1, 1, 1]))
0.0
"""
predict = np.array(predict)
@ -117,12 +115,12 @@ def mbd(predict, actual):
Here the model overpredicts
>>> actual = [1,2,3];predict = [2,3,4]
>>> np.around(mbd(predict,actual),decimals = 2)
>>> float(np.around(mbd(predict,actual),decimals = 2))
50.0
Here the model underpredicts
>>> actual = [1,2,3];predict = [0,1,1]
>>> np.around(mbd(predict,actual),decimals = 2)
>>> float(np.around(mbd(predict,actual),decimals = 2))
-66.67
"""
predict = np.array(predict)

2
machine_learning/similarity_search.py
View File

@ -153,7 +153,7 @@ def cosine_similarity(input_a: np.ndarray, input_b: np.ndarray) -> float:
>>> cosine_similarity(np.array([1, 2]), np.array([6, 32]))
0.9615239476408232
"""
return np.dot(input_a, input_b) / (norm(input_a) * norm(input_b))
return float(np.dot(input_a, input_b) / (norm(input_a) * norm(input_b)))
if __name__ == "__main__":

6
machine_learning/support_vector_machines.py
View File

@ -14,11 +14,11 @@ def norm_squared(vector: ndarray) -> float:
Returns:
float: squared second norm of vector
>>> norm_squared([1, 2])
>>> int(norm_squared([1, 2]))
5
>>> norm_squared(np.asarray([1, 2]))
>>> int(norm_squared(np.asarray([1, 2])))
5
>>> norm_squared([0, 0])
>>> int(norm_squared([0, 0]))
0
"""
return np.dot(vector, vector)

8
maths/euclidean_distance.py
View File

@ -13,13 +13,13 @@ def euclidean_distance(vector_1: Vector, vector_2: Vector) -> VectorOut:
"""
Calculate the distance between the two endpoints of two vectors.
A vector is defined as a list, tuple, or numpy 1D array.
>>> euclidean_distance((0, 0), (2, 2))
>>> float(euclidean_distance((0, 0), (2, 2)))
2.8284271247461903
>>> euclidean_distance(np.array([0, 0, 0]), np.array([2, 2, 2]))
>>> float(euclidean_distance(np.array([0, 0, 0]), np.array([2, 2, 2])))
3.4641016151377544
>>> euclidean_distance(np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8]))
>>> float(euclidean_distance(np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8])))
8.0
>>> euclidean_distance([1, 2, 3, 4], [5, 6, 7, 8])
>>> float(euclidean_distance([1, 2, 3, 4], [5, 6, 7, 8]))
8.0
"""
return np.sqrt(np.sum((np.asarray(vector_1) - np.asarray(vector_2)) ** 2))

2
maths/euler_method.py
View File

@ -26,7 +26,7 @@ def explicit_euler(
... return y
>>> y0 = 1
>>> y = explicit_euler(f, y0, 0.0, 0.01, 5)
>>> y[-1]
>>> float(y[-1])
144.77277243257308
"""
n = int(np.ceil((x_end - x0) / step_size))

4
maths/euler_modified.py
View File

@ -24,13 +24,13 @@ def euler_modified(
>>> def f1(x, y):
... return -2*x*(y**2)
>>> y = euler_modified(f1, 1.0, 0.0, 0.2, 1.0)
>>> y[-1]
>>> float(y[-1])
0.503338255442106
>>> import math
>>> def f2(x, y):
... return -2*y + (x**3)*math.exp(-2*x)
>>> y = euler_modified(f2, 1.0, 0.0, 0.1, 0.3)
>>> y[-1]
>>> float(y[-1])
0.5525976431951775
"""
n = int(np.ceil((x_end - x0) / step_size))

16
maths/gaussian.py
View File

@ -5,18 +5,18 @@ 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:
def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> float:
"""
>>> gaussian(1)
>>> float(gaussian(1))
0.24197072451914337
>>> gaussian(24)
>>> float(gaussian(24))
3.342714441794458e-126
>>> gaussian(1, 4, 2)
>>> float(gaussian(1, 4, 2))
0.06475879783294587
>>> gaussian(1, 5, 3)
>>> float(gaussian(1, 5, 3))
0.05467002489199788
Supports NumPy Arrays
@ -29,7 +29,7 @@ def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int:
5.05227108e-15, 1.02797736e-18, 7.69459863e-23, 2.11881925e-27,
2.14638374e-32, 7.99882776e-38, 1.09660656e-43])
>>> gaussian(15)
>>> float(gaussian(15))
5.530709549844416e-50
>>> gaussian([1,2, 'string'])
@ -47,10 +47,10 @@ def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int:
...
OverflowError: (34, 'Result too large')
>>> gaussian(10**-326)
>>> float(gaussian(10**-326))
0.3989422804014327
>>> gaussian(2523, mu=234234, sigma=3425)
>>> float(gaussian(2523, mu=234234, sigma=3425))
0.0
"""
return 1 / sqrt(2 * pi * sigma**2) * exp(-((x - mu) ** 2) / (2 * sigma**2))

2
maths/minkowski_distance.py
View File

@ -19,7 +19,7 @@ def minkowski_distance(
>>> minkowski_distance([1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], 2)
8.0
>>> import numpy as np
>>> np.isclose(5.0, minkowski_distance([5.0], [0.0], 3))
>>> bool(np.isclose(5.0, minkowski_distance([5.0], [0.0], 3)))
True
>>> minkowski_distance([1.0], [2.0], -1)
Traceback (most recent call last):

8
maths/numerical_analysis/adams_bashforth.py
View File

@ -102,7 +102,7 @@ class AdamsBashforth:
>>> def f(x, y):
... return x + y
>>> y = AdamsBashforth(f, [0, 0.2, 0.4], [0, 0, 0.04], 0.2, 1).step_3()
>>> y[3]
>>> float(y[3])
0.15533333333333332
>>> AdamsBashforth(f, [0, 0.2], [0, 0], 0.2, 1).step_3()
@ -140,9 +140,9 @@ class AdamsBashforth:
... return x + y
>>> y = AdamsBashforth(
... f, [0, 0.2, 0.4, 0.6], [0, 0, 0.04, 0.128], 0.2, 1).step_4()
>>> y[4]
>>> float(y[4])
0.30699999999999994
>>> y[5]
>>> float(y[5])
0.5771083333333333
>>> AdamsBashforth(f, [0, 0.2, 0.4], [0, 0, 0.04], 0.2, 1).step_4()
@ -185,7 +185,7 @@ class AdamsBashforth:
>>> y = AdamsBashforth(
... f, [0, 0.2, 0.4, 0.6, 0.8], [0, 0.02140, 0.02140, 0.22211, 0.42536],
... 0.2, 1).step_5()
>>> y[-1]
>>> float(y[-1])
0.05436839444444452
>>> AdamsBashforth(f, [0, 0.2, 0.4], [0, 0, 0.04], 0.2, 1).step_5()

2
maths/numerical_analysis/runge_kutta.py
View File

@ -19,7 +19,7 @@ def runge_kutta(f, y0, x0, h, x_end):
... return y
>>> y0 = 1
>>> y = runge_kutta(f, y0, 0.0, 0.01, 5)
>>> y[-1]
>>> float(y[-1])
148.41315904125113
"""
n = int(np.ceil((x_end - x0) / h))

4
maths/numerical_analysis/runge_kutta_fehlberg_45.py
View File

@ -34,12 +34,12 @@ def runge_kutta_fehlberg_45(
>>> def f(x, y):
... return 1 + y**2
>>> y = runge_kutta_fehlberg_45(f, 0, 0, 0.2, 1)
>>> y[1]
>>> float(y[1])
0.2027100937470787
>>> def f(x,y):
... return x
>>> y = runge_kutta_fehlberg_45(f, -1, 0, 0.2, 0)
>>> y[1]
>>> float(y[1])
-0.18000000000000002
>>> y = runge_kutta_fehlberg_45(5, 0, 0, 0.1, 1)
Traceback (most recent call last):

2
maths/numerical_analysis/runge_kutta_gills.py
View File

@ -34,7 +34,7 @@ def runge_kutta_gills(
>>> def f(x, y):
... return (x-y)/2
>>> y = runge_kutta_gills(f, 0, 3, 0.2, 5)
>>> y[-1]
>>> float(y[-1])
3.4104259225717537
>>> def f(x,y):

2
maths/softmax.py
View File

@ -28,7 +28,7 @@ def softmax(vector):
The softmax vector adds up to one. We need to ceil to mitigate for
precision
>>> np.ceil(np.sum(softmax([1,2,3,4])))
>>> float(np.ceil(np.sum(softmax([1,2,3,4]))))
1.0
>>> vec = np.array([5,5])

6
neural_network/two_hidden_layers_neural_network.py
View File

@ -64,7 +64,7 @@ class TwoHiddenLayerNeuralNetwork:
>>> nn = TwoHiddenLayerNeuralNetwork(input_val, output_val)
>>> res = nn.feedforward()
>>> array_sum = np.sum(res)
>>> np.isnan(array_sum)
>>> bool(np.isnan(array_sum))
False
"""
# Layer_between_input_and_first_hidden_layer is the layer connecting the
@ -105,7 +105,7 @@ class TwoHiddenLayerNeuralNetwork:
>>> res = nn.feedforward()
>>> nn.back_propagation()
>>> updated_weights = nn.second_hidden_layer_and_output_layer_weights
>>> (res == updated_weights).all()
>>> bool((res == updated_weights).all())
False
"""
@ -171,7 +171,7 @@ class TwoHiddenLayerNeuralNetwork:
>>> first_iteration_weights = nn.feedforward()
>>> nn.back_propagation()
>>> updated_weights = nn.second_hidden_layer_and_output_layer_weights
>>> (first_iteration_weights == updated_weights).all()
>>> bool((first_iteration_weights == updated_weights).all())
False
"""
for iteration in range(1, iterations + 1):

8
other/bankers_algorithm.py
View File

@ -87,9 +87,11 @@ class BankersAlgorithm:
This function builds an index control dictionary to track original ids/indices
of processes when altered during execution of method "main"
Return: {0: [a: int, b: int], 1: [c: int, d: int]}
>>> (BankersAlgorithm(test_claim_vector, test_allocated_res_table,
... test_maximum_claim_table)._BankersAlgorithm__need_index_manager()
... ) # doctest: +NORMALIZE_WHITESPACE
>>> index_control = BankersAlgorithm(
... test_claim_vector, test_allocated_res_table, test_maximum_claim_table
... )._BankersAlgorithm__need_index_manager()
>>> {key: [int(x) for x in value] for key, value
... in index_control.items()} # doctest: +NORMALIZE_WHITESPACE
{0: [1, 2, 0, 3], 1: [0, 1, 3, 1], 2: [1, 1, 0, 2], 3: [1, 3, 2, 0],
4: [2, 0, 0, 3]}
"""

2
physics/in_static_equilibrium.py
View File

@ -53,7 +53,7 @@ def in_static_equilibrium(
# summation of moments is zero
moments: NDArray[float64] = cross(location, forces)
sum_moments: float = sum(moments)
return abs(sum_moments) < eps
return bool(abs(sum_moments) < eps)
if __name__ == "__main__":

4
requirements.txt
View File

@ -1,7 +1,7 @@
beautifulsoup4
fake_useragent
imageio
keras ; python_version < '3.12'
keras
lxml
matplotlib
numpy
@ -17,7 +17,7 @@ rich
scikit-learn
statsmodels
sympy
tensorflow
tensorflow ; python_version < '3.13'
tweepy
# yulewalker # uncomment once audio_filters/equal_loudness_filter.py is fixed
typing_extensions

0
web_programming/get_user_tweets.py → web_programming/get_user_tweets.py.DISABLED
View File