Merge branch 'master' into fix-sphinx/build_docs-warning-for-maths/zellers_congruence

Author: MaximSmolskiy

.github/workflows/build.yml

@@ -10,7 +10,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      - uses: astral-sh/setup-uv@v4
+      - uses: astral-sh/setup-uv@v5
         with:
           enable-cache: true
           cache-dependency-glob: uv.lock

.github/workflows/project_euler.yml

@@ -15,7 +15,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      - uses: astral-sh/setup-uv@v4
+      - uses: astral-sh/setup-uv@v5
       - uses: actions/setup-python@v5
         with:
           python-version: 3.x
@@ -25,7 +25,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      - uses: astral-sh/setup-uv@v4
+      - uses: astral-sh/setup-uv@v5
       - uses: actions/setup-python@v5
         with:
           python-version: 3.x

.github/workflows/ruff.yml

@@ -12,5 +12,5 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      - uses: astral-sh/setup-uv@v4
+      - uses: astral-sh/setup-uv@v5
       - run: uvx ruff check --output-format=github .

.github/workflows/sphinx.yml

@@ -26,7 +26,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      - uses: astral-sh/setup-uv@v4
+      - uses: astral-sh/setup-uv@v5
       - uses: actions/setup-python@v5
         with:
           python-version: 3.13

computer_vision/README.md

@@ -8,4 +8,3 @@ Image processing and computer vision are a little different from each other. Ima
 While computer vision comes from modelling image processing using the techniques of machine learning, computer vision applies machine learning to recognize patterns for interpretation of images (much like the process of visual reasoning of human vision).
 
 * <https://en.wikipedia.org/wiki/Computer_vision>
-* <https://www.datarobot.com/blog/introduction-to-computer-vision-what-it-is-and-how-it-works/>

data_structures/binary_tree/mirror_binary_tree.py

@@ -56,6 +56,8 @@ def mirror(self) -> Node:
 def make_tree_seven() -> Node:
     r"""
     Return a binary tree with 7 nodes that looks like this:
+    ::
+
            1
          /   \
         2     3
@@ -81,13 +83,15 @@ def make_tree_seven() -> Node:
 def make_tree_nine() -> Node:
     r"""
     Return a binary tree with 9 nodes that looks like this:
-          1
-         /  \
-        2    3
-       / \    \
-      4   5    6
-     / \   \
-    7   8   9
+    ::
+
+            1
+           / \
+          2   3
+         / \   \
+        4   5   6
+       / \   \
+      7   8   9
 
     >>> tree_nine = make_tree_nine()
     >>> len(tree_nine)
@@ -117,23 +121,25 @@ def main() -> None:
     >>> tuple(tree.mirror())
     (6, 3, 1, 9, 5, 2, 8, 4, 7)
 
-    nine_tree:
-          1
-         /  \
-        2    3
-       / \    \
-      4   5    6
-     / \   \
-    7   8   9
-
-    The mirrored tree looks like this:
+    nine_tree::
+
+            1
+           / \
+          2   3
+         / \   \
+        4   5   6
+       / \   \
+      7   8   9
+
+    The mirrored tree looks like this::
+
           1
-         /  \
-        3    2
-       /    / \
-      6    5   4
-          /   / \
-         9   8   7
+         / \
+        3   2
+       /   / \
+      6   5   4
+         /   / \
+        9   8   7
     """
     trees = {"zero": Node(0), "seven": make_tree_seven(), "nine": make_tree_nine()}
     for name, tree in trees.items():

electronics/electric_power.py

@@ -23,20 +23,22 @@ def electric_power(voltage: float, current: float, power: float) -> tuple:
     >>> electric_power(voltage=2, current=4, power=2)
     Traceback (most recent call last):
         ...
-    ValueError: Only one argument must be 0
+    ValueError: Exactly one argument must be 0
     >>> electric_power(voltage=0, current=0, power=2)
     Traceback (most recent call last):
         ...
-    ValueError: Only one argument must be 0
+    ValueError: Exactly one argument must be 0
     >>> electric_power(voltage=0, current=2, power=-4)
     Traceback (most recent call last):
         ...
     ValueError: Power cannot be negative in any electrical/electronics system
     >>> electric_power(voltage=2.2, current=2.2, power=0)
     Result(name='power', value=4.84)
+    >>> electric_power(current=0, power=6, voltage=2)
+    Result(name='current', value=3.0)
     """
     if (voltage, current, power).count(0) != 1:
-        raise ValueError("Only one argument must be 0")
+        raise ValueError("Exactly one argument must be 0")
     elif power < 0:
         raise ValueError(
             "Power cannot be negative in any electrical/electronics system"
@@ -48,7 +50,7 @@ def electric_power(voltage: float, current: float, power: float) -> tuple:
     elif power == 0:
         return Result("power", float(round(abs(voltage * current), 2)))
     else:
-        raise ValueError("Exactly one argument must be 0")
+        raise AssertionError
 
 
 if __name__ == "__main__":

linear_algebra/src/gaussian_elimination_pivoting.py

@@ -22,40 +22,33 @@ def solve_linear_system(matrix: np.ndarray) -> np.ndarray:
     >>> solution = solve_linear_system(np.column_stack((A, B)))
     >>> np.allclose(solution, np.array([2., 3., -1.]))
     True
-    >>> solve_linear_system(np.array([[0, 0], [0, 0]],  dtype=float))
-    array([nan, nan])
+    >>> solve_linear_system(np.array([[0, 0, 0]], dtype=float))
+    Traceback (most recent call last):
+        ...
+    ValueError: Matrix is not square
+    >>> solve_linear_system(np.array([[0, 0, 0], [0, 0, 0]], dtype=float))
+    Traceback (most recent call last):
+        ...
+    ValueError: Matrix is singular
     """
     ab = np.copy(matrix)
     num_of_rows = ab.shape[0]
     num_of_columns = ab.shape[1] - 1
     x_lst: list[float] = []
 
-    # Lead element search
-    for column_num in range(num_of_rows):
-        for i in range(column_num, num_of_columns):
-            if abs(ab[i][column_num]) > abs(ab[column_num][column_num]):
-                ab[[column_num, i]] = ab[[i, column_num]]
-                if ab[column_num, column_num] == 0.0:
-                    raise ValueError("Matrix is not correct")
-            else:
-                pass
-        if column_num != 0:
-            for i in range(column_num, num_of_rows):
-                ab[i, :] -= (
-                    ab[i, column_num - 1]
-                    / ab[column_num - 1, column_num - 1]
-                    * ab[column_num - 1, :]
-                )
+    if num_of_rows != num_of_columns:
+        raise ValueError("Matrix is not square")
 
-    # Upper triangular matrix
     for column_num in range(num_of_rows):
+        # Lead element search
         for i in range(column_num, num_of_columns):
             if abs(ab[i][column_num]) > abs(ab[column_num][column_num]):
                 ab[[column_num, i]] = ab[[i, column_num]]
-                if ab[column_num, column_num] == 0.0:
-                    raise ValueError("Matrix is not correct")
-            else:
-                pass
+
+        # Upper triangular matrix
+        if abs(ab[column_num, column_num]) < 1e-8:
+            raise ValueError("Matrix is singular")
+
         if column_num != 0:
             for i in range(column_num, num_of_rows):
                 ab[i, :] -= (

linear_algebra/src/lib.py

@@ -46,7 +46,6 @@ class Vector:
         change_component(pos: int, value: float): changes specified component
         euclidean_length(): returns the euclidean length of the vector
         angle(other: Vector, deg: bool): returns the angle between two vectors
-        TODO: compare-operator
     """
 
     def __init__(self, components: Collection[float] | None = None) -> None:
@@ -96,6 +95,16 @@ def __sub__(self, other: Vector) -> Vector:
         else:  # error case
             raise Exception("must have the same size")
 
+    def __eq__(self, other: object) -> bool:
+        """
+        performs the comparison between two vectors
+        """
+        if not isinstance(other, Vector):
+            return NotImplemented
+        if len(self) != len(other):
+            return False
+        return all(self.component(i) == other.component(i) for i in range(len(self)))
+
     @overload
     def __mul__(self, other: float) -> Vector: ...
 

machine_learning/linear_regression.py

@@ -41,6 +41,14 @@ def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):
     :param theta    : Feature vector (weight's for our model)
     ;param return    : Updated Feature's, using
                        curr_features - alpha_ * gradient(w.r.t. feature)
+    >>> import numpy as np
+    >>> data_x = np.array([[1, 2], [3, 4]])
+    >>> data_y = np.array([5, 6])
+    >>> len_data = len(data_x)
+    >>> alpha = 0.01
+    >>> theta = np.array([0.1, 0.2])
+    >>> run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta)
+    array([0.196, 0.343])
     """
     n = len_data
 
@@ -58,6 +66,12 @@ def sum_of_square_error(data_x, data_y, len_data, theta):
     :param len_data  : len of the dataset
     :param theta     : contains the feature vector
     :return          : sum of square error computed from given feature's
+
+    Example:
+    >>> vc_x = np.array([[1.1], [2.1], [3.1]])
+    >>> vc_y = np.array([1.2, 2.2, 3.2])
+    >>> round(sum_of_square_error(vc_x, vc_y, 3, np.array([1])),3)
+    np.float64(0.005)
     """
     prod = np.dot(theta, data_x.transpose())
     prod -= data_y.transpose()
@@ -93,6 +107,11 @@ def mean_absolute_error(predicted_y, original_y):
     :param predicted_y   : contains the output of prediction (result vector)
     :param original_y    : contains values of expected outcome
     :return          : mean absolute error computed from given feature's
+
+    >>> predicted_y = [3, -0.5, 2, 7]
+    >>> original_y = [2.5, 0.0, 2, 8]
+    >>> mean_absolute_error(predicted_y, original_y)
+    0.5
     """
     total = sum(abs(y - predicted_y[i]) for i, y in enumerate(original_y))
     return total / len(original_y)
@@ -114,4 +133,7 @@ def main():
 
 
 if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()
     main()