Reduce the complexity of boolean_algebra/quine_mc_cluskey.py (#8604)

* Reduce the complexity of boolean_algebra/quine_mc_cluskey.py

* updating DIRECTORY.md

* Fix

* Fix review issues

* Fix

* Fix review issues

---------

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>

boolean_algebra/quine_mc_cluskey.py

@@ -74,10 +74,7 @@ def is_for_table(string1: str, string2: str, count: int) -> bool:
     """
     list1 = list(string1)
     list2 = list(string2)
-    count_n = 0
-    for i in range(len(list1)):
-        if list1[i] != list2[i]:
-            count_n += 1
+    count_n = sum(item1 != item2 for item1, item2 in zip(list1, list2))
     return count_n == count
 
 
@@ -92,40 +89,34 @@ def selection(chart: list[list[int]], prime_implicants: list[str]) -> list[str]:
     temp = []
     select = [0] * len(chart)
     for i in range(len(chart[0])):
-        count = 0
-        rem = -1
-        for j in range(len(chart)):
-            if chart[j][i] == 1:
-                count += 1
-                rem = j
+        count = sum(row[i] == 1 for row in chart)
         if count == 1:
+            rem = max(j for j, row in enumerate(chart) if row[i] == 1)
             select[rem] = 1
-    for i in range(len(select)):
-        if select[i] == 1:
-            for j in range(len(chart[0])):
-                if chart[i][j] == 1:
-                    for k in range(len(chart)):
-                        chart[k][j] = 0
-            temp.append(prime_implicants[i])
+    for i, item in enumerate(select):
+        if item != 1:
+            continue
+        for j in range(len(chart[0])):
+            if chart[i][j] != 1:
+                continue
+            for row in chart:
+                row[j] = 0
+        temp.append(prime_implicants[i])
     while True:
-        max_n = 0
-        rem = -1
-        count_n = 0
-        for i in range(len(chart)):
-            count_n = chart[i].count(1)
-            if count_n > max_n:
-                max_n = count_n
-                rem = i
+        counts = [chart[i].count(1) for i in range(len(chart))]
+        max_n = max(counts)
+        rem = counts.index(max_n)
 
         if max_n == 0:
             return temp
 
         temp.append(prime_implicants[rem])
 
-        for i in range(len(chart[0])):
-            if chart[rem][i] == 1:
-                for j in range(len(chart)):
-                    chart[j][i] = 0
+        for j in range(len(chart[0])):
+            if chart[rem][j] != 1:
+                continue
+            for i in range(len(chart)):
+                chart[i][j] = 0
 
 
 def prime_implicant_chart(

ciphers/rsa_key_generator.py

@@ -2,8 +2,7 @@ import os
 import random
 import sys
 
-from . import cryptomath_module as cryptoMath  # noqa: N812
-from . import rabin_miller as rabinMiller  # noqa: N812
+from . import cryptomath_module, rabin_miller
 
 
 def main() -> None:
@@ -13,20 +12,26 @@ def main() -> None:
 
 
 def generate_key(key_size: int) -> tuple[tuple[int, int], tuple[int, int]]:
-    print("Generating prime p...")
-    p = rabinMiller.generate_large_prime(key_size)
-    print("Generating prime q...")
-    q = rabinMiller.generate_large_prime(key_size)
+    """
+    >>> random.seed(0) # for repeatability
+    >>> public_key, private_key = generate_key(8)
+    >>> public_key
+    (26569, 239)
+    >>> private_key
+    (26569, 2855)
+    """
+    p = rabin_miller.generate_large_prime(key_size)
+    q = rabin_miller.generate_large_prime(key_size)
     n = p * q
 
-    print("Generating e that is relatively prime to (p - 1) * (q - 1)...")
+    # Generate e that is relatively prime to (p - 1) * (q - 1)
     while True:
         e = random.randrange(2 ** (key_size - 1), 2 ** (key_size))
-        if cryptoMath.gcd(e, (p - 1) * (q - 1)) == 1:
+        if cryptomath_module.gcd(e, (p - 1) * (q - 1)) == 1:
             break
 
-    print("Calculating d that is mod inverse of e...")
-    d = cryptoMath.find_mod_inverse(e, (p - 1) * (q - 1))
+    # Calculate d that is mod inverse of e
+    d = cryptomath_module.find_mod_inverse(e, (p - 1) * (q - 1))
 
     public_key = (n, e)
     private_key = (n, d)

computer_vision/flip_augmentation.py

@@ -32,13 +32,13 @@ def main() -> None:
         letter_code = random_chars(32)
         file_name = paths[index].split(os.sep)[-1].rsplit(".", 1)[0]
         file_root = f"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
-        cv2.imwrite(f"/{file_root}.jpg", image, [cv2.IMWRITE_JPEG_QUALITY, 85])
+        cv2.imwrite(f"{file_root}.jpg", image, [cv2.IMWRITE_JPEG_QUALITY, 85])
         print(f"Success {index+1}/{len(new_images)} with {file_name}")
         annos_list = []
         for anno in new_annos[index]:
             obj = f"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
             annos_list.append(obj)
-        with open(f"/{file_root}.txt", "w") as outfile:
+        with open(f"{file_root}.txt", "w") as outfile:
             outfile.write("\n".join(line for line in annos_list))
 
 

data_structures/binary_tree/binary_search_tree.py

@@ -1,5 +1,62 @@
-"""
+r"""
 A binary search Tree
+
+Example
+              8
+             / \
+            3   10
+           / \    \
+          1   6    14
+             / \   /
+            4   7 13
+
+>>> t = BinarySearchTree()
+>>> t.insert(8, 3, 6, 1, 10, 14, 13, 4, 7)
+>>> print(" ".join(repr(i.value) for i in t.traversal_tree()))
+8 3 1 6 4 7 10 14 13
+>>> print(" ".join(repr(i.value) for i in t.traversal_tree(postorder)))
+1 4 7 6 3 13 14 10 8
+>>> t.remove(20)
+Traceback (most recent call last):
+    ...
+ValueError: Value 20 not found
+>>> BinarySearchTree().search(6)
+Traceback (most recent call last):
+    ...
+IndexError: Warning: Tree is empty! please use another.
+
+Other example:
+
+>>> testlist = (8, 3, 6, 1, 10, 14, 13, 4, 7)
+>>> t = BinarySearchTree()
+>>> for i in testlist:
+...     t.insert(i)
+
+Prints all the elements of the list in order traversal
+>>> print(t)
+{'8': ({'3': (1, {'6': (4, 7)})}, {'10': (None, {'14': (13, None)})})}
+
+Test existence
+>>> t.search(6) is not None
+True
+>>> t.search(-1) is not None
+False
+
+>>> t.search(6).is_right
+True
+>>> t.search(1).is_right
+False
+
+>>> t.get_max().value
+14
+>>> t.get_min().value
+1
+>>> t.empty()
+False
+>>> for i in testlist:
+...     t.remove(i)
+>>> t.empty()
+True
 """
 
 from collections.abc import Iterable
@@ -20,6 +77,10 @@ class Node:
             return str(self.value)
         return pformat({f"{self.value}": (self.left, self.right)}, indent=1)
 
+    @property
+    def is_right(self) -> bool:
+        return self.parent is not None and self is self.parent.right
+
 
 class BinarySearchTree:
     def __init__(self, root: Node | None = None):
@@ -35,18 +96,13 @@ class BinarySearchTree:
         if new_children is not None:  # reset its kids
             new_children.parent = node.parent
         if node.parent is not None:  # reset its parent
-            if self.is_right(node):  # If it is the right children
+            if node.is_right:  # If it is the right child
                 node.parent.right = new_children
             else:
                 node.parent.left = new_children
         else:
             self.root = new_children
 
-    def is_right(self, node: Node) -> bool:
-        if node.parent and node.parent.right:
-            return node == node.parent.right
-        return False
-
     def empty(self) -> bool:
         return self.root is None
 
@@ -119,22 +175,26 @@ class BinarySearchTree:
         return node
 
     def remove(self, value: int) -> None:
-        node = self.search(value)  # Look for the node with that label
-        if node is not None:
-            if node.left is None and node.right is None:  # If it has no children
-                self.__reassign_nodes(node, None)
-            elif node.left is None:  # Has only right children
-                self.__reassign_nodes(node, node.right)
-            elif node.right is None:  # Has only left children
-                self.__reassign_nodes(node, node.left)
-            else:
-                tmp_node = self.get_max(
-                    node.left
-                )  # Gets the max value of the left branch
-                self.remove(tmp_node.value)  # type: ignore
-                node.value = (
-                    tmp_node.value  # type: ignore
-                )  # Assigns the value to the node to delete and keep tree structure
+        # Look for the node with that label
+        node = self.search(value)
+        if node is None:
+            msg = f"Value {value} not found"
+            raise ValueError(msg)
+
+        if node.left is None and node.right is None:  # If it has no children
+            self.__reassign_nodes(node, None)
+        elif node.left is None:  # Has only right children
+            self.__reassign_nodes(node, node.right)
+        elif node.right is None:  # Has only left children
+            self.__reassign_nodes(node, node.left)
+        else:
+            predecessor = self.get_max(
+                node.left
+            )  # Gets the max value of the left branch
+            self.remove(predecessor.value)  # type: ignore
+            node.value = (
+                predecessor.value  # type: ignore
+            )  # Assigns the value to the node to delete and keep tree structure
 
     def preorder_traverse(self, node: Node | None) -> Iterable:
         if node is not None:
@@ -177,55 +237,6 @@ def postorder(curr_node: Node | None) -> list[Node]:
     return node_list
 
 
-def binary_search_tree() -> None:
-    r"""
-    Example
-                  8
-                 / \
-                3   10
-               / \    \
-              1   6    14
-                 / \   /
-                4   7 13
-
-    >>> t = BinarySearchTree()
-    >>> t.insert(8, 3, 6, 1, 10, 14, 13, 4, 7)
-    >>> print(" ".join(repr(i.value) for i in t.traversal_tree()))
-    8 3 1 6 4 7 10 14 13
-    >>> print(" ".join(repr(i.value) for i in t.traversal_tree(postorder)))
-    1 4 7 6 3 13 14 10 8
-    >>> BinarySearchTree().search(6)
-    Traceback (most recent call last):
-        ...
-    IndexError: Warning: Tree is empty! please use another.
-    """
-    testlist = (8, 3, 6, 1, 10, 14, 13, 4, 7)
-    t = BinarySearchTree()
-    for i in testlist:
-        t.insert(i)
-
-    # Prints all the elements of the list in order traversal
-    print(t)
-
-    if t.search(6) is not None:
-        print("The value 6 exists")
-    else:
-        print("The value 6 doesn't exist")
-
-    if t.search(-1) is not None:
-        print("The value -1 exists")
-    else:
-        print("The value -1 doesn't exist")
-
-    if not t.empty():
-        print("Max Value: ", t.get_max().value)  # type: ignore
-        print("Min Value: ", t.get_min().value)  # type: ignore
-
-    for i in testlist:
-        t.remove(i)
-        print(t)
-
-
 if __name__ == "__main__":
     import doctest
 

digital_image_processing/morphological_operations/erosion_operation.py

@@ -21,6 +21,7 @@ def rgb2gray(rgb: np.array) -> np.array:
 def gray2binary(gray: np.array) -> np.array:
     """
     Return binary image from gray image
+
     >>> gray2binary(np.array([[127, 255, 0]]))
     array([[False,  True, False]])
     >>> gray2binary(np.array([[0]]))

digital_image_processing/rotation/rotation.py

@@ -10,12 +10,12 @@ def get_rotation(
 ) -> np.ndarray:
     """
     Get image rotation
-    :param img: np.array
+    :param img: np.ndarray
     :param pt1: 3x2 list
     :param pt2: 3x2 list
     :param rows: columns image shape
     :param cols: rows image shape
-    :return: np.array
+    :return: np.ndarray
     """
     matrix = cv2.getAffineTransform(pt1, pt2)
     return cv2.warpAffine(img, matrix, (rows, cols))

graphs/greedy_best_first.py

@@ -6,14 +6,32 @@ 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],
+# 0's are free path whereas 1's are obstacles
+TEST_GRIDS = [
+    [
+        [0, 0, 0, 0, 0, 0, 0],
+        [0, 1, 0, 0, 0, 0, 0],
+        [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],
+    ],
+    [
+        [0, 0, 0, 1, 1, 0, 0],
+        [0, 0, 0, 0, 1, 0, 1],
+        [0, 0, 0, 1, 1, 0, 0],
+        [0, 1, 0, 0, 1, 0, 0],
+        [1, 0, 0, 1, 1, 0, 1],
+        [0, 0, 0, 0, 0, 0, 0],
+    ],
+    [
+        [0, 0, 1, 0, 0],
+        [0, 1, 0, 0, 0],
+        [0, 0, 1, 0, 1],
+        [1, 0, 0, 1, 1],
+        [0, 0, 0, 0, 0],
+    ],
 ]
 
 delta = ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right
@@ -65,10 +83,14 @@ class Node:
     def __lt__(self, other) -> bool:
         return self.f_cost < other.f_cost
 
+    def __eq__(self, other) -> bool:
+        return self.pos == other.pos
+
 
 class GreedyBestFirst:
     """
-    >>> gbf = GreedyBestFirst((0, 0), (len(grid) - 1, len(grid[0]) - 1))
+    >>> grid = TEST_GRIDS[2]
+    >>> gbf = GreedyBestFirst(grid, (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])
@@ -78,11 +100,14 @@ class GreedyBestFirst:
     >>> 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)]
+    [(0, 0), (1, 0), (2, 0), (2, 1), (3, 1), (4, 1), (4, 2), (4, 3),
+     (4, 4)]
     """
 
-    def __init__(self, start: tuple[int, int], goal: tuple[int, int]):
+    def __init__(
+        self, grid: list[list[int]], start: tuple[int, int], goal: tuple[int, int]
+    ):
+        self.grid = grid
         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)
 
@@ -114,14 +139,6 @@ class GreedyBestFirst:
 
                 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]
@@ -131,28 +148,22 @@ class GreedyBestFirst:
         """
         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 [
+            Node(
+                pos_x,
+                pos_y,
+                self.target.pos_x,
+                self.target.pos_y,
+                parent.g_cost + 1,
+                parent,
             )
-        return successors
+            for action in delta
+            if (
+                0 <= (pos_x := parent.pos_x + action[1]) < len(self.grid[0])
+                and 0 <= (pos_y := parent.pos_y + action[0]) < len(self.grid)
+                and self.grid[pos_y][pos_x] == 0
+            )
+        ]
 
     def retrace_path(self, node: Node | None) -> Path:
         """
@@ -168,18 +179,21 @@ class GreedyBestFirst:
 
 
 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 idx, grid in enumerate(TEST_GRIDS):
+        print(f"==grid-{idx + 1}==")
 
+        init = (0, 0)
+        goal = (len(grid) - 1, len(grid[0]) - 1)
         for elem in grid:
             print(elem)
+
+        print("------")
+
+        greedy_bf = GreedyBestFirst(grid, 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)

maths/average_median.py

@@ -19,7 +19,9 @@ def median(nums: list) -> int | float:
     Returns:
         Median.
     """
-    sorted_list = sorted(nums)
+    # The sorted function returns list[SupportsRichComparisonT@sorted]
+    # which does not support `+`
+    sorted_list: list[int] = sorted(nums)
     length = len(sorted_list)
     mid_index = length >> 1
     return (

maths/euler_modified.py

@@ -5,7 +5,7 @@ import numpy as np
 
 def euler_modified(
     ode_func: Callable, y0: float, x0: float, step_size: float, x_end: float
-) -> np.array:
+) -> np.ndarray:
     """
     Calculate solution at each step to an ODE using Euler's Modified Method
     The Euler Method is straightforward to implement, but can't give accurate solutions.

maths/gaussian_error_linear_unit.py

@@ -13,7 +13,7 @@ This script is inspired by a corresponding research paper.
 import numpy as np
 
 
-def sigmoid(vector: np.array) -> np.array:
+def sigmoid(vector: np.ndarray) -> np.ndarray:
     """
     Mathematical function sigmoid takes a vector x of K real numbers as input and
     returns 1/ (1 + e^-x).
@@ -25,7 +25,7 @@ def sigmoid(vector: np.array) -> np.array:
     return 1 / (1 + np.exp(-vector))
 
 
-def gaussian_error_linear_unit(vector: np.array) -> np.array:
+def gaussian_error_linear_unit(vector: np.ndarray) -> np.ndarray:
     """
     Implements the Gaussian Error Linear Unit (GELU) function
 

maths/jaccard_similarity.py

@@ -14,7 +14,11 @@ Jaccard similarity is widely used with MinHashing.
 """
 
 
-def jaccard_similarity(set_a, set_b, alternative_union=False):
+def jaccard_similarity(
+    set_a: set[str] | list[str] | tuple[str],
+    set_b: set[str] | list[str] | tuple[str],
+    alternative_union=False,
+):
     """
     Finds the jaccard similarity between two sets.
     Essentially, its intersection over union.
@@ -37,41 +41,52 @@ def jaccard_similarity(set_a, set_b, alternative_union=False):
     >>> set_b = {'c', 'd', 'e', 'f', 'h', 'i'}
     >>> jaccard_similarity(set_a, set_b)
     0.375
-
     >>> jaccard_similarity(set_a, set_a)
     1.0
-
     >>> jaccard_similarity(set_a, set_a, True)
     0.5
-
     >>> set_a = ['a', 'b', 'c', 'd', 'e']
     >>> set_b = ('c', 'd', 'e', 'f', 'h', 'i')
     >>> jaccard_similarity(set_a, set_b)
     0.375
+    >>> set_a = ('c', 'd', 'e', 'f', 'h', 'i')
+    >>> set_b = ['a', 'b', 'c', 'd', 'e']
+    >>> jaccard_similarity(set_a, set_b)
+    0.375
+    >>> set_a = ('c', 'd', 'e', 'f', 'h', 'i')
+    >>> set_b = ['a', 'b', 'c', 'd']
+    >>> jaccard_similarity(set_a, set_b, True)
+    0.2
+    >>> set_a = {'a', 'b'}
+    >>> set_b = ['c', 'd']
+    >>> jaccard_similarity(set_a, set_b)
+    Traceback (most recent call last):
+        ...
+    ValueError: Set a and b must either both be sets or be either a list or a tuple.
     """
 
     if isinstance(set_a, set) and isinstance(set_b, set):
-        intersection = len(set_a.intersection(set_b))
+        intersection_length = len(set_a.intersection(set_b))
 
         if alternative_union:
-            union = len(set_a) + len(set_b)
+            union_length = len(set_a) + len(set_b)
         else:
-            union = len(set_a.union(set_b))
+            union_length = len(set_a.union(set_b))
 
-        return intersection / union
+        return intersection_length / union_length
 
-    if isinstance(set_a, (list, tuple)) and isinstance(set_b, (list, tuple)):
+    elif isinstance(set_a, (list, tuple)) and isinstance(set_b, (list, tuple)):
         intersection = [element for element in set_a if element in set_b]
 
         if alternative_union:
-            union = len(set_a) + len(set_b)
-            return len(intersection) / union
+            return len(intersection) / (len(set_a) + len(set_b))
         else:
-            union = set_a + [element for element in set_b if element not in set_a]
+            # Cast set_a to list because tuples cannot be mutated
+            union = list(set_a) + [element for element in set_b if element not in set_a]
             return len(intersection) / len(union)
-
-        return len(intersection) / len(union)
-    return None
+    raise ValueError(
+        "Set a and b must either both be sets or be either a list or a tuple."
+    )
 
 
 if __name__ == "__main__":

maths/newton_raphson.py

@@ -1,16 +1,20 @@
 """
-    Author: P Shreyas Shetty
-    Implementation of Newton-Raphson method for solving equations of kind
-    f(x) = 0. It is an iterative method where solution is found by the expression
-        x[n+1] = x[n] + f(x[n])/f'(x[n])
-    If no solution exists, then either the solution will not be found when iteration
-    limit is reached or the gradient f'(x[n]) approaches zero. In both cases, exception
-    is raised. If iteration limit is reached, try increasing maxiter.
-    """
+Author: P Shreyas Shetty
+Implementation of Newton-Raphson method for solving equations of kind
+f(x) = 0. It is an iterative method where solution is found by the expression
+    x[n+1] = x[n] + f(x[n])/f'(x[n])
+If no solution exists, then either the solution will not be found when iteration
+limit is reached or the gradient f'(x[n]) approaches zero. In both cases, exception
+is raised. If iteration limit is reached, try increasing maxiter.
+"""
+
 import math as m
+from collections.abc import Callable
+
+DerivativeFunc = Callable[[float], float]
 
 
-def calc_derivative(f, a, h=0.001):
+def calc_derivative(f: DerivativeFunc, a: float, h: float = 0.001) -> float:
     """
     Calculates derivative at point a for function f using finite difference
     method
@@ -18,7 +22,14 @@ def calc_derivative(f, a, h=0.001):
     return (f(a + h) - f(a - h)) / (2 * h)
 
 
-def newton_raphson(f, x0=0, maxiter=100, step=0.0001, maxerror=1e-6, logsteps=False):
+def newton_raphson(
+    f: DerivativeFunc,
+    x0: float = 0,
+    maxiter: int = 100,
+    step: float = 0.0001,
+    maxerror: float = 1e-6,
+    logsteps: bool = False,
+) -> tuple[float, float, list[float]]:
     a = x0  # set the initial guess
     steps = [a]
     error = abs(f(a))
@@ -36,7 +47,7 @@ def newton_raphson(f, x0=0, maxiter=100, step=0.0001, maxerror=1e-6, logsteps=Fa
     if logsteps:
         # If logstep is true, then log intermediate steps
         return a, error, steps
-    return a, error
+    return a, error, []
 
 
 if __name__ == "__main__":

maths/qr_decomposition.py

@@ -1,7 +1,7 @@
 import numpy as np
 
 
-def qr_householder(a):
+def qr_householder(a: np.ndarray):
     """Return a QR-decomposition of the matrix A using Householder reflection.
 
     The QR-decomposition decomposes the matrix A of shape (m, n) into an

maths/sigmoid.py

@@ -11,7 +11,7 @@ https://en.wikipedia.org/wiki/Sigmoid_function
 import numpy as np
 
 
-def sigmoid(vector: np.array) -> np.array:
+def sigmoid(vector: np.ndarray) -> np.ndarray:
     """
     Implements the sigmoid function
 

maths/tanh.py

@@ -12,12 +12,12 @@ https://en.wikipedia.org/wiki/Activation_function
 import numpy as np
 
 
-def tangent_hyperbolic(vector: np.array) -> np.array:
+def tangent_hyperbolic(vector: np.ndarray) -> np.ndarray:
     """
         Implements the tanh function
 
         Parameters:
-            vector: np.array
+            vector: np.ndarray
 
         Returns:
             tanh (np.array): The input numpy array after applying tanh.