[pull] master from TheAlgorithms:master

DIRECTORY.md

@@ -169,6 +169,7 @@
   * [Prefix Conversions](conversions/prefix_conversions.py)
   * [Prefix Conversions String](conversions/prefix_conversions_string.py)
   * [Pressure Conversions](conversions/pressure_conversions.py)
+  * [Rectangular To Polar](conversions/rectangular_to_polar.py)
   * [Rgb Cmyk Conversion](conversions/rgb_cmyk_conversion.py)
   * [Rgb Hsv Conversion](conversions/rgb_hsv_conversion.py)
   * [Roman Numerals](conversions/roman_numerals.py)

computer_vision/intensity_based_segmentation.py

@@ -0,0 +1,62 @@
+# Source: "https://www.ijcse.com/docs/IJCSE11-02-03-117.pdf"
+
+# Importing necessary libraries
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image
+
+
+def segment_image(image: np.ndarray, thresholds: list[int]) -> np.ndarray:
+    """
+    Performs image segmentation based on intensity thresholds.
+
+    Args:
+        image: Input grayscale image as a 2D array.
+        thresholds: Intensity thresholds to define segments.
+
+    Returns:
+        A labeled 2D array where each region corresponds to a threshold range.
+
+    Example:
+        >>> img = np.array([[80, 120, 180], [40, 90, 150], [20, 60, 100]])
+        >>> segment_image(img, [50, 100, 150])
+        array([[1, 2, 3],
+               [0, 1, 2],
+               [0, 1, 1]], dtype=int32)
+    """
+    # Initialize segmented array with zeros
+    segmented = np.zeros_like(image, dtype=np.int32)
+
+    # Assign labels based on thresholds
+    for i, threshold in enumerate(thresholds):
+        segmented[image > threshold] = i + 1
+
+    return segmented
+
+
+if __name__ == "__main__":
+    # Load the image
+    image_path = "path_to_image"  # Replace with your image path
+    original_image = Image.open(image_path).convert("L")
+    image_array = np.array(original_image)
+
+    # Define thresholds
+    thresholds = [50, 100, 150, 200]
+
+    # Perform segmentation
+    segmented_image = segment_image(image_array, thresholds)
+
+    # Display the results
+    plt.figure(figsize=(10, 5))
+
+    plt.subplot(1, 2, 1)
+    plt.title("Original Image")
+    plt.imshow(image_array, cmap="gray")
+    plt.axis("off")
+
+    plt.subplot(1, 2, 2)
+    plt.title("Segmented Image")
+    plt.imshow(segmented_image, cmap="tab20")
+    plt.axis("off")
+
+    plt.show()

conversions/rectangular_to_polar.py

@@ -0,0 +1,32 @@
+import math
+
+
+def rectangular_to_polar(real: float, img: float) -> tuple[float, float]:
+    """
+    https://en.wikipedia.org/wiki/Polar_coordinate_system
+
+    >>> rectangular_to_polar(5,-5)
+    (7.07, -45.0)
+    >>> rectangular_to_polar(-1,1)
+    (1.41, 135.0)
+    >>> rectangular_to_polar(-1,-1)
+    (1.41, -135.0)
+    >>> rectangular_to_polar(1e-10,1e-10)
+    (0.0, 45.0)
+    >>> rectangular_to_polar(-1e-10,1e-10)
+    (0.0, 135.0)
+    >>> rectangular_to_polar(9.75,5.93)
+    (11.41, 31.31)
+    >>> rectangular_to_polar(10000,99999)
+    (100497.76, 84.29)
+    """
+
+    mod = round(math.sqrt((real**2) + (img**2)), 2)
+    ang = round(math.degrees(math.atan2(img, real)), 2)
+    return (mod, ang)
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()

data_structures/arrays/sudoku_solver.py

@@ -23,7 +23,7 @@ def cross(items_a, items_b):
     + [cross(rs, cs) for rs in ("ABC", "DEF", "GHI") for cs in ("123", "456", "789")]
 )
 units = {s: [u for u in unitlist if s in u] for s in squares}
-peers = {s: set(sum(units[s], [])) - {s} for s in squares}  # noqa: RUF017
+peers = {s: {x for u in units[s] for x in u} - {s} for s in squares}
 
 
 def test():
@@ -172,7 +172,8 @@ def unitsolved(unit):
 
 def from_file(filename, sep="\n"):
     "Parse a file into a list of strings, separated by sep."
-    return open(filename).read().strip().split(sep)
+    with open(filename) as file:
+        return file.read().strip().split(sep)
 
 
 def random_puzzle(assignments=17):

dynamic_programming/all_construct.py

@@ -8,9 +8,10 @@
 
 def all_construct(target: str, word_bank: list[str] | None = None) -> list[list[str]]:
     """
-        returns the list containing all the possible
-        combinations a string(target) can be constructed from
-        the given list of substrings(word_bank)
+    returns the list containing all the possible
+    combinations a string(`target`) can be constructed from
+    the given list of substrings(`word_bank`)
+
     >>> all_construct("hello", ["he", "l", "o"])
     [['he', 'l', 'l', 'o']]
     >>> all_construct("purple",["purp","p","ur","le","purpl"])

dynamic_programming/combination_sum_iv.py

@@ -1,24 +1,25 @@
 """
 Question:
-You are given an array of distinct integers and you have to tell how many
-different ways of selecting the elements from the array are there such that
-the sum of chosen elements is equal to the target number tar.
+    You are given an array of distinct integers and you have to tell how many
+    different ways of selecting the elements from the array are there such that
+    the sum of chosen elements is equal to the target number tar.
 
 Example
 
 Input:
-N = 3
-target = 5
-array = [1, 2, 5]
+    * N = 3
+    * target = 5
+    * array = [1, 2, 5]
 
 Output:
-9
+    9
 
 Approach:
-The basic idea is to go over recursively to find the way such that the sum
-of chosen elements is “tar”. For every element, we have two choices
-    1. Include the element in our set of chosen elements.
-    2. Don't include the element in our set of chosen elements.
+    The basic idea is to go over recursively to find the way such that the sum
+    of chosen elements is `target`. For every element, we have two choices
+
+        1. Include the element in our set of chosen elements.
+        2. Don't include the element in our set of chosen elements.
 """
 
 

dynamic_programming/fizz_buzz.py

@@ -3,11 +3,12 @@
 
 def fizz_buzz(number: int, iterations: int) -> str:
     """
-    Plays FizzBuzz.
-    Prints Fizz if number is a multiple of 3.
-    Prints Buzz if its a multiple of 5.
-    Prints FizzBuzz if its a multiple of both 3 and 5 or 15.
-    Else Prints The Number Itself.
+    | Plays FizzBuzz.
+    | Prints Fizz if number is a multiple of ``3``.
+    | Prints Buzz if its a multiple of ``5``.
+    | Prints FizzBuzz if its a multiple of both ``3`` and ``5`` or ``15``.
+    | Else Prints The Number Itself.
+
     >>> fizz_buzz(1,7)
     '1 2 Fizz 4 Buzz Fizz 7 '
     >>> fizz_buzz(1,0)

dynamic_programming/knapsack.py

@@ -11,7 +11,7 @@ def mf_knapsack(i, wt, val, j):
     """
     This code involves the concept of memory functions. Here we solve the subproblems
     which are needed unlike the below example
-    F is a 2D array with -1s filled up
+    F is a 2D array with ``-1`` s filled up
     """
     global f  # a global dp table for knapsack
     if f[i][j] < 0:
@@ -45,22 +45,24 @@ def knapsack_with_example_solution(w: int, wt: list, val: list):
     the several possible optimal subsets.
 
     Parameters
-    ---------
+    ----------
 
-    W: int, the total maximum weight for the given knapsack problem.
-    wt: list, the vector of weights for all items where wt[i] is the weight
-    of the i-th item.
-    val: list, the vector of values for all items where val[i] is the value
-    of the i-th item
+    * `w`: int, the total maximum weight for the given knapsack problem.
+    * `wt`: list, the vector of weights for all items where ``wt[i]`` is the weight
+       of the ``i``-th item.
+    * `val`: list, the vector of values for all items where ``val[i]`` is the value
+      of the ``i``-th item
 
     Returns
     -------
-    optimal_val: float, the optimal value for the given knapsack problem
-    example_optional_set: set, the indices of one of the optimal subsets
-    which gave rise to the optimal value.
+
+    * `optimal_val`: float, the optimal value for the given knapsack problem
+    * `example_optional_set`: set, the indices of one of the optimal subsets
+      which gave rise to the optimal value.
 
     Examples
-    -------
+    --------
+
     >>> knapsack_with_example_solution(10, [1, 3, 5, 2], [10, 20, 100, 22])
     (142, {2, 3, 4})
     >>> knapsack_with_example_solution(6, [4, 3, 2, 3], [3, 2, 4, 4])
@@ -104,19 +106,19 @@ def _construct_solution(dp: list, wt: list, i: int, j: int, optimal_set: set):
     a filled DP table and the vector of weights
 
     Parameters
-    ---------
-
-    dp: list of list, the table of a solved integer weight dynamic programming problem
+    ----------
 
-    wt: list or tuple, the vector of weights of the items
-    i: int, the index of the item under consideration
-    j: int, the current possible maximum weight
-    optimal_set: set, the optimal subset so far. This gets modified by the function.
+    * `dp`: list of list, the table of a solved integer weight dynamic programming
+      problem
+    * `wt`: list or tuple, the vector of weights of the items
+    * `i`: int, the index of the item under consideration
+    * `j`: int, the current possible maximum weight
+    * `optimal_set`: set, the optimal subset so far. This gets modified by the function.
 
     Returns
     -------
-    None
 
+    ``None``
     """
     # for the current item i at a maximum weight j to be part of an optimal subset,
     # the optimal value at (i, j) must be greater than the optimal value at (i-1, j).

dynamic_programming/longest_common_substring.py

@@ -1,15 +1,19 @@
 """
-Longest Common Substring Problem Statement: Given two sequences, find the
-longest common substring present in both of them. A substring is
-necessarily continuous.
-Example: "abcdef" and "xabded" have two longest common substrings, "ab" or "de".
-Therefore, algorithm should return any one of them.
+Longest Common Substring Problem Statement:
+    Given two sequences, find the
+    longest common substring present in both of them. A substring is
+    necessarily continuous.
+
+Example:
+    ``abcdef`` and ``xabded`` have two longest common substrings, ``ab`` or ``de``.
+    Therefore, algorithm should return any one of them.
 """
 
 
 def longest_common_substring(text1: str, text2: str) -> str:
     """
     Finds the longest common substring between two strings.
+
     >>> longest_common_substring("", "")
     ''
     >>> longest_common_substring("a","")

dynamic_programming/longest_increasing_subsequence.py

@@ -4,11 +4,13 @@
 This is a pure Python implementation of Dynamic Programming solution to the longest
 increasing subsequence of a given sequence.
 
-The problem is  :
-Given an array, to find the longest and increasing sub-array in that given array and
-return it.
-Example: [10, 22, 9, 33, 21, 50, 41, 60, 80] as input will return
-         [10, 22, 33, 41, 60, 80] as output
+The problem is:
+    Given an array, to find the longest and increasing sub-array in that given array and
+    return it.
+
+Example:
+    ``[10, 22, 9, 33, 21, 50, 41, 60, 80]`` as input will return
+    ``[10, 22, 33, 41, 60, 80]`` as output
 """
 
 from __future__ import annotations
@@ -17,6 +19,7 @@
 def longest_subsequence(array: list[int]) -> list[int]:  # This function is recursive
     """
     Some examples
+
     >>> longest_subsequence([10, 22, 9, 33, 21, 50, 41, 60, 80])
     [10, 22, 33, 41, 60, 80]
     >>> longest_subsequence([4, 8, 7, 5, 1, 12, 2, 3, 9])