Merge branch 'TheAlgorithms:master' into master

Author: Epik-Whale463

.github/workflows/build.yml

@@ -12,20 +12,24 @@ 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:
           path: ~/.cache/pip
           key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }}
       - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip setuptools six wheel
+          python -m pip install --upgrade pip setuptools wheel
           python -m pip install pytest-cov -r requirements.txt
       - name: Run tests
         # 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

.pre-commit-config.yaml

@@ -16,7 +16,7 @@ repos:
       - id: auto-walrus
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.6.7
+    rev: v0.6.8
     hooks:
       - id: ruff
       - id: ruff-format
@@ -42,7 +42,7 @@ repos:
         pass_filenames: false
 
   - repo: https://github.com/abravalheri/validate-pyproject
-    rev: v0.19
+    rev: v0.20.2
     hooks:
       - id: validate-pyproject
 

DIRECTORY.md

@@ -22,6 +22,7 @@
   * [Rat In Maze](backtracking/rat_in_maze.py)
   * [Sudoku](backtracking/sudoku.py)
   * [Sum Of Subsets](backtracking/sum_of_subsets.py)
+  * [Word Ladder](backtracking/word_ladder.py)
   * [Word Search](backtracking/word_search.py)
 
 ## Bit Manipulation
@@ -291,6 +292,13 @@
     * [Stack With Doubly Linked List](data_structures/stacks/stack_with_doubly_linked_list.py)
     * [Stack With Singly Linked List](data_structures/stacks/stack_with_singly_linked_list.py)
     * [Stock Span Problem](data_structures/stacks/stock_span_problem.py)
+  * Suffix Tree
+    * Example
+      * [Example Usage](data_structures/suffix_tree/example/example_usage.py)
+    * [Suffix Tree](data_structures/suffix_tree/suffix_tree.py)
+    * [Suffix Tree Node](data_structures/suffix_tree/suffix_tree_node.py)
+    * Tests
+      * [Test Suffix Tree](data_structures/suffix_tree/tests/test_suffix_tree.py)
   * Trie
     * [Radix Tree](data_structures/trie/radix_tree.py)
     * [Trie](data_structures/trie/trie.py)
@@ -1336,7 +1344,6 @@
   * [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)

backtracking/word_break.py

@@ -0,0 +1,71 @@
+"""
+Word Break Problem is a well-known problem in computer science.
+Given a string and a dictionary of words, the task is to determine if
+the string can be segmented into a sequence of one or more dictionary words.
+
+Wikipedia: https://en.wikipedia.org/wiki/Word_break_problem
+"""
+
+
+def backtrack(input_string: str, word_dict: set[str], start: int) -> bool:
+    """
+    Helper function that uses backtracking to determine if a valid
+    word segmentation is possible starting from index 'start'.
+
+    Parameters:
+    input_string (str): The input string to be segmented.
+    word_dict (set[str]): A set of valid dictionary words.
+    start (int): The starting index of the substring to be checked.
+
+    Returns:
+    bool: True if a valid segmentation is possible, otherwise False.
+
+    Example:
+    >>> backtrack("leetcode", {"leet", "code"}, 0)
+    True
+
+    >>> backtrack("applepenapple", {"apple", "pen"}, 0)
+    True
+
+    >>> backtrack("catsandog", {"cats", "dog", "sand", "and", "cat"}, 0)
+    False
+    """
+
+    # Base case: if the starting index has reached the end of the string
+    if start == len(input_string):
+        return True
+
+    # Try every possible substring from 'start' to 'end'
+    for end in range(start + 1, len(input_string) + 1):
+        if input_string[start:end] in word_dict and backtrack(
+            input_string, word_dict, end
+        ):
+            return True
+
+    return False
+
+
+def word_break(input_string: str, word_dict: set[str]) -> bool:
+    """
+    Determines if the input string can be segmented into a sequence of
+    valid dictionary words using backtracking.
+
+    Parameters:
+    input_string (str): The input string to segment.
+    word_dict (set[str]): The set of valid words.
+
+    Returns:
+    bool: True if the string can be segmented into valid words, otherwise False.
+
+    Example:
+    >>> word_break("leetcode", {"leet", "code"})
+    True
+
+    >>> word_break("applepenapple", {"apple", "pen"})
+    True
+
+    >>> word_break("catsandog", {"cats", "dog", "sand", "and", "cat"})
+    False
+    """
+
+    return backtrack(input_string, word_dict, 0)

backtracking/word_ladder.py

@@ -0,0 +1,100 @@
+"""
+Word Ladder is a classic problem in computer science.
+The problem is to transform a start word into an end word
+by changing one letter at a time.
+Each intermediate word must be a valid word from a given list of words.
+The goal is to find a transformation sequence
+from the start word to the end word.
+
+Wikipedia: https://en.wikipedia.org/wiki/Word_ladder
+"""
+
+import string
+
+
+def backtrack(
+    current_word: str, path: list[str], end_word: str, word_set: set[str]
+) -> list[str]:
+    """
+    Helper function to perform backtracking to find the transformation
+    from the current_word to the end_word.
+
+    Parameters:
+    current_word (str): The current word in the transformation sequence.
+    path (list[str]): The list of transformations from begin_word to current_word.
+    end_word (str): The target word for transformation.
+    word_set (set[str]): The set of valid words for transformation.
+
+    Returns:
+    list[str]: The list of transformations from begin_word to end_word.
+               Returns an empty list if there is no valid
+                transformation from current_word to end_word.
+
+    Example:
+    >>> backtrack("hit", ["hit"], "cog", {"hot", "dot", "dog", "lot", "log", "cog"})
+    ['hit', 'hot', 'dot', 'lot', 'log', 'cog']
+
+    >>> backtrack("hit", ["hit"], "cog", {"hot", "dot", "dog", "lot", "log"})
+    []
+
+    >>> backtrack("lead", ["lead"], "gold", {"load", "goad", "gold", "lead", "lord"})
+    ['lead', 'lead', 'load', 'goad', 'gold']
+
+    >>> backtrack("game", ["game"], "code", {"came", "cage", "code", "cade", "gave"})
+    ['game', 'came', 'cade', 'code']
+    """
+
+    # Base case: If the current word is the end word, return the path
+    if current_word == end_word:
+        return path
+
+    # Try all possible single-letter transformations
+    for i in range(len(current_word)):
+        for c in string.ascii_lowercase:  # Try changing each letter
+            transformed_word = current_word[:i] + c + current_word[i + 1 :]
+            if transformed_word in word_set:
+                word_set.remove(transformed_word)
+                # Recur with the new word added to the path
+                result = backtrack(
+                    transformed_word, [*path, transformed_word], end_word, word_set
+                )
+                if result:  # valid transformation found
+                    return result
+                word_set.add(transformed_word)  # backtrack
+
+    return []  # No valid transformation found
+
+
+def word_ladder(begin_word: str, end_word: str, word_set: set[str]) -> list[str]:
+    """
+    Solve the Word Ladder problem using Backtracking and return
+    the list of transformations from begin_word to end_word.
+
+    Parameters:
+    begin_word (str): The word from which the transformation starts.
+    end_word (str): The target word for transformation.
+    word_list (list[str]): The list of valid words for transformation.
+
+    Returns:
+    list[str]: The list of transformations from begin_word to end_word.
+               Returns an empty list if there is no valid transformation.
+
+    Example:
+    >>> word_ladder("hit", "cog", ["hot", "dot", "dog", "lot", "log", "cog"])
+    ['hit', 'hot', 'dot', 'lot', 'log', 'cog']
+
+    >>> word_ladder("hit", "cog", ["hot", "dot", "dog", "lot", "log"])
+    []
+
+    >>> word_ladder("lead", "gold", ["load", "goad", "gold", "lead", "lord"])
+    ['lead', 'lead', 'load', 'goad', 'gold']
+
+    >>> word_ladder("game", "code", ["came", "cage", "code", "cade", "gave"])
+    ['game', 'came', 'cade', 'code']
+    """
+
+    if end_word not in word_set:  # no valid transformation possible
+        return []
+
+    # Perform backtracking starting from the begin_word
+    return backtrack(begin_word, [begin_word], end_word, word_set)

computer_vision/haralick_descriptors.py

@@ -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]]: