Merge pull request #2 from sahildando/code-optimization-and-documentation-db02f

Update from task f57a45c6-dc70-4d29-86c9-ae22966db02f

Author: sahildando

.gitignore

@@ -1,110 +1,36 @@
-# Byte-compiled / optimized / DLL files
+```
+# Compiled Python files
+*.pyc
 __pycache__/
-*.py[cod]
-*$py.class
 
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a Python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
+# Dependencies
+.venv/
+venv/
 pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-.hypothesis/
-.pytest_cache/
-
-# Translations
-*.mo
-*.pot
+pipenv.lock
 
-# Django stuff:
+# Logs and temp files
 *.log
-local_settings.py
-db.sqlite3
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/
+*.tmp
+*.swp
 
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# pyenv
-.python-version
-
-# celery beat schedule file
-celerybeat-schedule
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
+# Environment
 .env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
+.env.local
+*.env.*
 
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
+# Coverage reports
+.coverage
+htmlcov/
+coverage/
 
-# mypy
-.mypy_cache/
+# Editors
+.vscode/
+.idea/
+*.swp
+*.swo
 
+# OS generated files
 .DS_Store
-.idea
-.try
-.vscode/
-.vs/
+Thumbs.db
+```

dynamic_programming/edit_distance.py

@@ -1,29 +1,78 @@
 """
-Author  : Turfa Auliarachman
-Date    : October 12, 2016
-
 This is a pure Python implementation of Dynamic Programming solution to the edit
-distance problem.
-
-The problem is :
-Given two strings A and B. Find the minimum number of operations to string B such that
-A = B. The permitted operations are removal,  insertion, and substitution.
+distance problem (also known as Levenshtein distance).
+
+The Problem:
+Given two strings A and B, find the minimum number of operations to transform
+string A into string B. The permitted operations are:
+1. Insertion - Add a character
+2. Deletion - Remove a character  
+3. Substitution - Replace one character with another
+
+Time Complexity: O(m × n) where m and n are the lengths of the two strings
+Space Complexity: O(m × n) for the DP table
+
+Applications:
+- Spell checkers and autocorrect
+- DNA sequence alignment in bioinformatics
+- Plagiarism detection
+- Speech recognition
+- Fuzzy string matching
+
+Example:
+    >>> EditDistance().min_dist_bottom_up("intention", "execution")
+    5
+    >>> # The 5 edits are: intention -> inention -> enention -> exention -> executon -> execution
 """
 
 
 class EditDistance:
     """
-    Use :
-    solver              = EditDistance()
-    editDistanceResult  = solver.solve(firstString, secondString)
+    A class to compute the edit distance between two strings using dynamic programming.
+    
+    This implementation provides both top-down (memoization) and bottom-up (tabulation)
+    approaches. The bottom-up approach is generally preferred for its iterative nature
+    and better space efficiency potential.
+    
+    Attributes:
+        word1 (str): First input string.
+        word2 (str): Second input string.
+        dp (list[list[int]]): Dynamic programming table for storing intermediate results.
+    
+    Example:
+        >>> solver = EditDistance()
+        >>> solver.min_dist_bottom_up("kitten", "sitting")
+        3
     """
-
+    
     def __init__(self):
+        """Initialize the EditDistance solver with empty strings."""
         self.word1 = ""
         self.word2 = ""
         self.dp = []
-
+    
     def __min_dist_top_down_dp(self, m: int, n: int) -> int:
+        """
+        Helper method for top-down dynamic programming with memoization.
+        
+        Recursively computes the minimum edit distance between word1[0:m+1] and 
+        word2[0:n+1] by considering three possible operations at each step.
+        
+        Args:
+            m (int): Current index in word1 (0-indexed).
+            n (int): Current index in word2 (0-indexed).
+        
+        Returns:
+            int: Minimum edit distance between the substrings.
+        
+        Base Cases:
+            - If m == -1: Need to insert all n+1 characters from word2
+            - If n == -1: Need to delete all m+1 characters from word1
+        
+        Recursive Case:
+            - If characters match: No operation needed, move diagonally
+            - Otherwise: Take minimum of insert, delete, replace + 1
+        """
         if m == -1:
             return n + 1
         elif n == -1:
@@ -38,51 +87,90 @@ def __min_dist_top_down_dp(self, m: int, n: int) -> int:
                 delete = self.__min_dist_top_down_dp(m - 1, n)
                 replace = self.__min_dist_top_down_dp(m - 1, n - 1)
                 self.dp[m][n] = 1 + min(insert, delete, replace)
-
+            
             return self.dp[m][n]
-
+    
     def min_dist_top_down(self, word1: str, word2: str) -> int:
         """
-        >>> EditDistance().min_dist_top_down("intention", "execution")
-        5
-        >>> EditDistance().min_dist_top_down("intention", "")
-        9
-        >>> EditDistance().min_dist_top_down("", "")
-        0
+        Calculate edit distance using top-down approach with memoization.
+        
+        This approach starts from the full problem and recursively breaks it down
+        into smaller subproblems, caching results to avoid redundant computations.
+        
+        Args:
+            word1 (str): The source string.
+            word2 (str): The target string.
+        
+        Returns:
+            int: Minimum number of operations to transform word1 into word2.
+        
+        Examples:
+            >>> EditDistance().min_dist_top_down("intention", "execution")
+            5
+            >>> EditDistance().min_dist_top_down("intention", "")
+            9
+            >>> EditDistance().min_dist_top_down("", "")
+            0
+            >>> EditDistance().min_dist_top_down("kitten", "sitting")
+            3
         """
         self.word1 = word1
         self.word2 = word2
         self.dp = [[-1 for _ in range(len(word2))] for _ in range(len(word1))]
-
+        
         return self.__min_dist_top_down_dp(len(word1) - 1, len(word2) - 1)
-
+    
     def min_dist_bottom_up(self, word1: str, word2: str) -> int:
         """
-        >>> EditDistance().min_dist_bottom_up("intention", "execution")
-        5
-        >>> EditDistance().min_dist_bottom_up("intention", "")
-        9
-        >>> EditDistance().min_dist_bottom_up("", "")
-        0
+        Calculate edit distance using bottom-up approach with tabulation.
+        
+        This approach builds the solution iteratively from smaller subproblems,
+        filling a DP table where dp[i][j] represents the edit distance between
+        the first i characters of word1 and first j characters of word2.
+        
+        Args:
+            word1 (str): The source string.
+            word2 (str): The target string.
+        
+        Returns:
+            int: Minimum number of operations to transform word1 into word2.
+        
+        Algorithm:
+            1. Initialize DP table of size (m+1) × (n+1)
+            2. Base cases: dp[i][0] = i (delete all), dp[0][j] = j (insert all)
+            3. For each cell, if characters match: dp[i][j] = dp[i-1][j-1]
+            4. Otherwise: dp[i][j] = 1 + min(insert, delete, replace)
+        
+        Examples:
+            >>> EditDistance().min_dist_bottom_up("intention", "execution")
+            5
+            >>> EditDistance().min_dist_bottom_up("intention", "")
+            9
+            >>> EditDistance().min_dist_bottom_up("", "")
+            0
+            >>> EditDistance().min_dist_bottom_up("kitten", "sitting")
+            3
+            >>> EditDistance().min_dist_bottom_up("horse", "ros")
+            3
         """
         self.word1 = word1
         self.word2 = word2
         m = len(word1)
         n = len(word2)
         self.dp = [[0 for _ in range(n + 1)] for _ in range(m + 1)]
-
+        
         for i in range(m + 1):
             for j in range(n + 1):
-                if i == 0:  # first string is empty
+                if i == 0:  # first string is empty - insert all characters
                     self.dp[i][j] = j
-                elif j == 0:  # second string is empty
+                elif j == 0:  # second string is empty - delete all characters
                     self.dp[i][j] = i
                 elif word1[i - 1] == word2[j - 1]:  # last characters are equal
                     self.dp[i][j] = self.dp[i - 1][j - 1]
                 else:
-                    insert = self.dp[i][j - 1]
-                    delete = self.dp[i - 1][j]
-                    replace = self.dp[i - 1][j - 1]
+                    insert = self.dp[i][j - 1]      # Insert character
+                    delete = self.dp[i - 1][j]      # Delete character
+                    replace = self.dp[i - 1][j - 1] # Replace character
                     self.dp[i][j] = 1 + min(insert, delete, replace)
         return self.dp[m][n]