Documentation

ciphers/mixed_keyword_cypher.py

@@ -1,3 +1,12 @@
+import string
+
+"""This function takes two parameters: a string variable named
+    'key' (with a default value of 'college') and a string variable
+     named 'pt' (with a default value of 'UNIVERSITY').
+     The function returns a string that is a transformation of the 'pt' argument
+      based on a key-shift cipher"""
+
+
 def mixed_keyword(key: str = "college", pt: str = "UNIVERSITY") -> str:
     """
 
@@ -21,23 +30,30 @@ def mixed_keyword(key: str = "college", pt: str = "UNIVERSITY") -> str:
     """
     key = key.upper()
     pt = pt.upper()
-    temp = []
-    for i in key:
-        if i not in temp:
-            temp.append(i)
+    ## changed below lines added sets functionality and remove for loops to get unique elemnt in list
+    temp = list(set(key))
     len_temp = len(temp)
     # print(temp)
-    alpha = []
+    # string module can used to generate alphabets list instead of using loops
+    alpha = list(string.ascii_uppercase)
     modalpha = []
-    for j in range(65, 91):
-        t = chr(j)
-        alpha.append(t)
-        if t not in temp:
-            temp.append(t)
+    for j in alpha:
+        if j not in temp:
+            temp.append(j)
+
     # print(temp)
     r = int(26 / 4)
-    # print(r)
+    print(r)
+    print(len_temp)
+    # for i in range(r*len_temp):
+    #     s = [temp[i] for _ in range(len_temp) if i < 26]
+    #     modalpha.append(s)
+
     k = 0
+    """These lines of code creates a dictionary by iterating over the list of
+    lists. Each letter in the alphabets list is mapped to a letter in a row of
+     the "modalpha" list. The mappings are stored in the dictionary with the
+     indices of the alphabets list as keys and the values fromthe corresponding modalpha lists as values"""
     for _ in range(r):
         s = []
         for _ in range(len_temp):
@@ -59,9 +75,10 @@ def mixed_keyword(key: str = "college", pt: str = "UNIVERSITY") -> str:
                 break
             k += 1
     print(d)
-    cypher = ""
-    for i in pt:
-        cypher += d[i]
+    cypher = "".join(d[c] for c in pt)
+    # cypher = ""
+    # for i in pt:
+    #     cypher += d[i]
     return cypher
 
 

divide_and_conquer/max_subarray_sum.py

@@ -26,6 +26,7 @@ def max_sum_from_start(array):
         array_sum += num
         if array_sum > max_sum:
             max_sum = array_sum
+
     return max_sum
 
 

divide_and_conquer/mergesort.py

@@ -1,5 +1,11 @@
 from __future__ import annotations
 
+"""
+The function creates an empty list called "sorted_array" with the length equal to the sum of the lengths of the two lists. It then initializes three pointers - pointer1, pointer2, and index - to 0.
+The function then enters a while loop that continues as long as pointer1 and pointer2 are less than the length of their respective lists. Within the loop, the function compares the values of the elements
+at the current pointer positions in the left and right lists, and adds the smaller element to the sorted_array. The pointer for the list that contained the smaller element is then incremented, as well as the index pointer for the sorted_array.
+"""
+
 
 def merge(left_half: list, right_half: list) -> list:
     """Helper function for mergesort.
@@ -34,7 +40,10 @@ def merge(left_half: list, right_half: list) -> list:
     pointer1 = 0  # pointer to current index for left Half
     pointer2 = 0  # pointer to current index for the right Half
     index = 0  # pointer to current index for the sorted array Half
-
+    """After the while loop finishes, the function runs two more while
+    loops to add any remaining elements from the left and right lists to
+    the sorted_array, in case one of the lists was longer than the other.
+    Finally, the function returns the sorted_array."""
     while pointer1 < len(left_half) and pointer2 < len(right_half):
         if left_half[pointer1] < right_half[pointer2]:
             sorted_array[index] = left_half[pointer1]

genetic_algorithm/basic_string.py

@@ -21,8 +21,11 @@
 random.seed(random.randint(0, 1000))
 
 
+# This function uses a genetic algorithm to evolve the string by randomly selecting characters from the genes list and comparing
+# the resulting string to the original string, repeating this process until the two strings match.
 def basic(target: str, genes: list[str], debug: bool = True) -> tuple[int, int, str]:
     """
+    These lines of code are testing a function called "basic"
     Verify that the target contains no genes besides the ones inside genes variable.
 
     >>> from string import ascii_lowercase
@@ -122,6 +125,9 @@ def evaluate(item: str, main_target: str = target) -> tuple[str, float]:
         ]
 
         # Select, crossover and mutate a new population.
+        """Select is responsible for selecting a second parent and generating a new population.
+           The function takes a tuple containing the first parent's string and its fitness score as input"""
+
         def select(parent_1: tuple[str, float]) -> list[str]:
             """Select the second parent and generate new population"""
             pop = []
@@ -139,13 +145,25 @@ def select(parent_1: tuple[str, float]) -> list[str]:
                 pop.append(mutate(child_2))
             return pop
 
+        """This function defines a crossover operation between two parent strings,
+         where a random slice point is chosen and the two parts of the parent strings
+         are combined to form two child strings. The function returns a tuple containing the two child strings."""
+
         def crossover(parent_1: str, parent_2: str) -> tuple[str, str]:
             """Slice and combine two string at a random point."""
             random_slice = random.randint(0, len(parent_1) - 1)
             child_1 = parent_1[:random_slice] + parent_2[random_slice:]
             child_2 = parent_2[:random_slice] + parent_1[random_slice:]
             return (child_1, child_2)
 
+        """This function takes a string, child, as an argument and returns a string.
+            The function mutates a random gene of the child by replacing it with a random gene from a list called "genes".
+            The mutation occurs only if a random number generated by the "random.uniform" function is less than a constant value called
+            "MUTATION_PROBABILITY".
+            The code first converts the string child to a list called "child_list". It then selects a random index from the child_list
+            using "random.randint" and replaces the value at that index with a random gene from "genes".
+            Finally, the function returns the mutated child as a string by joining the "child_list"."""
+
         def mutate(child: str) -> str:
             """Mutate a random gene of a child with another one from the list."""
             child_list = list(child)