AlgorithmAndLeetCode · pull · May 10, 2025 · May 10, 2025 · May 10, 2025 · May 10, 2025
diff --git a/boolean_algebra/and_gate.py b/boolean_algebra/and_gate.py
@@ -1,8 +1,8 @@
 """
-An AND Gate is a logic gate in boolean algebra which results to 1 (True) if both the
-inputs are 1, and 0 (False) otherwise.
+An AND Gate is a logic gate in boolean algebra which results to 1 (True) if all the
+inputs are 1 (True), and 0 (False) otherwise.
 
-Following is the truth table of an AND Gate:
+Following is the truth table of a Two Input AND Gate:
     ------------------------------
     | Input 1 | Input 2 | Output |
     ------------------------------
@@ -12,7 +12,7 @@
     |    1    |    1    |    1   |
     ------------------------------
 
-Refer - https://www.geeksforgeeks.org/logic-gates-in-python/
+Refer - https://www.geeksforgeeks.org/logic-gates/
 """
 
 
@@ -32,6 +32,18 @@ def and_gate(input_1: int, input_2: int) -> int:
     return int(input_1 and input_2)
 
 
+def n_input_and_gate(inputs: list[int]) -> int:
+    """
+    Calculate AND of a list of input values
+
+    >>> n_input_and_gate([1, 0, 1, 1, 0])
+    0
+    >>> n_input_and_gate([1, 1, 1, 1, 1])
+    1
+    """
+    return int(all(inputs))
+
+
 if __name__ == "__main__":
     import doctest
 

diff --git a/data_structures/binary_tree/lowest_common_ancestor.py b/data_structures/binary_tree/lowest_common_ancestor.py
@@ -15,6 +15,8 @@ def swap(a: int, b: int) -> tuple[int, int]:
     (4, 3)
     >>> swap(67, 12)
     (12, 67)
+    >>> swap(3,-4)
+    (-4, 3)
     """
     a ^= b
     b ^= a
@@ -25,6 +27,23 @@ def swap(a: int, b: int) -> tuple[int, int]:
 def create_sparse(max_node: int, parent: list[list[int]]) -> list[list[int]]:
     """
     creating sparse table which saves each nodes 2^i-th parent
+    >>> max_node = 6
+    >>> parent = [[0, 0, 1, 1, 2, 2, 3]] + [[0] * 7 for _ in range(19)]
+    >>> parent = create_sparse(max_node=max_node, parent=parent)
+    >>> parent[0]
+    [0, 0, 1, 1, 2, 2, 3]
+    >>> parent[1]
+    [0, 0, 0, 0, 1, 1, 1]
+    >>> parent[2]
+    [0, 0, 0, 0, 0, 0, 0]
+
+    >>> max_node = 1
+    >>> parent = [[0, 0]] + [[0] * 2 for _ in range(19)]
+    >>> parent = create_sparse(max_node=max_node, parent=parent)
+    >>> parent[0]
+    [0, 0]
+    >>> parent[1]
+    [0, 0]
     """
     j = 1
     while (1 << j) < max_node:
@@ -38,6 +57,21 @@ def create_sparse(max_node: int, parent: list[list[int]]) -> list[list[int]]:
 def lowest_common_ancestor(
     u: int, v: int, level: list[int], parent: list[list[int]]
 ) -> int:
+    """
+    Return the lowest common ancestor between u and v
+
+    >>> level = [-1, 0, 1, 1, 2, 2, 2]
+    >>> parent = [[0, 0, 1, 1, 2, 2, 3],[0, 0, 0, 0, 1, 1, 1]] + \
+                    [[0] * 7 for _ in range(17)]
+    >>> lowest_common_ancestor(u=4, v=5, level=level, parent=parent)
+    2
+    >>> lowest_common_ancestor(u=4, v=6, level=level, parent=parent)
+    1
+    >>> lowest_common_ancestor(u=2, v=3, level=level, parent=parent)
+    1
+    >>> lowest_common_ancestor(u=6, v=6, level=level, parent=parent)
+    6
+    """
     # u must be deeper in the tree than v
     if level[u] < level[v]:
         u, v = swap(u, v)
@@ -68,6 +102,26 @@ def breadth_first_search(
     sets every nodes direct parent
     parent of root node is set to 0
     calculates depth of each node from root node
+    >>> level = [-1] * 7
+    >>> parent = [[0] * 7 for _ in range(20)]
+    >>> graph = {1: [2, 3], 2: [4, 5], 3: [6], 4: [], 5: [], 6: []}
+    >>> level, parent = breadth_first_search(
+    ...     level=level, parent=parent, max_node=6, graph=graph, root=1)
+    >>> level
+    [-1, 0, 1, 1, 2, 2, 2]
+    >>> parent[0]
+    [0, 0, 1, 1, 2, 2, 3]
+
+
+    >>> level = [-1] * 2
+    >>> parent = [[0] * 2 for _ in range(20)]
+    >>> graph = {1: []}
+    >>> level, parent = breadth_first_search(
+    ...     level=level, parent=parent, max_node=1, graph=graph, root=1)
+    >>> level
+    [-1, 0]
+    >>> parent[0]
+    [0, 0]
     """
     level[root] = 0
     q: Queue[int] = Queue(maxsize=max_node)

diff --git a/dynamic_programming/longest_common_substring.py b/dynamic_programming/longest_common_substring.py
@@ -43,22 +43,25 @@ def longest_common_substring(text1: str, text2: str) -> str:
     if not (isinstance(text1, str) and isinstance(text2, str)):
         raise ValueError("longest_common_substring() takes two strings for inputs")
 
+    if not text1 or not text2:
+        return ""
+
     text1_length = len(text1)
     text2_length = len(text2)
 
     dp = [[0] * (text2_length + 1) for _ in range(text1_length + 1)]
-    ans_index = 0
-    ans_length = 0
+    end_pos = 0
+    max_length = 0
 
     for i in range(1, text1_length + 1):
         for j in range(1, text2_length + 1):
             if text1[i - 1] == text2[j - 1]:
                 dp[i][j] = 1 + dp[i - 1][j - 1]
-                if dp[i][j] > ans_length:
-                    ans_index = i
-                    ans_length = dp[i][j]
+                if dp[i][j] > max_length:
+                    end_pos = i
+                    max_length = dp[i][j]
 
-    return text1[ans_index - ans_length : ans_index]
+    return text1[end_pos - max_length : end_pos]
 
 
 if __name__ == "__main__":

diff --git a/project_euler/problem_095/__init__.py b/project_euler/problem_095/__init__.py
diff --git a/project_euler/problem_095/sol1.py b/project_euler/problem_095/sol1.py
@@ -0,0 +1,164 @@
+"""
+Project Euler Problem 95: https://projecteuler.net/problem=95
+
+Amicable Chains
+
+The proper divisors of a number are all the divisors excluding the number itself.
+For example, the proper divisors of 28 are 1, 2, 4, 7, and 14.
+As the sum of these divisors is equal to 28, we call it a perfect number.
+
+Interestingly the sum of the proper divisors of 220 is 284 and
+the sum of the proper divisors of 284 is 220, forming a chain of two numbers.
+For this reason, 220 and 284 are called an amicable pair.
+
+Perhaps less well known are longer chains.
+For example, starting with 12496, we form a chain of five numbers:
+    12496 -> 14288 -> 15472 -> 14536 -> 14264 (-> 12496 -> ...)
+
+Since this chain returns to its starting point, it is called an amicable chain.
+
+Find the smallest member of the longest amicable chain with
+no element exceeding one million.
+
+Solution is doing the following:
+- Get relevant prime numbers
+- Iterate over product combination of prime numbers to generate all non-prime
+  numbers up to max number, by keeping track of prime factors
+- Calculate the sum of factors for each number
+- Iterate over found some factors to find longest chain
+"""
+
+from math import isqrt
+
+
+def generate_primes(max_num: int) -> list[int]:
+    """
+    Calculates the list of primes up to and including `max_num`.
+
+    >>> generate_primes(6)
+    [2, 3, 5]
+    """
+    are_primes = [True] * (max_num + 1)
+    are_primes[0] = are_primes[1] = False
+    for i in range(2, isqrt(max_num) + 1):
+        if are_primes[i]:
+            for j in range(i * i, max_num + 1, i):
+                are_primes[j] = False
+
+    return [prime for prime, is_prime in enumerate(are_primes) if is_prime]
+
+
+def multiply(
+    chain: list[int],
+    primes: list[int],
+    min_prime_idx: int,
+    prev_num: int,
+    max_num: int,
+    prev_sum: int,
+    primes_degrees: dict[int, int],
+) -> None:
+    """
+    Run over all prime combinations to generate non-prime numbers.
+
+    >>> chain = [0] * 3
+    >>> primes_degrees = {}
+    >>> multiply(
+    ...     chain=chain,
+    ...     primes=[2],
+    ...     min_prime_idx=0,
+    ...     prev_num=1,
+    ...     max_num=2,
+    ...     prev_sum=0,
+    ...     primes_degrees=primes_degrees,
+    ... )
+    >>> chain
+    [0, 0, 1]
+    >>> primes_degrees
+    {2: 1}
+    """
+
+    min_prime = primes[min_prime_idx]
+    num = prev_num * min_prime
+
+    min_prime_degree = primes_degrees.get(min_prime, 0)
+    min_prime_degree += 1
+    primes_degrees[min_prime] = min_prime_degree
+
+    new_sum = prev_sum * min_prime + (prev_sum + prev_num) * (min_prime - 1) // (
+        min_prime**min_prime_degree - 1
+    )
+    chain[num] = new_sum
+
+    for prime_idx in range(min_prime_idx, len(primes)):
+        if primes[prime_idx] * num > max_num:
+            break
+
+        multiply(
+            chain=chain,
+            primes=primes,
+            min_prime_idx=prime_idx,
+            prev_num=num,
+            max_num=max_num,
+            prev_sum=new_sum,
+            primes_degrees=primes_degrees.copy(),
+        )
+
+
+def find_longest_chain(chain: list[int], max_num: int) -> int:
+    """
+    Finds the smallest element of longest chain
+
+    >>> find_longest_chain(chain=[0, 0, 0, 0, 0, 0, 6], max_num=6)
+    6
+    """
+
+    max_len = 0
+    min_elem = 0
+    for start in range(2, len(chain)):
+        visited = {start}
+        elem = chain[start]
+        length = 1
+
+        while elem > 1 and elem <= max_num and elem not in visited:
+            visited.add(elem)
+            elem = chain[elem]
+            length += 1
+
+        if elem == start and length > max_len:
+            max_len = length
+            min_elem = start
+
+    return min_elem
+
+
+def solution(max_num: int = 1000000) -> int:
+    """
+    Runs the calculation for numbers <= `max_num`.
+
+    >>> solution(10)
+    6
+    >>> solution(200000)
+    12496
+    """
+
+    primes = generate_primes(max_num)
+    chain = [0] * (max_num + 1)
+    for prime_idx, prime in enumerate(primes):
+        if prime**2 > max_num:
+            break
+
+        multiply(
+            chain=chain,
+            primes=primes,
+            min_prime_idx=prime_idx,
+            prev_num=1,
+            max_num=max_num,
+            prev_sum=0,
+            primes_degrees={},
+        )
+
+    return find_longest_chain(chain=chain, max_num=max_num)
+
+
+if __name__ == "__main__":
+    print(f"{solution() = }")