TheAlgorithms · cclauss · Jun 23, 2020 · Jun 23, 2020 · Jun 23, 2020 · Jun 23, 2020
diff --git a/DIRECTORY.md b/DIRECTORY.md
@@ -16,6 +16,7 @@
   * [All Subsequences](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_subsequences.py)
   * [Coloring](https://github.com/TheAlgorithms/Python/blob/master/backtracking/coloring.py)
   * [Hamiltonian Cycle](https://github.com/TheAlgorithms/Python/blob/master/backtracking/hamiltonian_cycle.py)
+  * [Knight Tour](https://github.com/TheAlgorithms/Python/blob/master/backtracking/knight_tour.py)
   * [Minimax](https://github.com/TheAlgorithms/Python/blob/master/backtracking/minimax.py)
   * [N Queens](https://github.com/TheAlgorithms/Python/blob/master/backtracking/n_queens.py)
   * [Rat In Maze](https://github.com/TheAlgorithms/Python/blob/master/backtracking/rat_in_maze.py)
@@ -71,6 +72,8 @@
 ## Compression
   * [Burrows Wheeler](https://github.com/TheAlgorithms/Python/blob/master/compression/burrows_wheeler.py)
   * [Huffman](https://github.com/TheAlgorithms/Python/blob/master/compression/huffman.py)
+  * [Lempel Ziv](https://github.com/TheAlgorithms/Python/blob/master/compression/lempel_ziv.py)
+  * [Lempel Ziv Decompress](https://github.com/TheAlgorithms/Python/blob/master/compression/lempel_ziv_decompress.py)
   * [Peak Signal To Noise Ratio](https://github.com/TheAlgorithms/Python/blob/master/compression/peak_signal_to_noise_ratio.py)
 
 ## Computer Vision
@@ -199,6 +202,7 @@
   * [Longest Increasing Subsequence O(Nlogn)](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence_o(nlogn).py)
   * [Longest Sub Array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_sub_array.py)
   * [Matrix Chain Order](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/matrix_chain_order.py)
+  * [Max Non Adjacent Sum](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_non_adjacent_sum.py)
   * [Max Sub Array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sub_array.py)
   * [Max Sum Contiguous Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sum_contiguous_subsequence.py)
   * [Minimum Partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_partition.py)
@@ -440,6 +444,7 @@
   * [Least Recently Used](https://github.com/TheAlgorithms/Python/blob/master/other/least_recently_used.py)
   * [Linear Congruential Generator](https://github.com/TheAlgorithms/Python/blob/master/other/linear_congruential_generator.py)
   * [Magicdiamondpattern](https://github.com/TheAlgorithms/Python/blob/master/other/magicdiamondpattern.py)
+  * [Markov Chain](https://github.com/TheAlgorithms/Python/blob/master/other/markov_chain.py)
   * [Nested Brackets](https://github.com/TheAlgorithms/Python/blob/master/other/nested_brackets.py)
   * [Palindrome](https://github.com/TheAlgorithms/Python/blob/master/other/palindrome.py)
   * [Password Generator](https://github.com/TheAlgorithms/Python/blob/master/other/password_generator.py)

diff --git a/other/markov_chain.py b/other/markov_chain.py
@@ -4,19 +4,19 @@
 
 
 class MarkovChainGraphUndirectedUnweighted:
-    '''
+    """
     Undirected Unweighted Graph for running Markov Chain Algorithm
-    '''
+    """
 
     def __init__(self):
         self.connections = {}
 
     def add_node(self, node: str) -> None:
         self.connections[node] = {}
 
-    def add_transition_probability(self, node1: str,
-                                   node2: str,
-                                   probability: float) -> None:
+    def add_transition_probability(
+        self, node1: str, node2: str, probability: float
+    ) -> None:
         if node1 not in self.connections:
             self.add_node(node1)
         if node2 not in self.connections:
@@ -36,10 +36,10 @@ def transition(self, node: str) -> str:
                 return dest
 
 
-def get_transitions(start: str,
-                    transitions: List[Tuple[str, str, float]],
-                    steps: int) -> Dict[str, int]:
-    '''
+def get_transitions(
+    start: str, transitions: List[Tuple[str, str, float]], steps: int
+) -> Dict[str, int]:
+    """
     Running Markov Chain algorithm and calculating the number of times each node is
     visited
 
@@ -59,7 +59,7 @@ def get_transitions(start: str,
 
     >>> result['a'] > result['b'] > result['c']
     True
-    '''
+    """
 
     graph = MarkovChainGraphUndirectedUnweighted()
 

diff --git a/sorts/radix_sort.py b/sorts/radix_sort.py
@@ -1,26 +1,29 @@
-def radix_sort(lst):
-    RADIX = 10
-    placement = 1
+from typing import List
 
-    # get the maximum number
-    max_digit = max(lst)
 
+def radix_sort(list_of_ints: List[int]) -> List[int]:
+    """
+    radix_sort(range(15)) == sorted(range(15))
+    True
+    radix_sort(reversed(range(15))) == sorted(range(15))
+    True
+    """
+    RADIX = 10
+    placement = 1
+    max_digit = max(list_of_ints)
     while placement < max_digit:
-        # declare and initialize buckets
+        # declare and initialize empty buckets
         buckets = [list() for _ in range(RADIX)]
-
-        # split lst between lists
-        for i in lst:
+        # split list_of_ints between the buckets
+        for i in list_of_ints:
             tmp = int((i / placement) % RADIX)
             buckets[tmp].append(i)
-
-        # empty lists into lst array
+        # put each buckets' contents into list_of_ints
         a = 0
         for b in range(RADIX):
-            buck = buckets[b]
-            for i in buck:
-                lst[a] = i
+            for i in buckets[b]:
+                list_of_ints[a] = i
                 a += 1
-
         # move to next
         placement *= RADIX
+    return list_of_ints