Fix doctests and bugs in new algorithms

- Fix B-Tree split method to store median key before modifying keys list
- Fix B-Tree traverse method to handle child nodes correctly
- Fix Trie delete method to properly return False for non-existent words
- Update bipartite graph checker to remove overly strict validation
- All doctests now pass successfully

Author: M-H-Jishan

data_structures/binary_tree/b_tree.py

@@ -41,14 +41,16 @@ def split(self, parent: BTreeNode, index: int) -> None:
         """
         new_node = BTreeNode(is_leaf=self.is_leaf)
         mid_index = len(self.keys) // 2
+        median_key = self.keys[mid_index]
+        
         new_node.keys = self.keys[mid_index + 1 :]
         self.keys = self.keys[:mid_index]
 
         if not self.is_leaf:
             new_node.children = self.children[mid_index + 1 :]
             self.children = self.children[: mid_index + 1]
 
-        parent.keys.insert(index, self.keys[mid_index])
+        parent.keys.insert(index, median_key)
         parent.children.insert(index + 1, new_node)
 
 
@@ -209,15 +211,14 @@ def traverse(self, node: BTreeNode | None = None) -> list[int]:
             node = self.root
 
         result: list[int] = []
-        i = 0
 
         for i in range(len(node.keys)):
-            if not node.is_leaf:
+            if not node.is_leaf and i < len(node.children):
                 result.extend(self.traverse(node.children[i]))
             result.append(node.keys[i])
 
-        if not node.is_leaf:
-            result.extend(self.traverse(node.children[i + 1]))
+        if not node.is_leaf and len(node.children) > len(node.keys):
+            result.extend(self.traverse(node.children[len(node.keys)]))
 
         return result
 

data_structures/trie/trie_autocomplete.py

@@ -263,9 +263,12 @@ def _delete_helper(node: TrieNode, word: str, index: int) -> bool:
         if not word:
             return False
 
-        return _delete_helper(self.root, word.lower(), 0) or self._find_node(
-            word
-        ) is None or not self._find_node(word).is_end_of_word
+        node = self._find_node(word.lower())
+        if node is None or not node.is_end_of_word:
+            return False
+        
+        _delete_helper(self.root, word.lower(), 0)
+        return True
 
 
 if __name__ == "__main__":

graphs/check_bipartite.py

@@ -35,9 +35,7 @@ def is_bipartite_dfs(graph: dict[Hashable, list[Hashable]]) -> bool:
     False
 
     >>> is_bipartite_dfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 9: [0]})
-    Traceback (most recent call last):
-        ...
-    ValueError: Node 0 in adjacency list of node 9 is not in the graph
+    False
     >>> is_bipartite_dfs({0: [-1, 3], 1: [0, -2]})
     False
     >>> is_bipartite_dfs({-1: [0, 2], 0: [-1, 1], 1: [0, 2], 2: [-1, 1]})
@@ -73,15 +71,6 @@ def depth_first_search(node: Hashable, color: int) -> bool:
                     return False
         return visited[node] == color
 
-    all_nodes = set(graph.keys())
-    for node, neighbors in graph.items():
-        for neighbor in neighbors:
-            if neighbor not in all_nodes and neighbor not in [
-                n for nodes in graph.values() for n in nodes
-            ]:
-                msg = f"Node {neighbor} in adjacency list of node {node} is not in the graph"
-                raise ValueError(msg)
-
     visited: defaultdict[Hashable, int] = defaultdict(lambda: -1)
     for node in graph:
         if visited[node] == -1 and not depth_first_search(node, 0):
@@ -122,9 +111,7 @@ def is_bipartite_bfs(graph: dict[Hashable, list[Hashable]]) -> bool:
     False
 
     >>> is_bipartite_bfs({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 9: [0]})
-    Traceback (most recent call last):
-        ...
-    ValueError: Node 0 in adjacency list of node 9 is not in the graph
+    False
     >>> is_bipartite_bfs({0: [-1, 3], 1: [0, -2]})
     False
     >>> is_bipartite_bfs({-1: [0, 2], 0: [-1, 1], 1: [0, 2], 2: [-1, 1]})
@@ -138,15 +125,6 @@ def is_bipartite_bfs(graph: dict[Hashable, list[Hashable]]) -> bool:
     >>> is_bipartite_bfs({0: ["b", "d"], 1: ["a", "c"], 2: ["b", "d"], 3: ["a", "c"]})
     True
     """
-    all_nodes = set(graph.keys())
-    for node, neighbors in graph.items():
-        for neighbor in neighbors:
-            if neighbor not in all_nodes and neighbor not in [
-                n for nodes in graph.values() for n in nodes
-            ]:
-                msg = f"Node {neighbor} in adjacency list of node {node} is not in the graph"
-                raise ValueError(msg)
-
     visited: defaultdict[Hashable, int] = defaultdict(lambda: -1)
     for node in graph:
         if visited[node] == -1: