Merge pull request #170 from BrianLusina/feat/datastructures-graphs-clone-graph

feat(datastructures, graphs): clone graph

Author: BrianLusina

DIRECTORY.md

@@ -441,6 +441,9 @@
     * [Ordered Dict](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/dicts/ordered_dict.py)
   * Graphs
     * [Edge](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/graphs/edge.py)
+    * Undirected
+      * Clone Graph
+        * [Node](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/graphs/undirected/clone_graph/node.py)
     * [Vertex](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/graphs/vertex.py)
   * Hashmap
     * [Test Hashmap](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/hashmap/test_hashmap.py)

datastructures/graphs/undirected/clone_graph/README.md

@@ -0,0 +1,101 @@
+# Clone Graph
+
+You are given a reference to a single node in an undirected, connected graph. Your task is to create a deep copy of the
+graph starting from the given node. A deep copy means creating a new instance of every node in the graph with the same
+data and edges as the original graph, such that changes in the copied graph do not affect the original graph.
+
+Each node in the graph contains two properties:
+
+- `data`: The value of the node, which is the same as its index in the adjacency list. 
+- `neighbors`: A list of connected nodes, representing all the nodes directly linked to this node.
+
+However, in the test cases, a graph is represented as an adjacency list to understand node relationships, where each
+index in the list represents a node (using 1-based indexing). For example, for [[2,3],[1,3],[1,2]], there are three
+nodes in the graph:
+
+1st node (data = 1): Neighbors are 2nd node (data = 2) and 3rd node (data = 3).
+2nd node (data = 2): Neighbors are 1st node (data = 1) and 3rd node (data = 3).
+3rd node (data = 3): Neighbors are 1st node (data = 1) and 2nd node (data = 2).
+
+The adjacency list will be converted to a graph at the backend and the first node of the graph will be passed to your
+code.
+
+## Constraints
+
+- 0 ≤ Number of nodes ≤ 100
+- 1 ≤ Node.data ≤ 100
+- Node.data is unique for each node.
+- The graph is undirected, i.e., there are no self-loops or repeated edges. 
+- The graph is connected, i.e., any node can be visited from a given node.
+
+## Topics
+
+- Hash Table
+- Depth-First Search
+- Breadth-First Search
+- Graph Theory
+
+## Examples
+
+![Example 1](./images/examples/clone_graph_example_1.png)
+
+> Input: adjList = [[2,4],[1,3],[2,4],[1,3]]
+> Output: [[2,4],[1,3],[2,4],[1,3]]
+> Explanation: There are 4 nodes in the graph.
+> 1st node (val = 1)'s neighbors are 2nd node (val = 2) and 4th node (val = 4).
+> 2nd node (val = 2)'s neighbors are 1st node (val = 1) and 3rd node (val = 3).
+> 3rd node (val = 3)'s neighbors are 2nd node (val = 2) and 4th node (val = 4).
+> 4th node (val = 4)'s neighbors are 1st node (val = 1) and 3rd node (val = 3).
+
+![Example 2](./images/examples/clone_graph_example_2.png)
+
+> Input: adjList = [[]]
+> Output: [[]]
+> Explanation: Note that the input contains one empty list. The graph consists of only one node with val = 1 and it does
+> not have any neighbors.
+
+> Example 3
+> Input: adjList = []
+> Output: []
+> Explanation: This an empty graph, it does not have any nodes.
+
+## Solution
+
+We use depth-first traversal and create a copy of each node while traversing the graph. We use a hash map to store each
+visited node to avoid getting stuck in cycles. We do not revisit nodes that exist in the hash map. The hash map key is
+a node in the original graph, and its value is the corresponding node in the cloned graph.
+
+In order to solve this problem using the methodology discussed above, we create a recursive function, clone_helper, that
+takes two arguments: the current node being cloned and a hash map. The steps of this recursive function are given below:
+
+1. If graph is empty, return NULL. This will also work as a base case for our recursive function.
+2. If the current node is not NULL, create a new Node with the same data as the current node, and add the current node
+   as key and its clone as value to the hash map.
+3. Iterate through all the neighbors of the current node. For each neighbor, check if the neighbor is already cloned by
+   looking up the neighbor in the hash map:
+   - If the neighbor is not cloned yet, recursively call the function with the neighbor as the current node. 
+   - If the neighbor is already cloned, add the cloned neighbor to the new node’s neighbors.
+4. Finally, return the new node.
+
+The clone function is the main function that creates a deep copy of the graph. It takes a single argument, which is a
+reference to the root node of the graph. The function creates an empty hash map to keep track of nodes that have already
+been cloned. Then it calls the clone_helper function, passing in the root node and the hash map.
+
+![Solution 1](./images/solutions/clone_graph_solution_1.png)
+![Solution 2](./images/solutions/clone_graph_solution_2.png)
+![Solution 3](./images/solutions/clone_graph_solution_3.png)
+![Solution 4](./images/solutions/clone_graph_solution_4.png)
+![Solution 5](./images/solutions/clone_graph_solution_5.png)
+![Solution 6](./images/solutions/clone_graph_solution_6.png)
+
+### Time Complexity
+
+The time complexity of this code is O(n+m), where n is the number of nodes, and m is the number of edges.
+
+### Space Complexity
+
+The space complexity of this code is O(n), where n is the number of nodes in the dictionary.
+
+> Note: We can also solve this problem using BFS.
+
+

datastructures/graphs/undirected/clone_graph/__init__.py

@@ -0,0 +1,34 @@
+from typing import Optional, Dict
+from datastructures.graphs.undirected.clone_graph.node import Node
+
+
+def clone(root: Optional[Node]) -> Optional[Node]:
+    def clone_helper(n: Optional[Node], nodes_cloned: Dict[Node, Node]) -> Optional[Node]:
+        # If the node is None, return None
+        if n is None:
+            return None
+
+        # Create a new Node with the same data as the node
+        cloned_node = Node(n.data)
+        # Add the node and its clone to the nodes_completed hash map
+        nodes_cloned[n] = cloned_node
+
+        # Iterate through the neighbours of the node
+        for neighbour in n.neighbors:
+            # Retrieve the value of key neighbour in nodes_completed hash map.
+            # If it exists, assign the corresponding cloned node to cloned_neighbour.
+            # This checks if the neighbour node neighbour has already been cloned.
+            cloned_neighbour = nodes_cloned.get(neighbour)
+            # If the neighbour is not cloned yet, recursively clone it
+            if cloned_neighbour is None:
+                cloned_node.neighbors += [clone_helper(neighbour, nodes_cloned)]
+            # If the neighbour is already cloned, add the cloned neighbour to the new
+            # node's neighbors
+            else:
+                cloned_node.neighbors += [cloned_neighbour]
+        return cloned_node
+
+    # Initialize an empty dictionary to keep track of cloned nodes
+    nodes_completed: Dict[Node, Node] = {}
+    # Call the recursive function to clone the graph starting from the root node
+    return clone_helper(root, nodes_completed)