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Please help with this program. Need to write a program called did-stack.py in python that uses the algorithm below without using an agency list. Will post a picture of the instructions and algorithm below thank you.

Please help with this program. Need to write a program called did-stack.py in python that uses the algorithm below without using an agency list. Will post a picture of the instructions and algorithm below thank you.

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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Please help with this program. Need to write a program called did-stack.py in python that uses the algorithm below without using an agency list. Will post a picture of the instructions and algorithm below thank you.
**Algorithm 2.3: Graph Depth-First Search with a Stack** **StackDFS(G, node) → visited** - **Input:** G = (V, E), a graph - `node`, the starting vertex in G - **Output:** `visited`, an array of size |V| such that `visited[i]` is TRUE if we have visited node i, FALSE otherwise 1. `S ← CreateStack()` 2. `visited ← CreateArray(|V|)` 3. `for i ← 0 to |V| do` 4.   `visited[i] ← FALSE` 5. `Push(S, node)` 6. `while not IsStackEmpty(S) do` 7.   `c ← Pop(S)` 8.   `visited[c] ← TRUE` 9.   `foreach u in AdjacencyList(G, c) do` 10.    `if not visited[u] then` 11.     `Push(S, u)` 12. `return visited` This algorithm performs a depth-first search (DFS) of a graph using a stack. It initializes the stack and a visited array to track which nodes have been explored. The algorithm iterates by pushing unvisited nodes onto the stack and marking them as visited once popped. This continues until the stack is empty, resulting in the visited array indicating all reached nodes from the starting vertex.
Transcribed Image Text:**Algorithm 2.3: Graph Depth-First Search with a Stack** **StackDFS(G, node) → visited** - **Input:** G = (V, E), a graph - `node`, the starting vertex in G - **Output:** `visited`, an array of size |V| such that `visited[i]` is TRUE if we have visited node i, FALSE otherwise 1. `S ← CreateStack()` 2. `visited ← CreateArray(|V|)` 3. `for i ← 0 to |V| do` 4.   `visited[i] ← FALSE` 5. `Push(S, node)` 6. `while not IsStackEmpty(S) do` 7.   `c ← Pop(S)` 8.   `visited[c] ← TRUE` 9.   `foreach u in AdjacencyList(G, c) do` 10.    `if not visited[u] then` 11.     `Push(S, u)` 12. `return visited` This algorithm performs a depth-first search (DFS) of a graph using a stack. It initializes the stack and a visited array to track which nodes have been explored. The algorithm iterates by pushing unvisited nodes onto the stack and marking them as visited once popped. This continues until the stack is empty, resulting in the visited array indicating all reached nodes from the starting vertex.
**Program Implementation for DFS with Stack in Python** **Instructions:** 1. **Objective**: Develop a program named `dfs-stack.py` that implements a graph depth-first search (DFS) using a stack as described in Algorithm 2.3. 2. **Restrictions**: - Do not use an adjacency list. Instead, utilize an adjacency matrix, which is a two-dimensional array (or a list of lists in Python). 3. **Flexibility**: - You can use any list methods such as `append`, `pop`, etc. **Implementation Details**: 1. **Algorithm Development**: - Create a final algorithmic solution using pseudocode, incorporating your logic alongside the logic in Algorithm 2.3. 2. **Documentation**: - Include your name and a Certificate of Authenticity as comments at the beginning of your code. Acknowledge any collaborations. 3. **User Input**: - Prompt the user for the number of vertices \( V \) in the graph \( G \). 4. **Adjacency Matrix Creation**: - Represent the graph using an adjacency matrix, \( M \), with dimensions \( V \times V \). - Initialize all elements of \( M \) to zero. 5. **Matrix Population**: - Allow the user to specify which matrix elements should be set to 1, indicating a connection (edge) between vertices. 6. **Matrix Display**: - Print the entire adjacency matrix to the screen. 7. **Vertex Selection**: - Prompt the user to specify the starting vertex (node) for the DFS. 8. **Algorithm Execution**: - Implement Algorithm 2.3 using the following modifications: - Use the adjacency matrix instead of an adjacency list. - At each iteration, print the current stack contents. - After processing, print the entire stack. 9. **Output**: - The program should show how the stack evolves during the DFS execution on graph \( G \). By following these steps, you will implement a depth-first search algorithm using a stack, effectively demonstrating understanding of graph traversal techniques.
Transcribed Image Text:**Program Implementation for DFS with Stack in Python** **Instructions:** 1. **Objective**: Develop a program named `dfs-stack.py` that implements a graph depth-first search (DFS) using a stack as described in Algorithm 2.3. 2. **Restrictions**: - Do not use an adjacency list. Instead, utilize an adjacency matrix, which is a two-dimensional array (or a list of lists in Python). 3. **Flexibility**: - You can use any list methods such as `append`, `pop`, etc. **Implementation Details**: 1. **Algorithm Development**: - Create a final algorithmic solution using pseudocode, incorporating your logic alongside the logic in Algorithm 2.3. 2. **Documentation**: - Include your name and a Certificate of Authenticity as comments at the beginning of your code. Acknowledge any collaborations. 3. **User Input**: - Prompt the user for the number of vertices \( V \) in the graph \( G \). 4. **Adjacency Matrix Creation**: - Represent the graph using an adjacency matrix, \( M \), with dimensions \( V \times V \). - Initialize all elements of \( M \) to zero. 5. **Matrix Population**: - Allow the user to specify which matrix elements should be set to 1, indicating a connection (edge) between vertices. 6. **Matrix Display**: - Print the entire adjacency matrix to the screen. 7. **Vertex Selection**: - Prompt the user to specify the starting vertex (node) for the DFS. 8. **Algorithm Execution**: - Implement Algorithm 2.3 using the following modifications: - Use the adjacency matrix instead of an adjacency list. - At each iteration, print the current stack contents. - After processing, print the entire stack. 9. **Output**: - The program should show how the stack evolves during the DFS execution on graph \( G \). By following these steps, you will implement a depth-first search algorithm using a stack, effectively demonstrating understanding of graph traversal techniques.
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