Implement Johnson's algorithm with tests; update Bellman-Ford and Dij…

graphs/bellman_ford.py

@@ -1,73 +1,184 @@
-from __future__ import annotations
+"""
+Bellman-Ford Algorithm implementation for single-source shortest path.
 
+The Bellman-Ford algorithm can handle negative edge weights and detect negative cycles.
+It has O(VE) time complexity where V is the number of vertices and E is the number of edges.
 
-def print_distance(distance: list[float], src):
-    print(f"Vertex\tShortest Distance from vertex {src}")
-    for i, d in enumerate(distance):
-        print(f"{i}\t\t{d}")
+Author: Zakariae Fakhri
+Date: August 2025
+"""
 
+from typing import Dict, Any, Optional, List, Tuple
+from collections import defaultdict
 
-def check_negative_cycle(
-    graph: list[dict[str, int]], distance: list[float], edge_count: int
-):
-    for j in range(edge_count):
-        u, v, w = (graph[j][k] for k in ["src", "dst", "weight"])
-        if distance[u] != float("inf") and distance[u] + w < distance[v]:
-            return True
-    return False
 
-
-def bellman_ford(
-    graph: list[dict[str, int]], vertex_count: int, edge_count: int, src: int
-) -> list[float]:
+class Graph:
     """
-    Returns shortest paths from a vertex src to all
-    other vertices.
-    >>> edges = [(2, 1, -10), (3, 2, 3), (0, 3, 5), (0, 1, 4)]
-    >>> g = [{"src": s, "dst": d, "weight": w} for s, d, w in edges]
-    >>> bellman_ford(g, 4, 4, 0)
-    [0.0, -2.0, 8.0, 5.0]
-    >>> g = [{"src": s, "dst": d, "weight": w} for s, d, w in edges + [(1, 3, 5)]]
-    >>> bellman_ford(g, 4, 5, 0)
-    Traceback (most recent call last):
-     ...
-    Exception: Negative cycle found
+    A lightweight graph class for algorithms that don't have access to the main Graph class.
     """
-    distance = [float("inf")] * vertex_count
-    distance[src] = 0.0
 
-    for _ in range(vertex_count - 1):
-        for j in range(edge_count):
-            u, v, w = (graph[j][k] for k in ["src", "dst", "weight"])
+    def __init__(self):
+        self.adjacency_list = defaultdict(list)
+        self.vertices = set()
+
+    def add_edge(self, source: Any, destination: Any, weight: float) -> None:
+        self.adjacency_list[source].append((destination, weight))
+        self.vertices.add(source)
+        self.vertices.add(destination)
 
-            if distance[u] != float("inf") and distance[u] + w < distance[v]:
-                distance[v] = distance[u] + w
+    def get_neighbors(self, vertex: Any) -> List[Tuple[Any, float]]:
+        return self.adjacency_list.get(vertex, [])
 
-    negative_cycle_exists = check_negative_cycle(graph, distance, edge_count)
-    if negative_cycle_exists:
-        raise Exception("Negative cycle found")
+    def get_vertices(self) -> set:
+        return self.vertices.copy()
 
-    return distance
+    def has_vertex(self, vertex: Any) -> bool:
+        return vertex in self.vertices
 
+    def get_vertex_count(self) -> int:
+        return len(self.vertices)
 
-if __name__ == "__main__":
-    import doctest
 
-    doctest.testmod()
+class BellmanFord:
+    """
+    Bellman-Ford algorithm implementation for single-source shortest path.
 
-    V = int(input("Enter number of vertices: ").strip())
-    E = int(input("Enter number of edges: ").strip())
+    This algorithm can handle graphs with negative edge weights and can detect
+    negative cycles. It's particularly useful as a preprocessing step in
+    Johnson's algorithm.
+    """
 
-    graph: list[dict[str, int]] = [{} for _ in range(E)]
+    def __init__(self, graph):
+        """
+        Initialize the Bellman-Ford algorithm with a graph.
+
+        Args:
+            graph: The weighted directed graph to process
+        """
+        self.graph = graph
+        self.distances = {}
+        self.predecessors = {}
+
+    def find_shortest_paths(self, start_vertex: Any) -> Optional[Dict[Any, float]]:
+        """
+        Find shortest paths from start_vertex to all other vertices.
+
+        Args:
+            start_vertex: The source vertex to start from
 
-    for i in range(E):
-        print("Edge ", i + 1)
-        src, dest, weight = (
-            int(x)
-            for x in input("Enter source, destination, weight: ").strip().split(" ")
-        )
-        graph[i] = {"src": src, "dst": dest, "weight": weight}
+        Returns:
+            Dictionary of vertex -> shortest distance, or None if negative cycle exists
+        """
+        if not self.graph.has_vertex(start_vertex):
+            raise ValueError(f"Start vertex {start_vertex} not found in graph")
+
+        # Initialize distances
+        self.distances = {vertex: float("inf") for vertex in self.graph.get_vertices()}
+        self.distances[start_vertex] = 0
+        self.predecessors = {vertex: None for vertex in self.graph.get_vertices()}
+
+        # Relax edges V-1 times
+        vertex_count = self.graph.get_vertex_count()
+
+        for iteration in range(vertex_count - 1):
+            updated = False
+
+            for vertex in self.graph.get_vertices():
+                if self.distances[vertex] != float("inf"):
+                    for neighbor, weight in self.graph.get_neighbors(vertex):
+                        new_distance = self.distances[vertex] + weight
+
+                        if new_distance < self.distances[neighbor]:
+                            self.distances[neighbor] = new_distance
+                            self.predecessors[neighbor] = vertex
+                            updated = True
+
+            # Early termination if no updates in this iteration
+            if not updated:
+                break
+
+        # Check for negative cycles
+        if self._has_negative_cycle():
+            return None
+
+        return self.distances.copy()
+
+    def _has_negative_cycle(self) -> bool:
+        """
+        Check if the graph contains a negative cycle.
+
+        Returns:
+            True if negative cycle exists, False otherwise
+        """
+        for vertex in self.graph.get_vertices():
+            if self.distances[vertex] != float("inf"):
+                for neighbor, weight in self.graph.get_neighbors(vertex):
+                    if self.distances[vertex] + weight < self.distances[neighbor]:
+                        return True
+        return False
+
+    def get_path(self, start_vertex: Any, end_vertex: Any) -> Optional[List[Any]]:
+        """
+        Get the shortest path from start_vertex to end_vertex.
+
+        Args:
+            start_vertex: Source vertex
+            end_vertex: Destination vertex
+
+        Returns:
+            List of vertices representing the path, or None if no path exists
+        """
+        if not self.distances or end_vertex not in self.distances:
+            return None
+
+        if self.distances[end_vertex] == float("inf"):
+            return None
+
+        path = []
+        current = end_vertex
+
+        while current is not None:
+            path.append(current)
+            current = self.predecessors.get(current)
+
+        path.reverse()
+
+        # Verify the path starts with start_vertex
+        if path[0] != start_vertex:
+            return None
+
+        return path
+
+    def get_distance(self, vertex: Any) -> float:
+        """
+        Get the shortest distance to a specific vertex.
+
+        Args:
+            vertex: The target vertex
+
+        Returns:
+            Shortest distance to the vertex
+        """
+        return self.distances.get(vertex, float("inf"))
+
+    @staticmethod
+    def detect_negative_cycle(graph) -> bool:
+        """
+        Static method to detect if a graph contains a negative cycle.
+
+        Args:
+            graph: The graph to check
+
+        Returns:
+            True if negative cycle exists, False otherwise
+        """
+        vertices = graph.get_vertices()
+        if not vertices:
+            return False
 
-    source = int(input("\nEnter shortest path source:").strip())
-    shortest_distance = bellman_ford(graph, V, E, source)
-    print_distance(shortest_distance, 0)
+        # Pick any vertex as start
+        start_vertex = next(iter(vertices))
+        bf = BellmanFord(graph)
+        result = bf.find_shortest_paths(start_vertex)
+
+        return result is None