|
| 1 | +from __future__ import annotations |
| 2 | + |
| 3 | + |
| 4 | +class Graph: |
| 5 | + def __init__(self, vertices: int) -> None: |
| 6 | + """ |
| 7 | + >>> graph = Graph(2) |
| 8 | + >>> graph.vertices |
| 9 | + 2 |
| 10 | + >>> len(graph.graph) |
| 11 | + 2 |
| 12 | + >>> len(graph.graph[0]) |
| 13 | + 2 |
| 14 | + """ |
| 15 | + self.vertices = vertices |
| 16 | + self.graph = [[0] * vertices for _ in range(vertices)] |
| 17 | + |
| 18 | + def print_solution(self, distances_from_source: list[int]) -> None: |
| 19 | + """ |
| 20 | + >>> Graph(0).print_solution([]) # doctest: +NORMALIZE_WHITESPACE |
| 21 | + Vertex Distance from Source |
| 22 | + """ |
| 23 | + print("Vertex \t Distance from Source") |
| 24 | + for vertex in range(self.vertices): |
| 25 | + print(vertex, "\t\t", distances_from_source[vertex]) |
| 26 | + |
| 27 | + def minimum_distance( |
| 28 | + self, distances_from_source: list[int], visited: list[bool] |
| 29 | + ) -> int: |
| 30 | + """ |
| 31 | + A utility function to find the vertex with minimum distance value, from the set |
| 32 | + of vertices not yet included in shortest path tree. |
| 33 | +
|
| 34 | + >>> Graph(3).minimum_distance([1, 2, 3], [False, False, True]) |
| 35 | + 0 |
| 36 | + """ |
| 37 | + |
| 38 | + # Initialize minimum distance for next node |
| 39 | + minimum = 1e7 |
| 40 | + min_index = 0 |
| 41 | + |
| 42 | + # Search not nearest vertex not in the shortest path tree |
| 43 | + for vertex in range(self.vertices): |
| 44 | + if distances_from_source[vertex] < minimum and visited[vertex] is False: |
| 45 | + minimum = distances_from_source[vertex] |
| 46 | + min_index = vertex |
| 47 | + return min_index |
| 48 | + |
| 49 | + def dijkstra(self, source: int) -> None: |
| 50 | + """ |
| 51 | + Function that implements Dijkstra's single source shortest path algorithm for a |
| 52 | + graph represented using adjacency matrix representation. |
| 53 | +
|
| 54 | + >>> Graph(4).dijkstra(1) # doctest: +NORMALIZE_WHITESPACE |
| 55 | + Vertex Distance from Source |
| 56 | + 0 10000000 |
| 57 | + 1 0 |
| 58 | + 2 10000000 |
| 59 | + 3 10000000 |
| 60 | + """ |
| 61 | + |
| 62 | + distances = [int(1e7)] * self.vertices # distances from the source |
| 63 | + distances[source] = 0 |
| 64 | + visited = [False] * self.vertices |
| 65 | + |
| 66 | + for _ in range(self.vertices): |
| 67 | + u = self.minimum_distance(distances, visited) |
| 68 | + visited[u] = True |
| 69 | + |
| 70 | + # Update dist value of the adjacent vertices |
| 71 | + # of the picked vertex only if the current |
| 72 | + # distance is greater than new distance and |
| 73 | + # the vertex in not in the shortest path tree |
| 74 | + for v in range(self.vertices): |
| 75 | + if ( |
| 76 | + self.graph[u][v] > 0 |
| 77 | + and visited[v] is False |
| 78 | + and distances[v] > distances[u] + self.graph[u][v] |
| 79 | + ): |
| 80 | + distances[v] = distances[u] + self.graph[u][v] |
| 81 | + |
| 82 | + self.print_solution(distances) |
| 83 | + |
| 84 | + |
| 85 | +if __name__ == "__main__": |
| 86 | + graph = Graph(9) |
| 87 | + graph.graph = [ |
| 88 | + [0, 4, 0, 0, 0, 0, 0, 8, 0], |
| 89 | + [4, 0, 8, 0, 0, 0, 0, 11, 0], |
| 90 | + [0, 8, 0, 7, 0, 4, 0, 0, 2], |
| 91 | + [0, 0, 7, 0, 9, 14, 0, 0, 0], |
| 92 | + [0, 0, 0, 9, 0, 10, 0, 0, 0], |
| 93 | + [0, 0, 4, 14, 10, 0, 2, 0, 0], |
| 94 | + [0, 0, 0, 0, 0, 2, 0, 1, 6], |
| 95 | + [8, 11, 0, 0, 0, 0, 1, 0, 7], |
| 96 | + [0, 0, 2, 0, 0, 0, 6, 7, 0], |
| 97 | + ] |
| 98 | + graph.dijkstra(0) |
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