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| 1 | +# Author: OMKAR PATHAK |
| 2 | + |
| 3 | +# A greedy algorithm example |
| 4 | + |
| 5 | +# Algorithm: |
| 6 | +# 1. T (the final spanning tree) is defined to be the empty set; |
| 7 | +# 2. For each vertex v of G, make the empty set out of v; |
| 8 | +# 3. Sort the edges of G in ascending (non-decreasing) order; |
| 9 | +# 4. For each edge (u, v) from the sored list of step 3. |
| 10 | +# If u and v belong to different sets |
| 11 | +# Add (u,v) to T; |
| 12 | +# Get together u and v in one single set; |
| 13 | +# 5. Return T |
| 14 | + |
| 15 | +# from collections import defaultdict |
| 16 | + |
| 17 | +class Graph: |
| 18 | + |
| 19 | + def __init__(self, vertices): |
| 20 | + self.vertices = vertices |
| 21 | + self.graph = [] |
| 22 | + |
| 23 | + # function to add an edge to graph |
| 24 | + def addEdge(self, fromEdge, toEdge, weight): |
| 25 | + self.graph.append([fromEdge, toEdge, weight]) |
| 26 | + |
| 27 | + # function to find the index of element i |
| 28 | + def find(self, parent, i): |
| 29 | + if parent[i] == i: |
| 30 | + return i |
| 31 | + return self.find(parent, parent[i]) |
| 32 | + |
| 33 | + # function that finds the union of two sets first and second |
| 34 | + def union(self, parent, rank, first, second): |
| 35 | + root_x = self.find(parent, first) |
| 36 | + root_y = self.find(parent, second) |
| 37 | + |
| 38 | + if rank[root_x] < rank[root_y]: |
| 39 | + parent[root_x] = root_y |
| 40 | + elif rank[root_x] > rank[root_y]: |
| 41 | + parent[root_y] = root_x |
| 42 | + # if ranks are same then increment its rank |
| 43 | + elif rank[root_x] == rank[root_y]: |
| 44 | + parent[root_y] = root_x |
| 45 | + rank[root_x] += 1 |
| 46 | + |
| 47 | + def kruskals(self): |
| 48 | + result = [] |
| 49 | + |
| 50 | + sortedIndex = 0 |
| 51 | + resultIndex = 0 |
| 52 | + |
| 53 | + # sort all the edges according to their weights inn ascending order |
| 54 | + self.graph = sorted(self.graph,key=lambda item: item[2]) |
| 55 | + |
| 56 | + parent = [] |
| 57 | + rank = [] |
| 58 | + |
| 59 | + for node in range(self.vertices): |
| 60 | + parent.append(node) |
| 61 | + rank.append(0) |
| 62 | + |
| 63 | + while resultIndex < self.vertices - 1: |
| 64 | + fromEdge, toEdge, weight = self.graph[sortedIndex] |
| 65 | + |
| 66 | + sortedIndex += 1 |
| 67 | + root_x = self.find(parent, fromEdge) |
| 68 | + root_y = self.find(parent, toEdge) |
| 69 | + |
| 70 | + if root_x != root_y: |
| 71 | + resultIndex += 1 |
| 72 | + result.append([fromEdge, toEdge, weight]) |
| 73 | + self.union(parent, rank, root_x, root_y) |
| 74 | + |
| 75 | + # print the contents of result[] to display the built MST |
| 76 | + print ('Constructed Kruskal\'s Minimum Spanning Tree: ') |
| 77 | + for u,v,weight in result: |
| 78 | + print('{} -> {} = {}'.format(u, v, weight)) |
| 79 | + |
| 80 | +if __name__ == '__main__': |
| 81 | + g = Graph(4) |
| 82 | + g.addEdge(0, 1, 10) |
| 83 | + g.addEdge(0, 2, 6) |
| 84 | + g.addEdge(0, 3, 5) |
| 85 | + g.addEdge(1, 3, 15) |
| 86 | + g.addEdge(2, 3, 4) |
| 87 | + |
| 88 | + g.kruskals() |
| 89 | + |
| 90 | + # OUTPUT: |
| 91 | + # Constructed Kruskal's Minimum Spanning Tree: |
| 92 | + # 2 -> 3 = 4 |
| 93 | + # 0 -> 3 = 5 |
| 94 | + # 0 -> 1 = 10 |
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