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Minimum Spanning Trees

Posted on Edited on In Valine:
Minimum Spanning Trees

Minimum Spanning Trees

  • Kruskal's Algorithm
    • O(n + m lg(n))
  • Prim's Algorithm
    • O(n lg(n) + m lg(n))
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import heapq
from collections import namedtuple


class UnionFind(object):
    def __init__(self, nodes):
        self.parent =  {v: -1 for v in nodes}
        self.rank = {r: 0 for r in nodes}


    def get_root(self, i):
        if self.parent[i] == -1:
            return i
        self.parent[i] = self.get_root(self.parent[i])
        return self.parent[i]


    def union(self, i, j):
        i_root = self.get_root(i)
        j_root = self.get_root(j)

        if i_root != j_root:
            if self.rank[i_root] == self.rank[j_root]:
                self.parent[i_root] = j_root
                self.rank[j_root] += 1
            elif self.rank[i_root] > self.rank[j_root]:
                self.parent[j_root] = i_root
            else:
                self.parent[i_root] = j_root

    def is_connected(self, i, j):
        return self.get_root(i) == self.get_root(j)


class MyHeap(object):
    def __init__(self, initial = None, key = None):
        if not key:
            self.key = lambda x: x.weight

        if initial:
           self._data = [(key(item), item) for item in initial]
           heapq.heapify(self._data)
        else:
           self._data = []

    def push(self, item):
        # print(self._data)
        heapq.heappush(self._data, (self.key(item), item))
      

    def pop(self):
       return heapq.heappop(self._data)[1]

    
    def empty(self):
        return False if self._data else True




# KruskalMST(G):
#     DisjointSets forest
#     foreach (Vertex v : G):
#         forest.makeSet(v)
  
#     PriorityQueue Q    // min edge weight
#     foreach (Edge e : G):
#         Q.insert(e)
  
#     Graph T = (V, {})
#     while |T.edges()| < n-1:
#         Edge (u, v) = Q.removeMin()
#         if forest.find(u) != forest.find(v):
#             T.addEdge(u, v) 
#             forest.union(forest.find(u)),
#     return T



def kruskal(graph):

    edge = namedtuple("edge",('u','v','weight'))        
    heap = MyHeap()
    union_find = UnionFind(graph.keys())

    path = []
    weight = 0

    for u in graph:
        for v in graph[u]:
            heap.push(edge(u,v,graph[u][v]))
                

    while len(path) < len(graph)-1:
        edge = heap.pop()
        if not union_find.is_connected(edge.u, edge.v):
            path.append(edge)
            weight += edge.weight
            union_find.union(edge.u,edge.v)

    return weight,path


# PrimMST(G, s):
#     foreach (Vertex v : G):
#         d[v] = +inf
#         p[v] = NULL
#     d[s] = 0
#     PriorityQueue Q // min distance, defined by d[v] 
#     Q.buildHeap(G.vertices())

#     Graph T // "labeled set"

#     repeat n times:
#         Vertex m = Q.removeMin()
#         T.add(m)
#         foreach (Vertex v : neighbors of m not in T):
#             if cost(v, m) < d[v]:
#                 d[v] = cost(v, m)
#                 p[v] = m


def prim(graph, root):

    # Input: G, Graph;
    #      s, vertex in G, starting vertex
    # Output: T, a minimum spanning tree (MST) of G

    prev = None
    path = []
    total = 0                   # Total cost of edges in tree
    visited = set()            # Set of vertices in tree
    Node = namedtuple("Node",("v","weight"))
    heap = MyHeap()   # Unexplored edges ordered by cost
    heap.push(Node(root,0))
    
    while not heap.empty():
        cur_node = heap.pop()
        if cur_node.v not in visited:
            visited.add(cur_node.v)
            total += cur_node.weight
            if prev:
                path.append((prev,cur_node.v,cur_node.weight))
            prev = cur_node.v
            for neighbour in graph[cur_node.v]:
                if neighbour not in visited:
                    heap.push(Node(neighbour, graph[cur_node.v][neighbour]))

    return total,path





    

    return weight,path

if __name__ == '__main__':
    graph_dict = {  "v1":{"v2": 32, "v4": 17},
                    "v2":{"v1":32, "v5": 45},
                    "v3":{"v7":5,"v4":18},
                    "v4":{"v3":18,"v1":17, "v5":10,"v8":3},
                    "v5":{"v4":10,"v2":45,"v9":25,"v6":28},
                    "v6":{"v5":28,"v10":6},
                    "v7":{"v3":5,"v8":59},
                    "v8":{"v4":3,"v7":59,"v9":4},
                    "v9":{"v8":4,"v5":25,"v10":12},
                    "v10":{"v9":12,"v6":6}
    }

    weight,path = prim(graph_dict, 'v1')
    print(weight)
    print(path)

    weight,path = kruskal(graph_dict)
    print(weight)
    print(path)