# Python program for Kruskal's algorithm to find
# Minimum Spanning Tree of a given connected,
# undirected and weighted graph

from collections import defaultdict

# Class to represent a graph


class Graph:

	def __init__(self, vertices):
		self.V = vertices # No. of vertices
		self.graph = [] # default dictionary
		# to store graph

	# function to add an edge to graph
	def addEdge(self, u, v, w):
		self.graph.append([u, v, w])

	# A utility function to find set of an element i
	# (uses path compression technique)
	def find(self, parent, i):
		if parent[i] == i:
			return i
		return self.find(parent, parent[i])

	# A function that does union of two sets of x and y
	# (uses union by rank)
	def union(self, parent, rank, x, y):
		xroot = self.find(parent, x)
		yroot = self.find(parent, y)

		# Attach smaller rank tree under root of
		# high rank tree (Union by Rank)
		if rank[xroot] < rank[yroot]:
			parent[xroot] = yroot
		elif rank[xroot] > rank[yroot]:
			parent[yroot] = xroot

		# If ranks are same, then make one as root
		# and increment its rank by one
		else:
			parent[yroot] = xroot
			rank[xroot] += 1

	# The main function to construct MST using Kruskal's
		# algorithm
	def KruskalMST(self):

		result = [] # This will store the resultant MST

		# An index variable, used for sorted edges
		i = 0

		# An index variable, used for result[]
		e = 0

		# Step 1: Sort all the edges in
		# non-decreasing order of their
		# weight. If we are not allowed to change the
		# given graph, we can create a copy of graph
		self.graph = sorted(self.graph,
							key=lambda item: item[2])

		parent = []
		rank = []

		# Create V subsets with single elements
		for node in range(self.V):
			parent.append(node)
			rank.append(0)

		# Number of edges to be taken is equal to V-1
		while e < self.V - 1:

			# Step 2: Pick the smallest edge and increment
			# the index for next iteration
			u, v, w = self.graph[i]
			i = i + 1
			x = self.find(parent, u)
			y = self.find(parent, v)

			# If including this edge doesn't
			# cause cycle, then include it in result
			# and increment the index of result
			# for next edge
			if x != y:
				e = e + 1
				result.append([u, v, w])
				self.union(parent, rank, x, y)
			# Else discard the edge

		minimumCost = 0
		print("Edges in the constructed MST")
		for u, v, weight in result:
			minimumCost += weight
			print("%d -- %d == %d" % (u, v, weight))
		print("Minimum Spanning Tree", minimumCost)


# Driver's code
if __name__ == '__main__':
	g = Graph(4)
	g.addEdge(0, 1, 10)
	g.addEdge(0, 2, 6)
	g.addEdge(0, 3, 5)
	g.addEdge(1, 3, 15)
	g.addEdge(2, 3, 4)

	# Function call
	g.KruskalMST()