PrivacyScanData/visualize/Visualization-of-popular-algorithms-in-Python/K Centers Problem/k_centers_problem.py

import networkx as nx
import matplotlib.pyplot as plt
import operator



def k_centers(G, n):
	centers = []
	cities = G.nodes()
	#add an arbitrary node, here, the first node,to the centers list
	centers.append((G.nodes())[0])
	cities.remove(centers[0]) 
	n = n-1 #since we have already added one center
	#choose n-1 centers
	while n!= 0:
		city_dict = {}
		for cty in cities:
			min_dist = float("inf")
			for c in centers:
				min_dist = min(min_dist,G[cty][c]['length'])
			city_dict[cty] = min_dist
		#print city_dict
		new_center = max(city_dict, key = city_dict[i])
		#print new_center
		centers.append(new_center)
		cities.remove(new_center)
		n = n-1
	#print centers
	return centers



#takes input from the file and creates a weighted undirected graph
def CreateGraph():
	G = nx.Graph()
	f = open('input.txt')
	n = int(f.readline()) #n denotes the number of cities
	wtMatrix = []
	for i in range(n):
		list1 = map(int, (f.readline()).split())
		wtMatrix.append(list1)
	#Adds egdes along with their weights to the graph 
	for i in range(n) :
		for j in range(n)[i:] :
				G.add_edge(i, j, length = wtMatrix[i][j]) 
	noc = int(f.readline()) #noc,here,denotes the number of centers
	return G, noc



#draws the graph and displays the weights on the edges
def DrawGraph(G, centers):
	pos = nx.spring_layout(G)
	color_map = ['blue'] * len(G.nodes())
 	#all the center nodes are marked with 'red'
	for c in centers:
		color_map[c] = 'red'
	nx.draw(G, pos, node_color = color_map, with_labels = True)  #with_labels=true is to show the node number in the output graph
	edge_labels = nx.get_edge_attributes(G, 'length')
	nx.draw_networkx_edge_labels(G, pos, edge_labels = edge_labels, font_size = 11) #prints weight on all the edges
	


#main function
if __name__ == "__main__":
	G,n = CreateGraph()
	centers = k_centers(G, n)
	DrawGraph(G, centers)
	plt.show()