Created Prims.py

graph/Prims Algorithm/Prims/Prims.py

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+import random
+import math
+import numpy as np
+
+# Prim's Minimum Spanning Tree Algorithm
+def primsAlgo(e,c,n):
+	sel_edge = ()
+	mincost = 0
+	near = [0 for x in range(n)]
+	t = [[0 for x in range(2)] for y in range(n-1)] 
+	print('Initially Minimum Spanning Tree: \n' ,np.matrix(t))
+	values = np.array(c)
+	index = np.argmin(values)
+	minedge = [index//n, index%n]
+
+	sel_edge = minedge
+	mincost += c[sel_edge[0]][sel_edge[1]]
+
+    #Select neighboring edge with minimum cost
+	t[0][0],t[0][1] = sel_edge[0],sel_edge[1]
+	for l in range(0,n):
+		if(c[l][sel_edge[0]] < c[l][sel_edge[1]]):
+			near[l] = sel_edge[0]
+		else:
+			near[l] = sel_edge[1]
+	near[sel_edge[0]],near[sel_edge[1]] = -1,-1
+	minm = math.inf
+	for i in range(1,n-1):
+		for j in range(n):
+			if(near[j] != -1):
+				if(c[j][near[j]] < minm):
+					minm = c[j][near[j]]
+					sel_edge[0] = j
+					sel_edge[1] = near[j]
+		minm = math.inf
+		t[i][0],t[i][1] = sel_edge[0],sel_edge[1]
+		mincost += c[sel_edge[0]][sel_edge[1]]
+		near[sel_edge[0]] = -1
+		for k in range(n):
+			if(near[k] != -1 and (c[k][near[k]] > c[k][sel_edge[0]])):
+				near[k] = sel_edge[0]
+	print('Resultant MST: \n', np.matrix(t))
+	return mincost
+
+n = random.randint(5,7)
+print('Number of vertices:', n)
+max_int = math.inf
+e = [[0 for x in range(n)] for y in range(n)] 
+c = [[max_int for x in range(n)] for y in range(n)] 
+for i in range(n):
+	for j in range(n):
+		if(i < j):
+			e[i][j]=1
+			c[i][j]=float(random.randint(10,50))
+		else:
+			e[i][j]=e[j][i]
+			c[i][j]=c[j][i]
+print('Edge Adjacency Matrix: \n', np.matrix(e))
+print('Cost Adjacency Matrix: \n',np.matrix(c))
+print ('Cost of MST: ', primsAlgo(e,c,n))