Python HC.median_min Exemples

Exemple #1

Fichier : median_clustering.py Projet : antarasb/Data-Mining

    for j in range(0, len(cluster)):
        dist1 = ((cluster[j][0] - c[0][0])**2 + (cluster[j][1] - c[0][1])**2 +
                 (cluster[j][2] - c[0][2])**2 + (cluster[j][3] - c[0][3])**2 +
                 (cluster[j][4] - c[0][4])**2) * 0.5
        dist2 = ((cluster[j][0] - c[1][0])**2 + (cluster[j][1] - c[1][1])**2 +
                 (cluster[j][2] - c[1][2])**2 + (cluster[j][3] - c[1][3])**2 +
                 (cluster[j][4] - c[1][4])**2) * 0.5
        dist3 = ((cluster[j][0] - c[2][0])**2 + (cluster[j][1] - c[2][1])**2 +
                 (cluster[j][2] - c[2][2])**2 + (cluster[j][3] - c[2][3])**2 +
                 (cluster[j][4] - c[2][4])**2) * 0.5
        dist4 = ((cluster[j][0] - c[3][0])**2 + (cluster[j][1] - c[3][1])**2 +
                 (cluster[j][2] - c[3][2])**2 + (cluster[j][3] - c[3][3])**2 +
                 (cluster[j][4] - c[3][4])**2) * 0.5

        min_dist = HC.median_min(dist1, dist2, dist3, dist4)

        #print 'Minimum is :', min_dist
        if min_dist == dist1:
            phi[j] = 0
            cluster1.append(cluster[j])
        elif min_dist == dist2:
            phi[j] = 1
            cluster2.append(cluster[j])
        elif min_dist == dist3:
            phi[j] = 2
            cluster3.append(cluster[j])
        elif min_dist == dist4:
            phi[j] = 3
            cluster4.append(cluster[j])

Exemple #2

Fichier : median_clustering.py Projet : AnirudhNarasimhamurthy/Data-Mining

counter=0

while (1):

	counter=counter + 1
	b=set(c)
	print 'The set c now is :', c
	
	
	for j in range(0, len(cluster)):
		dist1=((cluster[j][0]- c[0][0])**2 + (cluster[j][1] - c[0][1])**2 +(cluster[j][2] - c[0][2])**2 + (cluster[j][3] - c[0][3])**2+ (cluster[j][4] - c[0][4])**2)*0.5
		dist2=((cluster[j][0]- c[1][0])**2 + (cluster[j][1] - c[1][1])**2 +(cluster[j][2] - c[1][2])**2 + (cluster[j][3] - c[1][3])**2+ (cluster[j][4] - c[1][4])**2)*0.5
		dist3=((cluster[j][0]- c[2][0])**2 + (cluster[j][1] - c[2][1])**2 +(cluster[j][2] - c[2][2])**2 + (cluster[j][3] - c[2][3])**2+ (cluster[j][4] - c[2][4])**2)*0.5
		dist4=((cluster[j][0]- c[3][0])**2 + (cluster[j][1] - c[3][1])**2 +(cluster[j][2] - c[3][2])**2 + (cluster[j][3] - c[3][3])**2+ (cluster[j][4] - c[3][4])**2)*0.5
		
		min_dist=HC.median_min(dist1,dist2,dist3,dist4)
		
		#print 'Minimum is :', min_dist
		if min_dist==dist1:
			phi[j]=0
			cluster1.append(cluster[j])
		elif min_dist==dist2:
			phi[j]=1
			cluster2.append(cluster[j])
		elif min_dist==dist3:
			phi[j]=2	
			cluster3.append(cluster[j])	
		elif min_dist==dist4:
			phi[j]=3
			cluster4.append(cluster[j])