fcm = FCM(data, x, 100) #create the fcm model
fcm.initializeCenters() #initialize the centers of clusters
current = 0
while ((current < fcm.max_iter) and (not fcm.membershipConvergence())
): #while the membership values not converging
fcm.updateMembershipDegrees()
fcm.updateCenters()
current += 1
"""temporaryClusters=fcm.makeClusters()
for item in temporaryClusters:
if len(item)==0:
maxrand=len(data)
randomIndex=random.randint(10,maxrand)-11
row = data[randomIndex]
item.append(row)"""
#print(fcm.membershipMatrix[0][0])
#plotData(temporaryClusters,fcm.data)
#plotCenters(fcm.centers)
finalClusters = fcm.makeClusters(
) #get clusters based on the membership values in the membership matrix(defuzzification)
print("after " + str(current) + " iterations:")
num = 0
for cluster in finalClusters:
num += 1
print("Cluster " + str(num) + " having " + str(len(cluster)) +
" datapoints")
plotData(finalClusters, fcm.data) #visualise each cluster in a color
#print(fcm.membershipMatrix)
#plotCenters(fcm.centers)
