distances = distances[:-DiscardLast]
elif type(DiscardLast) is float:
distances = distances[:-int(DiscardLast * len(distances))]
callDerivative = lambda value: list(
map(lambda x: x[1] - x[0], zip(value, value[0] + value[:-1])))
distancesDD = callDerivative(callDerivative(distances))
mx = max(distancesDD)
for i in range(len(distancesDD) - 1, -1, -1):
if distancesDD[i] == mx:
return i, distances[i]
return None
for i in range(90, 280, 10):
# for i in range(145,200,5):
# for i in range(155,165,1):# 160 makes silhouette score max
kn = i
esp = getEsp(kn, DiscardLast=0.1)[1]
test = testK.Copy()
# test.ReadData('data.csv')
result = test.DBSCAN(eps=esp, min_samples=kn)
result.ShowLabelInfo(output=False)
silScore = result.getScore(method='Silhouette')
davScore = result.getScore(method='DaviesBouldin')
calScore = result.getScore(method='CalinskiHarabasz')
print('K:', kn, 'ESP:{:.4f}'.format(esp), 'SIL:{:.4f}'.format(silScore),
'CAL:{:.4f}'.format(calScore), 'DAV:{:.4f}'.format(davScore), 'CNT:',
sum(result.distributionInfo['Num']), 'LB:',
len(result.distributionInfo['Num']), 'LC',
result.distributionInfo['Num'])
from Data import Data
import matplotlib.pyplot
import time
SSE = [] # sum of the squared errors
TestUpperBound = 15
test = Data()
test.ReadData('data.csv')
# test = test.pca(0.95)
for k in range(1,TestUpperBound):
# print('Now @ k = {0}'.format(k))
start=time.time()
testK = test.Copy()
result = testK.KMeans(k)
result.ShowLabelInfo(output=False)
time1=time.time()-start
silScore = result.getScore(method='Silhouette')
calScore = result.getScore(method='CalinskiHarabasz')
davScore = result.getScore(method='DaviesBouldin')
print('TIME:{:.2f}'.format(time1),'K:',k,'SIL:{:.4f}'.format(silScore),'CAL:{:.4f}'.format(calScore),'DAV:{:.4f}'.format(davScore),'CNT:',sum(result.distributionInfo['Num']),'LC',result.distributionInfo['Num'])
SSE.append(testK.midResult.inertia_)
x = range(1,TestUpperBound)
matplotlib.pyplot.figure(figsize=(5,5))
matplotlib.pyplot.xlabel('k')
matplotlib.pyplot.ylabel('SSE')
matplotlib.pyplot.plot(x,SSE,'o-')
matplotlib.pyplot.show()
