def clusterize():
[points, label] = weeklydataset_shogun('/home/work/Projects/EclipseProjects/thesis/Scripts/cpu_mod.csv', [])
# points, label = weeklydataset_sg_ndata('/media/4AC0AB31C0AB21E5/Documents and Settings/Claudio/Documenti/Thesis/Workloads/MSClaudio/ews/ewsdata2.csv', [])
# print points
clusteredpoints, cdata = create_clustered_samples(points, 10, 1)
# clusteredpoints, cdata = create_clustered_samples_ndata(points, 3, 1)
cluster0 = clusteredpoints[0][1]
cluster1 = clusteredpoints[1][1]
cluster2 = clusteredpoints[2][1]
cluster3 = clusteredpoints[3][1]
cluster4 = clusteredpoints[4][1]
cluster5 = clusteredpoints[5][1]
cluster6 = clusteredpoints[6][1]
cluster7 = clusteredpoints[7][1]
cluster8 = clusteredpoints[8][1]
cluster9 = clusteredpoints[9][1]
#
# clusterlen = [len(cluster0[0]), len(cluster1[0]), len(cluster2[0]),len(cluster3[0]),len(cluster4[0]), len(cluster5[0]),
# len(cluster6[0]), len(cluster7[0]), len(cluster8[0]),len(cluster9[0])]
# m = max(clusterlen)
# minimum = min(clusterlen)
#
# maxcluster = clusterlen.index(m)
# mincluster = clusterlen.index(minimum)
# input, target = aggregateby10mins_sg(clusteredpoints[mincluster][0])
print "Cluster0 points: %d" % len(cluster0)
print "Cluster1 points: %d" % len(cluster1)
print "Cluster2 points: %d" % len(cluster2)
print "Cluster3 points: %d" % len(cluster3)
print "Cluster4 points: %d" % len(cluster4)
print "Cluster5 points: %d" % len(cluster5)
print "Cluster6 points: %d" % len(cluster6)
print "Cluster7 points: %d" % len(cluster7)
print "Cluster8 points: %d" % len(cluster8)
print "Cluster9 points: %d" % len(cluster9)
input = []
target = []
numcluster = 0
for cluster in clusteredpoints:
inp, tar = aggregateby10mins_sg_mean(cluster[0], numcluster)
# inp, tar = aggregateby10mins_sg_ndata(cluster[1], numcluster)
input.append(inp)
target.append(tar)
numcluster += 1
# input, target = [aggregateby10mins_sg_ndata(cluster[0]) for cluster in clusteredpoints]
traininput = []
traintarget = []
testinput = []
testtarget = []
for i in range(len(input)):
trainin, traintar, testin, testtar = traintest(input[i], target[i], 20, 1)
traininput.append(trainin)
traintarget.append(traintar)
testinput.append(testin)
testtarget.append(testtar)
return traininput, traintarget, testinput, testtarget, cdata
def main_hmm():
# [points, label] = weeklydataset_shogun('/home/work/Projects/EclipseProjects/thesis/Scripts/cpu_mod.csv', [])
## [points, label] = weeklydataset_sg_ndata('/media/4AC0AB31C0AB21E5/Documents and Settings/Claudio/Documenti/Thesis/Workloads/MSClaudio/ews/ewsdata2.csv', [])
## print points
# clusteredpoints, cdata = create_clustered_samples(points, 10, 1)
## clusteredpoints, cdata = create_clustered_samples_ndata(points, 3, 1)
#
clusteredpoints = []
clusteredpoints.append(genfromtxt("cluster0.csv", delimiter=';'))
clusteredpoints.append(genfromtxt("cluster1.csv", delimiter=';'))
clusteredpoints.append(genfromtxt("cluster2.csv", delimiter=';'))
clusteredpoints.append(genfromtxt("cluster3.csv", delimiter=';'))
clusteredpoints.append(genfromtxt("cluster4.csv", delimiter=';'))
clusteredpoints.append(genfromtxt("cluster5.csv", delimiter=';'))
clusteredpoints.append(genfromtxt("cluster6.csv", delimiter=';'))
clusteredpoints.append(genfromtxt("cluster7.csv", delimiter=';'))
clusteredpoints.append(genfromtxt("cluster8.csv", delimiter=';'))
clusteredpoints.append(genfromtxt("cluster9.csv", delimiter=';'))
cluster0 = clusteredpoints[0][1]
cluster1 = clusteredpoints[1][1]
cluster2 = clusteredpoints[2][1]
cluster3 = clusteredpoints[3][1]
cluster4 = clusteredpoints[4][1]
cluster5 = clusteredpoints[5][1]
cluster6 = clusteredpoints[6][1]
cluster7 = clusteredpoints[7][1]
cluster8 = clusteredpoints[8][1]
cluster9 = clusteredpoints[9][1]
##
## clusterlen = [len(cluster0[0]), len(cluster1[0]), len(cluster2[0]),len(cluster3[0]),len(cluster4[0]), len(cluster5[0]),
## len(cluster6[0]), len(cluster7[0]), len(cluster8[0]),len(cluster9[0])]
## m = max(clusterlen)
## minimum = min(clusterlen)
##
## maxcluster = clusterlen.index(m)
## mincluster = clusterlen.index(minimum)
## input, target = aggregateby10mins_sg(clusteredpoints[mincluster][0])
print "Cluster0 points: %d" % len(cluster0)
print "Cluster1 points: %d" % len(cluster1)
print "Cluster2 points: %d" % len(cluster2)
print "Cluster3 points: %d" % len(cluster3)
print "Cluster4 points: %d" % len(cluster4)
print "Cluster5 points: %d" % len(cluster5)
print "Cluster6 points: %d" % len(cluster6)
print "Cluster7 points: %d" % len(cluster7)
print "Cluster8 points: %d" % len(cluster8)
print "Cluster9 points: %d" % len(cluster9)
clusterlen = [len(cluster0), len(cluster1), len(cluster2), len(cluster3), len(cluster4), len(cluster5), len(cluster6), len(cluster7), len(cluster8), len(cluster9)]
minimum = min(clusterlen)
mincluster = clusterlen.index(70400)
input = []
target = []
numcluster = 0
# input, target = aggregateby10mins_sg_ndata(points[1], 0)
for cluster in clusteredpoints:
inp, tar = aggregateby10mins_sg_mean(cluster[0], numcluster)
# inp, tar = aggregateby10mins_sg_ndata(cluster[1], numcluster)
input.append(inp)
target.append(tar)
numcluster += 1
# input, target = [aggregateby10mins_sg_ndata(cluster[0]) for cluster in clusteredpoints]
traininput = []
traintarget = []
testinput = []
testtarget = []
for i in range(len(input)):
trainin, traintar, testin, testtar = traintest(input[i], target[i], 20, 1)
traininput.append(trainin)
traintarget.append(traintar)
testinput.append(testin)
testtarget.append(testtar)
# traininput, traintarget, testinput, testtarget = traintest(input, target, 20, 1)
# models = [HMM(target[j], testinput[j], testtarget[j], 6, 6, max(target[j])) for j in range(len(target))]
print "cluster maximum = %f" % max(target[mincluster])
models = hmm(target[mincluster], testinput[mincluster], testtarget[mincluster], 6, 6, max(target[mincluster]))
# vs = [hmm_req(models[j], target[j], testinput[j], testtarget[j], max(target[j])) for j in range(len(target)-1)]
## model = hmm(target, testinput, testtarget, 6, 6, max(target))
v = hmm_req(models, target[mincluster], testinput[mincluster], testtarget[mincluster], max(target[mincluster]))
# counter = 0
#
# for v in vs:
lastest_states = [v[i][0][len(v[i][0])-1] for i in range(len(v)-1)]
print lastest_states
ttarget = []
for state in lastest_states:
li = models.getEmission(state)
m = (max(li)* 2.0)/3.0
el = pylab.find(array(li) > m)
maxes = nlargest(10, li)
maxvals = [li.index(maxval) for maxval in maxes]
ttarget.append(maxvals)
# counter += 1
## sme = sme_calc(ttarget, testtarget[counter])
sme = sme_calc(ttarget, testtarget[mincluster])
print "SME = %f" % sme
# counter += 1
return models