def main_mcmc():
[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', [])
clusteredpoints, cdata = create_clustered_samples(points, 10, 1)
# clusteredpoints, cdata = create_clustered_samples_ndata(points, 3, 1)
# cluster0 = clusteredpoints[0]
# cluster1 = clusteredpoints[1]
# cluster2 = clusteredpoints[2]
# cluster3 = clusteredpoints[3]
# cluster4 = clusteredpoints[4]
# cluster5 = clusteredpoints[5]
# cluster6 = clusteredpoints[0]
# cluster7 = clusteredpoints[1]
# cluster8 = clusteredpoints[2]
# cluster9 = clusteredpoints[3]
#
# 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)
target = []
numcluster = 0
for cluster in clusteredpoints:
target.append(aggregateby10mins_sg_mcmc(cluster[0], numcluster))
# target.append(aggregateby10mins_sg_mcmc_ndata(cluster[1], numcluster))
numcluster += 1
return target
def main(argv):
#[train, labels] = weeklydataset('/home/work/Projects/EclipseProjects/thesis/Scripts/cpu_500.csv', [])
[points, labels] = weeklydataset_shogun('/home/work/Projects/EclipseProjects/thesis/Scripts/cpu_500.csv', [])
clusteredpoints, cdata = create_clustered_samples(points, 5, 1)
# print len(clusteredpoints[0][2])
train ,test = traintest(clusteredpoints[0], 20, 1)
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 create_clusters():
[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]
print type(cluster0)
savetxt("cluster0.csv", matrix(clusteredpoints[0]), delimiter=';')
savetxt("cluster1.csv", matrix(clusteredpoints[1]), delimiter=';')
savetxt("cluster2.csv", matrix(clusteredpoints[2]), delimiter=';')
savetxt("cluster3.csv", matrix(clusteredpoints[3]), delimiter=';')
savetxt("cluster4.csv", matrix(clusteredpoints[4]), delimiter=';')
savetxt("cluster5.csv", matrix(clusteredpoints[5]), delimiter=';')
savetxt("cluster6.csv", matrix(clusteredpoints[6]), delimiter=';')
savetxt("cluster7.csv", matrix(clusteredpoints[7]), delimiter=';')
savetxt("cluster8.csv", matrix(clusteredpoints[8]), delimiter=';')
savetxt("cluster9.csv", matrix(clusteredpoints[9]), delimiter=';')
#
# 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)
