readLocation = "../../runs/clean/models/"
if __name__ == "__main__":
files = os.listdir(readLocation)
suppress.suppress(2)
for f in files:
print f
#It is a data file.
if f.split('.')[-1] == 'dat':
#Open files
fn = dataio.loadData(readLocation + str(f))
fn.matrixToModel(fn.modelList)
sigma = IntegerRange(0, fn.obs)
alldata = []
for i in fn.assignedData:
alldata += i
print "hmm silhouette:" + str(
hmmextra.hmmSilhoutte(alldata, fn.models, sigma))
print "inter-model dist:" + str(
markov_anneal._fitness(fn.models, fn.assignedData, sigma))
print "Outliers:" + str(len(fn.out))
print "Clusters per models:" + str(
[len(i) for i in fn.assignedData])
print ""
suppress.restore(2)
"""
trainData = markov_anneal.train(sData.values()[0:700], \
numModels, \
states, obs, \
iterations = 20, \
printBest = False, \
clustering = "kmeans", \
verbose = False)
"""
sigma = IntegerRange(0, obs)
bd2 = []
for j in bd:
bd2 += j
s = hmmextra.hmmSilhoutte(bd2, bm, sigma)
f = markov_anneal._fitness(bm, bd, sigma)
print "models: " + str(n) + " states:" + str(o) + \
" Silhouette:" + str(s) + " inter-distance:" + str(f)
if s > bestSil:
bestSil = s
bestModels = bm
bestData = bd
bestOut = out
bestStates = states
bestInter = f
sigma = IntegerRange(0, obs)
suppress.suppress(2)
from ghmm import *
suppress.restore(2)
readLocation = "../../runs/clean/models/"
if __name__ == "__main__":
files = os.listdir(readLocation)
suppress.suppress(2)
for f in files:
print f
#It is a data file.
if f.split('.')[-1] == 'dat':
#Open files
fn = dataio.loadData(readLocation + str(f))
fn.matrixToModel(fn.modelList)
sigma = IntegerRange(0, fn.obs)
alldata = []
for i in fn.assignedData:
alldata += i
print "hmm silhouette:" + str(hmmextra.hmmSilhoutte(alldata, fn.models, sigma))
print "inter-model dist:" + str(markov_anneal._fitness(fn.models, fn.assignedData, sigma))
print "Outliers:" + str(len(fn.out))
print "Clusters per models:" + str([len(i) for i in fn.assignedData])
print ""
suppress.restore(2)
"""
trainData = markov_anneal.train(sData.values()[0:700], \
numModels, \
states, obs, \
iterations = 20, \
printBest = False, \
clustering = "kmeans", \
verbose = False)
"""
sigma = IntegerRange(0, obs)
bd2 = []
for j in bd:
bd2 += j
s = hmmextra.hmmSilhoutte(bd2, bm, sigma)
f = markov_anneal._fitness(bm, bd, sigma)
print "models: " + str(n) + " states:" + str(o) + \
" Silhouette:" + str(s) + " inter-distance:" + str(f)
if s > bestSil:
bestSil = s
bestModels = bm
bestData = bd
bestOut = out
bestStates = states
bestInter = f
sigma = IntegerRange(0, obs)
bd2 = []