import os
import random
import suppress
warnings.simplefilter("ignore")
readLocation = "../data/sensor_data/small_54_64.dat"
splitLen = 8
if __name__ == "__main__":
oData = dataio.loadData(readLocation)
obs = 2**oData.data.shape[1]
states = splitLen
sData = markov_anneal.splitActivityMax(oData.cd[0:50000], splitLen)
scores = [0, 0]
entropys = [0, 0]
for i in range(2, 26):
print "Models:" + str(i)
bestScore = -1
bestModels = []
bestData = []
bestOut = []
for j in range(2):
suppress.suppress(2)
bm, bd, out = markov_anneal.train(sData, i, states, obs, \
iterations = 9, outliers = False, voidOutput = False)
import os
import random
import suppress
warnings.simplefilter("ignore")
readLocation = "../data/sensor_data/small_54_64.dat"
splitLen = 8
if __name__ == "__main__":
oData = dataio.loadData(readLocation)
obs = 2**oData.data.shape[1]
states = splitLen
sData = markov_anneal.splitActivityMax(oData.cd[0:50000], splitLen)
scores = [0, 0]
entropys = [0, 0]
for i in range(2, 26):
print "Models:" + str(i)
bestScore = -1
bestModels = []
bestData = []
bestOut = []
for j in range(2):
suppress.suppress(2)
bm, bd, out = markov_anneal.train(sData, i, states, obs, \
iterations = 9, outliers = False, voidOutput = False)
mData = dataio.loadData(modelFile)
mData.matrixToModel(mData.modelList)
models = mData.models
for d in validDays:
print "Day " + str(d)
print " Getting data."
#Iterate over all valid days.
cd, td = bbdata.comp(st, et, \
vDays = [d], \
comp = compress, \
sens = sensors)
print " Splitting."
#Get the split calculation finished.
sData = markov_anneal.splitActivityMax(cd, td, splitLen)
print " Calculating."
sigma = IntegerRange(0, 2**len(sensors))
val, counts = analysis.ratio(sData.values(), models, sigma)
tdMatrix.append(counts)
#Save output matrix.
foo = bbdata.Dataset(None)
foo.tdMatrix = numpy.array(tdMatrix)
dataio.saveData(writeLocation, foo)
