print "Beginning loading and training"
# For testing purpose. print input for test data
# each line in output corresponds to one input data field (record)
# print inDataTest
timestamps = {}
# Add training data to CE object
for i in xrange(len(outDataTrain)):
# recording time stamps before and after adding to measure load time
firstTS = time.time()
algorithmTest.addSingleObservation(inDataTrain[:][i], outDataTrain[i])
secondTS = time.time()
timestamps["load" + str(i)] = secondTS - firstTS
# training CE using the added data, while the training time is measured
firstTS = time.time()
algorithmTest.clusterAndTrain()
secondTS = time.time()
timestamps["train"] = secondTS - firstTS
print "Done: loading and training"
print "Beginning execution"
runningTotal = 0
for executeSample in range(testRecStop - testRecStart + 1):
# computing output of test data using trained CE (time measured)
# Saving error for each test data.
firstTS = time.time()
algoRes = algorithmTest.deNormalizeSnippet(
algorithmTest.executeAndCluster(list(inDataTest[executeSample])), -1)
secondTS = time.time()
timestamps["test" + str(executeSample)] = secondTS - firstTS
timestamps["delta" + str(executeSample)] = \
print "Beginning loading and training"
# For testing purpose. print input for test data
# each line in output corresponds to one input data field (record)
# print inDataTest
timestamps = {}
# Add training data to CE object
for i in xrange(len(outDataTrain)):
# recording time stamps before and after adding to measure load time
firstTS = time.time()
algorithmTest.addSingleObservation(inDataTrain[:][i], outDataTrain[i])
secondTS = time.time()
timestamps["load" + str(i)] = secondTS - firstTS
# training CE using the added data, while the training time is measured
firstTS = time.time()
algorithmTest.clusterAndTrain()
secondTS = time.time()
timestamps["train"] = secondTS - firstTS
print "Done: loading and training"
print "Beginning execution"
runningTotal = 0
for executeSample in range(testRecStop - testRecStart + 1):
# computing output of test data using trained CE (time measured)
# Saving error for each test data.
firstTS = time.time()
algoRes = algorithmTest.deNormalizeSnippet(
algorithmTest.executeAndCluster(list(inDataTest[executeSample])),-1)
secondTS = time.time()
timestamps["test" + str(executeSample)] = secondTS - firstTS
timestamps["delta" + str(executeSample)] = \
