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)] = \
abs(np.asarray(algorithmTest.deNormalizeSnippet(\
algorithmTest.classify(\
algorithmTest.normalizeSnippet(\
outDataTest[executeSample], -1), -1), -1))\
- np.asarray(algoRes))
if printFlag == True:
print algorithmTest.deNormalizeSnippet(\
algorithmTest.classify(\
algorithmTest.normalizeSnippet(\
outDataTest[executeSample], -1), -1), -1), \
algoRes
runningTotal += outDataTest[executeSample]
print "Done: execution"
# computing average of the output test data
avgActual = runningTotal / (1.0 * numExecuteSamples)
# calculating the loading time of the whole training dataset
netLoadingTime = 0
for i in range(numTrainingSamples):
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)] = \
abs(np.asarray(algorithmTest.deNormalizeSnippet(\
algorithmTest.classify(\
algorithmTest.normalizeSnippet(\
outDataTest[executeSample], -1), -1), -1))\
- np.asarray(algoRes))
if printFlag == True:
print algorithmTest.deNormalizeSnippet(\
algorithmTest.classify(\
algorithmTest.normalizeSnippet(\
outDataTest[executeSample], -1), -1), -1), \
algoRes
runningTotal += outDataTest[executeSample]
print "Done: execution"
# computing average of the output test data
avgActual = runningTotal/(1.0*numExecuteSamples)
# calculating the loading time of the whole training dataset
netLoadingTime = 0
