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)] = \ 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]
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)] = \ 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]