firstTS = time.time(); algorithmTest.train(); secondTS = time.time(); teslaTimestamps["train"] = secondTS - firstTS; runningTotal = 0; for executeSample in range(numExecuteSamples): inputRow = next(inputReader); outputRow = next(outputReader); if (len(inputRow) > 0): input1 = float(inputRow[0]); input2 = float(inputRow[1]); output = float(outputRow[0]); firstTS = time.time(); theor = algorithmTest.execute([input1, input2]); secondTS = time.time(); teslaTimestamps["test" + str(executeSample)] = secondTS - firstTS; teslaTimestamps["delta" + str(executeSample)] = abs(output - theor); runningTotal += output; avgActual = runningTotal/(1.0*numExecuteSamples); netLoadingTime = 0; for i in range(numTrainingSamples): netLoadingTime += teslaTimestamps["load" + str(i)]; netExecuteTime = 0; runningMAE = 0.0; for i in range(numExecuteSamples): netExecuteTime += teslaTimestamps["test" + str(i)];
print(str(trainingSample)) firstTS = time.time() algorithmTest.train() secondTS = time.time() teslaTimestamps["train"] = secondTS - firstTS runningTotal = 0 for executeSample in range(numExecuteSamples): inputRow = next(inputReader) outputRow = next(outputReader) if (len(inputRow) > 0): inputs = [float(x) for x in inputRow[0:numInputs]] output = float(outputRow[0]) firstTS = time.time() theor = algorithmTest.execute(inputs) secondTS = time.time() teslaTimestamps["test" + str(executeSample)] = secondTS - firstTS teslaTimestamps["delta" + str(executeSample)] = abs(output - theor) runningTotal += output avgActual = runningTotal / (1.0 * numExecuteSamples) netLoadingTime = 0 for i in range(numTrainingSamples): netLoadingTime += teslaTimestamps["load" + str(i)] netExecuteTime = 0 runningMAE = 0.0 for i in range(numExecuteSamples): netExecuteTime += teslaTimestamps["test" + str(i)] runningMAE += teslaTimestamps["delta" + str(i)]
for executeSample in range(numExecuteSamples): try: inputRow = next(inputReader); outputRow = next(outputReader); except: inputFile.seek(0); outputFile.seek(0); inputRow = next(inputReader); outputRow = next(outputReader); if (len(inputRow) > 0): inputVec = [ float(x) for x in inputRow ] output = float(outputRow[0]); firstTS = time.time(); theor = algorithmTest.execute(inputVec); secondTS = time.time(); teslaTimestamps["test" + str(executeSample)] = secondTS - firstTS; teslaError["delta" + str(executeSample)] = abs(output - theor); runningTotal += output; else: print("Error reading test samples"); sys.exit(); netLoadingTime = 0; for i in range(numTrainingSamples): netLoadingTime += teslaTimestamps["load" + str(i)]; netExecuteTime = 0; runningMAE = 0.0; for i in range(numExecuteSamples):
print(str(trainingSample)) firstTS = time.time() algorithmTest.train() secondTS = time.time() teslaTimestamps["train"] = secondTS - firstTS runningTotal = 0 for executeSample in range(numExecuteSamples): inputRow = next(inputReader) outputRow = next(outputReader) if (len(inputRow) > 0): input1 = float(inputRow[0]) output = float(outputRow[0]) firstTS = time.time() theor = algorithmTest.execute([input1]) secondTS = time.time() teslaTimestamps["test" + str(executeSample)] = secondTS - firstTS teslaTimestamps["delta" + str(executeSample)] = abs(output - theor) runningTotal += output avgActual = runningTotal / (1.0 * numExecuteSamples) netLoadingTime = 0 for i in range(numTrainingSamples): netLoadingTime += teslaTimestamps["load" + str(i)] netExecuteTime = 0 runningMAE = 0.0 for i in range(numExecuteSamples): netExecuteTime += teslaTimestamps["test" + str(i)]
secondTS = time.time(); teslaTimestamps["train"] = secondTS - firstTS; runningActual = []; for executeSample in range(numExecuteSamples): inputRow = next(inputReader); outputRow = next(outputReader); if (len(inputRow) > 0): inputs = inputRow[:numInputsToUse]; inputs = [float(x) for x in inputs]; # input1 = float(inputRow[0]); # input2 = float(inputRow[1]); output = float(outputRow[0]); firstTS = time.time(); theor = algorithmTest.execute(inputs); # theor = 0 if (theor < 0.0) else theor; # print(str(theor) + "\t" + str(output)); # theor = algorithmTest.execute([input1, input2]); secondTS = time.time(); teslaTimestamps["test" + str(executeSample)] = secondTS - firstTS; teslaTimestamps["delta" + str(executeSample)] = abs(output - theor); runningActual.append(output); runningActualHigh = [x for x in runningActual if x > 0]; avgActual = sum(runningActualHigh)/(1.0*len(runningActualHigh)); netLoadingTime = 0; for i in range(numTrainingSamples): netLoadingTime += teslaTimestamps["load" + str(i)];