TOTALDATASET = len(featureValueListForAllTrainingImages) INCREMENTS = int(TOTALDATASET * PERCENT_INCREMENT / 100) PERCEPTRON_TIME = {} while dataset < TOTALDATASET: startTimer = time.time() print("Training ON {0} to {1} data".format(dataset, dataset + INCREMENTS)) ImageLabelZipList = zip( featureValueListForAllTrainingImages[dataset:dataset + INCREMENTS], actualLabelForTrainingList[dataset:dataset + INCREMENTS]) for featureValueListPerImage, actualLabel in ImageLabelZipList: perceptronClassifier.runModel(True, featureValueListPerImage, actualLabel) endTimer = time.time() print("TESTING our model that is TRAINED ON {0} to {1} data".format( 0, dataset + INCREMENTS)) errorPrediction = 0 total = 0 featureValueListForAllTestingImages, actualLabelList = dataClassifier.extractFeatures( samples.test_lines_itr, samples.test_labelsLines_itr) for featureValueListPerImage, actualLabel in zip( featureValueListForAllTestingImages, actualLabelList): errorPrediction += perceptronClassifier.runModel( False, featureValueListPerImage, actualLabel)