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)
