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)
total += 1
samples.initTestIters()
print("Error is", errorPrediction, "out of Total of ", total)
errorRate = (errorPrediction * 100) / total
print(errorRate, "%")
dataset += INCREMENTS
PERCEPTRON_TIME[dataset] = ((endTimer - startTimer), errorRate)
plt.plot([1, 2, 3], [2, 3, 4])
plt.ylabel('Error Rate')
plt.xlabel('DataSet')
plt.show()
print(PERCEPTRON_TIME)
samples.closeFiles()
