# Calling the basic feature extractor
features = mp.extract_basic_features(ALL_TRAINING_IMAGES[example_number],
DATA_WIDTH, DATA_HEIGHT)
#mp.extract_advanced_features(ALL_TRAINING_IMAGES[example_number],DATA_WIDTH, DATA_HEIGHT)
#mp.extract_advanced_features(ALL_TRAINING_IMAGES[2075],DATA_WIDTH, DATA_HEIGHT)
# Compute parameters for a Naive Bayes classifier using the basic feature
# extractor
#mp.compute_statistics(ALL_TRAINING_IMAGES, ALL_TRAINING_LABELS, DATA_WIDTH,DATA_HEIGHT, mp.extract_basic_features)
# Making predictions on validation data
#predicted_labels = mp.classify(ALL_VALIDATION_IMAGES, DATA_WIDTH, DATA_HEIGHT,mp.extract_basic_features)
#with advance features
#mp.compute_statistics(ALL_TRAINING_IMAGES, ALL_TRAINING_LABELS, DATA_WIDTH, DATA_HEIGHT, mp.extract_advanced_features)
#predicted_labels = mp.classify(ALL_VALIDATION_IMAGES, DATA_WIDTH, DATA_HEIGHT, mp.extract_advanced_features)
#with final features
mp.compute_statistics(ALL_TRAINING_IMAGES, ALL_TRAINING_LABELS, DATA_WIDTH,
DATA_HEIGHT, mp.extract_final_features)
predicted_labels = mp.classify(ALL_VALIDATION_IMAGES, DATA_WIDTH,
DATA_HEIGHT, mp.extract_final_features)
correct_count = 0.0
for ei in range(len(predicted_labels)):
if (ALL_VALIDATION_LABELS[ei] == predicted_labels[ei]):
correct_count += 1
print "Correct prediction: " + str(correct_count / len(predicted_labels))
print("--- %s seconds ---" % (time.time() - start_time))
print "Printing digit example #" + str(example_number + 1) + " with label: " \ + str(ALL_TRAINING_LABELS[example_number]) _print_digit_image(ALL_TRAINING_IMAGES[example_number]) print # Calling the basic feature extractor # features = mp.extract_basic_features(ALL_TRAINING_IMAGES[example_number], # DATA_WIDTH, DATA_HEIGHT) # # # # Compute parameters for a Naive Bayes classifier using the basic feature # # # extractor mp.compute_statistics(ALL_TRAINING_IMAGES, ALL_TRAINING_LABELS, DATA_WIDTH, DATA_HEIGHT, mp.extract_basic_features) # Making predictions on validation data predicted_labels = mp.classify(ALL_VALIDATION_IMAGES, DATA_WIDTH, DATA_HEIGHT, mp.extract_basic_features) correct_count = 0.0 for ei in range(len(predicted_labels)): if (ALL_VALIDATION_LABELS[ei] == predicted_labels[ei]): correct_count += 1 print "Correct prediction: " + str(correct_count / len(predicted_labels)) # features = mp.extract_advanced_features(ALL_TRAINING_IMAGES[example_number], DATA_WIDTH, DATA_HEIGHT) # Compute parameters for a Naive Bayes classifier using the basic feature # extractor mp.compute_statistics(ALL_TRAINING_IMAGES, ALL_TRAINING_LABELS, DATA_WIDTH, DATA_HEIGHT,mp.extract_advanced_features) # Making predictions on validation data
