def load_data(path,batch_size=25):
all_files=utils.get_all_files(path)
all_files=utils.append_path(path,all_files)
images=utils.read_images(all_files)
images=utils.flatten_images(images)
images=map(utils.normalize,images)
images=np.array(images)
n_batches=get_number_of_batches(batch_size,len(images))
def get_batch(i):
return images[i * batch_size: (i+1) * batch_size]
batches=map(get_batch,range(n_batches))
batches = [np.array(batch) for batch in batches]
print("Dataset loaded")
return np.array(batches)
channel_train, y_true, channel_len = load_raw_labeled_data()
# fit lda
print('Model training..')
classifier = lda()
classifier.fit(channel_train, y_true)
precision_clf = classifier.score(channel_train, y_true)
prediction = classifier.predict(channel_train)
balanced_acc = balanced_accuracy_score(y_true, prediction)
kappa = cohen_kappa_score(y_true, prediction)
# plot learning curve
plot_learning_curve(classifier, 'learning curve of LDA', channel_train, y_true)
print('train-accuracy: ', precision_clf)
print('balanced-accuracy: ', balanced_acc)
print('kappa: ', kappa)
# prepare test data
print('Prepare test data..')
imgs = load_raw_images_data(test_data_path,
rescale_ratio=0.25,
preserve_range_after_rescale=True)
channel_test, _ = flatten_images(imgs)
print('Model predict..')
predictions = classifier.predict(channel_test)
print('Reconstruct..')
sample_img = imgs[0]
sample_img = reconstruct_image(sample_img, predictions)
imsave(f'{img_save_path}/{data_id}_lda.png', sample_img)
def flatten(self):
self.frames=utils.flatten_images(self.frames)
