def get_dictionary_data(n_comp=20, zero_index=False):
unlabeled = util.load_unlabeled_training(flatten=False)
height, width = 32, 32
n_images = 10000
patch_size = (8, 8)
unlabeled = util.standardize(unlabeled)
np.random.shuffle(unlabeled)
print('Extracting reference patches...')
patches = np.empty((0, 64))
t0 = time()
for image in unlabeled[:n_images, :, :]:
data = np.array(extract_patches_2d(image, patch_size,
max_patches=0.01))
data = data.reshape(data.shape[0], -1)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0) + 1e-20
patches = np.concatenate([patches, data])
print('done in %.2fs.' % (time() - t0))
# whiten the patches
z = zca.ZCA()
z.fit(patches)
z.transform(patches)
print('Learning the dictionary...')
t0 = time()
dico = MiniBatchDictionaryLearning(n_components=n_comp, alpha=1)
V = dico.fit(patches).components_
dt = time() - t0
print('done in %.2fs.' % dt)
#plt.figure(figsize=(4.2, 4))
#for i, comp in enumerate(V[:100]):
# plt.subplot(10, 10, i + 1)
# plt.imshow(comp.reshape(patch_size), cmap=plt.cm.gray_r,
# interpolation='nearest')
# plt.xticks(())
# plt.yticks(())
#plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
#plt.show()
labeled_data, labels = util.load_labeled_training(flatten=False,
zero_index=True)
labeled_data = util.standardize(labeled_data)
test_data = util.load_all_test(flatten=False)
test_data = util.standardize(test_data)
#util.render_matrix(test_data, flattened=False)
print('Reconstructing the training images...')
t0 = time()
reconstructed_images = np.empty((0, 32, 32))
for i, image in enumerate(labeled_data):
data = extract_patches_2d(image, patch_size)
data = data.reshape(data.shape[0], -1)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0) + 1e-20
code = dico.transform(data)
patches = np.dot(code, V)
z.transform(patches)
patches = patches.reshape(len(data), *patch_size)
data = reconstruct_from_patches_2d(patches, (width, height))
data = data.reshape(1, 32, 32)
reconstructed_images = np.concatenate([reconstructed_images, data])
print('done in %.2fs.' % (time() - t0))
# flatten
n, x, y = reconstructed_images.shape
training_images = reconstructed_images.reshape(
reconstructed_images.shape[0],
reconstructed_images.shape[1] * reconstructed_images.shape[2])
assert training_images.shape == (n, x * y)
print('Reconstructing the test images...')
t0 = time()
reconstructed_test_images = np.empty((0, 32, 32))
for image in test_data:
data = extract_patches_2d(image, patch_size)
data = data.reshape(data.shape[0], -1)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0) + 1e-20
code = dico.transform(data)
patches = np.dot(code, V)
z.transform(patches)
patches = patches.reshape(len(data), *patch_size)
data = reconstruct_from_patches_2d(patches, (width, height))
data = data.reshape(1, 32, 32)
reconstructed_test_images = np.concatenate(
[reconstructed_test_images, data])
print('done in %.2fs.' % (time() - t0))
# flatten
n, x, y = reconstructed_test_images.shape
test_images = reconstructed_test_images.reshape(
reconstructed_test_images.shape[0],
reconstructed_test_images.shape[1] *
reconstructed_test_images.shape[2])
assert test_images.shape == (n, x * y)
return (training_images, labels, test_images)
def get_dictionary_data(n_comp=20, zero_index=True):
unlabeled = util.load_unlabeled_training(flatten=False)
height, width = 32, 32
n_images = 10000
patch_size = (8, 8)
unlabeled = util.standardize(unlabeled)
np.random.shuffle(unlabeled)
print('Extracting reference patches...')
patches = np.empty((0, 64))
t0 = time()
for image in unlabeled[:n_images, :, :]:
data = np.array(extract_patches_2d(image, patch_size, max_patches=0.10))
data = data.reshape(data.shape[0], -1)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0) + 1e-20
patches = np.concatenate([patches, data])
print('done in %.2fs.' % (time() - t0))
# whiten the patches
z = zca.ZCA()
z.fit(patches)
z.transform(patches)
print('Learning the dictionary...')
t0 = time()
dico = MiniBatchDictionaryLearning(n_components=n_comp, alpha=1)
V = dico.fit(patches).components_
dt = time() - t0
print('done in %.2fs.' % dt)
#plt.figure(figsize=(4.2, 4))
#for i, comp in enumerate(V[:100]):
# plt.subplot(10, 10, i + 1)
# plt.imshow(comp.reshape(patch_size), cmap=plt.cm.gray_r,
# interpolation='nearest')
# plt.xticks(())
# plt.yticks(())
#plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
#plt.show()
labeled_data, labels = util.load_labeled_training(flatten=False, zero_index=True)
labeled_data = util.standardize(labeled_data)
test_data = util.load_all_test(flatten=False)
test_data = util.standardize(test_data)
#util.render_matrix(test_data, flattened=False)
print('Training SVM with the training images...')
t0 = time()
reconstructed_images = np.empty((0, 64))
multiplied_labels = np.empty((0))
for i in range(len(labeled_data)):
image = labeled_data[i, :, :]
label = labels[i]
data = extract_patches_2d(image, patch_size, max_patches=0.50)
data = data.reshape(data.shape[0], -1)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0) + 1e-20
code = dico.transform(data)
patches = np.dot(code, V)
z.transform(patches)
reconstructed_images = np.concatenate([reconstructed_images, patches])
extended_labels = np.asarray([label] * len(patches))
multiplied_labels = np.concatenate([multiplied_labels, extended_labels])
print(reconstructed_images.shape, multiplied_labels.shape)
svc = SVC()
#print('Getting cross-val scores...')
#scores = cross_validation.cross_val_score(svc, reconstructed_images, multiplied_labels, cv=10)
#print('cross-val scores:', scores)
#print('cross-val mean:', np.mean(scores))
#print('cross-val variance:', np.var(scores))
print('done in %.2fs.' % (time() - t0))
svc.fit(reconstructed_images, multiplied_labels)
print('Reconstructing the test images...')
t0 = time()
predictions = []
for i, image in enumerate(test_data):
data = extract_patches_2d(image, patch_size, max_patches=0.25)
data = data.reshape(data.shape[0], -1)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0) + 1e-20
code = dico.transform(data)
patches = np.dot(code, V)
z.transform(patches)
pred = svc.predict(patches)
print('Variance in the predictions:', np.var(pred))
predictions.append(mode(pred))
print('done in %.2fs.' % (time() - t0))
predictions += 1
util.write_results(predictions, 'svm_patches_25_percent_20_comp.csv')
from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix, DefaultViewConverter
from pylearn2.datasets import preprocessing
from pylearn2.format.target_format import convert_to_one_hot
import pylab as plt
import cPickle as pickle
import numpy as np
import util
import dictionary_learning
if __name__ == "__main__":
#train_data, train_labels = util.load_labeled_training(flatten=True, zero_index=True)
#train_data = util.standardize(train_data)
test_data = util.load_all_test(flatten=True)
test_data = util.standardize(test_data)
#train_data_20, _, test_data_20 = dictionary_learning.get_dictionary_data(n_comp=20, zero_index=True)
#train_data_100, _, test_data_100 = dictionary_learning.get_dictionary_data(n_comp=100, zero_index=True)
# convert the training labels into one-hot format, as required by the pylearn2 model
#train_labels = convert_to_one_hot(train_labels, dtype='int64', max_labels=7, mode='stack')
# pickle the data
#serial.save('training_data_for_pylearn2.pkl', train_data)
#serial.save('training_data_20_components_for_pylearn2.pkl', train_data_20)
#serial.save('training_data_100_components_for_pylearn2.pkl', train_data_100)
#serial.save('training_labels_for_pylearn2.pkl', train_labels)
def get_dictionary_data(n_comp=20, zero_index=False):
unlabeled = util.load_unlabeled_training(flatten=False)
height, width = 32, 32
n_images = 10000
patch_size = (8, 8)
unlabeled = util.standardize(unlabeled)
np.random.shuffle(unlabeled)
print('Extracting reference patches...')
patches = np.empty((0, 64))
t0 = time()
for image in unlabeled[:n_images, :, :]:
data = np.array(extract_patches_2d(image, patch_size, max_patches=0.01))
data = data.reshape(data.shape[0], -1)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0) + 1e-20
patches = np.concatenate([patches, data])
print('done in %.2fs.' % (time() - t0))
# whiten the patches
z = zca.ZCA()
z.fit(patches)
z.transform(patches)
print('Learning the dictionary...')
t0 = time()
dico = MiniBatchDictionaryLearning(n_components=n_comp, alpha=1)
V = dico.fit(patches).components_
dt = time() - t0
print('done in %.2fs.' % dt)
#plt.figure(figsize=(4.2, 4))
#for i, comp in enumerate(V[:100]):
# plt.subplot(10, 10, i + 1)
# plt.imshow(comp.reshape(patch_size), cmap=plt.cm.gray_r,
# interpolation='nearest')
# plt.xticks(())
# plt.yticks(())
#plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
#plt.show()
labeled_data, labels = util.load_labeled_training(flatten=False, zero_index=True)
labeled_data = util.standardize(labeled_data)
test_data = util.load_all_test(flatten=False)
test_data = util.standardize(test_data)
#util.render_matrix(test_data, flattened=False)
print('Reconstructing the training images...')
t0 = time()
reconstructed_images = np.empty((0, 32, 32))
for i, image in enumerate(labeled_data):
data = extract_patches_2d(image, patch_size)
data = data.reshape(data.shape[0], -1)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0) + 1e-20
code = dico.transform(data)
patches = np.dot(code, V)
z.transform(patches)
patches = patches.reshape(len(data), *patch_size)
data = reconstruct_from_patches_2d(patches, (width, height))
data = data.reshape(1, 32, 32)
reconstructed_images = np.concatenate([reconstructed_images, data])
print('done in %.2fs.' % (time() - t0))
# flatten
n, x, y = reconstructed_images.shape
training_images = reconstructed_images.reshape(reconstructed_images.shape[0], reconstructed_images.shape[1]*reconstructed_images.shape[2])
assert training_images.shape == (n, x*y)
print('Reconstructing the test images...')
t0 = time()
reconstructed_test_images = np.empty((0, 32, 32))
for image in test_data:
data = extract_patches_2d(image, patch_size)
data = data.reshape(data.shape[0], -1)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0) + 1e-20
code = dico.transform(data)
patches = np.dot(code, V)
z.transform(patches)
patches = patches.reshape(len(data), *patch_size)
data = reconstruct_from_patches_2d(patches, (width, height))
data = data.reshape(1, 32, 32)
reconstructed_test_images = np.concatenate([reconstructed_test_images, data])
print('done in %.2fs.' % (time() - t0))
# flatten
n, x, y = reconstructed_test_images.shape
test_images = reconstructed_test_images.reshape(reconstructed_test_images.shape[0], reconstructed_test_images.shape[1]*reconstructed_test_images.shape[2])
assert test_images.shape == (n, x*y)
return (training_images, labels, test_images)