def test_zca_dataset():
"""
Tests the ZCA_Dataset class.
"""
# Preparation
rng = np.random.RandomState([2014, 11, 4])
start = 0
stop = 990
num_examples = 1000
num_feat = 5
num_classes = 2
# random_dense_design_matrix has values that are centered and of
# unit stdev, which is not useful to test the ZCA.
# So, we replace its value by an uncentered uniform one.
raw = random_dense_design_matrix(rng, num_examples, num_feat, num_classes)
x = rng.uniform(low=-0.5, high=2.0, size=(num_examples, num_feat))
x = x.astype(np.float32)
raw.X = x
zca = ZCA(filter_bias=0.0)
zca.apply(raw, can_fit=True)
zca_dataset = ZCA_Dataset(raw, zca, start, stop)
# Testing general behaviour
mean = zca_dataset.X.mean(axis=0)
var = zca_dataset.X.std(axis=0)
assert_allclose(mean, np.zeros(num_feat), atol=1e-2)
assert_allclose(var, np.ones(num_feat), atol=1e-2)
# Testing mapback()
y = zca_dataset.mapback(zca_dataset.X)
assert_allclose(x[start:stop], y)
# Testing mapback_for_viewer()
y = zca_dataset.mapback_for_viewer(zca_dataset.X)
z = x/np.abs(x).max(axis=0)
assert_allclose(z[start:stop], y, rtol=1e-2)
# Testing adjust_for_viewer()
y = zca_dataset.adjust_for_viewer(x.T).T
z = x/np.abs(x).max(axis=0)
assert_allclose(z, y)
# Testing adjust_to_be_viewed_with()
y = zca_dataset.adjust_to_be_viewed_with(x, 2*x, True)
z = zca_dataset.adjust_for_viewer(x)
assert_allclose(z/2, y)
y = zca_dataset.adjust_to_be_viewed_with(x, 2*x, False)
z = x/np.abs(x).max()
assert_allclose(z/2, y)
# Testing has_targets()
assert zca_dataset.has_targets()
def test_zca_dataset():
"""
Test that a ZCA dataset can be constructed without crashing. No
attempt to verify correctness of behavior.
"""
rng = np.random.RandomState([2014, 11, 4])
num_examples = 5
dim = 3
num_classes = 2
raw = random_dense_design_matrix(rng, num_examples, dim, num_classes)
zca = ZCA()
zca.apply(raw, can_fit=True)
zca_dataset = ZCA_Dataset(raw, zca, start=1, stop=4)
parser.add_argument('-o')
args = parser.parse_args()
model_path = '/data/lisa/exp/wuzhen/conv2d/' + args.folder + '/convolutional_network_best.pkl'
import os
#X = np.load(os.environ['PYLEARN2_DATA_PATH'] + '/faceEmo/test_X.npy')
#y = np.load(os.environ['PYLEARN2_DATA_PATH'] + '/faceEmo/test_y.npy')
#print 'X.shape before', X.shape
X = np.load('test_input.npy')
X = X.astype('float32')
test_set = DenseDesignMatrix(X)
preproc = ZCA()
preproc.fit(test_set.X)
preproc.apply(test_set)
X = test_set.X
X = X.reshape(X.shape[0], 48, 48, 1).astype('float32')
f = open(model_path, 'rb')
mlp = cPickle.load(f)
X_theano = mlp.get_input_space().make_batch_theano()
#X_theano = T.tensor4()
y_theano = mlp.fprop(X_theano)
func = function(inputs=[X_theano], outputs=y_theano)
batch_size = mlp.batch_size
from pylearn2.datasets.mnist import MNIST
train = MNIST(which_set = 'train')
from pylearn2.datasets.preprocessing import ZCA
zca = ZCA()
zca.apply(train, can_fit=True)
from pylearn2.utils import serial
serial.save('mnist_zca.pkl', zca)
from pylearn2.datasets.mnist import MNIST
train = MNIST(which_set='train')
from pylearn2.datasets.preprocessing import ZCA
zca = ZCA()
zca.apply(train, can_fit=True)
from pylearn2.utils import serial
serial.save('mnist_zca.pkl', zca)
