# Set PCA subclass from argument.
if args.algorithm == 'cov_eig':
PCAImpl = CovEigPCA
elif args.algorithm == 'svd':
PCAImpl = SVDPCA
elif args.algorithm == 'online':
PCAImpl = OnlinePCA
conf['minibatch_size'] = args.minibatch_size
else:
# This should never happen.
raise NotImplementedError(args.algorithm)
# Load precomputed PCA transformation if requested; otherwise compute it.
if args.load_file:
pca = Block.load(args.load_file)
else:
print "... computing PCA"
pca = PCAImpl(**conf)
pca.train(train_data)
# Save the computed transformation.
pca.save(args.save_file)
# Apply the transformation to test and valid subsets.
inputs = tensor.matrix()
pca_transform = theano.function([inputs], pca(inputs))
valid_pca = pca_transform(valid_data)
test_pca = pca_transform(test_data)
print >> sys.stderr, "New shapes:", map(numpy.shape, [valid_pca, test_pca])
# TODO: Compute ALC here when the code using the labels is ready.
# Set PCA subclass from argument.
if args.algorithm == 'cov_eig':
PCAImpl = CovEigPCA
elif args.algorithm == 'svd':
PCAImpl = SVDPCA
elif args.algorithm == 'online':
PCAImpl = OnlinePCA
conf['minibatch_size'] = args.minibatch_size
else:
# This should never happen.
raise NotImplementedError(args.algorithm)
# Load precomputed PCA transformation if requested; otherwise compute it.
if args.load_file:
pca = Block.load(args.load_file)
else:
print "... computing PCA"
pca = PCAImpl(**conf)
pca.train(train_data)
# Save the computed transformation.
pca.save(args.save_file)
# Apply the transformation to test and valid subsets.
inputs = tensor.matrix()
pca_transform = theano.function([inputs], pca(inputs))
valid_pca = pca_transform(valid_data)
test_pca = pca_transform(test_data)
print >> sys.stderr, "New shapes:", map(numpy.shape, [valid_pca, test_pca])
# TODO: Compute ALC here when the code using the labels is ready.
