def test_random_sparsecode():
n_samples = 50
n_features = 50
n_basis = 30
np.random.seed(0)
# sample data from centered unit variance guaussian process
x = np.random.normal(loc=0.0, scale=1.0, size=(n_samples, n_features))
# lets take the first samples as dictionary
D = x[:n_basis].T
# encode all samples according to D
encoded = sparse_encode(D, x)
assert_equal(encoded.shape, (n_samples, n_basis))
# check that the first sample are encoded using a single component (trivial
# sparse code)
for i in xrange(n_basis):
expected_code = np.zeros(n_basis)
expected_code[i] = 1.0
assert_almost_equal(encoded[i], expected_code, decimal=2)
# check that the remaining samples could also be encoding with a sparse code
avg_nb_nonzeros = (encoded[n_basis:] != 0).sum(axis=1).mean()
assert avg_nb_nonzeros <= 10
def test_parallel_sparse_code():
n_samples = 50
n_features = 20
n_basis = 10
np.random.seed(0)
# sample data from centered unit variance guaussian process
x = np.random.normal(size=(n_samples, n_features))
# lets take the first samples as dictionary
D = x[:n_basis].T
encoded_1 = sparse_encode(D, x )
encoded_2 = SparseEncoder(D, n_cores=2)(x)
assert_almost_equal(encoded_1, encoded_2)