def test_sparse_filtering_iter_fit(): X = np.random.standard_normal((10, 2)) X, = theano_floatx(X) sf = SparseFiltering(2, 10, max_iter=10) for i, info in enumerate(sf.iter_fit(X)): if i >= 10: break
def test_sparse_filtering_fit(): X = np.random.standard_normal((10, 2)) X, = theano_floatx(X) sf = SparseFiltering(2, 10, max_iter=10) sf.fit(X)
def test_sparse_filtering_transform(): X = np.random.standard_normal((10, 2)) sf = SparseFiltering(2, 10, max_iter=10) sf.transform(X)