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)
