def test(store_inverse): preprocessed_X = copy.copy(self.X) preprocessor = ZCA(store_inverse=store_inverse) dataset = DenseDesignMatrix(X=preprocessed_X, preprocessor=preprocessor, fit_preprocessor=True) preprocessed_X = dataset.get_design_matrix() assert_allclose(self.X, preprocessor.inverse(preprocessed_X))
def test(store_inverse): rng = np.random.RandomState([1, 2, 3]) X = as_floatX(rng.randn(15, 10)) preprocessed_X = copy.copy(X) preprocessor = ZCA(store_inverse=store_inverse) dataset = DenseDesignMatrix(X=preprocessed_X, preprocessor=preprocessor, fit_preprocessor=True) preprocessed_X = dataset.get_design_matrix() assert_allclose(X, preprocessor.inverse(preprocessed_X))