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))
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))