def test_rff_feature_map_comp_theano_execute(): if not theano_available: raise SkipTest("Theano not available.") D = 2 x = np.random.randn(D) m = 10 sigma = 1. omega, u = rff_sample_basis(D, m, sigma) for i in range(m): rff_feature_map_comp_theano(x, omega[:, i], u[i])
def test_rff_feature_map_comp_theano_result_equals_manual(): if not theano_available: raise SkipTest("Theano not available.") D = 2 x = np.random.randn(D) m = 10 sigma = 1. omega, u = rff_sample_basis(D, m, sigma) phi_manual = rff_feature_map_single(x, omega, u) for i in range(m): # phi_manual is a monte carlo average, so have to normalise by np.sqrt(m) here phi = rff_feature_map_comp_theano(x, omega[:, i], u[i]) / np.sqrt(m) assert_close(phi, phi_manual[i])