38b1a68f9f74ebb1a0f8cf2f73a9e606f7c022c2,nilearn/decoding/tests/test_same_api.py,,test_smoothlasso_and_tv_same_for_pure_l1_another_test,#Any#,167

Before Change

    l1_ratio = 1.
    max_iter = 20

    for standardize in [True, False]:
        sl = BaseSpaceNet(alphas=alpha, l1_ratios=l1_ratio,
                          penalty="smooth-lasso", max_iter=max_iter,
                          mask=mask, is_classif=False,
                          standardize=standardize, verbose=0).fit(X, y)
        tvl1 = BaseSpaceNet(alphas=alpha, l1_ratios=l1_ratio, penalty="tv-l1",
                        max_iter=max_iter, mask=mask, is_classif=False,
                        standardize=standardize, verbose=0).fit(X, y)

    // should be exactly the same (except for numerical errors)
    np.testing.assert_array_almost_equal(sl.coef_, tvl1.coef_, decimal=decimal)


def test_coef_shape():

After Change

    //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

    dim = (16, 16)
    W_init = np.zeros(dim)
    W_init[2:6, 3:7] = 1
    np.random.seed(0)
    n = 10
    p = dim[0] * dim[1]
    X = np.ones((n, 1)) + W_init.ravel().T
    X += np.random.randn(n, p)
    y = np.dot(X, W_init.ravel())
    mask = np.ones(X.shape[1]).astype(np.bool).reshape(dim)
    alpha = .1
    l1_ratio = 1.
    max_iter = 20

Instances