f5d882c106aa2202d03ead930f7af2ee5d612b4c,nilearn/regions/rena_clustering.py,,_compute_weights,#Any#Any#,19

Before Change

        Weights corresponding to all edges in the mask.
        shape: (n_edges,)
    
    data = masker.inverse_transform(masked_data).get_data()

    weights_deep = np.sum(np.diff(data, axis=2) ** 2, axis=-1).ravel()
    weights_right = np.sum(np.diff(data, axis=1) ** 2, axis=-1).ravel()

After Change

    n_samples, n_features = X.shape

    mask = mask_img.get_data().astype("bool")
    shape = mask.shape

    data = np.empty((shape[0], shape[1], shape[2], n_samples))
    for sample in range(n_samples):
        data[:, :, :, sample] = \

Instances