d8590f94ae7afaec4a6228c93d4c688d57965f38,nilearn/_utils/testing.py,,generate_group_sparse_gaussian_graphs,#Any#Any#Any#Any#Any#Any#,490

Before Change

    n_samples = random_state.randint(min_n_samples, high=max_n_samples,
                                 size=len(precisions))

    for n, prec in zip(n_samples, precisions):
        signals.append(random_state.multivariate_normal(
            mean, -scipy.linalg.inv(prec), (n,)))

    return signals, precisions, topology

After Change

            raise ValueError("Failed generating a positive definite precision "
                             "matrix. Decreasing n_features can help solving "
                             "this problem.")
        print(np.linalg.cond(prec))
        precisions.append(prec)

    // Returns the topology matrix of precision matrices.

Instances