0ff34ac18a47cdfa9878310b12ff6b5648106bd8,geomstats/learning/frechet_mean.py,,_adaptive_gradient_descent,#Any#Any#Any#Any#Any#Any#Any#,206

Before Change

        logs = metric.log(point=points, base_point=next_mean)
        next_tangent_mean = gs.einsum("nk,nj->j", weights, logs)
        next_tangent_mean /= sum_weights
        norm_next_tangent_mean = gs.linalg.norm(next_tangent_mean)
        if norm_next_tangent_mean < norm_current_tangent_mean:
            current_mean = next_mean
            current_tangent_mean = next_tangent_mean
            norm_current_tangent_mean = norm_next_tangent_mean

After Change

            current_mean = next_mean
            current_tangent_mean = next_tangent_mean
            sq_norm_current_tangent_mean = sq_norm_next_tangent_mean
            tau = min(TAU_MAX, TAU_MUL_UP * tau)
        else:
            tau = max(TAU_MIN, TAU_MUL_DOWN * tau)

    if iteration == n_max_iterations:

Instances