cb39a024f222364076056b39e925c0be51834629,geomstats/learning/frechet_mean.py,,_default_gradient_descent,#Any#Any#Any#Any#Any#Any#Any#,91

Before Change

    n_points = gs.shape(points)[0]

    if weights is None:
        weights = gs.ones((n_points, 1))
    weights = gs.array(weights)

    mean = points[0]
    if point_type == "vector":
        weights = gs.to_ndarray(weights, to_ndim=2, axis=1)
        mean = gs.to_ndarray(mean, to_ndim=2)
    if point_type == "matrix":
        weights = gs.to_ndarray(weights, to_ndim=3, axis=1)
        mean = gs.to_ndarray(mean, to_ndim=3)

    if n_points == 1:
        return mean

    sum_weights = gs.sum(weights)
    sq_dists_between_iterates = []
    iteration = 0
    sq_dist = gs.array([[0.]])
    var = gs.array([[0.]])

    while iteration < max_iter:
        condition = ~gs.logical_or(
            gs.isclose(var, 0.),
            gs.less_equal(sq_dist, epsilon * var))
        if not (condition or iteration == 0):
            break
        logs = metric.log(point=points, base_point=mean)

        tangent_mean = gs.einsum(einsum_str, weights, logs)
        tangent_mean /= sum_weights

        estimate_next = metric.exp(tangent_vec=tangent_mean, base_point=mean)

        sq_dist = metric.squared_dist(estimate_next, mean)
        sq_dists_between_iterates.append(sq_dist)

        var = variance(
            points=points,
            weights=weights,
            metric=metric,
            base_point=estimate_next,
            point_type=point_type)

        mean = estimate_next
        iteration += 1

    if iteration == max_iter:
        logging.warning(
            "Maximum number of iterations {} reached. "
            "The mean may be inaccurate".format(max_iter))

    if verbose:
        logging.info("n_iter: {}, final variance: {}, final dist: {}".format(
            iteration, var, sq_dist))

    mean = gs.to_ndarray(mean, to_ndim=2)
    return mean


def _ball_gradient_descent(points, metric, weights, max_iter,

After Change

    n_points = gs.shape(points)[0]

    if weights is None:
        weights = gs.ones((n_points,))
        //weights = gs.ones((n_points, 1))
    // weights = gs.array(weights)

    mean = points[0]

Instances