0c9b9ac2bba3a52e5175a24c217b1f3ab8581e08,geomstats/learning/frechet_mean.py,,_default_gradient_descent,#Any#Any#Any#Any#Any#Any#Any#,81

Before Change

        einsum_str = "n,nj->j"
    if point_type == "matrix":
        einsum_str = "n,nij->ij"
        points = gs.to_ndarray(points, to_ndim=3)
    n_points = gs.shape(points)[0]

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

    mean = points[0]

    if n_points == 1:
        return mean

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

After Change

        points = gs.to_ndarray(points, to_ndim=2)
        einsum_str = "n,nj->j"
    if point_type == "matrix":
        points = gs.to_ndarray(points, to_ndim=3)
        einsum_str = "n,nij->ij"
    n_points = gs.shape(points)[0]

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

    mean = points[0]

    if n_points == 1:
        return mean

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

    while iteration < max_iter:
        print(var)
        print(sq_dist)
        var_is_0 = gs.isclose(var, 0.)
        sq_dist_is_small = gs.less_equal(sq_dist, epsilon * var)
        condition = ~gs.logical_or(var_is_0, sq_dist_is_small)
        if not (condition or iteration == 0):
            break
        logs = metric.log(point=points, base_point=mean)

Instances