e4833d449452dc9b47a4e23c22c4b2b8abc7a682,geomstats/learning/exponential_barycenter.py,,_default_gradient_descent,#Any#Any#Any#Any#Any#Any#Any#,14

Before Change

        The exponential_barycenter of the input points.
    
    ndim = 2 if group.default_point_type == "vector" else 3
    if gs.ndim(gs.array(points)) < ndim or len(points) == 1:
        return points[0] if len(points) == 1 else points

    n_points = points.shape[0]
    if weights is None:
        weights = gs.ones((n_points,))
    weights = gs.cast(weights, gs.float32)

After Change

        inv_mean = group.inverse(mean)
        print(inv_mean.shape)
        print(points.shape)
        centered_points = group.compose(inv_mean, points)
        logs = group.log_from_identity(point=centered_points)
        tangent_mean = step * gs.einsum(
            "n, nk...->k...", weights / sum_weights, logs)
        mean_next = group.compose(

Instances