e4eaec6cbd34c7abc59fcf29a95dab3c9f2810b8,geomstats/riemannian_metric.py,RiemannianMetric,variance,#RiemannianMetric#Any#Any#Any#,190

Before Change

            weights = gs.ones(n_points)

        weights = gs.array(weights)
        n_weights = gs.shape(weights)[0]
        assert n_points == n_weights
        sum_weights = gs.sum(weights)

        if base_point is None:
            base_point = self.mean(points, weights)

        variance = 0

        for i in range(n_points):
            weight_i = weights[i]
            point_i = points[i]

            sq_dist = self.squared_dist(base_point, point_i)

            variance += weight_i * sq_dist
        variance /= sum_weights

        return variance

After Change

        n_points = gs.shape(points)[0]
        assert n_points > 0

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

        weights = gs.array(weights)
        //n_weights = gs.shape(weights)[0]

        sum_weights = gs.sum(weights)

        if base_point is None:
            base_point = self.mean(points, weights)

        variance = 0.

        sq_dists = self.squared_dist(base_point, points)
        variance += gs.einsum("n,nj->nj", weights, sq_dists)

        //for i in range(n_points):
        //    weight_i = weights[i]

Instances