f968d7bec6552b6986731a4dfbb0a524d01a29a2,examples/plot_kmean_poincare_disk.py,,main,#,22

Before Change

    Centroids = K_means.fit(X=Merged_Clusters, max_iter=5)

    Data_Labels = gs.array([])

    for data in Merged_Clusters:

        Data_Labels = gs.append(Data_Labels, K_means.predict(data))

    print("Centroids", Centroids)

    visualization.plot(

After Change

    Merged_Clusters = gs.concatenate((Cluster_1, Cluster_2), axis=0)
    manifold = HyperbolicSpace(dimension=2, point_type="poincare")
    metric = HyperbolicMetric(dimension=2, point_type="poincare")

    manifold_e = HyperbolicSpace(dimension=2)
    metric_e = HyperbolicMetric(dimension=2)
    x_b = gs.rand(10,2) * 1/2
    x_e = manifold_e.intrinsic_to_extrinsic_coords(gs.rand(10,2))
    e = metric_e.dist(x_e, x_e)
    b = metric.dist(x_b, x_b+0.1 )

    print("b shape", b.shape)
    print("e shape", e.shape)
    visualization.plot(
            Merged_Clusters,
            ax=ax,
            space="H2_poincare_disk",
            marker=".",
            color="black",
            point_type=manifold.point_type)

    k_means = RiemannianKMeans(riemannian_metric=metric,
                               n_clusters=2,
                               init="random",
                               )

    centroids = k_means.fit(X=Merged_Clusters, max_iter=2)

    labels = k_means.predict(X=Merged_Clusters)

    visualization.plot(
            centroids,
            ax=ax,

Instances