knn_kwargs["p"] = label_p_order
ideal_point = np.ones(n_objectives)
+ ideal_point_2d = ideal_point.reshape(1, -1)
if metric in {
"braycurtis",
cdist_kwargs["p"] = label_p_order
return sp.spatial.distance.cdist(
normalized_matrix,
- ideal_point.reshape(1, -1), # reshape ideal_point to 2D
+ ideal_point_2d,
metric=metric,
**cdist_kwargs,
).flatten()
if n_samples == 1:
return sp.spatial.distance.cdist(
normalized_matrix,
- ideal_point.reshape(1, -1),
+ ideal_point_2d,
metric=label_kmeans_metric,
**cdist_kwargs,
).flatten()
cluster_centers = kmeans.cluster_centers_
cluster_distances_to_ideal = sp.spatial.distance.cdist(
cluster_centers,
- ideal_point.reshape(1, -1),
+ ideal_point_2d,
metric=label_kmeans_metric,
**cdist_kwargs,
).flatten()