metacluster.py 文件源码

def compute_meta_centroid_set(self, C):
        print("Intermediate clusters", C.shape)

        # By eye, it looks like the top 60%-80% of the
        # remaining clusters are stable...

        nc = int(self.subcluster_pcut * self.subcluster_kn)
        clf = SpectralClustering(n_clusters=nc, affinity="precomputed")

        S = cosine_affinity(C)
        labels = clf.fit_predict(S)

        meta_clusters = []
        meta_cluster_size = []
        for i in range(labels.max() + 1):
            idx = labels == i
            mu = C[idx].mean(axis=0)
            mu /= np.linalg.norm(mu)
            meta_clusters.append(mu)
            meta_cluster_size.append(idx.sum())

        return np.array(meta_clusters)