cluster.py 文件源码

def _clusterAffinity(aff, k, imdb, cls_idx):
    """ Cluster error correlation matrix using spectral clustering into k cluster, 
        show the class labels in each cluster. 
    """
    # clustering model
    spectral = SpectralClustering(n_clusters=k,
                                  eigen_solver='arpack',
                                  affinity="precomputed")
    print 'Performing clustering...'
    labels = spectral.fit_predict(aff)

    # print out all labels
    for i in xrange(k):
        find_idx = np.where(labels==i)[0]
        print 'The list of classes in cluster {}'.format(i)
        print [imdb.classes[id] for id in find_idx]
        print '--------------------------------------------'

    return labels

if __name__ == '__main__':
    # TODO: debug code if necessary

    pass