Python matplotlib.cm 模块，spectral() 实例源码

def multi_cut_spectral(cluster_list, affinity_matrix, dist_matrix, n_jobs=-1,
                       sample_names=None):
    """Perform a spectral clustering with variable cluster sizes.

    Parameters
    ----------
    cluster_list : array-like
        Contains the list of the number of clusters to use at each step.
    affinity_matrix : array-like
        Precomputed affinity matrix.
    dist_matrix : array-like
        Precomputed distance matrix between points.

    Returns
    -------
    queue_y : array-like
        Array to be visualised on the y-axis. Contains the list of average
        silhouette for each number of clusters present in cluster_list.

    """
    def _internal(cluster_list, affinity_matrix, dist_matrix,
                  idx, n_jobs, n, queue_y):
        for i in range(idx, n, n_jobs):
            sp = SpectralClustering(n_clusters=cluster_list[i],
                                    affinity='precomputed',
                                    norm_laplacian=True,
                                    n_init=1000)
            sp.fit(affinity_matrix)

            save_results_clusters("res_spectral_{:03d}_clust.csv"
                                  .format(cluster_list[i]),
                                  sample_names, sp.labels_)

            silhouette_list = silhouette_samples(dist_matrix, sp.labels_,
                                                 metric="precomputed")
            queue_y[i] = np.mean(silhouette_list)

    n = len(cluster_list)
    if n_jobs == -1:
        n_jobs = min(mp.cpu_count(), n)
    queue_y = mp.Array('d', [0.] * n)
    ps = []
    try:
        for idx in range(n_jobs):
            p = mp.Process(target=_internal,
                           args=(cluster_list, affinity_matrix, dist_matrix,
                                 idx, n_jobs, n, queue_y))
            p.start()
            ps.append(p)

        for p in ps:
            p.join()
    except (KeyboardInterrupt, SystemExit):
        extra.term_processes(ps, 'Exit signal received\n')
    except BaseException as e:
        extra.term_processes(ps, 'ERROR: %s\n' % e)
    return queue_y