def poisson_se_tsne(data, k, n_runs=<NUM_LIT:10>, init='<STR_LIT>', **se_params):
clusters = []<EOL>tsne = TSNE(<NUM_LIT:2>)<EOL>km = KMeans(k)<EOL>for i in range(n_runs):<EOL><INDENT>m, w, ll = poisson_estimate_state(data, k, **se_params)<EOL>tsne_w = tsne.fit_transform(w.T)<EOL>clust = km.fit_predict(tsne_w)<EOL>clusters.append(clust)<EOL><DEDENT>clusterings = np.vstack(clusters)<EOL>consensus = CE.cluster_ensembles(clusterings, verbose=False, N_clusters_max=k)<EOL>init_m, init_w = nmf_init(data, consensus, k, '<STR_LIT>')<EOL>M, W, ll = poisson_estimate_state(data, k, init_means=init_m, init_weights=init_w, **se_params)<EOL>return M, W, ll<EOL>
runs tsne-consensus-poissonSE