path = 'J://utils' sys.path.append(path) from sklearn import cluster import common_utils as utils import clustering_utils as cl_utils import classification_utils as cutils X, _ = cl_utils.generate_synthetic_data_2d_clusters(n_samples=300, n_centers=4, cluster_std=0.60) utils.plot_data_2d(X) X, _ = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=300) utils.plot_data_2d(X) X, _ = cutils.generate_nonlinear_synthetic_data_classification3(n_samples=300) utils.plot_data_2d(X) scoring = 's_score' agg_estimator = cluster.AgglomerativeClustering() agg_grid = { 'linkage': ['ward', 'complete', 'average'], 'n_clusters': list(range(2, 7)) } agg_final_model = cl_utils.grid_search_best_model_clustering(agg_estimator, agg_grid, X, scoring=scoring) cl_utils.plot_model_2d_clustering(agg_final_model, X)
import sys path = 'J://utils' sys.path.append(path) from sklearn import cluster, manifold import common_utils as utils import clustering_utils as cl_utils import classification_utils as cutils X, _ = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=300) utils.plot_data_2d(X) X, _ = cutils.generate_nonlinear_synthetic_data_classification3(n_samples=300) utils.plot_data_2d(X) tsne = manifold.TSNE() X_tsne = tsne.fit_transform(X) utils.plot_data_2d(X_tsne) scoring = 's_score' kmeans_estimator = cluster.KMeans() kmeans_grid = {'n_clusters': list(range(2, 7))} kmeans_final_model = cl_utils.grid_search_best_model_clustering( kmeans_estimator, kmeans_grid, X, scoring=scoring) print(kmeans_final_model.labels_) print(kmeans_final_model.cluster_centers_) cl_utils.plot_model_2d_clustering(kmeans_final_model, X)
import sys path = 'E://utils' sys.path.append(path) from sklearn import cluster, mixture import common_utils as utils import clustering_utils as cl_utils X, _ = cl_utils.generate_synthetic_data_2d_clusters(n_samples=300, n_centers=4, cluster_std=0.60) utils.plot_data_2d(X) scoring = 's_score' gmm_estimator = mixture.GaussianMixture(n_components=3) gmm_grid = {'n_components': list(range(10, 40))} gmm_estimator.fit(X) gmm_estimator.predict(X) gmm_final_model = cl_utils.grid_search_best_model_clustering(gmm_estimator, gmm_grid, X, scoring=scoring) cl_utils.plot_model_2d_clustering(gmm_estimator, X)