# -*- coding: utf-8 -*-

 """

 Demo of DBSCAN clustering algorithm

 Finds core samples of high density and expands clusters from them.

 """

 print(__doc__)

 # 引入相关包

 import numpy as np

 from sklearn.cluster import DBSCAN

 from sklearn import metrics

 from sklearn.datasets.samples_generator import make_blobs

 from sklearn.preprocessing import StandardScaler

 import matplotlib.pyplot as plt

 # 初始化样本数据

 centers = [[1, 1], [-1, -1], [1, -1]]

 X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,

                             random_state=0)

 X = StandardScaler().fit_transform(X)

 # 计算DBSCAN

 db = DBSCAN(eps=0.3, min_samples=10).fit(X)

 core_samples_mask = np.zeros_like(db.labels_, dtype=bool)

 core_samples_mask[db.core_sample_indices_] = True

 labels = db.labels_

 # 聚类的结果

 n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)

 n_noise_ = list(labels).count(-1)

 print('Estimated number of clusters: %d' % n_clusters_)

 print('Estimated number of noise points: %d' % n_noise_)

 print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))

 print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))

 print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))

 print("Adjusted Rand Index: %0.3f"

       % metrics.adjusted_rand_score(labels_true, labels))

 print("Adjusted Mutual Information: %0.3f"

       % metrics.adjusted_mutual_info_score(labels_true, labels,

                                            average_method='arithmetic'))

 print("Silhouette Coefficient: %0.3f"

       % metrics.silhouette_score(X, labels))

 # 绘出结果

 unique_labels = set(labels)

 colors = [plt.cm.Spectral(each)

           for each in np.linspace(0, 1, len(unique_labels))]

 for k, col in zip(unique_labels, colors):

     if k == -1:

         col = [0, 0, 0, 1]

     class_member_mask = (labels == k)

     xy = X[class_member_mask & core_samples_mask]

     plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),

              markeredgecolor='k', markersize=14)

     xy = X[class_member_mask & ~core_samples_mask]

     plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),

              markeredgecolor='k', markersize=6)

 plt.title('Estimated number of clusters: %d' % n_clusters_)

 plt.show()