content: # -*- coding: utf-8 -*-
  """
  ===================================
  Demo of DBSCAN clustering algorithm
  ===================================
  
  Finds core samples of high density and expands clusters from them.
  
  """
  print(__doc__)
  
  import numpy as np
  from scipy.spatial import distance
  from sklearn.cluster import DBSCAN
  from sklearn import metrics
  from sklearn.datasets.samples_generator import make_blobs
  
  
  ##############################################################################
  # Generate sample data
  centers = [[1, 1], [-1, -1], [1, -1]]
  X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4)
  
  ##############################################################################
  # Compute similarities
  D = distance.squareform(distance.pdist(X))
  S = 1 - (D / np.max(D))
  
  ##############################################################################
  # Compute DBSCAN
  db = DBSCAN(eps=0.95, min_samples=10).fit(S)
  core_samples = db.core_sample_indices_
  labels = db.labels_
  
  # Number of clusters in labels, ignoring noise if present.
  n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
  
  print('Estimated number of clusters: %d' % n_clusters_)
  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))
  print("Silhouette Coefficient: %0.3f"
        % metrics.silhouette_score(D, labels, metric='precomputed'))
  
  ##############################################################################
  # Plot result
  import pylab as pl
  from itertools import cycle
  
  pl.close('all')
  pl.figure(1)
  pl.clf()
  
  # Black removed and is used for noise instead.
  colors = cycle('bgrcmybgrcmybgrcmybgrcmy')
  for k, col in zip(set(labels), colors):
      if k == -1:
          # Black used for noise.
          col = 'k'
          markersize = 6
      class_members = [index[0] for index in np.argwhere(labels == k)]
      cluster_core_samples = [index for index in core_samples
                              if labels[index] == k]
      for index in class_members:
          x = X[index]
          if index in core_samples and k != -1:
              markersize = 14
          else:
              markersize = 6
          pl.plot(x[0], x[1], 'o', markerfacecolor=col,
                  markeredgecolor='k', markersize=markersize)
  
  pl.title('Estimated number of clusters: %d' % n_clusters_)
  pl.show()
  