old_contents: # -*- 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 sklearn.cluster import DBSCAN
  from sklearn import metrics
  from sklearn.datasets.samples_generator import make_blobs
  from sklearn.preprocessing import StandardScaler
  
  
  ##############################################################################
  # Generate sample data
  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)
  
  ##############################################################################
  # Compute DBSCAN
  db = DBSCAN(eps=0.3, min_samples=10).fit(X)
  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(X, labels))
  
  ##############################################################################
  # Plot result
  import pylab as pl
  
  # Black removed and is used for noise instead.
  unique_labels = set(labels)
  colors = pl.cm.Spectral(np.linspace(0, 1, len(unique_labels)))
  for k, col in zip(unique_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()
  
new_contents: # -*- 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 sklearn.cluster import DBSCAN
  from sklearn import metrics
  from sklearn.datasets.samples_generator import make_blobs
  from sklearn.preprocessing import StandardScaler
  
  
  ##############################################################################
  # Generate sample data
  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)
  
  ##############################################################################
  # Compute 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_
  
  # 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(X, labels))
  
  ##############################################################################
  # Plot result
  import pylab as pl
  
  # Black removed and is used for noise instead.
  unique_labels = set(labels)
  colors = pl.cm.Spectral(np.linspace(0, 1, len(unique_labels)))
  for k, col in zip(unique_labels, colors):
      if k == -1:
          # Black used for noise.
          col = 'k'
  
      class_member_mask = (labels == k)
  
      xy = X[class_member_mask & core_samples_mask]
      pl.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
              markeredgecolor='k', markersize=14)
  
      xy = X[class_member_mask & ~core_samples_mask]
      pl.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
              markeredgecolor='k', markersize=6)
  
  pl.title('Estimated number of clusters: %d' % n_clusters_)
  pl.show()
  
subject: Call plot twice per class label rather than for every point.