old_contents: from dpconverge.data_set import DataSet
  from matplotlib import pyplot
  from sklearn.datasets.samples_generator import make_blobs
  
  n_features = 2
  points_per_feature = 100
  centers = [[2, 1.35], [2, 2], [2, 3], [2.5, 1.5], [2.5, 2], [2.5, 2.5]]
  
  blob1, y1 = make_blobs(
      n_samples=1000,
      n_features=1,
      centers=centers[0],
      cluster_std=[0.1, 0.15],
      random_state=1
  )
  
  blob2, y2 = make_blobs(
      n_samples=6000,
      n_features=1,
      centers=centers[1],
      cluster_std=[0.2, 0.3],
      random_state=2
  )
  
  blob3, y3 = make_blobs(
      n_samples=3000,
      n_features=1,
      centers=centers[2],
      cluster_std=[0.2, 0.1],
      random_state=2
  )
  
  blob4, y4 = make_blobs(
      n_samples=250,
      n_features=1,
      centers=centers[3],
      cluster_std=[0.1, 0.1],
      random_state=2
  )
  
  blob5, y5 = make_blobs(
      n_samples=250,
      n_features=1,
      centers=centers[4],
      cluster_std=[0.1, 0.1],
      random_state=3
  )
  
  ds = DataSet(parameter_count=2)
  
  ds.add_blob(1, blob1)
  ds.add_blob(2, blob2)
  ds.add_blob(3, blob3)
  ds.add_blob(4, blob4)
  ds.add_blob(5, blob5)
  
  ds.plot_blobs(ds.classifications, x_lim=[0, 4], y_lim=[0, 4])
  
  component_count = 16
  iteration_count = 100
  
  ds.cluster(
      component_count=component_count,
      burn_in=0,
      iteration_count=iteration_count,
      random_seed=1
  )
  
  valid_components = ds.get_valid_components()
  
  print "Recommended component count: ", len(valid_components)
  
  for i in range(component_count):
      if i in valid_components:
          ds.plot_iteration_traces(i)
  
  for i in range(component_count):
      if i not in valid_components:
          print "Possible invalid Component"
          ds.plot_iteration_traces(i)
  
  ds.plot_animated_trace()
  ds.plot_log_likelihood_trace()
  pyplot.show()
  
new_contents: from dpconverge.data_set import DataSet
  from matplotlib import pyplot
  from sklearn.datasets.samples_generator import make_blobs
  
  n_features = 2
  points_per_feature = 100
  centers = [[2, 1.35], [2, 2], [2, 3], [2.5, 1.5], [2.5, 2], [2.5, 2.5]]
  
  blob1, y1 = make_blobs(
      n_samples=1000,
      n_features=1,
      centers=centers[0],
      cluster_std=[0.1, 0.15],
      random_state=1
  )
  
  blob2, y2 = make_blobs(
      n_samples=6000,
      n_features=1,
      centers=centers[1],
      cluster_std=[0.2, 0.3],
      random_state=2
  )
  
  blob3, y3 = make_blobs(
      n_samples=3000,
      n_features=1,
      centers=centers[2],
      cluster_std=[0.2, 0.1],
      random_state=2
  )
  
  blob4, y4 = make_blobs(
      n_samples=250,
      n_features=1,
      centers=centers[3],
      cluster_std=[0.1, 0.1],
      random_state=2
  )
  
  blob5, y5 = make_blobs(
      n_samples=250,
      n_features=1,
      centers=centers[4],
      cluster_std=[0.1, 0.1],
      random_state=3
  )
  
  ds = DataSet(parameter_count=2)
  
  ds.add_blob(1, blob1)
  ds.add_blob(2, blob2)
  ds.add_blob(3, blob3)
  ds.add_blob(4, blob4)
  ds.add_blob(5, blob5)
  
  ds.plot_blobs(ds.labels, x_lim=[0, 4], y_lim=[0, 4])
  
  component_count = 16
  iteration_count = 100
  
  ds.cluster(
      component_count=component_count,
      burn_in=0,
      iteration_count=iteration_count,
      random_seed=1
  )
  
  valid_components = ds.get_valid_components()
  
  print "Recommended component count: ", len(valid_components)
  
  for i in range(component_count):
      if i in valid_components:
          ds.plot_iteration_traces(i)
  
  for i in range(component_count):
      if i not in valid_components:
          print "Possible invalid Component"
          ds.plot_iteration_traces(i)
  
  ds.plot_animated_trace()
  ds.plot_log_likelihood_trace()
  pyplot.show()
  
subject: update medium complex DP script to use labels