#! /usr/bin/env python3
#
def faithful_classify ( ):

#*****************************************************************************80
#
## faithful_classify() uses a KMeans model to classify new data.
#
#  Discussion:
#
#    Clustering data.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    26 September 2023
#
#  Author:
#
#    John Burkardt
#
  import matplotlib.pyplot as plt
  import numpy as np
  import platform
  from sklearn.cluster import KMeans

  print ( '' )
  print ( 'faithful_kmeans_classify():' )
  print ( '  Python version: %s' % ( platform.python_version ( ) ) )
  print ( '  Use a KMeans model to classify new data.' )
#
#  Read the data.
#
  data = np.loadtxt ( 'faithful_normalized.txt' )
#
#  Create x and y.
#
  x = data[:,0]
  y = data[:,1]
#
#  Call kmeans()
#
  k = 2
  kmeans = KMeans ( n_clusters = k, n_init = 'auto' )
  kmeans.fit ( data )
  Z = kmeans.cluster_centers_
  C = kmeans.labels_
#
#  Create a few new data items
#
  data2 = np.array ( [ 
    [-0.1, 0.3 ],
    [ 0.3, 0.3 ],
    [ 0.4, 0.8 ],
    [ 0.9, 0.5 ] ] )
  x2 = data2[:,0]
  y2 = data2[:,1]
#
#  Classify them using the KMeans clusters.
#
  C2 = kmeans.predict ( data2 )
#
#  Display the results.
#
  plt.clf ( )
  plt.plot ( data[C==0,0], data[label==0,1], 'c.', markersize = 10 )
  plt.plot ( data[C==1,0], data[label==1,1], 'm.', markersize = 10 )
  plt.plot ( data2[C2==0,0], data2[C2==0,1], 'c*', markersize = 20 )
  plt.plot ( data2[C2==1,0], data2[C2==1,1], 'm*', markersize = 20 )
  plt.plot ( Z[0,0], Z[0,1], 'bo', markersize = 15 )
  plt.plot ( Z[1,0], Z[1,1], 'ro', markersize = 15 )
  plt.xlabel ( '<-- Duration -->', fontsize = 16 )
  plt.ylabel ( '<-- Wait -->', fontsize = 16 )
  plt.title ( 'Classify new data using kmeans()', fontsize = 16 )
  plt.grid ( True )
  filename = 'faithful_classify.png'
  plt.savefig ( filename )
  plt.show ( )
  plt.close ( )

  print ( '' )
  print ( '  Graphics saved as "%s"' % ( filename ) )
#
#  Terminate.
#
  print ( '' )
  print ( 'faithful_classify():' )
  print ( '  Normal end of execution.' )

  return

if ( __name__ == '__main__' ):
  faithful_classify ( )