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

#*****************************************************************************80
#
## faithful_lloyd() applies a version of Lloyd's algorithm to data.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    27 September 2023
#
#  Author:
#
#    John Burkardt
#
  import matplotlib.pyplot as plt
  import numpy as np
  import platform

  print ( '' )
  print ( 'faithful_lloyd():' )
  print ( '  Python version: %s' % ( platform.python_version ( ) ) )
  print ( '  Implement Lloyd algorithm to the Old Faithful data.' )
#
#  Read the data.
#
  data = np.loadtxt ( 'faithful_normalized.txt' )
  x = data[:,0]
  y = data[:,1]
#
#  Display the data.
#
  plt.clf ( )
  plt.plot ( x, y, 'k.', markersize = 10 )
  plt.xlabel ( '<-- Duration (normalized) -->', fontsize = 16 )
  plt.ylabel ( '<-- Wait (normalized) -->', fontsize = 16 )
  plt.title ( 'Old Faithful eruption durations and waits', fontsize = 16 )
  plt.grid ( True )
  plt.axis ( 'equal' )
  filename = 'faithful_lloyd_data.png'
  plt.savefig ( filename )
  plt.show ( )
  print ( '' )
  print ( '  Graphics saved as "%s"' % ( filename ) )
#
#  Determine clusters using arbitrary centers.
#
  Z = np.array ( [
    [ 0.4, 0.6 ],
    [ 0.6, 0.3 ] ] )

  for iteration in range ( 0, 4 ):

    d0 = ( x - Z[0,0] )**2 + ( y - Z[0,1] )**2
    d1 = ( x - Z[1,0] )**2 + ( y - Z[1,1] )**2
    c0 = np.where ( d0 < d1 )
    c1 = np.where ( d1 < d0 )
    n0 = len ( d0[c0] )
    n1 = len ( d1[c1] ) 
    e0 = sum ( d0[c0] )
    e1 = sum ( d1[c1] )

    print ( '  Step ', iteration )
    print ( '    Cluster energy = ', e0 + e1, '=', e0, '+', e1 )
    print ( '    Cluster sizes  = ', n0 + n1, '=', n0, '+', n1 )

    plt.clf ( )
    plt.plot ( x[c0], y[c0], 'c.', markersize = 10 )
    plt.plot ( x[c1], y[c1], 'm.', markersize = 10 )
    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 )
    s = ( 'Clusters on iteration ' + str ( iteration ) )
    plt.title ( s, fontsize = 16 )
    plt.grid ( True )
    filename = 'faithful_lloyd_iteration' + str ( iteration ) + '.png'
    plt.savefig ( filename )
    plt.show ( )
#
#  Prepare for next step by updating cluster centers.
#
    Z[0,0] = np.mean ( x[c0] )
    Z[0,1] = np.mean ( y[c0] )
    Z[1,0] = np.mean ( x[c1] )
    Z[1,1] = np.mean ( y[c1] )
#
#  Terminate.
#
  print ( '' )
  print ( 'faithful_lloyd():' )
  print ( '  Normal end of execution.' )

  return

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