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

#*****************************************************************************80
#
## blob_classify_logistic_multi() repeatedly applies logistic regression.
#
#  Discussion:
#
#    The data involves three groups of "blobs".
#
#    By applying logistic regression 3 times, we can find boundaries between
#    each group of blobs and the other two.
#
#  Licensing:
#
#    This code is distributed under the MIT license.
#
#  Modified:
#
#    07 July 2023
#
#  Author:
#
#    John Burkardt
#
  from sklearn.svm import LinearSVC
  from sklearn.datasets import make_blobs

  import matplotlib.pyplot as plt
  import mglearn
  import numpy as np
  import platform
  import sklearn

  print ( '' )
  print ( 'blob_classify_logistic_multi()' )
  print ( '  python version: %s' % ( platform.python_version ( ) ) )
  print ( '  scikit-learn version %s' % ( sklearn.__version__ ) )
  print ( '  Use LogisticRegression() on three sets of blobs.' )
  print ( '  Find a line between each blob and the other two.' ) 
#
#  Define the data.
#
  X, y = make_blobs ( random_state = 42 )
#
#  Plot the three groups of blobs.
#
  plt.clf ( )
  mglearn.discrete_scatter ( X[:,0], X[:,1], y )
  plt.xlabel ( 'Feature 0' )
  plt.ylabel ( 'Feature 1' )
  plt.legend ( [ "Class 0", "Class 1", "Class 2" ] )
  plt.grid ( True )
  filename = 'blob_data.png'
  plt.savefig ( filename )
  print ( '  Graphics saved as "' + filename + '"' )
#
#  Define the model.
#
  linear_svm = LinearSVC ( ).fit ( X, y )
  print ( "" )
  print ( "  Coefficient shape: ", linear_svm.coef_.shape )
  print ( "  Intercept shape:   ", linear_svm.intercept_.shape )
#
#  Plot the results.
#
  plt.clf ( )
  mglearn.discrete_scatter ( X[:,0], X[:,1], y )
  line = np.linspace ( -15, 15 )
  for coef, intercept, color in zip ( 
    linear_svm.coef_, 
    linear_svm.intercept_,
    mglearn.cm3.colors ):
    plt.plot ( line, - ( line * coef[0] + intercept ) / coef[1], c = color )

  plt.ylim ( -10, 15 )
  plt.xlim ( -10,  8 )
  plt.xlabel ( 'Feature 0' )
  plt.ylabel ( 'Feature 1' )
  plt.legend ( [ "Class 0", "Class 1", "Class 2",
                 "Line class 0", "Line class 1", "Line class2" ],
    loc = ( 1.01, 0.3 ) )
  filename = 'blob_boundaries.png'
  plt.savefig ( filename )
  print ( '  Graphics saved as "' + filename + '"' )
#
#  Terminate.
#
  print ( '' )
  print ( 'blob_classify_logistic_multi()' )
  print ( '  Normal end of execution.' )

  return

def timestamp ( ):

#*****************************************************************************80
#
## timestamp() prints the date as a timestamp.
#
#  Licensing:
#
#    This code is distributed under the MIT license. 
#
#  Modified:
#
#    21 August 2019
#
#  Author:
#
#    John Burkardt
#
  import time

  t = time.time ( )
  print ( time.ctime ( t ) )

  return

if ( __name__ == '__main__' ):
  timestamp ( )
  blob_classify_logistic_multi ( )
  timestamp ( )