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

#*****************************************************************************80
#
## handcrafted_classify_svm_rbf() classifies handcrafted data using SVM with RBF.
#
#  Licensing:
#
#    This code is distributed under the MIT license.
#
#  Modified:
#
#    03 August 2023
#
#  Author:
#
#    Andreas Mueller, Sarah Guido.
#    Modifications by John Burkardt.
#
#  Reference:
#
#    Andreas Mueller, Sarah Guido,
#    Introduction to Machine Learning with Python,
#    OReilly, 2017,
#    ISBN: 978-1-449-36941-5
#
  from sklearn.svm import LinearSVC
  from sklearn.svm import SVC
  import matplotlib.pyplot as plt
  import mglearn
  import numpy as np
  import platform
  import sklearn

  print ( '' )
  print ( 'handcrafted_classify_svm_rbf():' )
  print ( '  Python version: ' + platform.python_version ( ) )
  print ( '  scikit-learn version: '+ sklearn.__version__ )
  print ( '  Classify data from the handcrafted dataset.' )
  print ( '  Use the support vector classifier with the RBF kernel.' )
  print ( '  Then vary the C and GAMMA parameters.' )
  print ( '' )
#
#  Generate the dataset.
#
  print ( '  Generate the handcrafted dataset, (X, y).' )
  X, y = mglearn.tools.make_handcrafted_dataset ( )
#
#  Fit the support vector classifier.
#
  svm = SVC ( 
    kernel = 'rbf',
    C = 10,
    gamma = 0.1 ).fit ( X, y )
#
#  Plot the data, boundary, and highlight the support vectors.
#
  plt.clf ( )
  mglearn.plots.plot_2d_separator ( svm, X, eps = 0.5 )
  mglearn.discrete_scatter ( X[:,0], X[:,1], y )
  sv = svm.support_vectors_
  sv_labels = svm.dual_coef_.ravel() > 0
  mglearn.discrete_scatter ( 
    sv[:,0], sv[:,1], 
    sv_labels, 
    s = 15, 
    markeredgewidth = 3 )
  plt.xlabel ( 'Feature 0' )
  plt.ylabel ( 'Feature 1' )
  plt.title ( 'RBF support vectors for handcrafted data' )
  filename = 'handcrafted_classify_svm_rbf_default.png'
  plt.savefig ( filename )
  print ( '  Graphics saved as "' + filename + '"' )
  plt.close ( )
#
#  Plot variations of C and Gamma
#
  plt.clf ( )
  fig, axes = plt.subplots ( 3, 3, figsize = ( 15, 10 ) )

  for ax, C in zip ( axes, [ -1.0, 0.0, 3.0 ] ):
    for a, gamma in zip ( ax, range ( -1, 2 ) ):
      mglearn.plots.plot_svm ( log_C = C, log_gamma = gamma, ax = a )
  axes[0,0].legend ( 
    [ "class 0", "class 1", "sv class 0", "sv class 1" ],
    ncol = 4, loc = ( 0.9, 1.2 ) )
  filename = 'handcrafted_classify_svm_rbf_variations.png'
  plt.savefig ( filename )
  print ( '  Graphics saved as "' + filename + '"' )
  plt.close ( )
#
#  Terminate.
#
  print ( '' )
  print ( 'handcrafted_classify_svm_rbf():' )
  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 ( )
  handcrafted_classify_svm_rbf ( )
  timestamp ( )