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

#*****************************************************************************80
#
## study_classify_logistic() applies logistic regression to student study data.
#
#  Licensing:
#
#    This code is distributed under the MIT license.
#
#  Modified:
#
#    17 June 2023
#
#  Author:
#
#    John Burkardt
#
  import matplotlib.pyplot as plt
  import numpy as np
  import platform
  import sklearn
  from sklearn import linear_model

  print ( '' )
  print ( 'study_classify_logistic()' )
  print ( '  python version: %s' % ( platform.python_version ( ) ) )
  print ( '  scikit-learn version %s' % ( sklearn.__version__ ) )
  print ( '  Use sklearn LogisticRegression() on the student study-time example.' )
#
#  Define the data.
#
  xvec = np.array ( [ 
   0.50, 0.75, 1.00, 1.25, 1.50, 
   1.75, 1.75, 2.00, 2.25, 2.50, 
   2.75, 3.00, 3.25, 3.50, 4.00, 
   4.25, 4.50, 4.75, 5.00, 5.00 ] )

  y = np.array ( [ 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1 ] )
#
#  Logistic regression expects the features to be a double-dimensioned array.
#
  x = np.reshape ( xvec, [ 20, 1 ] )
#
#  Define the model.
#
  lr = linear_model.LogisticRegression ( )
  lr.fit ( x, y )
  a = np.ndarray.flatten ( lr.coef_ )
  b = lr.intercept_
  print ( '' )
  print ( 'Linear model is y = a * x + b:' )
  print ( '  a = ', a, '  b = ', b )
  cutoff = - b / a
  print ( '' )
  print ( "  cutoff", cutoff )
  print ( "  score", lr.score ( x, y ) )
  print ( "  mean", np.mean ( y ) )
#
#  Plot the results.
#
  plt.clf ( )
  plt.scatter ( xvec, y )
  x_eval = np.linspace ( 0.0, 6.0, 101 )
  y_eval = logistic_fun ( a * x_eval + b )
  plt.plot ( x_eval, y_eval )
  plt.axvline ( x = cutoff, linestyle = '--', color = 'red' )
  plt.title ( 'study logistic regression example' )
  plt.grid ( True )
  filename = 'study_classify_logistic.png'
  plt.savefig ( filename )
  print ( '' )
  print ( '  Graphics saved as "%s"' % ( filename ) )
  plt.show ( block = False )
#
#  Terminate.
#
  print ( '' )
  print ( 'study_classify_logistic()' )
  print ( '  Normal end of execution.' )

  return

def logistic_fun ( x ):

#*****************************************************************************80
#
## logistic_fun() evaluates the logistic function.
#
#  Licensing:
#
#    This code is distributed under the MIT license.
#
#  Modified:
#
#    08 January 2020
#
#  Author:
#
#    John Burkardt
#
#  Input:
#
#    real x, the arguments.
#
#  Output:
#
#    real value, the logistic function evaluated at x.
#
  import numpy as np

  value = 1.0 / ( 1.0 + np.exp ( - x ) );

  return value

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 ( )
  study_classify_logistic ( )
  timestamp ( )