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

#*****************************************************************************80
#
## admit() does logistic regression on college admission data.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    13 October 2019
#
#  Author:
#
#    John Burkardt
#
  from sklearn.linear_model import LogisticRegression
  import matplotlib.pyplot as plt
  import numpy as np

  print ( '' )
  print ( 'admit():' )
  print ( '  We have M=100 english and math test scores' )
  print ( '  and the corresponding college admission decision adm.' )
  print ( '  Consider the model' )
  print ( '    y(x) = 1 / ( 1 + exp ( - w\'x ) )' )
  print ( '  Determine weights w, and apply to 3 new cases.' )
#
#  Read the data: (x1=english, x2=math, adm=admit(0/1))
#
  data = np.loadtxt ( 'admit_data.txt' )
  eng = data[:,0]
  mat = data[:,1]
  adm = data[:,2]
  n = len ( eng )

  X = np.zeros ( [ n, 3 ] )
  X[:,0] = 1
  X[:,1] = eng[:]
  X[:,2] = mat[:]

  y = adm[:]
#
#  Fit the logistic regression model to the data.
#
  classifier = LogisticRegression(random_state=0).fit ( X, y )
#
#  Evaluate the model on the original data.
#
  yp = classifier.predict ( X )
#
#  Compute MSE
#
  mse = ( 1.0 / n ) * sum ( ( yp - adm )**2 )
  print ( '  MSE = ', mse )
#
#  Construct the coefficient array W.
#
  w = classifier.coef_[0].copy()
  w[0] = classifier.intercept_

  print ( '' )
  print ( '  Computed weights W = ', w )
#
#  Display data.
#
  emin = np.min ( eng )
  emax = np.max ( eng )
  mmin = ( - w[0] - w[1] * emin ) / w[2]
  mmax = ( - w[0] - w[1] * emax ) / w[2]

  plt.plot ( X[y==0,1], X[y==0,2], 'r.', markersize = 15 )
  plt.plot ( X[y==1,1], X[y==1,2], 'b.', markersize = 15 )
  plt.plot ( [emin,emax], [mmin,mmax], 'k-', linewidth = 3 )
  plt.xlabel ( '<-- English Exam -->' )
  plt.ylabel ( '<-- Math Exam -->' )
  plt.title ( 'Blue = Admitted, Red = Not admitted', fontsize = 16 )
  plt.grid ( True )
  filename = 'admit.png'
  plt.savefig ( filename )
  plt.show ( )
  print ( '' )
  print ( '  Graphics saved as "%s"' % ( filename ) )
#
#  Evaluate the admissions formula for new data.
#
#  Raw values:
#    1  46  81
#    2  26  75
#    3  30  66
#
  X2 = np.array ( [ \
    [ 1.0, 46, 81 ], \
    [ 1.0, 26, 75 ], \
    [ 1.0, 30, 66 ] ] )
  print ( '  New student data:' )
  print ( X2 )
  y2 = classifier.predict ( X2 )
  print ( '  Admit new student? (0/1)' )
  print ( y2 )
  p2 = classifier.predict_proba ( X2 )
  print ( '  Strength of decision (0/1)' )
  print ( p2 )
#
#  Terminate.
#
  print ( '' )
  print ( 'admit():' )
  print ( '  Normal end of execution.' )

  return

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