#! /usr/bin/env python3
#
def exercise4 ( ):

#*****************************************************************************80
#
## exercise3() applies logistic regression to caesarian delivery data.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    12 February 2022
#
#  Author:
#
#    John Burkardt
#
  from sklearn.linear_model import LogisticRegression
  import matplotlib.pyplot as plt
  import numpy as np

  print ( '' )
  print ( 'exercise4():' )
  print ( '  Seek parameters for logistic regression example.' )
  print ( '  We have N examples of D-dimensional data X.' )
  print ( '  For a model' )
  print ( '    y(x) = 1 / ( 1 + exp ( - ( w(0) + w(1)*h + w(2)*t ) ) )' )
  print ( '  determine the weights w.' )
#
#  Read the data file.
#
  data = np.loadtxt ( 'caesarian_data.txt' )
#
#   
#  Third column is Y (comfortable = 1, no = 0).
#
  age = data[:,0]
  num = data[:,1]
  tim = data[:,2]
  pre = data[:,3]
  hrt = data[:,4]
  cae = data[:,5]
  n = len ( age )
  d = 6
#
#  Create a data array X = [ 1 | age | num | tim | pre | hrt ].
#
  X = np.zeros ( [ n, d ] )
  X[:,0] = 1
  X[:,1] = age
  X[:,2] = num
  X[:,3] = tim
  X[:,4] = pre
  X[:,5] = hrt

  y = cae.copy ( )
#
#  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 ( ( y - yp )**2 )
  print ( '  MSE = ', mse )
#
#  For some reason, w[0] does NOT contain the intercept.
#  What is in w[0] I don't know.  
#  Luckily, the intercept is available, so let's get it and copy
#  it into w[0], where it belongs.
#
  w = classifier.coef_[0].copy()
  w[0] = classifier.intercept_

  print ( '' )
  print ( '  Computed weights W = ', w )
#
#  To evaluate new data, ynew = classifier.predict ( Xnew )
#
  Xnew = np.array ( [ \
    [ 1, 29, 2, 2, 0, 1 ], \
    [ 1, 33, 2, 0, 2, 0 ], \
    [ 1, 22, 0, 1, 1, 0 ] ] )

  ynew = classifier.predict ( Xnew )

  print ( '  New data:' )
  print ( Xnew )
  print ( '  Caesarian recommendation?' )
  print ( ynew )
#
#  Terminate.
#
  print ( '' )
  print ( 'exercise4():' )
  print ( '  Normal end of execution.' )

  return

if ( __name__ == '__main__' ):
  exercise4 ( )