#! /usr/bin/env python3
#
def regression_1d_data ( data_num ):

#*****************************************************************************80
#
## regression_1d_data creates data for 1D regression.
#
#  Licensing:
#
#    This code is distributed under the MIT license. 
#
#  Modified:
#
#    25 April 2019
#
#  Author:
#
#    John Burkardt
#
#  Parameters:
#
#    Input, integer data_num, the number of data items to create.
#
#    Output, real data(data_num,2), the regression data.
#
  import numpy as np

  xmin = - 1.0
  xmax = + 2.0
  x = xmin + ( xmax - xmin ) * np.random.rand ( data_num )

  y = 0.5 * x**2 + x + 1.5
  y = y + 0.30 * np.random.randn ( data_num )

  data = np.zeros ( [ data_num, 2 ] )
  data[:,0] = x
  data[:,1] = y 
  
  return data

def regression_1d_data_test ( ):

#*****************************************************************************80
#
## regression_1d_data_test tests regression_1d_dataE.
#
#  Licensing:
#
#    This code is distributed under the MIT license.
#
#  Modified:
#
#    25 April 2019
#
#  Author:
#
#    John Burkardt
#
  import matplotlib.pyplot as plt
  import numpy as np
  import platform

  print ( '' )
  print ( 'regression_1d_data_test:' )
  print ( '  Python version: %s' % ( platform.python_version ( ) ) )
  print ( '  Create training and testing data for 1d linear regression.' )

  print ( '' )
  seed = 123456789
  np.random.seed ( seed )
  print ( '  Using random number seed %d' % ( seed ) )

  train_num = 100
  print ( '  Generating %d training items' % ( train_num ) )
  train_data = regression_1d_data ( train_num )
  filename = 'regression_1d_train.txt'
  r8mat_write ( filename, train_num, 2, train_data )
  print ( '  Training data written to "%s".' % ( filename ) )

  test_num = 10
  print ( '  Generating %d testing items' % ( test_num ) )
  test_data = regression_1d_data ( test_num )
  filename = 'regression_1d_test.txt'
  r8mat_write ( filename, test_num, 2, test_data )
  print ( '  Training data written to "%s".' % ( filename ) )
#
#  Plot the data.
#
  plt.plot ( train_data[:,0], train_data[:,1], 'bo' )
  plt.plot ( test_data[:,0], test_data[:,1], 'ro' )
  plt.grid ( True )
  plt.xlabel ( '<--- X --->', fontsize = 16 )
  plt.ylabel ( '<--- Y --->', fontsize = 16 )
  plt.title ( 'Data: train (blue), test (red)', fontsize = 16 )
  filename = 'regression_1d_data.png'
  plt.savefig ( filename )
  print ( '' )
  print ( '  Graphics saved as "%s"' % ( filename ) )
  plt.show ( )
#
#  Terminate.
#
  print ( '' )
  print ( 'regression_1d_data_test:' )
  print ( '  Normal end of execution.' )
  return

def r8mat_write ( filename, m, n, a ):

#*****************************************************************************80
#
## R8MAT_WRITE writes an R8MAT to a file.
#
#  Licensing:
#
#    This code is distributed under the MIT license.
#
#  Modified:
#
#    12 October 2014
#
#  Author:
#
#    John Burkardt
#
#  Parameters:
#
#    Input, string FILENAME, the name of the output file.
#
#    Input, integer M, the number of rows in A.
#
#    Input, integer N, the number of columns in A.
#
#    Input, real A(M,N), the matrix.
#
  output = open ( filename, 'w' )

  for i in range ( 0, m ):
    for j in range ( 0, n ):
      s = '  %g' % ( a[i,j] )
      output.write ( s )
    output.write ( '\n' )

  output.close ( )

  return

def timestamp ( ):

#*****************************************************************************80
#
## TIMESTAMP prints the date as a timestamp.
#
#  Licensing:
#
#    This code is distributed under the MIT license. 
#
#  Modified:
#
#    06 April 2013
#
#  Author:
#
#    John Burkardt
#
#  Parameters:
#
#    None
#
  import time

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

  return

if ( __name__ == '__main__' ):
  timestamp ( )
  regression_1d_data_test ( )
  timestamp ( )