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

#*****************************************************************************80
#
## boston() uses keras to analyze Boston housing data.
#
#  Discussion:
#
#    The data and the indexes of training and test subsets have been 
#    incorporated into this program as separate functions.
#
#    In other words, this file can be run stand-alone.
#
#  Licensing:
#
#    This code is distributed under the MIT license.
#
#  Modified:
#
#    27 October 2019
#
#  Author:
#
#    Original version by Francois Chollet;
#    This version by John Burkardt.
#
#  Reference:
#
#    Francois Chollet,
#    Deep Learning with Python,
#    Manning, 2018,
#    ISBN: 9781617294433.
#
  import keras
  import numpy as np
  import platform
  import tensorflow
  
  print ( '' )
  print ( 'boston():' )
  print ( '  python version:     ' + platform.python_version ( ) )
  print ( '  numpy version:      ' + np.version.version )
  print ( '  tensorflow version: ' + tensorflow.__version__ )
  print ( '  keras version:      ' + keras.__version__ )
  print ( '' )
  print ( '  Neural network to classify the Boston housing data.' )
  print ( '  The data is read from external files, not internal datasets.' )
#
#  Load the datasets.
#
  data = boston_housing_data ( )
  data = data[:,1:]
  print ( data.shape )

  train_index = boston_housing_train_index ( )

  test_index = boston_housing_test_index ( )
#
#  Set up the training and test targets (selling prices)
#
  targets = data[:,13]

  train_targets = targets[train_index]
  print ( train_targets.shape )

  test_targets = targets[test_index]
  print ( test_targets.shape )
#
#  Set up the training and test data.
#
  train_data = data[train_index,:]
  print ( train_data.shape )

  test_data = data[test_index,:]
  print ( test_data.shape )
#
#  The rest of the program is the same as "boston_housing.py"...
#
#  Exhibit the contents of one item of the training data:
#
  print ( train_targets[0:3], train_targets[-3:] )
#
#  Normalize the data.
#
  mean = train_data.mean ( axis = 0 )
  train_data = train_data - mean
  std = train_data.std ( axis = 0 )
  train_data = train_data / std

  test_data = test_data - mean
  test_data = test_data / std
#
#  Define the network architecture.
#
#  Because we are going to instantiate the same model several times, we
#  implement it as a function.
#
  from keras import models
  from keras import layers

  def build_model ( ):
    model = models.Sequential()
    model.add ( layers.Dense ( 64, activation = 'relu', input_shape = ( train_data.shape[1], ) ) )
    model.add ( layers.Dense ( 64, activation = 'relu' ) )
    model.add ( layers.Dense ( 1 ) )
    model.compile ( \
      optimizer = 'rmsprop', \
      loss = 'mse', \
      metrics = [ 'mae' ] )
    return model
#
#  Do K-fold validation
#
  k = 4
  num_val_samples = len ( train_data ) // k
  num_epochs = 100
  all_scores = []

  for i in range ( k ):

    print ( '  Processing fold #', i )

    val_data = train_data[i*num_val_samples:(i+1)*num_val_samples]
    val_targets = train_targets[i*num_val_samples:(i+1)*num_val_samples]

    partial_train_data = np.concatenate ( \
      [ train_data[:i*num_val_samples], \
        train_data[:(i+1)*num_val_samples:]], \
      axis = 0 )
    partial_train_targets = np.concatenate ( \
      [ train_targets[:i*num_val_samples], \
        train_targets[:(i+1)*num_val_samples:]], \
      axis = 0 )

    model = build_model ( )

    model.fit ( \
      partial_train_data, \
      partial_train_targets, \
      epochs = num_epochs, \
      batch_size = 1, \
      verbose = 0 )

    val_mse, val_mae = model.evaluate ( val_data, val_targets, verbose = 0 )

    all_scores.append ( val_mae )

  print ( all_scores )
  print ( np.mean ( all_scores ) )
#
#  Save the validation logs at each fold.
#
  num_epochs = 500
  all_mae_histories = []

  for i in range ( k ):

    print ( '  Processing fold #', i )

    val_data = train_data[i*num_val_samples:(i+1)*num_val_samples]
    val_targets = train_targets[i*num_val_samples:(i+1)*num_val_samples]

    partial_train_data = np.concatenate ( \
      [ train_data[:i*num_val_samples], \
        train_data[:(i+1)*num_val_samples:]], \
      axis = 0 )
    partial_train_targets = np.concatenate ( \
      [ train_targets[:i*num_val_samples], \
        train_targets[:(i+1)*num_val_samples:]], \
      axis = 0 )

    model = build_model ( )

    history = model.fit ( \
      partial_train_data, \
      partial_train_targets, \
      validation_data = ( val_data, val_targets ), \
      epochs = num_epochs, \
      batch_size = 1, \
      verbose = 0 )

#   mae_history = history.history [ 'val_mean_absolute_error' ]
    mae_history = history.history [ 'val_mae' ]

    all_mae_histories.append ( mae_history )
#
#  Build the history of successive k-fold validation scores.
#
  average_mae_history = [ \
    np.mean ( [ x[i] for x in all_mae_histories ] ) for i in range ( num_epochs ) ]
#
#  Display the validation scores.
#
  import matplotlib.pyplot as plt

  plt.plot ( range ( 1, len ( average_mae_history ) + 1 ), average_mae_history )
  plt.xlabel ( 'Epochs' )
  plt.ylabel ( 'Validation MAE' )
  plt.title ( 'Evolution of validation MAEs' )
  filename = 'boston_validation_mae.png'
  plt.savefig ( filename )
  print ( '' )
  print ( '  Graphics saved in "' + filename + '"' )
  plt.show ( )
#
#  Redisplay, omitting first 10 values.
#
  def smooth_curve ( points, factor = 0.9 ):
    smoothed_points = []
    for point in points:
      if ( smoothed_points ):
        previous = smoothed_points[-1]
        smoothed_points.append ( previous * factor + point * ( 1.0 - factor ) )
      else:
        smoothed_points.append ( point )
    return smoothed_points

  smoothed_mae_history = smooth_curve ( average_mae_history[10:] )

  plt.plot ( range ( 1, len ( smoothed_mae_history ) + 1 ), smoothed_mae_history )
  plt.xlabel ( 'Epochs' )
  plt.ylabel ( 'Validation MAE' )
  plt.title ( 'Evolution of validation MAEs (smoothed)' )
  filename = 'boston_validation_mae_smoothed.png'
  plt.savefig ( filename )
  print ( '' )
  print ( '  Graphics saved in "' + filename + '"' )
  plt.show ( )
#
#  Training the final model.
#
  model = build_model ( )
  model.fit ( train_data, train_targets, epochs = 80, batch_size = 16, \
    verbose = 0 )
  test_mse_score, test_mae_score = model.evaluate ( test_data, test_targets )

  print ( test_mae_score )
#
#  Terminate.
#
  print ( '' )
  print ( 'boston():' )
  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:
#
#    06 April 2013
#
#  Author:
#
#    John Burkardt
#
  import time

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

  return None

def boston_housing_data ( ):

  import numpy as np

  data = np.array ( \
[[1.00000e+00, 6.32000e-03, 1.80000e+01, 2.31000e+00, 0.00000e+00, 5.38000e-01,
  6.57500e+00, 6.52000e+01, 4.09000e+00, 1.00000e+00, 2.96000e+02, 1.53000e+01,
  3.96900e+02, 4.98000e+00, 2.40000e+01],
  [2.00000e+00, 2.73100e-02, 0.00000e+00, 7.07000e+00, 0.00000e+00, 4.69000e-01,
  6.42100e+00, 7.89000e+01, 4.96710e+00, 2.00000e+00, 2.42000e+02, 1.78000e+01,
  3.96900e+02, 9.14000e+00, 2.16000e+01],
  [3.00000e+00, 2.72900e-02, 0.00000e+00, 7.07000e+00, 0.00000e+00, 4.69000e-01,
  7.18500e+00, 6.11000e+01, 4.96710e+00, 2.00000e+00, 2.42000e+02, 1.78000e+01,
  3.92830e+02, 4.03000e+00, 3.47000e+01],
  [4.00000e+00, 3.23700e-02, 0.00000e+00, 2.18000e+00, 0.00000e+00, 4.58000e-01,
  6.99800e+00, 4.58000e+01, 6.06220e+00, 3.00000e+00, 2.22000e+02, 1.87000e+01,
  3.94630e+02, 2.94000e+00, 3.34000e+01],
  [5.00000e+00, 6.90500e-02, 0.00000e+00, 2.18000e+00, 0.00000e+00, 4.58000e-01,
  7.14700e+00, 5.42000e+01, 6.06220e+00, 3.00000e+00, 2.22000e+02, 1.87000e+01,
  3.96900e+02, 5.33000e+00, 3.62000e+01],
  [6.00000e+00, 2.98500e-02, 0.00000e+00, 2.18000e+00, 0.00000e+00, 4.58000e-01,
  6.43000e+00, 5.87000e+01, 6.06220e+00, 3.00000e+00, 2.22000e+02, 1.87000e+01,
  3.94120e+02, 5.21000e+00, 2.87000e+01],
  [7.00000e+00, 8.82900e-02, 1.25000e+01, 7.87000e+00, 0.00000e+00, 5.24000e-01,
  6.01200e+00, 6.66000e+01, 5.56050e+00, 5.00000e+00, 3.11000e+02, 1.52000e+01,
  3.95600e+02, 1.24300e+01, 2.29000e+01],
  [8.00000e+00, 1.44550e-01, 1.25000e+01, 7.87000e+00, 0.00000e+00, 5.24000e-01,
  6.17200e+00, 9.61000e+01, 5.95050e+00, 5.00000e+00, 3.11000e+02, 1.52000e+01,
  3.96900e+02, 1.91500e+01, 2.71000e+01],
  [9.00000e+00, 2.11240e-01, 1.25000e+01, 7.87000e+00, 0.00000e+00, 5.24000e-01,
  5.63100e+00, 1.00000e+02, 6.08210e+00, 5.00000e+00, 3.11000e+02, 1.52000e+01,
  3.86630e+02, 2.99300e+01, 1.65000e+01],
  [1.00000e+01, 1.70040e-01, 1.25000e+01, 7.87000e+00, 0.00000e+00, 5.24000e-01,
  6.00400e+00, 8.59000e+01, 6.59210e+00, 5.00000e+00, 3.11000e+02, 1.52000e+01,
  3.86710e+02, 1.71000e+01, 1.89000e+01],
  [1.10000e+01, 2.24890e-01, 1.25000e+01, 7.87000e+00, 0.00000e+00, 5.24000e-01,
  6.37700e+00, 9.43000e+01, 6.34670e+00, 5.00000e+00, 3.11000e+02, 1.52000e+01,
  3.92520e+02, 2.04500e+01, 1.50000e+01],
  [1.20000e+01, 1.17470e-01, 1.25000e+01, 7.87000e+00, 0.00000e+00, 5.24000e-01,
  6.00900e+00, 8.29000e+01, 6.22670e+00, 5.00000e+00, 3.11000e+02, 1.52000e+01,
  3.96900e+02, 1.32700e+01, 1.89000e+01],
  [1.30000e+01, 9.37800e-02, 1.25000e+01, 7.87000e+00, 0.00000e+00, 5.24000e-01,
  5.88900e+00, 3.90000e+01, 5.45090e+00, 5.00000e+00, 3.11000e+02, 1.52000e+01,
  3.90500e+02, 1.57100e+01, 2.17000e+01],
  [1.40000e+01, 6.29760e-01, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  5.94900e+00, 6.18000e+01, 4.70750e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.96900e+02, 8.26000e+00, 2.04000e+01],
  [1.50000e+01, 6.37960e-01, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  6.09600e+00, 8.45000e+01, 4.46190e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.80020e+02, 1.02600e+01, 1.82000e+01],
  [1.60000e+01, 6.27390e-01, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  5.83400e+00, 5.65000e+01, 4.49860e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.95620e+02, 8.47000e+00, 1.99000e+01],
  [1.70000e+01, 1.05393e+00, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  5.93500e+00, 2.93000e+01, 4.49860e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.86850e+02, 6.58000e+00, 2.31000e+01],
  [1.80000e+01, 7.84200e-01, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  5.99000e+00, 8.17000e+01, 4.25790e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.86750e+02, 1.46700e+01, 1.75000e+01],
  [1.90000e+01, 8.02710e-01, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  5.45600e+00, 3.66000e+01, 3.79650e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  2.88990e+02, 1.16900e+01, 2.02000e+01],
  [2.00000e+01, 7.25800e-01, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  5.72700e+00, 6.95000e+01, 3.79650e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.90950e+02, 1.12800e+01, 1.82000e+01],
  [2.10000e+01, 1.25179e+00, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  5.57000e+00, 9.81000e+01, 3.79790e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.76570e+02, 2.10200e+01, 1.36000e+01],
  [2.20000e+01, 8.52040e-01, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  5.96500e+00, 8.92000e+01, 4.01230e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.92530e+02, 1.38300e+01, 1.96000e+01],
  [2.30000e+01, 1.23247e+00, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  6.14200e+00, 9.17000e+01, 3.97690e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.96900e+02, 1.87200e+01, 1.52000e+01],
  [2.40000e+01, 9.88430e-01, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  5.81300e+00, 1.00000e+02, 4.09520e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.94540e+02, 1.98800e+01, 1.45000e+01],
  [2.50000e+01, 7.50260e-01, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  5.92400e+00, 9.41000e+01, 4.39960e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.94330e+02, 1.63000e+01, 1.56000e+01],
  [2.60000e+01, 8.40540e-01, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  5.59900e+00, 8.57000e+01, 4.45460e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.03420e+02, 1.65100e+01, 1.39000e+01],
  [2.70000e+01, 6.71910e-01, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  5.81300e+00, 9.03000e+01, 4.68200e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.76880e+02, 1.48100e+01, 1.66000e+01],
  [2.80000e+01, 9.55770e-01, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  6.04700e+00, 8.88000e+01, 4.45340e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.06380e+02, 1.72800e+01, 1.48000e+01],
  [2.90000e+01, 7.72990e-01, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  6.49500e+00, 9.44000e+01, 4.45470e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.87940e+02, 1.28000e+01, 1.84000e+01],
  [3.00000e+01, 1.00245e+00, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  6.67400e+00, 8.73000e+01, 4.23900e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.80230e+02, 1.19800e+01, 2.10000e+01],
  [3.10000e+01, 1.13081e+00, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  5.71300e+00, 9.41000e+01, 4.23300e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.60170e+02, 2.26000e+01, 1.27000e+01],
  [3.20000e+01, 1.35472e+00, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  6.07200e+00, 1.00000e+02, 4.17500e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.76730e+02, 1.30400e+01, 1.45000e+01],
  [3.30000e+01, 1.38799e+00, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  5.95000e+00, 8.20000e+01, 3.99000e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  2.32600e+02, 2.77100e+01, 1.32000e+01],
  [3.40000e+01, 1.15172e+00, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  5.70100e+00, 9.50000e+01, 3.78720e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  3.58770e+02, 1.83500e+01, 1.31000e+01],
  [3.50000e+01, 1.61282e+00, 0.00000e+00, 8.14000e+00, 0.00000e+00, 5.38000e-01,
  6.09600e+00, 9.69000e+01, 3.75980e+00, 4.00000e+00, 3.07000e+02, 2.10000e+01,
  2.48310e+02, 2.03400e+01, 1.35000e+01],
  [3.60000e+01, 6.41700e-02, 0.00000e+00, 5.96000e+00, 0.00000e+00, 4.99000e-01,
  5.93300e+00, 6.82000e+01, 3.36030e+00, 5.00000e+00, 2.79000e+02, 1.92000e+01,
  3.96900e+02, 9.68000e+00, 1.89000e+01],
  [3.70000e+01, 9.74400e-02, 0.00000e+00, 5.96000e+00, 0.00000e+00, 4.99000e-01,
  5.84100e+00, 6.14000e+01, 3.37790e+00, 5.00000e+00, 2.79000e+02, 1.92000e+01,
  3.77560e+02, 1.14100e+01, 2.00000e+01],
  [3.80000e+01, 8.01400e-02, 0.00000e+00, 5.96000e+00, 0.00000e+00, 4.99000e-01,
  5.85000e+00, 4.15000e+01, 3.93420e+00, 5.00000e+00, 2.79000e+02, 1.92000e+01,
  3.96900e+02, 8.77000e+00, 2.10000e+01],
  [3.90000e+01, 1.75050e-01, 0.00000e+00, 5.96000e+00, 0.00000e+00, 4.99000e-01,
  5.96600e+00, 3.02000e+01, 3.84730e+00, 5.00000e+00, 2.79000e+02, 1.92000e+01,
  3.93430e+02, 1.01300e+01, 2.47000e+01],
  [4.00000e+01, 2.76300e-02, 7.50000e+01, 2.95000e+00, 0.00000e+00, 4.28000e-01,
  6.59500e+00, 2.18000e+01, 5.40110e+00, 3.00000e+00, 2.52000e+02, 1.83000e+01,
  3.95630e+02, 4.32000e+00, 3.08000e+01],
  [4.10000e+01, 3.35900e-02, 7.50000e+01, 2.95000e+00, 0.00000e+00, 4.28000e-01,
  7.02400e+00, 1.58000e+01, 5.40110e+00, 3.00000e+00, 2.52000e+02, 1.83000e+01,
  3.95620e+02, 1.98000e+00, 3.49000e+01],
  [4.20000e+01, 1.27440e-01, 0.00000e+00, 6.91000e+00, 0.00000e+00, 4.48000e-01,
  6.77000e+00, 2.90000e+00, 5.72090e+00, 3.00000e+00, 2.33000e+02, 1.79000e+01,
  3.85410e+02, 4.84000e+00, 2.66000e+01],
  [4.30000e+01, 1.41500e-01, 0.00000e+00, 6.91000e+00, 0.00000e+00, 4.48000e-01,
  6.16900e+00, 6.60000e+00, 5.72090e+00, 3.00000e+00, 2.33000e+02, 1.79000e+01,
  3.83370e+02, 5.81000e+00, 2.53000e+01],
  [4.40000e+01, 1.59360e-01, 0.00000e+00, 6.91000e+00, 0.00000e+00, 4.48000e-01,
  6.21100e+00, 6.50000e+00, 5.72090e+00, 3.00000e+00, 2.33000e+02, 1.79000e+01,
  3.94460e+02, 7.44000e+00, 2.47000e+01],
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  5.90500e+00, 5.32000e+01, 3.15230e+00, 2.40000e+01, 6.66000e+02, 2.02000e+01,
  3.88220e+02, 1.14500e+01, 2.06000e+01],
  [4.89000e+02, 1.50860e-01, 0.00000e+00, 2.77400e+01, 0.00000e+00, 6.09000e-01,
  5.45400e+00, 9.27000e+01, 1.82090e+00, 4.00000e+00, 7.11000e+02, 2.01000e+01,
  3.95090e+02, 1.80600e+01, 1.52000e+01],
  [4.90000e+02, 1.83370e-01, 0.00000e+00, 2.77400e+01, 0.00000e+00, 6.09000e-01,
  5.41400e+00, 9.83000e+01, 1.75540e+00, 4.00000e+00, 7.11000e+02, 2.01000e+01,
  3.44050e+02, 2.39700e+01, 7.00000e+00],
  [4.91000e+02, 2.07460e-01, 0.00000e+00, 2.77400e+01, 0.00000e+00, 6.09000e-01,
  5.09300e+00, 9.80000e+01, 1.82260e+00, 4.00000e+00, 7.11000e+02, 2.01000e+01,
  3.18430e+02, 2.96800e+01, 8.10000e+00],
  [4.92000e+02, 1.05740e-01, 0.00000e+00, 2.77400e+01, 0.00000e+00, 6.09000e-01,
  5.98300e+00, 9.88000e+01, 1.86810e+00, 4.00000e+00, 7.11000e+02, 2.01000e+01,
  3.90110e+02, 1.80700e+01, 1.36000e+01],
  [4.93000e+02, 1.11320e-01, 0.00000e+00, 2.77400e+01, 0.00000e+00, 6.09000e-01,
  5.98300e+00, 8.35000e+01, 2.10990e+00, 4.00000e+00, 7.11000e+02, 2.01000e+01,
  3.96900e+02, 1.33500e+01, 2.01000e+01],
  [4.94000e+02, 1.73310e-01, 0.00000e+00, 9.69000e+00, 0.00000e+00, 5.85000e-01,
  5.70700e+00, 5.40000e+01, 2.38170e+00, 6.00000e+00, 3.91000e+02, 1.92000e+01,
  3.96900e+02, 1.20100e+01, 2.18000e+01],
  [4.95000e+02, 2.79570e-01, 0.00000e+00, 9.69000e+00, 0.00000e+00, 5.85000e-01,
  5.92600e+00, 4.26000e+01, 2.38170e+00, 6.00000e+00, 3.91000e+02, 1.92000e+01,
  3.96900e+02, 1.35900e+01, 2.45000e+01],
  [4.96000e+02, 1.78990e-01, 0.00000e+00, 9.69000e+00, 0.00000e+00, 5.85000e-01,
  5.67000e+00, 2.88000e+01, 2.79860e+00, 6.00000e+00, 3.91000e+02, 1.92000e+01,
  3.93290e+02, 1.76000e+01, 2.31000e+01],
  [4.97000e+02, 2.89600e-01, 0.00000e+00, 9.69000e+00, 0.00000e+00, 5.85000e-01,
  5.39000e+00, 7.29000e+01, 2.79860e+00, 6.00000e+00, 3.91000e+02, 1.92000e+01,
  3.96900e+02, 2.11400e+01, 1.97000e+01],
  [4.98000e+02, 2.68380e-01, 0.00000e+00, 9.69000e+00, 0.00000e+00, 5.85000e-01,
  5.79400e+00, 7.06000e+01, 2.89270e+00, 6.00000e+00, 3.91000e+02, 1.92000e+01,
  3.96900e+02, 1.41000e+01, 1.83000e+01],
  [4.99000e+02, 2.39120e-01, 0.00000e+00, 9.69000e+00, 0.00000e+00, 5.85000e-01,
  6.01900e+00, 6.53000e+01, 2.40910e+00, 6.00000e+00, 3.91000e+02, 1.92000e+01,
  3.96900e+02, 1.29200e+01, 2.12000e+01],
  [5.00000e+02, 1.77830e-01, 0.00000e+00, 9.69000e+00, 0.00000e+00, 5.85000e-01,
  5.56900e+00, 7.35000e+01, 2.39990e+00, 6.00000e+00, 3.91000e+02, 1.92000e+01,
  3.95770e+02, 1.51000e+01, 1.75000e+01],
  [5.01000e+02, 2.24380e-01, 0.00000e+00, 9.69000e+00, 0.00000e+00, 5.85000e-01,
  6.02700e+00, 7.97000e+01, 2.49820e+00, 6.00000e+00, 3.91000e+02, 1.92000e+01,
  3.96900e+02, 1.43300e+01, 1.68000e+01],
  [5.02000e+02, 6.26300e-02, 0.00000e+00, 1.19300e+01, 0.00000e+00, 5.73000e-01,
  6.59300e+00, 6.91000e+01, 2.47860e+00, 1.00000e+00, 2.73000e+02, 2.10000e+01,
  3.91990e+02, 9.67000e+00, 2.24000e+01],
  [5.03000e+02, 4.52700e-02, 0.00000e+00, 1.19300e+01, 0.00000e+00, 5.73000e-01,
  6.12000e+00, 7.67000e+01, 2.28750e+00, 1.00000e+00, 2.73000e+02, 2.10000e+01,
  3.96900e+02, 9.08000e+00, 2.06000e+01],
  [5.04000e+02, 6.07600e-02, 0.00000e+00, 1.19300e+01, 0.00000e+00, 5.73000e-01,
  6.97600e+00, 9.10000e+01, 2.16750e+00, 1.00000e+00, 2.73000e+02, 2.10000e+01,
  3.96900e+02, 5.64000e+00, 2.39000e+01],
  [5.05000e+02, 1.09590e-01, 0.00000e+00, 1.19300e+01, 0.00000e+00, 5.73000e-01,
  6.79400e+00, 8.93000e+01, 2.38890e+00, 1.00000e+00, 2.73000e+02, 2.10000e+01,
  3.93450e+02, 6.48000e+00, 2.20000e+01],
  [5.06000e+02, 4.74100e-02, 0.00000e+00, 1.19300e+01, 0.00000e+00, 5.73000e-01,
  6.03000e+00, 8.08000e+01, 2.50500e+00, 1.00000e+00, 2.73000e+02, 2.10000e+01,
  3.96900e+02, 7.88000e+00, 1.19000e+01] ]
  )

  return data

def boston_housing_test_index ( ):

  import numpy as np

  data = np.array ( \
[  2,   5,   7,   8,   9,  17,  19,  24,  25,  26,  28,  29,  32,  33,  35,  36,  37,  41,
  48,  52,  59,  62,  69,  71,  78,  79,  82,  91,  92,  95,  97,  98,  99, 104, 105, 110,
 112, 113, 115, 125, 129, 130, 132, 135, 140, 143, 144, 146, 151, 152, 155, 157, 162, 168,
 184, 185, 188, 195, 196, 198, 199, 202, 207, 209, 210, 212, 215, 217, 218, 219, 220, 228,
 231, 237, 238, 239, 241, 245, 247, 252, 254, 255, 256, 257, 258, 259, 267, 269, 270, 275,
 276, 277, 278, 280, 287, 289, 291, 294, 295, 298, 299, 300, 307, 313, 314, 319, 321, 322,
 323, 326, 329, 331, 332, 335, 337, 345, 346, 350, 351, 353, 354, 355, 361, 363, 364, 369,
 375, 378, 379, 380, 381, 385, 388, 390, 393, 395, 397, 399, 402, 404, 405, 406, 409, 410,
 411, 412, 416, 420, 421, 422, 423, 426, 430, 435, 436, 438, 446, 449, 450, 454, 456, 470,
 473, 475, 482, 485, 489, 494, 495, 496, 498, 500, 504 ] )

  return data

def boston_housing_train_index ( ):

  import numpy as np

  data = np.array ( \
[  0,   1,   3,   4,   6,  10,  11,  12,  13,  14,  15,  16,  18,  20,  21,  22,  23,  27,
  30,  31,  34,  38,  39,  40,  42,  43,  44,  45,  46,  47,  49,  50,  51,  53,  54,  55,
  56,  57,  58,  60,  61,  63,  64,  65,  66,  67,  68,  70,  72,  73,  74,  75,  76,  77,
  80,  81,  83,  84,  85,  86,  87,  88,  89,  90,  93,  94,  96, 100, 101, 102, 103, 106,
 107, 108, 109, 111, 114, 116, 117, 118, 119, 120, 121, 122, 123, 124, 126, 127, 128, 131,
 133, 134, 136, 137, 138, 139, 141, 142, 145, 147, 148, 149, 150, 153, 154, 156, 158, 159,
 160, 161, 163, 164, 165, 166, 167, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179,
 180, 181, 182, 183, 186, 187, 189, 190, 191, 192, 193, 194, 197, 200, 201, 203, 204, 205,
 206, 208, 211, 213, 214, 216, 221, 222, 223, 224, 225, 226, 227, 229, 230, 232, 233, 234,
 235, 236, 240, 242, 243, 244, 246, 248, 249, 250, 251, 253, 260, 261, 262, 263, 264, 265,
 266, 268, 271, 272, 273, 274, 279, 281, 282, 283, 284, 285, 286, 288, 290, 292, 293, 296,
 297, 301, 302, 303, 304, 305, 306, 308, 309, 310, 311, 312, 315, 316, 317, 318, 320, 324,
 325, 327, 328, 330, 333, 334, 336, 338, 339, 340, 341, 342, 343, 344, 347, 348, 349, 352,
 356, 357, 358, 359, 360, 362, 365, 366, 367, 368, 370, 371, 372, 373, 374, 376, 377, 382,
 383, 384, 386, 387, 389, 391, 392, 394, 396, 398, 400, 401, 403, 407, 408, 413, 414, 415,
 417, 418, 419, 424, 425, 427, 428, 429, 431, 432, 433, 434, 437, 439, 440, 441, 442, 443,
 444, 445, 447, 448, 451, 452, 453, 455, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466,
 467, 468, 469, 471, 472, 474, 476, 477, 478, 479, 480, 481, 483, 484, 486, 487, 488, 490,
 491, 492, 493, 497, 499, 501, 502, 503, 505 ] )

  return data

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