#! /usr/bin/env python
#
def counterfeit_detection_brute ( n, coin, correct ):

#*****************************************************************************80
#
## counterfeit_detection_brute detects counterfeit coins.
#
#  Discussion:
#
#    We are given the weights of N coins, and the correct weight of a single 
#    coin.  We are asked to identify the counterfeit coins, that is, those
#    with incorrect weight.  We don't know how many such coins there are.
#
#    We simply use brute force.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    22 March 2019
#
#  Author:
#
#    John Burkardt
#
#  Reference:
#
#    Kurt Bryan, Tanya Leise,
#    Making do with less: an introduction to compressed sensing,
#    SIAM Review,
#    Volume 55, Number 3, September 2013, pages 547-566.
#
#  Parameters:
#
#    Input, integer n, the number of coins.
#
#    Input, real coin(n), the weights of the coins.
#
#    Input, real correct, the correct weight of a single coin.
#
#    Output, integer suspect(*), the indices of the suspected counterfeit coins.
#
  import numpy as np

  suspect = np.nonzero ( coin != correct )
  suspect = np.ravel ( suspect )

  return suspect

def counterfeit_detection_brute_test ( ):

#*****************************************************************************80
#
## counterfeit_detection_brute_test tests counterfeit_detection_brute.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    22 March 2019
#
#  Author:
#
#    John Burkardt
#
  import numpy as np
  import platform

  print ( '' )
  print ( 'counterfeit_detection_brute_test' )
  print ( '  Python version: %s' % ( platform.python_version ( ) ) )
  print ( '  Test counterfeit_detection_brute,' )
  print ( '  which seeks to identify multiple counterfeit coins among n coins' )
  print ( '  using brute force.' )
#
#  Generate the problem.
#
  n = 100
  correct = 17.0
  coin = correct * np.ones ( n )
  fake_num = np.random.randint ( 3, 11 )
  fake_index = np.random.choice ( n, fake_num, replace = False )
  fake_index.sort( )
  coin[fake_index] = correct + 3.0 * np.random.normal ( fake_num )
#
#  Report the fakes.
#
  print ( '' )
  print ( '  There were %d fakes' % ( fake_num ) )
  print ( '' )
  print ( '  Indices of fakes:' )
  print ( '' )
  for i in range ( 0, fake_num ):
    print ( '  %d:  %d  %g' % ( i, fake_index[i], coin[fake_index[i]] ) )
#
#  Detect and report the fakes.
#
  suspect = counterfeit_detection_brute ( n, coin, correct )
  suspect_num = len ( suspect )

  print ( '' )
  print ( '  The function found %d suspects.' % ( suspect_num ) )
  print ( '' )
  print ( '  Indices of suspects:' )
  print ( '' )
  for i in range ( 0, suspect_num ):
    print ( '  %d:  %d  %g' % ( i, suspect[i], coin[suspect[i]] ) )

  return

if ( __name__ == '__main__' ):
  from timestamp import timestamp
  timestamp ( )
  counterfeit_detection_brute_test ( )
  timestamp ( )