class Foo {
    
    ## Object initializer:
    sub Foo (%args) {
      $this->{bar} = $arg{baz} ;
    }

    ## a Perl function:
    sub bar($x , $y) {
      $this->add($x , $y) ;
    }
    
    ## a C function that will be turned into XS:
    sub[C] int add( int x , int y ) {
      int res = x + y ;
      return res ;
    }
    
  }

##</code><code>##


  AI::NNEasy::NN::tanh

  AI::NNEasy::NN::feedforward::run
  
  AI::NNEasy::NN::backprop::hiddenToOutput
  AI::NNEasy::NN::backprop::hiddenOrInputToHidden


##</code><code>##


class AI::NNEasy::NN[0.01] {

  ...

  *tanh = \&tanh_c ;

  sub tanh_pl ($value) {
    if    ($value > 20)  { return 1 ;}
    elsif ($value < -20) { return -1 ;}
    else {
      my $x = exp($value) ;
      my $y = exp(-$value) ;
      return ($x-$y)/($x+$y) ;
    }
  }
  
  sub[C] double tanh_c ( SV* self , double value ) {
    if      ( value > 20 ) { return 1 ;}
    else if ( value < -20 ) { return -1 ;}
    else {
      double x = Perl_exp(value) ;
      double y = Perl_exp(-value) ;
      double ret = (x-y)/(x+y) ;
      return ret ;
    }
  }

  ...

}

##</code><code>##


  use AI::NNEasy ;

  my $nn = AI::NNEasy->new(
  'xor.nne' , ## file to save the NN.
  [0,1] ,     ## Output types of the NN.
  0.1 ,       ## Maximal error for output.
  2 ,         ## Number of inputs.
  1 ,         ## Number of outputs.
  ) ;
  
  
  ## Our set of inputs and outputs to learn:
  my @set = (
  [0,0] => [0],
  [0,1] => [1],
  [1,0] => [1],
  [1,1] => [0],
  );
  
  ## learn the inputs:
  $nn->learn_set( \@set ) ;

  ## Save the NN:
  $nn->save ;
  
  ## Use the NN:
  
  my $out = $nn->run_get_winner([0,0]) ;
  print "0 0 => @$out\n" ; ## 0 0 => 0

  my $out = $nn->run_get_winner([0,1]) ;
  print "0 1 => @$out\n" ; ## 0 1 => 1