AI-NNEasy

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lib/AI/NNEasy.hploo  view on Meta::CPAN

    double er = 0 ;
    int learn_ok = 0 ;
        
    for (i = 0 ; i <= av_len(set_av) ; i+=2) {
      SV* set_in = *av_fetch(set_av, i ,0) ;
      SV* set_out = *av_fetch(set_av, i+1 ,0) ;

      PUSHMARK(SP) ;
        XPUSHs( nn );
        XPUSHs( set_in );
      PUTBACK ;
      call_method("run", G_DISCARD) ;
      
      PUSHMARK(SP) ;
        XPUSHs( nn );
        XPUSHs( set_out );
      PUTBACK ;
      call_method("learn", G_SCALAR) ;
    }
    
    for (i = 0 ; i <= av_len(set_av) ; i+=2) {
      SV* set_in = *av_fetch(set_av, i ,0) ;
      SV* set_out = *av_fetch(set_av, i+1 ,0) ;

      PUSHMARK(SP) ;
        XPUSHs( nn );
        XPUSHs( set_in );
      PUTBACK ;
      call_method("run", G_DISCARD) ;
      
      PUSHMARK(SP) ;
        XPUSHs( nn );
        XPUSHs( set_out );
      PUTBACK ;
      call_method("RMSErr", G_SCALAR) ;
      
      SPAGAIN ;
      ret = POPs ;
      er = SvNV(ret) ;
      if (er < 0) er *= -1 ;
      if (er < error_ok) ++learn_ok ;
      err += er ;
      
      if ( verbose ) sv_catpvf(print_verbose , "%s => %s > %f\n" ,

lib/AI/NNEasy.hploo  view on Meta::CPAN

  print "1 1 => @$out\n" ; ## 1 1 => 0
  
  ## or just interate through the @set:
  for (my $i = 0 ; $i < @set ; $i+=2) {       
    my $out = $nn->run_get_winner($set[$i]) ;
    print "@{$set[$i]}) => @$out\n" ;
  }

=> METHODS

==> new ( FILE , @OUTPUT_TYPES , ERROR_OK , IN_SIZE , OUT_SIZE , @HIDDEN_LAYERS , %CONF )

*> FILE
The file path to save the NN. Default: 'nneasy.nne'.

*> @OUTPUT_TYPES
An array of outputs that the NN can have, so the NN can find the nearest number in this
list to give your the right output.

*> ERROR_OK
The maximal error of the calculated output.

If not defined ERROR_OK will be calculated by the minimal difference between 2 types at
@OUTPUT_TYPES dived by 2:

  @OUTPUT_TYPES = [0 , 0.5 , 1] ;
  
  ERROR_OK = (1 - 0.5) / 2 = 0.25 ;

*> IN_SIZE
The input size (number of nodes in the inpute layer).

*> OUT_SIZE
The output size (number of nodes in the output layer).

*> @HIDDEN_LAYERS

lib/AI/NNEasy.hploo  view on Meta::CPAN

*> @OUT
The values of the output for the input above.

*> N
Number of times that this input should be learned. Default: 100

Example:

  $nn->learn( [0,1] , [1] , 10 ) ;

==> learn_set (@SET , OK_OUTPUTS , LIMIT , VERBOSE)

Learn a set of inputs until get the right error for the outputs.

*> @SET
A list of inputs and outputs.

*> OK_OUTPUTS
Minimal number of outputs that should be OK when calculating the erros.

By default I<OK_OUTPUTS> should have the same size of number of different
inouts in the @SET.

*> LIMIT
Limit of interations when learning. Default: 30000

*> VERBOSE
If TRUE turn verbose method ON when learning.

==> get_set_error (@SET , OK_OUTPUTS)
Get the actual error of a set in the NN. If the returned error is bigger than
I<ERROR_OK> defined on I<new()> you should learn or relearn the set.

==> run (@INPUT)
Run a input and return the output calculated by the NN based in what the NN already have learned.

==> run_get_winner (@INPUT)
Same of I<run()>, but the output will return the nearest output value based in the
I<@OUTPUT_TYPES> defined at I<new()>.

For example an input I<[0,1]> learned that have
the output I<[1]>, actually will return something like 0.98324 as output and
not 1, since the error never should be 0. So, with I<run_get_winner()>
we get the output of I<run()>, let's say that is 0.98324, and find what output
is near of this number, that in this case should be 1. An output [0], will return
by I<run()> something like 0.078964, and I<run_get_winner()> return 0.

=> Samples

lib/AI/NNEasy.hploo  view on Meta::CPAN

is [1,1], the nodes I<output1> and I<output2> should give to us a number near 1,
let's say 0.98654. And if the output for [0,0] is [0,0], I<output1> and I<output2> should give to us a number near 0,
let's say 0.078875.

What is hard in a NN is to find this I<weights>. By default L<AI::NNEasy> uses
I<backprop> as learning algorithm. With I<backprop> it pastes the inputs through
the Neural Network and adjust the I<weights> using random numbers until we find
a set of I<weights> that give to us the right output.

The secret of a NN is the number of hidden layers and nodes/neurons for each layer.
Basically the best way to define the hidden layers is 1 layer of (INPUT_NODES+OUTPUT_NODES).
So, a layer of 2 input nodes and 1 output node, should have 3 nodes in the hidden layer.
This definition exists because the number of inputs define the maximal variability of
the inputs (N**2 for bollean inputs), and the output defines if the variability is reduced by some logic restriction, like
int the XOR example, where we have 2 inputs and 1 output, so, hidden is 3. And as we can see in the
logic we have 3 groups of inputs:

  0 0 => 0 # false
  0 1 => 1 # or
  1 0 => 1 # or
  1 1 => 1 # true

lib/AI/NNEasy.pm  view on Meta::CPAN

    double er = 0 ;
    int learn_ok = 0 ;
        
    for (i = 0 ; i <= av_len(set_av) ; i+=2) {
      SV* set_in = *av_fetch(set_av, i ,0) ;
      SV* set_out = *av_fetch(set_av, i+1 ,0) ;

      PUSHMARK(SP) ;
        XPUSHs( nn );
        XPUSHs( set_in );
      PUTBACK ;
      call_method("run", G_DISCARD) ;
      
      PUSHMARK(SP) ;
        XPUSHs( nn );
        XPUSHs( set_out );
      PUTBACK ;
      call_method("learn", G_SCALAR) ;
    }
    
    for (i = 0 ; i <= av_len(set_av) ; i+=2) {
      SV* set_in = *av_fetch(set_av, i ,0) ;
      SV* set_out = *av_fetch(set_av, i+1 ,0) ;

      PUSHMARK(SP) ;
        XPUSHs( nn );
        XPUSHs( set_in );
      PUTBACK ;
      call_method("run", G_DISCARD) ;
      
      PUSHMARK(SP) ;
        XPUSHs( nn );
        XPUSHs( set_out );
      PUTBACK ;
      call_method("RMSErr", G_SCALAR) ;
      
      SPAGAIN ;
      ret = POPs ;
      er = SvNV(ret) ;
      if (er < 0) er *= -1 ;
      if (er < error_ok) ++learn_ok ;
      err += er ;
      
      if ( verbose ) sv_catpvf(print_verbose , "%s => %s > %f\n" ,

lib/AI/NNEasy.pm  view on Meta::CPAN

  print "1 1 => @$out\n" ; ## 1 1 => 0
  
  ## or just interate through the @set:
  for (my $i = 0 ; $i < @set ; $i+=2) {
    my $out = $nn->run_get_winner($set[$i]) ;
    print "@{$set[$i]}) => @$out\n" ;
  }

=head1 METHODS

=head2 new ( FILE , @OUTPUT_TYPES , ERROR_OK , IN_SIZE , OUT_SIZE , @HIDDEN_LAYERS , %CONF )

=over 4

=item FILE

The file path to save the NN. Default: 'nneasy.nne'.

=item @OUTPUT_TYPES

An array of outputs that the NN can have, so the NN can find the nearest number in this
list to give your the right output.

=item ERROR_OK

The maximal error of the calculated output.

If not defined ERROR_OK will be calculated by the minimal difference between 2 types at
@OUTPUT_TYPES dived by 2:

  @OUTPUT_TYPES = [0 , 0.5 , 1] ;
  
  ERROR_OK = (1 - 0.5) / 2 = 0.25 ;

=item IN_SIZE

The input size (number of nodes in the inpute layer).

=item OUT_SIZE

The output size (number of nodes in the output layer).

lib/AI/NNEasy.pm  view on Meta::CPAN

=item N

Number of times that this input should be learned. Default: 100

Example:

  $nn->learn( [0,1] , [1] , 10 ) ;

=back

=head2 learn_set (@SET , OK_OUTPUTS , LIMIT , VERBOSE)

Learn a set of inputs until get the right error for the outputs.

=over 4

=item @SET

A list of inputs and outputs.

=item OK_OUTPUTS

Minimal number of outputs that should be OK when calculating the erros.

By default I<OK_OUTPUTS> should have the same size of number of different
inouts in the @SET.

=item LIMIT

Limit of interations when learning. Default: 30000

=item VERBOSE

If TRUE turn verbose method ON when learning.

=back

=head2 get_set_error (@SET , OK_OUTPUTS)

Get the actual error of a set in the NN. If the returned error is bigger than
I<ERROR_OK> defined on I<new()> you should learn or relearn the set.

=head2 run (@INPUT)

Run a input and return the output calculated by the NN based in what the NN already have learned.

=head2 run_get_winner (@INPUT)

Same of I<run()>, but the output will return the nearest output value based in the
I<@OUTPUT_TYPES> defined at I<new()>.

For example an input I<[0,1]> learned that have
the output I<[1]>, actually will return something like 0.98324 as output and
not 1, since the error never should be 0. So, with I<run_get_winner()>
we get the output of I<run()>, let's say that is 0.98324, and find what output
is near of this number, that in this case should be 1. An output [0], will return
by I<run()> something like 0.078964, and I<run_get_winner()> return 0.

=head1 Samples

lib/AI/NNEasy.pm  view on Meta::CPAN

is [1,1], the nodes I<output1> and I<output2> should give to us a number near 1,
let's say 0.98654. And if the output for [0,0] is [0,0], I<output1> and I<output2> should give to us a number near 0,
let's say 0.078875.

What is hard in a NN is to find this I<weights>. By default L<AI::NNEasy> uses
I<backprop> as learning algorithm. With I<backprop> it pastes the inputs through
the Neural Network and adjust the I<weights> using random numbers until we find
a set of I<weights> that give to us the right output.

The secret of a NN is the number of hidden layers and nodes/neurons for each layer.
Basically the best way to define the hidden layers is 1 layer of (INPUT_NODES+OUTPUT_NODES).
So, a layer of 2 input nodes and 1 output node, should have 3 nodes in the hidden layer.
This definition exists because the number of inputs define the maximal variability of
the inputs (N**2 for bollean inputs), and the output defines if the variability is reduced by some logic restriction, like
int the XOR example, where we have 2 inputs and 1 output, so, hidden is 3. And as we can see in the
logic we have 3 groups of inputs:

  0 0 => 0 # false
  0 1 => 1 # or
  1 0 => 1 # or
  1 1 => 1 # true