AI-NeuralNet-Simple
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---#YAML:1.0name: AI-NeuralNet-Simpleversion: 0.11license: perlgenerated_by: ExtUtils::MakeMaker version 6.31distribution_type: modulerequires:Log::Agent: 0.208Sub::Uplevel: 0Test::Exception: 0.15Test::More: 0.48_01meta-spec:version: 1.2author:- Curtis"Ovid"Poe <ovid@cpan.org>
To install this module type the following:perl Makefile.PLmakemake testmake installDEPENDENCIESThis module requires these other modules and libraries:Inline::CCOPYRIGHT AND LICENCECopyright (C) 2003 Curtis"Ovid"Poe <eop_divo_sitruc@yahoo.com>Reverse"eop_divo_sitruc"tosendme email.This library is free software; you can redistribute it and/or modify
av_store(av, i, build_rv(av2));}returnbuild_rv(av);}#define EXPORT_VERSION 1#define EXPORTED_ITEMS 9/** Exports the C data structures to the Perl worldforserialization* by Storable. We don't want to duplicate the logic of Storable here* even though we have todosome low-level Perl object construction.** The structure wereturnis an array reference, which contains the* following items:** 0 the export version number, in caseformatchanges later* 1 the amount of neurons in the input layer* 2 the amount of neurons in the hidden layer* 3 the amount of neurons in the output layer
free(input);free(output);returnmax_error;}SV* c_infer(inthandle, SV*array_ref){NEURAL_NETWORK*n= c_get_network(handle);inti;AV*perl_array,*result= newAV();/* feed the data */perl_array = get_array(array_ref);for(i = 0; i < n->size.input; i++)n->tmp[i] = get_float_element(perl_array, i);c_feed(n, n->tmp, NULL, 0);/*readthe results */for(i = 0; i < n->size.output; i++) {av_push(result, newSVnv(n->neuron.output[i]));}returnnewRV_noinc((SV*) result);}void c_feed(NEURAL_NETWORK*n, double*input, double*output,intlearn){inti;for(i=0; i < n->size.input; i++) {n->neuron.input[i] = input[i];}
c_feed_forward(n);if(learn) c_back_propagate(n);}/** The original author of this code is M. Tim Jones <mtj@cogitollc.com> and* writtenforthe book"AI Application Programming", by Charles River Media.** It's been so heavily modified that it bears little resemblance to the* original, but credit should begivenwhere credit is due. Therefore ...** Copyright (c) 2003 Charles River Media. All rights reserved.** modification, is hereby granted without fee provided that the following* conditions are met:** 1. Redistributions of source code must retain the above copyright* notice, this list of conditions and the following disclaimer. 2.* Redistributions in binary form must reproduce the above copyright* notice, this list of conditions and the following disclaimer in the* documentation and/or other materials providedwiththe distribution. 3.
examples/game_ai.pl view on Meta::CPAN
[POOR, NO, NO, 1], HIDE,[POOR, NO, NO, 0], WANDER,[POOR, YES, NO, 0], WANDER,]);my$format="%8s %5s %3s %7s %6s\n";my@actions=qw/attack run wander hide/;printf$format,qw/Health Knife Gun Enemies Action/;display_result($net,2,1,1,1);display_result($net,2,0,0,2);display_result($net,2,0,1,2);display_result($net,2,0,1,3);display_result($net,1,1,0,0);display_result($net,1,0,1,2);display_result($net,0,1,0,3);while(1) {"Type 'quit' to exit\n";my$health= prompt("Am I in poor, average, or good health? ",qr/^(?i:[pag])/);my$knife= prompt("Do I have a knife? ",qr/^(?i:[yn])/);my$gun= prompt("Do I have a gun? ",qr/^(?i:[yn])/);my$enemies= prompt("How many enemies can I see? ",qr/^\d+$/);$health=substr$health, 0, 1;$health=~tr/pag/012/;
examples/game_ai.pl view on Meta::CPAN
printf"I think I will %s!\n\n",$actions[$net->winner([$health,$knife,$gun,$enemies])];}subprompt{my($message,$domain) =@_;my$valid_response= 0;my$response;do{$message;chomp($response= <STDIN>);exitifsubstr(lc$response, 0, 1) eq'q';$valid_response=$response=~ /$domain/;}until$valid_response;return$response;}subdisplay_result{my($net,@data) =@_;my$result=$net->winner(\@data);my@health=qw/Poor Average Good/;my@knife=qw/No Yes/;my@gun=qw/No Yes/;printf$format,$health[$_[1]],$knife[$_[2]],$gun[$_[3]],$_[4],# number of enemies$actions[$result];}
lib/AI/NeuralNet/Simple.pm view on Meta::CPAN
$net->train([0,1],[0,1]);$net->train([0,0],[1,0]);}printf"Answer: %d\n",$net->winner([1,1]);printf"Answer: %d\n",$net->winner([1,0]);printf"Answer: %d\n",$net->winner([0,1]);printf"Answer: %d\n\n",$net->winner([0,0]);=head1 ABSTRACTThis module is a simple neural net designed for those who have an interestin artificial intelligence but need a "gentle" introduction. This is notintended to replace any of the neural net modules currently available on theCPAN.=head1 DESCRIPTION=head2 The DisclaimerPlease note that the following information is terribly incomplete. That'sdeliberate. Anyone familiar with neural networks is going to laugh themselvessilly at how simplistic the following information is and the astute reader willnotice that I've raised far more questions than I've answered.So why am I doing this? Because I'm giving I<just enough> information forsomeone new to neural networks to have enough of an idea of what's going on sothey can actually use this module and then move on to something more powerful,if interested.=head2 The BiologyA neural network, at its simplest, is merely an attempt to mimic nature's"design" of a brain. Like many successful ventures in the field of artificialintelligence, we find that blatantly ripping off natural designs has allowed usto solve many problems that otherwise might prove intractable. Fortunately,Mother Nature has not chosen to apply for patents.Our brains are comprised of neurons connected to one another by axons. Theaxon makes the actual connection to a neuron via a synapse. When neuronsreceive information, they process it and feed this information to other neuronswho in turn process the information and send it further until eventuallycommands are sent to various parts of the body and muscles twitch, emotions arefelt and we start eyeing our neighbor's popcorn in the movie theater, wondering
lib/AI/NeuralNet/Simple.pm view on Meta::CPAN
cannot be usedwiththe type of network that C<AI::NeuralNet::Simple> employs.This module uses the sigmoid activation function. (More information aboutthese can be found by reading the information in the L<SEE ALSO> section or byjust searchingwithGoogle.)Once the activation function is applied, the output is then sent through thenextsynapse, where it will be multiplied by w4 and the process willcontinue.=head2 C<AI::NeuralNet::Simple> architectureThe architecture used by this module has (at present) 3 fixed layers ofneurons: an input, hidden, and output layer. In practice, a 3 layer network isapplicable to many problems for which a neural network is appropriate, but thisis not always the case. In this module, we've settled on a fixed 3 layernetwork for simplicity.Here's how a three layer network might learn "logical or". First, we need todetermine how many inputs and outputs we'll have. The inputs are simple, we'llchoose two inputs as this is the minimum necessary to teach a network thisconcept. For the outputs, we'll also choose two neurons, with the neuron withthe highest output value being the "true" or "false" response that we arelooking for. We'll only have one neuron for the hidden layer. Thus, we get anetwork that resembles the following:Input Hidden Outputinput1 ----> n1 -+ +----> n4 ---> output1\ /n3/ \input2 ----> n2 -+ +----> n5 ---> output2Let's say that output 1 will correspond to "false" and output 2 will correspondto true. If we feed 1 (or true) or both input 1 and input 2, we hope that output2 will be true and output 1 will be false. The following table should illustratethe expected results:input output1 2 1 2----- ------1 1 0 11 0 0 10 1 0 10 0 1 0The type of network we use is a forward-feed back error propagation network,referred to as a back-propagation network, for short. The way it works issimple. When we feed in our input, it travels from the input to hidden layersand then to the output layers. This is the "feed forward" part. We thencompare the output to the expected results and measure how far off we are. Wethen adjust the weights on the "output to hidden" synapses, measure the erroron the hidden nodes and then adjust the weights on the "hidden to input"synapses. This is what is referred to as "back error propagation".We continue this process until the amount of error is small enough that we aresatisfied. In reality, we will rarely if ever get precise results from thenetwork, but we learn various strategies to interpret the results. In theexample above, we use a "winner takes all" strategy. Which ever of the outputnodes has the greatest value will be the "winner", and thus the answer.In the examples directory, you will find a program named "logical_or.pl" whichdemonstrates the above process.=head2 Building a networkIn creating a new neural network, there are three basic steps:
lib/AI/NeuralNet/Simple.pm view on Meta::CPAN
With more complete neural net packages, you can also pick which activation=item 2 TrainingThis involves feeding the neural network enough data until the error rate islow enough to be acceptable. Often we have a large data set and merely keepiterating until the desired error rate is achieved.=item 3 Measuring resultsOne frequent mistake made with neural networks is failing to test the networkwith different data from the training data. It's quite possible for abackpropagation network to hit what is known as a "local minimum" which is nottruly where it should be. This will cause false results. To check for this,after training we often feed in other known good data for verification. If theresults are not satisfactory, perhaps a different number of neurons per layershould be tried or a different set of training data should be supplied.=back=head1 Programming C<AI::NeuralNet::Simple>=head2 C<new($input, $hidden, $output)>C<new()> accepts three integers. These number represent the number of nodes inthe input, hidden, and output layers, respectively. To create the "logical or"network described earlier:my $net = AI::NeuralNet::Simple->new(2,1,2);By default, the activation function for the neurons is the sigmoid functionS() with delta = 1:S(x) = 1 / (1 + exp(-delta * x))but you can change the delta after creation. You can also use a bipolar
lib/AI/NeuralNet/Simple.pm view on Meta::CPAN
Returns whether the network currently uses a bipolar activation function.function or not.You should not change the activation function during the traning.=head2 C<train(\@input, \@output)>This method trains the network to associate the input data set with the outputdata set. Representing the "logical or" is as follows:$net->train([1,1] => [0,1]);$net->train([1,0] => [0,1]);$net->train([0,1] => [0,1]);$net->train([0,0] => [1,0]);Note that a one pass through the data is seldom sufficient to train a network.In the example "logical or" program, we actually run this data through thenetwork ten thousand times.for (1 .. 10000) {$net->train([1,1] => [0,1]);$net->train([1,0] => [0,1]);$net->train([0,1] => [0,1]);$net->train([0,0] => [1,0]);}The routine returns the Mean Squared Error (MSE) representing how far thenetwork answered.It is far preferable to use C<train_set()> as this lets you control the MSEover the training set and it is more efficient because there are less memorycopies back and forth.=head2 C<train_set(\@dataset, [$iterations, $mse])>Similar to train, this method allows us to train an entire data set at once.It is typically faster than calling individual "train" methods. The first
lib/AI/NeuralNet/Simple.pm view on Meta::CPAN
->train_set(\@training_data);If you choose a lower learning rate, you will train the network slower, but youmay get a better accuracy. A higher learning rate will train the networkfaster, but it can have a tendancy to"overshoot"the answerwhenlearning andnot learn as accurately.=head2 C<infer(\@input)>This method, if provided with an input array reference, will return an arrayreference corresponding to the output values that it is guessing. Note thatthese values will generally be close, but not exact. For example, with the"logical or" program, you might expect results similar to:use Data::Dumper;print Dumper $net->infer([1,1]);$VAR1 = ['0.00993729281477686','0.990100297418451'];That clearly has the second output item being close to 1, so as a helper methodfor use with a winner take all strategy, we have ...=head2 C<winner(\@input)>This method returns the index of the highest value from inferred results:print $net->winner([1,1]); # will likely print "1"For a more comprehensive example of how this is used, see the"examples/game_ai.pl" program.=head1 EXPORTNone by default.
lib/AI/NeuralNet/Simple.pm view on Meta::CPAN
copyright (c) 1990 by Massachussetts Institute of Technology.This book is a decent introduction to neural networks in general. The forwardfeed back error propogation is but one of many types.=head1 AUTHORSCurtis "Ovid" Poe, C<ovid [at] cpan [dot] org>Multiple network support, persistence, export of MSE (mean squared error),training until MSE below a given threshold and customization of theactivation function added by Raphael Manfredi C<Raphael_Manfredi@pobox.com>.=head1 COPYRIGHT AND LICENSECopyright (c) 2003-2005 by Curtis "Ovid" PoeCopyright (c) 2006 by Raphael ManfrediThis library is free software; you can redistribute it and/or modifyit under the same terms as Perl itself.
t/10nn_simple.t view on Meta::CPAN
ok($net->train([1,1], [0,1]),'Calling train() with valid data should succeed');for(1 .. 10000) {$net->train([1,1],[0,1]);$net->train([1,0],[0,1]);$net->train([0,1],[0,1]);$net->train([0,0],[1,0]);}can_ok($net,'winner');is($net->winner([1,1]), 1,'... and it should return the index of the highest valued result');is($net->winner([1,0]), 1,'... and it should return the index of the highest valued result');is($net->winner([0,1]), 1,'... and it should return the index of the highest valued result');is($net->winner([0,0]), 0,'... and it should return the index of the highest valued result');# teach the network logical 'and' using the tanh() activation with delta=2$net=$CLASS->new(2,1,2);$net->delta(2);$net->use_bipolar(1);my$mse=$net->train_set([[1,1] => [0,1],[1,0] => [1,0],[0,1] => [1,0],[0,0] => [1,0],
t/30nn_storable.t view on Meta::CPAN
my$net=$CLASS->new(2,1,2);$net->delta(2);$net->use_bipolar(5);for(1 .. 10000) {$net->train([1,1],[0,1]);$net->train([1,0],[0,1]);$net->train([0,1],[0,1]);$net->train([0,0],[1,0]);}is($net->winner([1,1]), 1,'... and it should return the index of the highest valued result');is($net->winner([1,0]), 1,'... and it should return the index of the highest valued result');is($net->winner([0,1]), 1,'... and it should return the index of the highest valued result');is($net->winner([0,0]), 0,'... and it should return the index of the highest valued result');ok(store($net,"t/store"),"store() succeeds");$net=undef;$net= retrieve("t/store");ok($net,"retrieve() succeeds");unlink't/store';can_ok($net,'learn_rate');is($net->delta, 2,'properly restored value of delta');is($net->use_bipolar, 5,'properly restored value of use_bipolar');is($net->winner([1,1]), 1,'... and it should return the index of the highest valued result');is($net->winner([1,0]), 1,'... and it should return the index of the highest valued result');is($net->winner([0,1]), 1,'... and it should return the index of the highest valued result');is($net->winner([0,0]), 0,'... and it should return the index of the highest valued result');
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