Hopfield Neural Network in perl6

[holyghost]

A small implementation of an Artificial Neural Network using Hopfield neurons, synapses and a simple training system :

unit module ann;

use ann::HopfieldSynaps;

class HopfieldNeuron is export {

        has @.inputsynapses;
        has @.outputsynapses;

        has $.input;

        method BUILD($y1 = 1000000.rand) {
                $.input = $y1;

        }

        method fire() {

                ### with training update weights
                loop (my $i = 0; $i < @.inputsynapses.length; $i++) {
                        if (@.inputsynapses[$i].weight * @.inputsynaps
+es[$i].outputneuron.input >= 0) {
                                @.inputsynapses[$i].outputneuron.input
+ = 1;
                        } else {
                                @.inputsynapses[$i].outputneuron.input
+ = 0;
                        }
                }
        }


}

unit module ann;

use ann::HopfieldNeuron;

class HopfieldSynaps is export {

        has $.weight;
        has $.inputneuron;
        has $.outputneuron;

        method BUILD($inputneuron, $outputneuron, $y1 = 1000000.rand) 
+{
                $.weight = $y1;
        }

};

unit module ann;

use ann::HopfieldNeuron;
use ann::HopfieldSynaps;

class HopfieldNN is export {

        has @.neurons;

        method BUILD($size) {

                @.neurons = ();
                loop (my $n = 0; $n < $size; $n++) {

                        push (@.neurons, HopfieldNeuron.new());

                }

                loop (my $m = 0; $m < $size; $m++) {
                        loop (my $j = 0; $j < $size; $j++) {
                                push(@.neurons[$j].inputsynapses, Hopf
+ieldSynaps.new());
                                @.neurons[$j].inputsynapses[$j].output
+neuron =
                                        @.neurons[$m];

                        }
                }
                loop (my $i = 0; $i < $size; $i++) {
                        loop (my $j = 0; $j < $size; $j++) {
                                push(@.neurons[$j].outputsynapses, Hop
+fieldSynaps.new());
                                @.neurons[$j].outputsynapses[$j].outpu
+tneuron =
                                        @.neurons[$i];

                        }
                }

        }

        ### repeat this to train the network
        method start(@inputs) {

                ### the inputs length is less than the full neuron lis
+t
                ### the first neurons made in the constructor are the 
+inputs
                ### of the network

                loop (my $i = 0; $i < @inputs.length; $i++) {

                        @.neurons[$i].input = @inputs[$i];
                }
                loop (my $j = 0; $j < @.neurons.length; $j++) {

                        @.neurons[$j].fire();

                }
        }

        method start2(@inputs) {
                ### without any traning, first neurons are for the inp
+ut pattern

                loop (my $n = 0; $n < @inputs.length; $n++) {
                        @.neurons[$n].input = @inputs[$n];
                }

                loop (my $i = 0; $i < @.neurons.length; $i++) {
                        loop (my $j = 0; $j < @.neurons.length; $j++) 
+{
                                loop (my $k = 0; $k < @.neurons.length
+; $k++) {

                                        if ($k == $j) { next; };

                                @.neurons[$i].inputsynapses[$j].weight
+ += (2 * @.neurons[$i].inputsynapses[$j].outputneuron.input - 1) * (2
+ * @.neurons[$i].inputsynapes[$k].outputneuron.input -1);
                                }
                        }
                }
        }
};