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, HopfieldSynaps.new());
                                @.neurons[$j].inputsynapses[$j].outputneuron =
                                        @.neurons[$m];

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

                        }
                }

        }

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

                ### the inputs length is less than the full neuron list
                ### 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();

                }
        }