neuralnetwork/build/lib/neurons.js

Rudimentary Neural Network in Typescript

//var key; // Temp variable for copying keys in a loop
"use strict";
var __extends = (this && this.__extends) || function (d, b) {
    for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p];
    function __() { this.constructor = d; }
    d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
};
/**
* Neural models for layers.
* Should contain a name and both forward and backward interfaces
*/
var Neuron = (function () {
    function Neuron() {
        this.name = Math.random().toString(36).substring(2);
    }
    Neuron.prototype.forward = function (input) {
        return input;
    };
    Neuron.prototype.backward = function (error, ouput) {
        return error;
    };
    return Neuron;
}());
exports.Neuron = Neuron;
var IdentityNeuron = (function (_super) {
    __extends(IdentityNeuron, _super);
    function IdentityNeuron() {
        _super.apply(this, arguments);
    }
    return IdentityNeuron;
}(Neuron));
exports.IdentityNeuron = IdentityNeuron;
var BiasNeuron = (function (_super) {
    __extends(BiasNeuron, _super);
    function BiasNeuron() {
        _super.apply(this, arguments);
    }
    BiasNeuron.prototype.forward = function (input) {
        return 1;
    };
    BiasNeuron.prototype.backward = function (error) {
        return 0;
    };
    return BiasNeuron;
}(Neuron));
exports.BiasNeuron = BiasNeuron;
var SigmoidNeuron = (function (_super) {
    __extends(SigmoidNeuron, _super);
    function SigmoidNeuron() {
        _super.apply(this, arguments);
    }
    SigmoidNeuron.prototype.forward = function (input) {
        return 1.0 / (1.0 + Math.exp(-input));
    };
    // Output is the activation value created by forward
    SigmoidNeuron.prototype.backward = function (err, output) {
        return output * (1 - output) * err;
    };
    return SigmoidNeuron;
}(Neuron));
exports.SigmoidNeuron = SigmoidNeuron;
var RectifiedLinearNeuron = (function (_super) {
    __extends(RectifiedLinearNeuron, _super);
    function RectifiedLinearNeuron() {
        _super.apply(this, arguments);
    }
    RectifiedLinearNeuron.prototype.forward = function (input) {
        return Math.max(0, input);
    };
    RectifiedLinearNeuron.prototype.backward = function (err, output) {
        return ((output > 0) ? err : 0);
    };
    return RectifiedLinearNeuron;
}(Neuron));
exports.RectifiedLinearNeuron = RectifiedLinearNeuron;

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