Javascript Neural Network (feed forward)

/**
 * Constructor A:
 * @param structure (ex. [4, 3, 2] 4 neurons in the input layer, 3 in the hidden layer and 2 in the output layer)
 *
 * Contructor B:
 * @param Brain dad
 * @param Brain mom
 * @param float mutation rate (0 = 0%, 1 = 100%)
 *
 * Examples:
 * to generate a new brain, use: new Brain([4, 3, 2])
 * to clone a brain, use: new Brain(brain_to_clone, brain_to_clone, 0)
 *
 */
var Brain = function(a, b, mrate) {

    // The mutation rate, will be set later
    this.mrate = 0;

    this.network;
    this.outputs;
    this.inputs;

    if (b == null) {
        // Generate a new brain
        var struct = a;
        this.network = new Array(struct.length-1);
        this.outputs = new Array(this.network.length);

        for(var l = 0; l < struct.length-1; l++) {
            this.network[l] = new Array(struct[l+1]);
            this.outputs[l] = new Array(this.network[l].length);

            var syn = struct[l] + 1;

            for(var n = 0; n < this.network[l].length; n++) {
                this.network[l][n] = new Array(syn);
                for(var w = 0; w < syn; w++) {
                    this.outputs[l][n] = 1;
                    this.network[l][n][w] = Brain.genWeight(1);
                }
            }
        }
    } else {

        // Generate a new brain given its parents
        var dad = a,
            mom = b;

        if (mrate == null) {
            this.mrate = dad.mrate;
        } else {
            this.mrate = mrate;
        }

        var d = dad.network;
        var m = mom.network;
        var src;

        this.network = new Array(dad.network.length);
        this.outputs = new Array(this.network.length);

        for(var l = 0; l < this.network.length; l++) {

            this.network[l] = new Array(d[l].length);
            this.outputs[l] = new Array(this.network[l].length);

            for(var n = 0; n < this.network[l].length; n++) {

                this.network[l][n] = new Array(d[l][n].length);

                src = Math.random() < .5 ? d[l][n] : m[l][n];

                for(var w = 0; w < this.network[l][n].length; w++)
                    this.network[l][n][w] = src[w] + (Math.random()*100 < this.mrate ? Brain.genWeight(.5) : 0);
            }
        }
    }

    // This is used to calculate the network outputs
    this.process = function(inp) {
        this.inputs = inp;
        for(var l = 0; l < this.network.length; l++) {

            if(l > 0)
                inp = this.outputs[l-1];

            for(var n = 0; n < this.network[l].length; n++) {
                this.outputs[l][n] = 0;
                for(var w = 0; w < this.network[l][n].length-1; w++) {
                    this.outputs[l][n]+= inp[w] * this.network[l][n][w];
                }
                this.outputs[l][n]+= this.network[l][n][this.network[l][n].length-1];

                this.outputs[l][n] = this.activate(this.outputs[l][n]);
            }

        }

        return this.outputs[this.outputs.length-1];
    }

    // Get the last outputs
    this.getOutputs = function() {
        return this.outputs[this.outputs.length-1];
    }

    // The activation function
    this.activate = function(x) {
        //return Math.sin(x*10.0);
        if(x > 40)
            return 1;
        if(x < -40)
            return -1;
        return 2.0/(1 + Math.exp(-x))-1;
    }

    // Clone a brain
    this.clone = function() {
        return new Brain(this, this, 0);
    }

    // Get the number of inputs
    this.inputs = function() {
        return this.network[0][0].length-1;
    }
}

// Generate a random number between -factor and +factor
Brain.genWeight = function(factor) {
    return (factor?factor:1)*(1-Math.random()*2);
}