Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Remove unnecessary function calls from loops, cleaning too much indir… #29

Open
wants to merge 1 commit into
base: gh-pages
Choose a base branch
from
Open

Remove unnecessary function calls from loops, cleaning too much indir… #29

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
137 changes: 82 additions & 55 deletions Neuroevolution.js
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ var Neuroevolution = function (options) {
* @return Logistic function output.
*/
activation: function (a) {
ap = (-a) / 1;
var ap = (-a) / 1;
return (1 / (1 + Math.exp(ap)))
},

Expand Down Expand Up @@ -85,8 +85,9 @@ var Neuroevolution = function (options) {
*/
Neuron.prototype.populate = function (nb) {
this.weights = [];
var rfunc = self.options.randomClamped;
for (var i = 0; i < nb; i++) {
this.weights.push(self.options.randomClamped());
this.weights.push(rfunc());
}
}

Expand Down Expand Up @@ -154,13 +155,14 @@ var Neuroevolution = function (options) {
index++;
for (var i in hiddens) {
// Repeat same process as first layer for each hidden layer.
var layer = new Layer(index);
layer.populate(hiddens[i], previousNeurons);
previousNeurons = hiddens[i];
this.layers.push(layer);
var hidden_layer = new Layer(index);
var ihidden = hiddens[i];
hidden_layer.populate(ihidden, previousNeurons);
previousNeurons = ihidden;
this.layers.push(hidden_layer);
index++;
}
var layer = new Layer(index);
layer = new Layer(index);
layer.populate(output, previousNeurons); // Number of input is equal to
// the size of the last hidden
// layer.
Expand All @@ -175,18 +177,22 @@ var Neuroevolution = function (options) {
* @return Network data.
*/
Network.prototype.getSave = function () {
var dneurons = []; // Number of Neurons per layer.
var dweights = []; // Weights of each Neuron's inputs.
var datas = {
neurons: [], // Number of Neurons per layer.
weights: [] // Weights of each Neuron's inputs.
neurons: dneurons,
weights: dweights
};

for (var i in this.layers) {
datas.neurons.push(this.layers[i].neurons.length);
for (var j in this.layers[i].neurons) {
for (var k in this.layers[i].neurons[j].weights) {
var neuron = this.layers[i].neurons;
dneurons.push(neuron.length);
for (var j in neuron) {
var weights = neuron[j].weights;
for (var k in weights) {
// push all input weights of each Neuron of each Layer into a flat
// array.
datas.weights.push(this.layers[i].neurons[j].weights[k]);
dweights.push(weights[k]);
}
}
}
Expand All @@ -204,19 +210,22 @@ var Neuroevolution = function (options) {
var index = 0;
var indexWeights = 0;
this.layers = [];
var sweights = save.weights;
for (var i in save.neurons) {
// Create and populate layers.
var layer = new Layer(index);
layer.populate(save.neurons[i], previousNeurons);
var sneuron = save.neurons[i];
layer.populate(sneuron, previousNeurons);
for (var j in layer.neurons) {
for (var k in layer.neurons[j].weights) {
var weights = layer.neurons[j].weights;
for (var k in weights) {
// Apply neurons weights to each Neuron.
layer.neurons[j].weights[k] = save.weights[indexWeights];
weights[k] = sweights[indexWeights];

indexWeights++; // Increment index of flat array.
}
}
previousNeurons = save.neurons[i];
previousNeurons = sneuron;
index++;
this.layers.push(layer);
}
Expand All @@ -230,35 +239,43 @@ var Neuroevolution = function (options) {
*/
Network.prototype.compute = function (inputs) {
// Set the value of each Neuron in the input layer.
for (var i in inputs) {
if (this.layers[0] && this.layers[0].neurons[i]) {
this.layers[0].neurons[i].value = inputs[i];
var layer0 = this.layers[0];
if(layer0)
{
var layer0_neurons = layer0.neurons;
for (var i in inputs) {
if (layer0_neurons[i]) {
layer0_neurons[i].value = inputs[i];
}
}
}

var prevLayer = this.layers[0]; // Previous layer is input layer.
for (var i = 1; i < this.layers.length; i++) {
for (var j in this.layers[i].neurons) {
var activation = self.options.activation;
for (var i = 1, len = this.layers.length; i < len; i++) {
var pneurons = prevLayer.neurons;
var lneurons = this.layers[i].neurons;
for (var j in lneurons) {
// For each Neuron in each layer.
var weights = lneurons[j].weights;
var sum = 0;
for (var k in prevLayer.neurons) {
for (var k in pneurons) {
// Every Neuron in the previous layer is an input to each Neuron in
// the next layer.
sum += prevLayer.neurons[k].value *
this.layers[i].neurons[j].weights[k];
sum += pneurons[k].value * weights[k];
}

// Compute the activation of the Neuron.
this.layers[i].neurons[j].value = self.options.activation(sum);
lneurons[j].value = activation(sum);
}
prevLayer = this.layers[i];
}

// All outputs of the Network.
var out = [];
var lastLayer = this.layers[this.layers.length - 1];
for (var i in lastLayer.neurons) {
out.push(lastLayer.neurons[i].value);
var lastLayer_neurons = this.layers[this.layers.length - 1].neurons;
for (var i in lastLayer_neurons) {
out.push(lastLayer_neurons[i].value);
}
return out;
}
Expand Down Expand Up @@ -302,15 +319,17 @@ var Neuroevolution = function (options) {
Generation.prototype.addGenome = function (genome) {
// Locate position to insert Genome into.
// The gnomes should remain sorted.
for (var i = 0; i < this.genomes.length; i++) {
var scoreSort = self.options.scoreSort;
for (var i = 0, len = this.genomes.length; i < len; i++) {
// Sort in descending order.
if (self.options.scoreSort < 0) {
if (genome.score > this.genomes[i].score) {
var score = this.genomes[i].score;
if (scoreSort < 0) {
if (genome.score > score) {
break;
}
// Sort in ascending order.
} else {
if (genome.score < this.genomes[i].score) {
if (genome.score < score) {
break;
}
}
Expand All @@ -330,25 +349,27 @@ var Neuroevolution = function (options) {
*/
Generation.prototype.breed = function (g1, g2, nbChilds) {
var datas = [];
var mutationRate = self.options.mutationRate;
var mutationRange = self.options.mutationRange;
var g2_weights = g2.network.weights;
for (var nb = 0; nb < nbChilds; nb++) {
// Deep clone of genome 1.
var data = JSON.parse(JSON.stringify(g1));
for (var i in g2.network.weights) {
var data_weights = data.network.weights;
for (var i in g2_weights) {
// Genetic crossover
// 0.5 is the crossover factor.
// FIXME Really should be a predefined constant.
if (Math.random() <= 0.5) {
data.network.weights[i] = g2.network.weights[i];
data_weights[i] = g2_weights[i];
}
}

// Perform mutation on some weights.
for (var i in data.network.weights) {
if (Math.random() <= self.options.mutationRate) {
data.network.weights[i] += Math.random() *
self.options.mutationRange *
2 -
self.options.mutationRange;
if (Math.random() <= mutationRate) {
data_weights[i] += Math.random() *
mutationRange * 2 - mutationRange;
}
}
datas.push(data);
Expand All @@ -365,34 +386,38 @@ var Neuroevolution = function (options) {
Generation.prototype.generateNextGeneration = function () {
var nexts = [];

for (var i = 0; i < Math.round(self.options.elitism *
self.options.population); i++) {
if (nexts.length < self.options.population) {
var population = self.options.population;
for (var i = 0, len= Math.round(self.options.elitism *
self.options.population); i < len; i++) {
if (nexts.length < population) {
// Push a deep copy of ith Genome's Nethwork.
nexts.push(JSON.parse(JSON.stringify(this.genomes[i].network)));
}
}

for (var i = 0; i < Math.round(self.options.randomBehaviour *
self.options.population); i++) {
var n = JSON.parse(JSON.stringify(this.genomes[0].network));
var funcRC = self.options.randomClamped;
var n = JSON.parse(JSON.stringify(this.genomes[0].network));
for (var i = 0, len = Math.round(self.options.randomBehaviour *
self.options.population); i < len; i++) {
for (var k in n.weights) {
n.weights[k] = self.options.randomClamped();
n.weights[k] = funcRC();
}
if (nexts.length < self.options.population) {
if (nexts.length < population) {
nexts.push(n);
}
}

var max = 0;
var nbChild = self.options.nbChild;
while (true) {
var genomes_max = this.genomes[max];
for (var i = 0; i < max; i++) {
// Create the children and push them to the nexts array.
var childs = this.breed(this.genomes[i], this.genomes[max],
(self.options.nbChild > 0 ? self.options.nbChild : 1));
var childs = this.breed(this.genomes[i], genomes_max,
(nbChild > 0 ? nbChild : 1));
for (var c in childs) {
nexts.push(childs[c].network);
if (nexts.length >= self.options.population) {
if (nexts.length >= population) {
// Return once number of children is equal to the
// population by generatino value.
return nexts;
Expand All @@ -417,7 +442,7 @@ var Neuroevolution = function (options) {
*/
var Generations = function () {
this.generations = [];
var currentGeneration = new Generation();
//var currentGeneration = new Generation();
}

/**
Expand All @@ -432,12 +457,14 @@ var Neuroevolution = function (options) {
// FIXME input, hiddens, output unused.

var out = [];
var network = self.options.network;
var network0 = network[0];
var network1 = network[1];
var network2 = network[2];
for (var i = 0; i < self.options.population; i++) {
// Generate the Network and save it.
var nn = new Network();
nn.perceptronGeneration(self.options.network[0],
self.options.network[1],
self.options.network[2]);
nn.perceptronGeneration(network0, network1, network2);
out.push(nn.getSave());
}

Expand Down
Loading