From 02be835802e046f570842d8e4c36be377be89639 Mon Sep 17 00:00:00 2001
From: atmandarji <atman.darji@gmail.com>
Date: Thu, 7 Mar 2019 12:02:09 +0530
Subject: [PATCH 1/2] New UI has been added and reset button has been added

---
 Neuroevolution.js        | 296 +++------------------------------------
 game.js                  | 120 ++++++++++++++--
 img/background.png       | Bin 3175 -> 4399 bytes
 img/bird.png             | Bin 382 -> 9001 bytes
 img/pipebottom.png       | Bin 1241 -> 3937 bytes
 img/pipetop.png          | Bin 1191 -> 3848 bytes
 img1/background.png      | Bin 0 -> 3175 bytes
 img1/bird.png            | Bin 0 -> 382 bytes
 {img => img1}/flappy.png | Bin
 img1/pipebottom.png      | Bin 0 -> 1241 bytes
 img1/pipetop.png         | Bin 0 -> 1191 bytes
 index.html               |  80 +++++++++--
 12 files changed, 201 insertions(+), 295 deletions(-)
 mode change 100644 => 100755 img/background.png
 mode change 100644 => 100755 img/bird.png
 mode change 100644 => 100755 img/pipebottom.png
 mode change 100644 => 100755 img/pipetop.png
 create mode 100755 img1/background.png
 create mode 100755 img1/bird.png
 rename {img => img1}/flappy.png (100%)
 mode change 100644 => 100755
 create mode 100755 img1/pipebottom.png
 create mode 100755 img1/pipetop.png

diff --git a/Neuroevolution.js b/Neuroevolution.js
index f1bc43d..7108bfb 100644
--- a/Neuroevolution.js
+++ b/Neuroevolution.js
@@ -1,118 +1,46 @@
-/**
- * Provides a set of classes and methods for handling Neuroevolution and
- * genetic algorithms.
- *
- * @param {options} An object of options for Neuroevolution.
- */
 var Neuroevolution = function (options) {
-	var self = this; // reference to the top scope of this module
-
-	// Declaration of module parameters (options) and default values
+	var self = this; 
 	self.options = {
-		/**
-		 * Logistic activation function.
-		 *
-		 * @param {a} Input value.
-		 * @return Logistic function output.
-		 */
 		activation: function (a) {
 			ap = (-a) / 1;
 			return (1 / (1 + Math.exp(ap)))
 		},
-
-		/**
-		 * Returns a random value between -1 and 1.
-		 *
-		 * @return Random value.
-		 */
 		randomClamped: function () {
 			return Math.random() * 2 - 1;
 		},
-
-		// various factors and parameters (along with default values).
-		network: [1, [1], 1], // Perceptron network structure (1 hidden
-		// layer).
-		population: 50, // Population by generation.
-		elitism: 0.2, // Best networks kepts unchanged for the next
-		// generation (rate).
-		randomBehaviour: 0.2, // New random networks for the next generation
-		// (rate).
-		mutationRate: 0.1, // Mutation rate on the weights of synapses.
-		mutationRange: 0.5, // Interval of the mutation changes on the
-		// synapse weight.
-		historic: 0, // Latest generations saved.
-		lowHistoric: false, // Only save score (not the network).
-		scoreSort: -1, // Sort order (-1 = desc, 1 = asc).
-		nbChild: 1 // Number of children by breeding.
-
+		network: [1, [1], 1],
+		population: 50,
+		elitism: 0.2,
+		randomBehaviour: 0.2,
+		mutationRate: 0.1,
+		mutationRange: 0.5,
+		historic: 0,
+		lowHistoric: false,
+		scoreSort: -1,
+		nbChild: 1
 	}
-
-	/**
-	 * Override default options.
-	 *
-	 * @param {options} An object of Neuroevolution options.
-	 * @return void
-	 */
 	self.set = function (options) {
 		for (var i in options) {
-			if (this.options[i] != undefined) { // Only override if the passed in value
-				// is actually defined.
+			if (this.options[i] != undefined) {
 				self.options[i] = options[i];
 			}
 		}
 	}
-
-	// Overriding default options with the pass in options
 	self.set(options);
-
-
-	/*NEURON**********************************************************************/
-	/**
-	 * Artificial Neuron class
-	 *
-	 * @constructor
-	 */
 	var Neuron = function () {
 		this.value = 0;
 		this.weights = [];
 	}
-
-	/**
-	 * Initialize number of neuron weights to random clamped values.
-	 *
-	 * @param {nb} Number of neuron weights (number of inputs).
-	 * @return void
-	 */
 	Neuron.prototype.populate = function (nb) {
 		this.weights = [];
 		for (var i = 0; i < nb; i++) {
 			this.weights.push(self.options.randomClamped());
 		}
 	}
-
-
-	/*LAYER***********************************************************************/
-	/**
-	 * Neural Network Layer class.
-	 *
-	 * @constructor
-	 * @param {index} Index of this Layer in the Network.
-	 */
 	var Layer = function (index) {
 		this.id = index || 0;
 		this.neurons = [];
 	}
-
-	/**
-	 * Populate the Layer with a set of randomly weighted Neurons.
-	 *
-	 * Each Neuron be initialied with nbInputs inputs with a random clamped
-	 * value.
-	 *
-	 * @param {nbNeurons} Number of neurons.
-	 * @param {nbInputs} Number of inputs.
-	 * @return void
-	 */
 	Layer.prototype.populate = function (nbNeurons, nbInputs) {
 		this.neurons = [];
 		for (var i = 0; i < nbNeurons; i++) {
@@ -121,39 +49,18 @@ var Neuroevolution = function (options) {
 			this.neurons.push(n);
 		}
 	}
-
-
-	/*NEURAL NETWORK**************************************************************/
-	/**
-	 * Neural Network class
-	 *
-	 * Composed of Neuron Layers.
-	 *
-	 * @constructor
-	 */
 	var Network = function () {
 		this.layers = [];
 	}
-
-	/**
-	 * Generate the Network layers.
-	 *
-	 * @param {input} Number of Neurons in Input layer.
-	 * @param {hidden} Number of Neurons per Hidden layer.
-	 * @param {output} Number of Neurons in Output layer.
-	 * @return void
-	 */
 	Network.prototype.perceptronGeneration = function (input, hiddens, output) {
 		var index = 0;
 		var previousNeurons = 0;
 		var layer = new Layer(index);
-		layer.populate(input, previousNeurons); // Number of Inputs will be set to
-		// 0 since it is an input layer.
-		previousNeurons = input; // number of input is size of previous layer.
+		layer.populate(input, previousNeurons);
+		previousNeurons = input;
 		this.layers.push(layer);
 		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];
@@ -161,59 +68,37 @@ var Neuroevolution = function (options) {
 			index++;
 		}
 		var layer = new Layer(index);
-		layer.populate(output, previousNeurons); // Number of input is equal to
-		// the size of the last hidden
-		// layer.
+		layer.populate(output, previousNeurons); 
 		this.layers.push(layer);
 	}
-
-	/**
-	 * Create a copy of the Network (neurons and weights).
-	 *
-	 * Returns number of neurons per layer and a flat array of all weights.
-	 *
-	 * @return Network data.
-	 */
 	Network.prototype.getSave = function () {
 		var datas = {
-			neurons: [], // Number of Neurons per layer.
-			weights: [] // Weights of each Neuron's inputs.
+			neurons: [], 
+			weights: [] 
 		};
 
 		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) {
-					// push all input weights of each Neuron of each Layer into a flat
-					// array.
 					datas.weights.push(this.layers[i].neurons[j].weights[k]);
 				}
 			}
 		}
 		return datas;
 	}
-
-	/**
-	 * Apply network data (neurons and weights).
-	 *
-	 * @param {save} Copy of network data (neurons and weights).
-	 * @return void
-	 */
 	Network.prototype.setSave = function (save) {
 		var previousNeurons = 0;
 		var index = 0;
 		var indexWeights = 0;
 		this.layers = [];
 		for (var i in save.neurons) {
-			// Create and populate layers.
 			var layer = new Layer(index);
 			layer.populate(save.neurons[i], previousNeurons);
 			for (var j in layer.neurons) {
 				for (var k in layer.neurons[j].weights) {
-					// Apply neurons weights to each Neuron.
 					layer.neurons[j].weights[k] = save.weights[indexWeights];
-
-					indexWeights++; // Increment index of flat array.
+					indexWeights++;
 				}
 			}
 			previousNeurons = save.neurons[i];
@@ -221,40 +106,25 @@ var Neuroevolution = function (options) {
 			this.layers.push(layer);
 		}
 	}
-
-	/**
-	 * Compute the output of an input.
-	 *
-	 * @param {inputs} Set of inputs.
-	 * @return Network output.
-	 */
 	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 prevLayer = this.layers[0]; // Previous layer is input layer.
+		var prevLayer = this.layers[0];
 		for (var i = 1; i < this.layers.length; i++) {
 			for (var j in this.layers[i].neurons) {
-				// For each Neuron in each layer.
 				var sum = 0;
 				for (var k in prevLayer.neurons) {
-					// 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];
 				}
-
-				// Compute the activation of the Neuron.
 				this.layers[i].neurons[j].value = self.options.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) {
@@ -262,53 +132,19 @@ var Neuroevolution = function (options) {
 		}
 		return out;
 	}
-
-
-	/*GENOME**********************************************************************/
-	/**
-	 * Genome class.
-	 *
-	 * Composed of a score and a Neural Network.
-	 *
-	 * @constructor
-	 *
-	 * @param {score}
-	 * @param {network}
-	 */
 	var Genome = function (score, network) {
 		this.score = score || 0;
 		this.network = network || null;
 	}
-
-
-	/*GENERATION******************************************************************/
-	/**
-	 * Generation class.
-	 *
-	 * Composed of a set of Genomes.
-	 *
-	 * @constructor
-	 */
 	var Generation = function () {
 		this.genomes = [];
 	}
-
-	/**
-	 * Add a genome to the generation.
-	 *
-	 * @param {genome} Genome to add.
-	 * @return void.
-	 */
 	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++) {
-			// Sort in descending order.
 			if (self.options.scoreSort < 0) {
 				if (genome.score > this.genomes[i].score) {
 					break;
 				}
-				// Sort in ascending order.
 			} else {
 				if (genome.score < this.genomes[i].score) {
 					break;
@@ -316,33 +152,17 @@ var Neuroevolution = function (options) {
 			}
 
 		}
-
-		// Insert genome into correct position.
 		this.genomes.splice(i, 0, genome);
 	}
-
-	/**
-	 * Breed to genomes to produce offspring(s).
-	 *
-	 * @param {g1} Genome 1.
-	 * @param {g2} Genome 2.
-	 * @param {nbChilds} Number of offspring (children).
-	 */
 	Generation.prototype.breed = function (g1, g2, nbChilds) {
 		var datas = [];
 		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) {
-				// 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];
 				}
 			}
-
-			// Perform mutation on some weights.
 			for (var i in data.network.weights) {
 				if (Math.random() <= self.options.mutationRate) {
 					data.network.weights[i] += Math.random() *
@@ -356,19 +176,12 @@ var Neuroevolution = function (options) {
 
 		return datas;
 	}
-
-	/**
-	 * Generate the next generation.
-	 *
-	 * @return Next generation data array.
-	 */
 	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) {
-				// Push a deep copy of ith Genome's Nethwork.
 				nexts.push(JSON.parse(JSON.stringify(this.genomes[i].network)));
 			}
 		}
@@ -387,14 +200,11 @@ var Neuroevolution = function (options) {
 		var max = 0;
 		while (true) {
 			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));
 				for (var c in childs) {
 					nexts.push(childs[c].network);
 					if (nexts.length >= self.options.population) {
-						// Return once number of children is equal to the
-						// population by generatino value.
 						return nexts;
 					}
 				}
@@ -405,35 +215,13 @@ var Neuroevolution = function (options) {
 			}
 		}
 	}
-
-
-	/*GENERATIONS*****************************************************************/
-	/**
-	 * Generations class.
-	 *
-	 * Hold's previous Generations and current Generation.
-	 *
-	 * @constructor
-	 */
 	var Generations = function () {
 		this.generations = [];
 		var currentGeneration = new Generation();
 	}
-
-	/**
-	 * Create the first generation.
-	 *
-	 * @param {input} Input layer.
-	 * @param {input} Hidden layer(s).
-	 * @param {output} Output layer.
-	 * @return First Generation.
-	 */
 	Generations.prototype.firstGeneration = function (input, hiddens, output) {
-		// FIXME input, hiddens, output unused.
-
 		var out = [];
 		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],
@@ -444,15 +232,8 @@ var Neuroevolution = function (options) {
 		this.generations.push(new Generation());
 		return out;
 	}
-
-	/**
-	 * Create the next Generation.
-	 *
-	 * @return Next Generation.
-	 */
 	Generations.prototype.nextGeneration = function () {
 		if (this.generations.length == 0) {
-			// Need to create first generation.
 			return false;
 		}
 
@@ -461,51 +242,22 @@ var Neuroevolution = function (options) {
 		this.generations.push(new Generation());
 		return gen;
 	}
-
-	/**
-	 * Add a genome to the Generations.
-	 *
-	 * @param {genome}
-	 * @return False if no Generations to add to.
-	 */
 	Generations.prototype.addGenome = function (genome) {
-		// Can't add to a Generation if there are no Generations.
 		if (this.generations.length == 0) return false;
-
-		// FIXME addGenome returns void.
 		return this.generations[this.generations.length - 1].addGenome(genome);
 	}
-
-
-	/*SELF************************************************************************/
 	self.generations = new Generations();
-
-	/**
-	 * Reset and create a new Generations object.
-	 *
-	 * @return void.
-	 */
 	self.restart = function () {
 		self.generations = new Generations();
 	}
-
-	/**
-	 * Create the next generation.
-	 *
-	 * @return Neural Network array for next Generation.
-	 */
 	self.nextGeneration = function () {
 		var networks = [];
 
 		if (self.generations.generations.length == 0) {
-			// If no Generations, create first.
 			networks = self.generations.firstGeneration();
 		} else {
-			// Otherwise, create next one.
 			networks = self.generations.nextGeneration();
 		}
-
-		// Create Networks from the current Generation.
 		var nns = [];
 		for (var i in networks) {
 			var nn = new Network();
@@ -514,7 +266,6 @@ var Neuroevolution = function (options) {
 		}
 
 		if (self.options.lowHistoric) {
-			// Remove old Networks.
 			if (self.generations.generations.length >= 2) {
 				var genomes =
 					self.generations
@@ -527,7 +278,6 @@ var Neuroevolution = function (options) {
 		}
 
 		if (self.options.historic != -1) {
-			// Remove older generations.
 			if (self.generations.generations.length > self.options.historic + 1) {
 				self.generations.generations.splice(0,
 					self.generations.generations.length - (self.options.historic + 1));
@@ -536,14 +286,6 @@ var Neuroevolution = function (options) {
 
 		return nns;
 	}
-
-	/**
-	 * Adds a new Genome with specified Neural Network and score.
-	 *
-	 * @param {network} Neural Network.
-	 * @param {score} Score value.
-	 * @return void.
-	 */
 	self.networkScore = function (network, score) {
 		self.generations.addGenome(new Genome(score, network.getSave()));
 	}
diff --git a/game.js b/game.js
index 0864638..ba0d9de 100644
--- a/game.js
+++ b/game.js
@@ -137,13 +137,29 @@ var Game = function(){
 	this.backgroundSpeed = 0.5;
 	this.backgroundx = 0;
 	this.maxScore = 0;
+	this.myMaxScore=0;
+	this.myScore=0;
 }
-
 Game.prototype.start = function(){
+	if(this.generation!=0){
+		var table = document.getElementById("genscore");
+	    var row = table.insertRow(this.generation);
+	    var cell1 = row.insertCell(0);
+	    var cell2 = row.insertCell(1);
+	    var cell3 = row.insertCell(2);
+	    cell1.style.textAlign = "center";
+	    cell2.style.textAlign = "center";
+	    cell3.style.textAlign = "center";
+	    cell1.innerHTML = this.generation;
+	    cell2.innerHTML = this.myScore;
+	    cell3.innerHTML = this.score;
+	}
 	this.interval = 0;
 	this.score = 0;
+	this.myScore=0;
 	this.pipes = [];
 	this.birds = [];
+	this.flag=false;
 
 	this.gen = Neuvol.nextGeneration();
 	for(var i in this.gen){
@@ -211,10 +227,17 @@ Game.prototype.update = function(){
 	this.interval++;
 	if(this.interval == this.spawnInterval){
 		this.interval = 0;
+		if(this.flag){
+			this.myScore++;
+		}
+		else{
+			this.flag=true;
+		}
 	}
 
 	this.score++;
 	this.maxScore = (this.score > this.maxScore) ? this.score : this.maxScore;
+	this.myMaxScore = (this.myScore > this.myMaxScore) ? this.myScore : this.myMaxScore;
 	var self = this;
 
 	if(FPS == 0){
@@ -264,12 +287,23 @@ Game.prototype.display = function(){
 		}
 	}
 
-	this.ctx.fillStyle = "white";
+	this.ctx.fillStyle = "black";
 	this.ctx.font="20px Oswald, sans-serif";
-	this.ctx.fillText("Score : "+ this.score, 10, 25);
-	this.ctx.fillText("Max Score : "+this.maxScore, 10, 50);
-	this.ctx.fillText("Generation : "+this.generation, 10, 75);
-	this.ctx.fillText("Alive : "+this.alives+" / "+Neuvol.options.population, 10, 100);
+	/*
+	this.ctx.fillText("Score : "+ this.myScore, 10, 25);
+	this.ctx.fillText("Max Score : "+this.myMaxScore, 10, 50);
+	this.ctx.fillText("Distance : "+ this.score, 10, 75);
+	this.ctx.fillText("Max Distance : "+this.maxScore, 10, 100);
+	this.ctx.fillText("Generation : "+this.generation, 10, 125);
+	this.ctx.fillText("Alive : "+this.alives+" / "+Neuvol.options.population, 10, 150);
+	*/
+
+	document.getElementById("p_score").innerHTML="Score : "+this.myScore;
+	document.getElementById("p_maxscore").innerHTML="Max Score : "+this.myMaxScore;
+	document.getElementById("p_distance").innerHTML="Distance : "+this.score;
+	document.getElementById("p_maxdistance").innerHTML="Max Distance : "+this.maxScore;
+	document.getElementById("p_gen").innerHTML="Generation : "+this.generation;
+	document.getElementById("p_alive").innerHTML="Alive : "+this.alives+"/"+Neuvol.options.population;
 
 	var self = this;
 	requestAnimationFrame(function(){
@@ -278,6 +312,17 @@ Game.prototype.display = function(){
 }
 
 window.onload = function(){
+	var c = document.getElementById("flappy");
+	var ctx = c.getContext("2d");
+	ctx.font = "30px Arial";
+	ctx.strokeText("Click here to start", 130, 256);
+	document.getElementById("Button").disabled = true;
+	document.getElementById("x1").disabled = true;
+	document.getElementById("x2").disabled = true;
+	document.getElementById("x3").disabled = true;
+	document.getElementById("x5").disabled = true;
+	document.getElementById("MAX").disabled = true;
+	document.getElementById("cui").disabled = true;
 	var sprites = {
 		bird:"./img/bird.png",
 		background:"./img/background.png",
@@ -299,7 +344,66 @@ window.onload = function(){
 
 	loadImages(sprites, function(imgs){
 		images = imgs;
-		start();
+		//start();
+	})
+}
+var isStarted = false;
+var mygamestart = function(){
+	if(!isStarted){
+		Neuvol = new Neuroevolution({
+			population:50,
+			network:[2, [2], 1],
+		});
+		game = new Game();
+		game.start();
+		game.update();
+		game.display();
+		isStarted = true;
+		document.getElementById("Button").disabled = false;
+		document.getElementById("x1").disabled = false;
+		document.getElementById("x2").disabled = false;
+		document.getElementById("x3").disabled = false;
+		document.getElementById("x5").disabled = false;
+		document.getElementById("MAX").disabled = false;
+		document.getElementById("cui").disabled = false;
+	}
+}
+var restart = function(){
+	var table = document.getElementById("genscore");
+    var rowCount = table.rows.length;
+    while(rowCount>1){
+    	rowCount--;
+    	table.deleteRow(rowCount);
+    }
+	Neuvol = new Neuroevolution({
+		population:50,
+		network:[2, [2], 1],
+	});
+	game = new Game();
+	game.start();
+	game.update();
+	game.display();
+	speed(60);
+	isStarted = true;
+}
+var isBW = true;
+var changeUI = function(){
+	var ref;
+	if(isBW){
+		isBW = false;
+		ref="img1"
+	}
+	else{
+		isBW = true;
+		ref="img"
+	}
+	var sprites = {
+		bird:"./"+ref+"/bird.png",
+		background:"./"+ref+"/background.png",
+		pipetop:"./"+ref+"/pipetop.png",
+		pipebottom:"./"+ref+"/pipebottom.png"
+	}
+	loadImages(sprites, function(imgs){
+		images = imgs;
 	})
-
 }
diff --git a/img/background.png b/img/background.png
old mode 100644
new mode 100755
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diff --git a/img/bird.png b/img/bird.png
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HcmV?d00001

diff --git a/index.html b/index.html
index 5e072f3..039d6b0 100644
--- a/index.html
+++ b/index.html
@@ -1,19 +1,79 @@
 <!DOCTYPE html>
 <html>
 <head>
-	<title>NeuroEvolution : Flappy Bird</title>
+	<link rel="shortcut icon" href="img/bird.png" />
+	<title>FlapAI Bird</title>
 	<link href='https://fonts.googleapis.com/css?family=Oswald' rel='stylesheet' type='text/css'>
+	<style>
+		canvas{border:2px solid #000000;}
+		.button {
+                background-color: black; /* Green */
+                border: none;
+                color: white;
+                padding: 8px 16px;
+                text-align: center;
+                text-decoration: none;
+                display: inline-block;
+                font-size: 16px;
+                margin: 10px 2px;
+                -webkit-transition-duration: 0.4s; /* Safari */
+                transition-duration: 0.4s;
+                cursor: pointer;
+            }
+
+            .button1 {
+                background-color: white; 
+                color: black; 
+                border: 2px solid black;
+            }
+
+            .button1:hover {
+                background-color: black;
+                color: white;
+            }
+            p{
+            	font-family:Oswald;
+            }
+	</style>
 </head>
 <body>
-	<canvas id="flappy" width="500" height="512"></canvas> <br/>
-	<button onclick="speed(60)">x1</button> 
-	<button onclick="speed(120)">x2</button> 
-	<button onclick="speed(180)">x3</button> 
-	<button onclick="speed(300)">x5</button> 
-	<button onclick="speed(0)">MAX</button> 
-	<br/>
-	<a href="http://github.com/xviniette/FlappyLearning">GitHub Repository</a>
+	<center>
+		<h1 style="font-family:Oswald;">FlapAI Bird</h1>
+		<table border=0>
+			<tr>
+				<td align="center" width=500px style="vertical-align:top;">
+					<p id="p_score"></p>
+					<p id="p_maxscore"></p>
+					<p id="p_distance"></p>
+					<p id="p_maxdistance"></p>
+					<p id="p_gen"></p>
+					<p id="p_alive"></p>
+				</td>
+				<td width=600px style="vertical-align:top;" align="center">
+					<canvas id="flappy" width="500" height="512" onclick="mygamestart()"></canvas> <br/>
+					<button class="button button1" id="Button" onclick="restart()">Restart</button>
+					<button class="button button1" id="x1" onclick="speed(60)">x1</button> 
+					<button class="button button1" id="x2" onclick="speed(120)">x2</button> 
+					<button class="button button1" id="x3" onclick="speed(180)">x3</button> 
+					<button class="button button1" id="x5" onclick="speed(300)">x5</button> 
+					<button class="button button1" id="MAX" onclick="speed(0)">MAX</button>
+				    <button class="button button1" id="cui" onclick="changeUI()">Change UI</button>
+				</td>
+				<td width=500px style="vertical-align:top;">
+					<table id="genscore" border=0 style="font-family:Oswald;">
+						<tr>
+							<td vlaign="top" align="center" width=500px>Generation Number</td>
+							<td vlaign="top" align="center" width=500px>Score</td>
+							<td vlaign="top" align="center" width=500px>Distance</td>
+						</tr>
+					</table>
+				</td>
+			</tr>
+		</table>  
+		<br/>
+		<p id="temp"></p>
+	</center>
 	<script src = "Neuroevolution.js"></script>
 	<script src = "game.js"></script>
 </body>
-</html>
\ No newline at end of file
+</html>

From a0190cfad2f72f6ef321c9f4b24e6a3e985223b5 Mon Sep 17 00:00:00 2001
From: atmandarji <atman.darji@gmail.com>
Date: Thu, 7 Mar 2019 12:06:07 +0530
Subject: [PATCH 2/2] Undo some changes

---
 Neuroevolution.js | 297 +++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 278 insertions(+), 19 deletions(-)

diff --git a/Neuroevolution.js b/Neuroevolution.js
index 7108bfb..82b910d 100644
--- a/Neuroevolution.js
+++ b/Neuroevolution.js
@@ -1,46 +1,118 @@
+/**
+ * Provides a set of classes and methods for handling Neuroevolution and
+ * genetic algorithms.
+ *
+ * @param {options} An object of options for Neuroevolution.
+ */
 var Neuroevolution = function (options) {
-	var self = this; 
+	var self = this; // reference to the top scope of this module
+
+	// Declaration of module parameters (options) and default values
 	self.options = {
+		/**
+		 * Logistic activation function.
+		 *
+		 * @param {a} Input value.
+		 * @return Logistic function output.
+		 */
 		activation: function (a) {
 			ap = (-a) / 1;
 			return (1 / (1 + Math.exp(ap)))
 		},
+
+		/**
+		 * Returns a random value between -1 and 1.
+		 *
+		 * @return Random value.
+		 */
 		randomClamped: function () {
 			return Math.random() * 2 - 1;
 		},
-		network: [1, [1], 1],
-		population: 50,
-		elitism: 0.2,
-		randomBehaviour: 0.2,
-		mutationRate: 0.1,
-		mutationRange: 0.5,
-		historic: 0,
-		lowHistoric: false,
-		scoreSort: -1,
-		nbChild: 1
+
+		// various factors and parameters (along with default values).
+		network: [1, [1], 1], // Perceptron network structure (1 hidden
+		// layer).
+		population: 50, // Population by generation.
+		elitism: 0.2, // Best networks kepts unchanged for the next
+		// generation (rate).
+		randomBehaviour: 0.2, // New random networks for the next generation
+		// (rate).
+		mutationRate: 0.1, // Mutation rate on the weights of synapses.
+		mutationRange: 0.5, // Interval of the mutation changes on the
+		// synapse weight.
+		historic: 0, // Latest generations saved.
+		lowHistoric: false, // Only save score (not the network).
+		scoreSort: -1, // Sort order (-1 = desc, 1 = asc).
+		nbChild: 1 // Number of children by breeding.
+
 	}
+
+	/**
+	 * Override default options.
+	 *
+	 * @param {options} An object of Neuroevolution options.
+	 * @return void
+	 */
 	self.set = function (options) {
 		for (var i in options) {
-			if (this.options[i] != undefined) {
+			if (this.options[i] != undefined) { // Only override if the passed in value
+				// is actually defined.
 				self.options[i] = options[i];
 			}
 		}
 	}
+
+	// Overriding default options with the pass in options
 	self.set(options);
+
+
+	/*NEURON**********************************************************************/
+	/**
+	 * Artificial Neuron class
+	 *
+	 * @constructor
+	 */
 	var Neuron = function () {
 		this.value = 0;
 		this.weights = [];
 	}
+
+	/**
+	 * Initialize number of neuron weights to random clamped values.
+	 *
+	 * @param {nb} Number of neuron weights (number of inputs).
+	 * @return void
+	 */
 	Neuron.prototype.populate = function (nb) {
 		this.weights = [];
 		for (var i = 0; i < nb; i++) {
 			this.weights.push(self.options.randomClamped());
 		}
 	}
+
+
+	/*LAYER***********************************************************************/
+	/**
+	 * Neural Network Layer class.
+	 *
+	 * @constructor
+	 * @param {index} Index of this Layer in the Network.
+	 */
 	var Layer = function (index) {
 		this.id = index || 0;
 		this.neurons = [];
 	}
+
+	/**
+	 * Populate the Layer with a set of randomly weighted Neurons.
+	 *
+	 * Each Neuron be initialied with nbInputs inputs with a random clamped
+	 * value.
+	 *
+	 * @param {nbNeurons} Number of neurons.
+	 * @param {nbInputs} Number of inputs.
+	 * @return void
+	 */
 	Layer.prototype.populate = function (nbNeurons, nbInputs) {
 		this.neurons = [];
 		for (var i = 0; i < nbNeurons; i++) {
@@ -49,18 +121,39 @@ var Neuroevolution = function (options) {
 			this.neurons.push(n);
 		}
 	}
+
+
+	/*NEURAL NETWORK**************************************************************/
+	/**
+	 * Neural Network class
+	 *
+	 * Composed of Neuron Layers.
+	 *
+	 * @constructor
+	 */
 	var Network = function () {
 		this.layers = [];
 	}
+
+	/**
+	 * Generate the Network layers.
+	 *
+	 * @param {input} Number of Neurons in Input layer.
+	 * @param {hidden} Number of Neurons per Hidden layer.
+	 * @param {output} Number of Neurons in Output layer.
+	 * @return void
+	 */
 	Network.prototype.perceptronGeneration = function (input, hiddens, output) {
 		var index = 0;
 		var previousNeurons = 0;
 		var layer = new Layer(index);
-		layer.populate(input, previousNeurons);
-		previousNeurons = input;
+		layer.populate(input, previousNeurons); // Number of Inputs will be set to
+		// 0 since it is an input layer.
+		previousNeurons = input; // number of input is size of previous layer.
 		this.layers.push(layer);
 		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];
@@ -68,37 +161,59 @@ var Neuroevolution = function (options) {
 			index++;
 		}
 		var layer = new Layer(index);
-		layer.populate(output, previousNeurons); 
+		layer.populate(output, previousNeurons); // Number of input is equal to
+		// the size of the last hidden
+		// layer.
 		this.layers.push(layer);
 	}
+
+	/**
+	 * Create a copy of the Network (neurons and weights).
+	 *
+	 * Returns number of neurons per layer and a flat array of all weights.
+	 *
+	 * @return Network data.
+	 */
 	Network.prototype.getSave = function () {
 		var datas = {
-			neurons: [], 
-			weights: [] 
+			neurons: [], // Number of Neurons per layer.
+			weights: [] // Weights of each Neuron's inputs.
 		};
 
 		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) {
+					// push all input weights of each Neuron of each Layer into a flat
+					// array.
 					datas.weights.push(this.layers[i].neurons[j].weights[k]);
 				}
 			}
 		}
 		return datas;
 	}
+
+	/**
+	 * Apply network data (neurons and weights).
+	 *
+	 * @param {save} Copy of network data (neurons and weights).
+	 * @return void
+	 */
 	Network.prototype.setSave = function (save) {
 		var previousNeurons = 0;
 		var index = 0;
 		var indexWeights = 0;
 		this.layers = [];
 		for (var i in save.neurons) {
+			// Create and populate layers.
 			var layer = new Layer(index);
 			layer.populate(save.neurons[i], previousNeurons);
 			for (var j in layer.neurons) {
 				for (var k in layer.neurons[j].weights) {
+					// Apply neurons weights to each Neuron.
 					layer.neurons[j].weights[k] = save.weights[indexWeights];
-					indexWeights++;
+
+					indexWeights++; // Increment index of flat array.
 				}
 			}
 			previousNeurons = save.neurons[i];
@@ -106,25 +221,40 @@ var Neuroevolution = function (options) {
 			this.layers.push(layer);
 		}
 	}
+
+	/**
+	 * Compute the output of an input.
+	 *
+	 * @param {inputs} Set of inputs.
+	 * @return Network output.
+	 */
 	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 prevLayer = this.layers[0];
+		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) {
+				// For each Neuron in each layer.
 				var sum = 0;
 				for (var k in prevLayer.neurons) {
+					// 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];
 				}
+
+				// Compute the activation of the Neuron.
 				this.layers[i].neurons[j].value = self.options.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) {
@@ -132,19 +262,53 @@ var Neuroevolution = function (options) {
 		}
 		return out;
 	}
+
+
+	/*GENOME**********************************************************************/
+	/**
+	 * Genome class.
+	 *
+	 * Composed of a score and a Neural Network.
+	 *
+	 * @constructor
+	 *
+	 * @param {score}
+	 * @param {network}
+	 */
 	var Genome = function (score, network) {
 		this.score = score || 0;
 		this.network = network || null;
 	}
+
+
+	/*GENERATION******************************************************************/
+	/**
+	 * Generation class.
+	 *
+	 * Composed of a set of Genomes.
+	 *
+	 * @constructor
+	 */
 	var Generation = function () {
 		this.genomes = [];
 	}
+
+	/**
+	 * Add a genome to the generation.
+	 *
+	 * @param {genome} Genome to add.
+	 * @return void.
+	 */
 	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++) {
+			// Sort in descending order.
 			if (self.options.scoreSort < 0) {
 				if (genome.score > this.genomes[i].score) {
 					break;
 				}
+				// Sort in ascending order.
 			} else {
 				if (genome.score < this.genomes[i].score) {
 					break;
@@ -152,17 +316,33 @@ var Neuroevolution = function (options) {
 			}
 
 		}
+
+		// Insert genome into correct position.
 		this.genomes.splice(i, 0, genome);
 	}
+
+	/**
+	 * Breed to genomes to produce offspring(s).
+	 *
+	 * @param {g1} Genome 1.
+	 * @param {g2} Genome 2.
+	 * @param {nbChilds} Number of offspring (children).
+	 */
 	Generation.prototype.breed = function (g1, g2, nbChilds) {
 		var datas = [];
 		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) {
+				// 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];
 				}
 			}
+
+			// Perform mutation on some weights.
 			for (var i in data.network.weights) {
 				if (Math.random() <= self.options.mutationRate) {
 					data.network.weights[i] += Math.random() *
@@ -176,12 +356,19 @@ var Neuroevolution = function (options) {
 
 		return datas;
 	}
+
+	/**
+	 * Generate the next generation.
+	 *
+	 * @return Next generation data array.
+	 */
 	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) {
+				// Push a deep copy of ith Genome's Nethwork.
 				nexts.push(JSON.parse(JSON.stringify(this.genomes[i].network)));
 			}
 		}
@@ -200,11 +387,14 @@ var Neuroevolution = function (options) {
 		var max = 0;
 		while (true) {
 			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));
 				for (var c in childs) {
 					nexts.push(childs[c].network);
 					if (nexts.length >= self.options.population) {
+						// Return once number of children is equal to the
+						// population by generatino value.
 						return nexts;
 					}
 				}
@@ -215,13 +405,35 @@ var Neuroevolution = function (options) {
 			}
 		}
 	}
+
+
+	/*GENERATIONS*****************************************************************/
+	/**
+	 * Generations class.
+	 *
+	 * Hold's previous Generations and current Generation.
+	 *
+	 * @constructor
+	 */
 	var Generations = function () {
 		this.generations = [];
 		var currentGeneration = new Generation();
 	}
+
+	/**
+	 * Create the first generation.
+	 *
+	 * @param {input} Input layer.
+	 * @param {input} Hidden layer(s).
+	 * @param {output} Output layer.
+	 * @return First Generation.
+	 */
 	Generations.prototype.firstGeneration = function (input, hiddens, output) {
+		// FIXME input, hiddens, output unused.
+
 		var out = [];
 		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],
@@ -232,8 +444,15 @@ var Neuroevolution = function (options) {
 		this.generations.push(new Generation());
 		return out;
 	}
+
+	/**
+	 * Create the next Generation.
+	 *
+	 * @return Next Generation.
+	 */
 	Generations.prototype.nextGeneration = function () {
 		if (this.generations.length == 0) {
+			// Need to create first generation.
 			return false;
 		}
 
@@ -242,22 +461,51 @@ var Neuroevolution = function (options) {
 		this.generations.push(new Generation());
 		return gen;
 	}
+
+	/**
+	 * Add a genome to the Generations.
+	 *
+	 * @param {genome}
+	 * @return False if no Generations to add to.
+	 */
 	Generations.prototype.addGenome = function (genome) {
+		// Can't add to a Generation if there are no Generations.
 		if (this.generations.length == 0) return false;
+
+		// FIXME addGenome returns void.
 		return this.generations[this.generations.length - 1].addGenome(genome);
 	}
+
+
+	/*SELF************************************************************************/
 	self.generations = new Generations();
+
+	/**
+	 * Reset and create a new Generations object.
+	 *
+	 * @return void.
+	 */
 	self.restart = function () {
 		self.generations = new Generations();
 	}
+
+	/**
+	 * Create the next generation.
+	 *
+	 * @return Neural Network array for next Generation.
+	 */
 	self.nextGeneration = function () {
 		var networks = [];
 
 		if (self.generations.generations.length == 0) {
+			// If no Generations, create first.
 			networks = self.generations.firstGeneration();
 		} else {
+			// Otherwise, create next one.
 			networks = self.generations.nextGeneration();
 		}
+
+		// Create Networks from the current Generation.
 		var nns = [];
 		for (var i in networks) {
 			var nn = new Network();
@@ -266,6 +514,7 @@ var Neuroevolution = function (options) {
 		}
 
 		if (self.options.lowHistoric) {
+			// Remove old Networks.
 			if (self.generations.generations.length >= 2) {
 				var genomes =
 					self.generations
@@ -278,6 +527,7 @@ var Neuroevolution = function (options) {
 		}
 
 		if (self.options.historic != -1) {
+			// Remove older generations.
 			if (self.generations.generations.length > self.options.historic + 1) {
 				self.generations.generations.splice(0,
 					self.generations.generations.length - (self.options.historic + 1));
@@ -286,7 +536,16 @@ var Neuroevolution = function (options) {
 
 		return nns;
 	}
+
+	/**
+	 * Adds a new Genome with specified Neural Network and score.
+	 *
+	 * @param {network} Neural Network.
+	 * @param {score} Score value.
+	 * @return void.
+	 */
 	self.networkScore = function (network, score) {
 		self.generations.addGenome(new Genome(score, network.getSave()));
 	}
 }
+