genetic algorithm implementation
This commit is contained in:
Robert Bendun
parent
82c3de94ef
commit
8e7b565dd7
@ -18,8 +18,10 @@
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<script src="configuration.js"></script>
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<script src="utilities.js"></script>
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<script src="logic/product.js"></script>
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<script src="logic/knowledge.js"></script>
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<script src="logic/time.js"></script>
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<script src="logic/shelf.js"></script>
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<script src="logic/evolution.js"></script>
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<script src="view/floor.js"></script>
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<script src="view/grid.js"></script>
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@ -28,9 +30,9 @@
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<script src="view/agentController.js"></script>
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<script src="logic/agent.js"></script>
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<script src="view/products.js"></script>
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<script src="logic/knowledge.js"></script>
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<script src="logic/shop.js"></script>
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<script src="logic/costMap.js"></script>
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<script src="view/visualisation.js"></script>
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<!-- Main script file -->
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<script src="main.js"></script>
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@ -77,7 +79,7 @@
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</div>
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<div class="logs-label">
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<span>LOGS</span>
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<span>INFO</span>
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</div>
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<div class="content">
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@ -279,7 +281,20 @@
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<div class="logs-wrapper">
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<div class="logs-content">
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<!-- All logs should go here -->
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<div>
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Nr <b id="populationIndex"></b> of <b id="populationSize"></b>
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</div>
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<div>
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Similarity:
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<b id="similarity"></b>
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</div>
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<div>
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Best: <b id="best"></b>
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</div>
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<div>Worst: <b id="worst"></b></div>
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<ul id="history">
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</ul>
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</div>
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</div>
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@ -10,8 +10,7 @@ class Agent extends AgentController {
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const cycle = async () => {
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const jobRequest = Products.instance.getJobRequest();
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if (jobRequest) {
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const requiredAmount = 50 - jobRequest.amount;
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await this.takeFromStore(jobRequest.product, requiredAmount);
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await this.takeFromStore(jobRequest.product, jobRequest.amount);
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await this.deliver(jobRequest.x, jobRequest.y);
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} else {
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await waitFor(1000);
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@ -44,7 +43,7 @@ class Agent extends AgentController {
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Products.instance.exchange(x, y, this.ownedProduct, 50);
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this.ownedProductAmount = 0;
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this.ownedProduct = Product.REGISTRY.empty;
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this.ownedProduct = null;
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}
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/**
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@ -36,43 +36,3 @@ class CostMap {
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}
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}
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}
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class CostMapVisualisation {
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static instance
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constructor(canvas) {
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this.canvas = canvas
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this.ctx = canvas.getContext('2d');
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canvas.width = 2000
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canvas.height = 900
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CostMapVisualisation.instance = this
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document.addEventListener('keydown', ({ key }) => {
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if (key !== 'c')
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return
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if (this.hasBeenUpdated) {
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this.ctx.clearRect(0, 0, 2000, 900);
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} else {
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this.update();
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}
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this.hasBeenUpdated = !this.hasBeenUpdated
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})
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}
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update() {
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CostMap.instance.values.forEach((cost, i) => {
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const y = Math.floor(i / 20) * 100;
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const x = (i % 20) * 100;
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let color = Math.floor((cost * 255)).toString(16)
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if (color.length == 1)
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color = "0" + color
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this.ctx.fillStyle = '#' + color + color + color;
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this.ctx.fillRect(x, y, 100, 100)
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})
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}
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}
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251
src/logic/evolution.js
Normal file
251
src/logic/evolution.js
Normal file
@ -0,0 +1,251 @@
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const ShelfCount = 17 * 6;
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const InRow = 3 * 6;
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// index to abstract coordinates
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function i2ac(index) {
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const y = Math.floor(index / InRow);
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const x = (index % InRow);
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if (y === 5 && x >= 6)
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return [x + 6, y];
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return [x, y];
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}
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function ac2i(x, y) {
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return y * InRow + x - (y === 5 && x >= 6) * 6;
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}
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// index to grid coordinates
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function i2gc(index) {
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const [x, y] = i2ac(index);
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return [x + Number(x > 11) + Number(x > 5), y + Math.floor((y + 1) / 2)];
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}
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function gc2i(x, y) {
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x -= Number(x >= 13) + Number(x >= 6);
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switch (y) {
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case 0: y = 0; break;
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case 2: y = 1; break;
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case 3: y = 2; break;
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case 5: y = 3; break;
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case 6: y = 4; break;
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case 8: y = 5; break;
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}
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return ac2i(x, y);
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}
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class Arrangement {
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generateRandom() {
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this.products = [...Array(ShelfCount).keys()].map(() => Knowledge.semanticNetwork.getRandom().name);
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this.productsAmount = null;
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this.similarities = null;
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}
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constructor(products = []) {
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this.products = products;
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this.productsAmount = null;
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this.similarities = null;
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}
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clone() {
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return JSON.parse(JSON.stringify(this.products));
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}
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simulate() {
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this.similarities ||= [...Array(ShelfCount)].map(() => 0);
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this.productsAmount ||= [...Array(ShelfCount)].map(() => 50);
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this.similarity = 0;
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this.products.forEach((product, i) => {
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let similarityWithNeighbours = 0;
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const neighbours = neighboursByIndex(i);
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for (const neighbour of neighbours) {
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similarityWithNeighbours += similarity(product, this.products[neighbour]);
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}
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similarityWithNeighbours /= neighbours.length;
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this.similarities[i] = 1 - similarityWithNeighbours;
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this.similarity += this.similarities[i];
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});
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this.similarity /= ShelfCount;
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}
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}
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const PopulationSize = 31 * 2;
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const PopulationToKeep = 0.3;
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const MutationRate = 0.2;
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class Evolution extends Arrangement {
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constructor() {
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super();
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this.generate();
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this.setup();
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this.productIndex = 0;
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this.productsAmount = [...Array(ShelfCount)].map(() => 0);
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}
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generate() {
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this.population = [...Array(PopulationSize)].map(() => {
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const arr = new Arrangement();
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arr.generateRandom();
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return arr;
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});
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}
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setup() {
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this.populationIndex = 0;
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this.population.forEach(x => x.simulate());
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this.min_similarity = this.population.reduce((p, c) => Math.min(p, c.similarity || 1), 1);
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this.max_similarity = this.population.reduce((p, c) => Math.max(p, c.similarity || 0), 0);
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}
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next(days) {
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for (let i = 0; i < Number(days) - 1; ++i) {
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this.step(false);
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}
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this.step(true);
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}
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step(render = true) {
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// Complete previous day
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this.products = this.population[this.populationIndex].products;
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this.simulate();
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this.productsAmount = [...Array(ShelfCount)].map((_, i) => Math.round(50 * this.similarities[i]));
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const productsSold = ShelfCount * 50 - this.productsAmount.reduce((p, c) => p + c, 0);
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// Setup new day
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this.populationIndex++;
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this.productIndex = 0;
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// Generate new population if needed
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if (this.populationIndex >= PopulationSize) {
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/**
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* @type{Arrangement[]}
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*/
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let newPopulation = [];
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this.population.sort((a, b) => a.similarity - b.similarity);
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let fitness = this.population.map(({ similarity }) => similarity);
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let sum = fitness.reduce((p, c) => p + c, 0);
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fitness = fitness.map(f => Math.pow(f / sum, 2));
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const D = PopulationToKeep * PopulationSize;
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for (let i = 0; i < this.population.length; ++i) {
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const candidateA = this.population[pick(fitness)];
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const candidateB = this.population[pick(fitness)];
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const candidate = crossover(candidateA, candidateB);
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mutate(candidate);
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newPopulation.push(candidate);
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}
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this.population = this.population.slice(0, D).concat(newPopulation).slice(0, PopulationSize);
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this.setup();
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}
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if (render) {
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this.update();
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console.log(productsSold)
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}
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}
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getJobRequest() {
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if (this.productIndex > ShelfCount)
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return null;
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for (; this.productIndex < ShelfCount; ++this.productIndex) {
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const inPopulation = this.population[this.populationIndex];
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let amount = 50;
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if (this.products[this.productIndex] === inPopulation.products[this.productIndex]) {
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if (this.productsAmount[this.productIndex] == 50)
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continue;
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amount -= this.productsAmount[this.productIndex];
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}
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const [x, y] = i2gc(this.productIndex++);
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return {
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product: Knowledge.semanticNetwork.findProductByName(this.population[this.populationIndex].products[this.productIndex]),
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amount,
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x, y
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}
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}
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}
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getSimilarity() {
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try {
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this.population[this.populationIndex].simulate();
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return this.population[this.populationIndex].similarity;
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} catch (e) {
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return this.similarity;
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}
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}
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}
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/**
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*
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* @param {Arrangement} arr
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*/
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function mutate(arr) {
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const { floor, random } = Math;
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for (let i = 0; i < ShelfCount / 6; ++i) {
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while (random() <= MutationRate) {
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const a = floor(random() * arr.products.length);
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const b = floor(random() * arr.products.length);
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if (a !== b) {
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let t = arr.products[a];
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arr.products[a] = arr.products[b];
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arr.products[b] = t;
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}
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}
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}
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}
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/**
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* @param {Arrangement} ca
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* @param {Arrangement} cb
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*/
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function crossover(ca, cb) {
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const child = [];
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for (let i = 0; i < ca.products.length; ++i)
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child.push(ca.similarities[i] > cb.similarities[i] ? ca.products[i] : cb.products[i]);
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return new Arrangement(child);
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}
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function neighboursByIndex(index) {
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const [bx, by] = i2ac(index);
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const at = (dx, dy) => {
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const x = bx + dx;
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const y = by + dy;
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if (by != y) {
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if ((by === 1 || by === 3) && (by - y < 0)) return [];
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if ((by === 2 || by === 4) && (by - y > 0)) return [];
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}
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if (x >= 0 && y >= 0) {
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const index = ac2i(x, y);
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if (index < ShelfCount)
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return [index];
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}
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return [];
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};
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return [at(0, -1), at(-1, 0), at(1, 0), at(0, 1)].flat();
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}
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// given two products, find how similar they are based on categories
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// similiary is defined as euclidian distance
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function similarity(leftProductName, rightProductName) {
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const lhs = Knowledge.semanticNetwork.findByName(leftProductName);
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const rhs = Knowledge.semanticNetwork.findByName(rightProductName);
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console.assert(lhs && rhs);
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const categories = new Set([...lhs.categories, ...rhs.categories]);
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const d = 1 / categories.size;
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let similarity = 0;
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for (const category of categories) {
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similarity += Math.pow(
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(lhs.categories.indexOf(category) >= 0 ? d : 0) -
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(rhs.categories.indexOf(category) >= 0 ? d : 0), 2);
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}
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return Math.sqrt(similarity);
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}
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@ -1,6 +1,6 @@
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class Time {
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isPaused = false;
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isPaused = true;
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timeMultiplier = 1;
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day = 0;
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@ -58,7 +58,13 @@ class Time {
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* Add days
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*/
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addDays(days) {
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Products.instance.next(days);
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Products.instance.update();
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const ul = document.querySelector('ul#history');
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this.setDay(this.day += Number(days));
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const li = document.createElement('li');
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li.innerHTML = `${this.day}: ${nice(Products.instance.max_similarity)}`;
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ul.appendChild(li);
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}
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}
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@ -10,12 +10,11 @@ window.addEventListener('DOMContentLoaded', async () => {
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const costMap = new CostMap();
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const time = new Time();
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const timeController = new TimeController();
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const costMapVisualisation = new CostMapVisualisation(costCanvas);
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const floor = new Floor(floorCanvas);
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const grid = new Grid(gridCanvas);
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const agent = new Agent(agentCanvas);
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await Knowladge.loadFromFile('/data/products.tsv')
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await Knowledge.loadFromFile('/data/products.tsv')
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const products = new Products(productsCanvas);
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const fpsElement = document.getElementById('fps');
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@ -107,6 +107,8 @@ main {
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height: 100%;
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width: 100%;
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overflow: auto;
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padding: 10px;
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box-sizing: border-box;
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}
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.logs-content::-webkit-scrollbar {
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@ -41,3 +41,13 @@ function Enum(...labels) {
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}
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return e
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}
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function pick(array) {
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let index = 0;
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for (let r = Math.random(); r >= 0; r -= array[index]) {
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index++;
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}
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return index - 1;
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}
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function nice(v) { return `${(v * 100).toFixed(1)}%` }
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@ -4,7 +4,7 @@ class AgentController {
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this.ctx = canvas.getContext('2d');
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this.setCanvasSize(canvas);
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this.ownedProduct = Product.REGISTRY.empty;
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this.ownedProduct = null;
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this.ownedProductAmount = 0;
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// Akcje
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@ -242,6 +242,7 @@ class AgentController {
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this.ctx.rotate(-rotation * Math.PI / 180);
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// Product
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if (this.ownedProduct) {
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this.ctx.font = '900 40px "Font Awesome 5 Free"';
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this.ctx.textAlign = 'center';
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this.ctx.fillStyle = 'white';
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@ -254,6 +255,7 @@ class AgentController {
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this.ctx.fillStyle = 'white';
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this.ctx.fillText(this.ownedProductAmount, 10, 35);
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}
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}
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this.ctx.translate(-(newX + (w / 2)), -(newY + (h / 2)));
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}
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@ -9,38 +9,20 @@ function filterReduce2D(array, reducer, filter, init) {
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return result;
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}
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class Products {
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class Products extends Evolution {
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/**
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* @type{Products}
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*/
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static instance
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constructor(canvas) {
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const { random, floor } = Math
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super();
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// this.generateRandom();
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this.gridProducts = []
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this.gridProductsAmount = []
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this.min = 50;
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for (let i = 0; i < 20; ++i) {
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const gridProductsColumn = []
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const gridProductsAmountColumn = []
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for (let j = 0; j < 9; ++j) {
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if (j % 3 !== 1 && (i+1) % 7 !== 0) {
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gridProductsColumn.push(Product.RANDOM_FROM_REGISTRY());
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const amount = floor(random() * 51);
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gridProductsAmountColumn.push(amount);
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this.min = this.min > amount ? amount : this.min;
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} else {
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gridProductsColumn.push('');
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gridProductsAmountColumn.push('');
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}
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}
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this.gridProducts.push(gridProductsColumn);
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this.gridProductsAmount.push(gridProductsAmountColumn);
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}
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this.ctx = canvas.getContext('2d');
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this.setCanvasSize(canvas);
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this.update();
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Products.instance = this
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@ -52,32 +34,46 @@ class Products {
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}
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update() {
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function write(tag, text) {
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const qr = document.querySelector(tag);
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if (qr) {
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qr.innerHTML = text;
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}
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}
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|
||||
|
||||
write('#similarity', nice(this.getSimilarity()));
|
||||
write('#populationIndex', this.populationIndex + 1);
|
||||
write('#populationSize', PopulationSize);
|
||||
write('#best', nice(this.max_similarity));
|
||||
write('#worst', nice(this.min_similarity));
|
||||
|
||||
this.clearCanvas();
|
||||
this.drawProducts();
|
||||
}
|
||||
|
||||
drawProducts() {
|
||||
for (let [x, line] of Grid.instance.grid.entries()) {
|
||||
for (let [y, type] of line.entries()) {
|
||||
if (type === GRID_FIELD_TYPE.SHELF) {
|
||||
let v = this.drawValue(x, y);
|
||||
this.product(x * 100, y * 100, v, x, y);
|
||||
}
|
||||
}
|
||||
}
|
||||
this.min = this.productsAmount?.reduce((p, c) => Math.min(p, c), 50) || 50;
|
||||
this.products.forEach((product, index) => {
|
||||
let [x, y] = i2gc(index);
|
||||
x *= 100; y *= 100;
|
||||
const amount = this.productsAmount === null ? 50 : this.productsAmount[index];
|
||||
this.drawAmount(x, y, amount);
|
||||
const { icon } = Knowledge.semanticNetwork.findProductByName(product);
|
||||
this.product(x, y, icon, amount);
|
||||
})
|
||||
}
|
||||
|
||||
drawValue(x, y) {
|
||||
drawAmount(x, y, amount) {
|
||||
let fontSize = 20;
|
||||
this.ctx.font = `${fontSize}px Montserrat`;
|
||||
this.ctx.textAlign = "left";
|
||||
this.ctx.fillStyle = 'white';
|
||||
let number = this.gridProductsAmount[x][y];
|
||||
this.ctx.fillText(number, (x * 100) + 10, (y * 100) + 90);
|
||||
return number;
|
||||
this.ctx.fillText(amount, x + 10, y + 90);
|
||||
}
|
||||
|
||||
product(x, y, v, productsKey, productsValue) {
|
||||
product(x, y, icon, amount) {
|
||||
let fontSize = 40;
|
||||
this.ctx.font = `900 ${fontSize}px "Font Awesome 5 Free"`;
|
||||
this.ctx.textAlign = "center";
|
||||
@ -86,17 +82,15 @@ class Products {
|
||||
// white - full shelf
|
||||
// red - empty shelf
|
||||
const t = Math.cbrt
|
||||
v = (v - this.min) / (50 - this.min);
|
||||
const v = (amount - this.min) / (50 - this.min);
|
||||
let color = Math.floor(t(v) * 255).toString(16)
|
||||
if (color.length == 1)
|
||||
color = "0" + color
|
||||
this.ctx.fillStyle = `#ff${color}${color}`;
|
||||
|
||||
let productsColumn = this.gridProducts[productsKey];
|
||||
let prdct = productsColumn[productsValue];
|
||||
this.ctx.font = '900 40px "Font Awesome 5 Free"';
|
||||
this.ctx.textAlign = 'center';
|
||||
this.ctx.fillText(prdct.icon, x+50, y+60);
|
||||
this.ctx.fillText(icon, x + 50, y + 60);
|
||||
}
|
||||
|
||||
clearCanvas() {
|
||||
@ -142,13 +136,13 @@ class Products {
|
||||
* @returns {{product: Product; amount: number} | null } Old shelf content
|
||||
*/
|
||||
exchange(x, y, incomingProduct, incomingAmount) {
|
||||
const product = this.gridProducts[x][y];
|
||||
const amount = this.gridProductsAmount[x][y];
|
||||
this.gridProducts[x][y] = incomingProduct;
|
||||
this.gridProductsAmount[x][y] = incomingAmount;
|
||||
const index = gc2i(x, y);
|
||||
const product = this.products[index];
|
||||
const amount = this.productsAmount[index];
|
||||
this.products[index] = incomingProduct.name;
|
||||
this.productsAmount[index] = incomingAmount;
|
||||
|
||||
this.min = filterReduce2D(this.gridProductsAmount, (p, c) => Math.min(p, c),
|
||||
x => typeof x === 'number', 50);
|
||||
this.min = this.productsAmount?.reduce((p, c) => Math.min(p, c), 50) || 50;
|
||||
|
||||
this.clearCanvas();
|
||||
this.drawProducts();
|
||||
@ -170,13 +164,6 @@ class Products {
|
||||
return this.exchange(x, y, incomingProduct, incomingAmount)
|
||||
}
|
||||
|
||||
getJobRequest() {
|
||||
const maybeProduct = this.filter((_product, amount, _i, _j, end) => amount < 50).sort((a, b) => {
|
||||
return a.amount - b.amount
|
||||
})
|
||||
return maybeProduct.length === 0 ? null : maybeProduct[0]
|
||||
}
|
||||
|
||||
findAvailableLocationsFor(product) {
|
||||
return this.filter((p, amount) => product.equals(p) && amount < 50)
|
||||
}
|
||||
|
||||
53
src/view/visualisation.js
Normal file
53
src/view/visualisation.js
Normal file
@ -0,0 +1,53 @@
|
||||
class Visualisation {
|
||||
static instance
|
||||
|
||||
constructor(canvas) {
|
||||
this.canvas = canvas
|
||||
this.ctx = canvas.getContext('2d');
|
||||
canvas.width = 2000
|
||||
canvas.height = 900
|
||||
|
||||
Visualisation.instance = this
|
||||
|
||||
document.addEventListener('keydown', ({ key }) => {
|
||||
const keys = {
|
||||
c: () => this.drawCostMap(),
|
||||
p: () => this.drawSimilarities(),
|
||||
};
|
||||
|
||||
this.ctx.clearRect(0, 0, 2000, 900);
|
||||
if (this.lastkey != key) {
|
||||
for (const k in keys)
|
||||
if (k == key) {
|
||||
keys[k]();
|
||||
this.lastkey = key;
|
||||
}
|
||||
} else
|
||||
this.lastkey = null;
|
||||
})
|
||||
}
|
||||
|
||||
drawSimilarities() {
|
||||
Products.instance.similarities.map((similarity, index) => {
|
||||
const [x, y] = i2gc(index);
|
||||
|
||||
let color = Math.floor(similarity * 120);
|
||||
this.ctx.fillStyle = `hsl(${color}, 100%, 50%)`;
|
||||
this.ctx.fillRect(x * 100, y * 100, 100, 100)
|
||||
})
|
||||
}
|
||||
|
||||
drawCostMap() {
|
||||
CostMap.instance.values.forEach((cost, i) => {
|
||||
const y = Math.floor(i / 20) * 100;
|
||||
const x = (i % 20) * 100;
|
||||
|
||||
let color = Math.floor((cost * 255)).toString(16)
|
||||
if (color.length == 1)
|
||||
color = "0" + color
|
||||
|
||||
this.ctx.fillStyle = '#' + color + color + color;
|
||||
this.ctx.fillRect(x, y, 100, 100)
|
||||
})
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user