整理桌面
This commit is contained in:
3
node_modules/neataptic/test/dist.js
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3
node_modules/neataptic/test/dist.js
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if (typeof global.neataptic === 'undefined') {
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global.neataptic = require('../dist/neataptic');
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}
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84
node_modules/neataptic/test/neat.js
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node_modules/neataptic/test/neat.js
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/* Import */
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var chai = require('chai');
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var assert = chai.assert;
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/* Shorten var names */
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var { Network, methods, config } = neataptic;
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/* Turn off warnings */
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config.warnings = false;
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/*******************************************************************************************
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Tests the effectiveness of evolution
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*******************************************************************************************/
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describe('Neat', function () {
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it('AND', async function () {
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this.timeout(40000);
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// Train the AND gate
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var trainingSet = [
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{ input: [0, 0], output: [0] },
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{ input: [0, 1], output: [0] },
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{ input: [1, 0], output: [0] },
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{ input: [1, 1], output: [1] }
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];
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var network = new Network(2, 1);
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var results = await network.evolve(trainingSet, {
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mutation: methods.mutation.FFW,
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equal: true,
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elitism: 10,
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mutationRate: 0.5,
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error: 0.03,
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threads: 1
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});
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assert.isBelow(results.error, 0.03);
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});
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it('XOR', async function () {
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this.timeout(40000);
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// Train the XOR gate
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var trainingSet = [
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{ input: [0, 0], output: [0] },
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{ input: [0, 1], output: [1] },
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{ input: [1, 0], output: [1] },
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{ input: [1, 1], output: [0] }
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];
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var network = new Network(2, 1);
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var results = await network.evolve(trainingSet, {
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mutation: methods.mutation.FFW,
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equal: true,
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elitism: 10,
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mutationRate: 0.5,
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error: 0.03,
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threads: 1
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});
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assert.isBelow(results.error, 0.03);
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});
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it('XNOR', async function () {
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this.timeout(60000);
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// Train the XNOR gate
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var trainingSet = [
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{ input: [0, 0], output: [1] },
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{ input: [0, 1], output: [0] },
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{ input: [1, 0], output: [0] },
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{ input: [1, 1], output: [1] }
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];
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var network = new Network(2, 1);
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var results = await network.evolve(trainingSet, {
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mutation: methods.mutation.FFW,
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equal: true,
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elitism: 10,
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mutationRate: 0.5,
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error: 0.03,
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threads: 1
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});
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assert.isBelow(results.error, 0.03);
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});
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});
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393
node_modules/neataptic/test/network.js
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393
node_modules/neataptic/test/network.js
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/* Import */
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var chai = require('chai');
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var assert = chai.assert;
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/* Shorten var names */
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var { architect, Network, methods, config } = neataptic;
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/* Turn off warnings */
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config.warnings = false;
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/* Functions used in the testing process */
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function checkMutation (method) {
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var network = new architect.Perceptron(2, 4, 4, 4, 2);
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network.mutate(methods.mutation.ADD_GATE);
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network.mutate(methods.mutation.ADD_BACK_CONN);
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network.mutate(methods.mutation.ADD_SELF_CONN);
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var originalOutput = [];
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var i, j;
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for (i = 0; i <= 10; i++) {
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for (j = 0; j <= 10; j++) {
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originalOutput.push(network.activate([i / 10, j / 10]));
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}
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}
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network.mutate(method);
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var mutatedOutput = [];
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for (i = 0; i <= 10; i++) {
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for (j = 0; j <= 10; j++) {
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mutatedOutput.push(network.activate([i / 10, j / 10]));
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}
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}
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assert.notDeepEqual(originalOutput, mutatedOutput, 'Output of original network should be different from the mutated network!');
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}
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function learnSet (set, iterations, error) {
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var network = new architect.Perceptron(set[0].input.length, 5, set[0].output.length);
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var options = {
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iterations: iterations,
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error: error,
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shuffle: true,
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rate: 0.3,
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momentum: 0.9
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};
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var results = network.train(set, options);
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assert.isBelow(results.error, error);
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}
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function testEquality (original, copied) {
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for (var j = 0; j < 50; j++) {
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var input = [];
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var a;
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for (a = 0; a < original.input; a++) {
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input.push(Math.random());
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}
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var ORout = original.activate([input]);
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var COout = copied instanceof Network ? copied.activate([input]) : copied([input]);
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for (a = 0; a < original.output; a++) {
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ORout[a] = ORout[a].toFixed(9);
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COout[a] = COout[a].toFixed(9);
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}
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assert.deepEqual(ORout, COout, copied instanceof Network
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? 'Original and JSON copied networks are not the same!'
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: 'Original and standalone networks are not the same!'
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);
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}
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}
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/*******************************************************************************************
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Test the performance of networks
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*******************************************************************************************/
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describe('Networks', function () {
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describe('Mutation', function () {
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it('ADD_NODE', function () {
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checkMutation(methods.mutation.ADD_NODE);
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});
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it('ADD_CONNECTION', function () {
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checkMutation(methods.mutation.ADD_CONN);
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});
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it('MOD_BIAS', function () {
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checkMutation(methods.mutation.MOD_BIAS);
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});
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it('MOD_WEIGHT', function () {
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checkMutation(methods.mutation.MOD_WEIGHT);
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});
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it('SUB_CONN', function () {
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checkMutation(methods.mutation.SUB_CONN);
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});
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it('SUB_NODE', function () {
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checkMutation(methods.mutation.SUB_NODE);
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});
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it('MOD_ACTIVATION', function () {
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checkMutation(methods.mutation.MOD_ACTIVATION);
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});
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it('ADD_GATE', function () {
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checkMutation(methods.mutation.ADD_GATE);
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});
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it('SUB_GATE', function () {
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checkMutation(methods.mutation.SUB_GATE);
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});
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it('ADD_SELF_CONN', function () {
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checkMutation(methods.mutation.ADD_SELF_CONN);
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});
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it('SUB_SELF_CONN', function () {
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checkMutation(methods.mutation.SUB_SELF_CONN);
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});
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it('ADD_BACK_CONN', function () {
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checkMutation(methods.mutation.ADD_BACK_CONN);
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});
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it('SUB_BACK_CONN', function () {
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checkMutation(methods.mutation.SUB_BACK_CONN);
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});
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it('SWAP_NODES', function () {
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checkMutation(methods.mutation.SWAP_NODES);
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});
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});
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describe('Structure', function () {
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it('Feed-forward', function () {
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this.timeout(30000);
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var network1 = new Network(2, 2);
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var network2 = new Network(2, 2);
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// mutate it a couple of times
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var i;
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for (i = 0; i < 100; i++) {
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network1.mutate(methods.mutation.ADD_NODE);
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network2.mutate(methods.mutation.ADD_NODE);
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}
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for (i = 0; i < 400; i++) {
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network1.mutate(methods.mutation.ADD_CONN);
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network2.mutate(methods.mutation.ADD_NODE);
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}
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// Crossover
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var network = Network.crossOver(network1, network2);
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// Check if the network is feed-forward correctly
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for (i = 0; i < network.connections.length; i++) {
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let from = network.nodes.indexOf(network.connections[i].from);
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let to = network.nodes.indexOf(network.connections[i].to);
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// Exception will be made for memory connections soon
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assert.isBelow(from, to, 'network is not feeding forward correctly');
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}
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});
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it('from/toJSON equivalency', function () {
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this.timeout(10000);
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var original, copy;
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original = new architect.Perceptron(Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1));
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copy = Network.fromJSON(original.toJSON());
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testEquality(original, copy);
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original = new Network(Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1));
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copy = Network.fromJSON(original.toJSON());
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testEquality(original, copy);
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original = new architect.LSTM(Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1));
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copy = Network.fromJSON(original.toJSON());
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testEquality(original, copy);
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original = new architect.GRU(Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1));
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copy = Network.fromJSON(original.toJSON());
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testEquality(original, copy);
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original = new architect.Random(Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 10 + 1), Math.floor(Math.random() * 5 + 1));
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copy = Network.fromJSON(original.toJSON());
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testEquality(original, copy);
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original = new architect.NARX(Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 10 + 1), Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1));
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copy = Network.fromJSON(original.toJSON());
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testEquality(original, copy);
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original = new architect.Hopfield(Math.floor(Math.random() * 5 + 1));
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copy = Network.fromJSON(original.toJSON());
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testEquality(original, copy);
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});
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it('standalone equivalency', function () {
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this.timeout(10000);
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var original;
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original = new architect.Perceptron(Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1));
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eval(original.standalone());
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testEquality(original, activate);
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original = new Network(Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1));
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eval(original.standalone());
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testEquality(original, activate);
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original = new architect.LSTM(Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1));
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eval(original.standalone());
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testEquality(original, activate);
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original = new architect.GRU(Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1));
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eval(original.standalone());
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testEquality(original, activate);
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original = new architect.Random(Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 10 + 1), Math.floor(Math.random() * 5 + 1));
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eval(original.standalone());
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testEquality(original, activate);
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original = new architect.NARX(Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1), Math.floor(Math.random() * 5 + 1));
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eval(original.standalone());
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testEquality(original, activate);
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original = new architect.Hopfield(Math.floor(Math.random() * 5 + 1));
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eval(original.standalone());
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testEquality(original, activate);
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});
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});
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describe('Learning capability', function () {
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it('AND gate', function () {
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learnSet([
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{ input: [0, 0], output: [0] },
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{ input: [0, 1], output: [0] },
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{ input: [1, 0], output: [0] },
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{ input: [1, 1], output: [1] }
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], 1000, 0.002);
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});
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it('XOR gate', function () {
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learnSet([
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{ input: [0, 0], output: [0] },
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{ input: [0, 1], output: [1] },
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{ input: [1, 0], output: [1] },
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{ input: [1, 1], output: [0] }
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], 3000, 0.002);
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});
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it('NOT gate', function () {
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learnSet([
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{ input: [0], output: [1] },
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{ input: [1], output: [0] }
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], 1000, 0.002);
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});
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it('XNOR gate', function () {
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learnSet([
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{ input: [0, 0], output: [1] },
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{ input: [0, 1], output: [0] },
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{ input: [1, 0], output: [0] },
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{ input: [1, 1], output: [1] }
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], 3000, 0.002);
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});
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it('OR gate', function () {
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learnSet([
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{ input: [0, 0], output: [0] },
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{ input: [0, 1], output: [1] },
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{ input: [1, 0], output: [1] },
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{ input: [1, 1], output: [1] }
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], 1000, 0.002);
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});
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it('SIN function', function () {
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this.timeout(30000);
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var set = [];
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while (set.length < 100) {
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var inputValue = Math.random() * Math.PI * 2;
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set.push({
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input: [inputValue / (Math.PI * 2)],
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output: [(Math.sin(inputValue) + 1) / 2]
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});
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}
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learnSet(set, 1000, 0.05);
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});
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it('Bigger than', function () {
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this.timeout(30000);
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var set = [];
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||||
for (var i = 0; i < 100; i++) {
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||||
var x = Math.random();
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var y = Math.random();
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var z = x > y ? 1 : 0;
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||||
set.push({ input: [x, y], output: [z] });
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}
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learnSet(set, 500, 0.05);
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||||
});
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it('LSTM XOR', function () {
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this.timeout(30000);
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||||
var lstm = new architect.LSTM(1, 1, 1);
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||||
|
||||
lstm.train([
|
||||
{ input: [0], output: [0] },
|
||||
{ input: [1], output: [1] },
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||||
{ input: [1], output: [0] },
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||||
{ input: [0], output: [1] },
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||||
{ input: [0], output: [0] }
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||||
], {
|
||||
error: 0.001,
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||||
iterations: 5000,
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||||
rate: 0.3
|
||||
});
|
||||
|
||||
lstm.activate([0]);
|
||||
|
||||
function getActivation(sensors) {
|
||||
return lstm.activate(sensors)[0];
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||||
}
|
||||
|
||||
assert.isBelow(0.9, getActivation([1]), 'LSTM error');
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||||
assert.isBelow(getActivation([1]), 0.1, 'LSTM error');
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||||
assert.isBelow(0.9, getActivation([0]), 'LSTM error');
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||||
assert.isBelow(getActivation([0]), 0.1, 'LSTM error');
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||||
});
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||||
it('GRU XOR', function () {
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||||
this.timeout(30000);
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||||
var gru = new architect.GRU(1, 2, 1);
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||||
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||||
gru.train([
|
||||
{ input: [0], output: [0] },
|
||||
{ input: [1], output: [1] },
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||||
{ input: [1], output: [0] },
|
||||
{ input: [0], output: [1] },
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||||
{ input: [0], output: [0] }
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||||
], {
|
||||
error: 0.001,
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||||
iterations: 5000,
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||||
rate: 0.1,
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||||
clear: true
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||||
});
|
||||
|
||||
gru.activate([0]);
|
||||
|
||||
function getActivation(sensors) {
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||||
return gru.activate(sensors)[0];
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||||
}
|
||||
|
||||
assert.isBelow(0.9, getActivation([1]), 'GRU error');
|
||||
assert.isBelow(getActivation([1]), 0.1, 'GRU error');
|
||||
assert.isBelow(0.9, getActivation([0]), 'GRU error');
|
||||
assert.isBelow(getActivation([0]), 0.1, 'GRU error');
|
||||
});
|
||||
it('NARX Sequence', function () {
|
||||
var narx = new architect.NARX(1, 5, 1, 3, 3);
|
||||
|
||||
// Train the XOR gate (in sequence!)
|
||||
var trainingData = [
|
||||
{ input: [0], output: [0] },
|
||||
{ input: [0], output: [0] },
|
||||
{ input: [0], output: [1] },
|
||||
{ input: [1], output: [0] },
|
||||
{ input: [0], output: [0] },
|
||||
{ input: [0], output: [0] },
|
||||
{ input: [0], output: [1] }
|
||||
];
|
||||
|
||||
narx.train(trainingData, {
|
||||
iterations: 1000,
|
||||
error: 0.005,
|
||||
rate: 0.05
|
||||
});
|
||||
|
||||
assert.isBelow(narx.test(trainingData).error, 0.005);
|
||||
});
|
||||
it('SIN + COS', function () {
|
||||
this.timeout(30000);
|
||||
var set = [];
|
||||
|
||||
while (set.length < 100) {
|
||||
var inputValue = Math.random() * Math.PI * 2;
|
||||
set.push({
|
||||
input: [inputValue / (Math.PI * 2)],
|
||||
output: [
|
||||
(Math.sin(inputValue) + 1) / 2,
|
||||
(Math.cos(inputValue) + 1) / 2
|
||||
]
|
||||
});
|
||||
}
|
||||
|
||||
learnSet(set, 1000, 0.05);
|
||||
});
|
||||
it('SHIFT', function () {
|
||||
var set = [];
|
||||
|
||||
for (var i = 0; i < 1000; i++) {
|
||||
var x = Math.random();
|
||||
var y = Math.random();
|
||||
var z = Math.random();
|
||||
|
||||
set.push({ input: [x, y, z], output: [z, x, y] });
|
||||
}
|
||||
|
||||
learnSet(set, 500, 0.03);
|
||||
});
|
||||
});
|
||||
});
|
||||
3
node_modules/neataptic/test/src.js
generated
vendored
Normal file
3
node_modules/neataptic/test/src.js
generated
vendored
Normal file
@@ -0,0 +1,3 @@
|
||||
if (typeof global.neataptic === 'undefined') {
|
||||
global.neataptic = require('../src/neataptic');
|
||||
}
|
||||
Reference in New Issue
Block a user