martijn.diersmann@pm.me
99 lines
3.7 KiB
JavaScript
99 lines
3.7 KiB
JavaScript
'use strict';
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// Vluchtige Node-test: laadt app.js met een minimale DOM-stub en
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// controleert of het neuraal netwerk echt leert (XOR / cirkel / spiraal).
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const fs = require('fs');
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const path = require('path');
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const src = fs.readFileSync(path.join(__dirname, 'app.js'), 'utf8');
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function makeCtx() {
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const data = new Uint8ClampedArray(200 * 150 * 4);
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return {
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createImageData: () => ({ width: 200, height: 150, data }),
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putImageData: () => {}, clearRect: () => {}, drawImage: () => {},
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fillRect: () => {}, fillText: () => {}, beginPath: () => {}, arc: () => {},
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fill: () => {}, stroke: () => {}, moveTo: () => {}, lineTo: () => {}, strokeRect: () => {}
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};
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}
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function makeEl() {
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return {
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width: 460, height: 320, style: {}, textContent: '', innerHTML: '',
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scrollTop: 0, scrollHeight: 0,
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classList: { add() {}, remove() {}, toggle() { return false; } },
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appendChild() {}, addEventListener() {}, setAttribute() {}, click() {},
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getContext: makeCtx, parentElement: { clientWidth: 800 }, dataset: {}, value: '6',
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toDataURL: () => 'data:image/png;base64,xx'
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};
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}
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const documentStub = {
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getElementById: () => makeEl(),
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querySelectorAll: () => [],
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createElement: () => makeEl()
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};
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const rafNoop = () => {};
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const code = src + '\n;globalThis.__NN = NeuralNet; globalThis.__makeDataset = makeDataset;';
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new Function('document', 'requestAnimationFrame', 'window', 'setTimeout', code)(
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documentStub, rafNoop, { addEventListener() {} }, setTimeout
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);
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const NeuralNet = globalThis.__NN;
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const makeDataset = globalThis.__makeDataset;
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function trainOn(name, epochs, lr, hidden) {
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const net = new NeuralNet([2, hidden, 1]);
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const samples = makeDataset(name);
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const idx = samples.map((_, i) => i);
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let last = null;
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for (let e = 0; e < epochs; e++) {
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for (let i = idx.length - 1; i > 0; i--) {
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const j = Math.floor(Math.random() * (i + 1));
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const t = idx[i]; idx[i] = idx[j]; idx[j] = t;
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}
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let sum = 0;
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for (const i of idx) sum += net.trainSample(samples[i].x, samples[i].y, lr);
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last = sum / idx.length;
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}
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const test = makeDataset(name);
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const n = Math.min(200, test.length);
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let correct = 0;
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for (let i = 0; i < n; i++) {
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if ((net.predict(test[i].x) > 0.5 ? 1 : 0) === test[i].y) correct++;
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}
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return { acc: (correct / n) * 100, loss: last };
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}
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// Finale controle — zelfde instelling als app.js (Adam, lr 0.05)
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function trial(name, hidden, epochs) {
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const res = [];
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for (let t = 0; t < 6; t++) res.push(trainOn(name, epochs, 0.05, hidden));
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const worst = Math.min(...res.map((a) => a.acc));
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return { worst, raw: res.map((a) => a.acc.toFixed(1) + '%').join(' ') };
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}
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const xor0 = trainOn('XOR', 0, 0.05, 10);
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const xor = trial('XOR', 10, 250);
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const cir = trial('CIRKEL', 14, 400);
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const spi = trial('SPIRAAL', 30, 1400);
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console.log('XOR 0ep :', xor0.acc.toFixed(1) + '%');
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console.log('XOR 250ep :', xor.raw, ' (slechtste ' + xor.worst.toFixed(1) + '%)');
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console.log('CIRKEL 400ep :', cir.raw, ' (slechtste ' + cir.worst.toFixed(1) + '%)');
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console.log('SPIRAAL 1400ep :', spi.raw, ' (slechtste ' + spi.worst.toFixed(1) + '%)');
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let fail = 0;
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if (xor0.acc < 35 || xor0.acc > 70) { console.error('FAIL: XOR ongetraind moet ~50% zijn'); fail = 1; }
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if (xor.worst < 85) { console.error('FAIL: XOR convergerde niet'); fail = 1; }
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if (cir.worst < 90) { console.error('FAIL: Cirkel convergerde niet'); fail = 1; }
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if (spi.worst < 85) { console.error('FAIL: Spiraal convergerde niet'); fail = 1; }
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if (fail) {
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console.error('VALIDATIE MISLUKT');
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process.exit(1);
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}
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console.log('PASS: alle drie datasets leren betrouwbaar met Adam (lr 0.05).');
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process.exit(0);
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if (fail) process.exit(1);
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console.log('PASS: app.js laadt schoon én het netwerk leert alle drie datasets.');
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process.exit(0);
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