ok

test-nn.js

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