{"schema":1,"generatedAt":"2026-09-26T21:06:26.531Z","origin":"local-build","runId":82891,"source":"// fly lab / xnor\n// generated from a bounded template by the room's coding simulation.\n// 2 inputs → 6 tanh neurons → 1 sigmoid output; full-batch gradient descent.\n// run: node --input-type=module -e \"import('./neural-network.mjs').then(m => console.log(m.run(m.config)))\"\n\nexport const config = {\n  \"name\": \"xnor\",\n  \"title\": \"learn when two inputs agree\",\n  \"targets\": [\n    1,\n    0,\n    0,\n    1\n  ],\n  \"seed\": 3812,\n  \"hidden\": 6,\n  \"epochs\": 2400,\n  \"rate\": 0.8\n};\n\nfunction randomGenerator(seed) {\n  return () => { seed = (Math.imul(seed, 1664525) + 1013904223) >>> 0; return seed / 4294967296; };\n}\n\nfunction createNetwork(config) {\n  const random = randomGenerator(config.seed);\n  const weight = () => (random() * 2 - 1) * 1.2;\n  return { input: Array.from({length: config.hidden}, () => [weight(), weight(), weight()]), output: Array.from({length: config.hidden + 1}, weight) };\n}\n\nfunction predict(network, x) {\n  const hidden = network.input.map(w => Math.tanh(w[0] * x[0] + w[1] * x[1] + w[2]));\n  const logit = hidden.reduce((sum, h, i) => sum + h * network.output[i], network.output.at(-1));\n  return { hidden, value: 1 / (1 + Math.exp(-logit)) };\n}\n\nfunction evaluate(network, targets) {\n  const inputs = [[0,0], [0,1], [1,0], [1,1]];\n  const predictions = inputs.map((input, i) => ({input, target: targets[i], probability: predict(network, input).value}));\n  const loss = predictions.reduce((sum, p) => sum - p.target * Math.log(Math.max(1e-12, p.probability)) - (1 - p.target) * Math.log(Math.max(1e-12, 1 - p.probability)), 0) / 4;\n  return {loss, accuracy: predictions.filter(p => Number(p.probability >= .5) === p.target).length / 4, predictions};\n}\n\nexport function* train(config) {\n  const network = createNetwork(config);\n  const inputs = [[0,0], [0,1], [1,0], [1,1]];\n  const history = [];\n  for (let epoch = 0; epoch <= config.epochs; epoch++) {\n    if (epoch % 40 === 0 || epoch === config.epochs) {\n      const metrics = evaluate(network, config.targets);\n      history.push({epoch, loss: metrics.loss, accuracy: metrics.accuracy});\n      yield {epoch, ...metrics, history: [...history], network: JSON.parse(JSON.stringify(network))};\n    }\n    if (epoch === config.epochs) break;\n    const gradInput = network.input.map(() => [0,0,0]);\n    const gradOutput = network.output.map(() => 0);\n    inputs.forEach((x, row) => {\n      const {hidden, value} = predict(network, x);\n      const error = (value - config.targets[row]) / inputs.length;\n      hidden.forEach((h, i) => {\n        gradOutput[i] += error * h;\n        const delta = error * network.output[i] * (1 - h * h);\n        gradInput[i][0] += delta * x[0];\n        gradInput[i][1] += delta * x[1];\n        gradInput[i][2] += delta;\n      });\n      gradOutput[config.hidden] += error;\n    });\n    network.input.forEach((weights, i) => weights.forEach((_, j) => { weights[j] -= config.rate * gradInput[i][j]; }));\n    network.output.forEach((_, i) => { network.output[i] -= config.rate * gradOutput[i]; });\n  }\n}\n\nexport function run(config) {\n  let result;\n  for (const step of train(config)) result = step;\n  return result;\n}\n","config":{"name":"xnor","title":"learn when two inputs agree","targets":[1,0,0,1],"seed":3812,"hidden":6,"epochs":2400,"rate":0.8},"brain":{"repository":"https://github.com/blendi-remade/fly-brain-minecraft","commit":"6cfa30175003ef25da68a237d5eda958f8047b82","dataset":"male-cns:v1.0","sha256":"e33df182bed7a6f3ea279daf4790a82b05706d3d41e819a6a80c0473e8c559f3","neurons":176422,"connections":6287749,"parameters":{"dt":0.5,"tau":20,"synTau":5,"rest":-52,"threshold":-45,"refractory":2.2,"delay":1.8,"weight":0.275,"gain":0.65,"kenyonGain":0.25},"seed":20260903,"simulatedMs":1150,"totalSpikes":74431,"bridge":"MN9 feeding rate → template keystrokes; full 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