import WASML from "wasml"

const wasml = new WASML()

// Create a model with 16 inputs and 3 action states.
await wasml.model(16, 3) // See below for full configuration options.

// Using `wasml.table(16, 3)` instead will solve this game far quicker!
// * Tabular optimisation becomes less feasible as state space grows (only 40x40=1600 states here)

// Add two hidden layers.
wasml.addLayers([
  { units: 32, activation: "sigmoid" },
  { units: 8, activation: "linear" },
])

// Compile the model.
wasml.compile({ loss: "meanSquaredError" })

// Array of a hundred empty samples.
// - It is not neccessary to pre-train the model, but can be useful.
const inputs = Array(100).from(Array.from({ length: 16 }, () => Math.random()))
const outputs = Array(100).from([1, 0, 0])
wasml.train(inputs, outputs)

// Predict the optimal action.
const input = Array.from({ length: 16 }, () => Math.random())
const result = wasml.predict(input)

// [?] Do something with the action.

// Reward the model.
wasml.reward(10.0)

import WASML from "wasml"

const wasml = new WASML()

// Load an exported model and restore the memory.
const model = await fetch('export.json').then(res => res.text())
wasml.import(model)

// Get the memory of the changed model in JSON form.
const json = wasml.export()

import { NeuralNetwork } from "wasml/network"

// Utilise the underlying NN.
const NN = new NeuralNetwork(
  2,
  2,
  [
    {
      activation: "sigmoid",
      units: 8,
    },
    {
      activation: "sigmoid",
      units: 2,
    },
  ],
  {
    loss: "meanSquaredError",
  },
  0.1
)

// Trains a neural network to determine the largest number in a set of 2 numbers.
for (let i = 0; i < 10000; i++) {
  let data: number[] = [Math.random(), Math.random()]
  let target: number[] = data[0] > data[1] ? [1, 0] : [0, 1]

  const result = NN.forward(data)
  NN.backward(target)
}

// Now attempt some static predictions.
console.log("Test 1: ", NN.forward([10, 20]))
console.log("Test 2: ", NN.forward([500, 1]))
console.log("Test 3: ", NN.forward([0.7, 0.99]))