let neuralNetwork;

let trainingData = [];

let totalPoints = 1000;

let circlePoints = 0;

​

function setup() {

  // Create a canvas

  createCanvas(400, 400);

​

  // Create the neural network

  neuralNetwork = ml5.neuralNetwork();

​

  // Generate training data

  for (let i = 0; i < totalPoints; i++) {

    let x = random(-1, 1);

    let y = random(-1, 1);

    let label = x * x + y * y < 1 ? 1 : 0; // Check if the point is inside the circle

    trainingData.push({ input: [x, y], output: [label] });

  }

​

  // Train the neural network

  neuralNetwork.addData(trainingData);

  neuralNetwork.normalizeData();

  neuralNetwork.train({ epochs: 32 }, finishedTraining);

}

​

function finishedTraining() {

  // Use the trained neural network to estimate pi

  for (let i = 0; i < totalPoints; i++) {

    let x = random(-1, 1);

    let y = random(-1, 1);

    let input = [x, y];

    neuralNetwork.predict(input, (err, result) => {

      if (result[0].value > 0.5) {

        circlePoints++;

      }

    });

  }

​

  // Calculate the estimated value of pi

  let piEstimate = 4 * (circlePoints / totalPoints);

​

  // Display the estimated value of pi

  textSize(32);

  textAlign(CENTER, CENTER);

  text("Estimated Pi: " + piEstimate, width / 2, height / 2);

}

​

function draw() {

  // Display a loading message while training

  background(220);

  textSize(32);

  textAlign(CENTER, CENTER);

  text("Training the neural network...", width / 2, height / 2);

}

​