This example if forked from Dan Shiffman and the Coding train 

https://thecodingtrain.com/learning/ml5/6.1-train-your-own.html

*/

let canvas;

​

function setup() {

  canvas= createCanvas(400, 400);

​

  //STEP 5

  // Only when clicking on the canvas

  canvas.mousePressed(addData);

​

  //STEP 1

  // Create ethe model

  const options = {

    inputs: ['x', 'y'], // TODO: support ['x', 'y']

    outputs: ['label'], // TODO: support ['label']

    debug: true,

    task: 'classification'

  }

  brain = ml5.neuralNetwork(options);

​

  // Train Model button

  select('#train').mousePressed(trainModel);

​

  background(0);

}

​

function draw() {

//not needed at moment 

}

​

// Add a data record

function addData() {

​

  //STEP 3

  // Get label

  let label = select('#label').value();

  // Add data

​

  brain.addData({

    x: mouseX,

    y: mouseY

  }, {

    label

  });

​

  //STEP 4

  // Draw circle to visualize training data

  stroke(255);

  noFill();

  ellipse(mouseX, mouseY, 32);

  fill(255);

  textSize(16);

  textAlign(CENTER, CENTER);

  text(label, mouseX, mouseY);

}

​

​

​

//STEP 6

// Train the model

function trainModel() {

  // ml5 will normalize data to a range between 0 and 1 for you.

  brain.normalizeData();

  // Train the model

  // Epochs: one cycle through all the training data

  brain.train({

    epochs: 20

  }, finishedTraining);

}

​

//STEP 7

// When the model is trained

function finishedTraining() {

  brain.classify([mouseX, mouseY], gotResults);

}

​

//STEP 8

// Got a result

function gotResults(error, results) {

  if (error) {

   // console.error(error);

    return;

  }

​

  // Show classification

  select('#classification').html(results[0].label);

  confidence = results[0].confidence;

  classificationResult = results[0].label;

​

 // console.log(results);

  // Predict again

  brain.classify([mouseX, mouseY], gotResults);

}