let model;

let targetLabel = 'C';

let state = 'collection';

let env, wave, trained_epochs;

​

let notes = {

  C: 261.62,

  D: 293.66,

  E: 329.63,

  F: 349.23,

  G: 392.00,

  A: 440.00

}

​

let colors = {

  C: 'rgba(255,  0,  0, 0.4)',

  D: 'rgba(  0, 255, 0, 0.4)',

  E: 'rgba( 0,  0, 255, 0.4)',

  F: 'rgba(255, 255, 0, 0.4)',

  G: 'rgba(0, 255, 255, 0.4)',

  A: 'rgba(255, 0, 255, 0.4)'

}

​

function setup() {

  createCanvas(400, 400);

  let  options = {

    inputs: ['x', 'y'],

    outputs: ['label'],

    task:  'classification',

    debug: 'true',

  }

  model = ml5.neuralNetwork(options);

  env = new p5.Envelope();

  env.setADSR(0.05, 0.1, 0.5, 1);

  env.setRange(1.2, 0); 

  wave = new p5.Oscillator(); 

  wave.setType('sine');

  wave.start();

  wave.freq(440);

  wave.amp(env);

  background(255);

}

​

function keyPressed() {

  if (key == 't') {

    state = 'training';

    console.log('starting training.');

    model.normalizeData();

    let options = {

      epochs: 200

    }

    trained_epochs = 200;

    model.train(options, whileTraining, finishedTraining);

  } else if (key == 'r' && state == 'prediction') {

    rx = random(400);

    ry = random(400);

    let pos = {

      x: rx,

      y: ry

    }

    model.classify(pos, autoResults);

  } else if (key == 'c' && state == 'prediction') {

    console.log('continue training');

    trained_epochs += 100;

    let options = { epochs: 100};

    model.train(options, whileTraining, finishedTraining);

  } else if (key == 's' && state == 'prediction') {

    console.log('export model.');

    model.save();

  }else {

    targetLabel = key.toUpperCase();

  }

}

​

function whileTraining(epochs, loss) {

  console.log(epochs);

}

​

function finishedTraining() {

  console.log('finished training. epochs trained: ', trained_epochs);

  state = 'prediction';

}

​

function mousePressed() {

  let inputs = {

    x: mouseX,

    y: mouseY

  }

  if (state == 'collection') {

    let target = {

      label: targetLabel

    }

    model.addData(inputs,target);

    stroke(0);

    noFill();

    ellipse(mouseX, mouseY, 24);

    fill(0);

    noStroke();

    textAlign(CENTER,CENTER);

    text(targetLabel, mouseX, mouseY);

    wave.freq(notes[targetLabel]);

    env.play();

  } else if (state == 'prediction') {

    model.classify(inputs, gotResults);

  }

}

​

function gotResults(error, results) {

  if (error) {

    console.error(error);

    return;

  }

  // console.log(results);

  let label = results[0].label;

  console.log(label);

  stroke(0);

  // fill(0, 0, 255, 100);

  fill(colors[label]);

  // console.log(colors[label]);

  ellipse(mouseX, mouseY, 24);

  fill(0);

  noStroke();

  textAlign(CENTER,CENTER);

  text(label, mouseX, mouseY);

  wave.freq(notes[label]);

  env.play();

}

​

function autoResults(error, results) {

  if (error) {

    console.error(error);

    return;

  }

  // console.log(results);

  let label = results[0].label;

  stroke(0);

  fill(colors[label]);

  // console.log(colors[label]);

  ellipse(rx, ry, 24);

  fill(0);

  noStroke();

  textAlign(CENTER,CENTER);

  text(label, rx, ry);

  wave.freq(notes[label]);

  env.play();

}

​

// function draw() {

//   background(220);

// }