let neuralNetwork;

​

function setup() {

  noCanvas();

  let nnOptions = {

    dataUrl: 'shoe.csv',

    inputs: ['price','distance', 'competition'],

    outputs: ['outcome'],

    task: 'regression',

    debug: true,

  };

  neuralNetwork = ml5.neuralNetwork(nnOptions, modelReady);

  priceTitle = createP('Price (type into a value between 50 and 150)');

  priceInput = createInput('100', 'text');

  distanceTitle = createP('Distance(over 400m type in long, lower 400m type in short)');

  distanceInput = createInput('long', 'text');

  competitionTitle = createP('Competition(yes or no)');

  competitionInput = createInput('yes', 'text');

  submitButton = createButton('Submit');

  submitButton.mousePressed(predict);

  resP = createP('results:')

}

​

function modelReady() {

  neuralNetwork.normalizeData();

  neuralNetwork.train({ epochs: 5 }, whileTraining, finishedTraining);

}

​

function whileTraining(epochs, log) {

  console.log('epochs', epochs)

  console.log('log', log.loss)

}

​

function finishedTraining() {

  predict();

}

​

function predict() {

  console.log('regression');

  const input = [priceInput.value(), distanceInput.value(), competitionInput.value()];

  neuralNetwork.classify(input, gotResults);

}

​

function gotResults(err, res) {

  if (res) {

    resP.html('results: ' + res[0].label + ', ' + res[0].confidence)

  }

}

​

​

​

​

​

​