//this example combines tthe Bare Conductive TouchBoard with ML5 neural //network classifyer code. 

//neural network methods forked from

//https://editor.p5js.org/ml5/sketches/eM-65FxlKkU

​

It requires a slightly adapted DataStream code to be uplaoded to the TouchBoard first so it works with WebUSB 

found here -->

https://github.com/j3nsykes/creativeML2020/tree/master/PhysicalTeachableMachines/ML5_neuralNet/ArduinoCode/CapacitiveTouch/DataStream_webUSB

*/

// The neural network is the brain

let brain;

// Interface

let dataButton;

let dataLabel;

let trainButton;

let classificationP;

let labelB = '';

let labelA = '';

let labelC = '';

//let labelD = '';

let confA = 0;

let confB = 0;

let confC = 0;

//let confD = 0;

​

//define electrodes to be used

var firstElectrode = 1; //start at one ignore first String

var lastElectrode = 11; //use if you do not want all electrodes in use. 

//remeber to update the inputs if you change the lastElectrode ID.

var totalInputs=10; //how many electrode inputs?

​

let classificationResult;

let confidence;

let sample = 0;

let pressed = false;

let classifierLabel;

let training = false;

let lastTraining = false;

let recording = false;

let font;

​

function preload() {

  font = loadFont('SourceCodePro-Regular.ttf');

​

}

​

​

function setup() {

  let canvas = createCanvas(650, 400);

  canvas.parent('sketch-holder');

  visualSetup();

​

  //interface

  //   classificationP = createP('waiting to train model');

​

  samplesP = createP('waiting for samples');

  samplesP.position(130, 60);

  // Create ethe model

  const options = {

    inputs: totalInputs, // add each electron

    outputs: 3, // add amount of output labels

    debug: true,

    task: 'classification'

  }

  brain = ml5.neuralNetwork(options);

​

​

  textFont(font);

  background(0);

}

​

​

// Train the model

function trainModel() {

  brain.normalizeData();

​

  let options = {

    epochs: 25

  }

​

  brain.train(options, finishedTraining);

}

​

// When the model is trained

function finishedTraining() {

  classify();

}

​

// Classify

function classify() {

​

  let inputs = getInputs();

  brain.classify(inputs, gotResults);

​

}

​

// Got a result

function gotResults(error, results) {

  if (error) {

    console.error(error);

    return;

  }

​

  //  Log output

  //console.log(results);

​

​

  classificationResult = results[0].label;

  confidence = results[0].confidence;

  updateClassification(results); //filter results

​

  // Classify again

  classify();

}

​

function updateClassification(results) {

​

  //filter the results

  var idA = results.filter(function(e) {

    return e.label == 'A';

  });

  //console.log(idA[0].confidence);

  labelA = idA[0].label;

  confA = idA[0].confidence;

​

  var idB = results.filter(function(e) {

    return e.label == 'B';

  });

  //console.log(idB[0].confidence);

  labelB = idB[0].label;

  confB = idB[0].confidence;

​

  //comment in and out for less/more options

  var idC = results.filter(function(e) {

    return e.label == 'C';

  });

  // console.log(idC[0].confidence);

  labelC = idC[0].label;

  confC = idC[0].confidence;

​

  /*

  //add extrra classification label filter

  var idD = results.filter(function(e) {

    return e.label == 'D';

  });

  // console.log(idB[0].confidence);

  labelD = idD[0].label;

  confD = idD[0].confidence;

 */

​

}

​

function getInputs() {

​

  let inputs = [];

  for (let i = firstElectrode; i < lastElectrode; i++) {

    inputs.push(float(filteredData[i])); //data needs to be float for training!!!

​

  }

  // console.log(inputs);

  return inputs;

}

​

​

// Add a data record

function addExample() {

​

  samplesP.html('samples: ' + sample++); //see amount of samples collected. 

​

  let inputs = getInputs();

  // Get frequency

  let target = classifierLabel;

  // Add data

  brain.addData(inputs, [target]);

​

}

​

​

​

function draw() {

  background('#092532');

  //handle incoming data

  readIncomingSerial();

  processData();

​

  controlsCheck(); //gui controls

​

​

  //comment  in to display text of filttered data

  //debug();

​

  //display  electrode visuals. 

  displayAllBars();

  displayTouch();

​

  // fill(255);

  // textSize(18);

  // text(classificationResult, 50, 300);

​

  ////////////////// ADD YOUR INTTERACTION !!!!! ///////////////////////////

  //check classification results

//   if (classificationResult === "A") {

//     fill(255, 0, 0, 100);

//     ellipse(75, 350, 50, 50);

//   }

​

//   if (classificationResult === "B") {

//     fill(0, 255, 0, 100);

//     ellipse(75, 350, 50, 50);

//   }

}

​

​

​

function controlsCheck() {

  if (training !== lastTraining) { //don't continuously train!

    if (training) {

      //  console.log('training');

      trainModel();

    }

  }

  lastTraining = training;

​

​

  if (recording) {

    addExample(); //add samples if button checked 

    //console.log('recording');

  }

}