Code viewer for World: Character recognition neur...
// Cloned by Sumit Khopkar on 3 Dec 2021 from World "Character recognition neural network" by "Coding Train" project 
// Please leave this clone trail here.
 

// Port of Character recognition neural network from here:
// https://github.com/CodingTrain/Toy-Neural-Network-JS/tree/master/examples/mnist
// with many modifications 


// --- defined by MNIST - do not change these ---------------------------------------

const PIXELS        = 28;                       // images in data set are tiny 
const PIXELSSQUARED = PIXELS * PIXELS;

// number of training and test exemplars in the data set:
const NOTRAIN = 60000;
const NOTEST  = 10000;



//--- can modify all these --------------------------------------------------

// no of nodes in network 
const noinput  = PIXELSSQUARED;
const nohidden = 64;
const nooutput = 3;

const learningrate = 0.1;   // default 0.1  

// should we train every timestep or not 
let do_training = true;

// how many to train and test per timestep 
const TRAINPERSTEP = 3;
const TESTPERSTEP  = 1;

// multiply it by this to magnify for display 
const ZOOMFACTOR    = 7;                        
const ZOOMPIXELS    = ZOOMFACTOR * PIXELS; 

// 3 rows of
// large image + 50 gap + small image    
// 50 gap between rows 

const canvaswidth = ( PIXELS + ZOOMPIXELS ) + 50;
const canvasheight = ( ZOOMPIXELS * 3 ) + 100;


const DOODLE_THICK = 18;    // thickness of doodle lines 
const DOODLE_BLUR = 3;      // blur factor applied to doodles 


let mnist;      
// all data is loaded into this 
// mnist.train_images
// mnist.train_labels
// mnist.test_images
// mnist.test_labels


let nn;

let trainrun = 1;
let train_index = 0;

let testrun = 1;
let test_index = 0;
let total_tests = 0;
let total_correct = 0;

// images in LHS:
let doodle, demo;
let doodle_exists = false;
let demo_exists = false;

let mousedrag = false;      // are we in the middle of a mouse drag drawing?  


// save inputs to global var to inspect
// type these names in console 
var train_inputs, test_inputs, demo_inputs, doodle_inputs;


// Matrix.randomize() is changed to point to this. Must be defined by user of Matrix. 

function randomWeight()
{
    return ( AB.randomFloatAtoB ( -0.5, 0.5 ) );
            // Coding Train default is -1 to 1
}    


// make run header bigger
AB.headerCSS ( { "max-height": "95vh" } );




//--- start of AB.msgs structure: ---------------------------------------------------------
// We output a serious of AB.msgs to put data at various places in the run header 
var thehtml;

  // 1 Doodle header 
  thehtml = "<hr> <h1> 1. Doodle </h1> Top row: Doodle (left) and shrunk (right). <br> " +
        " Draw your doodle in top LHS. <button onclick='wipeDoodle();' class='normbutton' >Clear doodle</button> <br> ";
   AB.msg ( thehtml, 1 );

  // 2 Doodle variable data (guess)
  
  // 3 Training header
  thehtml = "<hr> <h1> 2. Training </h1> Middle row: Training image magnified (left) and original (right). <br>  " +
        " <button onclick='do_training = false;' class='normbutton' >Stop training</button> <br> ";
  AB.msg ( thehtml, 3 );
     
  // 4 variable training data 
  
  // 5 Testing header
  thehtml = "<h3> Hidden tests </h3> " ;
  AB.msg ( thehtml, 5 );
           
  // 6 variable testing data 
  
  // 7 Demo header 
  thehtml = "<hr> <h1> 3. Demo </h1> Bottom row: Test image magnified (left) and  original (right). <br>" +
        " The network is <i>not</i> trained on any of these images. <br> " +
        " <button onclick='makeDemo();' class='normbutton' >Demo test image</button> <br> ";
   AB.msg ( thehtml, 7 );
   
  // 8 Demo variable data (random demo ID)
  // 9 Demo variable data (changing guess)
  
const greenspan = "<span style='font-weight:bold; font-size:x-large; color:darkgreen'> "  ;

//--- end of AB.msgs structure: ---------------------------------------------------------

//CA686//

function sigmoid(x) {
  return 1 / (1 + Math.exp(-x));
}

function dsigmoid(y) {
  // return sigmoid(x) * (1 - sigmoid(x));
  return y * (1 - y);
}

class Matrix {
  constructor(rows, cols) {
    this.rows = rows;
    this.cols = cols;
    this.data = [];

    for (let i = 0; i < this.rows; i++) {
      this.data[i] = [];
      for (let j = 0; j < this.cols; j++) {
        this.data[i][j] = 0;
      }
    }
  }

  static fromArray(arr) {
    let m = new Matrix(arr.length, 1);
    for (let i = 0; i < arr.length; i++) {
      m.data[i][0] = arr[i];
    }
    return m;
  }

  static subtract(a, b) {
    // Return a new Matrix a-b
    let result = new Matrix(a.rows, a.cols);
    for (let i = 0; i < result.rows; i++) {
      for (let j = 0; j < result.cols; j++) {
        result.data[i][j] = a.data[i][j] - b.data[i][j];
      }
    }
    return result;
  }

  toArray() {
    let arr = [];
    for (let i = 0; i < this.rows; i++) {
      for (let j = 0; j < this.cols; j++) {
        arr.push(this.data[i][j]);
      }
    }
    return arr;
  }

  randomize() {
    for (let i = 0; i < this.rows; i++) {
      for (let j = 0; j < this.cols; j++) {
        this.data[i][j] = Math.random() * 2 - 1;
      }
    }
  }

  add(n) {
    if (n instanceof Matrix) {
      for (let i = 0; i < this.rows; i++) {
        for (let j = 0; j < this.cols; j++) {
          this.data[i][j] += n.data[i][j];
        }
      }
    } else {
      for (let i = 0; i < this.rows; i++) {
        for (let j = 0; j < this.cols; j++) {
          this.data[i][j] += n;
        }
      }
    }
  }

  static transpose(matrix) {
    let result = new Matrix(matrix.cols, matrix.rows);
    for (let i = 0; i < matrix.rows; i++) {
      for (let j = 0; j < matrix.cols; j++) {
        result.data[j][i] = matrix.data[i][j];
      }
    }
    return result;
  }

  static multiply(a, b) {
    // Matrix product
    console.log("static multiply");
    if (a.cols !== b.rows) {
      console.log('Columns of A must match rows of B.')
      return undefined;
    }
    let result = new Matrix(a.rows, b.cols);
    for (let i = 0; i < result.rows; i++) {
      for (let j = 0; j < result.cols; j++) {
        // Dot product of values in col
        let sum = 0;
        for (let k = 0; k < a.cols; k++) {
          sum += a.data[i][k] * b.data[k][j];
        }
        result.data[i][j] = sum;
      }
    }
    return result;
  }

  multiply(n) {
    console.log("non-static multiply");
    if (n instanceof Matrix) {
      // hadamard product
      for (let i = 0; i < this.rows; i++) {
        for (let j = 0; j < this.cols; j++) {
          this.data[i][j] *= n.data[i][j];
        }
      }
    } else {
      // Scalar product
      for (let i = 0; i < this.rows; i++) {
        for (let j = 0; j < this.cols; j++) {
          this.data[i][j] *= n;
        }
      }
    }
  }

  map(func) {
    // Apply a function to every element of matrix
    for (let i = 0; i < this.rows; i++) {
      for (let j = 0; j < this.cols; j++) {
        let val = this.data[i][j];
        this.data[i][j] = func(val);
      }
    }
  }

  static map(matrix, func) {
    let result = new Matrix(matrix.rows, matrix.cols);
    // Apply a function to every element of matrix
    for (let i = 0; i < matrix.rows; i++) {
      for (let j = 0; j < matrix.cols; j++) {
        let val = matrix.data[i][j];
        result.data[i][j] = func(val);
      }
    }
    return result;
  }

  print() {
    console.table(this.data);
  }
}


if (typeof module !== 'undefined') {
  module.exports = Matrix;
}

class NeuralNetwork {
  //Added code for learning rate
  constructor(input_nodes, hidden_nodes, output_nodes, learning_rate) {
    console.log("Constructor");
    console.error("error");
    this.input_nodes = input_nodes;
    this.hidden_nodes = hidden_nodes;
    this.output_nodes = output_nodes;

    this.weights_ih = new Matrix(this.hidden_nodes, this.input_nodes);
    this.weights_ho = new Matrix(this.output_nodes, this.hidden_nodes);
    this.weights_ih.randomize();
    this.weights_ho.randomize();

    this.bias_h = new Matrix(this.hidden_nodes, 1);
    this.bias_o = new Matrix(this.output_nodes, 1);
    this.bias_h.randomize();
    this.bias_o.randomize();
    this.learning_rate = learning_rate;
  }

  feedforward(input_array) {

    // Generating the Hidden Outputs
    let inputs = Matrix.fromArray(input_array);
    let hidden = Matrix.multiply(this.weights_ih, inputs);
    hidden.add(this.bias_h);
    // activation function!
    hidden.map(sigmoid);

    // Generating the output's output!
    let output = Matrix.multiply(this.weights_ho, hidden);
    output.add(this.bias_o);
    output.map(sigmoid);

    // Sending back to the caller!
    return output.toArray();
  }

  train(input_array, target_array) {
    // Generating the Hidden Outputs
    let inputs = Matrix.fromArray(input_array);
    console.log("inputs", inputs);
    let hidden = Matrix.multiply(this.weights_ih, inputs);
    hidden.add(this.bias_h);
    // activation function!
    hidden.map(sigmoid);

    // Generating the output's output!
    console.log("this.weights_ho", this.weights_ho);
    let outputs = Matrix.multiply(this.weights_ho, hidden);
    outputs.add(this.bias_o);
    outputs.map(sigmoid);

    // Convert array to matrix object
    let targets = Matrix.fromArray(target_array);

    // Calculate the error
    // ERROR = TARGETS - OUTPUTS
    let output_errors = Matrix.subtract(targets, outputs);
    console.log("output_errors", output_errors);
    console.log("outputs", outputs);
    // let gradient = outputs * (1 - outputs);
    // Calculate gradient
    let gradients = Matrix.map(outputs, dsigmoid);
    console.log("gradients", gradients);
    gradients.multiply(output_errors);
    gradients.multiply(this.learning_rate);


    // Calculate deltas
    let hidden_T = Matrix.transpose(hidden);
    let weight_ho_deltas = Matrix.multiply(gradients, hidden_T);

    // Adjust the weights by deltas
    this.weights_ho.add(weight_ho_deltas);
    // Adjust the bias by its deltas (which is just the gradients)
    this.bias_o.add(gradients);

    // Calculate the hidden layer errors
    let who_t = Matrix.transpose(this.weights_ho);
    let hidden_errors = Matrix.multiply(who_t, output_errors);

    // Calculate hidden gradient
    let hidden_gradient = Matrix.map(hidden, dsigmoid);
    hidden_gradient.multiply(hidden_errors);
    hidden_gradient.multiply(this.learning_rate);

    // Calcuate input->hidden deltas
    let inputs_T = Matrix.transpose(inputs);
    let weight_ih_deltas = Matrix.multiply(hidden_gradient, inputs_T);

    this.weights_ih.add(weight_ih_deltas);
    // Adjust the bias by its deltas (which is just the gradients)
    this.bias_h.add(hidden_gradient);
    // outputs.print();
    // targets.print();
    // error.print();
  }

  predict(input_array) {

    // Generating the Hidden Outputs
    let inputs = Matrix.fromArray(input_array);
    let hidden = Matrix.multiply(this.weights_ih, inputs);
    hidden.add(this.bias_h);
    // activation function!
    //hidden.map(this.activation_function.func);
    hidden.map(sigmoid);

    // Generating the output's output!
    let output = Matrix.multiply(this.weights_ho, hidden);
    output.add(this.bias_o);
    //output.map(this.activation_function.func);
    output.map(sigmoid);

    // Sending back to the caller!
    return output.toArray();
  }

}

const len = 784;
const totalData = 500;

const CAT = 0;
const RAINBOW = 1;
const TRAIN = 2;

let catsData;
let trainsData;
let rainbowsData;

let cats = {};
let trains = {};
let rainbows = {};

var doodle_list = [];
var doodle_list_data = [];
var doodle_num_list = [];

var epochCounter = 0;

function preload() {
    catsData = loadBytes('uploads/sumitkhopkar25/cats1000.bin');
    trainsData = loadBytes('uploads/sumitkhopkar25/trains1000.bin');
    rainbowsData = loadBytes('uploads/sumitkhopkar25/rainbows1000.bin');
}

/*function prepareData(category, data, label) {
    category.training = [];
    category.testing = [];
    for (let i = 0; i < totalData; i++) {
      let offset = i * len;
      let threshold = floor(0.8 * totalData);
      if (i < threshold) {
        category.training[i] = data.bytes.subarray(offset, offset + len);
        category.training[i].label = label;
      } else {
        category.testing[i - threshold] = data.bytes.subarray(offset, offset + len);
        category.testing[i - threshold].label = label;
      }
    }
    console.log("category.training", category.training);
    console.log("cats.training", cats.training);
}*/
//CA686//

function setup() 
{
  createCanvas ( canvaswidth, canvasheight );

  doodle = createGraphics ( ZOOMPIXELS, ZOOMPIXELS );       // doodle on larger canvas 
  doodle.pixelDensity(1);
  
// JS load other JS 
// maybe have a loading screen while loading the JS and the data set 

    AB.loadingScreen();

//CA686//
/*$.getScript ( "/uploads/codingtrain/matrix.js", function()
{
$.getScript ( "/uploads/codingtrain/nn.js", function()
{
    $.getScript ( "/uploads/codingtrain/mnist.js", function()
    {*/
        console.log ("All JS loaded");
        nn = new NeuralNetwork(  noinput, nohidden, nooutput, learningrate);
        //nn.setLearningRate ( learningrate );
        loadData();
    /*});
});
});*/
 
 console.log("cats.training", cats.training);
//CA686//
}



// load data set from local file (on this server)

function loadData()    
{
  //CA680//
  /*loadMNIST ( function(data)    
  {
    mnist = data;
    console.log ("All data loaded into mnist object:")
    console.log(mnist);
    AB.removeLoading();     // if no loading screen exists, this does nothing 
  });*/
  // Preparing the data

  doodle_list = [cats, rainbows, trains];
  doodle_list_data = [catsData, rainbowsData, trainsData];
  doodle_num_list = [CAT, RAINBOW, TRAIN];

  for(let j = 0; j < doodle_list.length; j++){
    console.log("j", j);
    doodle_list[j].training = [];
    doodle_list[j].testing = [];
    for (let i = 0; i < totalData; i++) {
      let offset = i * len;
      let threshold = floor(0.8 * totalData);
      if (i < threshold) {
        doodle_list[j].training[i] = doodle_list_data[j].bytes.subarray(offset, offset + len);
        doodle_list[j].training[i].label = doodle_num_list[j];
      } else {
        doodle_list[j].testing[i - threshold] = doodle_list_data[j].bytes.subarray(offset, offset + len);
        doodle_list[j].testing[i - threshold].label = doodle_num_list[j];
      }
    }
    console.log("doodle_list[j].training", doodle_list[j].training);
  }

  console.log("cats.training", cats.training);
  
  /*prepareData(cats, catsData, CAT);
  prepareData(rainbows, rainbowsData, RAINBOW);
  prepareData(trains, trainsData, TRAIN);*/
  AB.removeLoading();     // if no loading screen exists, this does nothing 
  //CA680//
}



function getImage ( img )      // make a P5 image object from a raw data array   
{
    let theimage  = createImage (PIXELS, PIXELS);    // make blank image, then populate it 
    theimage.loadPixels();        
    
    for (let i = 0; i < PIXELSSQUARED ; i++) 
    {
        let bright = img[i];
        let index = i * 4;
        theimage.pixels[index + 0] = bright;
        theimage.pixels[index + 1] = bright;
        theimage.pixels[index + 2] = bright;
        theimage.pixels[index + 3] = 255;
    }
    
    theimage.updatePixels();
    return theimage;
}


function getInputs ( img )      // convert img array into normalised input array 
{
    let inputs = [];
    for (let i = 0; i < PIXELSSQUARED ; i++)          
    {
        let bright = img[i];
        inputs[i] = bright / 255;       // normalise to 0 to 1
    } 
    return ( inputs );
}

//CA686//
function trainEpoch(training) {
  shuffle(training, true);
  //console.log(training);
  // Train for one epoch
  for (let i = 0; i < training.length; i++) {
    let data = training[i];
    let inputs = Array.from(data).map(x => x / 255);
    let label = training[i].label;
    let targets = [0, 0, 0];
    targets[label] = 1;
    console.log("inputs", inputs);
    console.log("targets", targets);
    console.log("nn.weights_ih", nn.weights_ih); 
    nn.train(inputs, targets);
  }
}

function testAll(testing) {

  let correct = 0;
  // Train for one epoch
  for (let i = 0; i < testing.length; i++) {
    // for (let i = 0; i < 1; i++) {
    let data = testing[i];
    let inputs = Array.from(data).map(x => x / 255);
    let label = testing[i].label;
    let guess = nn.predict(inputs);

    let m = max(guess);
    let classification = guess.indexOf(m);
    // console.log(guess);
    // console.log(classification);
    // console.log(label);

    if (classification === label) {
      correct++;
    }
  }
  let percent = 100 * correct / testing.length;
  return percent;

}
//CA686//
 

function trainit (show)        // train the network with a single exemplar, from global var "train_index", show visual on or off 
{
  //CA686//
  /*let img   = mnist.train_images[train_index];
  let label = mnist.train_labels[train_index];
  
  // optional - show visual of the image 
  if (show)                
  {
    var theimage = getImage ( img );    // get image from data array 
    image ( theimage,   0,                ZOOMPIXELS+50,    ZOOMPIXELS,     ZOOMPIXELS  );      // magnified 
    image ( theimage,   ZOOMPIXELS+50,    ZOOMPIXELS+50,    PIXELS,         PIXELS      );      // original
  }

  // set up the inputs
  let inputs = getInputs ( img );       // get inputs from data array 

  // set up the outputs
  let targets = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
  targets[label] = 1;       // change one output location to 1, the rest stay at 0 

  // console.log(train_index);
  // console.log(inputs);
  // console.log(targets);

  train_inputs = inputs;        // can inspect in console 
  nn.train ( inputs, targets );

  thehtml = " trainrun: " + trainrun + "<br> no: " + train_index ;
  AB.msg ( thehtml, 4 );

  train_index++;
  if ( train_index == NOTRAIN ) 
  {
    train_index = 0;
    console.log( "finished trainrun: " + trainrun );
    trainrun++;
  }*/
  // Randomizing the data
  let training = [];
  console.log("cats in training", cats);
  training = training.concat(cats.training);
  training = training.concat(rainbows.training);
  training = training.concat(trains.training);
  console.log("training", training);
  trainEpoch(training);
  epochCounter++;
  console.log("Epoch: " + epochCounter);
}


function testit()    // test the network with a single exemplar, from global var "test_index"
{ 
  //CA686//
  /*let img   = mnist.test_images[test_index];
  let label = mnist.test_labels[test_index];

  // set up the inputs
  let inputs = getInputs ( img ); 
  
  test_inputs = inputs;        // can inspect in console 
  let prediction    = nn.predict(inputs);       // array of outputs 
  let guess         = findMax(prediction);      // the top output 

  total_tests++;
  if (guess == label)  total_correct++;

  let percent = (total_correct / total_tests) * 100 ;
  
  thehtml =  " testrun: " + testrun + "<br> no: " + total_tests + " <br> " +
        " correct: " + total_correct + "<br>" +
        "  score: " + greenspan + percent.toFixed(2) + "</span>";
  AB.msg ( thehtml, 6 );

  test_index++;
  if ( test_index == NOTEST ) 
  {
    console.log( "finished testrun: " + testrun + " score: " + percent.toFixed(2) );
    testrun++;
    test_index = 0;
    total_tests = 0;
    total_correct = 0;
  }*/

  let testing = [];
  testing = testing.concat(cats.testing);
  testing = testing.concat(rainbows.testing);
  testing = testing.concat(trains.testing);

  let percent = testAll(testing);
  console.log("Percent: " + nf(percent, 2, 2) + "%");

  let inputs = [];
  let img = get();
  img.resize(28, 28);
  img.loadPixels();
  for (let i = 0; i < len; i++) {
    let bright = img.pixels[i * 4];
    inputs[i] = (255 - bright) / 255.0;
  }

  let guess = nn.predict(inputs);
  // console.log(guess);
  let m = max(guess);
  let classification = guess.indexOf(m);
  if (classification === CAT) {
    console.log("cat");
  } else if (classification === RAINBOW) {
    console.log("rainbow");
  } else if (classification === TRAIN) {
    console.log("train");
  }

  //CA686//

}




//--- find no.1 (and maybe no.2) output nodes ---------------------------------------
// (restriction) assumes array values start at 0 (which is true for output nodes) 


function find12 (a)         // return array showing indexes of no.1 and no.2 values in array 
{
  let no1 = 0;
  let no2 = 0;
  let no1value = 0;     
  let no2value = 0;
  
  for (let i = 0; i < a.length; i++) 
  {
    if (a[i] > no1value)   // new no1
    {
      // old no1 becomes no2
      no2 = no1;
      no2value = no1value;
      // now put in the new no1
      no1 = i;
      no1value = a[i];
    }
    else if (a[i] > no2value)  // new no2 
    {
      no2 = i;
      no2value = a[i];
    }
  }
  
  var b = [ no1, no2 ];
  return b;
}



// just get the maximum - separate function for speed - done many times 
// find our guess - the max of the output nodes array

function findMax (a)        
{
  let no1 = 0;
  let no1value = 0;     
  
  for (let i = 0; i < a.length; i++) 
  {
    if (a[i] > no1value) 
    {
      no1 = i;
      no1value = a[i];
    }
  }
  
  return no1;
}




// --- the draw function -------------------------------------------------------------
// every step:
 
function draw() 
{
  // check if libraries and data loaded yet:
  //CA686//
  //if ( typeof mnist == 'undefined' ) return;
  console.log("doodle_list", doodle_list);
  if ( typeof doodle_list == 'undefined' || doodle_list.length == 0 ) return;
  //CA686//

// how can we get white doodle on black background on yellow canvas?
//        background('#ffffcc');    doodle.background('black');

background ('black');

AB.queryDataExists ( function ( exists )		// asynchronous - need callback function 
{
  if ( exists ){

    AB.restoreData ( function ( nn )            
    {
      // object returned from server is an array of blocks 
      // console.log ( "Restoring " + a.length + " blocks from server" );
      console.log(nn)
    });
  }
  else{
    if ( do_training ){
      // do some training per step 
      
      for (let i = 0; i < TRAINPERSTEP; i++) 
      {
        if (i == 0)    trainit(true);    // show only one per step - still flashes by  
        else           trainit(false);
      }
      
    // do some testing per step 
      for (let i = 0; i < TESTPERSTEP; i++) 
        testit();
  
      AB.saveData ( nn ); 
    }
  }
});

//throw new Error("Something went badly wrong!");
// keep drawing demo and doodle images 
// and keep guessing - we will update our guess as time goes on 
  
if ( demo_exists )
{
  drawDemo();
  guessDemo();
}
if ( doodle_exists ) 
{
  drawDoodle();
  guessDoodle();
}


// detect doodle drawing 
// (restriction) the following assumes doodle starts at 0,0 

  if ( mouseIsPressed )         // gets called when we click buttons, as well as if in doodle corner  
  {
     // console.log ( mouseX + " " + mouseY + " " + pmouseX + " " + pmouseY );
     var MAX = ZOOMPIXELS + 20;     // can draw up to this pixels in corner 
     if ( (mouseX < MAX) && (mouseY < MAX) && (pmouseX < MAX) && (pmouseY < MAX) )
     {
        mousedrag = true;       // start a mouse drag 
        doodle_exists = true;
        doodle.stroke('white');
        doodle.strokeWeight( DOODLE_THICK );
        doodle.line(mouseX, mouseY, pmouseX, pmouseY);      
     }
  }
  else 
  {
      // are we exiting a drawing
      if ( mousedrag )
      {
            mousedrag = false;
            // console.log ("Exiting draw. Now blurring.");
            doodle.filter (BLUR, DOODLE_BLUR);    // just blur once 
            //   console.log (doodle);
      }
  }
}




//--- demo -------------------------------------------------------------
// demo some test image and predict it
// get it from test set so have not used it in training


function makeDemo()
{
    demo_exists = true;
    var  i = AB.randomIntAtoB ( 0, NOTEST - 1 );  
    
    demo        = mnist.test_images[i];     
    var label   = mnist.test_labels[i];
    
   thehtml =  "Test image no: " + i + "<br>" + 
            "Classification: " + label + "<br>" ;
   AB.msg ( thehtml, 8 );
   
   // type "demo" in console to see raw data 
}


function drawDemo()
{
    var theimage = getImage ( demo );
     //  console.log (theimage);
     
    image ( theimage,   0,                canvasheight - ZOOMPIXELS,    ZOOMPIXELS,     ZOOMPIXELS  );      // magnified 
    image ( theimage,   ZOOMPIXELS+50,    canvasheight - ZOOMPIXELS,    PIXELS,         PIXELS      );      // original
}


function guessDemo()
{
   let inputs = getInputs ( demo ); 
   
  demo_inputs = inputs;  // can inspect in console 
  
  let prediction    = nn.predict(inputs);       // array of outputs 
  let guess         = findMax(prediction);      // the top output 

   thehtml =   " We classify it as: " + greenspan + guess + "</span>" ;
   AB.msg ( thehtml, 9 );
}




//--- doodle -------------------------------------------------------------

function drawDoodle()
{
    // doodle is createGraphics not createImage
    let theimage = doodle.get();
    // console.log (theimage);
    
    image ( theimage,   0,                0,    ZOOMPIXELS,     ZOOMPIXELS  );      // original 
    image ( theimage,   ZOOMPIXELS+50,    0,    PIXELS,         PIXELS      );      // shrunk
}
      
      
function guessDoodle() 
{
   // doodle is createGraphics not createImage
   let img = doodle.get();
  
  img.resize ( PIXELS, PIXELS );     
  img.loadPixels();

  // set up inputs   
  let inputs = [];
  for (let i = 0; i < PIXELSSQUARED ; i++) 
  {
     inputs[i] = img.pixels[i * 4] / 255;
  }
  
  doodle_inputs = inputs;     // can inspect in console 

  // feed forward to make prediction 
  let prediction    = nn.predict(inputs);       // array of outputs 
  let b             = find12(prediction);       // get no.1 and no.2 guesses  

  thehtml =   " We classify it as: " + greenspan + b[0] + "</span> <br>" +
            " No.2 guess is: " + greenspan + b[1] + "</span>";
  AB.msg ( thehtml, 2 );
}


function wipeDoodle()    
{
    doodle_exists = false;
    doodle.background('black');
}




// --- debugging --------------------------------------------------
// in console
// showInputs(demo_inputs);
// showInputs(doodle_inputs);


function showInputs ( inputs )
// display inputs row by row, corresponding to square of pixels 
{
    var str = "";
    for (let i = 0; i < inputs.length; i++) 
    {
      if ( i % PIXELS == 0 )    str = str + "\n";                                   // new line for each row of pixels 
      var value = inputs[i];
      str = str + " " + value.toFixed(2) ; 
    }
    console.log (str);
}