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Code viewer for World: Character recognition neur... 

// Cloned by Dom on 20 Dec 2019 from World "Character recognition neural network" by "Coding Train" project 
// Please leave this clone trail here.



//***********************DC***********************//

//In this script I will attempt to integrate the synaptic.js library for an alternative neural network for MNIST

// Import stylesheets



 
 // Porting in entire script by Jason Mayes at https://codepen.io/h-cal/pen/pVKKev
 


// Cloned by Dom on 21 Dec 2019 from World "Testing" by Michael Ryan 
// Please leave this clone trail here.
 
document.write ( `

<h1> Dom's Web page </h1>

<p>
Insert whatever HTML you want in here.
</p>

` );


function loadScripts(callbackFunction)


{
    $.getScript("uploads/dom84/synaptic.js", function( data, textStatus, jqxhr ) {
      console.log( data ); // Data returned
      console.log( textStatus ); // Success
      console.log( jqxhr.status ); // 200
      console.log( "Synaptic Load was performed." );
    
//    $.getScript ( "/uploads/codingtrain/mnist.js", function()
  //      {   console.log ("MNIST loaded");
           // nn = new NeuralNetwork(  noinput, nohidden, nooutput );
        //    nn.setLearningRate ( learningrate );
          //  loadData();
    //    });
      
      callbackFunction();
    });
}



function Perceptron(input, hidden, output)
{
	// create the layers
	var inputLayer = new Layer(input);
	var hiddenLayer = new Layer(hidden);
	var outputLayer = new Layer(output);

	// connect the layers
	inputLayer.project(hiddenLayer);
	hiddenLayer.project(outputLayer);

	// set the layers
	this.set({
		input: inputLayer,
		hidden: [hiddenLayer],
		output: outputLayer
	});
}


function main()
{
    console.log("We have imported our external scripts, now we can use them");
        
    
    var Neuron = synaptic.Neuron;
    var Layer = synaptic.Layer;
    var Network = synaptic.Network;
    var Trainer = synaptic.Trainer;
    var Architect = synaptic.Architect;
    
    
    // extend the prototype chain
    Perceptron.prototype = new Network();         
    Perceptron.prototype.constructor = Perceptron;
    
    console.log("Didn't crash");
    
function Perceptron(input, hidden, hidden2, output)
{
  // create the layers
  var inputLayer = new Layer(input);
  var hiddenLayer = new Layer(hidden);
  var hidden2Layer = new Layer(hidden2);
  var outputLayer = new Layer(output);

  // connect the layers
  inputLayer.project(hiddenLayer, Layer.connectionType.ALL_TO_ALL);
  hiddenLayer.project(hidden2Layer, Layer.connectionType.ALL_TO_ALL);
  hidden2Layer.project(outputLayer, Layer.connectionType.ALL_TO_ALL);

  // set the layers
  this.set({
    input: inputLayer,
    hidden: [hiddenLayer, hidden2Layer],
    output: outputLayer
  });
  
  Perceptron.prototype = new Network();
Perceptron.prototype.constructor = Perceptron;

var interval = 1000;
var ranger = document.getElementById('ranger');
var domSpeed = document.getElementById('domSpeed');

ranger.addEventListener('input', function(e) {
interval = this.value;
domSpeed.innerText = 'Change speed of classification! Currently: ' + interval + 'ms';
});

var context = document.getElementById('mnist').getContext('2d');
var result = document.getElementById('result');
var current = 0;

var myPerceptron = new Perceptron(784,7,5,10);
var myTrainer = new Trainer(myPerceptron);
var set = mnist.set(8000, 2000);

var trainingSet = set.training;
var testSet = set.test;


function train() {
  var learningRate = 0.03;
  for (var i = 0; i < 100; i++) {
    for (var n = 0; n < trainingSet.length; n++) {
      myPerceptron.activate(trainingSet[n].input);
      myPerceptron.propagate(learningRate, trainingSet[n].output);
    }
  }

  var jsdom = document.getElementById('json');
  jsdom.innerText = JSON.stringify(myPerceptron.toJSON());
}


function itterate() {
  var digit = testSet[current];
  mnist.draw(digit.input, context);
  var resultSet = myPerceptron.activate(testSet[current].input);
  var max = 0;
  var index = 0;

  for (var j = 0; j < resultSet.length; j++) {
    if (resultSet[j] > max) {
      max = resultSet[j];
      index = j;
    }
  }

  result.innerText = index;
  current++;

  if (current === testSet.length) {
    current = 0;
  }
  setTimeout(itterate, interval);
}

function loadNetwork() {
  myPerceptron = Network.fromJSON(JSON.parse('{"neurons":[{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"},{"trace":{"elegibility":{},"extended":{}},"state":0,"old":0,"activation":0,"bias":0,"layer":"input","squash":"LOGISTIC"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weight":-2.355978389538985,"gater":null},{"from":795,"to":804,"weight":-1.8752692018291177,"gater":null},{"from":795,"to":805,"weight":-7.490935316268493,"gater":null}]}'));
}

}
}

loadScripts(main);




/**

var Neuron = synaptic.Neuron,
  Layer = synaptic.Layer,
  Network = synaptic.Network,
  Trainer = synaptic.Trainer,
  Architect = synaptic.Architect;   **/




// extend the prototype chain


//train();
loadNetwork();
itterate();

// For this demo pre load a previously trained network I made.


















// Commenting out entire 'Doodle Script for the time being'




/**

 

// 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 = 10;

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 = 30;
const TESTPERSTEP  = 5;

// 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
}    



// CSS trick 
// make run header bigger 
 $("#runheaderbox").css ( { "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: ---------------------------------------------------------




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();
 
 $.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 );
            nn.setLearningRate ( learningrate );
            loadData();
        });
   });
 });
}



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

function loadData()    
{
  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 
  });
}



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 );
}

 

function trainit (show)        // train the network with a single exemplar, from global var "train_index", show visual on or off 
{
  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++;
  }
}


function testit()    // test the network with a single exemplar, from global var "test_index"
{ 
  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;
  }
}




//--- 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) 
    {
      no1 = i;
      no1value = a[i];
    }
    else if (a[i] > no2value) 
    {
      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:
  if ( typeof mnist == 'undefined' ) return;


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

      background ('black');
    
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();
}

  // 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);
}


**/