Code viewer for World: Smart Doodle -Max Pooling

// Cloned by Paul Geoghegan on 13 Dec 2020 from World "Smart Doodle & Char Recognition Neural Network " by Paul Geoghegan 
// Please leave this clone trail here.
 


// Cloned by Paul Geoghegan on 8 Dec 2020 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 --------------------------------------------------

// MYCODE: Variable for doodle and demo correct perecentage statistics 
let numDoodleDraw =0;
let doodleCorrectCnt = 0;
let doodlePercentage =0;

let numDemotestCnt =0;
let demoCorrectCnt = 0;
let demoPercentage =0;

const max_doodles = 20;  // const setting max saved & restored doodles to 20   
var saved_doodles;  // variable to store a single doodle 28x28 pixel image 
var saved_d_array = []; // variable to store array of saved doodle 28x28 pixel image 
var doodle_test_cntr =0;
var test_Saved_Dcount =0;
var Saved_Doodle_Ctr = -1;
var draw_saved_Doodles = false; 

let stride =7; 



// no of nodes in network 
const noinput  = PIXELSSQUARED;
const nohidden = 64;
// MYCODE : reduce no hidden nodes to 1 and 5 
//const nohidden = 1; //  20% accuracy reached with 4 training runs 
//const nohidden = 5;  // 73% accuracy reached with 4 training runs 

const nooutput = 10;

// MYCODE :  reduce learning rate to 0.03 (30% of default)  
//const learningrate = 0.03;   // default 0.1  
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 

// MYCODE: Reduce ZOOMFACTOR TO 3  
//const ZOOMFACTOR    = 3;   
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 * 4 ) + 30;

//const DOODLE_THICK = 30;    // MYCODE : increase thickness of doodle line to 30

//const DOODLE_THICK = 9;    // MYCODE : reduce thickness of doodle line to 9

//const DOODLE_BLUR = 7;      // MYCODE : increase blur factor to 7 applied to doodles 

const DOODLE_THICK = 18;    // thickness of doodle lines 

const DOODLE_BLUR = 9;      // 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
}    


// MYCODE : Change width of run header to allow for doddle correct incorect buttons  

// CSS trick   
// make run header bigger  // MYCODE : and run headerwider  
 $("#runheaderbox").css ( { "max-height": "95vh", "max-width": "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;


// MYCODE :  Change Doodle header colour to Purple and add two Doodle Correct /Incorrect User interface Buttons     

if ( AB.onDesktop() )
{
    if ( AB.runloggedin )
    {
    // 1 Doodle header 
                
    thehtml = "<hr> <h1> <span style= color:purple> 1. Doodle (User Input) </span></h1> Top row: Doodle (left) and shrunk (right). " +
            " <span style='font-weight:bold; color:purple'>Draw your doodle (using mouse)</span> in top LHS. <br><br>" + 
            " <button onclick='wipeDoodle();' class='normbutton' > Clear doodle</button>  " 
        
     // MYCODE: add record doodle button and restore saved doodles button    
        + "<button onclick='recordDoodle();' class='normbutton' > Record doodle</button> "+
        "<button onclick='deleteRecordDoodle();' class='normbutton' > Delete Recorded doodle(s)</button> " + 
        " <button onclick='saveData();' class='normbutton' >Save Recorded doodles</button> <br> <br>" + 
        " <button onclick='restoreDoodleData();' class='normbutton' >Restore Saved doodles</button> "+ 
        "<button onclick='draw_Rec_doodles();' class='normbutton' >Draw Recorded doodles</button> " + 
        "<button onclick='test_Rec_doodles();' class='normbutton' >Test Recorded doodles</button><br> <br>" + 
        
       // MYCODE :   Add two Doodle Correct /Incorrect User interface Buttons   
       " Click if doodle is: " + 
        " <button onclick='doodleStats(true);' class='normbutton' > Correct</button> <button onclick='doodleStats(false);'"+
        
        
        " class='normbutton' > Incorrect</button> <br>";
        AB.msg ( thehtml, 1 );
                        
                       
       }  // end of if AB.runloggedin         

}
else
{

// 1 Doodle header 
  thehtml = "<hr> <h1> <span style= color:purple> 1. Doodle (User Input) </span></h1> Top row: Doodle (left) and shrunk (right). " +
        " <span style='font-weight:bold; color:purple'>Draw your doodle (using mouse)</span> in top LHS.<br>" + 
        " <button onclick='wipeDoodle();' class='normbutton' > Clear doodle</button>  " 
        
     // MYCODE: add record doodle button  
        + "<button onclick='recordDoodle();' class='normbutton' > Record doodle</button> <br> " +
     
       
       // MYCODE :   Add two Doodle Correct /Incorrect User interface Buttons   
       " Click if doodle is: " + 
        " <button onclick='doodleStats(true);' class='normbutton' > Correct</button> <button onclick='doodleStats(false);'"+
        " class='normbutton' > Incorrect</button> <br>";
        AB.msg ( thehtml, 1 );    

} // end of else  


  // 2 Doodle variable data (guess)
  
  // 3 Training header
  thehtml = "<hr> <h1> <span style= color:green>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 
  
  
  // MYCODE :  Change Deemo header colour to Purple and add correct /Incorrect User interface Buttons    
  // 7 Demo header 
  thehtml = "<hr> <h1><span style= color:purple> 3. Demo (User Input) </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>"+ 
        "  <span style='font-weight:bold; color:purple'> Click if demo is:</span> <button onclick='demoStats(true);'"+
        " class='normbutton' > Correct</button> <button onclick='demoStats(false);'"+
        " class='normbutton' > Incorrect</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: ---------------------------------------------------------


// MYCODE : ============== Deleating Saving and Restoring Doodle data to/from AB world =================

function deleteRecordDoodle()

{
    while (saved_d_array.length >0)
    {
    saved_d_array.shift();    
    }
    console.log (saved_d_array)  // check content of deleted array 
}


function saveData()     // save saved_d_array to server 
{
    
    console.log ( "Saving " + saved_d_array.length + " doodles to server" );
    AB.saveData ( saved_d_array);  

}

function restoreDoodleData()
{
 
  AB.queryDataExists ( function ( exists )                // asynchronous - need callback function 
                        {
                              
                              AB.restoreData ( function ( a )            
                                   {
                                   // object returned from server is an array of dooodles 
                                  saved_d_array = a;  
                                console.log ( "Restoring " + a.length + " doodles from server" );
                                console.log (saved_d_array)  // check content of restored array data 
                
                                    });    
                               
                        }); // end of AB.queryDataExists code blk

}

// MYCODE :  Testing and Drawing Recorded Doodle set (max 20)  

// MYCODE :  to display Correct Doodle Guess Percentage Statistics =====  

function doodleStats(arg) // boolean arg
{
// print out Percentage stats on NN system correct doodle guess 
    numDoodleDraw ++;

    if (arg) doodleCorrectCnt ++; 

    doodlePercentage = (doodleCorrectCnt*100)/numDoodleDraw;

 return;
}      
// MYCODE :  to display Correct Demo Guess Percentage Statistics =====  
    
function demoStats(arg) // boolean arg
{
// print out Percentage stats on NN system correct demo guess 
    numDemotestCnt ++;

    if (arg) demoCorrectCnt ++; 

     demoPercentage = (demoCorrectCnt*100)/numDemotestCnt;

 return;
}          

// MYCODE :  Reording doodles function for AB Save and Restore for repeat testing 


function draw_Rec_doodles()  // MyCODE: Trigger the drawing of recorded doodles inside Draw function
{

draw_saved_Doodles = true; 

Saved_Doodle_Ctr ++

}

// MYCODE : For drwawing images from saved doodles array  =====  

function drawSaveddoodles()
{
    
    if (Saved_Doodle_Ctr < saved_d_array.length)
    {
    
    let theimage = saved_d_array [Saved_Doodle_Ctr] ;
      console.log (theimage);
    
    // MYCODE: Draw saved doodle images is the same canvas space as demo draw 
    image ( theimage,   0,                canvasheight - ZOOMPIXELS,    ZOOMPIXELS,     ZOOMPIXELS  );      // magnified 
    image ( theimage,   ZOOMPIXELS+50,    canvasheight - ZOOMPIXELS,    PIXELS,         PIXELS      );      // original

    }
    
}


function recordDoodle()
{
 
 if (doodle_exists)
 {
 
   let theimage = doodle.get();
   
  console.log ("record doodle image" + theimage);


 if (saved_d_array.length< max_doodles)  // max saved & restored doodles to 20  
 {
  saved_d_array.push(theimage);  // variable to store array of saved doodles 
  console.log(saved_d_array.length); 
  console.log(saved_d_array);  
 }
 
   
}// end of if doodle_exists if statement   

return;     
}
    





function test_Rec_doodles()
{

  
  let img = saved_d_array [doodle_test_cntr] ;
      console.log (img);
  
  doodle_test_cntr ++;   // increment  Saved doodle Array index   
 console.log (doodle_test_cntr);

  if (doodle_test_cntr > saved_d_array.length -1) doodle_test_cntr =0;  // reset Saved doodle Array index   
  
  
  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;
  }
  
  let prediction    = nn.predict(inputs);       // array of outputs 
  let b             = find12(prediction);       // get no.1 and no.2 guesses  

// MYCODE : change Doodle display to output percentage statistics   
  thehtml =   " We classify it as: " + greenspan + b[0] + "</span> <br>" +
            " No.2 guess is: " + greenspan + b[1] + "</span> <br>" + "  Correct Guess: "
             +   greenspan + doodlePercentage.toFixed(2) + "% </span>" + 
             "  "+" Num of Doodles: " + greenspan+ numDoodleDraw;
  AB.msg ( thehtml, 2 );


}





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 trainit END 


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>" +
        
    // MYCODE : addition of "%" symbol to score result for clarity    
        
        "  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) 
    {
      no2value = no1value  // MYCODE : suggested change to code to correct glitch 
      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();
  }

  if (draw_saved_Doodles)  // If user have clicked on Test Saved Doodles button  
  {
    drawSaveddoodles();

  }


// 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);
      }
  }
} // MYCODE : End of Draw function bracket marker




//--- 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 ("draw demo image" + 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 
  
 // MYCODE : change Demo display to output percentage statistics   

   thehtml =   " We classify it as: " + greenspan + guess + "</span>" + "  Correct Guess: "
             +   greenspan + demoPercentage.toFixed(2) + "% </span>" + 
             "  "+" Num of Demo tests: " + greenspan+ numDemotestCnt;
   AB.msg ( thehtml, 10 );
}




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

function drawDoodle()
{
    // doodle is createGraphics not createImage
    let theimage = doodle.get();
    console.log (" the doodle image " +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();
   
   console.log("the size of the doodle is "+ img)
   
   // MYCODE : Applying Pooling with a stride factor of 7  (196x196 => 28x28)
 
 /// MYCODE: This code is partly ported from: 
 //https://github.com/CodingTrain/website/tree/main/learning/ml5/8.3-cnn-2/P5 -Author Daniel Shiffman
 
 // stride = 7  defined as a global variable   
 
 let pooled;
 pooled = createImage(196/stride, 196 / stride); // a poled image of (196 /7): 28 x 28  
 
 img.loadPixels();
 
 // Pooling
  pooled.loadPixels();
  for (let x = 0; x < 196- 1; x += stride) {
    for (let y = 0; y < 196 - 1; y += stride) {
      let rgb = pooling(img, x, y);

      let px = x / stride;
      let py = y / stride;
      let pix = index(px, py, pooled);
      pooled.pixels[pix + 0] = rgb.r;
      pooled.pixels[pix + 1] = rgb.g;
      pooled.pixels[pix + 2] = rgb.b;
      pooled.pixels[pix + 3] = 255;
    }
  }
  pooled.updatePixels();
 
 
  function pooling(img, x, y) {
  let brightR = -Infinity;
  let brightG = -Infinity;
  let brightB = -Infinity;
  for (let i = 0; i < stride; i++) {
    for (let j = 0; j < stride; j++) {
      let pix = index(x + i, y + j, img);
      let r = img.pixels[pix + 0];
      let g = img.pixels[pix + 1];
      let b = img.pixels[pix + 2];
      brightR = max(brightR, r);
      brightG = max(brightG, g);
      brightB = max(brightB, b);
    }
  }
  return {
    r: brightR,
    g: brightG,
    b: brightB
  };
}
   
   
function index(x, y, img) {
  return (x + y * img.width) * 4;
}   
   
   ///===============
  
  
  pooled.resize ( PIXELS, PIXELS ); // MYCODE : May not be meeded as poole image should already be 28 x 28 pixels      
  pooled.loadPixels();

  // set up inputs   
  let inputs = [];
  for (let i = 0; i < PIXELSSQUARED ; i++) 
  {
     inputs[i] = pooled.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  

// MYCODE : change Doodle display to output percentage statistics   
  thehtml =   " We classify it as: " + greenspan + b[0] + "</span> <br>" +
            " No.2 guess is: " + greenspan + b[1] + "</span> <br>" + "  Correct Guess: "
             +   greenspan + doodlePercentage.toFixed(2) + "% </span>" + 
             "  "+" Num of Doodles: " + greenspan+ numDoodleDraw;
  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);
}