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// Cloned by Vishu Bhatnagar on 10 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 --------------------------------------------------

// no of nodes in network 
const noinput = PIXELSSQUARED;
const nohidden = 300;
const nooutput = 10;

const learningrate = 0.2;   // 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 = 12;    // 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, doodle_label;
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;


// variable used 
let doodleTestData = [];
let correct = 0;
let incorrect = 0;
var numberList;
var doodle_guess;


// 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 = `<div><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> 
  Select your classification and click on button to get score: 
  <span>
      <select id='numberlist'>
          <option value=''></option>
          <option value='0'>0</option>
          <option value='1'>1</option>
          <option value='2'>2</option>
          <option value='3'>3</option>
          <option value='4'>4</option>
          <option value='5'>5</option>
          <option value='6'>6</option>
          <option value='7'>7</option>
          <option value='8'>8</option>
          <option value='9'>9</option>
      </select>
  </span>
  <button onclick='saveDoodleDataAndScore();' class='normbutton'>Get Score</button><br/>
  <p id="showAccuracy" style="display:none">
  <span>Correct Predictions:</span><span id="correctCount"></span><br/>
  <span>Incorrect Predictions:</span><span id="incorrectCount"></span><br/>
  <span>
    Accuracy is : <span id="predictionScore"></span>
  </span>
  </p>
  <span>
    For future better accuracy lets save the helpul data and use next time. 
  </span>
  <button onclick='sendDatatoServer();' class='normbutton'>Save</button></div>`;
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);
    
    //vishu- hide accuracy span
    // $('showAccuracy').css('display:none');

    // 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();
                getDataFromServer();
            });
        });
    });
}



// 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 
    doodle_guess = find12(nn.predict(reduceInput(inputs)));       // get no.1 and no.2 guesses  

    thehtml = " We classify it as: " + greenspan + doodle_guess[0] + "</span> <br>" +
        " No.2 guess is: " + greenspan + doodle_guess[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);
}


// vishu- for restoring data from server if user uploads some test data;
function getDataFromServer() {
    console.log("Restoring doodle test data from server")
    AB.restoreData((e) => {
        doodleTestData = e;
        console.log('Data restored successfully')
    });
}

function sendDatatoServer() {
    if (doodleTestData) {
        console.log('Sending data to server')
        AB.saveData(doodleTestData)
        console.log('transimission complete')
    } else {
        console.log('transmission failed...')
    }
}

// vishu - for updating doodle score and saving the clasification result in doodle trainig set
function saveDoodleDataAndScore() {
    if (doodleTestData) {
        console.log('Saving data with label and doodle to test set');
        //doodle = doodle_inputs;
        doodle_label = $("#numberlist").val();
        let y = new InitializeDoodleAndLabel(doodle_inputs, doodle_label)
        doodleTestData.push(y)
        console.log('local test data' + doodleTestData);
        updateDoodlePercent();
        wipeDoodle();
        $("#numberlist").val('');
    }
}

function updateDoodlePercent() {
    // checking logic and updating score
    let correct = 0;
    let incorrect = 0;
    for (let t = 0; t < doodleTestData.length; t++) {
        let o;
        let n = doodleTestData[t].doodle;
        let r = find12((nn.predict(reduceInput(n))));
        if (r[0] == doodleTestData[t].label) {
            correct++;
            console.log("Predicted values are: " + r[0] +" --- Correct Label: " + doodleTestData[t].label);
        } 
        else if(r[1] == doodleTestData[t].label){
            correct++;
            console.log("Predicted values are: " + r[1] +" --- Correct Label: " + doodleTestData[t].label);
        } 
        else {
            incorrect++;
            console.log('Incorrect Predicted values are: '+r[0]+' ,'+ r[1]+' Correct Label is: '+ doodleTestData[t].label)
        }
    }
    console.log("Got " + correct + " correct outputs");
    console.log("Got " + incorrect + " incorrect outputs");
    let t = ((correct / doodleTestData.length) * 100).toFixed(2);
    $('#showAccuracy').css('display','block');
    $('#predictionScore').text(t + '%');
    $('#correctCount').text(correct);
    $('#correctCount').css('color', 'green');
    $('#incorrectCount').text(incorrect);
    $('#incorrectCount').css('color', 'red');
    $('#predictionScore').css('display', 'inline-block');
    if(t>50){
        $('#predictionScore').css('color', 'green');
    } else{
        $('#predictionScore').css('color', 'red');
    }
    console.log('accuracy score is ' + t + '%');
}

class InitializeDoodleAndLabel {
    constructor(e, t) {
        this.doodle = e;
        this.label = t;
    }
}


// vishu -- a convolution approach Classifier 
function reduceInput(e) {
    var t = []
    for (var o = 0; o < 784; o++) {
        t[o] = 0;
    }
    var n, r, s;
    var l = 0;
    var i = 27;
    var d = 0;
    var a = 27;
    for (o = 0; o < 28; o++) {
        found = false;
        for (n = 0; n < 28; n++)
            if (e[28 * o + n] != 0) {
                found = true;
            };
        if (found) {
            l = o;
            break;
        }
    }
    for (o = 27; o >= 0; o--) {
        found = false;
        for (n = 0; n < 28; n++)
            if (e[28 * o + n] != 0)
                found = true;
        if (found) {
            i = o;
            break;
        }
    }
    for (n = 0; n < 28; n++) {
        found = false;
        for (o = 0; o < 28; o++)
            if (e[28 * o + n] != 0) {
                found = true
            };
        if (found) {
            d = n;
            break;
        }
    }
    for (n = 27; n >= 0; n--) {
        found = false;
        for (o = 0; o < 28; o++)
            if (e[28 * o + n] != 0) {
                found = true
            };
        if (found) {
            a = n;
            break;
        }
    }
    var c = i - l + 1;
    var u = a - d + 1;
    var g = 20 / c;
    if (20 / u < g) {
        g = 20 / u;
    }
    var m = Math.floor(u * g);
    var f = Math.round((28 - m) / 2);
    var h = Math.floor(c * g);
    var D = Math.round((28 - h) / 2);
    for (o = 0; o < 784; o++){ 
        t[o] = 0;
    }
    for (n = 0; n < m; n++) {
        for (o = 0; o < h; o++) {
            s = Math.floor(n / g) + d;
            r = Math.floor(o / g) + l;
            t[28 * (o + D) + n + f] = e[28 * r + s];
        }
    }
    return t;
}