// Cloned by Fahad Mattoo on 3 Dec 2020 from World "Character recognition NN_KM" by Karl Murphy
// Please leave this clone trail here.
const PIXELS=28,PIXELSSQUARED=PIXELS*PIXELS,NOTRAIN=6e4,NOTEST=1e4,noinput=PIXELSSQUARED,nohidden=64,nooutput=10,learningrate=.1;let do_training=!0;const TRAINPERSTEP=30,TESTPERSTEP=5,ZOOMFACTOR=8,ZOOMPIXELS=8*PIXELS,canvaswidth=PIXELS+ZOOMPIXELS+50,canvasheight=3*ZOOMPIXELS+100,DOODLE_THICK=18,DOODLE_BLUR=1;let mnist,nn,doodle,demo,trainrun=1,train_index=0,testrun=1,test_index=0,total_tests=0,total_correct=0,doodle_exists=!1,demo_exists=!1,mousedrag=!1;var train_inputs,test_inputs,demo_inputs,doodle_inputs,thehtml;function randomWeight(){return AB.randomFloatAtoB(-.5,.5)}$("#runheaderbox").css({"max-height":"95vh"}),thehtml="<hr> <h1> 1. Doodle </h1> Top row: Doodle (left) and shrunk (right). <br> Draw your doodle in top LHS. <button onclick='wipeDoodle();' class='normbutton' >Clear doodle</button> <br> ",AB.msg(thehtml,1),thehtml="<hr> <h1> 2. Training </h1> Middle row: Training image magnified (left) and original (right). <br> <button onclick='do_training = false;' class='normbutton' >Stop training</button> <br> ",AB.msg(thehtml,3),thehtml="<h3> Hidden tests </h3> ",AB.msg(thehtml,5),thehtml="<hr> <h1> 3. Demo </h1> Bottom row: Test image magnified (left) and original (right). <br> The network is <i>not</i> trained on any of these images. <br> <button onclick='makeDemo();' class='normbutton' >Demo test image</button> <br> ",AB.msg(thehtml,7);const greenspan="<span style='font-weight:bold; font-size:x-large; color:darkgreen'> ";function setup(){createCanvas(canvaswidth,canvasheight),(doodle=createGraphics(280,280)).background("white"),doodle.stroke("black"),doodle.pixelDensity(1),doodle.canvas.id="sketchpad",AB.loadingScreen(),$.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)).setLearningRate(learningrate),loadData()})})})}function loadData(){loadMNIST(function(t){mnist=t,console.log("All data loaded into mnist object:"),console.log(mnist),AB.removeLoading()})}function getImage(t){let e=createImage(PIXELS,PIXELS);e.loadPixels();for(let n=0;n<PIXELSSQUARED;n++){let o=t[n],a=4*n;e.pixels[a+0]=o,e.pixels[a+1]=o,e.pixels[a+2]=o,e.pixels[a+3]=255}return e.updatePixels(),e}function getInputs(t){let e=[];for(let n=0;n<PIXELSSQUARED;n++){let o=t[n];e[n]=o/255}return e}function trainit(t){let e=mnist.train_images[train_index],n=mnist.train_labels[train_index];if(t){var o=getImage(e);image(o,0,ZOOMPIXELS+50,ZOOMPIXELS,ZOOMPIXELS),image(o,ZOOMPIXELS+50,ZOOMPIXELS+50,PIXELS,PIXELS)}let a=getInputs(e),i=[0,0,0,0,0,0,0,0,0,0];i[n]=1,train_inputs=a,nn.train(a,i),thehtml=" trainrun: "+trainrun+"<br> no: "+train_index,AB.msg(thehtml,4),++train_index==NOTRAIN&&(train_index=0,console.log("finished trainrun: "+trainrun),trainrun++)}function testit(){let t=mnist.test_images[test_index],e=mnist.test_labels[test_index],n=getInputs(t);test_inputs=n;let o=findMax(nn.predict(n));total_tests++,o==e&&total_correct++;let a=total_correct/total_tests*100;thehtml=" testrun: "+testrun+"<br> no: "+total_tests+" <br> correct: "+total_correct+"<br> score: "+greenspan+a.toFixed(2)+"</span>",AB.msg(thehtml,6),++test_index==NOTEST&&(console.log("finished testrun: "+testrun+" score: "+a.toFixed(2)),testrun++,test_index=0,total_tests=0,total_correct=0)}function find12(t){let e=0,n=0,o=0,a=0;for(let i=0;i<t.length;i++)t[i]>o?(e=i,o=t[i]):t[i]>a&&(n=i,a=t[i]);return[e,n]}function findMax(t){let e=0,n=0;for(let o=0;o<t.length;o++)t[o]>n&&(e=o,n=t[o]);return e}function draw(){if(void 0!==mnist){if(background("white"),do_training){for(let t=0;t<TRAINPERSTEP;t++)trainit(0==t);for(let t=0;t<TESTPERSTEP;t++)testit()}if(demo_exists&&(drawDemo(),guessDemo()),doodle_exists&&(drawDoodle(),guessDoodle()),mouseIsPressed){var t=ZOOMPIXELS+20;mouseX<t&&mouseY<t&&pmouseX<t&&pmouseY<t&&(mousedrag=!0,doodle_exists=!0,doodle.stroke("black"),doodle.strokeWeight(DOODLE_THICK),doodle.line(mouseX,mouseY,pmouseX,pmouseY))}else mousedrag&&(mousedrag=!1,doodle.filter(BLUR,DOODLE_BLUR))}}function makeDemo(){demo_exists=!0;var t=AB.randomIntAtoB(0,NOTEST-1);demo=mnist.test_images[t];var e=mnist.test_labels[t];thehtml="Test image no: "+t+"<br>Classification: "+e+"<br>",AB.msg(thehtml,8)}function drawDemo(){var t=getImage(demo);image(t,0,canvasheight-ZOOMPIXELS,ZOOMPIXELS,ZOOMPIXELS),image(t,ZOOMPIXELS+50,canvasheight-ZOOMPIXELS,PIXELS,PIXELS)}function guessDemo(){let t=getInputs(demo);demo_inputs=t;let e=findMax(nn.predict(t));thehtml=" We classify it as: "+greenspan+e+"</span>",AB.msg(thehtml,9)}function drawDoodle(){let t=doodle.get();image(t,0,0,ZOOMPIXELS,ZOOMPIXELS),image(t,ZOOMPIXELS+50,0,PIXELS,PIXELS)}function centerImage(t){for(var e=0,n=0,o=t.length,a=t[0].length,i=0,s=0;s<o;s++)for(var r=0;r<a;r++){var l=1-t[s][r];i+=l,n+=s*l,e+=r*l}e/=i,n/=i;var d=Math.round(o/2-n);return{transX:Math.round(a/2-e),transY:d}}function getBoundingRectangle(t,e){for(var n=t.length,o=t[0].length,a=o,i=n,s=-1,r=-1,l=0;l<n;l++)for(var d=0;d<o;d++)t[l][d]<e&&(a>d&&(a=d),s<d&&(s=d),i>l&&(i=l),r<l&&(r=l));return{minY:i,minX:a,maxY:r,maxX:s}}function imageDataToGrayscale(t){for(var e=[],n=0;n<t.height;n++){e[n]=[];for(var o=0;o<t.width;o++){var a=4*n*t.width+4*o;0===t.data[a+3]&&(t.data[a]=255,t.data[a+1]=255,t.data[a+2]=255),t.data[a+3]=255,e[n][o]=t.data[4*n*t.width+4*o+0]/255}}return e}function guessDoodle(){const t=document.getElementById("sketchpad"),e=t.getContext("2d");let n=e.getImageData(0,0,280,280);grayscaleImg=imageDataToGrayscale(n);const o=getBoundingRectangle(grayscaleImg,.01),a=centerImage(grayscaleImg),i=document.createElement("canvas");i.width=n.width,i.height=n.height;const s=i.getContext("2d"),r=o.maxX+1-o.minX,l=o.maxY+1-o.minY,d=190/(r>l?r:l);s.translate(t.width/2,t.height/2),s.scale(d,d),s.translate(-t.width/2,-t.height/2),s.translate(a.transX,a.transY),s.drawImage(e.canvas,0,0),n=s.getImageData(0,0,280,280),grayscaleImg=imageDataToGrayscale(n);const g=new Array(784),m=[];for(var c=0;c<28;c++)for(var h=0;h<28;h++){let t=0;for(let e=0;e<10;e++)for(let n=0;n<10;n++)t+=grayscaleImg[10*c+e][10*h+n];t=1-t/100,g[28*h+c]=(t-.5)/.5}for(c=0;c<28;c++)for(h=0;h<28;h++){const t=e.getImageData(10*h,10*c,10,10),n=255*(.5-g[28*h+c]/2);m.push(Math.round((255-n)/255*100)/100);for(let e=0;e<400;e+=4)t.data[e]=n,t.data[e+1]=n,t.data[e+2]=n,t.data[e+3]=255}doodle_inputs=m,console.log("nnInput2"),console.log(m);let u=find12(nn.predict(m));thehtml=" We classify it as: "+greenspan+u[0]+"</span> <br> No.2 guess is: "+greenspan+u[1]+"</span>",AB.msg(thehtml,2)}function wipeDoodle(){doodle_exists=!1,doodle.background("white")}function showInputs(t){var e="";for(let n=0;n<t.length;n++){n%PIXELS==0&&(e+="\n"),e=e+" "+t[n].toFixed(2)}console.log(e)}