// Cloned by Abdelshafa Abdala on 14 Nov 2021 from World "Character recognition neural network (clone by Adrian Sweeney)" by Adrian Sweeney
// 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=7,ZOOMPIXELS=7*PIXELS,canvaswidth=PIXELS+ZOOMPIXELS+28,canvasheight=3*ZOOMPIXELS+100,DOODLE_THICK=15,DOODLE_BLUR=2;var canvas=document.createElement("canvas"),ctx=canvas.getContext("2d");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)}$("#runheaderbox").css({"max-height":"95vh"}),thehtml="<hr> <h1> <font color='orange'> 1. Doodle </h1> </font> <font color='black'> Top row: Doodle (left) and shrunk (right). <br> Draw your doodle in top LHS. <br><button onclick='wipeDoodle();' class='normbutton' style='background-color:pink;color:black'>Clear doodle</button> <br> ",AB.msg(thehtml,1),thehtml="<hr> <h1> <font color='orange'> 2. Training </h1> </font> Middle row: Training image magnified (left) and original (right). <br> <button onclick='do_training = false;' class='normbutton' style='background-color:pink;color:black' >Stop training</button> <br> ",AB.msg(thehtml,3),thehtml="<h3> <font color='pink'> Hidden tests </h3> </font>",AB.msg(thehtml,5),thehtml="<hr> <h1> <font color='orange'> 3. Demo </h1> </font>Bottom row: Test image magnified (left) and original (right). <br> The network is <i>not</i> trained on any of these images. <br> <button onclick='makeDemo();' class='normbutton'style='background-color:pink;color:black' >Demo test image</button> <br> ",AB.msg(thehtml,7);const greenspan="<span style='font-weight:bold; font-size:x-large; color:#39ff14'> ";function setup(){bg=loadImage("uploads/ajsweeney/trainbg.jpg"),createCanvas(canvaswidth,canvasheight),doodle=createGraphics(ZOOMPIXELS,ZOOMPIXELS),AB.loadingScreen(),$.getScript("/uploads/codingtrain/matrix.js",function(){$.getScript("/uploads/ajsweeney/neural.js",function(){$.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()}),doodle.background("black")}function getImage(t){let e=createImage(PIXELS,PIXELS);e.loadPixels();for(let o=0;o<PIXELSSQUARED;o++){let n=t[o],s=4*o;e.pixels[s+0]=n,e.pixels[s+1]=n,e.pixels[s+2]=n,e.pixels[s+3]=255}return e.updatePixels(),e}function getInputs(t){let e=[];for(let o=0;o<PIXELSSQUARED;o++){let n=t[o];e[o]=n/255}return e}function trainit(t){let e=mnist.train_images[train_index],o=mnist.train_labels[train_index];if(t){var n=getImage(e);image(n,0,ZOOMPIXELS+50,ZOOMPIXELS,ZOOMPIXELS),image(n,ZOOMPIXELS+30,ZOOMPIXELS+50,PIXELS,PIXELS)}let s=getInputs(e),i=[0,0,0,0,0,0,0,0,0,0];i[o]=1,train_inputs=s,nn.train(s,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],o=getInputs(t);test_inputs=o;let n=findMax(nn.predict(o));total_tests++,n==e&&total_correct++;let s=total_correct/total_tests*100;thehtml=" testrun: "+testrun+"<br> no: "+total_tests+" <br> correct: "+total_correct+"<br> score: "+greenspan+s.toFixed(2)+"</span>",AB.msg(thehtml,6),++test_index==NOTEST&&(console.log("finished testrun: "+testrun+" score: "+s.toFixed(2)),testrun++,test_index=0,total_tests=0,total_correct=0)}function find12(t){let e=0,o=0,n=0,s=0;for(let i=0;i<t.length;i++)t[i]>n?(e=i,n=t[i]):t[i]>s&&(o=i,s=t[i]);return[e,o]}function findMax(t){let e=0,o=0;for(let n=0;n<t.length;n++)t[n]>o&&(e=n,o=t[n]);return e}function draw(){if(background(bg),void 0!==mnist){if(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){console.log(mouseX+" "+mouseY+" "+pmouseX+" "+pmouseY);var t=ZOOMPIXELS+20;mouseX<t&&mouseY<t&&pmouseX<t&&pmouseY<t&&(mousedrag=!0,doodle_exists=!0,doodle.stroke("white"),doodle.strokeCap(PROJECT),variableRect(mouseX,mouseY,pmouseX,pmouseY),variableRect(mouseX,mouseY,pmouseX,pmouseY))}else mousedrag&&(mousedrag=!1,console.log("Exiting draw. Now blurring at value: "),doodle.filter(BLUR,2),console.log(doodle))}}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);console.log(t),image(t,0,canvasheight-ZOOMPIXELS,ZOOMPIXELS,ZOOMPIXELS),image(t,ZOOMPIXELS+30,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();console.log(t),image(t,0,0,ZOOMPIXELS,ZOOMPIXELS),image(t,ZOOMPIXELS+30,0,PIXELS,PIXELS),console.log("Doodle completed"),console.log(t)}function guessDoodle(){let t=doodle.get();t.resize(PIXELS,PIXELS),t.loadPixels();let e=[];for(let o=0;o<PIXELSSQUARED;o++)e[o]=t.pixels[4*o]/255;doodle_inputs=e;let o=find12(nn.predict(e));thehtml=" We classify it as: "+greenspan+o[0]+"</span> <br> No.2 guess is: "+greenspan+o[1]+"</span>",AB.msg(thehtml,2)}function wipeDoodle(){doodle_exists=!1,doodle.background("black")}function showInputs(t){var e="";for(let o=0;o<t.length;o++){o%PIXELS==0&&(e+="\n"),e=e+" "+t[o].toFixed(2)}console.log(e)}function variableRect(t,e,o,n){let s=abs(t-o)+abs(e-n);doodle.stroke("white"),doodle.strokeWeight(Math.min(10,s/2)),console.log(doodle.strokeWeight),smooth(),doodle.rect(t,e,9,9),doodle.rect(t,e,8,8)}