// Cloned by Abdelshafa Abdala on 1 Dec 2021 from World "Character recognition neural network Practical 2" by Pratiksha Biradar
// 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=50,TESTPERSTEP=5,ZOOMFACTOR=5,ZOOMPIXELS=5*PIXELS,canvaswidth=PIXELS+ZOOMPIXELS+50,canvasheight=3*ZOOMPIXELS+100,DOODLE_THICK=14,DOODLE_BLUR=2;let mnist,nn,doodle,demo,nnType=1,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)}$("#runheaderbox").css({"max-height":"95vh"}),thehtml=" <button id='save' onclick='saveData();' class='normbutton mybutton' >Save work</button> ",AB.msg(thehtml,1),thehtml="<hr> <h1> 1. Doodle </h1> Top row: Doodle (left) and shrunk (right). <br> Draw your doodle in top LHS. <button onclick='wipeDoodle();' class='normbutton' >Clear doodle</button> <br> ",AB.msg(thehtml,2),thehtml="<hr> <h1> 2. Training </h1> Middle row: Training image magnified (left) and original (right). <br> <button onclick='do_training = false;' class='normbutton' >Stop training</button> <br> ",AB.msg(thehtml,4),thehtml="<h3> Hidden tests </h3> ",AB.msg(thehtml,6),thehtml="<hr> <h1> 3. Demo </h1> Bottom row: Test image magnified (left) and original (right). <br> The network is <i>not</i> trained on any of these images. <br> <button onclick='makeDemo();' class='normbutton' >Demo test image</button> <br> ",AB.msg(thehtml,8);const greenspan="<span style='font-weight:bold; font-size:x-large; color:darkgreen'> ";function setup(){createCanvas(canvaswidth,canvasheight),(doodle=createGraphics(ZOOMPIXELS,ZOOMPIXELS)).pixelDensity(1),AB.loadingScreen(),$.getScript("/uploads/codingtrain/matrix.js",function(){$.getScript("/uploads/biradap2/nn.js",function(){$.getScript("/uploads/codingtrain/mnist.js",function(){$.getJSON("/uploads/biradap2/_data.0646079000.json",function(t){console.log("All JS loaded"),1===nnType?(nn=NeuralNetwork.deserialize(t),console.log("Activation function used here is: "+nn.getActivationFunction()),console.log("Sigmoid function with loaded json")):2===nnType?(nn=new NeuralNetwork(noinput,nohidden,nooutput,learningrate,tanh),console.log("Tanh function is used without loaded json")):3===nnType?(nn=new NeuralNetwork(noinput,nohidden,nooutput,learningrate,sigmoid),console.log("Activation function used here is: "+nn.getActivationFunction()),console.log("Loading Default Neural Network")):4===nnType?(nn=new NeuralNetwork(noinput,nohidden,nooutput,learningrate,relu),console.log("Relu function is used without loaded json")):console.log("No neural network has been specified"),loadData()})})})})}function saveData(){AB.saveData(nn)}function restoreData(){AB.restoreData(function(t){nn=NeuralNetwork.deserialize(t),loadData(),console.log("AB.restoredata"+NeuralNetwork.deserialize(t).serialize())})}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],i=4*n;e.pixels[i+0]=o,e.pixels[i+1]=o,e.pixels[i+2]=o,e.pixels[i+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 i=getInputs(e),s=[0,0,0,0,0,0,0,0,0,0];s[n]=1,train_inputs=i,nn.train(i,s),thehtml=" trainrun: "+trainrun+"<br> no: "+train_index,AB.msg(thehtml,5),++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 i=total_correct/total_tests*100;thehtml=" testrun: "+testrun+"<br> no: "+total_tests+" <br> correct: "+total_correct+"<br> score: "+greenspan+i.toFixed(2)+"</span>",AB.msg(thehtml,7),++test_index==NOTEST&&(console.log("finished testrun: "+testrun+" score: "+i.toFixed(2)),testrun++,test_index=0,total_tests=0,total_correct=0)}function find12(t){let e=0,n=0,o=0,i=0;for(let s=0;s<t.length;s++)t[s]>o?(e=s,o=t[s]):t[s]>i&&(n=s,i=t[s]);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("yellow"),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("white"),doodle.smooth(),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,9)}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,10)}function drawDoodle(){let t=doodle.get();image(t,0,0,ZOOMPIXELS,ZOOMPIXELS),image(t,ZOOMPIXELS+50,0,PIXELS,PIXELS)}function guessDoodle(){let t=doodle.get();t.resize(PIXELS,PIXELS),t.loadPixels();let e=[];for(let n=0;n<PIXELSSQUARED;n++)e[n]=t.pixels[4*n]/255;doodle_inputs=e;let n=find12(nn.predict(e));thehtml=" We classify it as: "+greenspan+n[0]+"</span> <br> No.2 guess is: "+greenspan+n[1]+"</span>",AB.msg(thehtml,3)}function wipeDoodle(){doodle_exists=!1,doodle.background("black")}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)}