​

function preload(){

  data = loadJSON("./data.json");

}

​

class Location{

  constructor(x,y,nx,ny,c,t){

    this.x = x;

    this.y = y;

    this.nx = nx;

    this.ny = ny

    this.c = c;

    this.t = t;

    this.hasCase = this.c > 0 ? 1 : 0;

  }

  display(){

    push();

    noStroke();

    if (this.c == 0 ) {

      fill(0);

    } else {

      fill(200,0,0);

    }

    if (this.t == 'known'){

      ellipse(this.x, this.y, 8, 8);

    } else {

      rectMode(CENTER)

      rect(this.x, this.y, 8, 8);

    }

    pop();

  }

}

​

class Neuron{

  constructor(n){

    this.weights = [];

    this.learningRate = 0.001;

    while(this.weights.length < n + 1){

      this.weights.push(random(-1,1));

    }

  }

  train(inputs, label){

    let res = this.classify(inputs);

    let g = label - res;

    if ( g!=0 ) {

      for (let i = 0; i < this.weights.length; i ++){

        this.weights[i] += g * inputs[i] * this.learningRate;

        // direction(g & inputs[i]) * how much to adjust(learningRate);

      }

      errorNo ++;

    }

  }

  classify(inputs){

    let raw = 0;

    inputs.forEach((i,idx) => {

      raw += i*this.weights[idx];

    });

    return this.activation(raw);

  }

  activation(raw){

    return raw > 0 ? 1 : 0;

  }

}

​

let locations = [];

let errorNo;

​

function setup() {

  // stupid p5

  let array = [];

  for (const e in data) {

    array.push(data[e]);

  }

  data = array;

​

  createCanvas(600, 600);

  data.forEach(l => {

    let x = map(l.long,-180,180,0,width);

    let y = map(l.lat,-90,90,0,height);

    let nx = map(l.long,-180,180,-1,1);

    let ny = map(l.lat,-90,90,-1,1);

    let tem = new Location(x,y,nx,ny,l.cases,'known');

    locations.push(tem);

  });

   neuron = new Neuron(2);

   bias = 1;

  //noLoop();

}

​

function draw(){

  background(255)

  if (errorNo != 0) {

    errorNo = 0;

    locations.forEach(l => {

      if(l.t == 'known') neuron.train([l.nx, l.ny, bias], l.hasCase);

      l.display();

      //train 1 iteration

    });

  } else {

    locations.forEach(l => {

      l.display();

      //just display

    });

  }

  text(errorNo + "   <- when it become 0, click to add new location that you want to classify, the added ones will be shown as squres.",5,5, width, 50);

  line(0,map((neuron.weights[0] - neuron.weights[2])/neuron.weights[1], -1, 1, 0, height), width, map(-1*(neuron.weights[0] + neuron.weights[2])/neuron.weights[1], -1, 1, 0, height));

  // drawing this line is the hardest lol

}

​

function mouseClicked(){

  if (errorNo < 5) {

    let nx = map(mouseX, 0, width, -1, 1);

    let ny = map(mouseY, 0, height, -1, 1);

    locations.push(new Location(mouseX, mouseY, nx, ny, neuron.classify([nx,ny,bias]), 'unknown'));

  }

}

​

​

​