const optimizer = tf.train.sgd(learningRate);

let x_vals = []

let y_vals = []

let angle

let m, b

let placeholder=200

​

function setup() {

  createCanvas(400, 400);

  m = tf.variable(tf.scalar(random(1)))

  b = tf.variable(tf.scalar(random(1)))

  angle = 45

}

​

function draw() {

  background(100);

  if (x_vals.length>0){

  const ys = tf.tensor1d(y_vals)

  optimizer.minimize(() => {loss(predict(x_vals),ys)},m);

  }

  // console.log(PI/mouseX)

  // angle +=0.01

  // console.log(angle*100)

  stroke(255)

  strokeWeight(5)

  for(let i = 0; i< xs.length; i++){

    let px = map(xs[i],0,1,0,400)

    let py = map(ys[i],1,0,0,400)

    point(px,py)

  }

  fill(255)

  translate(200,200)

  angleMode(DEGREES)

  rotate(angle)

  line(0,282.842712475,0,-282.842712475)

}

​

function mousePressed(){

  x_vals.push(map(mouseX,0,400,0,1))

  y_vals.push(map(mouseY,0,400,1,0))

}

​

function loss(pred,labels){

  //pred.sub(label).square().mean()

  return pred.sub(label).square().mean()

}

​

function predict(x) {

  const xs = tf.variable(tf.tensor1d(x))

  //y = mx + b

  const ys = xs.mul(m).add(b)

  return ys

}

​