Fil/.block

## .block
license: gpl-3.0
height: 960

## README.md

      
    Raw
  

              README.md
            
          
    Computing K-Means within a d3.forceSimulation loop.
Forked from mbostock's block: Collision Detection

  
## index.html
<!DOCTYPE html>
<meta charset="utf-8">
<canvas width="960" height="960"></canvas>
<script src="https://d3js.org/d3.v4.min.js"></script>
<script>

var canvas = document.querySelector("canvas"),
    context = canvas.getContext("2d"),
    width = canvas.width,
    height = canvas.height,
    tau = 2 * Math.PI;

var nodes = d3.range(1000).map(function(i) {
  return {
    r: Math.random() * 14 + 4
  };
});

  var K = 20,
      centers = d3.range(K).map(i => [0,0]),
      colorscale = d3.scaleOrdinal(d3.schemeCategory20c);

var simulation = d3.forceSimulation(nodes)
    .velocityDecay(0.2)
    .force("x", d3.forceX().strength(0.002))
    .force("y", d3.forceY().strength(0.002))
    .force('kmeans', function(){

          // a central point to re-init empty groups
          var m = [d3.mean(centers.map(d => d[0] || 0)),
                   d3.mean(centers.map(d => d[1] || 0))];

          // the order is important: move the centers before re-making the groups
          // so that the centers follow the general movement and keep "their"
          // points, instead of having points pass through them

          // 1. move K-centers towards the barycenter of their group
          centers.forEach((c,i) => {
            c[0] = d3.mean(nodes.filter(d => d.group == i).map(d => d.x)) || m[0];
            c[1] = d3.mean(nodes.filter(d => d.group == i).map(d => d.y)) || m[1];
          });

          // 2. group each point according to its closest K-center
          nodes.forEach(d => {
             d.group = d3.scan(centers.map(c => {
               var dx = d.x - c[0],
                   dy = d.y - c[1];
               return  (dx*dx + dy*dy);
             }));
          });

        }
      )

    .force("collide", d3.forceCollide().radius(function(d) { return d.r + 0.5; }).iterations(2))
/*
    .force("circle", function(alpha){
      nodes.forEach(d => {
        let k = Math.sqrt(d.y*d.y + d.x * d.x);
        d.vx += d.y/k * 5 * alpha;
        d.vy -= d.x/k * 5 * alpha;
      })
    })
    */
    .on("tick", ticked);

function ticked() {
  context.clearRect(0, 0, width, height);
  context.save();
  context.translate(width / 2, height / 2);

  nodes.forEach(function(d) {
  context.beginPath();

    context.moveTo(d.x + d.r, d.y);
    context.arc(d.x, d.y, d.r, 0, tau);
  context.fillStyle = colorscale(d.group);
  context.fill();
  context.strokeStyle = "#333";
  context.stroke();


  });

  context.restore();
}

</script>


## thumbnail.png

      
    Raw
  

              thumbnail.png
	<!DOCTYPE html>
	<meta charset="utf-8">
	<canvas width="960" height="960"></canvas>
	<script src="https://d3js.org/d3.v4.min.js"></script>
	<script>

	var canvas = document.querySelector("canvas"),
	context = canvas.getContext("2d"),
	width = canvas.width,
	height = canvas.height,
	tau = 2 * Math.PI;

	var nodes = d3.range(1000).map(function(i) {
	return {
	r: Math.random() * 14 + 4
	};
	});

	var K = 20,
	centers = d3.range(K).map(i => [0,0]),
	colorscale = d3.scaleOrdinal(d3.schemeCategory20c);

	var simulation = d3.forceSimulation(nodes)
	.velocityDecay(0.2)
	.force("x", d3.forceX().strength(0.002))
	.force("y", d3.forceY().strength(0.002))
	.force('kmeans', function(){

	// a central point to re-init empty groups
	var m = [d3.mean(centers.map(d => d[0] \|\| 0)),
	d3.mean(centers.map(d => d[1] \|\| 0))];

	// the order is important: move the centers before re-making the groups
	// so that the centers follow the general movement and keep "their"
	// points, instead of having points pass through them

	// 1. move K-centers towards the barycenter of their group
	centers.forEach((c,i) => {
	c[0] = d3.mean(nodes.filter(d => d.group == i).map(d => d.x)) \|\| m[0];
	c[1] = d3.mean(nodes.filter(d => d.group == i).map(d => d.y)) \|\| m[1];
	});

	// 2. group each point according to its closest K-center
	nodes.forEach(d => {
	d.group = d3.scan(centers.map(c => {
	var dx = d.x - c[0],
	dy = d.y - c[1];
	return (dxdx + dydy);
	}));
	});

	}
	)

	.force("collide", d3.forceCollide().radius(function(d) { return d.r + 0.5; }).iterations(2))
	/*
	.force("circle", function(alpha){
	nodes.forEach(d => {
	let k = Math.sqrt(d.yd.y + d.x d.x);
	d.vx += d.y/k * 5 * alpha;
	d.vy -= d.x/k * 5 * alpha;
	})
	})
	*/
	.on("tick", ticked);

	function ticked() {
	context.clearRect(0, 0, width, height);
	context.save();
	context.translate(width / 2, height / 2);

	nodes.forEach(function(d) {
	context.beginPath();

	context.moveTo(d.x + d.r, d.y);
	context.arc(d.x, d.y, d.r, 0, tau);
	context.fillStyle = colorscale(d.group);
	context.fill();
	context.strokeStyle = "#333";
	context.stroke();


	});

	context.restore();
	}

	</script>
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