Function regressGaussian

src/dataset.ts:95–134

(numSamples: number, noise: number)

}

export function regressGaussian(numSamples: number, noise: number):
  Example2D[] {
  let points: Example2D[] = [];

  let labelScale = d3.scale.linear()
    .domain([0, 2])
    .range([1, 0])
    .clamp(true);

  let gaussians = [
    [-4, 2.5, 1],
    [0, 2.5, -1],
    [4, 2.5, 1],
    [-4, -2.5, -1],
    [0, -2.5, 1],
    [4, -2.5, -1]
  ];

  function getLabel(x, y) {
    // Choose the one that is maximum in abs value.
    let label = 0;
    gaussians.forEach(([cx, cy, sign]) => {
      let newLabel = sign * labelScale(dist({x, y}, {x: cx, y: cy}));
      if (Math.abs(newLabel) > Math.abs(label)) {
        label = newLabel;
      }
    });
    return label;
  }
  let radius = 6;
  for (let i = 0; i < numSamples; i++) {
    let x = randUniform(-radius, radius);
    let y = randUniform(-radius, radius);
    let noiseX = randUniform(-radius, radius) * noise;
    let noiseY = randUniform(-radius, radius) * noise;
    let label = getLabel(x + noiseX, y + noiseY);
    points.push({x, y, label});
  };
  return points;
}

export function classifySpiralData(numSamples: number, noise: number):
    Example2D[] {