Function toNormalizedTensors

polynomial-regression/index.js:116–149

(xyData, order)

//   yStddev: Standard deviation of y.
//   Normalized powers of y: an Tensor2D of shape [batchSize, 1].
function toNormalizedTensors(xyData, order) {
  const batchSize = xyData.length;
  const xData = xyData.map(xy => xy[0]);
  const yData = xyData.map(xy => xy[1]);
  const yMean = mean(yData);
  const yStddev = stddev(yData);
  const yNormalized = normalizeVector(yData, yMean, yStddev);
  const normalizedXPowers = [];
  const xPowerMeans = [];
  const xPowerStddevs = [];
  for (let i = 0; i < order; ++i) {
    const xPower = xData.map(x => Math.pow(x, i + 1));
    const xPowerMean = mean(xPower);
    xPowerMeans.push(xPowerMean);
    const xPowerStddev = stddev(xPower);
    xPowerStddevs.push(xPowerStddev);
    const normalizedXPower = normalizeVector(xPower, xPowerMean, xPowerStddev);
    normalizedXPowers.push(normalizedXPower);
  }
  const xArrayData = [];
  for (let i = 0; i < xData.length; ++i) {
    for (let j = 0; j < order + 1; ++j) {
      if (j === 0) {
        xArrayData.push(1);
      } else {
        xArrayData.push(normalizedXPowers[j - 1][i]);
      }
    }
  }
  return [
    xPowerMeans, xPowerStddevs, tf.tensor2d(xArrayData, [batchSize, order + 1]),
    yMean, yStddev, tf.tensor2d(yNormalized, [batchSize, 1])
  ];
}

// Fit a model for polynomial regression.
//