Function setdiff1dAsync_

tfjs-core/src/ops/setdiff1d_async.ts:51–88

* Computes the difference between two lists of numbers. * * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out` * that represents all values that are in `x` but not in `y`. The returned * Tensor `out` is sorted in the same order that the numbers appear in `x` * (duplicate

(
    x: Tensor|TensorLike, y: Tensor|TensorLike)

 * @doc {heading: 'Tensors', subheading: 'Transformations'}
 */
async function setdiff1dAsync_(
    x: Tensor|TensorLike, y: Tensor|TensorLike): Promise<[Tensor, Tensor]> {
  const $x = convertToTensor(x, 'x', 'setdiff1d');
  const $y = convertToTensor(y, 'y', 'setdiff1d');

  util.assert(
      $x.dtype === $y.dtype,
      () => `x and y should have the same dtype, but got x (${
          $x.dtype}) and y (${$y.dtype}).`);

  util.assert(
      $x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);

  util.assert(
      $y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);

  const xVals = await $x.data();
  const yVals = await $y.data();
  const ySet = new Set(yVals);

  let outputSize = 0;
  for (let i = 0; i < xVals.length; i++) {
    if (!ySet.has(xVals[i])) {
      outputSize++;
    }
  }

  const buffer = new TensorBuffer([outputSize], $x.dtype);
  const indices = new TensorBuffer([outputSize], 'int32');
  for (let i = 0, p = 0; i < xVals.length; i++) {
    if (!ySet.has(xVals[i])) {
      buffer.values[p] = xVals[i];
      indices.values[p] = i;
      p++;
    }
  }
  return [buffer.toTensor(), indices.toTensor()];
}
export const setdiff1dAsync = setdiff1dAsync_;