Function stringNGrams_

tfjs-core/src/ops/string/string_n_grams.ts:67–97

* Creates ngrams from ragged string data. * * This op accepts a ragged tensor with 1 ragged dimension containing only * strings and outputs a ragged tensor with 1 ragged dimension containing ngrams * of that string, joined along the innermost axis. * * ```js * const result = tf.string.stringN

(
    data: Tensor1D|TensorLike, dataSplits: Tensor|TensorLike, separator: string,
    nGramWidths: number[], leftPad: string, rightPad: string, padWidth: number,
    preserveShortSequences: boolean)

 * @doc {heading: 'Operations', subheading: 'String'}
 */
function stringNGrams_(
    data: Tensor1D|TensorLike, dataSplits: Tensor|TensorLike, separator: string,
    nGramWidths: number[], leftPad: string, rightPad: string, padWidth: number,
    preserveShortSequences: boolean): NamedTensorMap {
  const $data = convertToTensor(data, 'data', 'stringNGrams', 'string');
  if ($data.dtype !== 'string') {
    throw new Error('Data must be of datatype string');
  }
  if ($data.shape.length !== 1) {
    throw new Error(`Data must be a vector, saw: ${$data.shape}`);
  }

  const $dataSplits = convertToTensor(dataSplits, 'dataSplits', 'stringNGrams');
  if ($dataSplits.dtype !== 'int32') {
    throw new Error('Data splits must be of datatype int32');
  }

  const attrs: StringNGramsAttrs = {
    separator,
    nGramWidths,
    leftPad,
    rightPad,
    padWidth,
    preserveShortSequences
  };

  const inputs: StringNGramsInputs = {data: $data, dataSplits: $dataSplits};
  const result: Tensor[] =
      ENGINE.runKernel(StringNGrams, inputs as {}, attrs as {});
  return {nGrams: result[0], nGramsSplits: result[1]};
}

export const stringNGrams = /* @__PURE__ */ op({stringNGrams_});