@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import { TensorBuffer } from '../tensor';
import { convertToTensor } from '../tensor_util_env';
import * as util from '../util';
/**
* 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`
* (duplicates are preserved). This operation also returns a Tensor indices that
* represents the position of each out element in `x`. In other words:
*
* `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`
*
* ```js
* const x = [1, 2, 3, 4, 5, 6];
* const y = [1, 3, 5];
*
* const [out, indices] = await tf.setdiff1dAsync(x, y);
* out.print(); // [2, 4, 6]
* indices.print(); // [1, 3, 5]
* ```
*
* @param x 1-D Tensor. Values to keep.
* @param y 1-D Tensor. Must have the same type as x. Values to exclude in the
* output.
* @returns Promise of Tensor tuple [out, indices].
* out: Tensor with the same type as x.
* indices: A Tensor of type int32.
*
* @doc {heading: 'Tensors', subheading: 'Transformations'}
*/
async function setdiff1dAsync_(x, y) {
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_;
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