@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2020 Google LLC. 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 { whereImpl } from '../backends/where_impl';
import { convertToTensor } from '../tensor_util_env';
/**
* Returns the coordinates of true elements of condition.
*
* The coordinates are returned in a 2-D tensor where the first dimension (rows)
* represents the number of true elements, and the second dimension (columns)
* represents the coordinates of the true elements. Keep in mind, the shape of
* the output tensor can vary depending on how many true values there are in
* input. Indices are output in row-major order. The resulting tensor has the
* shape `[numTrueElems, condition.rank]`.
*
* This is analogous to calling the python `tf.where(cond)` without an x or y.
*
* ```js
* const cond = tf.tensor1d([false, false, true], 'bool');
* const result = await tf.whereAsync(cond);
* result.print();
* ```
*
* @doc {heading: 'Operations', subheading: 'Logical'}
*/
async function whereAsync_(condition) {
const $condition = convertToTensor(condition, 'condition', 'whereAsync', 'bool');
const vals = await $condition.data();
const res = whereImpl($condition.shape, vals);
if (condition !== $condition) {
$condition.dispose();
}
return res;
}
export const whereAsync = whereAsync_;
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