@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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TypeScript
/**
* @license
* Copyright 2018 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 { Tensor, Tensor2D } from '../tensor';
import { TensorLike } from '../types';
/**
* Returns the truth value of `NOT x` element-wise.
*
* ```js
* const a = tf.tensor1d([false, true], 'bool');
*
* a.logicalNot().print();
* ```
*
* @param x The input tensor. Must be of dtype 'bool'.
*/
/** @doc {heading: 'Operations', subheading: 'Logical'} */
declare function logicalNot_<T extends Tensor>(x: T | TensorLike): T;
/**
* Returns the truth value of `a AND b` element-wise. Supports broadcasting.
*
* ```js
* const a = tf.tensor1d([false, false, true, true], 'bool');
* const b = tf.tensor1d([false, true, false, true], 'bool');
*
* a.logicalAnd(b).print();
* ```
*
* @param a The first input tensor. Must be of dtype bool.
* @param b The second input tensor. Must be of dtype bool.
*/
/** @doc {heading: 'Operations', subheading: 'Logical'} */
declare function logicalAnd_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
/**
* Returns the truth value of `a OR b` element-wise. Supports broadcasting.
*
* ```js
* const a = tf.tensor1d([false, false, true, true], 'bool');
* const b = tf.tensor1d([false, true, false, true], 'bool');
*
* a.logicalOr(b).print();
* ```
* @param a The first input tensor. Must be of dtype bool.
* @param b The second input tensor. Must be of dtype bool.
*/
/** @doc {heading: 'Operations', subheading: 'Logical'} */
declare function logicalOr_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
/**
* Returns the truth value of `a XOR b` element-wise. Supports broadcasting.
*
* ```js
* const a = tf.tensor1d([false, false, true, true], 'bool');
* const b = tf.tensor1d([false, true, false, true], 'bool');
*
* a.logicalXor(b).print();
* ```
*
* @param a The first input tensor. Must be of dtype bool.
* @param b The second input tensor. Must be of dtype bool.
*/
/** @doc {heading: 'Operations', subheading: 'Logical'} */
declare function logicalXor_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
/**
* Returns the elements, either `a` or `b` depending on the `condition`.
*
* If the condition is true, select from `a`, otherwise select from `b`.
*
* ```js
* const cond = tf.tensor1d([false, false, true], 'bool');
* const a = tf.tensor1d([1 , 2, 3]);
* const b = tf.tensor1d([-1, -2, -3]);
*
* a.where(cond, b).print();
* ```
*
* @param condition The input condition. Must be of dtype bool.
* @param a If `condition` is rank 1, `a` may have a higher rank but
* its first dimension must match the size of `condition`.
* @param b A tensor with the same shape and type as `a`.
*/
/** @doc {heading: 'Operations', subheading: 'Logical'} */
declare function where_<T extends Tensor>(condition: Tensor | TensorLike, a: T | TensorLike, b: T | TensorLike): T;
/**
* 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'} */
declare function whereAsync_(condition: Tensor | TensorLike): Promise<Tensor2D>;
export declare const logicalAnd: typeof logicalAnd_;
export declare const logicalNot: typeof logicalNot_;
export declare const logicalOr: typeof logicalOr_;
export declare const logicalXor: typeof logicalXor_;
export declare const where: typeof where_;
export declare const whereAsync: typeof whereAsync_;
export {};