gx

chenyc

2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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/**
 * @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 { convertToTensor } from '../tensor_util_env';
import { assertAndGetBroadcastShape } from './broadcast_util';
import { logicalAnd } from './logical_and';
import { logicalNot } from './logical_not';
import { logicalOr } from './logical_or';
import { op } from './operation';
/**
 * 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'}
 */
function logicalXor_(a, b) {
    const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');
    const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');
    assertAndGetBroadcastShape($a.shape, $b.shape);
    // x ^ y = (x | y) & ~(x & y)
    return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));
}
export const logicalXor = /* @__PURE__ */ op({ logicalXor_ });
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