| 1 | 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {expectArraysClose} from '../test_util';\n\ndescribeWithFlags('logicalAnd', ALL_ENVS, () => {\n it('Tensor1D.', async () => {\n let a = tf.tensor1d([1, 0, 0], 'bool');\n let b = tf.tensor1d([0, 1, 0], 'bool');\n expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 0]);\n\n a = tf.tensor1d([0, 0, 0], 'bool');\n b = tf.tensor1d([0, 0, 0], 'bool');\n expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 0]);\n\n a = tf.tensor1d([1, 1], 'bool');\n b = tf.tensor1d([1, 1], 'bool');\n expectArraysClose(await tf.logicalAnd(a, b).data(), [1, 1]);\n });\n it('mismatched Tensor1D shapes', () => {\n const a = tf.tensor1d([1, 0], 'bool');\n const b = tf.tensor1d([0, 1, 0], 'bool');\n const f = () => {\n tf.logicalAnd(a, b);\n };\n expect(f).toThrowError();\n });\n\n it('Tensor2D', async () => {\n let a = tf.tensor2d([[1, 0, 1], [0, 0, 0]], [2, 3], 'bool');\n let b = tf.tensor2d([[0, 0, 0], [0, 1, 0]], [2, 3], 'bool');\n expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 0, 0, 0, 0]);\n\n a = tf.tensor2d([[0, 0, 0], [1, 1, 1]], [2, 3], 'bool');\n b = tf.tensor2d([[0, 0, 0], [1, 1, 1]], [2, 3], 'bool');\n expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 0, 1, 1, 1]);\n });\n it('broadcasting Tensor2D shapes', async () => {\n const a = tf.tensor2d([[1], [0]], [2, 1], 'bool');\n const b = tf.tensor2d([[0, 1, 0], [0, 1, 0]], [2, 3], 'bool');\n expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 1, 0, 0, 0, 0]);\n });\n\n it('Tensor3D', async () => {\n let a = tf.tensor3d([[[1], [0], [1]], [[0], [0], [1]]], [2, 3, 1], 'bool');\n let b = tf.tensor3d([[[0], [0], [1]], [[1], [0], [0]]], [2, 3, 1], 'bool');\n expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 1, 0, 0, 0]);\n\n a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'bool');\n b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'bool');\n expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 0, 1, 1, 1]);\n });\n it('broadcasting Tensor3D shapes', async () => {\n const a = tf.tensor3d(\n [[[1, 0], [0, 0], [1, 1]], [[0, 0], [0, 1], [0, 0]]], [2, 3, 2],\n 'bool');\n const b =\n tf.tensor3d([[[0], [0], [1]], [[1], [0], [0]]], [2, 3, 1], 'bool');\n expectArraysClose(\n await tf.logicalAnd(a, b).data(), [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0]);\n });\n\n it('Tensor4D', async () => {\n let a = tf.tensor4d([1, 0, 1, 0], [2, 2, 1, 1], 'bool');\n let b = tf.tensor4d([0, 1, 1, 0], [2, 2, 1, 1], 'bool');\n expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 1, 0]);\n\n a = tf.tensor4d([0, 0, 0, 0], [2, 2, 1, 1], 'bool');\n b = tf.tensor4d([0, 0, 0, 0], [2, 2, 1, 1], 'bool');\n expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 0, 0]);\n\n a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'bool');\n b = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'bool');\n expectArraysClose(await tf.logicalAnd(a, b).data(), [1, 1, 1, 1]);\n });\n it('broadcasting Tensor4D shapes', async () => {\n const a = tf.tensor4d([1, 0, 1, 0], [2, 2, 1, 1], 'bool');\n const b = tf.tensor4d(\n [[[[1, 0]], [[0, 0]]], [[[0, 0]], [[1, 1]]]], [2, 2, 1, 2], 'bool');\n expectArraysClose(\n await tf.logicalAnd(a, b).data(), [1, 0, 0, 0, 0, 0, 0, 0]);\n });\n\n it('TensorLike', async () => {\n const a = [true, false, false];\n const b = [false, true, false];\n expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 0]);\n });\n\n it('TensorLike Chained', async () => {\n const a = tf.tensor1d([1, 0, 0], 'bool');\n const b = [false, true, false];\n expectArraysClose(await a.logicalAnd(b).data(), [0, 0, 0]);\n });\n\n it('throws when passed a as a non-tensor', () => {\n expect(() => tf.logicalAnd({} as tf.Tensor, tf.scalar(1, 'bool')))\n .toThrowError(/Argument 'a' passed to 'logicalAnd' must be a Tensor/);\n });\n it('throws when passed b as a non-tensor', () => {\n expect(() => tf.logicalAnd(tf.scalar(1, 'bool'), {} as tf.Tensor))\n .toThrowError(/Argument 'b' passed to 'logicalAnd' must be a Tensor/);\n });\n\n it('accepts a tensor-like object', async () => {\n const a = [1, 0, 0, 1];\n const b = [0, 1, 0, 1];\n expectArraysClose(await tf.logicalAnd(a, b).data(), [0, 0, 0, 1]);\n });\n});\n"]} |