/**
 * @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 * as tf from '../../index';
import { ALL_ENVS, describeWithFlags } from '../../jasmine_util';
import { expectArraysClose } from '../../test_util';
describeWithFlags('cosineDistance', ALL_ENVS, () => {
    it('1D', async () => {
        const predictions = tf.tensor1d([1, 2, 3]);
        const label = tf.tensor1d([0.3, -0.6, -0.1]);
        const y = tf.losses.cosineDistance(label, predictions, 0);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), 1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1));
    });
    it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
        const predictions = tf.tensor1d([1, 2, 3]);
        const label = tf.tensor1d([0.3, -0.6, -0.1]);
        const weights = tf.scalar(0.1);
        const y = tf.losses.cosineDistance(label, predictions, 0, weights);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), (1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1);
    });
    it('1D - weighted - Reduction.NONE', async () => {
        const predictions = tf.tensor1d([1, 2, 3]);
        const label = tf.tensor1d([0.3, -0.6, -0.1]);
        const weights = tf.scalar(0.1);
        const y = tf.losses.cosineDistance(label, predictions, 0, weights, tf.Reduction.NONE);
        expect(y.shape).toEqual([1]);
        expectArraysClose(await y.data(), [(1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1]);
    });
    it('1D - Reduction.MEAN', async () => {
        const predictions = tf.tensor1d([1, 2, 3]);
        const label = tf.tensor1d([0.3, -0.6, -0.1]);
        const y = tf.losses.cosineDistance(label, predictions, 0, undefined, tf.Reduction.MEAN);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), (1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)));
    });
    it('1D - weighted - Reduction.MEAN', async () => {
        const predictions = tf.tensor1d([1, 2, 3]);
        const label = tf.tensor1d([0.3, -0.6, -0.1]);
        const weights = tf.scalar(0.1);
        const y = tf.losses.cosineDistance(label, predictions, 0, weights, tf.Reduction.MEAN);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), ((1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1) / 0.1);
    });
    it('2D', async () => {
        const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
        const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
        const y = tf.losses.cosineDistance(label, predictions, 1);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), ((1 - (4 * 1 + 8 * 9 + 12 * 2)) + (1 - (8 * -5 + 1 * -2 + 3 * 6))) / 2);
    });
    it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
        const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
        const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
        const weights = tf.tensor2d([3, 0], [2, 1]);
        const y = tf.losses.cosineDistance(label, predictions, 1, weights);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), ((1 - (4 * 1 + 8 * 9 + 12 * 2)) * 3 +
            (1 - (8 * -5 + 1 * -2 + 3 * 6)) * 0) /
            1);
    });
    it('2D - weighted - Reduction.NONE', async () => {
        const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
        const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
        const weights = tf.tensor2d([3, 0], [2, 1]);
        const y = tf.losses.cosineDistance(label, predictions, 1, weights, tf.Reduction.NONE);
        expect(y.shape).toEqual([2, 1]);
        expectArraysClose(await y.data(), [
            (1 - (4 * 1 + 8 * 9 + 12 * 2)) * 3, (1 - (8 * -5 + 1 * -2 + 3 * 6)) * 0
        ]);
    });
    it('2D - Reduction.MEAN', async () => {
        const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
        const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
        const y = tf.losses.cosineDistance(label, predictions, 1, undefined, tf.Reduction.MEAN);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), ((1 - (4 * 1 + 8 * 9 + 12 * 2)) + (1 - (8 * -5 + 1 * -2 + 3 * 6))) / 2);
    });
    it('2D - weighted - Reduction.MEAN', async () => {
        const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
        const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
        const weights = tf.tensor2d([3, 0], [2, 1]);
        const y = tf.losses.cosineDistance(label, predictions, 1, weights, tf.Reduction.MEAN);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), ((1 - (4 * 1 + 8 * 9 + 12 * 2)) * 3 +
            (1 - (8 * -5 + 1 * -2 + 3 * 6)) * 0) /
            3);
    });
    it('throws when passed label as a non-tensor', () => {
        const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
        const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
        const e = /Argument 'labels' passed to 'cosineDistance' must be a Tensor/;
        expect(() => tf.losses.cosineDistance({}, predictions, 0, weights, tf.Reduction.MEAN))
            .toThrowError(e);
    });
    it('throws when passed label as a non-tensor', () => {
        const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
        const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
        const e = new RegExp('Argument \'predictions\' passed to \'cosineDistance\' ' +
            'must be a Tensor');
        expect(() => tf.losses.cosineDistance(label, {}, 0, weights, tf.Reduction.MEAN))
            .toThrowError(e);
    });
    it('throws when passed weights as a non-tensor', () => {
        const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
        const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
        const e = /Argument 'weights' passed to 'cosineDistance' must be a Tensor/;
        expect(() => tf.losses.cosineDistance(label, predictions, 0, {}, tf.Reduction.MEAN))
            .toThrowError(e);
    });
    it('accepts a tensor-like object', async () => {
        const predictions = [1, 2, 3];
        const label = [0.3, -0.6, -0.1];
        const weights = 0.1;
        const y = tf.losses.cosineDistance(label, predictions, 0, weights, tf.Reduction.NONE);
        expect(y.shape).toEqual([1]);
        expectArraysClose(await y.data(), [(1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1]);
    });
});
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,MAAM,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAC7B,iBAAiB,CACb,MAAM,CAAC,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,GAAG,GAAG,CAAC,GAAG,CAAC,GAAG,GAAG,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,GAAG,GAAG,CAAC,CAAC,CAAC;IACrE,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC","sourcesContent":["/**\n * @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 */\nimport * as tf from '../../index';\nimport {ALL_ENVS, describeWithFlags} from '../../jasmine_util';\nimport {expectArraysClose} from '../../test_util';\n\ndescribeWithFlags('cosineDistance', ALL_ENVS, () => {\n  it('1D', async () => {\n    const predictions = tf.tensor1d([1, 2, 3]);\n    const label = tf.tensor1d([0.3, -0.6, -0.1]);\n\n    const y = tf.losses.cosineDistance(label, predictions, 0);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), 1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1));\n  });\n\n  it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {\n    const predictions = tf.tensor1d([1, 2, 3]);\n    const label = tf.tensor1d([0.3, -0.6, -0.1]);\n    const weights = tf.scalar(0.1);\n\n    const y = tf.losses.cosineDistance(label, predictions, 0, weights);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(\n        await y.data(), (1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1);\n  });\n\n  it('1D - weighted - Reduction.NONE', async () => {\n    const predictions = tf.tensor1d([1, 2, 3]);\n    const label = tf.tensor1d([0.3, -0.6, -0.1]);\n    const weights = tf.scalar(0.1);\n\n    const y = tf.losses.cosineDistance(\n        label, predictions, 0, weights, tf.Reduction.NONE);\n\n    expect(y.shape).toEqual([1]);\n    expectArraysClose(\n        await y.data(), [(1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1]);\n  });\n\n  it('1D - Reduction.MEAN', async () => {\n    const predictions = tf.tensor1d([1, 2, 3]);\n    const label = tf.tensor1d([0.3, -0.6, -0.1]);\n\n    const y = tf.losses.cosineDistance(\n        label, predictions, 0, undefined, tf.Reduction.MEAN);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), (1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)));\n  });\n\n  it('1D - weighted - Reduction.MEAN', async () => {\n    const predictions = tf.tensor1d([1, 2, 3]);\n    const label = tf.tensor1d([0.3, -0.6, -0.1]);\n    const weights = tf.scalar(0.1);\n\n    const y = tf.losses.cosineDistance(\n        label, predictions, 0, weights, tf.Reduction.MEAN);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(\n        await y.data(), ((1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1) / 0.1);\n  });\n\n  it('2D', async () => {\n    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);\n    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);\n\n    const y = tf.losses.cosineDistance(label, predictions, 1);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(\n        await y.data(),\n        ((1 - (4 * 1 + 8 * 9 + 12 * 2)) + (1 - (8 * -5 + 1 * -2 + 3 * 6))) / 2);\n  });\n\n  it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {\n    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);\n    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);\n    const weights = tf.tensor2d([3, 0], [2, 1]);\n\n    const y = tf.losses.cosineDistance(label, predictions, 1, weights);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(\n        await y.data(),\n        ((1 - (4 * 1 + 8 * 9 + 12 * 2)) * 3 +\n         (1 - (8 * -5 + 1 * -2 + 3 * 6)) * 0) /\n            1);\n  });\n\n  it('2D - weighted - Reduction.NONE', async () => {\n    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);\n    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);\n    const weights = tf.tensor2d([3, 0], [2, 1]);\n\n    const y = tf.losses.cosineDistance(\n        label, predictions, 1, weights, tf.Reduction.NONE);\n\n    expect(y.shape).toEqual([2, 1]);\n    expectArraysClose(await y.data(), [\n      (1 - (4 * 1 + 8 * 9 + 12 * 2)) * 3, (1 - (8 * -5 + 1 * -2 + 3 * 6)) * 0\n    ]);\n  });\n\n  it('2D - Reduction.MEAN', async () => {\n    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);\n    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);\n\n    const y = tf.losses.cosineDistance(\n        label, predictions, 1, undefined, tf.Reduction.MEAN);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(\n        await y.data(),\n        ((1 - (4 * 1 + 8 * 9 + 12 * 2)) + (1 - (8 * -5 + 1 * -2 + 3 * 6))) / 2);\n  });\n\n  it('2D - weighted - Reduction.MEAN', async () => {\n    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);\n    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);\n    const weights = tf.tensor2d([3, 0], [2, 1]);\n\n    const y = tf.losses.cosineDistance(\n        label, predictions, 1, weights, tf.Reduction.MEAN);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(\n        await y.data(),\n        ((1 - (4 * 1 + 8 * 9 + 12 * 2)) * 3 +\n         (1 - (8 * -5 + 1 * -2 + 3 * 6)) * 0) /\n            3);\n  });\n\n  it('throws when passed label as a non-tensor', () => {\n    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);\n    const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);\n\n    const e = /Argument 'labels' passed to 'cosineDistance' must be a Tensor/;\n    expect(\n        () => tf.losses.cosineDistance(\n            {} as tf.Tensor, predictions, 0, weights, tf.Reduction.MEAN))\n        .toThrowError(e);\n  });\n\n  it('throws when passed label as a non-tensor', () => {\n    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);\n    const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);\n\n    const e = new RegExp(\n        'Argument \\'predictions\\' passed to \\'cosineDistance\\' ' +\n        'must be a Tensor');\n    expect(\n        () => tf.losses.cosineDistance(\n            label, {} as tf.Tensor, 0, weights, tf.Reduction.MEAN))\n        .toThrowError(e);\n  });\n\n  it('throws when passed weights as a non-tensor', () => {\n    const predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);\n    const label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);\n\n    const e = /Argument 'weights' passed to 'cosineDistance' must be a Tensor/;\n    expect(\n        () => tf.losses.cosineDistance(\n            label, predictions, 0, {} as tf.Tensor, tf.Reduction.MEAN))\n        .toThrowError(e);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const predictions = [1, 2, 3];\n    const label = [0.3, -0.6, -0.1];\n    const weights = 0.1;\n\n    const y = tf.losses.cosineDistance(\n        label, predictions, 0, weights, tf.Reduction.NONE);\n\n    expect(y.shape).toEqual([1]);\n    expectArraysClose(\n        await y.data(), [(1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1]);\n  });\n});\n"]}