@tensorflow/tfjs-core
Version:
Hardware-accelerated JavaScript library for machine intelligence
125 lines • 23.5 kB
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 * as tf from '../index';
import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
import { expectArraysClose, expectArraysEqual } from '../test_util';
describeWithFlags('moments', ALL_ENVS, () => {
it('basic', async () => {
const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
const { mean, variance } = tf.moments(a);
expect(mean.dtype).toBe('float32');
expect(variance.dtype).toBe('float32');
expectArraysClose(await mean.data(), 7 / 6);
expectArraysClose(await variance.data(), 1.1389);
});
it('propagates NaNs', async () => {
const a = tf.tensor2d([1, 2, 3, NaN, 0, 1], [3, 2]);
const { mean, variance } = tf.moments(a);
expect(mean.dtype).toBe('float32');
expect(variance.dtype).toBe('float32');
expectArraysEqual(await mean.data(), NaN);
expectArraysEqual(await variance.data(), NaN);
});
it('moments(int32) => float32', async () => {
const a = tf.tensor1d([1, 5, 7, 3], 'int32');
const { mean, variance } = tf.moments(a);
expect(mean.dtype).toBe('float32');
expect(variance.dtype).toBe('float32');
expectArraysClose(await mean.data(), 4);
expectArraysClose(await variance.data(), 5);
});
it('moments(bool) => float32', async () => {
const a = tf.tensor1d([true, false, false, true, true], 'bool');
const { mean, variance } = tf.moments(a);
expect(mean.dtype).toBe('float32');
expect(variance.dtype).toBe('float32');
expectArraysClose(await mean.data(), 3 / 5);
expectArraysClose(await variance.data(), 0.23999998);
});
it('2D array with keep dim', async () => {
const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
const { mean, variance } = tf.moments(a, null, true /* keepDims */);
expect(mean.shape).toEqual([1, 1]);
expect(mean.dtype).toBe('float32');
expect(variance.shape).toEqual([1, 1]);
expect(variance.dtype).toBe('float32');
expectArraysClose(await mean.data(), [7 / 6]);
expectArraysClose(await variance.data(), [1.138889]);
});
it('axis=0 in 2D array', async () => {
const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
const { mean, variance } = tf.moments(a, [0]);
expect(mean.shape).toEqual([2]);
expect(mean.dtype).toBe('float32');
expect(variance.shape).toEqual([2]);
expect(variance.dtype).toBe('float32');
expectArraysClose(await mean.data(), [4 / 3, 1]);
expectArraysClose(await variance.data(), [1.556, 2 / 3]);
});
it('axis=1 in 2D array', async () => {
const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
const { mean, variance } = tf.moments(a, [1]);
expect(mean.dtype).toBe('float32');
expect(mean.shape).toEqual([3]);
expect(variance.dtype).toBe('float32');
expect(variance.shape).toEqual([3]);
expectArraysClose(await mean.data(), [1.5, 1.5, 0.5]);
expectArraysClose(await variance.data(), [0.25, 2.25, 0.25]);
});
it('2D, axis=1 provided as number', async () => {
const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
const { mean, variance } = tf.moments(a, 1);
expect(mean.shape).toEqual([2]);
expect(mean.dtype).toBe('float32');
expect(variance.shape).toEqual([2]);
expect(variance.dtype).toBe('float32');
expectArraysClose(await mean.data(), [2, 1 / 3]);
expectArraysClose(await variance.data(), [2 / 3, 0.222]);
});
it('2D, axis=-1 provided as number', async () => {
const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
const { mean, variance } = tf.moments(a, -1);
expect(mean.shape).toEqual([2]);
expect(mean.dtype).toBe('float32');
expect(variance.shape).toEqual([2]);
expect(variance.dtype).toBe('float32');
expectArraysClose(await mean.data(), [2, 1 / 3]);
expectArraysClose(await variance.data(), [2 / 3, 0.222]);
});
it('axis=0,1 in 2D array', async () => {
const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
const { mean, variance } = tf.moments(a, [0, 1]);
expect(mean.shape).toEqual([]);
expect(mean.dtype).toBe('float32');
expect(variance.shape).toEqual([]);
expect(variance.dtype).toBe('float32');
expectArraysClose(await mean.data(), [7 / 6]);
expectArraysClose(await variance.data(), [1.1389]);
});
it('throws when passed a non-tensor', () => {
expect(() => tf.moments({}))
.toThrowError(/Argument 'x' passed to 'moments' must be a Tensor/);
});
it('accepts a tensor-like object', async () => {
const { mean, variance } = tf.moments([1, 2, 3, 0, 0, 1]);
expect(mean.dtype).toBe('float32');
expect(variance.dtype).toBe('float32');
expectArraysClose(await mean.data(), 7 / 6);
expectArraysClose(await variance.data(), 1.1389);
});
});
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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, expectArraysEqual} from '../test_util';\n\ndescribeWithFlags('moments', ALL_ENVS, () => {\n  it('basic', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const {mean, variance} = tf.moments(a);\n\n    expect(mean.dtype).toBe('float32');\n    expect(variance.dtype).toBe('float32');\n    expectArraysClose(await mean.data(), 7 / 6);\n    expectArraysClose(await variance.data(), 1.1389);\n  });\n\n  it('propagates NaNs', async () => {\n    const a = tf.tensor2d([1, 2, 3, NaN, 0, 1], [3, 2]);\n    const {mean, variance} = tf.moments(a);\n\n    expect(mean.dtype).toBe('float32');\n    expect(variance.dtype).toBe('float32');\n    expectArraysEqual(await mean.data(), NaN);\n    expectArraysEqual(await variance.data(), NaN);\n  });\n\n  it('moments(int32) => float32', async () => {\n    const a = tf.tensor1d([1, 5, 7, 3], 'int32');\n    const {mean, variance} = tf.moments(a);\n\n    expect(mean.dtype).toBe('float32');\n    expect(variance.dtype).toBe('float32');\n    expectArraysClose(await mean.data(), 4);\n    expectArraysClose(await variance.data(), 5);\n  });\n\n  it('moments(bool) => float32', async () => {\n    const a = tf.tensor1d([true, false, false, true, true], 'bool');\n    const {mean, variance} = tf.moments(a);\n\n    expect(mean.dtype).toBe('float32');\n    expect(variance.dtype).toBe('float32');\n    expectArraysClose(await mean.data(), 3 / 5);\n    expectArraysClose(await variance.data(), 0.23999998);\n  });\n\n  it('2D array with keep dim', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const {mean, variance} = tf.moments(a, null, true /* keepDims */);\n\n    expect(mean.shape).toEqual([1, 1]);\n    expect(mean.dtype).toBe('float32');\n    expect(variance.shape).toEqual([1, 1]);\n    expect(variance.dtype).toBe('float32');\n    expectArraysClose(await mean.data(), [7 / 6]);\n    expectArraysClose(await variance.data(), [1.138889]);\n  });\n\n  it('axis=0 in 2D array', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const {mean, variance} = tf.moments(a, [0]);\n\n    expect(mean.shape).toEqual([2]);\n    expect(mean.dtype).toBe('float32');\n    expect(variance.shape).toEqual([2]);\n    expect(variance.dtype).toBe('float32');\n    expectArraysClose(await mean.data(), [4 / 3, 1]);\n    expectArraysClose(await variance.data(), [1.556, 2 / 3]);\n  });\n\n  it('axis=1 in 2D array', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const {mean, variance} = tf.moments(a, [1]);\n\n    expect(mean.dtype).toBe('float32');\n    expect(mean.shape).toEqual([3]);\n    expect(variance.dtype).toBe('float32');\n    expect(variance.shape).toEqual([3]);\n    expectArraysClose(await mean.data(), [1.5, 1.5, 0.5]);\n    expectArraysClose(await variance.data(), [0.25, 2.25, 0.25]);\n  });\n\n  it('2D, axis=1 provided as number', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);\n    const {mean, variance} = tf.moments(a, 1);\n\n    expect(mean.shape).toEqual([2]);\n    expect(mean.dtype).toBe('float32');\n    expect(variance.shape).toEqual([2]);\n    expect(variance.dtype).toBe('float32');\n    expectArraysClose(await mean.data(), [2, 1 / 3]);\n    expectArraysClose(await variance.data(), [2 / 3, 0.222]);\n  });\n\n  it('2D, axis=-1 provided as number', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);\n    const {mean, variance} = tf.moments(a, -1);\n\n    expect(mean.shape).toEqual([2]);\n    expect(mean.dtype).toBe('float32');\n    expect(variance.shape).toEqual([2]);\n    expect(variance.dtype).toBe('float32');\n    expectArraysClose(await mean.data(), [2, 1 / 3]);\n    expectArraysClose(await variance.data(), [2 / 3, 0.222]);\n  });\n\n  it('axis=0,1 in 2D array', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const {mean, variance} = tf.moments(a, [0, 1]);\n\n    expect(mean.shape).toEqual([]);\n    expect(mean.dtype).toBe('float32');\n    expect(variance.shape).toEqual([]);\n    expect(variance.dtype).toBe('float32');\n    expectArraysClose(await mean.data(), [7 / 6]);\n    expectArraysClose(await variance.data(), [1.1389]);\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.moments({} as tf.Tensor))\n        .toThrowError(/Argument 'x' passed to 'moments' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const {mean, variance} = tf.moments([1, 2, 3, 0, 0, 1]);\n\n    expect(mean.dtype).toBe('float32');\n    expect(variance.dtype).toBe('float32');\n    expectArraysClose(await mean.data(), 7 / 6);\n    expectArraysClose(await variance.data(), 1.1389);\n  });\n});\n"]}