@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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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 { buffer } from './buffer';
import { op } from './operation';
import { MPRandGauss } from './rand_util';
/**
* Creates a `tf.Tensor` with values sampled from a normal distribution.
*
* ```js
* tf.randomNormal([2, 2]).print();
* ```
*
* @param shape An array of integers defining the output tensor shape.
* @param mean The mean of the normal distribution.
* @param stdDev The standard deviation of the normal distribution.
* @param dtype The data type of the output.
* @param seed The seed for the random number generator.
*
* @doc {heading: 'Tensors', subheading: 'Random'}
*/
function randomNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {
if (dtype != null && dtype === 'bool') {
throw new Error(`Unsupported data type ${dtype}`);
}
const randGauss = new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);
const res = buffer(shape, dtype);
for (let i = 0; i < res.values.length; i++) {
res.values[i] = randGauss.nextValue();
}
return res.toTensor();
}
export const randomNormal = op({ randomNormal_ });
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