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@tensorflow/tfjs-core

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Hardware-accelerated JavaScript library for machine intelligence

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/** * @license * Copyright 2020 Google Inc. 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 {Tensor} from '../tensor'; import {DataType, Rank, ShapeMap} from '../types'; import {buffer} from './array_ops'; 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_<R extends Rank>( shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32', seed?: number): Tensor<R> { if (dtype != null && (dtype as DataType) === '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_});