@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 { ENGINE } from '../engine';
import { Multinomial } from '../kernel_names';
import { convertToTensor } from '../tensor_util_env';
import { op } from './operation';
import { reshape } from './reshape';
/**
* Creates a `tf.Tensor` with values drawn from a multinomial distribution.
*
* ```js
* const probs = tf.tensor([.75, .25]);
* tf.multinomial(probs, 3).print();
* ```
*
* @param logits 1D array with unnormalized log-probabilities, or
* 2D array of shape `[batchSize, numOutcomes]`. See the `normalized`
* parameter.
* @param numSamples Number of samples to draw for each row slice.
* @param seed The seed number.
* @param normalized Whether the provided `logits` are normalized true
* probabilities (sum to 1). Defaults to false.
* @return 1D array of shape `[numSamples]`, or 2D array of shape
* `[batchSize, numSamples]`, depending on the rank of the input.
*
* @doc {heading: 'Tensors', subheading: 'Random'}
*/
function multinomial_(logits, numSamples, seed, normalized = false) {
const $logits = convertToTensor(logits, 'logits', 'multinomial');
const numOutcomes = $logits.size;
const origRank = $logits.rank;
if (numOutcomes < 2) {
throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ` +
`${numOutcomes}.`);
}
if (origRank > 2) {
throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);
}
// TODO(lina128): Investigate correct seed behavior. The code seems not allow
// setting see to 0.
seed = seed || Math.random();
// The kernel only accepts (and returns) rank 2 tensors.
const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;
const inputs = { logits: logits2D };
const attrs = { numSamples, seed, normalized };
// tslint:disable-next-line: no-unnecessary-type-assertion
const res = ENGINE.runKernel(Multinomial, inputs, attrs);
// tslint:disable-next-line:no-unnecessary-type-assertion
return origRank === 1 ? reshape(res, [res.size]) : res;
}
export const multinomial = op({ multinomial_ });
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