@stdlib/nlp
Version:
Natural language processing.
91 lines (79 loc) • 2.14 kB
TypeScript
/*
* @license Apache-2.0
*
* Copyright (c) 2021 The Stdlib Authors.
*
* 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.
*/
// TypeScript Version: 4.1
/**
* Interface defining function options.
*/
interface Options {
/**
* Dirichlet hyper-parameter of topic vector theta (default: `50/K`).
*/
alpha?: number;
/**
* Dirichlet hyper-parameter for word vector phi (default: `0.1`).
*/
beta?: number;
}
/**
* Term probabilities.
*/
interface Term {
/**
* Word.
*/
word: string;
/**
* Probability.
*/
prob: number;
}
/**
* Model output.
*/
interface Model {
/**
* Fits model for the given corpus.
*
* @param iter - number of sampling iterations
* @param burnin - number of estimates that are thrown away at the beginning
* @param thin - number of estimates discarded in-between iterations
*/
fit( iter: number, burnin: number, thin: number ): void;
/**
* Returns the `no` terms with the highest probabilities for chosen topic `k`.
*
* @param k - topic index
* @param no - number of terms to retrieve (default: 10)
*/
getTerms( k: number, no?: number ): Array<Term>;
}
/**
* Latent Dirichlet Allocation via collapsed Gibbs sampling.
*
* @param documents - document corpus
* @param K - number of topics
* @param options - options object
* @param options.alpha - Dirichlet hyper-parameter of topic vector theta
* @param options.beta - Dirichlet hyper-parameter for word vector phi
* @throws second argument must be a positive integer
* @throws must provide valid options
* @returns model object
*/
declare function lda( documents: Array<string>, K: number, options?: Options ): Model;
// EXPORTS //
export = lda;