LDA.rar_LDA 文档主题_java LDA_lda_lda java_lda模型

* (C) Copyright 2005, Gregor Heinrich (gregor :: arbylon : net) (This file is * part of the org.knowceans experimental software packages.) */ /* * LdaGibbsSampler is free software; you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by the Free * Software Foundation; either version 2 of the License, or (at your option) any * later version. */ /* * LdaGibbsSampler is distributed in the hope that it will be useful, but * WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more * details. */ /* * You should have received a copy of the GNU General Public License along with * this program; if not, write to the Free Software Foundation, Inc., 59 Temple * Place, Suite 330, Boston, MA 02111-1307 USA */ /* * Created on Mar 6, 2005 */ package com.xh.lda; import java.text.DecimalFormat; import java.text.NumberFormat; /** * Gibbs sampler for estimating the best assignments of topics for words and * documents in a corpus. The algorithm is introduced in Tom Griffiths' paper * "Gibbs sampling in the generative model of Latent Dirichlet Allocation" * (2002). * * @author heinrich */ public class LdaGibbsSampler { /** * document data (term lists) */ int[][] documents; /** * vocabulary size */ int V; /** * number of topics */ int K; /** * Dirichlet parameter (document--topic associations) */ double alpha; /** * Dirichlet parameter (topic--term associations) */ double beta; /** * topic assignments for each word. */ int z[][]; /** * cwt[i][j] number of instances of word i (term?) assigned to topic j. */ int[][] nw; /** * na[i][j] number of words in document i assigned to topic j. */ int[][] nd; /** * nwsum[j] total number of words assigned to topic j. */ int[] nwsum; /** * nasum[i] total number of words in document i. */ int[] ndsum; /** * cumulative statistics of theta */ double[][] thetasum; /** * cumulative statistics of phi */ double[][] phisum; /** * size of statistics */ int numstats; /** * sampling lag (?) */ private static int THIN_INTERVAL = 20; /** * burn-in period */ private static int BURN_IN = 100; /** * max iterations */ private static int ITERATIONS = 1000; /** * sample lag (if -1 only one sample taken) */ private static int SAMPLE_LAG; private static int dispcol = 0; /** * Initialise the Gibbs sampler with data. * * @param V * vocabulary size * @param data */ public LdaGibbsSampler(int[][] documents, int V) { this.documents = documents; this.V = V; } /** * Initialisation: Must start with an assignment of observations to topics ? * Many alternatives are possible, I chose to perform random assignments * with equal probabilities * * @param K * number of topics * @return z assignment of topics to words */ public void initialState(int K) { int i; int M = documents.length; // initialise count variables. nw = new int[V][K]; nd = new int[M][K]; nwsum = new int[K]; ndsum = new int[M]; // The z_i are are initialised to values in [1,K] to determine the // initial state of the Markov chain. z = new int[M][]; for (int m = 0; m < M; m++) { int N = documents[m].length; z[m] = new int[N]; for (int n = 0; n < N; n++) { int topic = (int) (Math.random() * K); z[m][n] = topic; // number of instances of word i assigned to topic j nw[documents[m][n]][topic]++; // number of words in document i assigned to topic j. nd[m][topic]++; // total number of words assigned to topic j. nwsum[topic]++; } // total number of words in document i ndsum[m] = N; } } /** * Main method: Select initial state ? Repeat a large number of times: 1. * Select an element 2. Update conditional on other elements. If * appropriate, output summary for each run. * * @param K * number of topics * @param alpha * symmetric prior parameter on document--topic associations * @param beta * symmetric prior parameter on topic--term associations */ public void gibbs(int K, double alpha, double beta) { this.K = K; this.alpha = alpha; this.beta = beta; // init sampler statistics if (SAMPLE_LAG > 0) { thetasum = new double[documents.length][K]; phisum = new double[K][V]; numstats = 0; } // initial state of the Markov chain: initialState(K); System.out.println("Sampling " + ITERATIONS + " iterations with burn-in of " + BURN_IN + " (B/S=" + THIN_INTERVAL + ")."); for (int i = 0; i < ITERATIONS; i++) { // for all z_i for (int m = 0; m < z.length; m++) { for (int n = 0; n < z[m].length; n++) { // (z_i = z[m][n]) // sample from p(z_i|z_-i, w) int topic = sampleFullConditional(m, n); z[m][n] = topic; } } if ((i < BURN_IN) && (i % THIN_INTERVAL == 0)) { // System.out.print("B"); dispcol++; } // display progress if ((i > BURN_IN) && (i % THIN_INTERVAL == 0)) { // System.out.print("S"); dispcol++; } // get statistics after burn-in if ((i > BURN_IN) && (SAMPLE_LAG > 0) && (i % SAMPLE_LAG == 0)) { updateParams(); // System.out.print("|"); if (i % THIN_INTERVAL != 0) dispcol++; } if (dispcol >= 100) { // System.out.println(); dispcol = 0; } } } /** * Sample a topic z_i from the full conditional distribution: p(z_i = j | * z_-i, w) = (n_-i,j(w_i) + beta)/(n_-i,j(.) + W * beta) * (n_-i,j(d_i) + * alpha)/(n_-i,.(d_i) + K * alpha) * * @param m * document * @param n * word */ private int sampleFullConditional(int m, int n) { // remove z_i from the count variables int topic = z[m][n]; nw[documents[m][n]][topic]--; nd[m][topic]--; nwsum[topic]--; ndsum[m]--; // do multinomial sampling via cumulative method: double[] p = new double[K]; for (int k = 0; k < K; k++) { p[k] = (nw[documents[m][n]][k] + beta) / (nwsum[k] + V * beta) * (n