LdaGibbsSampler.java lda代碼

/* * (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 org.knowceans.gibbstest;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     */    private 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)                * (nd[m][k] + alpha) / (ndsum[m] + K * alpha);        }        // cumulate multinomial parameters        for (int k = 1; k < p.length; k++) {            p[k] += p[k - 1];        }        // scaled sample because of unnormalised p[]        double u = Math.random() * p[K - 1];        for (topic = 0; topic < p.length; topic++) {            if (u < p[topic])                break;        }        // add newly estimated z_i to count variables        nw[documents[m][n]][topic]++;        nd[m][topic]++;        nwsum[topic]++;        ndsum[m]++;        return topic;    }    /**     * Add to the statistics the values of theta and phi for the current state.     */    private void updateParams() {        for (int m = 0; m < documents.length; m++) {            for (int k = 0; k < K; k++) {                thetasum[m][k] += (nd[m][k] + alpha) / (ndsum[m] + K * alpha);            }        }        for (int k = 0; k < K; k++) {            for (int w = 0; w < V; w++) {                phisum[k][w] += (nw[w][k] + beta) / (nwsum[k] + V * beta);            }        }        numstats++;    }    /**     * Retrieve estimated document--topic associations. If sample lag > 0 then     * the mean value of all sampled statistics for theta[][] is taken.     *      * @return theta multinomial mixture of document topics (M x K)     */    public double[][] getTheta() {        double[][] theta = new double[documents.length][K];        if (SAMPLE_LAG > 0) {            for (int m = 0; m < documents.length; m++) {                for (int k = 0; k < K; k++) {                    theta[m][k] = thetasum[m][k] / numstats;                }            }        } else {            for (int m = 0; m < documents.length; m++) {                for (int k = 0; k < K; k++) {                    theta[m][k] = (nd[m][k] + alpha) / (ndsum[m] + K * alpha);                }            }        }        return theta;    }    /**     * Retrieve estimated topic--word associations. If sample lag > 0 then the     * mean value of all sampled statistics for phi[][] is taken.     *      * @return phi multinomial mixture of topic words (K x V)     */    public double[][] getPhi() {        double[][] phi = new double[K][V];        if (SAMPLE_LAG > 0) {            for (int k = 0; k < K; k++) {                for (int w = 0; w < V; w++) {                    phi[k][w] = phisum[k][w] / numstats;                }            }        } else {            for (int k = 0; k < K; k++) {                for (int w = 0; w < V; w++) {                    phi[k][w] = (nw[w][k] + beta) / (nwsum[k] + V * beta);                }            }        }        return phi;    }    /**     * Print table of multinomial data     *      * @param data     *            vector of evidence     * @param fmax     *            max frequency in display     * @return the scaled histogram bin values     */    public static void hist(double[] data, int fmax) {        double[] hist = new double[data.length];        // scale maximum        double hmax = 0;        for (int i = 0; i < data.length; i++) {            hmax = Math.max(data[i], hmax);        }        double shrink = fmax / hmax;        for (int i = 0; i < data.length; i++) {            hist[i] = shrink * data[i];        }        NumberFormat nf = new DecimalFormat("00");        String scale = "";        for (int i = 1; i < fmax / 10 + 1; i++) {            scale += "    .    " + i % 10;        }        System.out.println("x" + nf.format(hmax / fmax) + "\t0" + scale);        for (int i = 0; i < hist.length; i++) {            System.out.print(i + "\t|");            for (int j = 0; j < Math.round(hist[i]); j++) {                if ((j + 1) % 10 == 0)                    System.out.print("]");                else                    System.out.print("|");            }            System.out.println();        }    }    /**     * Configure the gibbs sampler     *      * @param iterations     *            number of total iterations     * @param burnIn     *            number of burn-in iterations     * @param thinInterval     *            update statistics interval     * @param sampleLag     *            sample interval (-1 for just one sample at the end)     */    public void configure(int iterations, int burnIn, int thinInterval,        int sampleLag) {        ITERATIONS = iterations;        BURN_IN = burnIn;        THIN_INTERVAL = thinInterval;        SAMPLE_LAG = sampleLag;    }    /**     * Driver with example data.     *      * @param args     */    public static void main(String[] args) {        // words in documents        int[][] documents = { {1, 4, 3, 2, 3, 1, 4, 3, 2, 3, 1, 4, 3, 2, 3, 6},            {2, 2, 4, 2, 4, 2, 2, 2, 2, 4, 2, 2},            {1, 6, 5, 6, 0, 1, 6, 5, 6, 0, 1, 6, 5, 6, 0, 0},            {5, 6, 6, 2, 3, 3, 6, 5, 6, 2, 2, 6, 5, 6, 6, 6, 0},            {2, 2, 4, 4, 4, 4, 1, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 0},            {5, 4, 2, 3, 4, 5, 6, 6, 5, 4, 3, 2}};        // vocabulary        int V = 7;        int M = documents.length;        // # topics        int K = 2;        // good values alpha = 2, beta = .5        double alpha = 2;        double beta = .5;        System.out.println("Latent Dirichlet Allocation using Gibbs Sampling.");        LdaGibbsSampler lda = new LdaGibbsSampler(documents, V);        lda.configure(10000, 2000, 100, 10);        lda.gibbs(K, alpha, beta);        double[][] theta = lda.getTheta();        double[][] phi = lda.getPhi();        System.out.println();        System.out.println();        System.out.println("Document--Topic Associations, Theta[d][k] (alpha="            + alpha + ")");        System.out.print("d\\k\t");        for (int m = 0; m < theta[0].length; m++) {            System.out.print("   " + m % 10 + "    ");        }        System.out.println();        for (int m = 0; m < theta.length; m++) {            System.out.print(m + "\t");            for (int k = 0; k < theta[m].length; k++) {                // System.out.print(theta[m][k] + " ");                System.out.print(shadeDouble(theta[m][k], 1) + " ");            }            System.out.println();        }        System.out.println();        System.out.println("Topic--Term Associations, Phi[k][w] (beta=" + beta            + ")");        System.out.print("k\\w\t");        for (int w = 0; w < phi[0].length; w++) {            System.out.print("   " + w % 10 + "    ");        }        System.out.println();        for (int k = 0; k < phi.length; k++) {            System.out.print(k + "\t");            for (int w = 0; w < phi[k].length; w++) {                // System.out.print(phi[k][w] + " ");                System.out.print(shadeDouble(phi[k][w], 1) + " ");            }            System.out.println();        }    }    static String[] shades = {"     ", ".    ", ":    ", ":.   ", "::   ",        "::.  ", ":::  ", ":::. ", ":::: ", "::::.", ":::::"};    static NumberFormat lnf = new DecimalFormat("00E0");    /**     * create a string representation whose gray value appears as an indicator     * of magnitude, cf. Hinton diagrams in statistics.     *      * @param d     *            value     * @param max     *            maximum value     * @return     */    public static String shadeDouble(double d, double max) {        int a = (int) Math.floor(d * 10 / max + 0.5);        if (a > 10 || a < 0) {            String x = lnf.format(d);            a = 5 - x.length();            for (int i = 0; i < a; i++) {                x += " ";            }            return "<" + x + ">";        }        return "[" + shades[a] + "]";    }}