lda-inference.c

latent dirichlet allocation的C实现代码

// (C) Copyright 2004, David M. Blei (blei [at] cs [dot] cmu [dot] edu)

// This file is part of LDA-C.

// LDA-C 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.

// LDA-C 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

#include "lda-inference.h"

/*
 * variational inference
 *
 */

double lda_inference(document* doc, lda_model* model, double* var_gamma, double** phi)
{
    double converged = 1;
    double phisum = 0, likelihood = 0;
    double likelihood_old = 0, oldphi[model->num_topics];
    int k, n, var_iter;
    double digamma_gam[model->num_topics];

    // compute posterior dirichlet

    for (k = 0; k < model->num_topics; k++)
    {
        var_gamma[k] = model->alpha + (doc->total/((double) model->num_topics));
        digamma_gam[k] = digamma(var_gamma[k]);
        for (n = 0; n < doc->length; n++)
            phi[n][k] = 1.0/model->num_topics;
    }
    var_iter = 0;

    while ((converged > VAR_CONVERGED) &&
           ((var_iter < VAR_MAX_ITER) || (VAR_MAX_ITER == -1)))
    {
	var_iter++;
	for (n = 0; n < doc->length; n++)
	{
            phisum = 0;
            for (k = 0; k < model->num_topics; k++)
            {
                oldphi[k] = phi[n][k];
                phi[n][k] =
                    digamma_gam[k] +
                    model->log_prob_w[k][doc->words[n]];

                if (k > 0)
                    phisum = log_sum(phisum, phi[n][k]);
                else
                    phisum = phi[n][k]; // note, phi is in log space
            }

            for (k = 0; k < model->num_topics; k++)
            {
                phi[n][k] = exp(phi[n][k] - phisum);
                var_gamma[k] =
                    var_gamma[k] + doc->counts[n]*(phi[n][k] - oldphi[k]);
                // !!! a lot of extra digamma's here because of how we're computing it
                // !!! but its more automatically updated too.
                digamma_gam[k] = digamma(var_gamma[k]);
            }
        }

        likelihood = compute_likelihood(doc, model, phi, var_gamma);
        assert(!isnan(likelihood));
        converged = (likelihood_old - likelihood) / likelihood_old;
        likelihood_old = likelihood;

        // printf("[LDA INF] %8.5f %1.3e\n", likelihood, converged);
    }
    return(likelihood);
}


/*
 * compute likelihood bound
 *
 */

double
compute_likelihood(document* doc, lda_model* model, double** phi, double* var_gamma)
{
    double likelihood = 0, digsum = 0, var_gamma_sum = 0, dig[model->num_topics];
    int k, n;

    for (k = 0; k < model->num_topics; k++)
    {
	dig[k] = digamma(var_gamma[k]);
	var_gamma_sum += var_gamma[k];
    }
    digsum = digamma(var_gamma_sum);

    likelihood =
	lgamma(model->alpha * model -> num_topics)
	- model -> num_topics * lgamma(model->alpha)
	- (lgamma(var_gamma_sum));

    for (k = 0; k < model->num_topics; k++)
    {
	likelihood +=
	    (model->alpha - 1)*(dig[k] - digsum) + lgamma(var_gamma[k])
	    - (var_gamma[k] - 1)*(dig[k] - digsum);

	for (n = 0; n < doc->length; n++)
	{
            if (phi[n][k] > 0)
            {
                likelihood += doc->counts[n]*
                    (phi[n][k]*((dig[k] - digsum) - log(phi[n][k])
                                + model->log_prob_w[k][doc->words[n]]));
            }
        }
    }
    return(likelihood);
}