NICE_VisLearning/optimization/mapestimation/MAPMultinomialGaussianPrior.cpp

/** 
* @file MAPMultinomialGaussianPrior.cpp
* @brief MAP estimation of multinomial with gaussian prior
* @author Erik Rodner
* @date 05/26/2008

*/

#include <iostream>
#include <limits>

#include "vislearning/optimization/mapestimation/MAPMultinomialGaussianPrior.h"

using namespace OBJREC;

using namespace std;
// refactor-nice.pl: check this substitution
// old: using namespace ice;
using namespace NICE;



MAPMultinomialGaussianPrior::MAPMultinomialGaussianPrior()
{
}

MAPMultinomialGaussianPrior::~MAPMultinomialGaussianPrior()
{
}

#undef DEBUG_OPTIMIZATION
#undef DEBUG_ANALYZEOPTIMIZATION

double MAPMultinomialGaussianPrior::objective ( 
		     // refactor-nice.pl: check this substitution
		     // old: const Vector & theta,
		     const NICE::Vector & theta,
		     // refactor-nice.pl: check this substitution
		     // old: const Vector & counts, 
		     const NICE::Vector & counts, 
		     // refactor-nice.pl: check this substitution
		     // old: const Vector & mu, 
		     const NICE::Vector & mu, 
		     double sigmaq )
{
    // calculate - log
    double sum_likelihood = 0.0;
    for ( size_t i = 0 ; i < counts.size() ; i++ )
    {
	double ti = (theta[i] < 10e-7) ? 10e-7 : theta[i];
	sum_likelihood += counts[i] * log( ti );
    }
    
    double sum_prior = 0.0;
    for ( size_t i = 0 ; i < counts.size() ; i++ )
    {
	double d = (theta[i] - mu[i]);
	sum_prior -= d*d*0.5;
    }
    
    sum_prior /= sigmaq;

#ifdef DEBUG_OPTIMIZATION
    fprintf (stderr, "log likelihood : %f\n", sum_likelihood );
    fprintf (stderr, "log prior : %f\n", sum_prior );
#endif

    return sum_likelihood + sum_prior;
}

// refactor-nice.pl: check this substitution
// old: void MAPMultinomialGaussianPrior::normalizeProb ( Vector & x )
void MAPMultinomialGaussianPrior::normalizeProb ( NICE::Vector & x )
{
    double sum = 0.0;
    for ( size_t i = 0 ; i < x.size() ; i++ )
	sum += x[i];

    for ( size_t i = 0 ; i < x.size() ; i++ )
	x[i] /= sum;
}

double MAPMultinomialGaussianPrior::estimateLambdaNonLinear (
    // refactor-nice.pl: check this substitution
    // old: const Vector & counts,
    const NICE::Vector & counts,
    // refactor-nice.pl: check this substitution
    // old: const Vector & mu,
    const NICE::Vector & mu,
    double sigmaq )
{
    // calculate lagrange multiplier
    const int maxIterations = 10000;
    const double eps = 10e-9;

    double count = 0;
    for ( size_t i = 0 ; i < counts.size() ; i++ )
	count += counts[i];

    double lambda = - count;
    int m = mu.size();
    int iterations = 0;
    double diff = 0.0;
    

    do {

	// calculate f(\lambda) = 0.5 m \lambda \sigma^2 + \sum_l \sqrt{ g_l(\lambda) } - 0.5
	double f = 0.5 * m * lambda * sigmaq - 0.5;
	double fabl = 0.5 * m * sigmaq;

	for ( size_t i = 0 ; i < mu.size() ; i++ )
	{
	    double phalf = 0.5 * (mu[i] + lambda * sigmaq);
	    double undersqrt = phalf*phalf + sigmaq * counts[i];
	    assert ( undersqrt >= 0.0 );
	    
	    // undersqrt = g_l(\lambda)

	    double gabl = (mu[i] + lambda * sigmaq) * sigmaq;
	    fabl -= gabl / sqrt(undersqrt);
	    f += sqrt(undersqrt);
	}

	double lambdanew = lambda - f / fabl;
	diff = fabs(f / fabl);

#ifdef DEBUG_OPTIMIZATION
	fprintf (stderr, "(%d) %f %f\n", iterations, lambdanew, diff );
#endif

	lambda = lambdanew;

#ifdef DEBUG_ANALYZEOPTIMIZATION

		// refactor-nice.pl: check this substitution
		// old: Vector theta (m);
		NICE::Vector theta (m);

	    // calculate theta estimation
		double sum = 0.0;
		for ( size_t i = 0 ; i < mu.size() ; i++ )
		{
		    double phalf = 0.5 * (mu[i] + lambda*sigmaq);
		    theta[i] = phalf + sqrt( phalf*phalf + sigmaq * counts[i] );
		    assert ( theta[i] >= 0.0 );
		    sum += theta[i];
		}

		fprintf (stderr, "OPT %f %d %f\n", sigmaq, iterations,
		    objective ( theta, counts, mu, sigmaq )  );
#endif
	iterations++;
    } while ( (iterations < maxIterations) && (diff > eps) );
    fprintf (stderr, "MAPMultinomialGaussianPrior: optimization finished (%d/%d iterations, %f last delta)\n",
	iterations, maxIterations, diff );
    fprintf (stderr, "MAPMultinomialGaussianPrior: final lambda: %f\n", lambda );

    return lambda;
}

double MAPMultinomialGaussianPrior::estimateLambda ( 
    // refactor-nice.pl: check this substitution
    // old: const Vector & counts, 
    const NICE::Vector & counts, 
    // refactor-nice.pl: check this substitution
    // old: const Vector & mu, 
    const NICE::Vector & mu, 
    double sigmaq )
{
    // calculate lagrange multiplier
    const int maxIterations = 10000;
    //const double eps = 10e-5;
    const double eps = 10e-9;

    int m = mu.size();

    double count = 0;
    for ( size_t i = 0 ; i < counts.size() ; i++ )
	count += counts[i];

    // using iterative formula, hopefully this converges
    double lambda = - count;
    double diff;
    int iterations = 0;

    do {
	// calculate evil sqrt sum
	double sqrsum = 0.0;
	for ( size_t i = 0 ; i < mu.size() ; i++ )
	{
	    double phalf = 0.5 * (mu[i] + lambda * sigmaq);
	    double undersqrt = phalf*phalf + sigmaq * counts[i];
	    assert ( undersqrt >= 0.0 );
	    sqrsum += sqrt( undersqrt );
	}
	double lambdanew = - (sqrsum - 0.5) * 2 / (sigmaq * m);

	diff = fabs(lambda-lambdanew);

#ifdef DEBUG_OPTIMIZATION
	fprintf (stderr, "(%d) %f %f\n", iterations, lambdanew, diff );
#endif
	lambda = lambdanew;

#ifdef DEBUG_ANALYZEOPTIMIZATION

		// refactor-nice.pl: check this substitution
		// old: Vector theta (m);
		NICE::Vector theta (m);
	    // calculate theta estimation
		double sum = 0.0;
		for ( size_t i = 0 ; i < mu.size() ; i++ )
		{
		    double phalf = 0.5 * (mu[i] + lambda*sigmaq);
		    theta[i] = phalf + sqrt( phalf*phalf + sigmaq * counts[i] );
		    assert ( theta[i] >= 0.0 );
		    sum += theta[i];
		}

		fprintf (stderr, "OPT %f %d %f\n", sigmaq, iterations,
		    objective ( theta, counts, mu, sigmaq )  );
#endif

	iterations++;
    } while ( (iterations < maxIterations) && (diff > eps) );
    fprintf (stderr, "MAPMultinomialGaussianPrior: optimization finished (%d/%d iterations, %f last delta)\n",
	iterations, maxIterations, diff );
    fprintf (stderr, "MAPMultinomialGaussianPrior: final lambda: %f\n", lambda );

    return lambda;
}

void MAPMultinomialGaussianPrior::estimate ( 
    // refactor-nice.pl: check this substitution
    // old: Vector & theta, 
    NICE::Vector & theta, 
    // refactor-nice.pl: check this substitution
    // old: const Vector & counts, 
    const NICE::Vector & counts, 
    // refactor-nice.pl: check this substitution
    // old: const Vector & mu, 
    const NICE::Vector & mu, 
    double sigmaq )
{
    theta = Vector(counts);
    normalizeProb ( theta );

#ifdef DEBUG_OPTIMIZATION
    fprintf (stderr, "objective (non informative) : %f\n",
	objective ( theta, counts, mu, sigmaq ) );
#endif

    double lambda = 1.0;
    if ( sigmaq < std::numeric_limits<double>::epsilon() )
    {
	fprintf (stderr, "MAPMultinomialGaussianPrior: skipping lambda optimization (sigmaq = 0.0)\n");
    } else {
	//lambda = estimateLambdaNonLinear ( counts, mu, sigmaq );
	lambda = estimateLambda ( counts, mu, sigmaq );
    }

    // calculate theta estimation
    double sum = 0.0;
    for ( size_t i = 0 ; i < mu.size() ; i++ )
    {
	double phalf = 0.5 * (mu[i] + lambda*sigmaq);
	theta[i] = phalf + sqrt( phalf*phalf + sigmaq * counts[i] );
	assert ( theta[i] >= 0.0 );
	sum += theta[i];
    }

#ifdef DEBUG_OPTIMIZATION
    fprintf (stderr, "objective (informative prior) : %f\n",
	objective ( theta, counts, mu, sigmaq ) );
    fprintf (stderr, "Sum over theta = %f\n", sum );
#endif
}

// refactor-nice.pl: check this substitution
// old: void MAPMultinomialGaussianPrior::estimate ( Vector & mapEstimate,
void MAPMultinomialGaussianPrior::estimate ( NICE::Vector & mapEstimate,
					     const VVector & likelihoodDistributionSamples,
			   		     const VVector & priorDistributionSamples,
					     double sigmaq )
{
    assert ( likelihoodDistributionSamples.size() == 1 );
    // refactor-nice.pl: check this substitution
    // old: const Vector & mlEstimate = likelihoodDistributionSamples[0];
    const NICE::Vector & mlEstimate = likelihoodDistributionSamples[0];

    // refactor-nice.pl: check this substitution
    // old: Vector mu;
    NICE::Vector mu;
    for ( VVector::const_iterator i = priorDistributionSamples.begin();
			    i != priorDistributionSamples.end(); i++ )
    {
	// refactor-nice.pl: check this substitution
	// old: const Vector & x = *i;
	const NICE::Vector & x = *i;
	if ( mu.size() == 0 ) mu = x;
	else                  mu = mu + x;
    }
    mu = mu * (1.0/priorDistributionSamples.size());

    mapEstimate.resize(mlEstimate.size());
    mapEstimate.set(0);
	    
    estimate ( mapEstimate, mlEstimate, mu, sigmaq );
    
    double sum = 0.0;
    for ( int i = 0 ; i < (int)mapEstimate.size() ; i++ )
	sum += mapEstimate[i];

    assert ( fabs(sum-1) < 10e-4 );
}