GMM.cpp

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/*
 * This program 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 3 of the License, or
 * (at your option) any later version.
 *
 * Written (W) 2011 Alesis Novik
 * Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
 */
#include <shogun/lib/config.h>

#ifdef HAVE_LAPACK

#include <shogun/clustering/GMM.h>
#include <shogun/clustering/KMeans.h>
#include <shogun/distance/EuclideanDistance.h>
#include <shogun/base/Parameter.h>
#include <shogun/mathematics/Math.h>
#include <shogun/mathematics/lapack.h>
#include <shogun/labels/MulticlassLabels.h>
#include <shogun/multiclass/KNN.h>

#include <vector>

using namespace shogun;
using namespace std;

CGMM::CGMM() : CDistribution(), m_components(), m_coefficients()
{
    register_params();
}

CGMM::CGMM(int32_t n, ECovType cov_type) : CDistribution(), m_components(), m_coefficients()
{
    m_coefficients.vector=SG_MALLOC(float64_t, n);
    m_coefficients.vlen=n;
    m_components = vector<CGaussian*>(n);

    for (int32_t i=0; i<n; i++)
    {
        m_components[i]=new CGaussian();
        SG_REF(m_components[i]);
        m_components[i]->set_cov_type(cov_type);
    }

    register_params();
}

CGMM::CGMM(vector<CGaussian*> components, SGVector<float64_t> coefficients, bool copy) : CDistribution()
{
    ASSERT(int32_t(components.size())==coefficients.vlen);

    if (!copy)
    {
        m_components=components;
        m_coefficients=coefficients;
        for (int32_t i=0; i<int32_t(components.size()); i++)
        {
            SG_REF(m_components[i]);
        }
    }
    else
    {
        m_coefficients = coefficients;
        m_components = vector<CGaussian*>(components.size());

        for (int32_t i=0; i<int32_t(components.size()); i++)
        {
            m_components[i]=new CGaussian();
            SG_REF(m_components[i]);
            m_components[i]->set_cov_type(components[i]->get_cov_type());

            SGVector<float64_t> old_mean=components[i]->get_mean();
            SGVector<float64_t> new_mean(old_mean.vlen);
            memcpy(new_mean.vector, old_mean.vector, old_mean.vlen*sizeof(float64_t));
            m_components[i]->set_mean(new_mean);

            SGVector<float64_t> old_d=components[i]->get_d();
            SGVector<float64_t> new_d(old_d.vlen);
            memcpy(new_d.vector, old_d.vector, old_d.vlen*sizeof(float64_t));
            m_components[i]->set_d(new_d);

            if (components[i]->get_cov_type()==FULL)
            {
                SGMatrix<float64_t> old_u=components[i]->get_u();
                SGMatrix<float64_t> new_u(old_u.num_rows, old_u.num_cols);
                memcpy(new_u.matrix, old_u.matrix, old_u.num_rows*old_u.num_cols*sizeof(float64_t));
                m_components[i]->set_u(new_u);
            }

            m_coefficients[i]=coefficients[i];
        }
    }

    register_params();
}

CGMM::~CGMM()
{
    if (!m_components.empty())
        cleanup();
}

void CGMM::cleanup()
{
    for (int32_t i = 0; i < int32_t(m_components.size()); i++)
        SG_UNREF(m_components[i]);

    m_components = vector<CGaussian*>();
    m_coefficients = SGVector<float64_t>();
}

bool CGMM::train(CFeatures* data)
{
    ASSERT(m_components.size() != 0);

    if (data)
    {
        if (!data->has_property(FP_DOT))
                SG_ERROR("Specified features are not of type CDotFeatures\n");
        set_features(data);
    }

    return true;
}

float64_t CGMM::train_em(float64_t min_cov, int32_t max_iter, float64_t min_change)
{
    if (!features)
        SG_ERROR("No features to train on.\n");

    CDotFeatures* dotdata=(CDotFeatures *) features;
    int32_t num_vectors=dotdata->get_num_vectors();

    SGMatrix<float64_t> alpha;

    if (m_components[0]->get_mean().vector==NULL)
    {
        CKMeans* init_k_means=new CKMeans(int32_t(m_components.size()), new CEuclideanDistance());
        init_k_means->train(dotdata);
        SGMatrix<float64_t> init_means=init_k_means->get_cluster_centers();

        alpha=alpha_init(init_means);

        SG_UNREF(init_k_means);

        max_likelihood(alpha, min_cov);
    }
    else
        alpha=SGMatrix<float64_t>(num_vectors,int32_t(m_components.size()));

    int32_t iter=0;
    float64_t log_likelihood_prev=0;
    float64_t log_likelihood_cur=0;
    float64_t* logPxy=SG_MALLOC(float64_t, num_vectors*m_components.size());
    float64_t* logPx=SG_MALLOC(float64_t, num_vectors);
    //float64_t* logPost=SG_MALLOC(float64_t, num_vectors*m_components.vlen);

    while (iter<max_iter)
    {
        log_likelihood_prev=log_likelihood_cur;
        log_likelihood_cur=0;

        for (int32_t i=0; i<num_vectors; i++)
        {
            logPx[i]=0;
            SGVector<float64_t> v=dotdata->get_computed_dot_feature_vector(i);
            for (int32_t j=0; j<int32_t(m_components.size()); j++)
            {
                logPxy[i*m_components.size()+j]=m_components[j]->compute_log_PDF(v)+CMath::log(m_coefficients[j]);
                logPx[i]+=CMath::exp(logPxy[i*m_components.size()+j]);
            }

            logPx[i]=CMath::log(logPx[i]);
            log_likelihood_cur+=logPx[i];

            for (int32_t j=0; j<int32_t(m_components.size()); j++)
            {
                //logPost[i*m_components.vlen+j]=logPxy[i*m_components.vlen+j]-logPx[i];
                alpha.matrix[i*m_components.size()+j]=CMath::exp(logPxy[i*m_components.size()+j]-logPx[i]);
            }
        }

        if (iter>0 && log_likelihood_cur-log_likelihood_prev<min_change)
            break;

        max_likelihood(alpha, min_cov);

        iter++;
    }

    SG_FREE(logPxy);
    SG_FREE(logPx);

    return log_likelihood_cur;
}

float64_t CGMM::train_smem(int32_t max_iter, int32_t max_cand, float64_t min_cov, int32_t max_em_iter, float64_t min_change)
{
    if (!features)
        SG_ERROR("No features to train on.\n");

    if (m_components.size()<3)
        SG_ERROR("Can't run SMEM with less than 3 component mixture model.\n");

    CDotFeatures* dotdata=(CDotFeatures *) features;
    int32_t num_vectors=dotdata->get_num_vectors();

    float64_t cur_likelihood=train_em(min_cov, max_em_iter, min_change);

    int32_t iter=0;
    float64_t* logPxy=SG_MALLOC(float64_t, num_vectors*m_components.size());
    float64_t* logPx=SG_MALLOC(float64_t, num_vectors);
    float64_t* logPost=SG_MALLOC(float64_t, num_vectors*m_components.size());
    float64_t* logPostSum=SG_MALLOC(float64_t, m_components.size());
    float64_t* logPostSum2=SG_MALLOC(float64_t, m_components.size());
    float64_t* logPostSumSum=SG_MALLOC(float64_t, m_components.size()*(m_components.size()-1)/2);
    float64_t* split_crit=SG_MALLOC(float64_t, m_components.size());
    float64_t* merge_crit=SG_MALLOC(float64_t, m_components.size()*(m_components.size()-1)/2);
    int32_t* split_ind=SG_MALLOC(int32_t, m_components.size());
    int32_t* merge_ind=SG_MALLOC(int32_t, m_components.size()*(m_components.size()-1)/2);

    while (iter<max_iter)
    {
        memset(logPostSum, 0, m_components.size()*sizeof(float64_t));
        memset(logPostSum2, 0, m_components.size()*sizeof(float64_t));
        memset(logPostSumSum, 0, (m_components.size()*(m_components.size()-1)/2)*sizeof(float64_t));
        for (int32_t i=0; i<num_vectors; i++)
        {
            logPx[i]=0;
            SGVector<float64_t> v=dotdata->get_computed_dot_feature_vector(i);
            for (int32_t j=0; j<int32_t(m_components.size()); j++)
            {
                logPxy[i*m_components.size()+j]=m_components[j]->compute_log_PDF(v)+CMath::log(m_coefficients[j]);
                logPx[i]+=CMath::exp(logPxy[i*m_components.size()+j]);
            }

            logPx[i]=CMath::log(logPx[i]);

            for (int32_t j=0; j<int32_t(m_components.size()); j++)
            {
                logPost[i*m_components.size()+j]=logPxy[i*m_components.size()+j]-logPx[i];
                logPostSum[j]+=CMath::exp(logPost[i*m_components.size()+j]);
                logPostSum2[j]+=CMath::exp(2*logPost[i*m_components.size()+j]);
            }

            int32_t counter=0;
            for (int32_t j=0; j<int32_t(m_components.size()); j++)
            {
                for (int32_t k=j+1; k<int32_t(m_components.size()); k++)
                {
                    logPostSumSum[counter]+=CMath::exp(logPost[i*m_components.size()+j]+logPost[i*m_components.size()+k]);
                    counter++;
                }
            }
        }

        int32_t counter=0;
        for (int32_t i=0; i<int32_t(m_components.size()); i++)
        {
            logPostSum[i]=CMath::log(logPostSum[i]);
            split_crit[i]=0;
            split_ind[i]=i;
            for (int32_t j=0; j<num_vectors; j++)
            {
                split_crit[i]+=(logPost[j*m_components.size()+i]-logPostSum[i]-logPxy[j*m_components.size()+i]+CMath::log(m_coefficients[i]))*
                                (CMath::exp(logPost[j*m_components.size()+i])/CMath::exp(logPostSum[i]));
            }
            for (int32_t j=i+1; j<int32_t(m_components.size()); j++)
            {
                merge_crit[counter]=CMath::log(logPostSumSum[counter])-(0.5*CMath::log(logPostSum2[i]))-(0.5*CMath::log(logPostSum2[j]));
                merge_ind[counter]=i*m_components.size()+j;
                counter++;
            }
        }
        CMath::qsort_backward_index(split_crit, split_ind, int32_t(m_components.size()));
        CMath::qsort_backward_index(merge_crit, merge_ind, int32_t(m_components.size()*(m_components.size()-1)/2));

        bool better_found=false;
        int32_t candidates_checked=0;
        for (int32_t i=0; i<int32_t(m_components.size()); i++)
        {
            for (int32_t j=0; j<int32_t(m_components.size()*(m_components.size()-1)/2); j++)
            {
                if (merge_ind[j]/int32_t(m_components.size()) != split_ind[i] && int32_t(merge_ind[j]%m_components.size()) != split_ind[i])
                {
                    candidates_checked++;
                    CGMM* candidate=new CGMM(m_components, m_coefficients, true);
                    candidate->train(features);
                    candidate->partial_em(split_ind[i], merge_ind[j]/int32_t(m_components.size()), merge_ind[j]%int32_t(m_components.size()), min_cov, max_em_iter, min_change);
                    float64_t cand_likelihood=candidate->train_em(min_cov, max_em_iter, min_change);

                    if (cand_likelihood>cur_likelihood)
                    {
                        cur_likelihood=cand_likelihood;
                        set_comp(candidate->get_comp());
                        set_coef(candidate->get_coef());

                        for (int32_t k=0; k<int32_t(candidate->get_comp().size()); k++)
                        {
                            SG_UNREF(candidate->get_comp()[k]);
                        }

                        better_found=true;
                        break;
                    }
                    else
                        delete candidate;

                    if (candidates_checked>=max_cand)
                        break;
                }
            }
            if (better_found || candidates_checked>=max_cand)
                break;
        }
        if (!better_found)
            break;
        iter++;
    }

    SG_FREE(logPxy);
    SG_FREE(logPx);
    SG_FREE(logPost);
    SG_FREE(split_crit);
    SG_FREE(merge_crit);
    SG_FREE(logPostSum);
    SG_FREE(logPostSum2);
    SG_FREE(logPostSumSum);
    SG_FREE(split_ind);
    SG_FREE(merge_ind);

    return cur_likelihood;
}

void CGMM::partial_em(int32_t comp1, int32_t comp2, int32_t comp3, float64_t min_cov, int32_t max_em_iter, float64_t min_change)
{
    CDotFeatures* dotdata=(CDotFeatures *) features;
    int32_t num_vectors=dotdata->get_num_vectors();

    float64_t* init_logPxy=SG_MALLOC(float64_t, num_vectors*m_components.size());
    float64_t* init_logPx=SG_MALLOC(float64_t, num_vectors);
    float64_t* init_logPx_fix=SG_MALLOC(float64_t, num_vectors);
    float64_t* post_add=SG_MALLOC(float64_t, num_vectors);

    for (int32_t i=0; i<num_vectors; i++)
    {
        init_logPx[i]=0;
        init_logPx_fix[i]=0;

        SGVector<float64_t> v=dotdata->get_computed_dot_feature_vector(i);
        for (int32_t j=0; j<int32_t(m_components.size()); j++)
        {
            init_logPxy[i*m_components.size()+j]=m_components[j]->compute_log_PDF(v)+CMath::log(m_coefficients[j]);
            init_logPx[i]+=CMath::exp(init_logPxy[i*m_components.size()+j]);
            if (j!=comp1 && j!=comp2 && j!=comp3)
            {
                init_logPx_fix[i]+=CMath::exp(init_logPxy[i*m_components.size()+j]);
            }
        }

        init_logPx[i]=CMath::log(init_logPx[i]);
        post_add[i]=CMath::log(CMath::exp(init_logPxy[i*m_components.size()+comp1]-init_logPx[i])+
                    CMath::exp(init_logPxy[i*m_components.size()+comp2]-init_logPx[i])+
                    CMath::exp(init_logPxy[i*m_components.size()+comp3]-init_logPx[i]));
    }

    vector<CGaussian*> components(3);
    SGVector<float64_t> coefficients(3);
    components[0]=m_components[comp1];
    components[1]=m_components[comp2];
    components[2]=m_components[comp3];
    coefficients.vector[0]=m_coefficients.vector[comp1];
    coefficients.vector[1]=m_coefficients.vector[comp2];
    coefficients.vector[2]=m_coefficients.vector[comp3];
    float64_t coef_sum=coefficients.vector[0]+coefficients.vector[1]+coefficients.vector[2];

    int32_t dim_n=components[0]->get_mean().vlen;

    float64_t alpha1=coefficients.vector[1]/(coefficients.vector[1]+coefficients.vector[2]);
    float64_t alpha2=coefficients.vector[2]/(coefficients.vector[1]+coefficients.vector[2]);

    float64_t noise_mag=SGVector<float64_t>::twonorm(components[0]->get_mean().vector, dim_n)*0.1/
                        CMath::sqrt((float64_t)dim_n);

    SGVector<float64_t>::add(components[1]->get_mean().vector, alpha1, components[1]->get_mean().vector, alpha2,
                components[2]->get_mean().vector, dim_n);

    for (int32_t i=0; i<dim_n; i++)
    {
        components[2]->get_mean().vector[i]=components[0]->get_mean().vector[i]+CMath::randn_double()*noise_mag;
        components[0]->get_mean().vector[i]=components[0]->get_mean().vector[i]+CMath::randn_double()*noise_mag;
    }

    coefficients.vector[1]=coefficients.vector[1]+coefficients.vector[2];
    coefficients.vector[2]=coefficients.vector[0]*0.5;
    coefficients.vector[0]=coefficients.vector[0]*0.5;

    if (components[0]->get_cov_type()==FULL)
    {
        SGMatrix<float64_t> c1=components[1]->get_cov();
        SGMatrix<float64_t> c2=components[2]->get_cov();
        SGVector<float64_t>::add(c1.matrix, alpha1, c1.matrix, alpha2, c2.matrix, dim_n*dim_n);

        components[1]->set_d(SGVector<float64_t>(SGMatrix<float64_t>::compute_eigenvectors(c1.matrix, dim_n, dim_n), dim_n));
        components[1]->set_u(c1);

        float64_t new_d=0;
        for (int32_t i=0; i<dim_n; i++)
        {
            new_d+=CMath::log(components[0]->get_d().vector[i]);
            for (int32_t j=0; j<dim_n; j++)
            {
                if (i==j)
                {
                    components[0]->get_u().matrix[i*dim_n+j]=1;
                    components[2]->get_u().matrix[i*dim_n+j]=1;
                }
                else
                {
                    components[0]->get_u().matrix[i*dim_n+j]=0;
                    components[2]->get_u().matrix[i*dim_n+j]=0;
                }
            }
        }
        new_d=CMath::exp(new_d*(1./dim_n));
        for (int32_t i=0; i<dim_n; i++)
        {
            components[0]->get_d().vector[i]=new_d;
            components[2]->get_d().vector[i]=new_d;
        }
    }
    else if(components[0]->get_cov_type()==DIAG)
    {
        SGVector<float64_t>::add(components[1]->get_d().vector, alpha1, components[1]->get_d().vector,
                    alpha2, components[2]->get_d().vector, dim_n);

        float64_t new_d=0;
        for (int32_t i=0; i<dim_n; i++)
        {
            new_d+=CMath::log(components[0]->get_d().vector[i]);
        }
        new_d=CMath::exp(new_d*(1./dim_n));
        for (int32_t i=0; i<dim_n; i++)
        {
            components[0]->get_d().vector[i]=new_d;
            components[2]->get_d().vector[i]=new_d;
        }
    }
    else if(components[0]->get_cov_type()==SPHERICAL)
    {
        components[1]->get_d().vector[0]=alpha1*components[1]->get_d().vector[0]+
                                                alpha2*components[2]->get_d().vector[0];

        components[2]->get_d().vector[0]=components[0]->get_d().vector[0];
    }

    CGMM* partial_candidate=new CGMM(components, coefficients);
    partial_candidate->train(features);

    float64_t log_likelihood_prev=0;
    float64_t log_likelihood_cur=0;
    int32_t iter=0;
    SGMatrix<float64_t> alpha(num_vectors, 3);
    float64_t* logPxy=SG_MALLOC(float64_t, num_vectors*3);
    float64_t* logPx=SG_MALLOC(float64_t, num_vectors);
    //float64_t* logPost=SG_MALLOC(float64_t, num_vectors*m_components.vlen);

    while (iter<max_em_iter)
    {
        log_likelihood_prev=log_likelihood_cur;
        log_likelihood_cur=0;

        for (int32_t i=0; i<num_vectors; i++)
        {
            logPx[i]=0;
            SGVector<float64_t> v=dotdata->get_computed_dot_feature_vector(i);
            for (int32_t j=0; j<3; j++)
            {
                logPxy[i*3+j]=components[j]->compute_log_PDF(v)+CMath::log(coefficients[j]);
                logPx[i]+=CMath::exp(logPxy[i*3+j]);
            }

            logPx[i]=CMath::log(logPx[i]+init_logPx_fix[i]);
            log_likelihood_cur+=logPx[i];

            for (int32_t j=0; j<3; j++)
            {
                //logPost[i*m_components.vlen+j]=logPxy[i*m_components.vlen+j]-logPx[i];
                alpha.matrix[i*3+j]=CMath::exp(logPxy[i*3+j]-logPx[i]+post_add[i]);
            }
        }

        if (iter>0 && log_likelihood_cur-log_likelihood_prev<min_change)
            break;

        partial_candidate->max_likelihood(alpha, min_cov);
        partial_candidate->get_coef().vector[0]=partial_candidate->get_coef().vector[0]*coef_sum;
        partial_candidate->get_coef().vector[1]=partial_candidate->get_coef().vector[1]*coef_sum;
        partial_candidate->get_coef().vector[2]=partial_candidate->get_coef().vector[2]*coef_sum;
        iter++;
    }

    m_coefficients.vector[comp1]=coefficients.vector[0];
    m_coefficients.vector[comp2]=coefficients.vector[1];
    m_coefficients.vector[comp3]=coefficients.vector[2];

    delete partial_candidate;
    SG_FREE(logPxy);
    SG_FREE(logPx);
    SG_FREE(init_logPxy);
    SG_FREE(init_logPx);
    SG_FREE(init_logPx_fix);
    SG_FREE(post_add);
}

void CGMM::max_likelihood(SGMatrix<float64_t> alpha, float64_t min_cov)
{
    CDotFeatures* dotdata=(CDotFeatures *) features;
    int32_t num_dim=dotdata->get_dim_feature_space();

    float64_t alpha_sum;
    float64_t alpha_sum_sum=0;
    float64_t* mean_sum;
    float64_t* cov_sum=NULL;

    for (int32_t i=0; i<alpha.num_cols; i++)
    {
        alpha_sum=0;
        mean_sum=SG_MALLOC(float64_t, num_dim);
        memset(mean_sum, 0, num_dim*sizeof(float64_t));

        for (int32_t j=0; j<alpha.num_rows; j++)
        {
            alpha_sum+=alpha.matrix[j*alpha.num_cols+i];
            SGVector<float64_t> v=dotdata->get_computed_dot_feature_vector(j);
            SGVector<float64_t>::add(mean_sum, alpha.matrix[j*alpha.num_cols+i], v.vector, 1, mean_sum, v.vlen);
        }

        for (int32_t j=0; j<num_dim; j++)
            mean_sum[j]/=alpha_sum;

        m_components[i]->set_mean(SGVector<float64_t>(mean_sum, num_dim));

        ECovType cov_type=m_components[i]->get_cov_type();

        if (cov_type==FULL)
        {
            cov_sum=SG_MALLOC(float64_t, num_dim*num_dim);
            memset(cov_sum, 0, num_dim*num_dim*sizeof(float64_t));
        }
        else if(cov_type==DIAG)
        {
            cov_sum=SG_MALLOC(float64_t, num_dim);
            memset(cov_sum, 0, num_dim*sizeof(float64_t));
        }
        else if(cov_type==SPHERICAL)
        {
            cov_sum=SG_MALLOC(float64_t, 1);
            cov_sum[0]=0;
        }

        for (int32_t j=0; j<alpha.num_rows; j++)
        {
            SGVector<float64_t> v=dotdata->get_computed_dot_feature_vector(j);
            SGVector<float64_t>::add(v.vector, 1, v.vector, -1, mean_sum, v.vlen);

            switch (cov_type)
            {
                case FULL:
                    cblas_dger(CblasRowMajor, num_dim, num_dim, alpha.matrix[j*alpha.num_cols+i], v.vector, 1, v.vector,
                                 1, (double*) cov_sum, num_dim);

                    break;
                case DIAG:
                    for (int32_t k=0; k<num_dim; k++)
                        cov_sum[k]+=v.vector[k]*v.vector[k]*alpha.matrix[j*alpha.num_cols+i];

                    break;
                case SPHERICAL:
                    float64_t temp=0;

                    for (int32_t k=0; k<num_dim; k++)
                        temp+=v.vector[k]*v.vector[k];

                    cov_sum[0]+=temp*alpha.matrix[j*alpha.num_cols+i];
                    break;
            }
        }

        switch (cov_type)
        {
            case FULL:
                for (int32_t j=0; j<num_dim*num_dim; j++)
                    cov_sum[j]/=alpha_sum;

                float64_t* d0;
                d0=SGMatrix<float64_t>::compute_eigenvectors(cov_sum, num_dim, num_dim);
                for (int32_t j=0; j<num_dim; j++)
                    d0[j]=CMath::max(min_cov, d0[j]);

                m_components[i]->set_d(SGVector<float64_t>(d0, num_dim));
                m_components[i]->set_u(SGMatrix<float64_t>(cov_sum, num_dim, num_dim));

                break;
            case DIAG:
                for (int32_t j=0; j<num_dim; j++)
                {
                    cov_sum[j]/=alpha_sum;
                    cov_sum[j]=CMath::max(min_cov, cov_sum[j]);
                }

                m_components[i]->set_d(SGVector<float64_t>(cov_sum, num_dim));

                break;
            case SPHERICAL:
                cov_sum[0]/=alpha_sum*num_dim;
                cov_sum[0]=CMath::max(min_cov, cov_sum[0]);

                m_components[i]->set_d(SGVector<float64_t>(cov_sum, 1));

                break;
        }

        m_coefficients.vector[i]=alpha_sum;
        alpha_sum_sum+=alpha_sum;
    }

    for (int32_t i=0; i<alpha.num_cols; i++)
        m_coefficients.vector[i]/=alpha_sum_sum;
}

int32_t CGMM::get_num_model_parameters()
{
    return 1;
}

float64_t CGMM::get_log_model_parameter(int32_t num_param)
{
    ASSERT(num_param==1);

    return CMath::log(m_components.size());
}

float64_t CGMM::get_log_derivative(int32_t num_param, int32_t num_example)
{
    SG_NOTIMPLEMENTED;
    return 0;
}

float64_t CGMM::get_log_likelihood_example(int32_t num_example)
{
    SG_NOTIMPLEMENTED;
    return 1;
}

float64_t CGMM::get_likelihood_example(int32_t num_example)
{
    return CMath::exp(get_log_likelihood_example(num_example));
}

SGVector<float64_t> CGMM::get_nth_mean(int32_t num)
{
    ASSERT(num<int32_t(m_components.size()));
    return m_components[num]->get_mean();
}

void CGMM::set_nth_mean(SGVector<float64_t> mean, int32_t num)
{
    ASSERT(num<int32_t(m_components.size()));
    m_components[num]->set_mean(mean);
}

SGMatrix<float64_t> CGMM::get_nth_cov(int32_t num)
{
    ASSERT(num<int32_t(m_components.size()));
    return m_components[num]->get_cov();
}

void CGMM::set_nth_cov(SGMatrix<float64_t> cov, int32_t num)
{
    ASSERT(num<int32_t(m_components.size()));
    m_components[num]->set_cov(cov);
}

SGVector<float64_t> CGMM::get_coef()
{
    return m_coefficients;
}

void CGMM::set_coef(const SGVector<float64_t> coefficients)
{
    m_coefficients=coefficients;
}

vector<CGaussian*> CGMM::get_comp()
{
    return m_components;
}

void CGMM::set_comp(vector<CGaussian*> components)
{
    for (int32_t i=0; i<int32_t(m_components.size()); i++)
    {
        SG_UNREF(m_components[i]);
    }

    m_components=components;

    for (int32_t i=0; i<int32_t(m_components.size()); i++)
    {
        SG_REF(m_components[i]);
    }
}

SGMatrix<float64_t> CGMM::alpha_init(SGMatrix<float64_t> init_means)
{
    CDotFeatures* dotdata=(CDotFeatures *) features;
    int32_t num_vectors=dotdata->get_num_vectors();

    SGVector<float64_t> label_num(init_means.num_cols);

    for (int32_t i=0; i<init_means.num_cols; i++)
        label_num.vector[i]=i;

    CKNN* knn=new CKNN(1, new CEuclideanDistance(), new CMulticlassLabels(label_num));
    knn->train(new CDenseFeatures<float64_t>(init_means));
    CMulticlassLabels* init_labels=(CMulticlassLabels*) knn->apply(features);

    SGMatrix<float64_t> alpha(num_vectors, int32_t(m_components.size()));
    memset(alpha.matrix, 0, num_vectors*m_components.size()*sizeof(float64_t));

    for (int32_t i=0; i<num_vectors; i++)
        alpha.matrix[i*m_components.size()+init_labels->get_int_label(i)]=1;

    SG_UNREF(init_labels);

    return alpha;
}

SGVector<float64_t> CGMM::sample()
{
    ASSERT(m_components.size());
    float64_t rand_num=CMath::random(float64_t(0), float64_t(1));
    float64_t cum_sum=0;
    for (int32_t i=0; i<m_coefficients.vlen; i++)
    {
        cum_sum+=m_coefficients.vector[i];
        if (cum_sum>=rand_num)
            return m_components[i]->sample();
    }
    return m_components[m_coefficients.vlen-1]->sample();
}

SGVector<float64_t> CGMM::cluster(SGVector<float64_t> point)
{
    SGVector<float64_t> answer(m_components.size()+1);
    answer.vector[m_components.size()]=0;

    for (int32_t i=0; i<int32_t(m_components.size()); i++)
    {
        answer.vector[i]=m_components[i]->compute_log_PDF(point)+CMath::log(m_coefficients[i]);
        answer.vector[m_components.size()]+=CMath::exp(answer.vector[i]);
    }
    answer.vector[m_components.size()]=CMath::log(answer.vector[m_components.size()]);

    return answer;
}

void CGMM::register_params()
{
    m_parameters->add((SGVector<CSGObject*>*) &m_components, "m_components", "Mixture components");
    m_parameters->add(&m_coefficients, "m_coefficients", "Mixture coefficients.");
}

#endif