KMeans.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) 1999-2008 Gunnar Raetsch
 * Written (W) 2007-2009 Soeren Sonnenburg
 * Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
 */

#include <shogun/clustering/KMeans.h>
#include <shogun/distance/Distance.h>
#include <shogun/features/Labels.h>
#include <shogun/features/SimpleFeatures.h>
#include <shogun/mathematics/Math.h>
#include <shogun/base/Parallel.h>

#ifdef HAVE_PTHREAD
#include <pthread.h>
#endif

#define MUSRECALC

#define PAR_THRESH  10

using namespace shogun;

CKMeans::CKMeans()
: CDistanceMachine()
{
    init();
}

CKMeans::CKMeans(int32_t k_, CDistance* d)
: CDistanceMachine()
{
    init();
    k=k_;
    set_distance(d);
}

CKMeans::~CKMeans()
{
    R.destroy_vector();
}

bool CKMeans::train_machine(CFeatures* data)
{
    ASSERT(distance);

    if (data)
        distance->init(data, data);

    ASSERT(distance->get_feature_type()==F_DREAL);

    CSimpleFeatures<float64_t>* lhs=
            (CSimpleFeatures<float64_t>*)distance->get_lhs();
    ASSERT(lhs);
    int32_t num=lhs->get_num_vectors();
    SG_UNREF(lhs);

    Weights=SGVector<float64_t>(num);
    for (int32_t i=0; i<num; i++)
        Weights.vector[i]=1.0;

    clustknb(false, NULL);
    Weights.destroy_vector();

    return true;
}

bool CKMeans::load(FILE* srcfile)
{
    SG_SET_LOCALE_C;
    SG_RESET_LOCALE;
    return false;
}

bool CKMeans::save(FILE* dstfile)
{
    SG_SET_LOCALE_C;
    SG_RESET_LOCALE;
    return false;
}

#ifndef DOXYGEN_SHOULD_SKIP_THIS
struct thread_data
{
    float64_t* x;
    CSimpleFeatures<float64_t>* y;
    float64_t* z;
    int32_t n1, n2, m;
    int32_t js, je; /* defines the matrix stripe */
    int32_t offs;
};
#endif // DOXYGEN_SHOULD_SKIP_THIS

namespace shogun
{
void *sqdist_thread_func(void * P)
{
    struct thread_data *TD=(struct thread_data*) P;
    float64_t* x=TD->x;
    CSimpleFeatures<float64_t>* y=TD->y;
    float64_t* z=TD->z;
    int32_t n1=TD->n1,
        m=TD->m,
        js=TD->js,
        je=TD->je,
        offs=TD->offs,
        j,i,k;

    for (j=js; j<je; j++)
    {
        int32_t vlen=0;
        bool vfree=false;
        float64_t* vec=y->get_feature_vector(j+offs, vlen, vfree);

        for (i=0; i<n1; i++)
        {
            float64_t sum=0;
            for (k=0; k<m; k++) 
                sum = sum + CMath::sq(x[i*m + k] - vec[k]);
            z[j*n1 + i] = sum;
        }

        y->free_feature_vector(vec, j, vfree);
    }
    return NULL;
} 
}

void CKMeans::clustknb(bool use_old_mus, float64_t *mus_start)
{
    ASSERT(distance && distance->get_feature_type()==F_DREAL);
    CSimpleFeatures<float64_t>* lhs = (CSimpleFeatures<float64_t>*) distance->get_lhs();
    ASSERT(lhs && lhs->get_num_features()>0 && lhs->get_num_vectors()>0);

    int32_t XSize=lhs->get_num_vectors();
    dimensions=lhs->get_num_features();
    int32_t i, changed=1;
    const int32_t XDimk=dimensions*k;
    int32_t iter=0;

    R.destroy_vector();
    R=SGVector<float64_t>(k);

    mus=SGMatrix<float64_t>(dimensions, k);

    int32_t *ClList=SG_CALLOC(int32_t, XSize);
    float64_t *weights_set=SG_CALLOC(float64_t, k);
    float64_t *dists=SG_CALLOC(float64_t, k*XSize);

    CSimpleFeatures<float64_t>* rhs_mus = new CSimpleFeatures<float64_t>(0);
    CFeatures* rhs_cache = distance->replace_rhs(rhs_mus);
 
    int32_t vlen=0;
    bool vfree=false;
    float64_t* vec=NULL;

    /* ClList=zeros(XSize,1) ; */
    memset(ClList, 0, sizeof(int32_t)*XSize);
    /* weights_set=zeros(k,1) ; */
    memset(weights_set, 0, sizeof(float64_t)*k);

    /* cluster_centers=zeros(dimensions, k) ; */
    memset(mus.matrix, 0, sizeof(float64_t)*XDimk);

    if (!use_old_mus)
    {
        for (i=0; i<XSize; i++) 
        {
            const int32_t Cl=CMath::random(0, k-1);
            int32_t j;
            float64_t weight=Weights.vector[i];

            weights_set[Cl]+=weight;
            ClList[i]=Cl;

            vec=lhs->get_feature_vector(i, vlen, vfree);

            for (j=0; j<dimensions; j++)
                mus.matrix[Cl*dimensions+j] += weight*vec[j];

            lhs->free_feature_vector(vec, i, vfree);
        }
        for (i=0; i<k; i++)
        {
            int32_t j;

            if (weights_set[i]!=0.0)
                for (j=0; j<dimensions; j++)
                    mus.matrix[i*dimensions+j] /= weights_set[i];
        }
    }
    else 
    {
        ASSERT(mus_start);

        rhs_mus->copy_feature_matrix(SGMatrix<float64_t>(mus_start,dimensions,k));
        float64_t* p_dists=dists;

        for(int32_t idx=0;idx<XSize;idx++,p_dists+=k)
            distances_rhs(p_dists,0,k,idx);
        p_dists=NULL;            

        for (i=0; i<XSize; i++)
        {
            float64_t mini=dists[i*k];
            int32_t Cl = 0, j;

            for (j=1; j<k; j++)
            {
                if (dists[i*k+j]<mini)
                {
                    Cl=j;
                    mini=dists[i*k+j];
                }
            }
            ClList[i]=Cl;
        }

        /* Compute the sum of all points belonging to a cluster 
         * and count the points */
        for (i=0; i<XSize; i++) 
        {
            const int32_t Cl = ClList[i];
            float64_t weight=Weights.vector[i];
            weights_set[Cl]+=weight;
#ifndef MUSRECALC
            vec=lhs->get_feature_vector(i, vlen, vfree);

            for (j=0; j<dimensions; j++)
                mus.matrix[Cl*dimensions+j] += weight*vec[j];

            lhs->free_feature_vector(vec, i, vfree);
#endif
        }
#ifndef MUSRECALC
        /* normalization to get the mean */ 
        for (i=0; i<k; i++)
        {
            if (weights_set[i]!=0.0)
            {
                int32_t j;
                for (j=0; j<dimensions; j++)
                    mus.matrix[i*dimensions+j] /= weights_set[i];
            }
        }
#endif
    }



    while (changed && (iter<max_iter))
    {
        iter++;
        if (iter==max_iter-1)
            SG_WARNING("kmeans clustering changed throughout %d iterations stopping...\n", max_iter-1);

        if (iter%1000 == 0)
            SG_INFO("Iteration[%d/%d]: Assignment of %i patterns changed.\n", iter, max_iter, changed);
        changed=0;

#ifdef MUSRECALC
        /* mus=zeros(dimensions, k) ; */
        memset(mus.matrix, 0, sizeof(float64_t)*XDimk);

        for (i=0; i<XSize; i++) 
        {
            int32_t j;
            int32_t Cl=ClList[i];
            float64_t weight=Weights.vector[i];

            vec=lhs->get_feature_vector(i, vlen, vfree);

            for (j=0; j<dimensions; j++)
                mus.matrix[Cl*dimensions+j] += weight*vec[j];

            lhs->free_feature_vector(vec, i, vfree);
        }
        for (i=0; i<k; i++)
        {
            int32_t j;

            if (weights_set[i]!=0.0)
                for (j=0; j<dimensions; j++)
                    mus.matrix[i*dimensions+j] /= weights_set[i];
        }
#endif

        rhs_mus->copy_feature_matrix(mus);

        for (i=0; i<XSize; i++)
        {
            /* ks=ceil(rand(1,XSize)*XSize) ; */
            const int32_t Pat= CMath::random(0, XSize-1);
            const int32_t ClList_Pat=ClList[Pat];
            int32_t imini, j;
            float64_t mini, weight;

            weight=Weights.vector[Pat];

            /* compute the distance of this point to all centers */
            for(int32_t idx_k=0;idx_k<k;idx_k++)
                dists[idx_k]=distance->distance(Pat,idx_k);
           
            /* [mini,imini]=min(dists(:,i)) ; */
            imini=0 ; mini=dists[0];
            for (j=1; j<k; j++)
                if (dists[j]<mini)
                {
                    mini=dists[j];
                    imini=j;
                }

            if (imini!=ClList_Pat)
            {
                changed= changed + 1;

                /* weights_set(imini) = weights_set(imini) + weight ; */
                weights_set[imini]+= weight;
                /* weights_set(j)     = weights_set(j)     - weight ; */
                weights_set[ClList_Pat]-= weight;

                vec=lhs->get_feature_vector(Pat, vlen, vfree);

                for (j=0; j<dimensions; j++)
                {
                    mus.matrix[imini*dimensions+j]-=(vec[j]
                            -mus.matrix[imini*dimensions+j])
                            *(weight/weights_set[imini]);
                }

                lhs->free_feature_vector(vec, Pat, vfree);

                /* mu_new = mu_old - (x - mu_old)/(n-1) */
                /* if weights_set(j)~=0 */
                if (weights_set[ClList_Pat]!=0.0)
                {
                    vec=lhs->get_feature_vector(Pat, vlen, vfree);

                    for (j=0; j<dimensions; j++)
                    {
                        mus.matrix[ClList_Pat*dimensions+j]-=
                                (vec[j]
                                        -mus.matrix[ClList_Pat
                                                *dimensions+j])
                                        *(weight/weights_set[ClList_Pat]);
                    }
                    lhs->free_feature_vector(vec, Pat, vfree);
                }
                else
                    /*  mus(:,j)=zeros(dimensions,1) ; */
                    for (j=0; j<dimensions; j++)
                        mus.matrix[ClList_Pat*dimensions+j]=0;

                /* ClList(i)= imini ; */
                ClList[Pat] = imini;
            }
        }
    }

    /* compute the ,,variances'' of the clusters */
    for (i=0; i<k; i++)
    {
        float64_t rmin1=0;
        float64_t rmin2=0;

        bool first_round=true;

        for (int32_t j=0; j<k; j++)
        {
            if (j!=i)
            {
                int32_t l;
                float64_t dist = 0;

                for (l=0; l<dimensions; l++)
                {
                    dist+=CMath::sq(
                            mus.matrix[i*dimensions+l]
                                    -mus.matrix[j*dimensions+l]);
                }

                if (first_round)
                {
                    rmin1=dist;
                    rmin2=dist;
                    first_round=false;
                }
                else
                {
                    if ((dist<rmin2) && (dist>=rmin1))
                        rmin2=dist;

                    if (dist<rmin1) 
                    {
                        rmin2=rmin1;
                        rmin1=dist;
                    }
                }
            }
        }

        R.vector[i]=(0.7*CMath::sqrt(rmin1)+0.3*CMath::sqrt(rmin2));
    }
        distance->replace_rhs(rhs_cache);
        delete rhs_mus;        
    SG_FREE(ClList);
    SG_FREE(weights_set);
    SG_FREE(dists);
    SG_UNREF(lhs);
}

void CKMeans::store_model_features()
{
    /* set lhs of underlying distance to cluster centers */
    CSimpleFeatures<float64_t>* cluster_centers=new CSimpleFeatures<float64_t>(
            mus);

    /* reset mus variable to avoid interference with above features */
    mus.do_free=false;
    mus.free_matrix();

    /* store cluster centers in lhs of distance variable */
    CFeatures* rhs=distance->get_rhs();
    distance->init(cluster_centers, rhs);
    SG_UNREF(rhs);
}

void CKMeans::init()
{
    max_iter=10000;
    k=3;
    dimensions=0;

    m_parameters->add(&max_iter, "max_iter", "Maximum number of iterations");
    m_parameters->add(&k, "k", "Parameter k");
    m_parameters->add(&dimensions, "dimensions", "Dimensions of data");
    m_parameters->add(&R, "R", "Cluster radiuses");
}