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 "clustering/KMeans.h"
#include "distance/Distance.h"
#include "features/Labels.h"
#include "features/SimpleFeatures.h"
#include "lib/Mathematics.h"
#include "base/Parallel.h"

#ifndef WIN32
#include <pthread.h>
#endif

#define MUSRECALC

#define PAR_THRESH  10

using namespace shogun;

CKMeans::CKMeans()
: CDistanceMachine(), max_iter(10000), k(3), dimensions(0), R(NULL),
    mus(NULL), Weights(NULL)
{
}

CKMeans::CKMeans(int32_t k_, CDistance* d)
: CDistanceMachine(), max_iter(10000), k(k_), dimensions(0), R(NULL),
    mus(NULL), Weights(NULL)
{
    set_distance(d);
}

CKMeans::~CKMeans()
{
    delete[] R;
    delete[] mus;
}

bool CKMeans::train(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=new float64_t[num];
    for (int32_t i=0; i<num; i++)
        Weights[i]=1.0;

    clustknb(false, NULL);
    delete[] Weights;

    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;

    delete[] R;
    R=new float64_t[k];

    delete[] mus;
    mus=new float64_t[XDimk];

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

    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) ; */
    for (i=0; i<XSize; i++) ClList[i]=0;
    /* weights_set=zeros(k,1) ; */
    for (i=0; i<k; i++) weights_set[i]=0;

    /* mus=zeros(dimensions, k) ; */
    for (i=0; i<XDimk; i++) mus[i]=0;

    if (!use_old_mus)
    {
        /* random clustering (select random subsets) */
        /*  ks=ceil(rand(1,XSize)*k);
         *  for i=1:k,
         *  actks= (ks==i);
         *  c=sum(actks);
         *  weights_set(i)=c;
         *
         *  ClList(actks)=i*ones(1, c);
         *
         *  if ~mus_recalc,
         *      if c>1
         *          mus(:,i) = sum(XData(:,actks)')'/c;
         *      elseif c>0
         *          mus(:,i) = XData(:,actks);
         *      end;
         *  end;
         *   end ; */

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

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

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

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

        rhs_mus->copy_feature_matrix(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[i];
            weights_set[Cl]+=weight;
#ifndef MUSRECALC
            vec=lhs->get_feature_vector(i, vlen, vfree);

            for (j=0; j<dimensions; j++)
                mus[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[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) ; */
        for (i=0; i<XDimk; i++) mus[i]=0;

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

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

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

        rhs_mus->copy_feature_matrix(mus,dimensions,k);

        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[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;

                /* mu_new=mu_old + (x - mu_old)/(n+1) */
                /* mus(:,imini)=mus(:,imini) + (XData(:,i) - mus(:,imini)) * (weight / weights_set(imini)) ; */
                vec=lhs->get_feature_vector(Pat, vlen, vfree);

                for (j=0; j<dimensions; j++)
                    mus[imini*dimensions+j]-=(vec[j]-mus[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)
                {
                    /* mus(:,j)=mus(:,j) - (XData(:,i) - mus(:,j)) * (weight/weights_set(j)) ; */
                    vec=lhs->get_feature_vector(Pat, vlen, vfree);

                    for (j=0; j<dimensions; j++)
                        mus[ClList_Pat*dimensions+j]-=(vec[j]-mus[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[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[i*dimensions+l]-mus[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[i]=(0.7*sqrt(rmin1)+0.3*sqrt(rmin2));
    }
        distance->replace_rhs(rhs_cache);
        delete rhs_mus;        

    free(ClList);
    free(weights_set);
    free(dists);
    SG_UNREF(lhs);
}