mysvmclassifier.cpp

本程序是一本很经典的知识响亮基的源代码 学起来比较方便 请大家使用

// MySVMClassifier.cpp: implementation of the CMySVMClassifier class.
//
//////////////////////////////////////////////////////////////////////

//#include "stdafx.h"
//#include "SVM.h"
#include "MySVMClassifier.h"

#ifdef _DEBUG
#undef THIS_FILE
static char THIS_FILE[]=__FILE__;
#define new DEBUG_NEW
#endif
# include "ctype.h"
long   verbosity;              /* verbosity level (0-4) */
long   kernel_cache_statistic;
//long verbosity;

/* /////////////////////////////////////////////////////////////// */

# define DEF_PRECISION_LINEAR    1E-8
# define DEF_PRECISION_NONLINEAR 1E-14

double *optimize_qp();
double *primal=0,*dual=0;
double init_margin=0.15;
long   init_iter=500,precision_violations=0;
double model_b;
double opt_precision=DEF_PRECISION_LINEAR;
#ifndef max
#define	max(A, B)	((A) > (B) ? (A) : (B))
#endif
#ifndef min
#define	min(A, B)	((A) < (B) ? (A) : (B))
#endif
#ifndef sqr
#define sqr(A)          ((A) * (A))
#endif
#ifndef ABS
#define	ABS(A)  	((A) > 0 ? (A) : (-(A)))
#endif
#define PREDICTOR 1
#define CORRECTOR 2


//////////////////////////////////////////////////////////////////////
// Construction/Destruction
//////////////////////////////////////////////////////////////////////

CMySVMClassifier::CMySVMClassifier()
{

}

CMySVMClassifier::~CMySVMClassifier()
{

}
double CMySVMClassifier::classify_example(MODEL *model, DOC *ex) 
     /* classifies one example */
{
  register long i;
  register double dist;

  if((model->kernel_parm.kernel_type == LINEAR) && (model->lin_weights))
    return(classify_example_linear(model,ex));
	   
  dist=0;
  for(i=1;i<model->sv_num;i++) {  
    dist+=kernel(&model->kernel_parm,model->supvec[i],ex)*model->alpha[i];
  }
  return(dist-model->b);
}

double CMySVMClassifier::classify_example_linear(MODEL *model, DOC *ex) 
     /* classifies example for linear kernel */
     
     /* important: the model must have the linear weight vector computed */
     /* use: add_weight_vector_to_linear_model(&model); */


     /* important: the feature numbers in the example to classify must */
     /*            not be larger than the weight vector!               */
{
  double sum=0;
  SVECTOR *f;

  for(f=ex->fvec;f;f=f->next)  
    sum+=f->factor*sprod_ns(model->lin_weights,f);
  return(sum-model->b);
}


CFLOAT CMySVMClassifier::kernel(KERNEL_PARM *kernel_parm, DOC *a, DOC *b) 
     /* calculate the kernel function */
{
  double sum=0;
  SVECTOR *fa,*fb;

  /* in case the constraints are sums of feature vector as represented
     as a list of SVECTOR's with their coefficient factor in the sum,
     take the kernel between all pairs */ 
  for(fa=a->fvec;fa;fa=fa->next) { 
    for(fb=b->fvec;fb;fb=fb->next) {
      if(fa->kernel_id == fb->kernel_id)
	sum+=fa->factor*fb->factor*single_kernel(kernel_parm,fa,fb);
    }
  }
  return((CFLOAT)sum);
}

CFLOAT CMySVMClassifier::single_kernel(KERNEL_PARM *kernel_parm, SVECTOR *a, SVECTOR *b) 
     /* calculate the kernel function between two vectors */
{
  kernel_cache_statistic++;
  switch(kernel_parm->kernel_type) {
    case 0: /* linear */ 
            return((CFLOAT)sprod_ss(a,b)); 
    case 1: /* polynomial */
            return((CFLOAT)pow(kernel_parm->coef_lin*sprod_ss(a,b)+kernel_parm->coef_const,(double)kernel_parm->poly_degree)); 
    case 2: /* radial basis function */
            return((CFLOAT)exp(-kernel_parm->rbf_gamma*(a->twonorm_sq-2*sprod_ss(a,b)+b->twonorm_sq)));
    case 3: /* sigmoid neural net */
            return((CFLOAT)tanh(kernel_parm->coef_lin*sprod_ss(a,b)+kernel_parm->coef_const)); 
    case 4: /* custom-kernel supplied in file kernel.h*/
            return((CFLOAT)custom_kernel(kernel_parm,a,b)); 
    default: printf("Error: Unknown kernel function\n"); exit(1);
  }
}


SVECTOR* CMySVMClassifier::create_svector(SVMWORD *words,char *userdefined,double factor)
{
  SVECTOR *vec;
  long    fnum,i;

  fnum=0;
  while(words[fnum].wnum) {
    fnum++;
  }
  fnum++;
  vec = (SVECTOR *)my_malloc(sizeof(SVECTOR));
  vec->words = (SVMWORD *)my_malloc(sizeof(SVMWORD)*(fnum));
  for(i=0;i<fnum;i++) { 
      vec->words[i]=words[i];
  }
  vec->twonorm_sq=sprod_ss(vec,vec);

  fnum=0;
  while(userdefined[fnum]) {
    fnum++;
  }
  fnum++;
  vec->userdefined = (char *)my_malloc(sizeof(char)*(fnum));
  for(i=0;i<fnum;i++) { 
      vec->userdefined[i]=userdefined[i];
  }
  vec->kernel_id=0;
  vec->next=NULL;
  vec->factor=factor;
  return(vec);
}

SVECTOR* CMySVMClassifier::copy_svector(SVECTOR *vec)
{
  SVECTOR *newvec=NULL;
  if(vec) {
    newvec=create_svector(vec->words,vec->userdefined,vec->factor);
    newvec->next=copy_svector(vec->next);
  }
  return(newvec);
}
    
void CMySVMClassifier::free_svector(SVECTOR *vec)
{
  if(vec) {
    free(vec->words);
    if(vec->userdefined)
      free(vec->userdefined);
    free_svector(vec->next);
    free(vec);
  }
}

double CMySVMClassifier::sprod_ss(SVECTOR *a, SVECTOR *b) 
     /* compute the inner product of two sparse vectors */
{
    register CFLOAT sum=0;
    register SVMWORD *ai,*bj;
    ai=a->words;
    bj=b->words;
    while (ai->wnum && bj->wnum) {
      if(ai->wnum > bj->wnum) {
	bj++;
      }
      else if (ai->wnum < bj->wnum) {
	ai++;
      }
      else {
	sum+=(CFLOAT)(ai->weight) * (CFLOAT)(bj->weight);
	ai++;
	bj++;
      }
    }
    return((double)sum);
}

SVECTOR* CMySVMClassifier::sub_ss(SVECTOR *a, SVECTOR *b) 
     /* compute the difference a-b of two sparse vectors */
     /* Note: SVECTOR lists are not followed, but only the first
	SVECTOR is used */
{
    SVECTOR *vec;
    register SVMWORD *sum,*sumi;
    register SVMWORD *ai,*bj;
    long veclength;
  
    ai=a->words;
    bj=b->words;
    veclength=0;
    while (ai->wnum && bj->wnum) {
      if(ai->wnum > bj->wnum) {
	veclength++;
	bj++;
      }
      else if (ai->wnum < bj->wnum) {
	veclength++;
	ai++;
      }
      else {
	veclength++;
	ai++;
	bj++;
      }
    }
    while (bj->wnum) {
      veclength++;
      bj++;
    }
    while (ai->wnum) {
      veclength++;
      ai++;
    }
    veclength++;

    sum=(SVMWORD *)my_malloc(sizeof(SVMWORD)*veclength);
    sumi=sum;
    ai=a->words;
    bj=b->words;
    while (ai->wnum && bj->wnum) {
      if(ai->wnum > bj->wnum) {
	(*sumi)=(*bj);
	sumi->weight*=(-1);
	sumi++;
	bj++;
      }
      else if (ai->wnum < bj->wnum) {
	(*sumi)=(*ai);
	sumi++;
	ai++;
      }
      else {
	(*sumi)=(*ai);
	sumi->weight-=bj->weight;
	if(sumi->weight != 0)
	  sumi++;
	ai++;
	bj++;
      }
    }
    while (bj->wnum) {
      (*sumi)=(*bj);
      sumi->weight*=(-1);
      sumi++;
      bj++;
    }
    while (ai->wnum) {
      (*sumi)=(*ai);
      sumi++;
      ai++;
    }
    sumi->wnum=0;

    vec=create_svector(sum,"",1.0);
    free(sum);

    return(vec);
}

SVECTOR* CMySVMClassifier::add_ss(SVECTOR *a, SVECTOR *b) 
     /* compute the sum a+b of two sparse vectors */
     /* Note: SVECTOR lists are not followed, but only the first
	SVECTOR is used */
{
    SVECTOR *vec;
    register SVMWORD *sum,*sumi;
    register SVMWORD *ai,*bj;
    long veclength;
  
    ai=a->words;
    bj=b->words;
    veclength=0;
    while (ai->wnum && bj->wnum) {
      if(ai->wnum > bj->wnum) {
	veclength++;
	bj++;
      }
      else if (ai->wnum < bj->wnum) {
	veclength++;
	ai++;
      }
      else {
	veclength++;
	ai++;
	bj++;
      }
    }
    while (bj->wnum) {
      veclength++;
      bj++;
    }
    while (ai->wnum) {
      veclength++;
      ai++;
    }
    veclength++;

    /*** is veclength=lengSequence(a)+lengthSequence(b)? ***/

    sum=(SVMWORD *)my_malloc(sizeof(WORD)*veclength);
    sumi=sum;
    ai=a->words;
    bj=b->words;
    while (ai->wnum && bj->wnum) {
      if(ai->wnum > bj->wnum) {
	(*sumi)=(*bj);
	sumi++;
	bj++;
      }
      else if (ai->wnum < bj->wnum) {
	(*sumi)=(*ai);
	sumi++;
	ai++;
      }
      else {
	(*sumi)=(*ai);
	sumi->weight+=bj->weight;
	if(sumi->weight != 0)
	  sumi++;
	ai++;
	bj++;
      }
    }
    while (bj->wnum) {
      (*sumi)=(*bj);
      sumi++;
      bj++;
    }
    while (ai->wnum) {
      (*sumi)=(*ai);
      sumi++;
      ai++;
    }
    sumi->wnum=0;

    vec=create_svector(sum,"",1.0);
    free(sum);

    return(vec);
}

SVECTOR* CMySVMClassifier::add_list_ss(SVECTOR *a) 
     /* computes the linear combination of the SVECTOR list weighted
	by the factor of each SVECTOR */
{
  SVECTOR *scaled,*oldsum,*sum,*f;
  SVMWORD    empty[2];
    
  if(a){
    sum=smult_s(a,a->factor);
    for(f=a->next;f;f=f->next) {
      scaled=smult_s(f,f->factor);
      oldsum=sum;
      sum=add_ss(sum,scaled);
      free_svector(oldsum);
      free_svector(scaled);
    }
    sum->factor=1.0;
  }
  else {
    empty[0].wnum=0;
    sum=create_svector(empty,"",1.0);
  }
  return(sum);
}

void CMySVMClassifier::append_svector_list(SVECTOR *a, SVECTOR *b) 
     /* appends SVECTOR b to the end of SVECTOR a. */
{
    SVECTOR *f;
    
    for(f=a;f->next;f=f->next);  /* find end of first vector list */
    f->next=b;                   /* append the two vector lists */
}

SVECTOR* CMySVMClassifier::smult_s(SVECTOR *a, double factor) 
     /* scale sparse vector a by factor */
{
    SVECTOR *vec;
    register SVMWORD *sum,*sumi;
    register SVMWORD *ai;
    long veclength;
  
    ai=a->words;
    veclength=0;
    while (ai->wnum) {
      veclength++;
      ai++;
    }
    veclength++;

    sum=(SVMWORD *)my_malloc(sizeof(SVMWORD)*veclength);
    sumi=sum;