www.pudn.com > SVM20.rar > svm_learn_main.cpp

// T3.cpp: implementation of the CT3 class. 
// 
////////////////////////////////////////////////////////////////////// 
 
#include "stdafx.h" 
#include "svm.h" 
#include "svm_learn.h" 
#include "svm_learn_main.h" 
 
#ifdef _DEBUG 
#undef THIS_FILE 
static char THIS_FILE[]=__FILE__; 
#define new DEBUG_NEW 
#endif 
static char temstr[200]; 
 
////////////////////////////////////////////////////////////////////// 
// Construction/Destruction 
////////////////////////////////////////////////////////////////////// 
//char modelfile[200];         /* file for resulting classifier */ 
void set_learn_parameters(LEARN_PARM* learn_parm,KERNEL_PARM* kernel_parm) 
{ 
  
   
  learn_parm->biased_hyperplane=com_param.biased_Hyperplane; 
  learn_parm->remove_inconsistent=com_param.remove_inconsitant; 
  learn_parm->skip_final_opt_check=com_param.final_test; 
  learn_parm->svm_maxqpsize=com_param.maximum_size; 
  learn_parm->svm_newvarsinqp=com_param.new_variable; 
  learn_parm->svm_iter_to_shrink=com_param.iteration_time; 
  learn_parm->svm_c=com_param.C; 
  learn_parm->transduction_posratio=com_param.fraction; 
  learn_parm->svm_costratio=com_param.cost_factor; 
  learn_parm->svm_costratio_unlab=1.0; 
  learn_parm->svm_unlabbound=1E-5; 
  learn_parm->epsilon_crit=0.001; 
  learn_parm->epsilon_a=1E-15; 
  learn_parm->compute_loo=com_param.loo; 
  learn_parm->rho=com_param.rho; 
  learn_parm->xa_depth=com_param.search_depth; 
 
  kernel_parm->kernel_type=com_param.kernel_type; 
  kernel_parm->poly_degree=com_param.poly_degree; 
  kernel_parm->rbf_gamma=com_param.rbf_gamma; 
  kernel_parm->coef_lin=com_param.poly_s; 
  kernel_parm->coef_const=com_param.poly_c; 
  //strcpy(kernel_parm->custom,com_param.); 
} 
 
 
int svm_learn_main () 
{   
  DOC *docs;  /* training examples */ 
  long *label,max_docs,max_words_doc; 
  long totwords,totdoc,ll,i; 
  KERNEL_CACHE kernel_cache; 
  LEARN_PARM learn_parm; 
  KERNEL_PARM kernel_parm; 
  MODEL model; 
  char docfile[200]; 
  char modelfile[200]; 
   
  if (com_pro.show_action) 
	  printm("begin to compute"); 
  if (com_pro.show_action) 
	printm("Scanning examples..."); 
 
  set_learn_parameters(&learn_parm,&kernel_parm); 
 
  strcpy(docfile,com_param.trainfile); 
  strcpy(modelfile,com_param.modelfile); 
//  kernel_cache_size=com_param.cache_size; 
 
 
  nol_ll(docfile,&max_docs,&max_words_doc,&ll); /* scan size of input file */ 
  max_words_doc+=2; 
  ll+=2; 
  max_docs+=2; 
 
 
  docs = (DOC *)my_malloc(sizeof(DOC)*max_docs); 
  label = (long *)my_malloc(sizeof(long)*max_docs); 
 
   
  read_documents(docfile,docs,label,max_words_doc,ll,&totwords,&totdoc); 
 
  if(kernel_parm.kernel_type == LINEAR) /* don't need the cache */ 
  {  
    svm_learn(docs,label,totdoc,totwords,&learn_parm,&kernel_parm,NULL,&model); 
  } 
  else  
  { 
    kernel_cache_init(&kernel_cache,totdoc,com_param.cache_size); 
    svm_learn(docs,label,totdoc,totwords,&learn_parm,&kernel_parm,&kernel_cache,&model); 
    kernel_cache_cleanup(&kernel_cache); 
  } 
 
  if (com_param.blWriteModel) 
	  write_model(modelfile,&model); 
 
  free(model.supvec); 
  free(model.alpha); 
  free(model.index); 
  for(i=0;i