www.pudn.com > svm_multiClass.rar > svm_classify.c

/***********************************************************************/ 
/*                                                                     */ 
/*   svm_classify.c                                                    */ 
/*                                                                     */ 
/*   Classification module of Support Vector Machine.                  */ 
/*                                                                     */ 
/*   Author: Thorsten Joachims                                         */ 
/*   Date: 02.07.02                                                    */ 
/*                                                                     */ 
/*   Copyright (c) 2002  Thorsten Joachims - All rights reserved       */ 
/*                                                                     */ 
/*   This software is available for non-commercial use only. It must   */ 
/*   not be modified and distributed without prior permission of the   */ 
/*   author. The author is not responsible for implications from the   */ 
/*   use of this software.                                             */ 
/*                                                                     */ 
/************************************************************************/ 
 
# include "svm_common.h" 
 
char docfile[200]; 
char modelfile[200]; 
char predictionsfile[200]; 
 
void read_input_parameters(int, char **, char *, char *, char *, long *,  
			   long *); 
void print_help(void); 
 
 
int main (int argc, char* argv[]) 
{ 
  DOC *doc;   /* test example */ 
  WORD *words; 
  long max_docs,max_words_doc,lld; 
  long totdoc=0,queryid,slackid; 
  long correct=0,incorrect=0,no_accuracy=0; 
  long res_a=0,res_b=0,res_c=0,res_d=0,wnum,pred_format; 
  long j; 
  double t1,runtime=0; 
  double dist,doc_label,costfactor; 
  char *line,*comment;  
  FILE *predfl,*docfl; 
  MODEL *model;  
 
  read_input_parameters(argc,argv,docfile,modelfile,predictionsfile, 
			&verbosity,&pred_format); 
 
  nol_ll(docfile,&max_docs,&max_words_doc,&lld); /* scan size of input file */ 
  max_words_doc+=2; 
  lld+=2; 
 
  line = (char *)my_malloc(sizeof(char)*lld); 
  words = (WORD *)my_malloc(sizeof(WORD)*(max_words_doc+10)); 
 
  model=read_model(modelfile); 
 
  if(model->kernel_parm.kernel_type == 0) { /* linear kernel */ 
    /* compute weight vector */ 
    add_weight_vector_to_linear_model(model); 
  } 
   
  if(verbosity>=2) { 
    printf("Classifying test examples.."); fflush(stdout); 
  } 
 
  if ((docfl = fopen (docfile, "r")) == NULL) 
  { perror (docfile); exit (1); } 
  if ((predfl = fopen (predictionsfile, "w")) == NULL) 
  { perror (predictionsfile); exit (1); } 
 
  while((!feof(docfl)) && fgets(line,(int)lld,docfl)) { 
    if(line[0] == '#') continue;  /* line contains comments */ 
    parse_document(line,words,&doc_label,&queryid,&slackid,&costfactor,&wnum, 
		   max_words_doc,&comment); 
    totdoc++; 
    if(model->kernel_parm.kernel_type == 0) {   /* linear kernel */ 
      for(j=0;(words[j]).wnum != 0;j++) {  /* Check if feature numbers   */ 
	if((words[j]).wnum>model->totwords) /* are not larger than in     */ 
	  (words[j]).wnum=0;               /* model. Remove feature if   */ 
      }                                        /* necessary.                 */ 
      doc = create_example(-1,0,0,0.0,create_svector(words,comment,1.0)); 
      t1=get_runtime(); 
      dist=classify_example_linear(model,doc); 
      runtime+=(get_runtime()-t1); 
      free_example(doc,1); 
    } 
    else {                             /* non-linear kernel */ 
      doc = create_example(-1,0,0,0.0,create_svector(words,comment,1.0)); 
      t1=get_runtime(); 
      dist=classify_example(model,doc); 
      runtime+=(get_runtime()-t1); 
      free_example(doc,1); 
    } 
    if(dist>0) { 
      if(pred_format==0) { /* old weired output format */ 
	fprintf(predfl,"%.8g:+1 %.8g:-1\n",dist,-dist); 
      } 
      if(doc_label>0) correct++; else incorrect++; 
      if(doc_label>0) res_a++; else res_b++; 
    } 
    else { 
      if(pred_format==0) { /* old weired output format */ 
	fprintf(predfl,"%.8g:-1 %.8g:+1\n",-dist,dist); 
      } 
      if(doc_label<0) correct++; else incorrect++; 
      if(doc_label>0) res_c++; else res_d++; 
    } 
    if(pred_format==1) { /* output the value of decision function */ 
      fprintf(predfl,"%.8g\n",dist); 
    } 
    if((int)(0.01+(doc_label*doc_label)) != 1)  
      { no_accuracy=1; } /* test data is not binary labeled */ 
    if(verbosity>=2) { 
      if(totdoc % 100 == 0) { 
	printf("%ld..",totdoc); fflush(stdout); 
      } 
    } 
  }   
  free(line); 
  free(words); 
  free_model(model,1); 
 
  if(verbosity>=2) { 
    printf("done\n"); 
 
/*   Note by Gary Boone                     Date: 29 April 2000        */ 
/*      o Timing is inaccurate. The timer has 0.01 second resolution.  */ 
/*        Because classification of a single vector takes less than    */ 
/*        0.01 secs, the timer was underflowing.                       */ 
    printf("Runtime (without IO) in cpu-seconds: %.2f\n", 
	   (float)(runtime/100.0)); 
     
  } 
  if((!no_accuracy) && (verbosity>=1)) { 
    printf("Accuracy on test set: %.2f%% (%ld correct, %ld incorrect, %ld total)\n",(float)(correct)*100.0/totdoc,correct,incorrect,totdoc); 
    printf("Precision/recall on test set: %.2f%%/%.2f%%\n",(float)(res_a)*100.0/(res_a+res_b),(float)(res_a)*100.0/(res_a+res_c)); 
  } 
 
  return(0); 
} 
 
void read_input_parameters(int argc, char **argv, char *docfile,  
			   char *modelfile, char *predictionsfile,  
			   long int *verbosity, long int *pred_format) 
{ 
  long i; 
   
  /* set default */ 
  strcpy (modelfile, "svm_model"); 
  strcpy (predictionsfile, "svm_predictions");  
  (*verbosity)=2; 
  (*pred_format)=1; 
 
  for(i=1;(i=argc) { 
    printf("\nNot enough input parameters!\n\n"); 
    print_help(); 
    exit(0); 
  } 
  strcpy (docfile, argv[i]); 
  strcpy (modelfile, argv[i+1]); 
  if((i+2) this help\n"); 
  printf("         -v [0..3]  -> verbosity level (default 2)\n"); 
  printf("         -f [0,1]   -> 0: old output format of V1.0\n"); 
  printf("                    -> 1: output the value of decision function (default)\n\n"); 
}