--- svm_common.h	Fri Sep  3 14:22:55 2004
+++ svm_common.h	Fri Jul  8 16:16:54 2005
@@ -284,6 +284,8 @@
 void   read_documents(char *, DOC ***, double **, long *, long *);
 int    parse_document(char *, WORD *, double *, long *, long *, double *, long *, long, char **);
 double *read_alphas(char *,long);
+void   set_learning_defaults(LEARN_PARM *learn_parm, KERNEL_PARM *kernel_parm);
+int    check_learning_parms(LEARN_PARM *learn_parm, KERNEL_PARM *kernel_parm);
 void   nol_ll(char *, long *, long *, long *);
 long   minl(long, long);
 long   maxl(long, long);
--- svm_learn_main.c	Wed Aug 25 19:03:59 2004
+++ svm_learn_main.c	Fri Jul  8 16:17:30 2005
@@ -109,38 +109,12 @@
   
   /* set default */
   strcpy (modelfile, "svm_model");
-  strcpy (learn_parm->predfile, "trans_predictions");
-  strcpy (learn_parm->alphafile, "");
   strcpy (restartfile, "");
   (*verbosity)=1;
-  learn_parm->biased_hyperplane=1;
-  learn_parm->sharedslack=0;
-  learn_parm->remove_inconsistent=0;
-  learn_parm->skip_final_opt_check=0;
-  learn_parm->svm_maxqpsize=10;
-  learn_parm->svm_newvarsinqp=0;
-  learn_parm->svm_iter_to_shrink=-9999;
-  learn_parm->maxiter=100000;
-  learn_parm->kernel_cache_size=40;
-  learn_parm->svm_c=0.0;
-  learn_parm->eps=0.1;
-  learn_parm->transduction_posratio=-1.0;
-  learn_parm->svm_costratio=1.0;
-  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=0;
-  learn_parm->rho=1.0;
-  learn_parm->xa_depth=0;
-  kernel_parm->kernel_type=0;
-  kernel_parm->poly_degree=3;
-  kernel_parm->rbf_gamma=1.0;
-  kernel_parm->coef_lin=1;
-  kernel_parm->coef_const=1;
-  strcpy(kernel_parm->custom,"empty");
   strcpy(type,"c");
 
+  set_learning_defaults(learn_parm, kernel_parm);
+
   for(i=1;(i<argc) && ((argv[i])[0] == '-');i++) {
     switch ((argv[i])[1]) 
       { 
@@ -215,74 +189,8 @@
     print_help();
     exit(0);
   }    
-  if((learn_parm->skip_final_opt_check) 
-     && (kernel_parm->kernel_type == LINEAR)) {
-    printf("\nIt does not make sense to skip the final optimality check for linear kernels.\n\n");
-    learn_parm->skip_final_opt_check=0;
-  }    
-  if((learn_parm->skip_final_opt_check) 
-     && (learn_parm->remove_inconsistent)) {
-    printf("\nIt is necessary to do the final optimality check when removing inconsistent \nexamples.\n");
-    wait_any_key();
-    print_help();
-    exit(0);
-  }    
-  if((learn_parm->svm_maxqpsize<2)) {
-    printf("\nMaximum size of QP-subproblems not in valid range: %ld [2..]\n",learn_parm->svm_maxqpsize); 
-    wait_any_key();
-    print_help();
-    exit(0);
-  }
-  if((learn_parm->svm_maxqpsize<learn_parm->svm_newvarsinqp)) {
-    printf("\nMaximum size of QP-subproblems [%ld] must be larger than the number of\n",learn_parm->svm_maxqpsize); 
-    printf("new variables [%ld] entering the working set in each iteration.\n",learn_parm->svm_newvarsinqp); 
-    wait_any_key();
-    print_help();
-    exit(0);
-  }
-  if(learn_parm->svm_iter_to_shrink<1) {
-    printf("\nMaximum number of iterations for shrinking not in valid range: %ld [1,..]\n",learn_parm->svm_iter_to_shrink);
-    wait_any_key();
-    print_help();
-    exit(0);
-  }
-  if(learn_parm->svm_c<0) {
-    printf("\nThe C parameter must be greater than zero!\n\n");
-    wait_any_key();
-    print_help();
-    exit(0);
-  }
-  if(learn_parm->transduction_posratio>1) {
-    printf("\nThe fraction of unlabeled examples to classify as positives must\n");
-    printf("be less than 1.0 !!!\n\n");
-    wait_any_key();
-    print_help();
-    exit(0);
-  }
-  if(learn_parm->svm_costratio<=0) {
-    printf("\nThe COSTRATIO parameter must be greater than zero!\n\n");
-    wait_any_key();
-    print_help();
-    exit(0);
-  }
-  if(learn_parm->epsilon_crit<=0) {
-    printf("\nThe epsilon parameter must be greater than zero!\n\n");
-    wait_any_key();
-    print_help();
-    exit(0);
-  }
-  if(learn_parm->rho<0) {
-    printf("\nThe parameter rho for xi/alpha-estimates and leave-one-out pruning must\n");
-    printf("be greater than zero (typically 1.0 or 2.0, see T. Joachims, Estimating the\n");
-    printf("Generalization Performance of an SVM Efficiently, ICML, 2000.)!\n\n");
-    wait_any_key();
-    print_help();
-    exit(0);
-  }
-  if((learn_parm->xa_depth<0) || (learn_parm->xa_depth>100)) {
-    printf("\nThe parameter depth for ext. xi/alpha-estimates must be in [0..100] (zero\n");
-    printf("for switching to the conventional xa/estimates described in T. Joachims,\n");
-    printf("Estimating the Generalization Performance of an SVM Efficiently, ICML, 2000.)\n");
+
+  if (!check_learning_parms(learn_parm, kernel_parm)) {
     wait_any_key();
     print_help();
     exit(0);
--- svm_classify.c	Wed Jul 14 13:50:00 2004
+++ svm_classify.c	Mon Aug  8 15:22:55 2005
@@ -78,19 +78,20 @@
 	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();
+    }
+    doc = create_example(-1,0,0,0.0,create_svector(words,comment,1.0));
+    t1=get_runtime();
+
+    if(model->kernel_parm.kernel_type == 0) {   /* linear kernel */
       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);
     }
+
+    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);
--- svm_common.c	Fri Aug 27 17:05:25 2004
+++ svm_common.c	Wed Sep  7 15:51:52 2005
@@ -887,6 +887,97 @@
   return(alpha);
 }
 
+void set_learning_defaults(LEARN_PARM *learn_parm, KERNEL_PARM *kernel_parm)
+{
+  learn_parm->type=CLASSIFICATION;
+  strcpy (learn_parm->predfile, "trans_predictions");
+  strcpy (learn_parm->alphafile, "");
+  learn_parm->biased_hyperplane=1;
+  learn_parm->sharedslack=0;
+  learn_parm->remove_inconsistent=0;
+  learn_parm->skip_final_opt_check=0;
+  learn_parm->svm_maxqpsize=10;
+  learn_parm->svm_newvarsinqp=0;
+  learn_parm->svm_iter_to_shrink=2;
+  learn_parm->maxiter=100000;
+  learn_parm->kernel_cache_size=40;
+  learn_parm->svm_c=0.0;
+  learn_parm->eps=0.1;
+  learn_parm->transduction_posratio=-1.0;
+  learn_parm->svm_costratio=1.0;
+  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=0;
+  learn_parm->rho=1.0;
+  learn_parm->xa_depth=0;
+
+  kernel_parm->kernel_type=LINEAR;
+  kernel_parm->poly_degree=3;
+  kernel_parm->rbf_gamma=1.0;
+  kernel_parm->coef_lin=1;
+  kernel_parm->coef_const=1;
+  strcpy(kernel_parm->custom,"empty");
+}
+
+int check_learning_parms(LEARN_PARM *learn_parm, KERNEL_PARM *kernel_parm)
+{
+  if((learn_parm->skip_final_opt_check) 
+     && (kernel_parm->kernel_type == LINEAR)) {
+    printf("\nIt does not make sense to skip the final optimality check for linear kernels.\n\n");
+    learn_parm->skip_final_opt_check=0;
+  }    
+  if((learn_parm->skip_final_opt_check) 
+     && (learn_parm->remove_inconsistent)) {
+    printf("\nIt is necessary to do the final optimality check when removing inconsistent \nexamples.\n");
+    return 0;
+  }    
+  if((learn_parm->svm_maxqpsize<2)) {
+    printf("\nMaximum size of QP-subproblems not in valid range: %ld [2..]\n",learn_parm->svm_maxqpsize); 
+    return 0;
+  }
+  if((learn_parm->svm_maxqpsize<learn_parm->svm_newvarsinqp)) {
+    printf("\nMaximum size of QP-subproblems [%ld] must be larger than the number of\n",learn_parm->svm_maxqpsize); 
+    printf("new variables [%ld] entering the working set in each iteration.\n",learn_parm->svm_newvarsinqp); 
+    return 0;
+  }
+  if(learn_parm->svm_iter_to_shrink<1) {
+    printf("\nMaximum number of iterations for shrinking not in valid range: %ld [1,..]\n",learn_parm->svm_iter_to_shrink);
+    return 0;
+  }
+  if(learn_parm->svm_c<0) {
+    printf("\nThe C parameter must be greater than zero!\n\n");
+    return 0;
+  }
+  if(learn_parm->transduction_posratio>1) {
+    printf("\nThe fraction of unlabeled examples to classify as positives must\n");
+    printf("be less than 1.0 !!!\n\n");
+    return 0;
+  }
+  if(learn_parm->svm_costratio<=0) {
+    printf("\nThe COSTRATIO parameter must be greater than zero!\n\n");
+    return 0;
+  }
+  if(learn_parm->epsilon_crit<=0) {
+    printf("\nThe epsilon parameter must be greater than zero!\n\n");
+    return 0;
+  }
+  if(learn_parm->rho<0) {
+    printf("\nThe parameter rho for xi/alpha-estimates and leave-one-out pruning must\n");
+    printf("be greater than zero (typically 1.0 or 2.0, see T. Joachims, Estimating the\n");
+    printf("Generalization Performance of an SVM Efficiently, ICML, 2000.)!\n\n");
+    return 0;
+  }
+  if((learn_parm->xa_depth<0) || (learn_parm->xa_depth>100)) {
+    printf("\nThe parameter depth for ext. xi/alpha-estimates must be in [0..100] (zero\n");
+    printf("for switching to the conventional xa/estimates described in T. Joachims,\n");
+    printf("Estimating the Generalization Performance of an SVM Efficiently, ICML, 2000.)\n");
+    return 0;
+  }
+  return 1;
+}
+
 void nol_ll(char *file, long int *nol, long int *wol, long int *ll) 
      /* Grep through file and count number of lines, maximum number of
         spaces per line, and longest line. */
--- Makefile	Fri Sep  3 15:56:45 2004
+++ Makefile	Thu Jul  7 14:27:03 2005
@@ -12,9 +12,14 @@
 LD=gcc                          # used linker
 LFLAGS=-O3                      # linker flags
 LIBS=-L. -lm                    # used libraries
+RANLIB=ranlib
 
 
-all: svm_learn_hideo svm_classify
+all: svm_learn_hideo svm_classify libsvmlight.a
+
+libsvmlight.a: svm_learn.o svm_common.o svm_hideo.o
+	$(AR) r $@ $^
+	$(RANLIB) $@
 
 tidy: 
 	rm -f *.o