ginitrain.cpp

SVM经典调试程序,内有说明,应用简便,可用做回归分类方面的计算

         count++;
	 nhits = (GINI_u32)atoi(argv[count++]);
	 validarg = GINI_TRUE;
      }

      if ( strcmp(argv[count],"-srch") == 0 )
      {
         count++;
	 srch = (GINI_u32)atoi(argv[count++]);
	 validarg = GINI_TRUE;
      }

      if ( strcmp(argv[count],"-csize") == 0 )
      {
         count++;
	 csize = (GINI_u32)atoi(argv[count++]);
	 validarg = GINI_TRUE;
      }

      if ( strcmp(argv[count],"-fpass") == 0 )
      {
         count++;
	 fpass = (GINI_u32)atoi(argv[count++]);
	 validarg = GINI_TRUE;
      }

      if ( validarg == GINI_FALSE )
      {
          PrintHelp();
	  printf("Invalid Argument: %s\n",argv[count]);
	  return 1;
      }
   }

   if ( count > argc-2 )
   {
       PrintHelp();
       return 1;
   }

   // Read the input training file and read the header.
   // Read in the data from the file
   FILE *fp = fopen(argv[argc-2],"r");
   if ( fp == (FILE*) GINI_NULL )
   {
      printf("Training File not Present\n");
      return 1;
   }

   FILE *fout = fopen(argv[argc-1],"w");
   if ( fout == (FILE*) GINI_NULL )
   {
      printf("Cannot Open Configuration File for writing\n");
      return 1;
   }

   GINI_float value;

   if ( ktype != GINISVMDTK )
   {
      // First read in the dimensionality of the input
      // vectors.
      fscanf(fp,"%f\n",&value);
      SVM_DIMENSION = (GINI_u32)value;
   }
   else
   {
      SVM_DIMENSION = 0;
   }

   // Number of classes
   fscanf(fp,"%f\n",&value);
   SVM_CLASS = (GINI_u32)value;

   // Total Number of training points
   fscanf(fp,"%f\n",&value);
   SVM_DATA = (GINI_u32)value;

   if ( srch == 0 )
   {
      srch = SVM_DATA;
   }

   if ( precomp == GINI_TRUE )
   {
      if ( SVM_DATA > csize )
      {
         printf("Cache size less than the total data points\n");
	 return 1;
      }
   }

   // Initialize the kernel depending on the ktype.
   switch (ktype)
   {
	   case GINISVMGAUSSIAN :
		  kernel = new GINI_GaussianKernel(p1,sp);
		  break;

	   case GINISVMPOLY :
		  kernel = new GINI_PolyKernel(p1,p2,(GINI_u32)p3,sp);
		  break;

	   case GINISVMDTK :
		  kernel = new GINI_DTKKernel(p1,p2,p3,p4);
		  SVM_DIMENSION = 0;
		  break;

	   case GINISVMTANH :
		  kernel = new GINI_TanhKernel(p1,sp);
		  break;
   }

   // Initialize the kernel cache with csize 
   kernel->InitializeCache(csize,SVM_DATA);  

   // Now define svm block and initialize it with the kernel
   GINI_SVMBlock *svmmachine = new GINI_SVMBlock(kernel);

   // Initialize the regularization constant array. For
   // this particular case all the values are fixed.
   GINI_double *inpC = new (GINI_double)[SVM_DATA];
   for ( GINI_u32 i = 0; i < SVM_DATA; i++ )
   {
      inpC[i] = C;
   }
 
   // Initialize the rate distortion factor.  Rate
   // distortion factor determines how stable the optimization
   // is and also controls sparsity of solution.
   if ( B == 0.0 )
   {
      B = ((GINI_double)SVM_CLASS/(SVM_CLASS-1))*log((GINI_double)SVM_CLASS); 
   }

   // Initialize the training 
   svmmachine->InitTraining( SVM_DATA,       // Total training data
		             SVM_DIMENSION,  // Feature dimension
			     SVM_CLASS,      // Total number of classes
			     inpC,           // Regularization constant array
			     B,              // Rate distortion factor
			     aeps,           // coefficient tolerance
			     keps,           // KKT tolerance
			     win,            // Random search window.
			     ceps,           // Tolerance for Cost function decrease
			     niter,          // Number of Iterations after which the
			     nhits,          // the cost function is computed.
			     srch,            // Maximum sv search window
			     liter,           // Maximum sv search window
			     fpass           // First level passes.
			 );

   // Data structures to read in the training data.
   GINI_double *testvec;
   GINI_double *label;
   GINI_double cval;

   for ( GINI_u32 i = 0; i < SVM_DATA; i++ )
   {
      if ( pflag == GINI_FALSE )
      {
         fscanf(fp,"%f\n",&value);
         if ( value >= SVM_CLASS)
         {
            printf("Improper Label: Expected < %d, Got %d\n",SVM_CLASS,(GINI_u32)value);
            return 1;
         }

         // Allocate memory for the label vector
         label = new GINI_double[SVM_CLASS];
         for ( GINI_u32 j = 0; j< SVM_CLASS; j++ )
         {
            if ( j != value)
            {
               label[j]= 0;
            }
            else
            {
               label[j] = 1;
            }
         }
      }
      else
      {
         // Allocate memory for the label vector
         label = new GINI_double[SVM_CLASS];
         for ( GINI_u32 j = 0; j< SVM_CLASS; j++ )
         {
            fscanf(fp,"%f\n",&value);
            if (( value > 1.0) || ( value < 0.0 ))
            {
               printf("%f is not a valid probability measure\n",value);
               return 1;
            }
	    label[j] = value;
         }
      }

      cval = 1.0;
      if ( cflag == GINI_TRUE )
      {
         // Read in the C value for this data point
         fscanf(fp,"%f\n",&value);
         cval = (GINI_double)value;
      }

      if ( sp == GINI_FALSE )
      {
         // If the data is in a non-sparse format
	 if ( ktype == GINISVMDTK )
         {
            // If this is a DTK kernel then we have to
	    // process variable length data.
            fscanf(fp,"%f\n",&value);
            testvec = new GINI_double[(GINI_u32)value+1];
	    testvec[0] = (GINI_double)value;

            for ( GINI_u32 j = 0; j< (GINI_u32)testvec[0]; j++ )
            {
               fscanf(fp,"%f\n",&value);
               testvec[j+1] = (GINI_double)value;
            }
         }
	 else
         {
            // Allocate memory for the training vector
            testvec = new GINI_double[SVM_DIMENSION];
            for ( GINI_u32 j = 0; j< SVM_DIMENSION; j++ )
            {
               fscanf(fp,"%f\n",&value);
               testvec[j] = (GINI_double)value;
            }
         }
      }
      else
      {
         // If the data is in a sparse format.
         fscanf(fp,"%f\n",&value);
         testvec = new GINI_double[(GINI_u32)(2*value)+1];
	 testvec[0] = (GINI_double)value;
         for ( GINI_u32 j = 0; j< (GINI_u32)testvec[0]; j++ )
         {
            // Read in the Index and then the value.
            fscanf(fp,"%f\n",&value);
            testvec[2*j+1] = (GINI_double)value;
            fscanf(fp,"%f\n",&value);
            testvec[2*j+2] = (GINI_double)value;
         }
      }

      // Insert training vector and its corresponding label.
      svmmachine->InsertTrainingData(label,testvec,cval);
   }
   fclose(fp);

   // Train the svm machine, with an upper limit of 20000
   // optimization steps.
   if ( svmmachine->StartTraining(precomp,20000,verbose) == GINI_FALSE )
   {
      printf(" SVM training Failed \n"); 
   }

   // End of training to clean up all the data structures.
   GINI_u32 unconverged = svmmachine->StopTraining();
   printf("Total KKT Violations = %d\n",unconverged);

   // Print out the parameters of SVM
   GINI_u32 nsv = svmmachine->GetSize();
   printf("Number of SVs=%d\n",nsv);

   printf("-------------------------------------------------------------------\n");
   printf("    BIAS Information for different classes                         \n");
   printf("-------------------------------------------------------------------\n");
   for ( GINI_u32 j = 0; j< SVM_CLASS; j++ )
   {
        printf("value of bias for class %d = %3.7f\n",j,svmmachine->GetThreshold(j));
   }



   // Write the SVM output 
   if ( svmmachine->Write(fout) == GINI_FALSE )
   {
      printf("Failed to save SVM parameter\n");
   }
   fclose(fout);

   // Free all the memory.
   delete svmmachine;
   delete kernel;
   delete [] inpC;
   return 0;
}