C++ (Cpp) eval_prediction Examples

Example #1

File: svm_struct_classify.c Project: YaMachineLearn/Project2-C

int main (int argc, char* argv[])
{
  long correct=0,incorrect=0,no_accuracy=0;
  long i;
  double t1,runtime=0;
  double avgloss=0,l;
  FILE *predfl;
  STRUCTMODEL model; 
  STRUCT_LEARN_PARM sparm;
  STRUCT_TEST_STATS teststats;
  SAMPLE testsample;
  LABEL y;

  svm_struct_classify_api_init(argc,argv);

  read_input_parameters(argc,argv,testfile,modelfile,predictionsfile,&sparm,
			&verbosity,&struct_verbosity);

  if(struct_verbosity>=1) {
    printf("Reading model..."); fflush(stdout);
  }
  model=read_struct_model(modelfile,&sparm);
  if(struct_verbosity>=1) {
    fprintf(stdout, "done.\n");
  }

  if(model.svm_model->kernel_parm.kernel_type == LINEAR) { /* linear kernel */
    /* compute weight vector */
    //add_weight_vector_to_linear_model(model.svm_model);
    //model.w=model.svm_model->lin_weights;
  }
  
  if(struct_verbosity>=1) {
    printf("Reading test examples..."); fflush(stdout);
  }
  testsample=read_struct_examples(testfile,&sparm);
  if(struct_verbosity>=1) {
    printf("done.\n"); fflush(stdout);
  }

  if(struct_verbosity>=1) {
    printf("Classifying test examples..."); fflush(stdout);
  }

  if ((predfl = fopen (predictionsfile, "w")) == NULL)
  { perror (predictionsfile); exit (1); }

  for(i=0;i<testsample.n;i++) {
    t1=get_runtime();
    y=classify_struct_example(testsample.examples[i].x,&model,&sparm);
    runtime+=(get_runtime()-t1);

    write_label(predfl,y);
    l=loss(testsample.examples[i].y,y,&sparm);
    avgloss+=l;
    if(l == 0) 
      correct++;
    else
      incorrect++;
    eval_prediction(i,testsample.examples[i],y,&model,&sparm,&teststats);

    if(empty_label(testsample.examples[i].y)) 
      { no_accuracy=1; } /* test data is not labeled */
    if(struct_verbosity>=2) {
      if((i+1) % 100 == 0) {
	printf("%ld..",i+1); fflush(stdout);
      }
    }
    free_label(y);
  }  
  avgloss/=testsample.n;
  fclose(predfl);

  if(struct_verbosity>=1) {
    printf("done\n");
    printf("Runtime (without IO) in cpu-seconds: %.2f\n",
	   (float)(runtime/100.0));    
  }
  if((!no_accuracy) && (struct_verbosity>=1)) {
    printf("Average loss on test set: %.4f\n",(float)avgloss);
    printf("Zero/one-error on test set: %.2f%% (%ld correct, %ld incorrect, %d total)\n",(float)100.0*incorrect/testsample.n,correct,incorrect,testsample.n);
  }
  print_struct_testing_stats(testsample,&model,&sparm,&teststats);
  free_struct_sample(testsample);
  free_struct_model(model);

  svm_struct_classify_api_exit();

  return(0);
}

Example #2

File: mex_svm_perf_classify.c Project: FelixPascal/BCILAB

void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
#endif
{
	long correct=0,incorrect=0,no_accuracy=0;
	long i;
	double t1,runtime=0;
	double avgloss=0,l;
#ifndef COMPILE_MEX_INTERFACE
	FILE *predfl;
#endif
	STRUCTMODEL model; 
	STRUCT_LEARN_PARM sparm;
	STRUCT_TEST_STATS teststats;
	SAMPLE testsample;
	LABEL y;
#ifdef COMPILE_MEX_INTERFACE
	int argc;
	char **argv;
	if (nrhs < 3) {
		print_help();
		return;
	}
	else if (nrhs==3) {
		argc=1;
		argv=(char **)my_malloc(MAX_ARGVS*sizeof(char *));
		argv[0]="OLR";
	}
	else
		create_argc_argv(prhs[3],&argc,&argv);
#endif
	svm_struct_classify_api_init(argc,argv);
	
#ifndef COMPILE_MEX_INTERFACE
	read_input_parameters(argc,argv,testfile,modelfile,predictionsfile,&sparm,
						  &verbosity,&struct_verbosity);
#else
	read_input_parameters(argc,argv,&sparm,&verbosity,&struct_verbosity);
#endif
	
	if(struct_verbosity>=1) {
		printf("Reading model..."); fflush(stdout);
	}
#ifndef COMPILE_MEX_INTERFACE
	model=read_struct_model(modelfile,&sparm);
#else
	model=read_struct_model(prhs[2],&sparm);
#endif
	if(struct_verbosity>=1) {
		fprintf(stdout, "done.\n");
	}
	
	if(model.svm_model->kernel_parm.kernel_type == LINEAR) { /* linear kernel */
		/* compute weight vector */
		add_weight_vector_to_linear_model(model.svm_model);
		model.w=model.svm_model->lin_weights;
	}
	
	if(struct_verbosity>=1) {
		printf("Reading test examples..."); fflush(stdout);
	}
#ifndef COMPILE_MEX_INTERFACE
	testsample=read_struct_examples(testfile,&sparm);
#else
	testsample=read_struct_examples(prhs,&sparm);
#endif
	if(struct_verbosity>=1) {
		printf("done.\n"); fflush(stdout);
	}
	
	if(struct_verbosity>=1) {
		printf("Classifying test examples..."); fflush(stdout);
	}
#ifndef COMPILE_MEX_INTERFACE
	if ((predfl = fopen (predictionsfile, "w")) == NULL)
	{ perror (predictionsfile); exit (1); }
#else
	mwSize rows=mxGetM(prhs[0]);
	mxArray *predictions=mxCreateDoubleMatrix(rows,1,mxREAL);
	double *pred_ptr=mxGetPr(predictions);
#endif
	for(i=0;i<testsample.n;i++) {
		t1=get_runtime();
		y=classify_struct_example(testsample.examples[i].x,&model,&sparm);
		runtime+=(get_runtime()-t1);
#ifndef COMPILE_MEX_INTERFACE
		write_label(predfl,y);
#else
		write_label(&pred_ptr,y);
#endif
		l=loss(testsample.examples[i].y,y,&sparm);
		avgloss+=l;
		if(l == 0) 
			correct++;
		else
			incorrect++;
		eval_prediction(i,testsample.examples[i],y,&model,&sparm,&teststats);
		
		if(empty_label(testsample.examples[i].y)) 
		{ no_accuracy=1; } /* test data is not labeled */
		if(struct_verbosity>=2) {
			if((i+1) % 100 == 0) {
				printf("%ld..",i+1); fflush(stdout);
			}
		}
		free_label(y);
	}
	avgloss/=testsample.n;
#ifndef COMPILE_MEX_INTERFACE
	fclose(predfl);
#endif
	if(struct_verbosity>=1) {
		printf("done\n");
		printf("Runtime (without IO) in cpu-seconds: %.2f\n",
			   (float)(runtime/100.0));    
	}
	if((!no_accuracy) && (struct_verbosity>=1)) {
		printf("Average loss on test set: %.4f\n",(float)avgloss);
		printf("Zero/one-error on test set: %.2f%% (%ld correct, %ld incorrect, %d total)\n",(float)100.0*incorrect/testsample.n,correct,incorrect,testsample.n);
	}
	print_struct_testing_stats(testsample,&model,&sparm,&teststats);
	free_struct_sample(testsample);
	free_struct_model(model);
	
	svm_struct_classify_api_exit();
#ifndef COMPILE_MEX_INTERFACE
	return(0);
#else
	plhs[0]=predictions;
#endif
}