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);
}
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
}