int _svm_learn (int argc, char* argv[])
{
char docfile[200]; /* file with training examples */
char modelfile[200]; /* file for resulting classifier */
char restartfile[200]; /* file with initial alphas */
DOC **docs; /* training examples */
long totwords,totdoc,i;
double *target;
double *alpha_in=NULL;
KERNEL_CACHE *kernel_cache;
LEARN_PARM learn_parm;
KERNEL_PARM kernel_parm;
MODEL *model=(MODEL *)my_malloc(sizeof(MODEL));
HIDEO_ENV *hideo_env=create_env();
model->td_pred=NULL;
model->n_td_pred=0;
_read_input_parameters(argc,argv,docfile,modelfile,restartfile,&verbosity,
&learn_parm,&kernel_parm);
read_documents(docfile,&docs,&target,&totwords,&totdoc);
if(restartfile[0]) alpha_in=read_alphas(restartfile,totdoc);
if(kernel_parm.kernel_type == LINEAR) { /* don't need the cache */
kernel_cache=NULL;
}
else {
/* Always get a new kernel cache. It is not possible to use the
same cache for two different training runs */
kernel_cache=kernel_cache_init(totdoc,learn_parm.kernel_cache_size);
}
if(learn_parm.type == CLASSIFICATION) {
svm_learn_classification(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,kernel_cache,model,alpha_in,hideo_env);
}
else if(learn_parm.type == REGRESSION) {
svm_learn_regression(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,&kernel_cache,model,hideo_env);
}
else if(learn_parm.type == RANKING) {
svm_learn_ranking(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,&kernel_cache,model,hideo_env);
}
else if(learn_parm.type == OPTIMIZATION) {
svm_learn_optimization(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,kernel_cache,model,alpha_in,hideo_env);
}
if(kernel_cache) {
/* Free the memory used for the cache. */
kernel_cache_cleanup(kernel_cache);
}
/* Warning: The model contains references to the original data 'docs'.
If you want to free the original data, and only keep the model, you
have to make a deep copy of 'model'. */
/* deep_copy_of_model=copy_model(model); */
write_model(modelfile,model);
free(alpha_in);
free_model(model,0);
for(i=0;i<totdoc;i++)
free_example(docs[i],1);
free(docs);
free(target);
free_env(hideo_env);
return(0);
}
int SVMLightRunner::librarySVMLearnMain(
int argc, char **argv, bool use_gmumr, SVMConfiguration &config
) {
LOG(
config.log,
LogLevel::DEBUG_LEVEL,
__debug_prefix__ + ".librarySVMLearnMain() Started."
);
DOC **docs; /* training examples */
long totwords,totdoc,i;
double *target;
double *alpha_in=NULL;
KERNEL_CACHE *kernel_cache;
LEARN_PARM learn_parm;
KERNEL_PARM kernel_parm;
MODEL *model=(MODEL *)my_malloc(sizeof(MODEL));
// GMUM.R changes {
librarySVMLearnReadInputParameters(
argc, argv, docfile, modelfile, restartfile, &verbosity, &learn_parm,
&kernel_parm, use_gmumr, config
);
kernel_parm.kernel_type = static_cast<long int>(config.kernel_type);
libraryReadDocuments(
docfile, &docs, &target, &totwords, &totdoc, use_gmumr, config
);
// GMUM.R changes }
if(restartfile[0]) alpha_in=read_alphas(restartfile,totdoc);
if(kernel_parm.kernel_type == LINEAR) { /* don't need the cache */
kernel_cache=NULL;
}
else {
/* Always get a new kernel cache. It is not possible to use the
* same cache for two different training runs */
kernel_cache=kernel_cache_init(totdoc,learn_parm.kernel_cache_size);
}
//gmum.r
init_global_params_QP();
if(learn_parm.type == CLASSIFICATION) {
svm_learn_classification(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,kernel_cache,model,alpha_in);
}
else if(learn_parm.type == REGRESSION) {
svm_learn_regression(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,&kernel_cache,model);
}
else if(learn_parm.type == RANKING) {
svm_learn_ranking(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,&kernel_cache,model);
}
else if(learn_parm.type == OPTIMIZATION) {
svm_learn_optimization(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,kernel_cache,model,alpha_in);
}
//gmum.r
config.iter = learn_parm.iterations;
if(kernel_cache) {
/* Free the memory used for the cache. */
kernel_cache_cleanup(kernel_cache);
}
/* Warning: The model contains references to the original data 'docs'.
If you want to free the original data, and only keep the model, you
have to make a deep copy of 'model'. */
/* deep_copy_of_model=copy_model(model); */
// GMUM.R changes {
if (!use_gmumr) {
write_model(modelfile,model);
} else {
SVMLightModelToSVMConfiguration(model, config);
}
// GMUM.R changes }
free(alpha_in);
free_model(model,0);
for(i=0;i<totdoc;i++)
free_example(docs[i],1);
free(docs);
free(target);
LOG(
config.log,
LogLevel::DEBUG_LEVEL,
__debug_prefix__ + ".librarySVMLearnMain() Done."
);
return(0);
}
