int main (int argc, char* argv[])
{
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));
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);
}
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);
}
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);
return(0);
}
/* call as model = mexsvmlearn(data,labels,options) */
void mexFunction(int nlhs, mxArray *plhs[],
int nrhs, const mxArray *prhs[])
{
char **argv;
int argc;
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;
/* check for valid calling format */
if ((nrhs != 3) || (nlhs != 1))
mexErrMsgTxt(ERR001);
if (mxGetM(prhs[0]) != mxGetM(prhs[1]))
mexErrMsgTxt(ERR002);
if (mxGetN(prhs[1]) != 1)
mexErrMsgTxt(ERR003);
/* reset static variables -- as a .DLL, static things are sticky */
global_init( );
/* convert the parameters (given in prhs[2]) into an argv/argc combination */
argv = make_argv((mxArray *)prhs[2],&argc); /* send the options */
/* this was originally supposed to be argc, argv, re-written for MATLAB ...
its cheesy - but it workss :: convert the options array into an argc,
argv pair and let svm_lite handle it from there. */
read_input_parameters(argc,argv,docfile,modelfile,restartfile,&verbosity,
&learn_parm,&kernel_parm);
extract_user_opts((mxArray *)prhs[2], &kernel_parm);
totdoc = mxGetM(prhs[0]);
totwords = mxGetN(prhs[0]);
/* prhs[0] = samples (mxn) array
prhs[1] = labels (mx1) array */
mexToDOC((mxArray *)prhs[0], (mxArray *)prhs[1], &docs, &target, NULL, NULL);
/* TODO modify to accept this array
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);
}
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);
}
else {
mexErrMsgTxt(ERR004);
}
if(kernel_cache) {
/* Free the memory used for the cache. */
kernel_cache_cleanup(kernel_cache);
}
/* **********************************
* After the training/learning portion has finished,
* copy the model back to the output arrays for MATLAB
* ********************************** */
store_model(&model, plhs);
free_kernel();
global_destroy( );
}
