void FULLCOND_rj_mix::update(void)
{
step_aborted = true;
/********* in principle like FULLCOND_rj::rj_step, but now
depending on the var_type functions of
FULLCOND_rj or FULLCOND_rj_int are used **********************/
while (step_aborted==true)
{
// two variables are randomly chosen
unsigned int i,j;
i= rand() % nvar;
j=i;
while(i==j)
j= rand() % nvar;
// decide which kind of step (birth, death, switch)
if (zeta(i,j) == 1)
{
if(preg_mods[j]->tell_var_type()=='d')
{
if(conditions == false)
FULLCOND_rj_int::death_step(i,j);
else
if(conditions_okay_d(i,j) ==true)
FULLCOND_rj_int::death_step(i,j);
}
else
{
if(conditions == false)
FULLCOND_rj::death_step(i,j);
else
if(conditions_okay_d(i,j) ==true)
FULLCOND_rj::death_step(i,j);
}
}
else if (zeta(j,i)==1)
{
if(preg_mods[j]->tell_var_type()=='d' ||
preg_mods[i]->tell_var_type()=='d')
{
if(conditions == false)
FULLCOND_rj_int::switch_step(i,j);
else
if(conditions_okay_s(i,j) ==true)
{
FULLCOND_rj_int::switch_step(i,j);
//cout<<"s_int: "<<i<<" "<<j<<endl;
}
}
else if(preg_mods[j]->tell_var_type()=='c' &&
preg_mods[i]->tell_var_type()=='c')
{
if(conditions == false)
FULLCOND_rj::switch_step(i,j);
else
if(conditions_okay_s(i,j) ==true)
FULLCOND_rj::switch_step(i,j);
}
}
else
{
if(preg_mods[j]->tell_var_type()=='d')
{
if(conditions == false)
FULLCOND_rj_int::birth_step(i,j);
else
if(conditions_okay_b(i,j) ==true)
FULLCOND_rj_int::birth_step(i,j);
}
else
{
if(conditions == false)
FULLCOND_rj::birth_step(i,j);
else
if(conditions_okay_b(i,j) ==true)
FULLCOND_rj::birth_step(i,j);
}
}
}
/************* from this point on like in Fullcond_rj::update ***************/
nrtrials++;
update_zeta();
//std::set < ST::string, unsigned int> freq;
if((optionsp->get_nriter() > optionsp->get_burnin()) &&
(optionsp->get_nriter() % (optionsp->get_step()) == 0))
{
store_model();
}
}
/* 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( );
}
