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#include "global_data.h"
#include "parser.h"
#include "learner.h"
#include "vw.h"
void save_predictor(vw& all, string reg_name, size_t current_pass);
void dispatch_example(vw& all, example& ec)
{
if (ec.test_only || !all.training)
all.l->predict(ec);
else
all.l->learn(ec);
all.l->finish_example(all, ec);
}
namespace LEARNER
{
void generic_driver(vw* all)
{
example* ec = NULL;
all->l->init_driver();
while ( true )
{
if ((ec = VW::get_example(all->p)) != NULL)//semiblocking operation.
{
if (ec->indices.size() > 1) // 1+ nonconstant feature. (most common case first)
dispatch_example(*all, *ec);
else if (ec->end_pass)
{
all->l->end_pass();
VW::finish_example(*all,ec);
}
else if (ec->tag.size() >= 4 && !strncmp((const char*) ec->tag.begin, "save", 4))
{// save state command
string final_regressor_name = all->final_regressor_name;
if ((ec->tag).size() >= 6 && (ec->tag)[4] == '_')
final_regressor_name = string(ec->tag.begin+5, (ec->tag).size()-5);
if (!all->quiet)
cerr << "saving regressor to " << final_regressor_name << endl;
save_predictor(*all, final_regressor_name, 0);
VW::finish_example(*all,ec);
}
else // empty example
dispatch_example(*all, *ec);
}
else if (parser_done(all->p))
{
all->l->end_examples();
return;
}
}
}
}
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