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#include <float.h>
#include "reductions.h"
namespace Scorer {
struct scorer{ vw* all; };
template <bool is_learn, float (*link)(float in)>
void predict_or_learn(scorer& s, LEARNER::base_learner& base, example& ec)
{
s.all->set_minmax(s.all->sd, ec.l.simple.label);
if (is_learn && ec.l.simple.label != FLT_MAX && ec.l.simple.weight > 0)
base.learn(ec);
else
base.predict(ec);
if(ec.l.simple.weight > 0 && ec.l.simple.label != FLT_MAX)
ec.loss = s.all->loss->getLoss(s.all->sd, ec.pred.scalar, ec.l.simple.label) * ec.l.simple.weight;
ec.pred.scalar = link(ec.pred.scalar);
}
// y = f(x) -> [0, 1]
float logistic(float in) { return 1.f / (1.f + exp(- in)); }
// http://en.wikipedia.org/wiki/Generalized_logistic_curve
// where the lower & upper asymptotes are -1 & 1 respectively
// 'glf1' stands for 'Generalized Logistic Function with [-1,1] range'
// y = f(x) -> [-1, 1]
float glf1(float in) { return 2.f / (1.f + exp(- in)) - 1.f; }
float id(float in) { return in; }
LEARNER::base_learner* setup(vw& all)
{
new_options(all, "Link options")
("link", po::value<string>()->default_value("identity"), "Specify the link function: identity, logistic or glf1");
add_options(all);
po::variables_map& vm = all.vm;
scorer& s = calloc_or_die<scorer>();
s.all = &all;
LEARNER::base_learner* base = setup_base(all);
LEARNER::learner<scorer>* l;
string link = vm["link"].as<string>();
if (!vm.count("link") || link.compare("identity") == 0)
l = &init_learner(&s, base, predict_or_learn<true, id>, predict_or_learn<false, id>);
else if (link.compare("logistic") == 0)
{
*all.file_options << " --link=logistic ";
l = &init_learner(&s, base, predict_or_learn<true, logistic>,
predict_or_learn<false, logistic>);
}
else if (link.compare("glf1") == 0)
{
*all.file_options << " --link=glf1 ";
l = &init_learner(&s, base, predict_or_learn<true, glf1>,
predict_or_learn<false, glf1>);
}
else
{
cerr << "Unknown link function: " << link << endl;
throw exception();
}
all.scorer = make_base(*l);
return all.scorer;
}
}
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