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#include <float.h>
#include "reductions.h"
using namespace LEARNER;
namespace Scorer {
struct scorer{
vw* all;
};
template <bool is_learn, float (*link)(float in)>
void predict_or_learn(scorer& s, 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 noop(float in)
{
return in;
}
base_learner* setup(vw& all, po::variables_map& vm)
{
po::options_description opts("Link options");
opts.add_options()
("link", po::value<string>()->default_value("identity"), "Specify the link function: identity, logistic or glf1");
vm = add_options(all, opts);
string link = vm["link"].as<string>();
scorer& s = calloc_or_die<scorer>();
s.all = &all;
learner<scorer>& l = init_learner(&s, all.l);
if (!vm.count("link") || link.compare("identity") == 0)
{
l.set_learn(predict_or_learn<true, noop> );
l.set_predict(predict_or_learn<false, noop> );
}
else if (link.compare("logistic") == 0)
{
*all.file_options << " --link=logistic ";
l.set_learn(predict_or_learn<true, logistic> );
l.set_predict(predict_or_learn<false, logistic>);
}
else if (link.compare("glf1") == 0)
{
*all.file_options << " --link=glf1 ";
l.set_learn(predict_or_learn<true, glf1>);
l.set_predict(predict_or_learn<false, glf1>);
}
else
{
cerr << "Unknown link function: " << link << endl;
throw exception();
}
return make_base(l);
}
}
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