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#ifndef _TIED_RESAMPLER_H_
#define _TIED_RESAMPLER_H_
#include <set>
#include <vector>
#include "sampler.h"
#include "slice_sampler.h"
#include "m.h"
template <class CRP>
struct TiedResampler {
explicit TiedResampler(double da, double db, double ss, double sr, double d=0.5, double s=1.0) :
d_alpha(da),
d_beta(db),
s_shape(ss),
s_rate(sr),
discount(d),
strength(s) {}
void Add(CRP* crp) {
crps.insert(crp);
crp->set_discount(discount);
crp->set_strength(strength);
assert(!crp->has_discount_prior());
assert(!crp->has_strength_prior());
}
void Remove(CRP* crp) {
crps.erase(crp);
}
size_t size() const {
return crps.size();
}
double LogLikelihood(double d, double s) const {
if (s <= -d) return -std::numeric_limits<double>::infinity();
double llh = Md::log_beta_density(d, d_alpha, d_beta) +
Md::log_gamma_density(d + s, s_shape, s_rate);
for (typename std::set<CRP*>::iterator it = crps.begin(); it != crps.end(); ++it)
llh += (*it)->log_crp_prob(d, s);
return llh;
}
double LogLikelihood() const {
return LogLikelihood(discount, strength);
}
struct DiscountResampler {
DiscountResampler(const TiedResampler& m) : m_(m) {}
const TiedResampler& m_;
double operator()(const double& proposed_discount) const {
return m_.LogLikelihood(proposed_discount, m_.strength);
}
};
struct AlphaResampler {
AlphaResampler(const TiedResampler& m) : m_(m) {}
const TiedResampler& m_;
double operator()(const double& proposed_strength) const {
return m_.LogLikelihood(m_.discount, proposed_strength);
}
};
void ResampleHyperparameters(MT19937* rng, const unsigned nloop = 5, const unsigned niterations = 10) {
if (size() == 0) { std::cerr << "EMPTY - not resampling\n"; return; }
const DiscountResampler dr(*this);
const AlphaResampler ar(*this);
for (int iter = 0; iter < nloop; ++iter) {
strength = slice_sampler1d(ar, strength, *rng, -discount + std::numeric_limits<double>::min(),
std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
double min_discount = std::numeric_limits<double>::min();
if (strength < 0.0) min_discount -= strength;
discount = slice_sampler1d(dr, discount, *rng, min_discount,
1.0, 0.0, niterations, 100*niterations);
}
strength = slice_sampler1d(ar, strength, *rng, -discount + std::numeric_limits<double>::min(),
std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
std::cerr << "TiedCRPs(d=" << discount << ",s="
<< strength << ") = " << LogLikelihood(discount, strength) << std::endl;
for (typename std::set<CRP*>::iterator it = crps.begin(); it != crps.end(); ++it)
(*it)->set_hyperparameters(discount, strength);
}
private:
std::set<CRP*> crps;
const double d_alpha, d_beta, s_shape, s_rate;
double discount, strength;
};
// split according to some criterion
template <class CRP>
struct BinTiedResampler {
explicit BinTiedResampler(unsigned nbins) :
resamplers(nbins, TiedResampler<CRP>(1,1,1,1)) {}
void Add(unsigned bin, CRP* crp) {
resamplers[bin].Add(crp);
}
void Remove(unsigned bin, CRP* crp) {
resamplers[bin].Remove(crp);
}
void ResampleHyperparameters(MT19937* rng) {
for (unsigned i = 0; i < resamplers.size(); ++i) {
std::cerr << "BIN " << i << " (" << resamplers[i].size() << " CRPs): " << std::flush;
resamplers[i].ResampleHyperparameters(rng);
}
}
double LogLikelihood() const {
double llh = 0;
for (unsigned i = 0; i < resamplers.size(); ++i)
llh += resamplers[i].LogLikelihood();
return llh;
}
private:
std::vector<TiedResampler<CRP> > resamplers;
};
#endif
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