diff options
Diffstat (limited to 'gi/pf/pyp_lm.cc')
| -rw-r--r-- | gi/pf/pyp_lm.cc | 12 |
1 files changed, 7 insertions, 5 deletions
diff --git a/gi/pf/pyp_lm.cc b/gi/pf/pyp_lm.cc index 7ebada13..104f356b 100644 --- a/gi/pf/pyp_lm.cc +++ b/gi/pf/pyp_lm.cc @@ -18,7 +18,7 @@ // I use templates to handle the recursive formalation of the prior, so // the order of the model has to be specified here, at compile time: -#define kORDER 3 +#define kORDER 4 using namespace std; using namespace tr1; @@ -114,7 +114,7 @@ template <unsigned N> struct PYPLM { if (aa <= -dd) return -std::numeric_limits<double>::infinity(); //double llh = Md::log_beta_density(dd, 10, 3) + Md::log_gamma_density(aa, 1, 1); double llh = Md::log_beta_density(dd, discount_a, discount_b) + - Md::log_gamma_density(aa, strength_s, strength_r); + Md::log_gamma_density(aa + dd, strength_s, strength_r); typename unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > >::const_iterator it; for (it = p.begin(); it != p.end(); ++it) llh += it->second.log_crp_prob(dd, aa); @@ -141,12 +141,14 @@ template <unsigned N> struct PYPLM { DiscountResampler dr(*this); AlphaResampler ar(*this); for (int iter = 0; iter < nloop; ++iter) { - strength = slice_sampler1d(ar, strength, *rng, 0.0, + strength = slice_sampler1d(ar, strength, *rng, -d + std::numeric_limits<double>::min(), std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations); - d = slice_sampler1d(dr, d, *rng, std::numeric_limits<double>::min(), + double min_discount = std::numeric_limits<double>::min(); + if (strength < 0.0) min_discount -= strength; + d = slice_sampler1d(dr, d, *rng, min_discount, 1.0, 0.0, niterations, 100*niterations); } - strength = slice_sampler1d(ar, strength, *rng, 0.0, + strength = slice_sampler1d(ar, strength, *rng, -d + std::numeric_limits<double>::min(), std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations); typename unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > >::iterator it; cerr << "PYPLM<" << N << ">(d=" << d << ",a=" << strength << ") = " << log_likelihood(d, strength) << endl; |