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-rw-r--r--gi/pf/align-lexonly-pyp.cc13
-rw-r--r--gi/pf/conditional_pseg.h68
-rw-r--r--gi/pf/pyp_lm.cc12
-rw-r--r--utils/ccrp.h4
-rw-r--r--utils/mfcr.h19
5 files changed, 84 insertions, 32 deletions
diff --git a/gi/pf/align-lexonly-pyp.cc b/gi/pf/align-lexonly-pyp.cc
index ac0590e0..13a3a487 100644
--- a/gi/pf/align-lexonly-pyp.cc
+++ b/gi/pf/align-lexonly-pyp.cc
@@ -68,14 +68,14 @@ struct AlignedSentencePair {
struct HierarchicalWordBase {
explicit HierarchicalWordBase(const unsigned vocab_e_size) :
- base(prob_t::One()), r(1,1,1,1), u0(-log(vocab_e_size)), l(1,prob_t::One()), v(1, prob_t::Zero()) {}
+ base(prob_t::One()), r(1,1,1,1,0.66,50.0), u0(-log(vocab_e_size)), l(1,prob_t::One()), v(1, prob_t::Zero()) {}
void ResampleHyperparameters(MT19937* rng) {
r.resample_hyperparameters(rng);
}
inline double logp0(const vector<WordID>& s) const {
- return s.size() * u0;
+ return Md::log_poisson(s.size(), 7.5) + s.size() * u0;
}
// return p0 of rule.e_
@@ -106,7 +106,7 @@ struct HierarchicalWordBase {
void Summary() const {
cerr << "NUMBER OF CUSTOMERS: " << r.num_customers() << " (d=" << r.discount() << ",s=" << r.strength() << ')' << endl;
for (MFCR<1,vector<WordID> >::const_iterator it = r.begin(); it != r.end(); ++it)
- cerr << " " << it->second.total_dish_count_ << " (on " << it->second.table_counts_.size() << " tables)" << TD::GetString(it->first) << endl;
+ cerr << " " << it->second.total_dish_count_ << " (on " << it->second.table_counts_.size() << " tables) " << TD::GetString(it->first) << endl;
}
prob_t base;
@@ -167,10 +167,9 @@ struct BasicLexicalAlignment {
}
void ResampleHyperparemeters() {
- cerr << " LLH_prev = " << Likelihood() << flush;
tmodel.ResampleHyperparameters(&*prng);
up0.ResampleHyperparameters(&*prng);
- cerr << "\tLLH_post = " << Likelihood() << endl;
+ cerr << " (base d=" << up0.r.discount() << ",s=" << up0.r.strength() << ")\n";
}
void ResampleCorpus();
@@ -218,7 +217,7 @@ void BasicLexicalAlignment::ResampleCorpus() {
up0.Increment(r);
}
}
- cerr << " LLH = " << tmodel.Likelihood() << endl;
+ cerr << " LLH = " << Likelihood() << endl;
}
void ExtractLetters(const set<WordID>& v, vector<vector<WordID> >* l, set<WordID>* letset = NULL) {
@@ -311,7 +310,7 @@ int main(int argc, char** argv) {
for (int i = 0; i < samples; ++i) {
for (int j = 65; j < 67; ++j) Debug(corpus[j]);
cerr << i << "\t" << x.tmodel.r.size() << "\t";
- if (i % 10 == 0) x.ResampleHyperparemeters();
+ if (i % 7 == 6) x.ResampleHyperparemeters();
x.ResampleCorpus();
if (i > (samples / 5) && (i % 10 == 9)) for (int j = 0; j < corpus.size(); ++j) AddSample(&corpus[j]);
}
diff --git a/gi/pf/conditional_pseg.h b/gi/pf/conditional_pseg.h
index ef73e332..8202778b 100644
--- a/gi/pf/conditional_pseg.h
+++ b/gi/pf/conditional_pseg.h
@@ -17,21 +17,66 @@
template <typename ConditionalBaseMeasure>
struct MConditionalTranslationModel {
explicit MConditionalTranslationModel(ConditionalBaseMeasure& rcp0) :
- rp0(rcp0), lambdas(1, prob_t::One()), p0s(1) {}
+ rp0(rcp0), d(0.5), strength(1.0), lambdas(1, prob_t::One()), p0s(1) {}
void Summary() const {
std::cerr << "Number of conditioning contexts: " << r.size() << std::endl;
for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) {
std::cerr << TD::GetString(it->first) << " \t(d=" << it->second.discount() << ",s=" << it->second.strength() << ") --------------------------" << std::endl;
for (MFCR<1,TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
- std::cerr << " " << -1 << '\t' << i2->first << std::endl;
+ std::cerr << " " << i2->second.total_dish_count_ << '\t' << i2->first << std::endl;
}
}
+ double log_likelihood(const double& dd, const double& aa) const {
+ 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, 1, 1) +
+ Md::log_gamma_density(dd + aa, 1, 1);
+ typename std::tr1::unordered_map<std::vector<WordID>, MFCR<1,TRule>, boost::hash<std::vector<WordID> > >::const_iterator it;
+ for (it = r.begin(); it != r.end(); ++it)
+ llh += it->second.log_crp_prob(dd, aa);
+ return llh;
+ }
+
+ struct DiscountResampler {
+ DiscountResampler(const MConditionalTranslationModel& m) : m_(m) {}
+ const MConditionalTranslationModel& m_;
+ double operator()(const double& proposed_discount) const {
+ return m_.log_likelihood(proposed_discount, m_.strength);
+ }
+ };
+
+ struct AlphaResampler {
+ AlphaResampler(const MConditionalTranslationModel& m) : m_(m) {}
+ const MConditionalTranslationModel& m_;
+ double operator()(const double& proposed_strength) const {
+ return m_.log_likelihood(m_.d, proposed_strength);
+ }
+ };
+
void ResampleHyperparameters(MT19937* rng) {
- for (RuleModelHash::iterator it = r.begin(); it != r.end(); ++it)
- it->second.resample_hyperparameters(rng);
- }
+ const unsigned nloop = 5;
+ const unsigned niterations = 10;
+ DiscountResampler dr(*this);
+ AlphaResampler ar(*this);
+ for (int iter = 0; iter < nloop; ++iter) {
+ strength = slice_sampler1d(ar, strength, *rng, -d + 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;
+ d = slice_sampler1d(dr, d, *rng, min_discount,
+ 1.0, 0.0, niterations, 100*niterations);
+ }
+ strength = slice_sampler1d(ar, strength, *rng, -d,
+ std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
+ typename std::tr1::unordered_map<std::vector<WordID>, MFCR<1,TRule>, boost::hash<std::vector<WordID> > >::iterator it;
+ std::cerr << "MConditionalTranslationModel(d=" << d << ",s=" << strength << ") = " << log_likelihood(d, strength) << std::endl;
+ for (it = r.begin(); it != r.end(); ++it) {
+ it->second.set_discount(d);
+ it->second.set_strength(strength);
+ }
+ }
int DecrementRule(const TRule& rule, MT19937* rng) {
RuleModelHash::iterator it = r.find(rule.f_);
@@ -46,7 +91,7 @@ struct MConditionalTranslationModel {
int IncrementRule(const TRule& rule, MT19937* rng) {
RuleModelHash::iterator it = r.find(rule.f_);
if (it == r.end()) {
- it = r.insert(make_pair(rule.f_, MFCR<1,TRule>(1.0, 1.0, 1.0, 1.0, 1e-9, 4.0))).first;
+ it = r.insert(make_pair(rule.f_, MFCR<1,TRule>(d, strength))).first;
}
p0s[0] = rp0(rule);
TableCount delta = it->second.increment(rule, p0s.begin(), lambdas.begin(), rng);
@@ -66,15 +111,7 @@ struct MConditionalTranslationModel {
}
prob_t Likelihood() const {
- prob_t p = prob_t::One();
-#if 0
- for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) {
- prob_t q; q.logeq(it->second.log_crp_prob());
- p *= q;
- for (CCRP_NoTable<TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
- p *= rp0(i2->first);
- }
-#endif
+ prob_t p; p.logeq(log_likelihood(d, strength));
return p;
}
@@ -83,6 +120,7 @@ struct MConditionalTranslationModel {
MFCR<1, TRule>,
boost::hash<std::vector<WordID> > > RuleModelHash;
RuleModelHash r;
+ double d, strength;
std::vector<prob_t> lambdas;
mutable std::vector<prob_t> p0s;
};
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;
diff --git a/utils/ccrp.h b/utils/ccrp.h
index e24130ac..439d7e1e 100644
--- a/utils/ccrp.h
+++ b/utils/ccrp.h
@@ -225,12 +225,12 @@ class CCRP {
StrengthResampler sr(*this);
for (int iter = 0; iter < nloop; ++iter) {
if (has_strength_prior()) {
- strength_ = slice_sampler1d(sr, strength_, *rng, -discount_,
+ strength_ = slice_sampler1d(sr, strength_, *rng, -discount_ + std::numeric_limits<double>::min(),
std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
}
if (has_discount_prior()) {
double min_discount = std::numeric_limits<double>::min();
- if (strength_ < 0.0) min_discount = -strength_;
+ if (strength_ < 0.0) min_discount -= strength_;
discount_ = slice_sampler1d(dr, discount_, *rng, min_discount,
1.0, 0.0, niterations, 100*niterations);
}
diff --git a/utils/mfcr.h b/utils/mfcr.h
index 6cc0ebf1..886f01ef 100644
--- a/utils/mfcr.h
+++ b/utils/mfcr.h
@@ -48,7 +48,7 @@ class MFCR {
discount_prior_strength_(std::numeric_limits<double>::quiet_NaN()),
discount_prior_beta_(std::numeric_limits<double>::quiet_NaN()),
strength_prior_shape_(std::numeric_limits<double>::quiet_NaN()),
- strength_prior_rate_(std::numeric_limits<double>::quiet_NaN()) {}
+ strength_prior_rate_(std::numeric_limits<double>::quiet_NaN()) { check_hyperparameters(); }
MFCR(double discount_strength, double discount_beta, double strength_shape, double strength_rate, double d = 0.9, double strength = 10.0) :
num_tables_(),
@@ -58,10 +58,23 @@ class MFCR {
discount_prior_strength_(discount_strength),
discount_prior_beta_(discount_beta),
strength_prior_shape_(strength_shape),
- strength_prior_rate_(strength_rate) {}
+ strength_prior_rate_(strength_rate) { check_hyperparameters(); }
+
+ void check_hyperparameters() {
+ if (discount_ < 0.0 || discount_ >= 1.0) {
+ std::cerr << "Bad discount: " << discount_ << std::endl;
+ abort();
+ }
+ if (strength_ <= -discount_) {
+ std::cerr << "Bad strength: " << strength_ << " (discount=" << discount_ << ")" << std::endl;
+ abort();
+ }
+ }
double discount() const { return discount_; }
double strength() const { return strength_; }
+ void set_discount(double d) { discount_ = d; check_hyperparameters(); }
+ void set_strength(double a) { strength_ = a; check_hyperparameters(); }
bool has_discount_prior() const {
return !std::isnan(discount_prior_strength_);
@@ -275,7 +288,7 @@ class MFCR {
}
if (has_discount_prior()) {
double min_discount = std::numeric_limits<double>::min();
- if (strength_ < 0.0) min_discount = -strength_;
+ if (strength_ < 0.0) min_discount -= strength_;
discount_ = slice_sampler1d(dr, discount_, *rng, min_discount,
1.0, 0.0, niterations, 100*niterations);
}
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-11 04:51:17 +0000