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-rw-r--r--gi/pf/align-lexonly-pyp.cc2
-rw-r--r--gi/pf/conditional_pseg.h2
-rw-r--r--gi/pf/learn_cfg.cc4
-rw-r--r--gi/pf/pyp_lm.cc22
4 files changed, 15 insertions, 15 deletions
diff --git a/gi/pf/align-lexonly-pyp.cc b/gi/pf/align-lexonly-pyp.cc
index 4ce7cf62..87f7f6b5 100644
--- a/gi/pf/align-lexonly-pyp.cc
+++ b/gi/pf/align-lexonly-pyp.cc
@@ -104,7 +104,7 @@ struct HierarchicalWordBase {
}
void Summary() const {
- cerr << "NUMBER OF CUSTOMERS: " << r.num_customers() << " (d=" << r.discount() << ",\\alpha=" << r.alpha() << ')' << endl;
+ cerr << "NUMBER OF CUSTOMERS: " << r.num_customers() << " (d=" << r.discount() << ",s=" << r.strength() << ')' << endl;
for (MFCR<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;
}
diff --git a/gi/pf/conditional_pseg.h b/gi/pf/conditional_pseg.h
index f9841cbf..86403d8d 100644
--- a/gi/pf/conditional_pseg.h
+++ b/gi/pf/conditional_pseg.h
@@ -22,7 +22,7 @@ struct MConditionalTranslationModel {
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() << ",\\alpha = " << it->second.alpha() << ") --------------------------" << std::endl;
+ std::cerr << TD::GetString(it->first) << " \t(d=" << it->second.discount() << ",s=" << it->second.strength() << ") --------------------------" << std::endl;
for (MFCR<TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
std::cerr << " " << -1 << '\t' << i2->first << std::endl;
}
diff --git a/gi/pf/learn_cfg.cc b/gi/pf/learn_cfg.cc
index 5b748311..bf157828 100644
--- a/gi/pf/learn_cfg.cc
+++ b/gi/pf/learn_cfg.cc
@@ -183,9 +183,9 @@ struct HieroLMModel {
nts[i].resample_hyperparameters(rng);
if (kHIERARCHICAL_PRIOR) {
q0.resample_hyperparameters(rng);
- cerr << "[base d=" << q0.discount() << ", alpha=" << q0.alpha() << "]";
+ cerr << "[base d=" << q0.discount() << ", s=" << q0.strength() << "]";
}
- cerr << " d=" << nts[0].discount() << ", alpha=" << nts[0].alpha() << endl;
+ cerr << " d=" << nts[0].discount() << ", s=" << nts[0].strength() << endl;
}
const BaseRuleModel base;
diff --git a/gi/pf/pyp_lm.cc b/gi/pf/pyp_lm.cc
index e5c44c8b..7ebada13 100644
--- a/gi/pf/pyp_lm.cc
+++ b/gi/pf/pyp_lm.cc
@@ -78,14 +78,14 @@ template <unsigned N> struct PYPLM {
backoff(vs, da, db, ss, sr),
discount_a(da), discount_b(db),
strength_s(ss), strength_r(sr),
- d(0.8), alpha(1.0), lookup(N-1) {}
+ d(0.8), strength(1.0), lookup(N-1) {}
void increment(WordID w, const vector<WordID>& context, MT19937* rng) {
const double bo = backoff.prob(w, context);
for (unsigned i = 0; i < N-1; ++i)
lookup[i] = context[context.size() - 1 - i];
typename unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > >::iterator it = p.find(lookup);
if (it == p.end())
- it = p.insert(make_pair(lookup, CCRP<WordID>(d,alpha))).first;
+ it = p.insert(make_pair(lookup, CCRP<WordID>(d,strength))).first;
if (it->second.increment(w, bo, rng))
backoff.increment(w, context, rng);
}
@@ -107,7 +107,7 @@ template <unsigned N> struct PYPLM {
}
double log_likelihood() const {
- return log_likelihood(d, alpha) + backoff.log_likelihood();
+ return log_likelihood(d, strength) + backoff.log_likelihood();
}
double log_likelihood(const double& dd, const double& aa) const {
@@ -125,15 +125,15 @@ template <unsigned N> struct PYPLM {
DiscountResampler(const PYPLM& m) : m_(m) {}
const PYPLM& m_;
double operator()(const double& proposed_discount) const {
- return m_.log_likelihood(proposed_discount, m_.alpha);
+ return m_.log_likelihood(proposed_discount, m_.strength);
}
};
struct AlphaResampler {
AlphaResampler(const PYPLM& m) : m_(m) {}
const PYPLM& m_;
- double operator()(const double& proposed_alpha) const {
- return m_.log_likelihood(m_.d, proposed_alpha);
+ double operator()(const double& proposed_strength) const {
+ return m_.log_likelihood(m_.d, proposed_strength);
}
};
@@ -141,25 +141,25 @@ template <unsigned N> struct PYPLM {
DiscountResampler dr(*this);
AlphaResampler ar(*this);
for (int iter = 0; iter < nloop; ++iter) {
- alpha = slice_sampler1d(ar, alpha, *rng, 0.0,
+ strength = slice_sampler1d(ar, strength, *rng, 0.0,
std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
d = slice_sampler1d(dr, d, *rng, std::numeric_limits<double>::min(),
1.0, 0.0, niterations, 100*niterations);
}
- alpha = slice_sampler1d(ar, alpha, *rng, 0.0,
+ strength = slice_sampler1d(ar, strength, *rng, 0.0,
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=" << alpha << ") = " << log_likelihood(d, alpha) << endl;
+ cerr << "PYPLM<" << N << ">(d=" << d << ",a=" << strength << ") = " << log_likelihood(d, strength) << endl;
for (it = p.begin(); it != p.end(); ++it) {
it->second.set_discount(d);
- it->second.set_alpha(alpha);
+ it->second.set_strength(strength);
}
backoff.resample_hyperparameters(rng, nloop, niterations);
}
PYPLM<N-1> backoff;
double discount_a, discount_b, strength_s, strength_r;
- double d, alpha;
+ double d, strength;
mutable vector<WordID> lookup; // thread-local
unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > > p;
};