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-rw-r--r--gi/pf/align-lexonly-pyp.cc2
-rw-r--r--gi/pf/align-lexonly.cc2
-rw-r--r--gi/pf/brat.cc2
-rw-r--r--gi/pf/conditional_pseg.h4
-rw-r--r--gi/pf/learn_cfg.cc4
-rw-r--r--gi/pf/pfbrat.cc2
-rw-r--r--gi/pf/pyp_lm.cc70
-rw-r--r--phrasinator/gibbs_train_plm.cc2
-rw-r--r--utils/ccrp.h95
-rw-r--r--utils/ccrp_nt.h52
-rw-r--r--utils/ccrp_onetable.h70
-rw-r--r--utils/mfcr.h58
12 files changed, 203 insertions, 160 deletions
diff --git a/gi/pf/align-lexonly-pyp.cc b/gi/pf/align-lexonly-pyp.cc
index e24cb457..4ce7cf62 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.d() << ",\\alpha=" << r.alpha() << ')' << endl;
+ cerr << "NUMBER OF CUSTOMERS: " << r.num_customers() << " (d=" << r.discount() << ",\\alpha=" << r.alpha() << ')' << 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/align-lexonly.cc b/gi/pf/align-lexonly.cc
index 8c1d689f..dbc9dc07 100644
--- a/gi/pf/align-lexonly.cc
+++ b/gi/pf/align-lexonly.cc
@@ -105,7 +105,7 @@ struct HierarchicalWordBase {
}
void Summary() const {
- cerr << "NUMBER OF CUSTOMERS: " << r.num_customers() << " (\\alpha=" << r.concentration() << ')' << endl;
+ cerr << "NUMBER OF CUSTOMERS: " << r.num_customers() << " (\\alpha=" << r.alpha() << ')' << endl;
for (CCRP_NoTable<vector<WordID> >::const_iterator it = r.begin(); it != r.end(); ++it)
cerr << " " << it->second << '\t' << TD::GetString(it->first) << endl;
}
diff --git a/gi/pf/brat.cc b/gi/pf/brat.cc
index 7b60ef23..c2c52760 100644
--- a/gi/pf/brat.cc
+++ b/gi/pf/brat.cc
@@ -191,7 +191,7 @@ struct UniphraseLM {
void ResampleHyperparameters(MT19937* rng) {
phrases_.resample_hyperparameters(rng);
gen_.resample_hyperparameters(rng);
- cerr << " " << phrases_.concentration();
+ cerr << " " << phrases_.alpha();
}
CCRP_NoTable<vector<int> > phrases_;
diff --git a/gi/pf/conditional_pseg.h b/gi/pf/conditional_pseg.h
index 2e9e38fc..f9841cbf 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.d() << ",\\alpha = " << it->second.alpha() << ") --------------------------" << std::endl;
+ std::cerr << TD::GetString(it->first) << " \t(d=" << it->second.discount() << ",\\alpha = " << it->second.alpha() << ") --------------------------" << std::endl;
for (MFCR<TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
std::cerr << " " << -1 << '\t' << i2->first << std::endl;
}
@@ -95,7 +95,7 @@ struct ConditionalTranslationModel {
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(\\alpha = " << it->second.concentration() << ") --------------------------" << std::endl;
+ std::cerr << TD::GetString(it->first) << " \t(\\alpha = " << it->second.alpha() << ") --------------------------" << std::endl;
for (CCRP_NoTable<TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2)
std::cerr << " " << i2->second << '\t' << i2->first << std::endl;
}
diff --git a/gi/pf/learn_cfg.cc b/gi/pf/learn_cfg.cc
index b2ca029a..5b748311 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.discount() << "]";
+ cerr << "[base d=" << q0.discount() << ", alpha=" << q0.alpha() << "]";
}
- cerr << " d=" << nts[0].discount() << ", alpha=" << nts[0].concentration() << endl;
+ cerr << " d=" << nts[0].discount() << ", alpha=" << nts[0].alpha() << endl;
}
const BaseRuleModel base;
diff --git a/gi/pf/pfbrat.cc b/gi/pf/pfbrat.cc
index 7b60ef23..c2c52760 100644
--- a/gi/pf/pfbrat.cc
+++ b/gi/pf/pfbrat.cc
@@ -191,7 +191,7 @@ struct UniphraseLM {
void ResampleHyperparameters(MT19937* rng) {
phrases_.resample_hyperparameters(rng);
gen_.resample_hyperparameters(rng);
- cerr << " " << phrases_.concentration();
+ cerr << " " << phrases_.alpha();
}
CCRP_NoTable<vector<int> > phrases_;
diff --git a/gi/pf/pyp_lm.cc b/gi/pf/pyp_lm.cc
index 2837e33c..0d85536c 100644
--- a/gi/pf/pyp_lm.cc
+++ b/gi/pf/pyp_lm.cc
@@ -50,16 +50,19 @@ template <unsigned N> struct PYPLM;
// uniform base distribution
template<> struct PYPLM<0> {
- PYPLM(unsigned vs) : p0(1.0 / vs) {}
- void increment(WordID w, const vector<WordID>& context, MT19937* rng) const {}
- void decrement(WordID w, const vector<WordID>& context, MT19937* rng) const {}
+ PYPLM(unsigned vs) : p0(1.0 / vs), draws() {}
+ void increment(WordID w, const vector<WordID>& context, MT19937* rng) { ++draws; }
+ void decrement(WordID w, const vector<WordID>& context, MT19937* rng) { --draws; assert(draws >= 0); }
double prob(WordID w, const vector<WordID>& context) const { return p0; }
+ void resample_hyperparameters(MT19937* rng, const unsigned nloop, const unsigned niterations) {}
+ double log_likelihood() const { return draws * log(p0); }
const double p0;
+ int draws;
};
// represents an N-gram LM
template <unsigned N> struct PYPLM {
- PYPLM(unsigned vs) : backoff(vs) {}
+ PYPLM(unsigned vs) : backoff(vs), d(0.8), alpha(1.0) {}
void increment(WordID w, const vector<WordID>& context, MT19937* rng) {
const double bo = backoff.prob(w, context);
static vector<WordID> lookup(N-1);
@@ -67,7 +70,7 @@ template <unsigned N> struct PYPLM {
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>(1,1,1,1))).first;
+ it = p.insert(make_pair(lookup, CCRP<WordID>(d,alpha))).first;
if (it->second.increment(w, bo, rng))
backoff.increment(w, context, rng);
}
@@ -89,7 +92,58 @@ template <unsigned N> struct PYPLM {
if (it == p.end()) return bo;
return it->second.prob(w, bo);
}
+
+ double log_likelihood() const {
+ return log_likelihood(d, alpha) + backoff.log_likelihood();
+ }
+
+ 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, 1, 1) + Md::log_gamma_density(aa, 1, 1);
+ 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);
+ return llh;
+ }
+
+ struct DiscountResampler {
+ DiscountResampler(const PYPLM& m) : m_(m) {}
+ const PYPLM& m_;
+ double operator()(const double& proposed_discount) const {
+ return m_.log_likelihood(proposed_discount, m_.alpha);
+ }
+ };
+
+ 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);
+ }
+ };
+
+ void resample_hyperparameters(MT19937* rng, const unsigned nloop = 5, const unsigned niterations = 10) {
+ DiscountResampler dr(*this);
+ AlphaResampler ar(*this);
+ for (int iter = 0; iter < nloop; ++iter) {
+ alpha = slice_sampler1d(ar, alpha, *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,
+ 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;
+ for (it = p.begin(); it != p.end(); ++it) {
+ it->second.set_discount(d);
+ it->second.set_alpha(alpha);
+ }
+ backoff.resample_hyperparameters(rng, nloop, niterations);
+ }
+
PYPLM<N-1> backoff;
+ double d, alpha;
unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > > p;
};
@@ -109,7 +163,7 @@ int main(int argc, char** argv) {
cerr << "Reading corpus...\n";
CorpusTools::ReadFromFile(conf["input"].as<string>(), &corpuse, &vocabe);
cerr << "E-corpus size: " << corpuse.size() << " sentences\t (" << vocabe.size() << " word types)\n";
-#define kORDER 5
+#define kORDER 3
PYPLM<kORDER> lm(vocabe.size());
vector<WordID> ctx(kORDER - 1, TD::Convert("<s>"));
int mci = corpuse.size() * 99 / 100;
@@ -126,6 +180,10 @@ int main(int argc, char** argv) {
if (SS > 0) lm.decrement(kEOS, ctx, &rng);
lm.increment(kEOS, ctx, &rng);
}
+ if (SS % 10 == 9) {
+ cerr << " [LLH=" << lm.log_likelihood() << "]" << endl;
+ if (SS % 20 == 19) lm.resample_hyperparameters(&rng);
+ } else { cerr << '.' << flush; }
}
double llh = 0;
unsigned cnt = 0;
diff --git a/phrasinator/gibbs_train_plm.cc b/phrasinator/gibbs_train_plm.cc
index 66b46011..54861dcb 100644
--- a/phrasinator/gibbs_train_plm.cc
+++ b/phrasinator/gibbs_train_plm.cc
@@ -252,7 +252,7 @@ struct UniphraseLM {
void ResampleHyperparameters(MT19937* rng) {
phrases_.resample_hyperparameters(rng);
gen_.resample_hyperparameters(rng);
- cerr << " d=" << phrases_.discount() << ",c=" << phrases_.concentration();
+ cerr << " d=" << phrases_.discount() << ",a=" << phrases_.alpha();
}
CCRP<vector<int> > phrases_;
diff --git a/utils/ccrp.h b/utils/ccrp.h
index 1a9e3ed5..d9a38089 100644
--- a/utils/ccrp.h
+++ b/utils/ccrp.h
@@ -17,35 +17,37 @@
template <typename Dish, typename DishHash = boost::hash<Dish> >
class CCRP {
public:
- CCRP(double disc, double conc) :
+ CCRP(double disc, double alpha) :
num_tables_(),
num_customers_(),
discount_(disc),
- concentration_(conc),
+ alpha_(alpha),
discount_prior_alpha_(std::numeric_limits<double>::quiet_NaN()),
discount_prior_beta_(std::numeric_limits<double>::quiet_NaN()),
- concentration_prior_shape_(std::numeric_limits<double>::quiet_NaN()),
- concentration_prior_rate_(std::numeric_limits<double>::quiet_NaN()) {}
+ alpha_prior_shape_(std::numeric_limits<double>::quiet_NaN()),
+ alpha_prior_rate_(std::numeric_limits<double>::quiet_NaN()) {}
CCRP(double d_alpha, double d_beta, double c_shape, double c_rate, double d = 0.9, double c = 1.0) :
num_tables_(),
num_customers_(),
discount_(d),
- concentration_(c),
+ alpha_(c),
discount_prior_alpha_(d_alpha),
discount_prior_beta_(d_beta),
- concentration_prior_shape_(c_shape),
- concentration_prior_rate_(c_rate) {}
+ alpha_prior_shape_(c_shape),
+ alpha_prior_rate_(c_rate) {}
double discount() const { return discount_; }
- double concentration() const { return concentration_; }
+ double alpha() const { return alpha_; }
+ void set_discount(double d) { discount_ = d; }
+ void set_alpha(double a) { alpha_ = a; }
bool has_discount_prior() const {
return !std::isnan(discount_prior_alpha_);
}
- bool has_concentration_prior() const {
- return !std::isnan(concentration_prior_shape_);
+ bool has_alpha_prior() const {
+ return !std::isnan(alpha_prior_shape_);
}
void clear() {
@@ -79,7 +81,7 @@ class CCRP {
DishLocations& loc = dish_locs_[dish];
bool share_table = false;
if (loc.total_dish_count_) {
- const double p_empty = (concentration_ + num_tables_ * discount_) * p0;
+ const double p_empty = (alpha_ + num_tables_ * discount_) * p0;
const double p_share = (loc.total_dish_count_ - loc.table_counts_.size() * discount_);
share_table = rng->SelectSample(p_empty, p_share);
}
@@ -113,7 +115,7 @@ class CCRP {
DishLocations& loc = dish_locs_[dish];
bool share_table = false;
if (loc.total_dish_count_) {
- const T p_empty = T(concentration_ + num_tables_ * discount_) * p0;
+ const T p_empty = T(alpha_ + num_tables_ * discount_) * p0;
const T p_share = T(loc.total_dish_count_ - loc.table_counts_.size() * discount_);
share_table = rng->SelectSample(p_empty, p_share);
}
@@ -180,63 +182,46 @@ class CCRP {
double prob(const Dish& dish, const double& p0) const {
const typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.find(dish);
- const double r = num_tables_ * discount_ + concentration_;
+ const double r = num_tables_ * discount_ + alpha_;
if (it == dish_locs_.end()) {
- return r * p0 / (num_customers_ + concentration_);
+ return r * p0 / (num_customers_ + alpha_);
} else {
return (it->second.total_dish_count_ - discount_ * it->second.table_counts_.size() + r * p0) /
- (num_customers_ + concentration_);
+ (num_customers_ + alpha_);
}
}
template <typename T>
T probT(const Dish& dish, const T& p0) const {
const typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.find(dish);
- const T r = T(num_tables_ * discount_ + concentration_);
+ const T r = T(num_tables_ * discount_ + alpha_);
if (it == dish_locs_.end()) {
- return r * p0 / T(num_customers_ + concentration_);
+ return r * p0 / T(num_customers_ + alpha_);
} else {
return (T(it->second.total_dish_count_ - discount_ * it->second.table_counts_.size()) + r * p0) /
- T(num_customers_ + concentration_);
+ T(num_customers_ + alpha_);
}
}
double log_crp_prob() const {
- return log_crp_prob(discount_, concentration_);
- }
-
- static double log_beta_density(const double& x, const double& alpha, const double& beta) {
- assert(x > 0.0);
- assert(x < 1.0);
- assert(alpha > 0.0);
- assert(beta > 0.0);
- const double lp = (alpha-1)*log(x)+(beta-1)*log(1-x)+lgamma(alpha+beta)-lgamma(alpha)-lgamma(beta);
- return lp;
- }
-
- static double log_gamma_density(const double& x, const double& shape, const double& rate) {
- assert(x >= 0.0);
- assert(shape > 0.0);
- assert(rate > 0.0);
- const double lp = (shape-1)*log(x) - shape*log(rate) - x/rate - lgamma(shape);
- return lp;
+ return log_crp_prob(discount_, alpha_);
}
// taken from http://en.wikipedia.org/wiki/Chinese_restaurant_process
// does not include P_0's
- double log_crp_prob(const double& discount, const double& concentration) const {
+ double log_crp_prob(const double& discount, const double& alpha) const {
double lp = 0.0;
if (has_discount_prior())
- lp = log_beta_density(discount, discount_prior_alpha_, discount_prior_beta_);
- if (has_concentration_prior())
- lp += log_gamma_density(concentration, concentration_prior_shape_, concentration_prior_rate_);
+ lp = Md::log_beta_density(discount, discount_prior_alpha_, discount_prior_beta_);
+ if (has_alpha_prior())
+ lp += Md::log_gamma_density(alpha, alpha_prior_shape_, alpha_prior_rate_);
assert(lp <= 0.0);
if (num_customers_) {
if (discount > 0.0) {
const double r = lgamma(1.0 - discount);
- lp += lgamma(concentration) - lgamma(concentration + num_customers_)
- + num_tables_ * log(discount) + lgamma(concentration / discount + num_tables_)
- - lgamma(concentration / discount);
+ lp += lgamma(alpha) - lgamma(alpha + num_customers_)
+ + num_tables_ * log(discount) + lgamma(alpha / discount + num_tables_)
+ - lgamma(alpha / discount);
assert(std::isfinite(lp));
for (typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.begin();
it != dish_locs_.end(); ++it) {
@@ -254,12 +239,12 @@ class CCRP {
}
void resample_hyperparameters(MT19937* rng, const unsigned nloop = 5, const unsigned niterations = 10) {
- assert(has_discount_prior() || has_concentration_prior());
+ assert(has_discount_prior() || has_alpha_prior());
DiscountResampler dr(*this);
ConcentrationResampler cr(*this);
for (int iter = 0; iter < nloop; ++iter) {
- if (has_concentration_prior()) {
- concentration_ = slice_sampler1d(cr, concentration_, *rng, 0.0,
+ if (has_alpha_prior()) {
+ alpha_ = slice_sampler1d(cr, alpha_, *rng, 0.0,
std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
}
if (has_discount_prior()) {
@@ -267,7 +252,7 @@ class CCRP {
1.0, 0.0, niterations, 100*niterations);
}
}
- concentration_ = slice_sampler1d(cr, concentration_, *rng, 0.0,
+ alpha_ = slice_sampler1d(cr, alpha_, *rng, 0.0,
std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
}
@@ -275,15 +260,15 @@ class CCRP {
DiscountResampler(const CCRP& crp) : crp_(crp) {}
const CCRP& crp_;
double operator()(const double& proposed_discount) const {
- return crp_.log_crp_prob(proposed_discount, crp_.concentration_);
+ return crp_.log_crp_prob(proposed_discount, crp_.alpha_);
}
};
struct ConcentrationResampler {
ConcentrationResampler(const CCRP& crp) : crp_(crp) {}
const CCRP& crp_;
- double operator()(const double& proposed_concentration) const {
- return crp_.log_crp_prob(crp_.discount_, proposed_concentration);
+ double operator()(const double& proposed_alpha) const {
+ return crp_.log_crp_prob(crp_.discount_, proposed_alpha);
}
};
@@ -295,7 +280,7 @@ class CCRP {
};
void Print(std::ostream* out) const {
- std::cerr << "PYP(d=" << discount_ << ",c=" << concentration_ << ") customers=" << num_customers_ << std::endl;
+ std::cerr << "PYP(d=" << discount_ << ",c=" << alpha_ << ") customers=" << num_customers_ << std::endl;
for (typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.begin();
it != dish_locs_.end(); ++it) {
(*out) << it->first << " (" << it->second.total_dish_count_ << " on " << it->second.table_counts_.size() << " tables): ";
@@ -320,15 +305,15 @@ class CCRP {
std::tr1::unordered_map<Dish, DishLocations, DishHash> dish_locs_;
double discount_;
- double concentration_;
+ double alpha_;
// optional beta prior on discount_ (NaN if no prior)
double discount_prior_alpha_;
double discount_prior_beta_;
- // optional gamma prior on concentration_ (NaN if no prior)
- double concentration_prior_shape_;
- double concentration_prior_rate_;
+ // optional gamma prior on alpha_ (NaN if no prior)
+ double alpha_prior_shape_;
+ double alpha_prior_rate_;
};
template <typename T,typename H>
diff --git a/utils/ccrp_nt.h b/utils/ccrp_nt.h
index 63b6f4c2..79321493 100644
--- a/utils/ccrp_nt.h
+++ b/utils/ccrp_nt.h
@@ -18,20 +18,20 @@ class CCRP_NoTable {
public:
explicit CCRP_NoTable(double conc) :
num_customers_(),
- concentration_(conc),
- concentration_prior_shape_(std::numeric_limits<double>::quiet_NaN()),
- concentration_prior_rate_(std::numeric_limits<double>::quiet_NaN()) {}
+ alpha_(conc),
+ alpha_prior_shape_(std::numeric_limits<double>::quiet_NaN()),
+ alpha_prior_rate_(std::numeric_limits<double>::quiet_NaN()) {}
CCRP_NoTable(double c_shape, double c_rate, double c = 10.0) :
num_customers_(),
- concentration_(c),
- concentration_prior_shape_(c_shape),
- concentration_prior_rate_(c_rate) {}
+ alpha_(c),
+ alpha_prior_shape_(c_shape),
+ alpha_prior_rate_(c_rate) {}
- double concentration() const { return concentration_; }
+ double alpha() const { return alpha_; }
- bool has_concentration_prior() const {
- return !std::isnan(concentration_prior_shape_);
+ bool has_alpha_prior() const {
+ return !std::isnan(alpha_prior_shape_);
}
void clear() {
@@ -73,16 +73,16 @@ class CCRP_NoTable {
double prob(const Dish& dish, const double& p0) const {
const unsigned at_table = num_customers(dish);
- return (at_table + p0 * concentration_) / (num_customers_ + concentration_);
+ return (at_table + p0 * alpha_) / (num_customers_ + alpha_);
}
double logprob(const Dish& dish, const double& logp0) const {
const unsigned at_table = num_customers(dish);
- return log(at_table + exp(logp0 + log(concentration_))) - log(num_customers_ + concentration_);
+ return log(at_table + exp(logp0 + log(alpha_))) - log(num_customers_ + alpha_);
}
double log_crp_prob() const {
- return log_crp_prob(concentration_);
+ return log_crp_prob(alpha_);
}
static double log_gamma_density(const double& x, const double& shape, const double& rate) {
@@ -95,14 +95,14 @@ class CCRP_NoTable {
// taken from http://en.wikipedia.org/wiki/Chinese_restaurant_process
// does not include P_0's
- double log_crp_prob(const double& concentration) const {
+ double log_crp_prob(const double& alpha) const {
double lp = 0.0;
- if (has_concentration_prior())
- lp += log_gamma_density(concentration, concentration_prior_shape_, concentration_prior_rate_);
+ if (has_alpha_prior())
+ lp += log_gamma_density(alpha, alpha_prior_shape_, alpha_prior_rate_);
assert(lp <= 0.0);
if (num_customers_) {
- lp += lgamma(concentration) - lgamma(concentration + num_customers_) +
- custs_.size() * log(concentration);
+ lp += lgamma(alpha) - lgamma(alpha + num_customers_) +
+ custs_.size() * log(alpha);
assert(std::isfinite(lp));
for (typename std::tr1::unordered_map<Dish, unsigned, DishHash>::const_iterator it = custs_.begin();
it != custs_.end(); ++it) {
@@ -114,10 +114,10 @@ class CCRP_NoTable {
}
void resample_hyperparameters(MT19937* rng, const unsigned nloop = 5, const unsigned niterations = 10) {
- assert(has_concentration_prior());
+ assert(has_alpha_prior());
ConcentrationResampler cr(*this);
for (int iter = 0; iter < nloop; ++iter) {
- concentration_ = slice_sampler1d(cr, concentration_, *rng, 0.0,
+ alpha_ = slice_sampler1d(cr, alpha_, *rng, 0.0,
std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
}
}
@@ -125,13 +125,13 @@ class CCRP_NoTable {
struct ConcentrationResampler {
ConcentrationResampler(const CCRP_NoTable& crp) : crp_(crp) {}
const CCRP_NoTable& crp_;
- double operator()(const double& proposed_concentration) const {
- return crp_.log_crp_prob(proposed_concentration);
+ double operator()(const double& proposed_alpha) const {
+ return crp_.log_crp_prob(proposed_alpha);
}
};
void Print(std::ostream* out) const {
- (*out) << "DP(alpha=" << concentration_ << ") customers=" << num_customers_ << std::endl;
+ (*out) << "DP(alpha=" << alpha_ << ") customers=" << num_customers_ << std::endl;
int cc = 0;
for (typename std::tr1::unordered_map<Dish, unsigned, DishHash>::const_iterator it = custs_.begin();
it != custs_.end(); ++it) {
@@ -153,11 +153,11 @@ class CCRP_NoTable {
return custs_.end();
}
- double concentration_;
+ double alpha_;
- // optional gamma prior on concentration_ (NaN if no prior)
- double concentration_prior_shape_;
- double concentration_prior_rate_;
+ // optional gamma prior on alpha_ (NaN if no prior)
+ double alpha_prior_shape_;
+ double alpha_prior_rate_;
};
template <typename T,typename H>
diff --git a/utils/ccrp_onetable.h b/utils/ccrp_onetable.h
index b63737d1..1fe01b0e 100644
--- a/utils/ccrp_onetable.h
+++ b/utils/ccrp_onetable.h
@@ -21,33 +21,33 @@ class CCRP_OneTable {
num_tables_(),
num_customers_(),
discount_(disc),
- concentration_(conc),
+ alpha_(conc),
discount_prior_alpha_(std::numeric_limits<double>::quiet_NaN()),
discount_prior_beta_(std::numeric_limits<double>::quiet_NaN()),
- concentration_prior_shape_(std::numeric_limits<double>::quiet_NaN()),
- concentration_prior_rate_(std::numeric_limits<double>::quiet_NaN()) {}
+ alpha_prior_shape_(std::numeric_limits<double>::quiet_NaN()),
+ alpha_prior_rate_(std::numeric_limits<double>::quiet_NaN()) {}
CCRP_OneTable(double d_alpha, double d_beta, double c_shape, double c_rate, double d = 0.9, double c = 1.0) :
num_tables_(),
num_customers_(),
discount_(d),
- concentration_(c),
+ alpha_(c),
discount_prior_alpha_(d_alpha),
discount_prior_beta_(d_beta),
- concentration_prior_shape_(c_shape),
- concentration_prior_rate_(c_rate) {}
+ alpha_prior_shape_(c_shape),
+ alpha_prior_rate_(c_rate) {}
double discount() const { return discount_; }
- double concentration() const { return concentration_; }
- void set_concentration(double c) { concentration_ = c; }
+ double alpha() const { return alpha_; }
+ void set_alpha(double c) { alpha_ = c; }
void set_discount(double d) { discount_ = d; }
bool has_discount_prior() const {
return !std::isnan(discount_prior_alpha_);
}
- bool has_concentration_prior() const {
- return !std::isnan(concentration_prior_shape_);
+ bool has_alpha_prior() const {
+ return !std::isnan(alpha_prior_shape_);
}
void clear() {
@@ -108,29 +108,29 @@ class CCRP_OneTable {
double prob(const Dish& dish, const double& p0) const {
const typename DishMapType::const_iterator it = dish_counts_.find(dish);
- const double r = num_tables_ * discount_ + concentration_;
+ const double r = num_tables_ * discount_ + alpha_;
if (it == dish_counts_.end()) {
- return r * p0 / (num_customers_ + concentration_);
+ return r * p0 / (num_customers_ + alpha_);
} else {
return (it->second - discount_ + r * p0) /
- (num_customers_ + concentration_);
+ (num_customers_ + alpha_);
}
}
template <typename T>
T probT(const Dish& dish, const T& p0) const {
const typename DishMapType::const_iterator it = dish_counts_.find(dish);
- const T r(num_tables_ * discount_ + concentration_);
+ const T r(num_tables_ * discount_ + alpha_);
if (it == dish_counts_.end()) {
- return r * p0 / T(num_customers_ + concentration_);
+ return r * p0 / T(num_customers_ + alpha_);
} else {
return (T(it->second - discount_) + r * p0) /
- T(num_customers_ + concentration_);
+ T(num_customers_ + alpha_);
}
}
double log_crp_prob() const {
- return log_crp_prob(discount_, concentration_);
+ return log_crp_prob(discount_, alpha_);
}
static double log_beta_density(const double& x, const double& alpha, const double& beta) {
@@ -152,19 +152,19 @@ class CCRP_OneTable {
// taken from http://en.wikipedia.org/wiki/Chinese_restaurant_process
// does not include P_0's
- double log_crp_prob(const double& discount, const double& concentration) const {
+ double log_crp_prob(const double& discount, const double& alpha) const {
double lp = 0.0;
if (has_discount_prior())
lp = log_beta_density(discount, discount_prior_alpha_, discount_prior_beta_);
- if (has_concentration_prior())
- lp += log_gamma_density(concentration, concentration_prior_shape_, concentration_prior_rate_);
+ if (has_alpha_prior())
+ lp += log_gamma_density(alpha, alpha_prior_shape_, alpha_prior_rate_);
assert(lp <= 0.0);
if (num_customers_) {
if (discount > 0.0) {
const double r = lgamma(1.0 - discount);
- lp += lgamma(concentration) - lgamma(concentration + num_customers_)
- + num_tables_ * log(discount) + lgamma(concentration / discount + num_tables_)
- - lgamma(concentration / discount);
+ lp += lgamma(alpha) - lgamma(alpha + num_customers_)
+ + num_tables_ * log(discount) + lgamma(alpha / discount + num_tables_)
+ - lgamma(alpha / discount);
assert(std::isfinite(lp));
for (typename DishMapType::const_iterator it = dish_counts_.begin();
it != dish_counts_.end(); ++it) {
@@ -180,12 +180,12 @@ class CCRP_OneTable {
}
void resample_hyperparameters(MT19937* rng, const unsigned nloop = 5, const unsigned niterations = 10) {
- assert(has_discount_prior() || has_concentration_prior());
+ assert(has_discount_prior() || has_alpha_prior());
DiscountResampler dr(*this);
ConcentrationResampler cr(*this);
for (int iter = 0; iter < nloop; ++iter) {
- if (has_concentration_prior()) {
- concentration_ = slice_sampler1d(cr, concentration_, *rng, 0.0,
+ if (has_alpha_prior()) {
+ alpha_ = slice_sampler1d(cr, alpha_, *rng, 0.0,
std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
}
if (has_discount_prior()) {
@@ -193,7 +193,7 @@ class CCRP_OneTable {
1.0, 0.0, niterations, 100*niterations);
}
}
- concentration_ = slice_sampler1d(cr, concentration_, *rng, 0.0,
+ alpha_ = slice_sampler1d(cr, alpha_, *rng, 0.0,
std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
}
@@ -201,20 +201,20 @@ class CCRP_OneTable {
DiscountResampler(const CCRP_OneTable& crp) : crp_(crp) {}
const CCRP_OneTable& crp_;
double operator()(const double& proposed_discount) const {
- return crp_.log_crp_prob(proposed_discount, crp_.concentration_);
+ return crp_.log_crp_prob(proposed_discount, crp_.alpha_);
}
};
struct ConcentrationResampler {
ConcentrationResampler(const CCRP_OneTable& crp) : crp_(crp) {}
const CCRP_OneTable& crp_;
- double operator()(const double& proposed_concentration) const {
- return crp_.log_crp_prob(crp_.discount_, proposed_concentration);
+ double operator()(const double& proposed_alpha) const {
+ return crp_.log_crp_prob(crp_.discount_, proposed_alpha);
}
};
void Print(std::ostream* out) const {
- (*out) << "PYP(d=" << discount_ << ",c=" << concentration_ << ") customers=" << num_customers_ << std::endl;
+ (*out) << "PYP(d=" << discount_ << ",c=" << alpha_ << ") customers=" << num_customers_ << std::endl;
for (typename DishMapType::const_iterator it = dish_counts_.begin(); it != dish_counts_.end(); ++it) {
(*out) << " " << it->first << " = " << it->second << std::endl;
}
@@ -233,15 +233,15 @@ class CCRP_OneTable {
DishMapType dish_counts_;
double discount_;
- double concentration_;
+ double alpha_;
// optional beta prior on discount_ (NaN if no prior)
double discount_prior_alpha_;
double discount_prior_beta_;
- // optional gamma prior on concentration_ (NaN if no prior)
- double concentration_prior_shape_;
- double concentration_prior_rate_;
+ // optional gamma prior on alpha_ (NaN if no prior)
+ double alpha_prior_shape_;
+ double alpha_prior_rate_;
};
template <typename T,typename H>
diff --git a/utils/mfcr.h b/utils/mfcr.h
index 396d0205..df988f51 100644
--- a/utils/mfcr.h
+++ b/utils/mfcr.h
@@ -43,29 +43,29 @@ class MFCR {
num_floors_(num_floors),
num_tables_(),
num_customers_(),
- d_(d),
+ discount_(d),
alpha_(alpha),
- d_prior_alpha_(std::numeric_limits<double>::quiet_NaN()),
- d_prior_beta_(std::numeric_limits<double>::quiet_NaN()),
+ discount_prior_alpha_(std::numeric_limits<double>::quiet_NaN()),
+ discount_prior_beta_(std::numeric_limits<double>::quiet_NaN()),
alpha_prior_shape_(std::numeric_limits<double>::quiet_NaN()),
alpha_prior_rate_(std::numeric_limits<double>::quiet_NaN()) {}
- MFCR(unsigned num_floors, double d_alpha, double d_beta, double alpha_shape, double alpha_rate, double d = 0.9, double alpha = 10.0) :
+ MFCR(unsigned num_floors, double discount_alpha, double discount_beta, double alpha_shape, double alpha_rate, double d = 0.9, double alpha = 10.0) :
num_floors_(num_floors),
num_tables_(),
num_customers_(),
- d_(d),
+ discount_(d),
alpha_(alpha),
- d_prior_alpha_(d_alpha),
- d_prior_beta_(d_beta),
+ discount_prior_alpha_(discount_alpha),
+ discount_prior_beta_(discount_beta),
alpha_prior_shape_(alpha_shape),
alpha_prior_rate_(alpha_rate) {}
- double d() const { return d_; }
+ double discount() const { return discount_; }
double alpha() const { return alpha_; }
- bool has_d_prior() const {
- return !std::isnan(d_prior_alpha_);
+ bool has_discount_prior() const {
+ return !std::isnan(discount_prior_alpha_);
}
bool has_alpha_prior() const {
@@ -122,15 +122,15 @@ class MFCR {
int floor = -1;
bool share_table = false;
if (loc.total_dish_count_) {
- const double p_empty = (alpha_ + num_tables_ * d_) * marg_p0;
- const double p_share = (loc.total_dish_count_ - loc.table_counts_.size() * d_);
+ const double p_empty = (alpha_ + num_tables_ * discount_) * marg_p0;
+ const double p_share = (loc.total_dish_count_ - loc.table_counts_.size() * discount_);
share_table = rng->SelectSample(p_empty, p_share);
}
if (share_table) {
- double r = rng->next() * (loc.total_dish_count_ - loc.table_counts_.size() * d_);
+ double r = rng->next() * (loc.total_dish_count_ - loc.table_counts_.size() * discount_);
for (typename std::list<TableCount>::iterator ti = loc.table_counts_.begin();
ti != loc.table_counts_.end(); ++ti) {
- r -= ti->count - d_;
+ r -= ti->count - discount_;
if (r <= 0.0) {
++ti->count;
floor = ti->floor;
@@ -206,25 +206,25 @@ class MFCR {
const double marg_p0 = std::inner_product(p0s.begin(), p0s.end(), lambdas.begin(), 0.0);
assert(marg_p0 <= 1.0);
const typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.find(dish);
- const double r = num_tables_ * d_ + alpha_;
+ const double r = num_tables_ * discount_ + alpha_;
if (it == dish_locs_.end()) {
return r * marg_p0 / (num_customers_ + alpha_);
} else {
- return (it->second.total_dish_count_ - d_ * it->second.table_counts_.size() + r * marg_p0) /
+ return (it->second.total_dish_count_ - discount_ * it->second.table_counts_.size() + r * marg_p0) /
(num_customers_ + alpha_);
}
}
double log_crp_prob() const {
- return log_crp_prob(d_, alpha_);
+ return log_crp_prob(discount_, alpha_);
}
// taken from http://en.wikipedia.org/wiki/Chinese_restaurant_process
// does not include draws from G_w's
double log_crp_prob(const double& d, const double& alpha) const {
double lp = 0.0;
- if (has_d_prior())
- lp = Md::log_beta_density(d, d_prior_alpha_, d_prior_beta_);
+ if (has_discount_prior())
+ lp = Md::log_beta_density(d, discount_prior_alpha_, discount_prior_beta_);
if (has_alpha_prior())
lp += Md::log_gamma_density(alpha, alpha_prior_shape_, alpha_prior_rate_);
assert(lp <= 0.0);
@@ -251,7 +251,7 @@ class MFCR {
}
void resample_hyperparameters(MT19937* rng, const unsigned nloop = 5, const unsigned niterations = 10) {
- assert(has_d_prior() || has_alpha_prior());
+ assert(has_discount_prior() || has_alpha_prior());
DiscountResampler dr(*this);
ConcentrationResampler cr(*this);
for (int iter = 0; iter < nloop; ++iter) {
@@ -259,8 +259,8 @@ class MFCR {
alpha_ = slice_sampler1d(cr, alpha_, *rng, 0.0,
std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
}
- if (has_d_prior()) {
- d_ = slice_sampler1d(dr, d_, *rng, std::numeric_limits<double>::min(),
+ if (has_discount_prior()) {
+ discount_ = slice_sampler1d(dr, discount_, *rng, std::numeric_limits<double>::min(),
1.0, 0.0, niterations, 100*niterations);
}
}
@@ -279,8 +279,8 @@ class MFCR {
struct ConcentrationResampler {
ConcentrationResampler(const MFCR& crp) : crp_(crp) {}
const MFCR& crp_;
- double operator()(const double& proposed_alpha) const {
- return crp_.log_crp_prob(crp_.d_, proposed_alpha);
+ double operator()(const double& proposediscount_alpha) const {
+ return crp_.log_crp_prob(crp_.discount_, proposediscount_alpha);
}
};
@@ -292,7 +292,7 @@ class MFCR {
};
void Print(std::ostream* out) const {
- (*out) << "MFCR(d=" << d_ << ",alpha=" << alpha_ << ") customers=" << num_customers_ << std::endl;
+ (*out) << "MFCR(d=" << discount_ << ",alpha=" << alpha_ << ") customers=" << num_customers_ << std::endl;
for (typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.begin();
it != dish_locs_.end(); ++it) {
(*out) << it->first << " (" << it->second.total_dish_count_ << " on " << it->second.table_counts_.size() << " tables): ";
@@ -317,12 +317,12 @@ class MFCR {
unsigned num_customers_;
std::tr1::unordered_map<Dish, DishLocations, DishHash> dish_locs_;
- double d_;
+ double discount_;
double alpha_;
- // optional beta prior on d_ (NaN if no prior)
- double d_prior_alpha_;
- double d_prior_beta_;
+ // optional beta prior on discount_ (NaN if no prior)
+ double discount_prior_alpha_;
+ double discount_prior_beta_;
// optional gamma prior on alpha_ (NaN if no prior)
double alpha_prior_shape_;
generated by cgit v1.2.3 (git 2.39.1) at 2024-09-21 11:03:00 +0000