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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
-rw-r--r--phrasinator/gibbs_train_plm.cc2
-rw-r--r--utils/ccrp.h106
-rw-r--r--utils/mfcr.h105
7 files changed, 131 insertions, 112 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;
};
diff --git a/phrasinator/gibbs_train_plm.cc b/phrasinator/gibbs_train_plm.cc
index 54861dcb..3b99e1b6 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() << ",a=" << phrases_.alpha();
+ cerr << " d=" << phrases_.discount() << ",s=" << phrases_.strength();
}
CCRP<vector<int> > phrases_;
diff --git a/utils/ccrp.h b/utils/ccrp.h
index c883c027..5f9db7a6 100644
--- a/utils/ccrp.h
+++ b/utils/ccrp.h
@@ -18,27 +18,27 @@
template <typename Dish, typename DishHash = boost::hash<Dish> >
class CCRP {
public:
- CCRP(double disc, double alpha) :
+ CCRP(double disc, double strength) :
num_tables_(),
num_customers_(),
discount_(disc),
- alpha_(alpha),
- discount_prior_alpha_(std::numeric_limits<double>::quiet_NaN()),
+ strength_(strength),
+ discount_prior_strength_(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()) {
+ strength_prior_shape_(std::numeric_limits<double>::quiet_NaN()),
+ strength_prior_rate_(std::numeric_limits<double>::quiet_NaN()) {
check_hyperparameters();
}
- CCRP(double d_alpha, double d_beta, double c_shape, double c_rate, double d = 0.9, double c = 1.0) :
+ CCRP(double d_strength, double d_beta, double c_shape, double c_rate, double d = 0.9, double c = 1.0) :
num_tables_(),
num_customers_(),
discount_(d),
- alpha_(c),
- discount_prior_alpha_(d_alpha),
+ strength_(c),
+ discount_prior_strength_(d_strength),
discount_prior_beta_(d_beta),
- alpha_prior_shape_(c_shape),
- alpha_prior_rate_(c_rate) {
+ strength_prior_shape_(c_shape),
+ strength_prior_rate_(c_rate) {
check_hyperparameters();
}
@@ -47,23 +47,23 @@ class CCRP {
std::cerr << "Bad discount: " << discount_ << std::endl;
abort();
}
- if (alpha_ <= -discount_) {
- std::cerr << "Bad strength: " << alpha_ << " (discount=" << discount_ << ")" << std::endl;
+ if (strength_ <= -discount_) {
+ std::cerr << "Bad strength: " << strength_ << " (discount=" << discount_ << ")" << std::endl;
abort();
}
}
double discount() const { return discount_; }
- double alpha() const { return alpha_; }
+ double strength() const { return strength_; }
void set_discount(double d) { discount_ = d; check_hyperparameters(); }
- void set_alpha(double a) { alpha_ = a; check_hyperparameters(); }
+ void set_strength(double a) { strength_ = a; check_hyperparameters(); }
bool has_discount_prior() const {
- return !std::isnan(discount_prior_alpha_);
+ return !std::isnan(discount_prior_strength_);
}
- bool has_alpha_prior() const {
- return !std::isnan(alpha_prior_shape_);
+ bool has_strength_prior() const {
+ return !std::isnan(strength_prior_shape_);
}
void clear() {
@@ -97,7 +97,7 @@ class CCRP {
DishLocations& loc = dish_locs_[dish];
bool share_table = false;
if (loc.total_dish_count_) {
- const double p_empty = (alpha_ + num_tables_ * discount_) * p0;
+ const double p_empty = (strength_ + 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);
}
@@ -131,7 +131,7 @@ class CCRP {
DishLocations& loc = dish_locs_[dish];
bool share_table = false;
if (loc.total_dish_count_) {
- const T p_empty = T(alpha_ + num_tables_ * discount_) * p0;
+ const T p_empty = T(strength_ + 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);
}
@@ -198,47 +198,47 @@ 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_ + alpha_;
+ const double r = num_tables_ * discount_ + strength_;
if (it == dish_locs_.end()) {
- return r * p0 / (num_customers_ + alpha_);
+ return r * p0 / (num_customers_ + strength_);
} else {
return (it->second.total_dish_count_ - discount_ * it->second.table_counts_.size() + r * p0) /
- (num_customers_ + alpha_);
+ (num_customers_ + strength_);
}
}
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_ + alpha_);
+ const T r = T(num_tables_ * discount_ + strength_);
if (it == dish_locs_.end()) {
- return r * p0 / T(num_customers_ + alpha_);
+ return r * p0 / T(num_customers_ + strength_);
} else {
return (T(it->second.total_dish_count_ - discount_ * it->second.table_counts_.size()) + r * p0) /
- T(num_customers_ + alpha_);
+ T(num_customers_ + strength_);
}
}
double log_crp_prob() const {
- return log_crp_prob(discount_, alpha_);
+ return log_crp_prob(discount_, strength_);
}
// 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& alpha) const {
+ double log_crp_prob(const double& discount, const double& strength) const {
double lp = 0.0;
if (has_discount_prior())
- lp = Md::log_beta_density(discount, discount_prior_alpha_, discount_prior_beta_);
- if (has_alpha_prior())
- lp += Md::log_gamma_density(alpha + discount, alpha_prior_shape_, alpha_prior_rate_);
+ lp = Md::log_beta_density(discount, discount_prior_strength_, discount_prior_beta_);
+ if (has_strength_prior())
+ lp += Md::log_gamma_density(strength + discount, strength_prior_shape_, strength_prior_rate_);
assert(lp <= 0.0);
if (num_customers_) {
if (discount > 0.0) {
const double r = lgamma(1.0 - discount);
- if (alpha)
- lp += lgamma(alpha) - lgamma(alpha / discount);
- lp += - lgamma(alpha + num_customers_)
- + num_tables_ * log(discount) + lgamma(alpha / discount + num_tables_);
+ if (strength)
+ lp += lgamma(strength) - lgamma(strength / discount);
+ lp += - lgamma(strength + num_customers_)
+ + num_tables_ * log(discount) + lgamma(strength / discount + num_tables_);
assert(std::isfinite(lp));
for (typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.begin();
it != dish_locs_.end(); ++it) {
@@ -247,8 +247,16 @@ class CCRP {
lp += lgamma(*ti - discount) - r;
}
}
+ } else if (!discount) { // discount == 0.0
+ lp += lgamma(strength) + num_tables_ * log(strength) - lgamma(strength + num_tables_);
+ assert(std::isfinite(lp));
+ for (typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.begin();
+ it != dish_locs_.end(); ++it) {
+ const DishLocations& cur = it->second;
+ lp += lgamma(cur.table_counts_.size());
+ }
} else {
- assert(!"not implemented yet");
+ assert(!"discount less than 0 detected!");
}
}
assert(std::isfinite(lp));
@@ -256,22 +264,22 @@ class CCRP {
}
void resample_hyperparameters(MT19937* rng, const unsigned nloop = 5, const unsigned niterations = 10) {
- assert(has_discount_prior() || has_alpha_prior());
+ assert(has_discount_prior() || has_strength_prior());
DiscountResampler dr(*this);
StrengthResampler sr(*this);
for (int iter = 0; iter < nloop; ++iter) {
- if (has_alpha_prior()) {
- alpha_ = slice_sampler1d(sr, alpha_, *rng, -discount_,
+ if (has_strength_prior()) {
+ strength_ = slice_sampler1d(sr, strength_, *rng, -discount_,
std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
}
if (has_discount_prior()) {
double min_discount = std::numeric_limits<double>::min();
- if (alpha_ < 0.0) min_discount = -alpha_;
+ if (strength_ < 0.0) min_discount = -strength_;
discount_ = slice_sampler1d(dr, discount_, *rng, min_discount,
1.0, 0.0, niterations, 100*niterations);
}
}
- alpha_ = slice_sampler1d(sr, alpha_, *rng, -discount_,
+ strength_ = slice_sampler1d(sr, strength_, *rng, -discount_,
std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
}
@@ -279,15 +287,15 @@ 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_.alpha_);
+ return crp_.log_crp_prob(proposed_discount, crp_.strength_);
}
};
struct StrengthResampler {
StrengthResampler(const CCRP& crp) : crp_(crp) {}
const CCRP& crp_;
- double operator()(const double& proposed_alpha) const {
- return crp_.log_crp_prob(crp_.discount_, proposed_alpha);
+ double operator()(const double& proposed_strength) const {
+ return crp_.log_crp_prob(crp_.discount_, proposed_strength);
}
};
@@ -299,7 +307,7 @@ class CCRP {
};
void Print(std::ostream* out) const {
- std::cerr << "PYP(d=" << discount_ << ",c=" << alpha_ << ") customers=" << num_customers_ << std::endl;
+ std::cerr << "PYP(d=" << discount_ << ",c=" << strength_ << ") 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): ";
@@ -324,15 +332,15 @@ class CCRP {
std::tr1::unordered_map<Dish, DishLocations, DishHash> dish_locs_;
double discount_;
- double alpha_;
+ double strength_;
// optional beta prior on discount_ (NaN if no prior)
- double discount_prior_alpha_;
+ double discount_prior_strength_;
double discount_prior_beta_;
- // optional gamma prior on alpha_ (NaN if no prior)
- double alpha_prior_shape_;
- double alpha_prior_rate_;
+ // optional gamma prior on strength_ (NaN if no prior)
+ double strength_prior_shape_;
+ double strength_prior_rate_;
};
template <typename T,typename H>
diff --git a/utils/mfcr.h b/utils/mfcr.h
index df988f51..aeaf599d 100644
--- a/utils/mfcr.h
+++ b/utils/mfcr.h
@@ -39,37 +39,37 @@ template <typename Dish, typename DishHash = boost::hash<Dish> >
class MFCR {
public:
- MFCR(unsigned num_floors, double d, double alpha) :
+ MFCR(unsigned num_floors, double d, double strength) :
num_floors_(num_floors),
num_tables_(),
num_customers_(),
discount_(d),
- alpha_(alpha),
- discount_prior_alpha_(std::numeric_limits<double>::quiet_NaN()),
+ strength_(strength),
+ discount_prior_strength_(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()) {}
+ strength_prior_shape_(std::numeric_limits<double>::quiet_NaN()),
+ strength_prior_rate_(std::numeric_limits<double>::quiet_NaN()) {}
- MFCR(unsigned num_floors, double discount_alpha, double discount_beta, double alpha_shape, double alpha_rate, double d = 0.9, double alpha = 10.0) :
+ MFCR(unsigned num_floors, double discount_strength, double discount_beta, double strength_shape, double strength_rate, double d = 0.9, double strength = 10.0) :
num_floors_(num_floors),
num_tables_(),
num_customers_(),
discount_(d),
- alpha_(alpha),
- discount_prior_alpha_(discount_alpha),
+ strength_(strength),
+ discount_prior_strength_(discount_strength),
discount_prior_beta_(discount_beta),
- alpha_prior_shape_(alpha_shape),
- alpha_prior_rate_(alpha_rate) {}
+ strength_prior_shape_(strength_shape),
+ strength_prior_rate_(strength_rate) {}
double discount() const { return discount_; }
- double alpha() const { return alpha_; }
+ double strength() const { return strength_; }
bool has_discount_prior() const {
- return !std::isnan(discount_prior_alpha_);
+ return !std::isnan(discount_prior_strength_);
}
- bool has_alpha_prior() const {
- return !std::isnan(alpha_prior_shape_);
+ bool has_strength_prior() const {
+ return !std::isnan(strength_prior_shape_);
}
void clear() {
@@ -122,7 +122,7 @@ class MFCR {
int floor = -1;
bool share_table = false;
if (loc.total_dish_count_) {
- const double p_empty = (alpha_ + num_tables_ * discount_) * marg_p0;
+ const double p_empty = (strength_ + 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);
}
@@ -206,44 +206,53 @@ 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_ * discount_ + alpha_;
+ const double r = num_tables_ * discount_ + strength_;
if (it == dish_locs_.end()) {
- return r * marg_p0 / (num_customers_ + alpha_);
+ return r * marg_p0 / (num_customers_ + strength_);
} else {
return (it->second.total_dish_count_ - discount_ * it->second.table_counts_.size() + r * marg_p0) /
- (num_customers_ + alpha_);
+ (num_customers_ + strength_);
}
}
double log_crp_prob() const {
- return log_crp_prob(discount_, alpha_);
+ return log_crp_prob(discount_, strength_);
}
// 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 log_crp_prob(const double& discount, const double& strength) const {
double lp = 0.0;
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_);
+ lp = Md::log_beta_density(discount, discount_prior_strength_, discount_prior_beta_);
+ if (has_strength_prior())
+ lp += Md::log_gamma_density(strength + discount, strength_prior_shape_, strength_prior_rate_);
assert(lp <= 0.0);
if (num_customers_) {
- if (d > 0.0) {
- const double r = lgamma(1.0 - d);
- lp += lgamma(alpha) - lgamma(alpha + num_customers_)
- + num_tables_ * log(d) + lgamma(alpha / d + num_tables_)
- - lgamma(alpha / d);
+ if (discount > 0.0) {
+ const double r = lgamma(1.0 - discount);
+ if (strength)
+ lp += lgamma(strength) - lgamma(strength / discount);
+ lp += - lgamma(strength + num_customers_)
+ + num_tables_ * log(discount) + lgamma(strength / discount + num_tables_);
assert(std::isfinite(lp));
for (typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.begin();
it != dish_locs_.end(); ++it) {
const DishLocations& cur = it->second;
for (std::list<TableCount>::const_iterator ti = cur.table_counts_.begin(); ti != cur.table_counts_.end(); ++ti) {
- lp += lgamma(ti->count - d) - r;
+ lp += lgamma(ti->count - discount) - r;
}
}
+ } else if (!discount) { // discount == 0.0
+ lp += lgamma(strength) + num_tables_ * log(strength) - lgamma(strength + num_tables_);
+ assert(std::isfinite(lp));
+ for (typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.begin();
+ it != dish_locs_.end(); ++it) {
+ const DishLocations& cur = it->second;
+ lp += lgamma(cur.table_counts_.size());
+ }
} else {
- assert(!"not implemented yet");
+ assert(!"discount less than 0 detected!");
}
}
assert(std::isfinite(lp));
@@ -251,20 +260,22 @@ class MFCR {
}
void resample_hyperparameters(MT19937* rng, const unsigned nloop = 5, const unsigned niterations = 10) {
- assert(has_discount_prior() || has_alpha_prior());
+ assert(has_discount_prior() || has_strength_prior());
DiscountResampler dr(*this);
- ConcentrationResampler cr(*this);
+ StrengthResampler sr(*this);
for (int iter = 0; iter < nloop; ++iter) {
- if (has_alpha_prior()) {
- alpha_ = slice_sampler1d(cr, alpha_, *rng, 0.0,
+ if (has_strength_prior()) {
+ strength_ = slice_sampler1d(sr, strength_, *rng, -discount_,
std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
}
if (has_discount_prior()) {
- discount_ = slice_sampler1d(dr, discount_, *rng, std::numeric_limits<double>::min(),
+ 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);
}
}
- alpha_ = slice_sampler1d(cr, alpha_, *rng, 0.0,
+ strength_ = slice_sampler1d(sr, strength_, *rng, -discount_,
std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
}
@@ -272,15 +283,15 @@ class MFCR {
DiscountResampler(const MFCR& crp) : crp_(crp) {}
const MFCR& crp_;
double operator()(const double& proposed_d) const {
- return crp_.log_crp_prob(proposed_d, crp_.alpha_);
+ return crp_.log_crp_prob(proposed_d, crp_.strength_);
}
};
- struct ConcentrationResampler {
- ConcentrationResampler(const MFCR& crp) : crp_(crp) {}
+ struct StrengthResampler {
+ StrengthResampler(const MFCR& crp) : crp_(crp) {}
const MFCR& crp_;
- double operator()(const double& proposediscount_alpha) const {
- return crp_.log_crp_prob(crp_.discount_, proposediscount_alpha);
+ double operator()(const double& proposediscount_strength) const {
+ return crp_.log_crp_prob(crp_.discount_, proposediscount_strength);
}
};
@@ -292,7 +303,7 @@ class MFCR {
};
void Print(std::ostream* out) const {
- (*out) << "MFCR(d=" << discount_ << ",alpha=" << alpha_ << ") customers=" << num_customers_ << std::endl;
+ (*out) << "MFCR(d=" << discount_ << ",strength=" << strength_ << ") 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): ";
@@ -318,15 +329,15 @@ class MFCR {
std::tr1::unordered_map<Dish, DishLocations, DishHash> dish_locs_;
double discount_;
- double alpha_;
+ double strength_;
// optional beta prior on discount_ (NaN if no prior)
- double discount_prior_alpha_;
+ double discount_prior_strength_;
double discount_prior_beta_;
- // optional gamma prior on alpha_ (NaN if no prior)
- double alpha_prior_shape_;
- double alpha_prior_rate_;
+ // optional gamma prior on strength_ (NaN if no prior)
+ double strength_prior_shape_;
+ double strength_prior_rate_;
};
template <typename T,typename H>
generated by cgit v1.2.3 (git 2.39.1) at 2024-09-21 10:58:51 +0000