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authorChris Dyer <cdyer@cs.cmu.edu>2012-03-03 17:16:58 -0500
committerChris Dyer <cdyer@cs.cmu.edu>2012-03-03 17:16:58 -0500
commit2579dd24d3833823527e688196276c2fab381b37 (patch)
treedf25825f29db546549fc469f912cef5a7e32c08f /utils/ccrp.h
parente0507d1aa96c6b1348e6a202beb95f63d8662258 (diff)
pyp lm, fixed hyperparameters inference
Diffstat (limited to 'utils/ccrp.h')
-rw-r--r--utils/ccrp.h95
1 files changed, 40 insertions, 55 deletions
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>