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-rw-r--r--.gitignore1
-rw-r--r--gi/pf/base_distributions.cc22
-rw-r--r--gi/pf/base_distributions.h21
-rw-r--r--gi/pf/conditional_pseg.h3
-rw-r--r--gi/pf/pfdist.cc6
-rw-r--r--gi/pf/pfnaive.cc4
-rw-r--r--phrasinator/gibbs_train_plm.cc8
-rw-r--r--utils/Makefile.am5
-rw-r--r--utils/m.h89
-rw-r--r--utils/m_test.cc75
-rw-r--r--utils/mfcr.h22
11 files changed, 194 insertions, 62 deletions
diff --git a/.gitignore b/.gitignore
index ab8bf2c7..4f75d153 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
mira/kbest_mira
+utils/m_test
sa-extract/calignment.c
sa-extract/calignment.so
sa-extract/cdat.c
diff --git a/gi/pf/base_distributions.cc b/gi/pf/base_distributions.cc
index d362fd76..d9761005 100644
--- a/gi/pf/base_distributions.cc
+++ b/gi/pf/base_distributions.cc
@@ -59,7 +59,7 @@ prob_t PhraseConditionalUninformativeUnigramBase::p0(const vector<WordID>& vsrc,
const int flen = vsrc.size() - start_src;
const int elen = vtrg.size() - start_trg;
prob_t p;
- p.logeq(log_poisson(elen, flen + 0.01)); // elen | flen ~Pois(flen + 0.01)
+ p.logeq(Md::log_poisson(elen, flen + 0.01)); // elen | flen ~Pois(flen + 0.01)
//p.logeq(log_poisson(elen, 1)); // elen | flen ~Pois(flen + 0.01)
for (int i = 0; i < elen; ++i)
p *= u(vtrg[i + start_trg]); // draw e_i ~Uniform
@@ -73,7 +73,7 @@ prob_t PhraseConditionalUninformativeBase::p0(const vector<WordID>& vsrc,
const int elen = vtrg.size() - start_trg;
prob_t p;
//p.logeq(log_poisson(elen, flen + 0.01)); // elen | flen ~Pois(flen + 0.01)
- p.logeq(log_poisson(elen, 1)); // elen | flen ~Pois(flen + 0.01)
+ p.logeq(Md::log_poisson(elen, 1)); // elen | flen ~Pois(flen + 0.01)
for (int i = 0; i < elen; ++i)
p *= kUNIFORM_TARGET; // draw e_i ~Uniform
return p;
@@ -113,7 +113,7 @@ prob_t PhraseConditionalBase::p0(const vector<WordID>& vsrc,
const int elen = vtrg.size() - start_trg;
prob_t uniform_src_alignment; uniform_src_alignment.logeq(-log(flen + 1));
prob_t p;
- p.logeq(log_poisson(elen, flen + 0.01)); // elen | flen ~Pois(flen + 0.01)
+ p.logeq(Md::log_poisson(elen, flen + 0.01)); // elen | flen ~Pois(flen + 0.01)
for (int i = 0; i < elen; ++i) { // for each position i in e-RHS
const WordID trg = vtrg[i + start_trg];
prob_t tp = prob_t::Zero();
@@ -139,9 +139,9 @@ prob_t PhraseJointBase::p0(const vector<WordID>& vsrc,
const int elen = vtrg.size() - start_trg;
prob_t uniform_src_alignment; uniform_src_alignment.logeq(-log(flen + 1));
prob_t p;
- p.logeq(log_poisson(flen, 1.0)); // flen ~Pois(1)
+ p.logeq(Md::log_poisson(flen, 1.0)); // flen ~Pois(1)
// elen | flen ~Pois(flen + 0.01)
- prob_t ptrglen; ptrglen.logeq(log_poisson(elen, flen + 0.01));
+ prob_t ptrglen; ptrglen.logeq(Md::log_poisson(elen, flen + 0.01));
p *= ptrglen;
p *= kUNIFORM_SOURCE.pow(flen); // each f in F ~Uniform
for (int i = 0; i < elen; ++i) { // for each position i in E
@@ -171,9 +171,9 @@ prob_t PhraseJointBase_BiDir::p0(const vector<WordID>& vsrc,
prob_t uniform_trg_alignment; uniform_trg_alignment.logeq(-log(elen + 1));
prob_t p1;
- p1.logeq(log_poisson(flen, 1.0)); // flen ~Pois(1)
+ p1.logeq(Md::log_poisson(flen, 1.0)); // flen ~Pois(1)
// elen | flen ~Pois(flen + 0.01)
- prob_t ptrglen; ptrglen.logeq(log_poisson(elen, flen + 0.01));
+ prob_t ptrglen; ptrglen.logeq(Md::log_poisson(elen, flen + 0.01));
p1 *= ptrglen;
p1 *= kUNIFORM_SOURCE.pow(flen); // each f in F ~Uniform
for (int i = 0; i < elen; ++i) { // for each position i in E
@@ -193,9 +193,9 @@ prob_t PhraseJointBase_BiDir::p0(const vector<WordID>& vsrc,
}
prob_t p2;
- p2.logeq(log_poisson(elen, 1.0)); // elen ~Pois(1)
+ p2.logeq(Md::log_poisson(elen, 1.0)); // elen ~Pois(1)
// flen | elen ~Pois(flen + 0.01)
- prob_t psrclen; psrclen.logeq(log_poisson(flen, elen + 0.01));
+ prob_t psrclen; psrclen.logeq(Md::log_poisson(flen, elen + 0.01));
p2 *= psrclen;
p2 *= kUNIFORM_TARGET.pow(elen); // each f in F ~Uniform
for (int i = 0; i < flen; ++i) { // for each position i in E
@@ -227,9 +227,9 @@ JumpBase::JumpBase() : p(200) {
for (int j = min_jump; j <= max_jump; ++j) {
prob_t& cp = cpd[j];
if (j < 0)
- cp.logeq(log_poisson(1.5-j, 1));
+ cp.logeq(Md::log_poisson(1.5-j, 1));
else if (j > 0)
- cp.logeq(log_poisson(j, 1));
+ cp.logeq(Md::log_poisson(j, 1));
cp.poweq(0.2);
z += cp;
}
diff --git a/gi/pf/base_distributions.h b/gi/pf/base_distributions.h
index a23ac32b..0d597c5c 100644
--- a/gi/pf/base_distributions.h
+++ b/gi/pf/base_distributions.h
@@ -13,24 +13,7 @@
#include "prob.h"
#include "tdict.h"
#include "sampler.h"
-
-inline double log_poisson(unsigned x, const double& lambda) {
- assert(lambda > 0.0);
- return log(lambda) * x - lgamma(x + 1) - lambda;
-}
-
-inline double log_binom_coeff(unsigned n, unsigned k) {
- assert(n >= k);
- if (n == k) return 0.0;
- return lgamma(n + 1) - lgamma(k + 1) - lgamma(n - k + 1);
-}
-
-// http://en.wikipedia.org/wiki/Negative_binomial_distribution
-inline double log_negative_binom(unsigned x, unsigned r, double p) {
- assert(p > 0.0);
- assert(p < 1.0);
- return log_binom_coeff(x + r - 1, x) + r * log(1 - p) + x * log(p);
-}
+#include "m.h"
inline std::ostream& operator<<(std::ostream& os, const std::vector<WordID>& p) {
os << '[';
@@ -68,7 +51,7 @@ struct Model1 {
struct PoissonUniformUninformativeBase {
explicit PoissonUniformUninformativeBase(const unsigned ves) : kUNIFORM(1.0 / ves) {}
prob_t operator()(const TRule& r) const {
- prob_t p; p.logeq(log_poisson(r.e_.size(), 1.0));
+ prob_t p; p.logeq(Md::log_poisson(r.e_.size(), 1.0));
prob_t q = kUNIFORM; q.poweq(r.e_.size());
p *= q;
return p;
diff --git a/gi/pf/conditional_pseg.h b/gi/pf/conditional_pseg.h
index 0aa5e8e0..2e9e38fc 100644
--- a/gi/pf/conditional_pseg.h
+++ b/gi/pf/conditional_pseg.h
@@ -6,6 +6,7 @@
#include <boost/functional/hash.hpp>
#include <iostream>
+#include "m.h"
#include "prob.h"
#include "ccrp_nt.h"
#include "mfcr.h"
@@ -210,7 +211,7 @@ struct ConditionalParallelSegementationModel {
prob_t AlignProbability(unsigned span) const {
prob_t p;
- p.logeq(aligns.logprob(span, log_poisson(span, 1.0)));
+ p.logeq(aligns.logprob(span, Md::log_poisson(span, 1.0)));
return p;
}
diff --git a/gi/pf/pfdist.cc b/gi/pf/pfdist.cc
index ef08a165..3d578db2 100644
--- a/gi/pf/pfdist.cc
+++ b/gi/pf/pfdist.cc
@@ -315,7 +315,7 @@ struct BackwardEstimate {
for (int i = 0; i < src_cov.size(); ++i)
if (!src_cov[i]) r.push_back(src_[i]);
const prob_t uniform_alignment(1.0 / r.size());
- e.logeq(log_poisson(trg_.size() - trg_cov, r.size() - 1)); // p(trg len remaining | src len remaining)
+ e.logeq(Md::log_poisson(trg_.size() - trg_cov, r.size() - 1)); // p(trg len remaining | src len remaining)
for (unsigned j = trg_cov; j < trg_.size(); ++j) {
prob_t p;
for (unsigned i = 0; i < r.size(); ++i)
@@ -352,7 +352,7 @@ struct BackwardEstimateSym {
if (!src_cov[i]) r.push_back(src_[i]);
r.push_back(0); // NULL word
const prob_t uniform_alignment(1.0 / r.size());
- e.logeq(log_poisson(trg_.size() - trg_cov, r.size() - 1)); // p(trg len remaining | src len remaining)
+ e.logeq(Md::log_poisson(trg_.size() - trg_cov, r.size() - 1)); // p(trg len remaining | src len remaining)
for (unsigned j = trg_cov; j < trg_.size(); ++j) {
prob_t p;
for (unsigned i = 0; i < r.size(); ++i)
@@ -367,7 +367,7 @@ struct BackwardEstimateSym {
r.pop_back();
const prob_t inv_uniform(1.0 / (trg_.size() - trg_cov + 1.0));
prob_t inv;
- inv.logeq(log_poisson(r.size(), trg_.size() - trg_cov));
+ inv.logeq(Md::log_poisson(r.size(), trg_.size() - trg_cov));
for (unsigned i = 0; i < r.size(); ++i) {
prob_t p;
for (unsigned j = trg_cov - 1; j < trg_.size(); ++j)
diff --git a/gi/pf/pfnaive.cc b/gi/pf/pfnaive.cc
index acba9d22..e1a53f5c 100644
--- a/gi/pf/pfnaive.cc
+++ b/gi/pf/pfnaive.cc
@@ -77,7 +77,7 @@ struct BackwardEstimateSym {
r.push_back(src_[i]);
r.push_back(0); // NULL word
const prob_t uniform_alignment(1.0 / r.size());
- e.logeq(log_poisson(trg_.size() - trg_cov, r.size() - 1)); // p(trg len remaining | src len remaining)
+ e.logeq(Md::log_poisson(trg_.size() - trg_cov, r.size() - 1)); // p(trg len remaining | src len remaining)
for (unsigned j = trg_cov; j < trg_.size(); ++j) {
prob_t p;
for (unsigned i = 0; i < r.size(); ++i)
@@ -92,7 +92,7 @@ struct BackwardEstimateSym {
r.pop_back();
const prob_t inv_uniform(1.0 / (trg_.size() - trg_cov + 1.0));
prob_t inv;
- inv.logeq(log_poisson(r.size(), trg_.size() - trg_cov));
+ inv.logeq(Md::log_poisson(r.size(), trg_.size() - trg_cov));
for (unsigned i = 0; i < r.size(); ++i) {
prob_t p;
for (unsigned j = trg_cov - 1; j < trg_.size(); ++j)
diff --git a/phrasinator/gibbs_train_plm.cc b/phrasinator/gibbs_train_plm.cc
index 29b3d7ea..66b46011 100644
--- a/phrasinator/gibbs_train_plm.cc
+++ b/phrasinator/gibbs_train_plm.cc
@@ -8,6 +8,7 @@
#include "dict.h"
#include "sampler.h"
#include "ccrp.h"
+#include "m.h"
using namespace std;
using namespace std::tr1;
@@ -95,11 +96,6 @@ void ReadCorpus(const string& filename, vector<vector<int> >* c, set<int>* vocab
if (in != &cin) delete in;
}
-double log_poisson(unsigned x, const double& lambda) {
- assert(lambda > 0.0);
- return log(lambda) * x - lgamma(x + 1) - lambda;
-}
-
struct UniphraseLM {
UniphraseLM(const vector<vector<int> >& corpus,
const set<int>& vocab,
@@ -128,7 +124,7 @@ struct UniphraseLM {
double log_p0(const vector<int>& phrase) const {
double len_logprob;
if (use_poisson_)
- len_logprob = log_poisson(phrase.size(), 1.0);
+ len_logprob = Md::log_poisson(phrase.size(), 1.0);
else
len_logprob = log(1 - p_end_) * (phrase.size() -1) + log(p_end_);
return log(uniform_word_) * phrase.size() + len_logprob;
diff --git a/utils/Makefile.am b/utils/Makefile.am
index 3e559c75..a1ea8270 100644
--- a/utils/Makefile.am
+++ b/utils/Makefile.am
@@ -7,11 +7,12 @@ TESTS = ts phmt mfcr_test
if HAVE_GTEST
noinst_PROGRAMS += \
dict_test \
+ m_test \
weights_test \
logval_test \
small_vector_test
-TESTS += small_vector_test logval_test weights_test dict_test
+TESTS += small_vector_test logval_test weights_test dict_test m_test
endif
reconstruct_weights_SOURCES = reconstruct_weights.cc
@@ -38,6 +39,8 @@ endif
phmt_SOURCES = phmt.cc
ts_SOURCES = ts.cc
+m_test_SOURCES = m_test.cc
+m_test_LDADD = $(GTEST_LDFLAGS) $(GTEST_LIBS)
dict_test_SOURCES = dict_test.cc
dict_test_LDADD = $(GTEST_LDFLAGS) $(GTEST_LIBS)
mfcr_test_SOURCES = mfcr_test.cc
diff --git a/utils/m.h b/utils/m.h
new file mode 100644
index 00000000..b25248c2
--- /dev/null
+++ b/utils/m.h
@@ -0,0 +1,89 @@
+#ifndef _M_H_
+#define _M_H_
+
+#include <cassert>
+#include <cmath>
+
+template <typename F>
+struct M {
+ // support [0, 1, 2 ...)
+ static inline F log_poisson(unsigned x, const F& lambda) {
+ assert(lambda > 0.0);
+ return std::log(lambda) * x - lgamma(x + 1) - lambda;
+ }
+
+ // support [0, 1, 2 ...)
+ static inline F log_geometric(unsigned x, const F& p) {
+ assert(p > 0.0);
+ assert(p < 1.0);
+ return std::log(1 - p) * x + std::log(p);
+ }
+
+ // log of the binomial coefficient
+ static inline F log_binom_coeff(unsigned n, unsigned k) {
+ assert(n >= k);
+ if (n == k) return 0.0;
+ return lgamma(n + 1) - lgamma(k + 1) - lgamma(n - k + 1);
+ }
+
+ // http://en.wikipedia.org/wiki/Negative_binomial_distribution
+ // support [0, 1, 2 ...)
+ static inline F log_negative_binom(unsigned x, unsigned r, const F& p) {
+ assert(p > 0.0);
+ assert(p < 1.0);
+ return log_binom_coeff(x + r - 1u, x) + r * std::log(F(1) - p) + x * std::log(p);
+ }
+
+ // this is the Beta function, *not* the beta probability density
+ // http://mathworld.wolfram.com/BetaFunction.html
+ static inline F log_beta_fn(const F& x, const F& y) {
+ return lgamma(x) + lgamma(y) - lgamma(x + y);
+ }
+
+ // support x >= 0.0
+ static F log_gamma_density(const F& x, const F& shape, const F& rate) {
+ assert(x >= 0.0);
+ assert(shape > 0.0);
+ assert(rate > 0.0);
+ return (shape-1)*std::log(x) - shape*std::log(rate) - x/rate - lgamma(shape);
+ }
+
+ // this is the Beta *density* p(x ; alpha, beta)
+ // support x \in (0,1)
+ static inline F log_beta_density(const F& x, const F& alpha, const F& beta) {
+ assert(x > 0.0);
+ assert(x < 1.0);
+ assert(alpha > 0.0);
+ assert(beta > 0.0);
+ return (alpha-1)*std::log(x)+(beta-1)*std::log(1-x) - log_beta_fn(alpha, beta);
+ }
+
+ // note: this has been adapted so that 0 is in the support of the distribution
+ // support [0, 1, 2 ...)
+ static inline F log_yule_simon(unsigned x, const F& rho) {
+ assert(rho > 0.0);
+ return std::log(rho) + log_beta_fn(x + 1, rho + 1);
+ }
+
+ // see http://www.gatsby.ucl.ac.uk/~ywteh/research/compling/hpylm.pdf
+ // when y=1, sometimes written x^{\overline{n}} or x^{(n)} "Pochhammer symbol"
+ static inline F log_generalized_factorial(const F& x, const F& n, const F& y = 1.0) {
+ assert(x > 0.0);
+ assert(y >= 0.0);
+ assert(n > 0.0);
+ if (!n) return 0.0;
+ if (y == F(1)) {
+ return lgamma(x + n) - lgamma(x);
+ } else if (y) {
+ return n * std::log(y) + lgamma(x/y + n) - lgamma(x/y);
+ } else { // y == 0.0
+ return n * std::log(x);
+ }
+ }
+
+};
+
+typedef M<double> Md;
+typedef M<double> Mf;
+
+#endif
diff --git a/utils/m_test.cc b/utils/m_test.cc
new file mode 100644
index 00000000..fca8f895
--- /dev/null
+++ b/utils/m_test.cc
@@ -0,0 +1,75 @@
+#include "m.h"
+
+#include <iostream>
+#include <gtest/gtest.h>
+#include <cassert>
+
+using namespace std;
+
+class MTest : public testing::Test {
+ public:
+ MTest() {}
+ protected:
+ virtual void SetUp() { }
+ virtual void TearDown() { }
+};
+
+TEST_F(MTest, Poisson) {
+ double prev = 1.0;
+ double tot = 0;
+ for (int i = 0; i < 10; ++i) {
+ double p = Md::log_poisson(i, 0.99);
+ cerr << "p(i=" << i << ") = " << exp(p) << endl;
+ EXPECT_LT(p, prev);
+ tot += exp(p);
+ prev = p;
+ }
+ cerr << " tot=" << tot << endl;
+ EXPECT_LE(tot, 1.0);
+}
+
+TEST_F(MTest, YuleSimon) {
+ double prev = 1.0;
+ double tot = 0;
+ for (int i = 0; i < 10; ++i) {
+ double p = Md::log_yule_simon(i, 1.0);
+ cerr << "p(i=" << i << ") = " << exp(p) << endl;
+ EXPECT_LT(p, prev);
+ tot += exp(p);
+ prev = p;
+ }
+ cerr << " tot=" << tot << endl;
+ EXPECT_LE(tot, 1.0);
+}
+
+TEST_F(MTest, LogGeometric) {
+ double prev = 1.0;
+ double tot = 0;
+ for (int i = 0; i < 10; ++i) {
+ double p = Md::log_geometric(i, 0.5);
+ cerr << "p(i=" << i << ") = " << exp(p) << endl;
+ EXPECT_LT(p, prev);
+ tot += exp(p);
+ prev = p;
+ }
+ cerr << " tot=" << tot << endl;
+ EXPECT_LE(tot, 1.0);
+}
+
+TEST_F(MTest, GeneralizedFactorial) {
+ for (double i = 0.3; i < 10000; i += 0.4) {
+ double a = Md::log_generalized_factorial(1.0, i);
+ double b = lgamma(1.0 + i);
+ EXPECT_FLOAT_EQ(a,b);
+ }
+ double gf_3_6 = 3.0 * 4.0 * 5.0 * 6.0 * 7.0 * 8.0;
+ EXPECT_FLOAT_EQ(Md::log_generalized_factorial(3.0, 6.0), std::log(gf_3_6));
+ double gf_314_6 = 3.14 * 4.14 * 5.14 * 6.14 * 7.14 * 8.14;
+ EXPECT_FLOAT_EQ(Md::log_generalized_factorial(3.14, 6.0), std::log(gf_314_6));
+}
+
+int main(int argc, char** argv) {
+ testing::InitGoogleTest(&argc, argv);
+ return RUN_ALL_TESTS();
+}
+
diff --git a/utils/mfcr.h b/utils/mfcr.h
index 3eb133fc..396d0205 100644
--- a/utils/mfcr.h
+++ b/utils/mfcr.h
@@ -12,6 +12,7 @@
#include <boost/functional/hash.hpp>
#include "sampler.h"
#include "slice_sampler.h"
+#include "m.h"
struct TableCount {
TableCount() : count(), floor() {}
@@ -218,31 +219,14 @@ class MFCR {
return log_crp_prob(d_, alpha_);
}
- 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;
- }
-
// 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 = log_beta_density(d, d_prior_alpha_, d_prior_beta_);
+ lp = Md::log_beta_density(d, d_prior_alpha_, d_prior_beta_);
if (has_alpha_prior())
- lp += log_gamma_density(alpha, alpha_prior_shape_, alpha_prior_rate_);
+ lp += Md::log_gamma_density(alpha, alpha_prior_shape_, alpha_prior_rate_);
assert(lp <= 0.0);
if (num_customers_) {
if (d > 0.0) {
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-11 02:06:23 +0000