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-rw-r--r--gi/pf/align-lexonly-pyp.cc207
1 files changed, 47 insertions, 160 deletions
diff --git a/gi/pf/align-lexonly-pyp.cc b/gi/pf/align-lexonly-pyp.cc
index 13a3a487..d68a4b8f 100644
--- a/gi/pf/align-lexonly-pyp.cc
+++ b/gi/pf/align-lexonly-pyp.cc
@@ -1,27 +1,18 @@
#include <iostream>
-#include <tr1/memory>
#include <queue>
-#include <boost/multi_array.hpp>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
-#include "array2d.h"
-#include "base_distributions.h"
-#include "monotonic_pseg.h"
-#include "conditional_pseg.h"
-#include "trule.h"
#include "tdict.h"
#include "stringlib.h"
#include "filelib.h"
-#include "dict.h"
+#include "array2d.h"
#include "sampler.h"
-#include "mfcr.h"
#include "corpus.h"
-#include "ngram_base.h"
+#include "pyp_tm.h"
using namespace std;
-using namespace tr1;
namespace po = boost::program_options;
void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
@@ -51,7 +42,7 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
}
}
-shared_ptr<MT19937> prng;
+MT19937* prng;
struct LexicalAlignment {
unsigned char src_index;
@@ -66,159 +57,59 @@ struct AlignedSentencePair {
Array2D<short> posterior;
};
-struct HierarchicalWordBase {
- explicit HierarchicalWordBase(const unsigned vocab_e_size) :
- base(prob_t::One()), r(1,1,1,1,0.66,50.0), u0(-log(vocab_e_size)), l(1,prob_t::One()), v(1, prob_t::Zero()) {}
-
- void ResampleHyperparameters(MT19937* rng) {
- r.resample_hyperparameters(rng);
- }
-
- inline double logp0(const vector<WordID>& s) const {
- return Md::log_poisson(s.size(), 7.5) + s.size() * u0;
- }
-
- // return p0 of rule.e_
- prob_t operator()(const TRule& rule) const {
- v[0].logeq(logp0(rule.e_));
- return r.prob(rule.e_, v.begin(), l.begin());
- }
-
- void Increment(const TRule& rule) {
- v[0].logeq(logp0(rule.e_));
- if (r.increment(rule.e_, v.begin(), l.begin(), &*prng).count) {
- base *= v[0] * l[0];
- }
- }
-
- void Decrement(const TRule& rule) {
- if (r.decrement(rule.e_, &*prng).count) {
- base /= prob_t(exp(logp0(rule.e_)));
- }
- }
-
- prob_t Likelihood() const {
- prob_t p; p.logeq(r.log_crp_prob());
- p *= base;
- return p;
+struct Aligner {
+ Aligner(const vector<vector<WordID> >& lets, int num_letters, vector<AlignedSentencePair>* c) :
+ corpus(*c),
+ model(lets, num_letters),
+ kNULL(TD::Convert("NULL")) {
+ assert(lets[kNULL].size() == 0);
}
- void Summary() const {
- cerr << "NUMBER OF CUSTOMERS: " << r.num_customers() << " (d=" << r.discount() << ",s=" << r.strength() << ')' << endl;
- for (MFCR<1,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;
- }
-
- prob_t base;
- MFCR<1,vector<WordID> > r;
- const double u0;
- const vector<prob_t> l;
- mutable vector<prob_t> v;
-};
-
-struct BasicLexicalAlignment {
- explicit BasicLexicalAlignment(const vector<vector<WordID> >& lets,
- const unsigned words_e,
- const unsigned letters_e,
- vector<AlignedSentencePair>* corp) :
- letters(lets),
- corpus(*corp),
- //up0(words_e),
- //up0("en.chars.1gram", letters_e),
- //up0("en.words.1gram"),
- up0(letters_e),
- //up0("en.chars.2gram"),
- tmodel(up0) {
- }
+ vector<AlignedSentencePair>& corpus;
+ PYPLexicalTranslation model;
+ const WordID kNULL;
- void InstantiateRule(const WordID src,
- const WordID trg,
- TRule* rule) const {
- static const WordID kX = TD::Convert("X") * -1;
- rule->lhs_ = kX;
- rule->e_ = letters[trg];
- rule->f_ = letters[src];
+ void ResampleHyperparameters() {
+ model.ResampleHyperparameters(prng);
}
void InitializeRandom() {
- const WordID kNULL = TD::Convert("NULL");
cerr << "Initializing with random alignments ...\n";
for (unsigned i = 0; i < corpus.size(); ++i) {
AlignedSentencePair& asp = corpus[i];
asp.a.resize(asp.trg.size());
for (unsigned j = 0; j < asp.trg.size(); ++j) {
- const unsigned char a_j = prng->next() * (1 + asp.src.size());
+ unsigned char& a_j = asp.a[j].src_index;
+ a_j = prng->next() * (1 + asp.src.size());
const WordID f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
- TRule r;
- InstantiateRule(f_a_j, asp.trg[j], &r);
- asp.a[j].is_transliteration = false;
- asp.a[j].src_index = a_j;
- if (tmodel.IncrementRule(r, &*prng))
- up0.Increment(r);
+ model.Increment(f_a_j, asp.trg[j], &*prng);
}
}
- cerr << " LLH = " << Likelihood() << endl;
- }
-
- prob_t Likelihood() const {
- prob_t p = tmodel.Likelihood();
- p *= up0.Likelihood();
- return p;
- }
-
- void ResampleHyperparemeters() {
- tmodel.ResampleHyperparameters(&*prng);
- up0.ResampleHyperparameters(&*prng);
- cerr << " (base d=" << up0.r.discount() << ",s=" << up0.r.strength() << ")\n";
+ cerr << "Corpus intialized randomly. LLH = " << model.Likelihood() << endl;
}
- void ResampleCorpus();
-
- const vector<vector<WordID> >& letters; // spelling dictionary
- vector<AlignedSentencePair>& corpus;
- //PhraseConditionalUninformativeBase up0;
- //PhraseConditionalUninformativeUnigramBase up0;
- //UnigramWordBase up0;
- //HierarchicalUnigramBase up0;
- HierarchicalWordBase up0;
- //CompletelyUniformBase up0;
- //FixedNgramBase up0;
- //ConditionalTranslationModel<PhraseConditionalUninformativeBase> tmodel;
- //ConditionalTranslationModel<PhraseConditionalUninformativeUnigramBase> tmodel;
- //ConditionalTranslationModel<UnigramWordBase> tmodel;
- //ConditionalTranslationModel<HierarchicalUnigramBase> tmodel;
- MConditionalTranslationModel<HierarchicalWordBase> tmodel;
- //ConditionalTranslationModel<FixedNgramBase> tmodel;
- //ConditionalTranslationModel<CompletelyUniformBase> tmodel;
-};
-
-void BasicLexicalAlignment::ResampleCorpus() {
- static const WordID kNULL = TD::Convert("NULL");
- for (unsigned i = 0; i < corpus.size(); ++i) {
- AlignedSentencePair& asp = corpus[i];
- SampleSet<prob_t> ss; ss.resize(asp.src.size() + 1);
- for (unsigned j = 0; j < asp.trg.size(); ++j) {
- TRule r;
- unsigned char& a_j = asp.a[j].src_index;
- WordID f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
- InstantiateRule(f_a_j, asp.trg[j], &r);
- if (tmodel.DecrementRule(r, &*prng))
- up0.Decrement(r);
-
- for (unsigned prop_a_j = 0; prop_a_j <= asp.src.size(); ++prop_a_j) {
- const WordID prop_f = (prop_a_j ? asp.src[prop_a_j - 1] : kNULL);
- InstantiateRule(prop_f, asp.trg[j], &r);
- ss[prop_a_j] = tmodel.RuleProbability(r);
+ void ResampleCorpus() {
+ for (unsigned i = 0; i < corpus.size(); ++i) {
+ AlignedSentencePair& asp = corpus[i];
+ SampleSet<prob_t> ss; ss.resize(asp.src.size() + 1);
+ for (unsigned j = 0; j < asp.trg.size(); ++j) {
+ unsigned char& a_j = asp.a[j].src_index;
+ const WordID e_j = asp.trg[j];
+ WordID f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
+ model.Decrement(f_a_j, e_j, prng);
+
+ for (unsigned prop_a_j = 0; prop_a_j <= asp.src.size(); ++prop_a_j) {
+ const WordID prop_f = (prop_a_j ? asp.src[prop_a_j - 1] : kNULL);
+ ss[prop_a_j] = model.Prob(prop_f, e_j);
+ }
+ a_j = prng->SelectSample(ss);
+ f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
+ model.Increment(f_a_j, e_j, prng);
}
- a_j = prng->SelectSample(ss);
- f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
- InstantiateRule(f_a_j, asp.trg[j], &r);
- if (tmodel.IncrementRule(r, &*prng))
- up0.Increment(r);
}
+ cerr << "LLH = " << model.Likelihood() << " " << model.UniqueConditioningContexts() << endl;
}
- cerr << " LLH = " << Likelihood() << endl;
-}
+};
void ExtractLetters(const set<WordID>& v, vector<vector<WordID> >* l, set<WordID>* letset = NULL) {
for (set<WordID>::const_iterator it = v.begin(); it != v.end(); ++it) {
@@ -240,8 +131,10 @@ void ExtractLetters(const set<WordID>& v, vector<vector<WordID> >* l, set<WordID
void Debug(const AlignedSentencePair& asp) {
cerr << TD::GetString(asp.src) << endl << TD::GetString(asp.trg) << endl;
Array2D<bool> a(asp.src.size(), asp.trg.size());
- for (unsigned j = 0; j < asp.trg.size(); ++j)
+ for (unsigned j = 0; j < asp.trg.size(); ++j) {
+ assert(asp.a[j].src_index <= asp.src.size());
if (asp.a[j].src_index) a(asp.a[j].src_index - 1, j) = true;
+ }
cerr << a << endl;
}
@@ -275,10 +168,9 @@ int main(int argc, char** argv) {
InitCommandLine(argc, argv, &conf);
if (conf.count("random_seed"))
- prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
+ prng = new MT19937(conf["random_seed"].as<uint32_t>());
else
- prng.reset(new MT19937);
-// MT19937& rng = *prng;
+ prng = new MT19937;
vector<vector<int> > corpuse, corpusf;
set<int> vocabe, vocabf;
@@ -304,23 +196,18 @@ int main(int argc, char** argv) {
ExtractLetters(vocabf, &letters, NULL);
letters[TD::Convert("NULL")].clear();
- BasicLexicalAlignment x(letters, vocabe.size(), letset.size(), &corpus);
- x.InitializeRandom();
+ Aligner aligner(letters, letset.size(), &corpus);
+ aligner.InitializeRandom();
+
const unsigned samples = conf["samples"].as<unsigned>();
for (int i = 0; i < samples; ++i) {
for (int j = 65; j < 67; ++j) Debug(corpus[j]);
- cerr << i << "\t" << x.tmodel.r.size() << "\t";
- if (i % 7 == 6) x.ResampleHyperparemeters();
- x.ResampleCorpus();
+ if (i % 7 == 6) aligner.ResampleHyperparameters();
+ aligner.ResampleCorpus();
if (i > (samples / 5) && (i % 10 == 9)) for (int j = 0; j < corpus.size(); ++j) AddSample(&corpus[j]);
}
for (unsigned i = 0; i < corpus.size(); ++i)
WriteAlignments(corpus[i]);
- //ModelAndData posterior(x, &corpus, vocabe, vocabf);
- x.tmodel.Summary();
- x.up0.Summary();
-
- //posterior.Sample();
return 0;
}
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-11 05:57:52 +0000