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-rw-r--r--gi/pf/Makefile.am7
-rw-r--r--gi/pf/align-tl.cc334
-rw-r--r--gi/pf/conditional_pseg.h11
-rw-r--r--gi/pf/nuisance_test.cc161
-rw-r--r--gi/pf/transliterations.cc193
-rw-r--r--gi/pf/transliterations.h20
6 files changed, 723 insertions, 3 deletions
diff --git a/gi/pf/Makefile.am b/gi/pf/Makefile.am
index 7cf9c14d..5e89f02a 100644
--- a/gi/pf/Makefile.am
+++ b/gi/pf/Makefile.am
@@ -1,12 +1,17 @@
-bin_PROGRAMS = cbgi brat dpnaive pfbrat pfdist itg pfnaive condnaive align-lexonly align-lexonly-pyp learn_cfg pyp_lm
+bin_PROGRAMS = cbgi brat dpnaive pfbrat pfdist itg pfnaive condnaive align-lexonly align-lexonly-pyp learn_cfg pyp_lm nuisance_test align-tl
noinst_LIBRARIES = libpf.a
+
libpf_a_SOURCES = base_distributions.cc reachability.cc cfg_wfst_composer.cc corpus.cc unigrams.cc ngram_base.cc
+nuisance_test_SOURCES = nuisance_test.cc transliterations.cc
+
align_lexonly_SOURCES = align-lexonly.cc
align_lexonly_pyp_SOURCES = align-lexonly-pyp.cc
+align_tl_SOURCES = align-tl.cc transliterations.cc
+
itg_SOURCES = itg.cc
pyp_lm_SOURCES = pyp_lm.cc
diff --git a/gi/pf/align-tl.cc b/gi/pf/align-tl.cc
new file mode 100644
index 00000000..0e0454e5
--- /dev/null
+++ b/gi/pf/align-tl.cc
@@ -0,0 +1,334 @@
+#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 "sampler.h"
+#include "mfcr.h"
+#include "corpus.h"
+#include "ngram_base.h"
+#include "transliterations.h"
+
+using namespace std;
+using namespace tr1;
+namespace po = boost::program_options;
+
+void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+ po::options_description opts("Configuration options");
+ opts.add_options()
+ ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples")
+ ("input,i",po::value<string>(),"Read parallel data from")
+ ("random_seed,S",po::value<uint32_t>(), "Random seed");
+ po::options_description clo("Command line options");
+ clo.add_options()
+ ("config", po::value<string>(), "Configuration file")
+ ("help,h", "Print this help message and exit");
+ po::options_description dconfig_options, dcmdline_options;
+ dconfig_options.add(opts);
+ dcmdline_options.add(opts).add(clo);
+
+ po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
+ if (conf->count("config")) {
+ ifstream config((*conf)["config"].as<string>().c_str());
+ po::store(po::parse_config_file(config, dconfig_options), *conf);
+ }
+ po::notify(*conf);
+
+ if (conf->count("help") || (conf->count("input") == 0)) {
+ cerr << dcmdline_options << endl;
+ exit(1);
+ }
+}
+
+shared_ptr<MT19937> prng;
+
+struct LexicalAlignment {
+ unsigned char src_index;
+ bool is_transliteration;
+ vector<pair<short, short> > derivation;
+};
+
+struct AlignedSentencePair {
+ vector<WordID> src;
+ vector<WordID> trg;
+ vector<LexicalAlignment> a;
+ 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;
+ }
+
+ 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) {
+ }
+
+ 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 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());
+ 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);
+ }
+ }
+ 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";
+ }
+
+ 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);
+ }
+ 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 = " << 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) {
+ vector<WordID>& letters = (*l)[*it];
+ if (letters.size()) continue; // if e and f have the same word
+
+ const string& w = TD::Convert(*it);
+
+ size_t cur = 0;
+ while (cur < w.size()) {
+ const size_t len = UTF8Len(w[cur]);
+ letters.push_back(TD::Convert(w.substr(cur, len)));
+ if (letset) letset->insert(letters.back());
+ cur += len;
+ }
+ }
+}
+
+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)
+ if (asp.a[j].src_index) a(asp.a[j].src_index - 1, j) = true;
+ cerr << a << endl;
+}
+
+void AddSample(AlignedSentencePair* asp) {
+ for (unsigned j = 0; j < asp->trg.size(); ++j)
+ asp->posterior(asp->a[j].src_index, j)++;
+}
+
+void WriteAlignments(const AlignedSentencePair& asp) {
+ bool first = true;
+ for (unsigned j = 0; j < asp.trg.size(); ++j) {
+ int src_index = -1;
+ int mc = -1;
+ for (unsigned i = 0; i <= asp.src.size(); ++i) {
+ if (asp.posterior(i, j) > mc) {
+ mc = asp.posterior(i, j);
+ src_index = i;
+ }
+ }
+
+ if (src_index) {
+ if (first) first = false; else cout << ' ';
+ cout << (src_index - 1) << '-' << j;
+ }
+ }
+ cout << endl;
+}
+
+int main(int argc, char** argv) {
+ po::variables_map conf;
+ InitCommandLine(argc, argv, &conf);
+
+ if (conf.count("random_seed"))
+ prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
+ else
+ prng.reset(new MT19937);
+// MT19937& rng = *prng;
+
+ vector<vector<int> > corpuse, corpusf;
+ set<int> vocabe, vocabf;
+ corpus::ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
+ cerr << "f-Corpus size: " << corpusf.size() << " sentences\n";
+ cerr << "f-Vocabulary size: " << vocabf.size() << " types\n";
+ cerr << "f-Corpus size: " << corpuse.size() << " sentences\n";
+ cerr << "f-Vocabulary size: " << vocabe.size() << " types\n";
+ assert(corpusf.size() == corpuse.size());
+
+ vector<AlignedSentencePair> corpus(corpuse.size());
+ for (unsigned i = 0; i < corpuse.size(); ++i) {
+ corpus[i].src.swap(corpusf[i]);
+ corpus[i].trg.swap(corpuse[i]);
+ corpus[i].posterior.resize(corpus[i].src.size() + 1, corpus[i].trg.size());
+ }
+ corpusf.clear(); corpuse.clear();
+
+ vocabf.insert(TD::Convert("NULL"));
+ vector<vector<WordID> > letters(TD::NumWords());
+ set<WordID> letset;
+ ExtractLetters(vocabe, &letters, &letset);
+ ExtractLetters(vocabf, &letters, NULL);
+ letters[TD::Convert("NULL")].clear();
+
+ Transliterations tl;
+
+ // TODO CONFIGURE THIS
+ int min_trans_src = 4;
+
+ cerr << "Initializing transliteration DPs ...\n";
+ for (int i = 0; i < corpus.size(); ++i) {
+ const vector<int>& src = corpus[i].src;
+ const vector<int>& trg = corpus[i].trg;
+ cerr << '.' << flush;
+ if (i % 80 == 79) cerr << endl;
+ for (int j = 0; j < src.size(); ++j) {
+ const vector<int>& src_let = letters[src[j]];
+ for (int k = 0; k < trg.size(); ++k) {
+ const vector<int>& trg_let = letters[trg[k]];
+ if (src_let.size() < min_trans_src)
+ tl.Forbid(src[j], trg[k]);
+ else
+ tl.Initialize(src[j], src_let, trg[k], trg_let);
+ }
+ }
+ }
+ cerr << endl;
+ tl.GraphSummary();
+
+ return 0;
+}
diff --git a/gi/pf/conditional_pseg.h b/gi/pf/conditional_pseg.h
index 8202778b..81ddb206 100644
--- a/gi/pf/conditional_pseg.h
+++ b/gi/pf/conditional_pseg.h
@@ -56,6 +56,12 @@ struct MConditionalTranslationModel {
};
void ResampleHyperparameters(MT19937* rng) {
+ typename std::tr1::unordered_map<std::vector<WordID>, MFCR<1,TRule>, boost::hash<std::vector<WordID> > >::iterator it;
+#if 1
+ for (it = r.begin(); it != r.end(); ++it) {
+ it->second.resample_hyperparameters(rng);
+ }
+#else
const unsigned nloop = 5;
const unsigned niterations = 10;
DiscountResampler dr(*this);
@@ -70,12 +76,12 @@ struct MConditionalTranslationModel {
}
strength = slice_sampler1d(ar, strength, *rng, -d,
std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
- typename std::tr1::unordered_map<std::vector<WordID>, MFCR<1,TRule>, boost::hash<std::vector<WordID> > >::iterator it;
std::cerr << "MConditionalTranslationModel(d=" << d << ",s=" << strength << ") = " << log_likelihood(d, strength) << std::endl;
for (it = r.begin(); it != r.end(); ++it) {
it->second.set_discount(d);
it->second.set_strength(strength);
}
+#endif
}
int DecrementRule(const TRule& rule, MT19937* rng) {
@@ -91,7 +97,8 @@ struct MConditionalTranslationModel {
int IncrementRule(const TRule& rule, MT19937* rng) {
RuleModelHash::iterator it = r.find(rule.f_);
if (it == r.end()) {
- it = r.insert(make_pair(rule.f_, MFCR<1,TRule>(d, strength))).first;
+ //it = r.insert(make_pair(rule.f_, MFCR<1,TRule>(d, strength))).first;
+ it = r.insert(make_pair(rule.f_, MFCR<1,TRule>(1,1,1,1,0.6, -0.12))).first;
}
p0s[0] = rp0(rule);
TableCount delta = it->second.increment(rule, p0s.begin(), lambdas.begin(), rng);
diff --git a/gi/pf/nuisance_test.cc b/gi/pf/nuisance_test.cc
new file mode 100644
index 00000000..0f44fe95
--- /dev/null
+++ b/gi/pf/nuisance_test.cc
@@ -0,0 +1,161 @@
+#include "ccrp.h"
+
+#include <vector>
+#include <iostream>
+
+#include "tdict.h"
+#include "transliterations.h"
+
+using namespace std;
+
+MT19937 rng;
+
+ostream& operator<<(ostream&os, const vector<int>& v) {
+ os << '[' << v[0];
+ if (v.size() == 2) os << ' ' << v[1];
+ return os << ']';
+}
+
+struct Base {
+ Base() : llh(), v(2), v1(1), v2(1), crp(0.25, 0.5) {}
+ inline double p0(const vector<int>& x) const {
+ double p = 0.75;
+ if (x.size() == 2) p = 0.25;
+ p *= 1.0 / 3.0;
+ if (x.size() == 2) p *= 1.0 / 3.0;
+ return p;
+ }
+ double est_deriv_prob(int a, int b, int seg) const {
+ assert(a > 0 && a < 4); // a \in {1,2,3}
+ assert(b > 0 && b < 4); // b \in {1,2,3}
+ assert(seg == 0 || seg == 1); // seg \in {0,1}
+ if (seg == 0) {
+ v[0] = a;
+ v[1] = b;
+ return crp.prob(v, p0(v));
+ } else {
+ v1[0] = a;
+ v2[0] = b;
+ return crp.prob(v1, p0(v1)) * crp.prob(v2, p0(v2));
+ }
+ }
+ double est_marginal_prob(int a, int b) const {
+ return est_deriv_prob(a,b,0) + est_deriv_prob(a,b,1);
+ }
+ int increment(int a, int b, double* pw = NULL) {
+ double p1 = est_deriv_prob(a, b, 0);
+ double p2 = est_deriv_prob(a, b, 1);
+ //p1 = 0.5; p2 = 0.5;
+ int seg = rng.SelectSample(p1,p2);
+ double tmp = 0;
+ if (!pw) pw = &tmp;
+ double& w = *pw;
+ if (seg == 0) {
+ v[0] = a;
+ v[1] = b;
+ w = crp.prob(v, p0(v)) / p1;
+ if (crp.increment(v, p0(v), &rng)) {
+ llh += log(p0(v));
+ }
+ } else {
+ v1[0] = a;
+ w = crp.prob(v1, p0(v1)) / p2;
+ if (crp.increment(v1, p0(v1), &rng)) {
+ llh += log(p0(v1));
+ }
+ v2[0] = b;
+ w *= crp.prob(v2, p0(v2));
+ if (crp.increment(v2, p0(v2), &rng)) {
+ llh += log(p0(v2));
+ }
+ }
+ return seg;
+ }
+ void increment(int a, int b, int seg) {
+ if (seg == 0) {
+ v[0] = a;
+ v[1] = b;
+ if (crp.increment(v, p0(v), &rng)) {
+ llh += log(p0(v));
+ }
+ } else {
+ v1[0] = a;
+ if (crp.increment(v1, p0(v1), &rng)) {
+ llh += log(p0(v1));
+ }
+ v2[0] = b;
+ if (crp.increment(v2, p0(v2), &rng)) {
+ llh += log(p0(v2));
+ }
+ }
+ }
+ void decrement(int a, int b, int seg) {
+ if (seg == 0) {
+ v[0] = a;
+ v[1] = b;
+ if (crp.decrement(v, &rng)) {
+ llh -= log(p0(v));
+ }
+ } else {
+ v1[0] = a;
+ if (crp.decrement(v1, &rng)) {
+ llh -= log(p0(v1));
+ }
+ v2[0] = b;
+ if (crp.decrement(v2, &rng)) {
+ llh -= log(p0(v2));
+ }
+ }
+ }
+ double log_likelihood() const {
+ return llh + crp.log_crp_prob();
+ }
+ double llh;
+ mutable vector<int> v, v1, v2;
+ CCRP<vector<int> > crp;
+};
+
+int main(int argc, char** argv) {
+ double tl = 0;
+ const int ITERS = 1000;
+ const int PARTICLES = 20;
+ const int DATAPOINTS = 50;
+ WordID x = TD::Convert("souvenons");
+ WordID y = TD::Convert("remember");
+ vector<WordID> src; TD::ConvertSentence("s o u v e n o n s", &src);
+ vector<WordID> trg; TD::ConvertSentence("r e m e m b e r", &trg);
+ Transliterations xx;
+ xx.Initialize(x, src, y, trg);
+ return 1;
+
+ for (int j = 0; j < ITERS; ++j) {
+ Base b;
+ vector<int> segs(DATAPOINTS);
+ SampleSet<double> ss;
+ vector<int> sss;
+ for (int i = 0; i < DATAPOINTS; i++) {
+ ss.clear();
+ sss.clear();
+ int x = ((i / 10) % 3) + 1;
+ int y = (i % 3) + 1;
+ //double ep = b.est_marginal_prob(x,y);
+ //cerr << "est p(" << x << "," << y << ") = " << ep << endl;
+ for (int n = 0; n < PARTICLES; ++n) {
+ double w;
+ int seg = b.increment(x,y,&w);
+ //cerr << seg << " w=" << w << endl;
+ ss.add(w);
+ sss.push_back(seg);
+ b.decrement(x,y,seg);
+ }
+ int seg = sss[rng.SelectSample(ss)];
+ b.increment(x, y, seg);
+ //cerr << "Selected: " << seg << endl;
+ //return 1;
+ segs[i] = seg;
+ }
+ tl += b.log_likelihood();
+ }
+ cerr << "LLH=" << tl / ITERS << endl;
+}
+
diff --git a/gi/pf/transliterations.cc b/gi/pf/transliterations.cc
new file mode 100644
index 00000000..6e0c2e93
--- /dev/null
+++ b/gi/pf/transliterations.cc
@@ -0,0 +1,193 @@
+#include "transliterations.h"
+
+#include <iostream>
+#include <vector>
+#include <tr1/unordered_map>
+
+#include "grammar.h"
+#include "bottom_up_parser.h"
+#include "hg.h"
+#include "hg_intersect.h"
+#include "filelib.h"
+#include "ccrp.h"
+#include "m.h"
+#include "lattice.h"
+#include "verbose.h"
+
+using namespace std;
+using namespace std::tr1;
+
+static WordID kX;
+static int kMAX_SRC_SIZE = 0;
+static vector<vector<WordID> > cur_trg_chunks;
+
+vector<GrammarIter*> tlttofreelist;
+
+static void InitTargetChunks(int max_size, const vector<WordID>& trg) {
+ cur_trg_chunks.clear();
+ vector<WordID> tmp;
+ unordered_set<vector<WordID>, boost::hash<vector<WordID> > > u;
+ for (int len = 1; len <= max_size; ++len) {
+ int end = trg.size() + 1;
+ end -= len;
+ for (int i = 0; i < end; ++i) {
+ tmp.clear();
+ for (int j = 0; j < len; ++j)
+ tmp.push_back(trg[i + j]);
+ if (u.insert(tmp).second) cur_trg_chunks.push_back(tmp);
+ }
+ }
+}
+
+struct TransliterationGrammarIter : public GrammarIter, public RuleBin {
+ TransliterationGrammarIter() { tlttofreelist.push_back(this); }
+ TransliterationGrammarIter(const TRulePtr& inr, int symbol) {
+ if (inr) {
+ r.reset(new TRule(*inr));
+ } else {
+ r.reset(new TRule);
+ }
+ TRule& rr = *r;
+ rr.lhs_ = kX;
+ rr.f_.push_back(symbol);
+ tlttofreelist.push_back(this);
+ }
+ virtual int GetNumRules() const {
+ if (!r) return 0;
+ return cur_trg_chunks.size();
+ }
+ virtual TRulePtr GetIthRule(int i) const {
+ TRulePtr nr(new TRule(*r));
+ nr->e_ = cur_trg_chunks[i];
+ //cerr << nr->AsString() << endl;
+ return nr;
+ }
+ virtual int Arity() const {
+ return 0;
+ }
+ virtual const RuleBin* GetRules() const {
+ if (!r) return NULL; else return this;
+ }
+ virtual const GrammarIter* Extend(int symbol) const {
+ if (symbol <= 0) return NULL;
+ if (!r || !kMAX_SRC_SIZE || r->f_.size() < kMAX_SRC_SIZE)
+ return new TransliterationGrammarIter(r, symbol);
+ else
+ return NULL;
+ }
+ TRulePtr r;
+};
+
+struct TransliterationGrammar : public Grammar {
+ virtual const GrammarIter* GetRoot() const {
+ return new TransliterationGrammarIter;
+ }
+ virtual bool HasRuleForSpan(int, int, int distance) const {
+ return (distance < kMAX_SRC_SIZE);
+ }
+};
+
+struct TInfo {
+ TInfo() : initialized(false) {}
+ bool initialized;
+ Hypergraph lattice; // may be empty if transliteration is not possible
+ prob_t est_prob; // will be zero if not possible
+};
+
+struct TransliterationsImpl {
+ TransliterationsImpl() {
+ kX = TD::Convert("X")*-1;
+ kMAX_SRC_SIZE = 4;
+ grammars.push_back(GrammarPtr(new TransliterationGrammar));
+ grammars.push_back(GrammarPtr(new GlueGrammar("S", "X")));
+ SetSilent(true);
+ }
+
+ void Initialize(WordID src, const vector<WordID>& src_lets, WordID trg, const vector<WordID>& trg_lets) {
+ if (src >= graphs.size()) graphs.resize(src + 1);
+ if (graphs[src][trg].initialized) return;
+ int kMAX_TRG_SIZE = 4;
+ InitTargetChunks(kMAX_TRG_SIZE, trg_lets);
+ ExhaustiveBottomUpParser parser("S", grammars);
+ Lattice lat(src_lets.size()), tlat(trg_lets.size());
+ for (unsigned i = 0; i < src_lets.size(); ++i)
+ lat[i].push_back(LatticeArc(src_lets[i], 0.0, 1));
+ for (unsigned i = 0; i < trg_lets.size(); ++i)
+ tlat[i].push_back(LatticeArc(trg_lets[i], 0.0, 1));
+ //cerr << "Creating lattice for: " << TD::Convert(src) << " --> " << TD::Convert(trg) << endl;
+ //cerr << "'" << TD::GetString(src_lets) << "' --> " << TD::GetString(trg_lets) << endl;
+ if (!parser.Parse(lat, &graphs[src][trg].lattice)) {
+ //cerr << "Failed to parse " << TD::GetString(src_lets) << endl;
+ abort();
+ }
+ if (HG::Intersect(tlat, &graphs[src][trg].lattice)) {
+ graphs[src][trg].est_prob = prob_t(1e-4);
+ } else {
+ graphs[src][trg].lattice.clear();
+ //cerr << "Failed to intersect " << TD::GetString(src_lets) << " ||| " << TD::GetString(trg_lets) << endl;
+ graphs[src][trg].est_prob = prob_t::Zero();
+ }
+ for (unsigned i = 0; i < tlttofreelist.size(); ++i)
+ delete tlttofreelist[i];
+ tlttofreelist.clear();
+ //cerr << "Number of paths: " << graphs[src][trg].lattice.NumberOfPaths() << endl;
+ graphs[src][trg].initialized = true;
+ }
+
+ const prob_t& EstimateProbability(WordID src, WordID trg) const {
+ assert(src < graphs.size());
+ const unordered_map<WordID, TInfo>& um = graphs[src];
+ const unordered_map<WordID, TInfo>::const_iterator it = um.find(trg);
+ assert(it != um.end());
+ assert(it->second.initialized);
+ return it->second.est_prob;
+ }
+
+ void Forbid(WordID src, WordID trg) {
+ if (src >= graphs.size()) graphs.resize(src + 1);
+ graphs[src][trg].est_prob = prob_t::Zero();
+ graphs[src][trg].initialized = true;
+ }
+
+ void GraphSummary() const {
+ double tlp = 0;
+ int tt = 0;
+ for (int i = 0; i < graphs.size(); ++i) {
+ const unordered_map<WordID, TInfo>& um = graphs[i];
+ unordered_map<WordID, TInfo>::const_iterator it;
+ for (it = um.begin(); it != um.end(); ++it) {
+ if (it->second.lattice.empty()) continue;
+ //cerr << TD::Convert(i) << " --> " << TD::Convert(it->first) << ": " << it->second.lattice.NumberOfPaths() << endl;
+ tlp += log(it->second.lattice.NumberOfPaths());
+ tt++;
+ }
+ }
+ tlp /= tt;
+ cerr << "E[log paths] = " << tlp << endl;
+ cerr << "exp(E[log paths]) = " << exp(tlp) << endl;
+ }
+
+ vector<unordered_map<WordID, TInfo> > graphs;
+ vector<GrammarPtr> grammars;
+};
+
+Transliterations::Transliterations() : pimpl_(new TransliterationsImpl) {}
+Transliterations::~Transliterations() { delete pimpl_; }
+
+void Transliterations::Initialize(WordID src, const vector<WordID>& src_lets, WordID trg, const vector<WordID>& trg_lets) {
+ pimpl_->Initialize(src, src_lets, trg, trg_lets);
+}
+
+prob_t Transliterations::EstimateProbability(WordID src, WordID trg) const {
+ return pimpl_->EstimateProbability(src,trg);
+}
+
+void Transliterations::Forbid(WordID src, WordID trg) {
+ pimpl_->Forbid(src, trg);
+}
+
+void Transliterations::GraphSummary() const {
+ pimpl_->GraphSummary();
+}
+
+
diff --git a/gi/pf/transliterations.h b/gi/pf/transliterations.h
new file mode 100644
index 00000000..a548aacf
--- /dev/null
+++ b/gi/pf/transliterations.h
@@ -0,0 +1,20 @@
+#ifndef _TRANSLITERATIONS_H_
+#define _TRANSLITERATIONS_H_
+
+#include <vector>
+#include "wordid.h"
+#include "prob.h"
+
+struct TransliterationsImpl;
+struct Transliterations {
+ explicit Transliterations();
+ ~Transliterations();
+ void Initialize(WordID src, const std::vector<WordID>& src_lets, WordID trg, const std::vector<WordID>& trg_lets);
+ void Forbid(WordID src, WordID trg);
+ void GraphSummary() const;
+ prob_t EstimateProbability(WordID src, WordID trg) const;
+ TransliterationsImpl* pimpl_;
+};
+
+#endif
+
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-11 16:00:23 +0000