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-rw-r--r--rst_parser/Makefile.am16
-rw-r--r--rst_parser/arc_factored.cc40
-rw-r--r--rst_parser/arc_factored.h7
-rw-r--r--rst_parser/arc_ff.cc120
-rw-r--r--rst_parser/arc_ff.h35
-rw-r--r--rst_parser/arc_ff_factory.h42
-rw-r--r--rst_parser/mst_train.cc37
-rw-r--r--rst_parser/rst_test.cc48
-rw-r--r--rst_parser/rst_train.cc (renamed from rst_parser/rst_parse.cc)102
9 files changed, 180 insertions, 267 deletions
diff --git a/rst_parser/Makefile.am b/rst_parser/Makefile.am
index 6e884f53..876c2237 100644
--- a/rst_parser/Makefile.am
+++ b/rst_parser/Makefile.am
@@ -1,22 +1,14 @@
bin_PROGRAMS = \
- mst_train rst_parse
-
-noinst_PROGRAMS = \
- rst_test
-
-TESTS = rst_test
+ mst_train rst_train
noinst_LIBRARIES = librst.a
-librst_a_SOURCES = arc_factored.cc arc_factored_marginals.cc rst.cc arc_ff.cc dep_training.cc
+librst_a_SOURCES = arc_factored.cc arc_factored_marginals.cc rst.cc arc_ff.cc dep_training.cc global_ff.cc
mst_train_SOURCES = mst_train.cc
mst_train_LDADD = librst.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a ../training/optimize.o -lz
-rst_parse_SOURCES = rst_parse.cc
-rst_parse_LDADD = librst.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
-
-rst_test_SOURCES = rst_test.cc
-rst_test_LDADD = librst.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
+rst_train_SOURCES = rst_train.cc
+rst_train_LDADD = librst.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
AM_CPPFLAGS = -W -Wall -Wno-sign-compare $(GTEST_CPPFLAGS) -I$(top_srcdir)/decoder -I$(top_srcdir)/training -I$(top_srcdir)/utils -I$(top_srcdir)/mteval -I../klm
diff --git a/rst_parser/arc_factored.cc b/rst_parser/arc_factored.cc
index 34c689f4..74bf7516 100644
--- a/rst_parser/arc_factored.cc
+++ b/rst_parser/arc_factored.cc
@@ -13,36 +13,30 @@ using namespace std::tr1;
using namespace boost;
void EdgeSubset::ExtractFeatures(const TaggedSentence& sentence,
- const std::vector<boost::shared_ptr<ArcFeatureFunction> >& ffs,
+ const ArcFeatureFunctions& ffs,
SparseVector<double>* features) const {
SparseVector<weight_t> efmap;
- for (int i = 0; i < ffs.size(); ++i) {
- const ArcFeatureFunction& ff= *ffs[i];
- for (int j = 0; j < h_m_pairs.size(); ++j) {
- efmap.clear();
- ff.EgdeFeatures(sentence, h_m_pairs[j].first,
- h_m_pairs[j].second,
- &efmap);
- (*features) += efmap;
- }
- for (int j = 0; j < roots.size(); ++j) {
- efmap.clear();
- ff.EgdeFeatures(sentence, -1, roots[j], &efmap);
- (*features) += efmap;
- }
+ for (int j = 0; j < h_m_pairs.size(); ++j) {
+ efmap.clear();
+ ffs.EdgeFeatures(sentence, h_m_pairs[j].first,
+ h_m_pairs[j].second,
+ &efmap);
+ (*features) += efmap;
+ }
+ for (int j = 0; j < roots.size(); ++j) {
+ efmap.clear();
+ ffs.EdgeFeatures(sentence, -1, roots[j], &efmap);
+ (*features) += efmap;
}
}
void ArcFactoredForest::ExtractFeatures(const TaggedSentence& sentence,
- const std::vector<boost::shared_ptr<ArcFeatureFunction> >& ffs) {
- for (int i = 0; i < ffs.size(); ++i) {
- const ArcFeatureFunction& ff = *ffs[i];
- for (int m = 0; m < num_words_; ++m) {
- for (int h = 0; h < num_words_; ++h) {
- ff.EgdeFeatures(sentence, h, m, &edges_(h,m).features);
- }
- ff.EgdeFeatures(sentence, -1, m, &root_edges_[m].features);
+ const ArcFeatureFunctions& ffs) {
+ for (int m = 0; m < num_words_; ++m) {
+ for (int h = 0; h < num_words_; ++h) {
+ ffs.EdgeFeatures(sentence, h, m, &edges_(h,m).features);
}
+ ffs.EdgeFeatures(sentence, -1, m, &root_edges_[m].features);
}
}
diff --git a/rst_parser/arc_factored.h b/rst_parser/arc_factored.h
index a271c8d4..c5481d80 100644
--- a/rst_parser/arc_factored.h
+++ b/rst_parser/arc_factored.h
@@ -17,14 +17,15 @@ struct TaggedSentence {
std::vector<WordID> pos;
};
-struct ArcFeatureFunction;
+struct ArcFeatureFunctions;
struct EdgeSubset {
EdgeSubset() {}
std::vector<short> roots; // unless multiroot trees are supported, this
// will have a single member
std::vector<std::pair<short, short> > h_m_pairs; // h,m start at 0
+ // assumes ArcFeatureFunction::PrepareForInput has already been called
void ExtractFeatures(const TaggedSentence& sentence,
- const std::vector<boost::shared_ptr<ArcFeatureFunction> >& ffs,
+ const ArcFeatureFunctions& ffs,
SparseVector<double>* features) const;
};
@@ -74,7 +75,7 @@ class ArcFactoredForest {
// set eges_[*].features
void ExtractFeatures(const TaggedSentence& sentence,
- const std::vector<boost::shared_ptr<ArcFeatureFunction> >& ffs);
+ const ArcFeatureFunctions& ffs);
const Edge& operator()(short h, short m) const {
return h >= 0 ? edges_(h, m) : root_edges_[m];
diff --git a/rst_parser/arc_ff.cc b/rst_parser/arc_ff.cc
index f9effbda..10885716 100644
--- a/rst_parser/arc_ff.cc
+++ b/rst_parser/arc_ff.cc
@@ -6,59 +6,81 @@
using namespace std;
-ArcFeatureFunction::~ArcFeatureFunction() {}
+struct ArcFFImpl {
+ ArcFFImpl() : kROOT("ROOT") {}
+ const string kROOT;
-void ArcFeatureFunction::PrepareForInput(const TaggedSentence&) {}
+ void PrepareForInput(const TaggedSentence& sentence) {
+ (void) sentence;
+ }
+
+ void EdgeFeatures(const TaggedSentence& sent,
+ short h,
+ short m,
+ SparseVector<weight_t>* features) const {
+ const bool is_root = (h == -1);
+ const string& head_word = (is_root ? kROOT : TD::Convert(sent.words[h]));
+ const string& head_pos = (is_root ? kROOT : TD::Convert(sent.pos[h]));
+ const string& mod_word = TD::Convert(sent.words[m]);
+ const string& mod_pos = TD::Convert(sent.pos[m]);
+ const bool dir = m < h;
+ int v = m - h;
+ if (v < 0) {
+ v= -1 - int(log(-v) / log(2));
+ } else {
+ v= int(log(v) / log(2));
+ }
+ static map<int, int> lenmap;
+ int& lenfid = lenmap[v];
+ if (!lenfid) {
+ ostringstream os;
+ if (v < 0) os << "LenL" << -v; else os << "LenR" << v;
+ lenfid = FD::Convert(os.str());
+ }
+ features->set_value(lenfid, 1.0);
+ const string& lenstr = FD::Convert(lenfid);
+ if (!is_root) {
+ static int modl = FD::Convert("ModLeft");
+ static int modr = FD::Convert("ModRight");
+ if (dir) features->set_value(modl, 1);
+ else features->set_value(modr, 1);
+ }
+ if (is_root) {
+ ostringstream os;
+ os << "ROOT:" << mod_pos;
+ features->set_value(FD::Convert(os.str()), 1.0);
+ os << "_" << lenstr;
+ features->set_value(FD::Convert(os.str()), 1.0);
+ } else { // not root
+ ostringstream os;
+ os << "HM:" << head_pos << '_' << mod_pos;
+ features->set_value(FD::Convert(os.str()), 1.0);
+ os << '_' << dir;
+ features->set_value(FD::Convert(os.str()), 1.0);
+ os << '_' << lenstr;
+ features->set_value(FD::Convert(os.str()), 1.0);
+ ostringstream os2;
+ os2 << "LexHM:" << head_word << '_' << mod_word;
+ features->set_value(FD::Convert(os2.str()), 1.0);
+ os2 << '_' << dir;
+ features->set_value(FD::Convert(os2.str()), 1.0);
+ os2 << '_' << lenstr;
+ features->set_value(FD::Convert(os2.str()), 1.0);
+ }
+ }
+};
-DistancePenalty::DistancePenalty(const string&) : fidw_(FD::Convert("Distance")), fidr_(FD::Convert("RootDistance")) {}
+ArcFeatureFunctions::ArcFeatureFunctions() : pimpl(new ArcFFImpl) {}
+ArcFeatureFunctions::~ArcFeatureFunctions() { delete pimpl; }
+
+void ArcFeatureFunctions::PrepareForInput(const TaggedSentence& sentence) {
+ pimpl->PrepareForInput(sentence);
+}
-void DistancePenalty::EdgeFeaturesImpl(const TaggedSentence& sent,
+void ArcFeatureFunctions::EdgeFeatures(const TaggedSentence& sentence,
short h,
short m,
SparseVector<weight_t>* features) const {
- const bool dir = m < h;
- const bool is_root = (h == -1);
- int v = m - h;
- if (v < 0) {
- v= -1 - int(log(-v) / log(2));
- } else {
- v= int(log(v) / log(2));
- }
- static map<int, int> lenmap;
- int& lenfid = lenmap[v];
- if (!lenfid) {
- ostringstream os;
- if (v < 0) os << "LenL" << -v; else os << "LenR" << v;
- lenfid = FD::Convert(os.str());
- }
- features->set_value(lenfid, 1.0);
- const string& lenstr = FD::Convert(lenfid);
- if (!is_root) {
- static int modl = FD::Convert("ModLeft");
- static int modr = FD::Convert("ModRight");
- if (dir) features->set_value(modl, 1);
- else features->set_value(modr, 1);
- }
- if (is_root) {
- ostringstream os;
- os << "ROOT:" << TD::Convert(sent.pos[m]);
- features->set_value(FD::Convert(os.str()), 1.0);
- os << "_" << lenstr;
- features->set_value(FD::Convert(os.str()), 1.0);
- } else { // not root
- ostringstream os;
- os << "HM:" << TD::Convert(sent.pos[h]) << '_' << TD::Convert(sent.pos[m]);
- features->set_value(FD::Convert(os.str()), 1.0);
- os << '_' << dir;
- features->set_value(FD::Convert(os.str()), 1.0);
- os << '_' << lenstr;
- features->set_value(FD::Convert(os.str()), 1.0);
- ostringstream os2;
- os2 << "LexHM:" << TD::Convert(sent.words[h]) << '_' << TD::Convert(sent.words[m]);
- features->set_value(FD::Convert(os2.str()), 1.0);
- os2 << '_' << dir;
- features->set_value(FD::Convert(os2.str()), 1.0);
- os2 << '_' << lenstr;
- features->set_value(FD::Convert(os2.str()), 1.0);
- }
+ pimpl->EdgeFeatures(sentence, h, m, features);
}
+
diff --git a/rst_parser/arc_ff.h b/rst_parser/arc_ff.h
index bc51fef4..52f311d2 100644
--- a/rst_parser/arc_ff.h
+++ b/rst_parser/arc_ff.h
@@ -7,37 +7,22 @@
#include "arc_factored.h"
struct TaggedSentence;
-class ArcFeatureFunction {
+struct ArcFFImpl;
+class ArcFeatureFunctions {
public:
- virtual ~ArcFeatureFunction();
+ ArcFeatureFunctions();
+ ~ArcFeatureFunctions();
// called once, per input, before any calls to EdgeFeatures
// used to initialize sentence-specific data structures
- virtual void PrepareForInput(const TaggedSentence& sentence);
+ void PrepareForInput(const TaggedSentence& sentence);
- inline void EgdeFeatures(const TaggedSentence& sentence,
- short h,
- short m,
- SparseVector<weight_t>* features) const {
- EdgeFeaturesImpl(sentence, h, m, features);
- }
- protected:
- virtual void EdgeFeaturesImpl(const TaggedSentence& sentence,
- short h,
- short m,
- SparseVector<weight_t>* features) const = 0;
-};
-
-class DistancePenalty : public ArcFeatureFunction {
- public:
- DistancePenalty(const std::string& param);
- protected:
- virtual void EdgeFeaturesImpl(const TaggedSentence& sentence,
- short h,
- short m,
- SparseVector<weight_t>* features) const;
+ void EdgeFeatures(const TaggedSentence& sentence,
+ short h,
+ short m,
+ SparseVector<weight_t>* features) const;
private:
- const int fidw_, fidr_;
+ ArcFFImpl* pimpl;
};
#endif
diff --git a/rst_parser/arc_ff_factory.h b/rst_parser/arc_ff_factory.h
deleted file mode 100644
index 4237fd5d..00000000
--- a/rst_parser/arc_ff_factory.h
+++ /dev/null
@@ -1,42 +0,0 @@
-#ifndef _ARC_FF_FACTORY_H_
-#define _ARC_FF_FACTORY_H_
-
-#include <string>
-#include <map>
-#include <boost/shared_ptr.hpp>
-
-struct ArcFFFactoryBase {
- virtual boost::shared_ptr<ArcFeatureFunction> Create(const std::string& param) const = 0;
-};
-
-template<class FF>
-struct ArcFFFactory : public ArcFFFactoryBase {
- boost::shared_ptr<ArcFeatureFunction> Create(const std::string& param) const {
- return boost::shared_ptr<ArcFeatureFunction>(new FF(param));
- }
-};
-
-struct ArcFFRegistry {
- boost::shared_ptr<ArcFeatureFunction> Create(const std::string& name, const std::string& param) const {
- std::map<std::string, ArcFFFactoryBase*>::const_iterator it = facts.find(name);
- assert(it != facts.end());
- return it->second->Create(param);
- }
-
- void Register(const std::string& name, ArcFFFactoryBase* fact) {
- ArcFFFactoryBase*& f = facts[name];
- assert(f == NULL);
- f = fact;
- }
- std::map<std::string, ArcFFFactoryBase*> facts;
-};
-
-std::ostream& operator<<(std::ostream& os, const ArcFFRegistry& reg) {
- for (std::map<std::string, ArcFFFactoryBase*>::const_iterator it = reg.facts.begin();
- it != reg.facts.end(); ++it) {
- os << " " << it->first << std::endl;
- }
- return os;
-}
-
-#endif
diff --git a/rst_parser/mst_train.cc b/rst_parser/mst_train.cc
index f0403d7e..0709e7c9 100644
--- a/rst_parser/mst_train.cc
+++ b/rst_parser/mst_train.cc
@@ -6,7 +6,6 @@
#include <boost/program_options/variables_map.hpp>
#include "arc_ff.h"
-#include "arc_ff_factory.h"
#include "stringlib.h"
#include "filelib.h"
#include "tdict.h"
@@ -22,7 +21,6 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
string cfg_file;
opts.add_options()
("training_data,t",po::value<string>()->default_value("-"), "File containing training data (jsent format)")
- ("feature_function,F",po::value<vector<string> >()->composing(), "feature function (multiple permitted)")
("weights,w",po::value<string>(), "Optional starting weights")
("output_every_i_iterations,I",po::value<unsigned>()->default_value(1), "Write weights every I iterations")
("regularization_strength,C",po::value<double>()->default_value(1.0), "Regularization strength")
@@ -74,12 +72,8 @@ int main(int argc, char** argv) {
int size = 1;
po::variables_map conf;
InitCommandLine(argc, argv, &conf);
- ArcFactoredForest af(5);
- ArcFFRegistry reg;
- reg.Register("DistancePenalty", new ArcFFFactory<DistancePenalty>);
+ ArcFeatureFunctions ffs;
vector<TrainingInstance> corpus;
- vector<boost::shared_ptr<ArcFeatureFunction> > ffs;
- ffs.push_back(boost::shared_ptr<ArcFeatureFunction>(new DistancePenalty("")));
TrainingInstance::ReadTraining(conf["training_data"].as<string>(), &corpus, rank, size);
vector<ArcFactoredForest> forests(corpus.size());
SparseVector<double> empirical;
@@ -88,22 +82,19 @@ int main(int argc, char** argv) {
TrainingInstance& cur = corpus[i];
if (rank == 0 && (i+1) % 10 == 0) { cerr << '.' << flush; flag = true; }
if (rank == 0 && (i+1) % 400 == 0) { cerr << " [" << (i+1) << "]\n"; flag = false; }
- for (int fi = 0; fi < ffs.size(); ++fi) {
- ArcFeatureFunction& ff = *ffs[fi];
- ff.PrepareForInput(cur.ts);
- SparseVector<weight_t> efmap;
- for (int j = 0; j < cur.tree.h_m_pairs.size(); ++j) {
- efmap.clear();
- ff.EgdeFeatures(cur.ts, cur.tree.h_m_pairs[j].first,
- cur.tree.h_m_pairs[j].second,
- &efmap);
- cur.features += efmap;
- }
- for (int j = 0; j < cur.tree.roots.size(); ++j) {
- efmap.clear();
- ff.EgdeFeatures(cur.ts, -1, cur.tree.roots[j], &efmap);
- cur.features += efmap;
- }
+ ffs.PrepareForInput(cur.ts);
+ SparseVector<weight_t> efmap;
+ for (int j = 0; j < cur.tree.h_m_pairs.size(); ++j) {
+ efmap.clear();
+ ffs.EdgeFeatures(cur.ts, cur.tree.h_m_pairs[j].first,
+ cur.tree.h_m_pairs[j].second,
+ &efmap);
+ cur.features += efmap;
+ }
+ for (int j = 0; j < cur.tree.roots.size(); ++j) {
+ efmap.clear();
+ ffs.EdgeFeatures(cur.ts, -1, cur.tree.roots[j], &efmap);
+ cur.features += efmap;
}
empirical += cur.features;
forests[i].resize(cur.ts.words.size());
diff --git a/rst_parser/rst_test.cc b/rst_parser/rst_test.cc
deleted file mode 100644
index 3bb95759..00000000
--- a/rst_parser/rst_test.cc
+++ /dev/null
@@ -1,48 +0,0 @@
-#include "arc_factored.h"
-
-#include <iostream>
-
-#include <Eigen/Dense>
-
-using namespace std;
-
-int main(int argc, char** argv) {
- // John saw Mary
- // (H -> M)
- // (1 -> 2) 20
- // (1 -> 3) 3
- // (2 -> 1) 20
- // (2 -> 3) 30
- // (3 -> 2) 0
- // (3 -> 1) 11
- // (0, 2) 10
- // (0, 1) 9
- // (0, 3) 9
- ArcFactoredForest af(3);
- af(0,1).edge_prob.logeq(20);
- af(0,2).edge_prob.logeq(3);
- af(1,0).edge_prob.logeq(20);
- af(1,2).edge_prob.logeq(30);
- af(2,1).edge_prob.logeq(0);
- af(2,0).edge_prob.logeq(11);
- af(-1,1).edge_prob.logeq(10);
- af(-1,0).edge_prob.logeq(9);
- af(-1,2).edge_prob.logeq(9);
- EdgeSubset tree;
-// af.MaximumEdgeSubset(&tree);
- prob_t z;
- af.EdgeMarginals(&z);
- cerr << "Z = " << abs(z) << endl;
- af.PickBestParentForEachWord(&tree);
- cerr << tree << endl;
- typedef Eigen::Matrix<prob_t, 2, 2> M3;
- M3 A = M3::Zero();
- A(0,0) = prob_t(1);
- A(1,0) = prob_t(3);
- A(0,1) = prob_t(2);
- A(1,1) = prob_t(4);
- prob_t det = A.determinant();
- cerr << det.as_float() << endl;
- return 0;
-}
-
diff --git a/rst_parser/rst_parse.cc b/rst_parser/rst_train.cc
index 9cc1359a..16673cdc 100644
--- a/rst_parser/rst_parse.cc
+++ b/rst_parser/rst_train.cc
@@ -7,13 +7,13 @@
#include "timing_stats.h"
#include "arc_ff.h"
-#include "arc_ff_factory.h"
#include "dep_training.h"
#include "stringlib.h"
#include "filelib.h"
#include "tdict.h"
#include "weights.h"
#include "rst.h"
+#include "global_ff.h"
using namespace std;
namespace po = boost::program_options;
@@ -23,7 +23,6 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
string cfg_file;
opts.add_options()
("training_data,t",po::value<string>()->default_value("-"), "File containing training data (jsent format)")
- ("feature_function,F",po::value<vector<string> >()->composing(), "feature function (multiple permitted)")
("q_weights,q",po::value<string>(), "Arc-factored weights for proposal distribution")
("samples,n",po::value<unsigned>()->default_value(1000), "Number of samples");
po::options_description clo("Command line options");
@@ -48,51 +47,55 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
int main(int argc, char** argv) {
po::variables_map conf;
InitCommandLine(argc, argv, &conf);
- ArcFactoredForest af(5);
- ArcFFRegistry reg;
- reg.Register("DistancePenalty", new ArcFFFactory<DistancePenalty>);
+ vector<weight_t> qweights(FD::NumFeats(), 0.0);
+ Weights::InitFromFile(conf["q_weights"].as<string>(), &qweights);
vector<TrainingInstance> corpus;
- vector<boost::shared_ptr<ArcFeatureFunction> > ffs;
- ffs.push_back(boost::shared_ptr<ArcFeatureFunction>(new DistancePenalty("")));
+ ArcFeatureFunctions ffs;
+ GlobalFeatureFunctions gff;
TrainingInstance::ReadTraining(conf["training_data"].as<string>(), &corpus);
vector<ArcFactoredForest> forests(corpus.size());
+ vector<prob_t> zs(corpus.size());
SparseVector<double> empirical;
bool flag = false;
for (int i = 0; i < corpus.size(); ++i) {
TrainingInstance& cur = corpus[i];
if ((i+1) % 10 == 0) { cerr << '.' << flush; flag = true; }
if ((i+1) % 400 == 0) { cerr << " [" << (i+1) << "]\n"; flag = false; }
- for (int fi = 0; fi < ffs.size(); ++fi) {
- ArcFeatureFunction& ff = *ffs[fi];
- ff.PrepareForInput(cur.ts);
- SparseVector<weight_t> efmap;
- for (int j = 0; j < cur.tree.h_m_pairs.size(); ++j) {
- efmap.clear();
- ff.EgdeFeatures(cur.ts, cur.tree.h_m_pairs[j].first,
- cur.tree.h_m_pairs[j].second,
- &efmap);
- cur.features += efmap;
- }
- for (int j = 0; j < cur.tree.roots.size(); ++j) {
- efmap.clear();
- ff.EgdeFeatures(cur.ts, -1, cur.tree.roots[j], &efmap);
- cur.features += efmap;
- }
+ SparseVector<weight_t> efmap;
+ ffs.PrepareForInput(cur.ts);
+ gff.PrepareForInput(cur.ts);
+ for (int j = 0; j < cur.tree.h_m_pairs.size(); ++j) {
+ efmap.clear();
+ ffs.EdgeFeatures(cur.ts, cur.tree.h_m_pairs[j].first,
+ cur.tree.h_m_pairs[j].second,
+ &efmap);
+ cur.features += efmap;
}
+ for (int j = 0; j < cur.tree.roots.size(); ++j) {
+ efmap.clear();
+ ffs.EdgeFeatures(cur.ts, -1, cur.tree.roots[j], &efmap);
+ cur.features += efmap;
+ }
+ efmap.clear();
+ gff.Features(cur.ts, cur.tree, &efmap);
+ cur.features += efmap;
empirical += cur.features;
forests[i].resize(cur.ts.words.size());
forests[i].ExtractFeatures(cur.ts, ffs);
+ forests[i].Reweight(qweights);
+ forests[i].EdgeMarginals(&zs[i]);
+ zs[i] = prob_t::One() / zs[i];
+ // cerr << zs[i] << endl;
+ forests[i].Reweight(qweights); // EdgeMarginals overwrites edge_prob
}
if (flag) cerr << endl;
- vector<weight_t> weights(FD::NumFeats(), 0.0);
- Weights::InitFromFile(conf["q_weights"].as<string>(), &weights);
MT19937 rng;
SparseVector<double> model_exp;
- SparseVector<double> sampled_exp;
+ SparseVector<double> weights;
+ Weights::InitSparseVector(qweights, &weights);
int samples = conf["samples"].as<unsigned>();
for (int i = 0; i < corpus.size(); ++i) {
- const int num_words = corpus[i].ts.words.size();
- forests[i].Reweight(weights);
+#if 0
forests[i].EdgeMarginals();
model_exp.clear();
for (int h = -1; h < num_words; ++h) {
@@ -104,23 +107,38 @@ int main(int argc, char** argv) {
model_exp += fmap * prob;
}
}
- //cerr << "TRUE EXP: " << model_exp << endl;
-
+ cerr << "TRUE EXP: " << model_exp << endl;
forests[i].Reweight(weights);
+#endif
+
TreeSampler ts(forests[i]);
- sampled_exp.clear();
- //ostringstream os; os << "Samples_" << samples;
- //Timer t(os.str());
- for (int n = 0; n < samples; ++n) {
- EdgeSubset tree;
- ts.SampleRandomSpanningTree(&tree, &rng);
- SparseVector<double> feats;
- tree.ExtractFeatures(corpus[i].ts, ffs, &feats);
- sampled_exp += feats;
- }
- sampled_exp /= samples;
- cerr << "L2 norm of diff @ " << samples << " samples: " << (model_exp - sampled_exp).l2norm() << endl;
+ prob_t zhat = prob_t::Zero();
+ SparseVector<prob_t> sampled_exp;
+ for (int n = 0; n < samples; ++n) {
+ EdgeSubset tree;
+ ts.SampleRandomSpanningTree(&tree, &rng);
+ SparseVector<double> qfeats, gfeats;
+ tree.ExtractFeatures(corpus[i].ts, ffs, &qfeats);
+ prob_t u; u.logeq(qfeats.dot(qweights));
+ const prob_t q = u / zs[i]; // proposal mass
+ gff.Features(corpus[i].ts, tree, &gfeats);
+ SparseVector<double> tot_feats = qfeats + gfeats;
+ u.logeq(tot_feats.dot(weights));
+ prob_t w = u / q;
+ zhat += w;
+ for (SparseVector<double>::const_iterator it = tot_feats.begin(); it != tot_feats.end(); ++it)
+ sampled_exp.add_value(it->first, w * prob_t(it->second));
+ }
+ sampled_exp /= zhat;
+ SparseVector<double> tot_m;
+ for (SparseVector<prob_t>::const_iterator it = sampled_exp.begin(); it != sampled_exp.end(); ++it)
+ tot_m.add_value(it->first, it->second.as_float());
+ //cerr << "DIFF: " << (tot_m - corpus[i].features) << endl;
+ const double eta = 0.03;
+ weights -= (tot_m - corpus[i].features) * eta;
}
+ cerr << "WEIGHTS.\n";
+ cerr << weights << endl;
return 0;
}
generated by cgit v1.2.3 (git 2.39.1) at 2024-09-22 01:33:47 +0000