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-rw-r--r--decoder/decoder.cc64
-rw-r--r--decoder/decoder.h9
-rw-r--r--mira/kbest_mira.cc62
-rw-r--r--pro-train/mr_pro_map.cc8
-rw-r--r--pro-train/mr_pro_reduce.cc16
-rw-r--r--training/Makefile.am8
-rw-r--r--training/augment_grammar.cc4
-rw-r--r--training/collapse_weights.cc6
-rw-r--r--training/compute_cllh.cc23
-rw-r--r--training/grammar_convert.cc8
-rw-r--r--training/mpi_batch_optimize.cc127
-rw-r--r--training/mpi_online_optimize.cc69
-rw-r--r--training/mr_optimize_reduce.cc19
-rw-r--r--utils/fdict.h2
-rw-r--r--utils/phmt.cc8
-rw-r--r--utils/weights.cc75
-rw-r--r--utils/weights.h22
-rw-r--r--vest/mr_vest_generate_mapper_input.cc6
18 files changed, 201 insertions, 335 deletions
diff --git a/decoder/decoder.cc b/decoder/decoder.cc
index 25eb2de4..4d4b6245 100644
--- a/decoder/decoder.cc
+++ b/decoder/decoder.cc
@@ -159,8 +159,7 @@ struct RescoringPass {
shared_ptr<ModelSet> models;
shared_ptr<IntersectionConfiguration> inter_conf;
vector<const FeatureFunction*> ffs;
- shared_ptr<Weights> w; // null == use previous weights
- vector<double> weight_vector;
+ shared_ptr<vector<weight_t> > weight_vector;
int fid_summary; // 0 == no summary feature
double density_prune; // 0 == don't density prune
double beam_prune; // 0 == don't beam prune
@@ -169,7 +168,7 @@ struct RescoringPass {
ostream& operator<<(ostream& os, const RescoringPass& rp) {
os << "[num_fn=" << rp.ffs.size();
if (rp.inter_conf) { os << " int_alg=" << *rp.inter_conf; }
- if (rp.w) os << " new_weights";
+ //if (rp.weight_vector.size() > 0) os << " new_weights";
if (rp.fid_summary) os << " summary_feature=" << FD::Convert(rp.fid_summary);
if (rp.density_prune) os << " density_prune=" << rp.density_prune;
if (rp.beam_prune) os << " beam_prune=" << rp.beam_prune;
@@ -181,13 +180,8 @@ struct DecoderImpl {
DecoderImpl(po::variables_map& conf, int argc, char** argv, istream* cfg);
~DecoderImpl();
bool Decode(const string& input, DecoderObserver*);
- void SetWeights(const vector<double>& weights) {
- init_weights = weights;
- for (int i = 0; i < rescoring_passes.size(); ++i) {
- if (rescoring_passes[i].models)
- rescoring_passes[i].models->SetWeights(weights);
- rescoring_passes[i].weight_vector = weights;
- }
+ vector<weight_t>& CurrentWeightVector() {
+ return *rescoring_passes.back().weight_vector;
}
void SetId(int next_sent_id) { sent_id = next_sent_id - 1; }
@@ -300,8 +294,7 @@ struct DecoderImpl {
OracleBleu oracle;
string formalism;
shared_ptr<Translator> translator;
- Weights w_init_weights; // used with initial parse
- vector<double> init_weights; // weights used with initial parse
+ shared_ptr<vector<weight_t> > init_weights; // weights used with initial parse
vector<shared_ptr<FeatureFunction> > pffs;
#ifdef FSA_RESCORING
CFGOptions cfg_options;
@@ -557,13 +550,18 @@ DecoderImpl::DecoderImpl(po::variables_map& conf, int argc, char** argv, istream
exit(1);
}
- // load initial feature weights (and possibly freeze feature set)
- if (conf.count("weights")) {
- w_init_weights.InitFromFile(str("weights",conf));
- w_init_weights.InitVector(&init_weights);
- init_weights.resize(FD::NumFeats());
+ // load perfect hash function for features
+ if (conf.count("cmph_perfect_feature_hash")) {
+ cerr << "Loading perfect hash function from " << conf["cmph_perfect_feature_hash"].as<string>() << " ...\n";
+ FD::EnableHash(conf["cmph_perfect_feature_hash"].as<string>());
+ cerr << " " << FD::NumFeats() << " features in map\n";
}
+ // load initial feature weights (and possibly freeze feature set)
+ init_weights.reset(new vector<weight_t>);
+ if (conf.count("weights"))
+ Weights::InitFromFile(str("weights",conf), init_weights.get());
+
// cube pruning pop-limit: we may want to configure this on a per-pass basis
pop_limit = conf["cubepruning_pop_limit"].as<int>();
@@ -582,9 +580,8 @@ DecoderImpl::DecoderImpl(po::variables_map& conf, int argc, char** argv, istream
RescoringPass& rp = rescoring_passes.back();
// only configure new weights if pass > 0, otherwise we reuse the initial chart weights
if (nth_pass_condition && conf.count(ws)) {
- rp.w.reset(new Weights);
- rp.w->InitFromFile(str(ws.c_str(), conf));
- rp.w->InitVector(&rp.weight_vector);
+ rp.weight_vector.reset(new vector<weight_t>());
+ Weights::InitFromFile(str(ws.c_str(), conf), rp.weight_vector.get());
}
bool has_stateful = false;
if (conf.count(ff)) {
@@ -624,11 +621,15 @@ DecoderImpl::DecoderImpl(po::variables_map& conf, int argc, char** argv, istream
}
// set up weight vectors since later phases may reuse weights from earlier phases
- const vector<double>* prev = &init_weights;
+ shared_ptr<vector<weight_t> > prev_weights = init_weights;
for (int pass = 0; pass < rescoring_passes.size(); ++pass) {
RescoringPass& rp = rescoring_passes[pass];
- if (!rp.w) { rp.weight_vector = *prev; } else { prev = &rp.weight_vector; }
- rp.models.reset(new ModelSet(rp.weight_vector, rp.ffs));
+ if (!rp.weight_vector) {
+ rp.weight_vector = prev_weights;
+ } else {
+ prev_weights = rp.weight_vector;
+ }
+ rp.models.reset(new ModelSet(*rp.weight_vector, rp.ffs));
string ps = "Pass1 "; ps[4] += pass;
if (!SILENT) show_models(conf,*rp.models,ps.c_str());
}
@@ -650,12 +651,6 @@ DecoderImpl::DecoderImpl(po::variables_map& conf, int argc, char** argv, istream
FD::Freeze(); // this means we can't see the feature names of not-weighted features
}
- if (conf.count("cmph_perfect_feature_hash")) {
- cerr << "Loading perfect hash function from " << conf["cmph_perfect_feature_hash"].as<string>() << " ...\n";
- FD::EnableHash(conf["cmph_perfect_feature_hash"].as<string>());
- cerr << " " << FD::NumFeats() << " features in map\n";
- }
-
// set up translation back end
if (formalism == "scfg")
translator.reset(new SCFGTranslator(conf));
@@ -685,7 +680,7 @@ DecoderImpl::DecoderImpl(po::variables_map& conf, int argc, char** argv, istream
}
if (!fsa_ffs.empty()) {
cerr<<"FSA: ";
- show_all_features(fsa_ffs,init_weights,cerr,cerr,true,true);
+ show_all_features(fsa_ffs,*init_weights,cerr,cerr,true,true);
}
#endif
@@ -733,7 +728,8 @@ bool Decoder::Decode(const string& input, DecoderObserver* o) {
if (del) delete o;
return res;
}
-void Decoder::SetWeights(const vector<double>& weights) { pimpl_->SetWeights(weights); }
+vector<weight_t>& Decoder::CurrentWeightVector() { return pimpl_->CurrentWeightVector(); }
+const vector<weight_t>& Decoder::CurrentWeightVector() const { return pimpl_->CurrentWeightVector(); }
void Decoder::SetSupplementalGrammar(const std::string& grammar_string) {
assert(pimpl_->translator->GetDecoderType() == "SCFG");
static_cast<SCFGTranslator&>(*pimpl_->translator).SetSupplementalGrammar(grammar_string);
@@ -774,7 +770,7 @@ bool DecoderImpl::Decode(const string& input, DecoderObserver* o) {
translator->ProcessMarkupHints(smeta.sgml_);
Timer t("Translation");
const bool translation_successful =
- translator->Translate(to_translate, &smeta, init_weights, &forest);
+ translator->Translate(to_translate, &smeta, *init_weights, &forest);
translator->SentenceComplete();
if (!translation_successful) {
@@ -812,7 +808,7 @@ bool DecoderImpl::Decode(const string& input, DecoderObserver* o) {
for (int pass = 0; pass < rescoring_passes.size(); ++pass) {
const RescoringPass& rp = rescoring_passes[pass];
- const vector<double>& cur_weights = rp.weight_vector;
+ const vector<weight_t>& cur_weights = *rp.weight_vector;
if (!SILENT) cerr << endl << " RESCORING PASS #" << (pass+1) << " " << rp << endl;
#ifdef FSA_RESCORING
cfg_options.maybe_output_source(forest);
@@ -933,7 +929,7 @@ bool DecoderImpl::Decode(const string& input, DecoderObserver* o) {
#endif
}
- const vector<double>& last_weights = (rescoring_passes.empty() ? init_weights : rescoring_passes.back().weight_vector);
+ const vector<double>& last_weights = (rescoring_passes.empty() ? *init_weights : *rescoring_passes.back().weight_vector);
// Oracle Rescoring
if(get_oracle_forest) {
diff --git a/decoder/decoder.h b/decoder/decoder.h
index 5491369f..9d009ffa 100644
--- a/decoder/decoder.h
+++ b/decoder/decoder.h
@@ -7,6 +7,8 @@
#include <boost/shared_ptr.hpp>
#include <boost/program_options/variables_map.hpp>
+#include "weights.h" // weight_t
+
#undef CP_TIME
//#define CP_TIME
#ifdef CP_TIME
@@ -39,7 +41,12 @@ struct Decoder {
Decoder(int argc, char** argv);
Decoder(std::istream* config_file);
bool Decode(const std::string& input, DecoderObserver* observer = NULL);
- void SetWeights(const std::vector<double>& weights);
+
+ // access this to either *read* or *write* to the decoder's last
+ // weight vector (i.e., the weights of the finest past)
+ std::vector<weight_t>& CurrentWeightVector();
+ const std::vector<weight_t>& CurrentWeightVector() const;
+
void SetId(int id);
~Decoder();
const boost::program_options::variables_map& GetConf() const { return conf; }
diff --git a/mira/kbest_mira.cc b/mira/kbest_mira.cc
index 6918a9a1..459a5e6f 100644
--- a/mira/kbest_mira.cc
+++ b/mira/kbest_mira.cc
@@ -32,21 +32,6 @@ namespace po = boost::program_options;
bool invert_score;
boost::shared_ptr<MT19937> rng;
-void SanityCheck(const vector<double>& w) {
- for (int i = 0; i < w.size(); ++i) {
- assert(!isnan(w[i]));
- assert(!isinf(w[i]));
- }
-}
-
-struct FComp {
- const vector<double>& w_;
- FComp(const vector<double>& w) : w_(w) {}
- bool operator()(int a, int b) const {
- return fabs(w_[a]) > fabs(w_[b]);
- }
-};
-
void RandomPermutation(int len, vector<int>* p_ids) {
vector<int>& ids = *p_ids;
ids.resize(len);
@@ -58,21 +43,6 @@ void RandomPermutation(int len, vector<int>* p_ids) {
}
}
-void ShowLargestFeatures(const vector<double>& w) {
- vector<int> fnums(w.size());
- for (int i = 0; i < w.size(); ++i)
- fnums[i] = i;
- vector<int>::iterator mid = fnums.begin();
- mid += (w.size() > 10 ? 10 : w.size());
- partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
- cerr << "TOP FEATURES:";
- --mid;
- for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
- cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
- }
- cerr << endl;
-}
-
bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
opts.add_options()
@@ -209,14 +179,16 @@ int main(int argc, char** argv) {
cerr << "Mismatched number of references (" << ds.size() << ") and sources (" << corpus.size() << ")\n";
return 1;
}
- // load initial weights
- Weights weights;
- weights.InitFromFile(conf["input_weights"].as<string>());
- SparseVector<double> lambdas;
- weights.InitSparseVector(&lambdas);
ReadFile ini_rf(conf["decoder_config"].as<string>());
Decoder decoder(ini_rf.stream());
+
+ // load initial weights
+ vector<weight_t>& dense_weights = decoder.CurrentWeightVector();
+ SparseVector<weight_t> lambdas;
+ Weights::InitFromFile(conf["input_weights"].as<string>(), &dense_weights);
+ Weights::InitSparseVector(dense_weights, &lambdas);
+
const double max_step_size = conf["max_step_size"].as<double>();
const double mt_metric_scale = conf["mt_metric_scale"].as<double>();
@@ -230,7 +202,6 @@ int main(int argc, char** argv) {
double tot_loss = 0;
int dots = 0;
int cur_pass = 0;
- vector<double> dense_weights;
SparseVector<double> tot;
tot += lambdas; // initial weights
normalizer++; // count for initial weights
@@ -240,27 +211,22 @@ int main(int argc, char** argv) {
vector<int> order;
RandomPermutation(corpus.size(), &order);
while (lcount <= max_iteration) {
- dense_weights.clear();
- weights.InitFromVector(lambdas);
- weights.InitVector(&dense_weights);
- decoder.SetWeights(dense_weights);
+ lambdas.init_vector(&dense_weights);
if ((cur_sent * 40 / corpus.size()) > dots) { ++dots; cerr << '.'; }
if (corpus.size() == cur_sent) {
cerr << " [AVG METRIC LAST PASS=" << (tot_loss / corpus.size()) << "]\n";
- ShowLargestFeatures(dense_weights);
+ Weights::ShowLargestFeatures(dense_weights);
cur_sent = 0;
tot_loss = 0;
dots = 0;
ostringstream os;
os << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << ".gz";
- weights.WriteToFile(os.str(), true, &msg);
SparseVector<double> x = tot;
x /= normalizer;
ostringstream sa;
sa << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << "-avg.gz";
- Weights ww;
- ww.InitFromVector(x);
- ww.WriteToFile(sa.str(), true, &msga);
+ x.init_vector(&dense_weights);
+ Weights::WriteToFile(os.str(), dense_weights, true, &msg);
++cur_pass;
RandomPermutation(corpus.size(), &order);
}
@@ -294,11 +260,11 @@ int main(int argc, char** argv) {
++cur_sent;
}
cerr << endl;
- weights.WriteToFile("weights.mira-final.gz", true, &msg);
+ Weights::WriteToFile("weights.mira-final.gz", dense_weights, true, &msg);
tot /= normalizer;
- weights.InitFromVector(tot);
+ tot.init_vector(dense_weights);
msg = "# MIRA tuned weights (averaged vector)";
- weights.WriteToFile("weights.mira-final-avg.gz", true, &msg);
+ Weights::WriteToFile("weights.mira-final-avg.gz", dense_weights, true, &msg);
cerr << "Optimization complete.\nAVERAGED WEIGHTS: weights.mira-final-avg.gz\n";
return 0;
}
diff --git a/pro-train/mr_pro_map.cc b/pro-train/mr_pro_map.cc
index 4324e8de..bc59285b 100644
--- a/pro-train/mr_pro_map.cc
+++ b/pro-train/mr_pro_map.cc
@@ -301,12 +301,8 @@ int main(int argc, char** argv) {
const unsigned gamma = conf["candidate_pairs"].as<unsigned>();
const unsigned xi = conf["best_pairs"].as<unsigned>();
string weightsf = conf["weights"].as<string>();
- vector<double> weights;
- {
- Weights w;
- w.InitFromFile(weightsf);
- w.InitVector(&weights);
- }
+ vector<weight_t> weights;
+ Weights::InitFromFile(weightsf, &weights);
string kbest_repo = conf["kbest_repository"].as<string>();
MkDirP(kbest_repo);
while(in) {
diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc
index 9b422f33..9caaa1d1 100644
--- a/pro-train/mr_pro_reduce.cc
+++ b/pro-train/mr_pro_reduce.cc
@@ -194,7 +194,7 @@ int main(int argc, char** argv) {
InitCommandLine(argc, argv, &conf);
string line;
vector<pair<bool, SparseVector<double> > > training, testing;
- SparseVector<double> old_weights;
+ SparseVector<weight_t> old_weights;
const bool tune_regularizer = conf.count("tune_regularizer");
if (tune_regularizer && !conf.count("testset")) {
cerr << "--tune_regularizer requires --testset to be set\n";
@@ -210,9 +210,9 @@ int main(int argc, char** argv) {
const double psi = conf["interpolation"].as<double>();
if (psi < 0.0 || psi > 1.0) { cerr << "Invalid interpolation weight: " << psi << endl; }
if (conf.count("weights")) {
- Weights w;
- w.InitFromFile(conf["weights"].as<string>());
- w.InitSparseVector(&old_weights);
+ vector<weight_t> dt;
+ Weights::InitFromFile(conf["weights"].as<string>(), &dt);
+ Weights::InitSparseVector(dt, &old_weights);
}
ReadCorpus(&cin, &training);
if (conf.count("testset")) {
@@ -220,8 +220,8 @@ int main(int argc, char** argv) {
ReadCorpus(rf.stream(), &testing);
}
cerr << "Number of features: " << FD::NumFeats() << endl;
- vector<double> x(FD::NumFeats(), 0.0); // x[0] is bias
- for (SparseVector<double>::const_iterator it = old_weights.begin();
+ vector<weight_t> x(FD::NumFeats(), 0.0); // x[0] is bias
+ for (SparseVector<weight_t>::const_iterator it = old_weights.begin();
it != old_weights.end(); ++it)
x[it->first] = it->second;
double tppl = 0.0;
@@ -257,7 +257,6 @@ int main(int argc, char** argv) {
sigsq = sp[best_i].first;
tppl = LearnParameters(training, testing, sigsq, conf["memory_buffers"].as<unsigned>(), &x);
}
- Weights w;
if (conf.count("weights")) {
for (int i = 1; i < x.size(); ++i)
x[i] = (x[i] * psi) + old_weights.get(i) * (1.0 - psi);
@@ -271,7 +270,6 @@ int main(int argc, char** argv) {
cout << "# " << sp[i].first << "\t" << sp[i].second << "\t" << smoothed[i] << endl;
}
}
- w.InitFromVector(x);
- w.WriteToFile("-");
+ Weights::WriteToFile("-", x);
return 0;
}
diff --git a/training/Makefile.am b/training/Makefile.am
index e075e417..6e2c06f5 100644
--- a/training/Makefile.am
+++ b/training/Makefile.am
@@ -12,9 +12,7 @@ bin_PROGRAMS = \
cllh_filter_grammar \
mpi_online_optimize \
mpi_batch_optimize \
- mpi_em_optimize \
compute_cllh \
- feature_expectations \
augment_grammar
noinst_PROGRAMS = \
@@ -29,12 +27,6 @@ mpi_online_optimize_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval
mpi_batch_optimize_SOURCES = mpi_batch_optimize.cc optimize.cc
mpi_batch_optimize_LDADD = $(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
-feature_expectations_SOURCES = feature_expectations.cc
-feature_expectations_LDADD = $(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
-
-mpi_em_optimize_SOURCES = mpi_em_optimize.cc optimize.cc
-mpi_em_optimize_LDADD = $(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
-
compute_cllh_SOURCES = compute_cllh.cc
compute_cllh_LDADD = $(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
diff --git a/training/augment_grammar.cc b/training/augment_grammar.cc
index df8d4ee8..e89a92d5 100644
--- a/training/augment_grammar.cc
+++ b/training/augment_grammar.cc
@@ -134,9 +134,7 @@ int main(int argc, char** argv) {
} else { ngram = NULL; }
extra_feature = conf.count("extra_lex_feature") > 0;
if (conf.count("collapse_weights")) {
- Weights w;
- w.InitFromFile(conf["collapse_weights"].as<string>());
- w.InitVector(&col_weights);
+ Weights::InitFromFile(conf["collapse_weights"].as<string>(), &col_weights);
}
clear_features = conf.count("clear_features_after_collapse") > 0;
gather_rules = false;
diff --git a/training/collapse_weights.cc b/training/collapse_weights.cc
index 4fb742fb..dc480f6c 100644
--- a/training/collapse_weights.cc
+++ b/training/collapse_weights.cc
@@ -59,10 +59,8 @@ int main(int argc, char** argv) {
InitCommandLine(argc, argv, &conf);
const string wfile = conf["weights"].as<string>();
const string gfile = conf["grammar"].as<string>();
- Weights wm;
- wm.InitFromFile(wfile);
- vector<double> w;
- wm.InitVector(&w);
+ vector<weight_t> w;
+ Weights::InitFromFile(wfile, &w);
MarginalMap e_tots;
MarginalMap f_tots;
prob_t tot;
diff --git a/training/compute_cllh.cc b/training/compute_cllh.cc
index 332f6d0c..b496d196 100644
--- a/training/compute_cllh.cc
+++ b/training/compute_cllh.cc
@@ -148,15 +148,6 @@ int main(int argc, char** argv) {
if (!InitCommandLine(argc, argv, &conf))
return false;
- // load initial weights
- Weights weights;
- if (conf.count("weights"))
- weights.InitFromFile(conf["weights"].as<string>());
-
- // freeze feature set
- //const bool freeze_feature_set = conf.count("freeze_feature_set");
- //if (freeze_feature_set) FD::Freeze();
-
// load cdec.ini and set up decoder
ReadFile ini_rf(conf["decoder_config"].as<string>());
Decoder decoder(ini_rf.stream());
@@ -165,17 +156,22 @@ int main(int argc, char** argv) {
abort();
}
+ // load weights
+ vector<weight_t>& weights = decoder.CurrentWeightVector();
+ if (conf.count("weights"))
+ Weights::InitFromFile(conf["weights"].as<string>(), &weights);
+
+ // freeze feature set
+ //const bool freeze_feature_set = conf.count("freeze_feature_set");
+ //if (freeze_feature_set) FD::Freeze();
+
vector<string> corpus; vector<int> ids;
ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus, &ids);
assert(corpus.size() > 0);
assert(corpus.size() == ids.size());
- vector<double> wv;
- weights.InitVector(&wv);
- decoder.SetWeights(wv);
TrainingObserver observer;
double objective = 0;
- bool converged = false;
observer.Reset();
if (rank == 0)
@@ -197,3 +193,4 @@ int main(int argc, char** argv) {
return 0;
}
+
diff --git a/training/grammar_convert.cc b/training/grammar_convert.cc
index 8d292f8a..bf8abb26 100644
--- a/training/grammar_convert.cc
+++ b/training/grammar_convert.cc
@@ -251,12 +251,10 @@ int main(int argc, char **argv) {
const bool is_split_input = (conf["format"].as<string>() == "split");
const bool is_json_input = is_split_input || (conf["format"].as<string>() == "json");
const bool collapse_weights = conf.count("collapse_weights");
- Weights wts;
vector<double> w;
- if (conf.count("weights")) {
- wts.InitFromFile(conf["weights"].as<string>());
- wts.InitVector(&w);
- }
+ if (conf.count("weights"))
+ Weights::InitFromFile(conf["weights"].as<string>(), &w);
+
if (collapse_weights && !w.size()) {
cerr << "--collapse_weights requires a weights file to be specified!\n";
exit(1);
diff --git a/training/mpi_batch_optimize.cc b/training/mpi_batch_optimize.cc
index 39a8af7d..cc5953f6 100644
--- a/training/mpi_batch_optimize.cc
+++ b/training/mpi_batch_optimize.cc
@@ -31,42 +31,12 @@ using namespace std;
using boost::shared_ptr;
namespace po = boost::program_options;
-void SanityCheck(const vector<double>& w) {
- for (int i = 0; i < w.size(); ++i) {
- assert(!isnan(w[i]));
- assert(!isinf(w[i]));
- }
-}
-
-struct FComp {
- const vector<double>& w_;
- FComp(const vector<double>& w) : w_(w) {}
- bool operator()(int a, int b) const {
- return fabs(w_[a]) > fabs(w_[b]);
- }
-};
-
-void ShowLargestFeatures(const vector<double>& w) {
- vector<int> fnums(w.size());
- for (int i = 0; i < w.size(); ++i)
- fnums[i] = i;
- vector<int>::iterator mid = fnums.begin();
- mid += (w.size() > 10 ? 10 : w.size());
- partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
- cerr << "TOP FEATURES:";
- for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
- cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
- }
- cerr << endl;
-}
-
bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
opts.add_options()
("input_weights,w",po::value<string>(),"Input feature weights file")
("training_data,t",po::value<string>(),"Training data")
("decoder_config,d",po::value<string>(),"Decoder configuration file")
- ("sharded_input,s",po::value<string>(), "Corpus and grammar files are 'sharded' so each processor loads its own input and grammar file. Argument is the directory containing the shards.")
("output_weights,o",po::value<string>()->default_value("-"),"Output feature weights file")
("optimization_method,m", po::value<string>()->default_value("lbfgs"), "Optimization method (sgd, lbfgs, rprop)")
("correction_buffers,M", po::value<int>()->default_value(10), "Number of gradients for LBFGS to maintain in memory")
@@ -88,14 +58,10 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
}
po::notify(*conf);
- if (conf->count("help") || !conf->count("input_weights") || !(conf->count("training_data") | conf->count("sharded_input")) || !conf->count("decoder_config")) {
+ if (conf->count("help") || !conf->count("input_weights") || !(conf->count("training_data")) || !conf->count("decoder_config")) {
cerr << dcmdline_options << endl;
return false;
}
- if (conf->count("training_data") && conf->count("sharded_input")) {
- cerr << "Cannot specify both --training_data and --sharded_input\n";
- return false;
- }
return true;
}
@@ -236,42 +202,9 @@ int main(int argc, char** argv) {
po::variables_map conf;
if (!InitCommandLine(argc, argv, &conf)) return 1;
- string shard_dir;
- if (conf.count("sharded_input")) {
- shard_dir = conf["sharded_input"].as<string>();
- if (!DirectoryExists(shard_dir)) {
- if (rank == 0) cerr << "Can't find shard directory: " << shard_dir << endl;
- return 1;
- }
- if (rank == 0)
- cerr << "Shard directory: " << shard_dir << endl;
- }
-
- // load initial weights
- Weights weights;
- if (rank == 0) { cerr << "Loading weights...\n"; }
- weights.InitFromFile(conf["input_weights"].as<string>());
- if (rank == 0) { cerr << "Done loading weights.\n"; }
-
- // freeze feature set (should be optional?)
- const bool freeze_feature_set = true;
- if (freeze_feature_set) FD::Freeze();
-
// load cdec.ini and set up decoder
vector<string> cdec_ini;
ReadConfig(conf["decoder_config"].as<string>(), &cdec_ini);
- if (shard_dir.size()) {
- if (rank == 0) {
- for (int i = 0; i < cdec_ini.size(); ++i) {
- if (cdec_ini[i].find("grammar=") == 0) {
- cerr << "!!! using sharded input and " << conf["decoder_config"].as<string>() << " contains a grammar specification:\n" << cdec_ini[i] << "\n VERIFY THAT THIS IS CORRECT!\n";
- }
- }
- }
- ostringstream g;
- g << "grammar=" << shard_dir << "/grammar." << rank << "_of_" << size << ".gz";
- cdec_ini.push_back(g.str());
- }
istringstream ini;
StoreConfig(cdec_ini, &ini);
if (rank == 0) cerr << "Loading grammar...\n";
@@ -282,22 +215,28 @@ int main(int argc, char** argv) {
}
if (rank == 0) cerr << "Done loading grammar!\n";
+ // load initial weights
+ if (rank == 0) { cerr << "Loading weights...\n"; }
+ vector<weight_t>& lambdas = decoder->CurrentWeightVector();
+ Weights::InitFromFile(conf["input_weights"].as<string>(), &lambdas);
+ if (rank == 0) { cerr << "Done loading weights.\n"; }
+
+ // freeze feature set (should be optional?)
+ const bool freeze_feature_set = true;
+ if (freeze_feature_set) FD::Freeze();
+
const int num_feats = FD::NumFeats();
if (rank == 0) cerr << "Number of features: " << num_feats << endl;
+ lambdas.resize(num_feats);
+
const bool gaussian_prior = conf.count("gaussian_prior");
- vector<double> means(num_feats, 0);
+ vector<weight_t> means(num_feats, 0);
if (conf.count("means")) {
if (!gaussian_prior) {
cerr << "Don't use --means without --gaussian_prior!\n";
exit(1);
}
- Weights wm;
- wm.InitFromFile(conf["means"].as<string>());
- if (num_feats != FD::NumFeats()) {
- cerr << "[ERROR] Means file had unexpected features!\n";
- exit(1);
- }
- wm.InitVector(&means);
+ Weights::InitFromFile(conf["means"].as<string>(), &means);
}
shared_ptr<BatchOptimizer> o;
if (rank == 0) {
@@ -309,26 +248,13 @@ int main(int argc, char** argv) {
cerr << "Optimizer: " << o->Name() << endl;
}
double objective = 0;
- vector<double> lambdas(num_feats, 0.0);
- weights.InitVector(&lambdas);
- if (lambdas.size() != num_feats) {
- cerr << "Initial weights file did not have all features specified!\n feats="
- << num_feats << "\n weights file=" << lambdas.size() << endl;
- lambdas.resize(num_feats, 0.0);
- }
vector<double> gradient(num_feats, 0.0);
- vector<double> rcv_grad(num_feats, 0.0);
+ vector<double> rcv_grad;
+ rcv_grad.clear();
bool converged = false;
vector<string> corpus;
- if (shard_dir.size()) {
- ostringstream os; os << shard_dir << "/corpus." << rank << "_of_" << size;
- ReadTrainingCorpus(os.str(), 0, 1, &corpus);
- cerr << os.str() << " has " << corpus.size() << " training examples. " << endl;
- if (corpus.size() > 500) { corpus.resize(500); cerr << " TRUNCATING\n"; }
- } else {
- ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus);
- }
+ ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus);
assert(corpus.size() > 0);
TrainingObserver observer;
@@ -341,19 +267,20 @@ int main(int argc, char** argv) {
if (rank == 0) {
cerr << "Starting decoding... (~" << corpus.size() << " sentences / proc)\n";
}
- decoder->SetWeights(lambdas);
for (int i = 0; i < corpus.size(); ++i)
decoder->Decode(corpus[i], &observer);
cerr << " process " << rank << '/' << size << " done\n";
fill(gradient.begin(), gradient.end(), 0);
- fill(rcv_grad.begin(), rcv_grad.end(), 0);
observer.SetLocalGradientAndObjective(&gradient, &objective);
double to = 0;
#ifdef HAVE_MPI
+ rcv_grad.resize(num_feats, 0.0);
mpi::reduce(world, &gradient[0], gradient.size(), &rcv_grad[0], plus<double>(), 0);
- mpi::reduce(world, objective, to, plus<double>(), 0);
swap(gradient, rcv_grad);
+ rcv_grad.clear();
+
+ mpi::reduce(world, objective, to, plus<double>(), 0);
objective = to;
#endif
@@ -378,7 +305,7 @@ int main(int argc, char** argv) {
for (int i = 0; i < gradient.size(); ++i)
gnorm += gradient[i] * gradient[i];
cerr << " GNORM=" << sqrt(gnorm) << endl;
- vector<double> old = lambdas;
+ vector<weight_t> old = lambdas;
int c = 0;
while (old == lambdas) {
++c;
@@ -387,9 +314,8 @@ int main(int argc, char** argv) {
assert(c < 5);
}
old.clear();
- SanityCheck(lambdas);
- ShowLargestFeatures(lambdas);
- weights.InitFromVector(lambdas);
+ Weights::SanityCheck(lambdas);
+ Weights::ShowLargestFeatures(lambdas);
converged = o->HasConverged();
if (converged) { cerr << "OPTIMIZER REPORTS CONVERGENCE!\n"; }
@@ -399,7 +325,7 @@ int main(int argc, char** argv) {
ostringstream vv;
vv << "Objective = " << objective << " (eval count=" << o->EvaluationCount() << ")";
const string svv = vv.str();
- weights.WriteToFile(fname, true, &svv);
+ Weights::WriteToFile(fname, lambdas, true, &svv);
} // rank == 0
int cint = converged;
#ifdef HAVE_MPI
@@ -411,3 +337,4 @@ int main(int argc, char** argv) {
}
return 0;
}
+
diff --git a/training/mpi_online_optimize.cc b/training/mpi_online_optimize.cc
index 32033c19..2ef4a2e7 100644
--- a/training/mpi_online_optimize.cc
+++ b/training/mpi_online_optimize.cc
@@ -31,35 +31,6 @@ namespace mpi = boost::mpi;
using namespace std;
namespace po = boost::program_options;
-void SanityCheck(const vector<double>& w) {
- for (int i = 0; i < w.size(); ++i) {
- assert(!isnan(w[i]));
- assert(!isinf(w[i]));
- }
-}
-
-struct FComp {
- const vector<double>& w_;
- FComp(const vector<double>& w) : w_(w) {}
- bool operator()(int a, int b) const {
- return fabs(w_[a]) > fabs(w_[b]);
- }
-};
-
-void ShowLargestFeatures(const vector<double>& w) {
- vector<int> fnums(w.size());
- for (int i = 0; i < w.size(); ++i)
- fnums[i] = i;
- vector<int>::iterator mid = fnums.begin();
- mid += (w.size() > 10 ? 10 : w.size());
- partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
- cerr << "TOP FEATURES:";
- for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
- cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
- }
- cerr << endl;
-}
-
bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
opts.add_options()
@@ -250,10 +221,25 @@ int main(int argc, char** argv) {
if (!InitCommandLine(argc, argv, &conf))
return 1;
+ vector<pair<string, int> > agenda;
+ if (!LoadAgenda(conf["training_agenda"].as<string>(), &agenda))
+ return 1;
+ if (rank == 0)
+ cerr << "Loaded agenda defining " << agenda.size() << " training epochs\n";
+
+ assert(agenda.size() > 0);
+
+ if (1) { // hack to load the feature hash functions -- TODO this should not be in cdec.ini
+ const string& cur_config = agenda[0].first;
+ const unsigned max_iteration = agenda[0].second;
+ ReadFile ini_rf(cur_config);
+ Decoder decoder(ini_rf.stream());
+ }
+
// load initial weights
- Weights weights;
+ vector<weight_t> init_weights;
if (conf.count("input_weights"))
- weights.InitFromFile(conf["input_weights"].as<string>());
+ Weights::InitFromFile(conf["input_weights"].as<string>(), &init_weights);
vector<int> frozen_fids;
if (conf.count("frozen_features")) {
@@ -310,19 +296,12 @@ int main(int argc, char** argv) {
rng.reset(new MT19937);
SparseVector<double> x;
- weights.InitSparseVector(&x);
+ Weights::InitSparseVector(init_weights, &x);
TrainingObserver observer;
int write_weights_every_ith = 100; // TODO configure
int titer = -1;
- vector<pair<string, int> > agenda;
- if (!LoadAgenda(conf["training_agenda"].as<string>(), &agenda))
- return 1;
- if (rank == 0)
- cerr << "Loaded agenda defining " << agenda.size() << " training epochs\n";
-
- vector<double> lambdas;
for (int ai = 0; ai < agenda.size(); ++ai) {
const string& cur_config = agenda[ai].first;
const unsigned max_iteration = agenda[ai].second;
@@ -331,6 +310,8 @@ int main(int argc, char** argv) {
// load cdec.ini and set up decoder
ReadFile ini_rf(cur_config);
Decoder decoder(ini_rf.stream());
+ vector<weight_t>& lambdas = decoder.CurrentWeightVector();
+ if (ai == 0) { lambdas.swap(init_weights); init_weights.clear(); }
if (rank == 0)
o->ResetEpoch(); // resets the learning rate-- TODO is this good?
@@ -341,15 +322,13 @@ int main(int argc, char** argv) {
#ifdef HAVE_MPI
mpi::timer timer;
#endif
- weights.InitFromVector(x);
- weights.InitVector(&lambdas);
+ x.init_vector(&lambdas);
++iter; ++titer;
observer.Reset();
- decoder.SetWeights(lambdas);
if (rank == 0) {
converged = (iter == max_iteration);
- SanityCheck(lambdas);
- ShowLargestFeatures(lambdas);
+ Weights::SanityCheck(lambdas);
+ Weights::ShowLargestFeatures(lambdas);
string fname = "weights.cur.gz";
if (iter % write_weights_every_ith == 0) {
ostringstream o; o << "weights.epoch_" << (ai+1) << '.' << iter << ".gz";
@@ -360,7 +339,7 @@ int main(int argc, char** argv) {
vv << "total iter=" << titer << " (of current config iter=" << iter << ") minibatch=" << size_per_proc << " sentences/proc x " << size << " procs. num_feats=" << x.size() << '/' << FD::NumFeats() << " passes_thru_data=" << (titer * size_per_proc / static_cast<double>(corpus.size())) << " eta=" << lr->eta(titer);
const string svv = vv.str();
cerr << svv << endl;
- weights.WriteToFile(fname, true, &svv);
+ Weights::WriteToFile(fname, lambdas, true, &svv);
}
for (int i = 0; i < size_per_proc; ++i) {
diff --git a/training/mr_optimize_reduce.cc b/training/mr_optimize_reduce.cc
index b931991d..15e28fa1 100644
--- a/training/mr_optimize_reduce.cc
+++ b/training/mr_optimize_reduce.cc
@@ -88,25 +88,19 @@ int main(int argc, char** argv) {
const bool use_b64 = conf["input_format"].as<string>() == "b64";
- Weights weights;
- weights.InitFromFile(conf["input_weights"].as<string>());
+ vector<weight_t> lambdas;
+ Weights::InitFromFile(conf["input_weights"].as<string>(), &lambdas);
const string s_obj = "**OBJ**";
int num_feats = FD::NumFeats();
cerr << "Number of features: " << num_feats << endl;
const bool gaussian_prior = conf.count("gaussian_prior");
- vector<double> means(num_feats, 0);
+ vector<weight_t> means(num_feats, 0);
if (conf.count("means")) {
if (!gaussian_prior) {
cerr << "Don't use --means without --gaussian_prior!\n";
exit(1);
}
- Weights wm;
- wm.InitFromFile(conf["means"].as<string>());
- if (num_feats != FD::NumFeats()) {
- cerr << "[ERROR] Means file had unexpected features!\n";
- exit(1);
- }
- wm.InitVector(&means);
+ Weights::InitFromFile(conf["means"].as<string>(), &means);
}
shared_ptr<BatchOptimizer> o;
const string omethod = conf["optimization_method"].as<string>();
@@ -124,8 +118,6 @@ int main(int argc, char** argv) {
cerr << "No state file found, assuming ITERATION 1\n";
}
- vector<double> lambdas(num_feats, 0);
- weights.InitVector(&lambdas);
double objective = 0;
vector<double> gradient(num_feats, 0);
// 0<TAB>**OBJ**=12.2;Feat1=2.3;Feat2=-0.2;
@@ -223,8 +215,7 @@ int main(int argc, char** argv) {
old.clear();
SanityCheck(lambdas);
ShowLargestFeatures(lambdas);
- weights.InitFromVector(lambdas);
- weights.WriteToFile(conf["output_weights"].as<string>(), false);
+ Weights::WriteToFile(conf["output_weights"].as<string>(), lambdas, false);
const bool conv = o->HasConverged();
if (conv) { cerr << "OPTIMIZER REPORTS CONVERGENCE!\n"; }
diff --git a/utils/fdict.h b/utils/fdict.h
index 771e8b91..f0871b9a 100644
--- a/utils/fdict.h
+++ b/utils/fdict.h
@@ -28,6 +28,8 @@ struct FD {
}
static void EnableHash(const std::string& cmph_file) {
#ifdef HAVE_CMPH
+ assert(dict_.max() == 0); // dictionary must not have
+ // been added to
hash_ = new PerfectHashFunction(cmph_file);
#endif
}
diff --git a/utils/phmt.cc b/utils/phmt.cc
index 1f59afaf..48d9f093 100644
--- a/utils/phmt.cc
+++ b/utils/phmt.cc
@@ -19,22 +19,18 @@ int main(int argc, char** argv) {
cerr << "LexFE = " << FD::Convert("LexFE") << endl;
cerr << "LexEF = " << FD::Convert("LexEF") << endl;
{
- Weights w;
vector<weight_t> v(FD::NumFeats());
v[FD::Convert("LexFE")] = 1.0;
v[FD::Convert("LexEF")] = 0.5;
- w.InitFromVector(v);
cerr << "Writing...\n";
- w.WriteToFile("weights.bin");
+ Weights::WriteToFile("weights.bin", v);
cerr << "Done.\n";
}
{
- Weights w;
vector<weight_t> v(FD::NumFeats());
cerr << "Reading...\n";
- w.InitFromFile("weights.bin");
+ Weights::InitFromFile("weights.bin", &v);
cerr << "Done.\n";
- w.InitVector(&v);
assert(v[FD::Convert("LexFE")] == 1.0);
assert(v[FD::Convert("LexEF")] == 0.5);
}
diff --git a/utils/weights.cc b/utils/weights.cc
index 0916b72a..c49000be 100644
--- a/utils/weights.cc
+++ b/utils/weights.cc
@@ -8,7 +8,10 @@
using namespace std;
-void Weights::InitFromFile(const std::string& filename, vector<string>* feature_list) {
+void Weights::InitFromFile(const string& filename,
+ vector<weight_t>* pweights,
+ vector<string>* feature_list) {
+ vector<weight_t>& weights = *pweights;
if (!SILENT) cerr << "Reading weights from " << filename << endl;
ReadFile in_file(filename);
istream& in = *in_file.stream();
@@ -47,16 +50,16 @@ void Weights::InitFromFile(const std::string& filename, vector<string>* feature_
int end = 0;
while(end < buf.size() && buf[end] != ' ') ++end;
const int fid = FD::Convert(buf.substr(start, end - start));
+ if (feature_list) { feature_list->push_back(buf.substr(start, end - start)); }
while(end < buf.size() && buf[end] == ' ') ++end;
val = strtod(&buf.c_str()[end], NULL);
if (isnan(val)) {
cerr << FD::Convert(fid) << " has weight NaN!\n";
abort();
}
- if (wv_.size() <= fid)
- wv_.resize(fid + 1);
- wv_[fid] = val;
- if (feature_list) { feature_list->push_back(FD::Convert(fid)); }
+ if (weights.size() <= fid)
+ weights.resize(fid + 1);
+ weights[fid] = val;
++weight_count;
if (!SILENT) {
if (weight_count % 50000 == 0) { cerr << '.' << flush; fl = true; }
@@ -76,8 +79,8 @@ void Weights::InitFromFile(const std::string& filename, vector<string>* feature_
cerr << "Hash function reports " << FD::NumFeats() << " keys but weights file contains " << num_keys[0] << endl;
abort();
}
- wv_.resize(num_keys[0]);
- in.get(reinterpret_cast<char*>(&wv_[0]), num_keys[0] * sizeof(weight_t));
+ weights.resize(num_keys[0]);
+ in.get(reinterpret_cast<char*>(&weights[0]), num_keys[0] * sizeof(weight_t));
if (!in.good()) {
cerr << "Error loading weights!\n";
abort();
@@ -85,7 +88,10 @@ void Weights::InitFromFile(const std::string& filename, vector<string>* feature_
}
}
-void Weights::WriteToFile(const std::string& fname, bool hide_zero_value_features, const string* extra) const {
+void Weights::WriteToFile(const string& fname,
+ const vector<weight_t>& weights,
+ bool hide_zero_value_features,
+ const string* extra) {
WriteFile out(fname);
ostream& o = *out.stream();
assert(o);
@@ -96,41 +102,54 @@ void Weights::WriteToFile(const std::string& fname, bool hide_zero_value_feature
o.precision(17);
const int num_feats = FD::NumFeats();
for (int i = 1; i < num_feats; ++i) {
- const weight_t val = (i < wv_.size() ? wv_[i] : 0.0);
+ const weight_t val = (i < weights.size() ? weights[i] : 0.0);
if (hide_zero_value_features && val == 0.0) continue;
o << FD::Convert(i) << ' ' << val << endl;
}
} else {
o.write("_PHWf", 5);
const size_t keys = FD::NumFeats();
- assert(keys <= wv_.size());
+ assert(keys <= weights.size());
o.write(reinterpret_cast<const char*>(&keys), sizeof(keys));
- o.write(reinterpret_cast<const char*>(&wv_[0]), keys * sizeof(weight_t));
+ o.write(reinterpret_cast<const char*>(&weights[0]), keys * sizeof(weight_t));
}
}
-void Weights::InitVector(std::vector<weight_t>* w) const {
- *w = wv_;
+void Weights::InitSparseVector(const vector<weight_t>& dv,
+ SparseVector<weight_t>* sv) {
+ sv->clear();
+ for (unsigned i = 1; i < dv.size(); ++i) {
+ if (dv[i]) sv->set_value(i, dv[i]);
+ }
}
-void Weights::InitSparseVector(SparseVector<weight_t>* w) const {
- for (int i = 1; i < wv_.size(); ++i) {
- const weight_t& weight = wv_[i];
- if (weight) w->set_value(i, weight);
+void Weights::SanityCheck(const vector<weight_t>& w) {
+ for (int i = 0; i < w.size(); ++i) {
+ assert(!isnan(w[i]));
+ assert(!isinf(w[i]));
}
}
-void Weights::InitFromVector(const std::vector<weight_t>& w) {
- wv_ = w;
- if (wv_.size() > FD::NumFeats())
- cerr << "WARNING: initializing weight vector has more features than the global feature dictionary!\n";
- wv_.resize(FD::NumFeats(), 0);
-}
+struct FComp {
+ const vector<weight_t>& w_;
+ FComp(const vector<weight_t>& w) : w_(w) {}
+ bool operator()(int a, int b) const {
+ return fabs(w_[a]) > fabs(w_[b]);
+ }
+};
-void Weights::InitFromVector(const SparseVector<weight_t>& w) {
- wv_.clear();
- wv_.resize(FD::NumFeats(), 0.0);
- for (int i = 1; i < FD::NumFeats(); ++i)
- wv_[i] = w.value(i);
+void Weights::ShowLargestFeatures(const vector<weight_t>& w) {
+ vector<int> fnums(w.size());
+ for (int i = 0; i < w.size(); ++i)
+ fnums[i] = i;
+ vector<int>::iterator mid = fnums.begin();
+ mid += (w.size() > 10 ? 10 : w.size());
+ partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
+ cerr << "TOP FEATURES:";
+ for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
+ cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
+ }
+ cerr << endl;
}
+
diff --git a/utils/weights.h b/utils/weights.h
index 7664810b..30f71db0 100644
--- a/utils/weights.h
+++ b/utils/weights.h
@@ -10,15 +10,21 @@ typedef double weight_t;
class Weights {
public:
- Weights() {}
- void InitFromFile(const std::string& fname, std::vector<std::string>* feature_list = NULL);
- void WriteToFile(const std::string& fname, bool hide_zero_value_features = true, const std::string* extra = NULL) const;
- void InitVector(std::vector<weight_t>* w) const;
- void InitSparseVector(SparseVector<weight_t>* w) const;
- void InitFromVector(const std::vector<weight_t>& w);
- void InitFromVector(const SparseVector<weight_t>& w);
+ static void InitFromFile(const std::string& fname,
+ std::vector<weight_t>* weights,
+ std::vector<std::string>* feature_list = NULL);
+ static void WriteToFile(const std::string& fname,
+ const std::vector<weight_t>& weights,
+ bool hide_zero_value_features = true,
+ const std::string* extra = NULL);
+ static void InitSparseVector(const std::vector<weight_t>& dv,
+ SparseVector<weight_t>* sv);
+ // check for infinities, NaNs, etc
+ static void SanityCheck(const std::vector<weight_t>& w);
+ // write weights with largest magnitude to cerr
+ static void ShowLargestFeatures(const std::vector<weight_t>& w);
private:
- std::vector<weight_t> wv_;
+ Weights();
};
#endif
diff --git a/vest/mr_vest_generate_mapper_input.cc b/vest/mr_vest_generate_mapper_input.cc
index b84c44bc..0c094fd5 100644
--- a/vest/mr_vest_generate_mapper_input.cc
+++ b/vest/mr_vest_generate_mapper_input.cc
@@ -223,16 +223,16 @@ struct oracle_directions {
cerr << "Forest repo: " << forest_repository << endl;
assert(DirectoryExists(forest_repository));
vector<string> features;
- weights.InitFromFile(weights_file, &features);
+ vector<weight_t> dorigin;
+ Weights::InitFromFile(weights_file, &dorigin, &features);
if (optimize_features.size())
features=optimize_features;
- weights.InitSparseVector(&origin);
+ Weights::InitSparseVector(dorigin, &origin);
fids.clear();
AddFeatureIds(features);
oracles.resize(dev_set_size);
}
- Weights weights;
void AddFeatureIds(vector<string> const& features) {
int i = fids.size();
fids.resize(fids.size()+features.size());
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-22 09:22:29 +0000