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diff --git a/decoder/cdec.cc b/decoder/cdec.cc
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+++ b/decoder/cdec.cc
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+#include <iostream>
+#include <fstream>
+#include <tr1/unordered_map>
+#include <tr1/unordered_set>
+
+#include <boost/shared_ptr.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "timing_stats.h"
+#include "translator.h"
+#include "phrasebased_translator.h"
+#include "aligner.h"
+#include "stringlib.h"
+#include "forest_writer.h"
+#include "hg_io.h"
+#include "filelib.h"
+#include "sampler.h"
+#include "sparse_vector.h"
+#include "tagger.h"
+#include "lextrans.h"
+#include "lexalign.h"
+#include "csplit.h"
+#include "weights.h"
+#include "tdict.h"
+#include "ff.h"
+#include "ff_factory.h"
+#include "hg_intersect.h"
+#include "apply_models.h"
+#include "viterbi.h"
+#include "kbest.h"
+#include "inside_outside.h"
+#include "exp_semiring.h"
+#include "sentence_metadata.h"
+
+using namespace std;
+using namespace std::tr1;
+using boost::shared_ptr;
+namespace po = boost::program_options;
+
+// some globals ...
+boost::shared_ptr<RandomNumberGenerator<boost::mt19937> > rng;
+static const double kMINUS_EPSILON = -1e-6; // don't be too strict
+
+namespace Hack { void MaxTrans(const Hypergraph& in, int beam_size); }
+namespace NgramCache { void Clear(); }
+
+void ShowBanner() {
+ cerr << "cdec v1.0 (c) 2009-2010 by Chris Dyer\n";
+}
+
+void ConvertSV(const SparseVector<prob_t>& src, SparseVector<double>* trg) {
+ for (SparseVector<prob_t>::const_iterator it = src.begin(); it != src.end(); ++it)
+ trg->set_value(it->first, it->second);
+}
+
+void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+ po::options_description opts("Configuration options");
+ opts.add_options()
+ ("formalism,f",po::value<string>(),"Decoding formalism; values include SCFG, FST, PB, LexTrans (lexical translation model, also disc training), CSplit (compound splitting), Tagger (sequence labeling), LexAlign (alignment only, or EM training)")
+ ("input,i",po::value<string>()->default_value("-"),"Source file")
+ ("grammar,g",po::value<vector<string> >()->composing(),"Either SCFG grammar file(s) or phrase tables file(s)")
+ ("weights,w",po::value<string>(),"Feature weights file")
+ ("no_freeze_feature_set,Z", "Do not freeze feature set after reading feature weights file")
+ ("feature_function,F",po::value<vector<string> >()->composing(), "Additional feature function(s) (-L for list)")
+ ("list_feature_functions,L","List available feature functions")
+ ("add_pass_through_rules,P","Add rules to translate OOV words as themselves")
+ ("k_best,k",po::value<int>(),"Extract the k best derivations")
+ ("unique_k_best,r", "Unique k-best translation list")
+ ("aligner,a", "Run as a word/phrase aligner (src & ref required)")
+ ("intersection_strategy,I",po::value<string>()->default_value("cube_pruning"), "Intersection strategy for incorporating finite-state features; values include Cube_pruning, Full")
+ ("cubepruning_pop_limit,K",po::value<int>()->default_value(200), "Max number of pops from the candidate heap at each node")
+ ("goal",po::value<string>()->default_value("S"),"Goal symbol (SCFG & FST)")
+ ("scfg_extra_glue_grammar", po::value<string>(), "Extra glue grammar file (Glue grammars apply when i=0 but have no other span restrictions)")
+ ("scfg_no_hiero_glue_grammar,n", "No Hiero glue grammar (nb. by default the SCFG decoder adds Hiero glue rules)")
+ ("scfg_default_nt,d",po::value<string>()->default_value("X"),"Default non-terminal symbol in SCFG")
+ ("scfg_max_span_limit,S",po::value<int>()->default_value(10),"Maximum non-terminal span limit (except \"glue\" grammar)")
+ ("show_tree_structure", "Show the Viterbi derivation structure")
+ ("show_expected_length", "Show the expected translation length under the model")
+ ("show_partition,z", "Compute and show the partition (inside score)")
+ ("show_cfg_search_space", "Show the search space as a CFG")
+ ("beam_prune", po::value<double>(), "Prune paths from +LM forest")
+ ("lexalign_use_null", "Support source-side null words in lexical translation")
+ ("tagger_tagset,t", po::value<string>(), "(Tagger) file containing tag set")
+ ("csplit_output_plf", "(Compound splitter) Output lattice in PLF format")
+ ("csplit_preserve_full_word", "(Compound splitter) Always include the unsegmented form in the output lattice")
+ ("extract_rules", po::value<string>(), "Extract the rules used in translation (de-duped) to this file")
+ ("graphviz","Show (constrained) translation forest in GraphViz format")
+ ("max_translation_beam,x", po::value<int>(), "Beam approximation to get max translation from the chart")
+ ("max_translation_sample,X", po::value<int>(), "Sample the max translation from the chart")
+ ("pb_max_distortion,D", po::value<int>()->default_value(4), "Phrase-based decoder: maximum distortion")
+ ("cll_gradient,G","Compute conditional log-likelihood gradient and write to STDOUT (src & ref required)")
+ ("crf_uniform_empirical", "If there are multple references use (i.e., lattice) a uniform distribution rather than posterior weighting a la EM")
+ ("feature_expectations","Write feature expectations for all features in chart (**OBJ** will be the partition)")
+ ("vector_format",po::value<string>()->default_value("b64"), "Sparse vector serialization format for feature expectations or gradients, includes (text or b64)")
+ ("combine_size,C",po::value<int>()->default_value(1), "When option -G is used, process this many sentence pairs before writing the gradient (1=emit after every sentence pair)")
+ ("forest_output,O",po::value<string>(),"Directory to write forests to")
+ ("minimal_forests,m","Write minimal forests (excludes Rule information). Such forests can be used for ML/MAP training, but not rescoring, etc.");
+ po::options_description clo("Command line options");
+ clo.add_options()
+ ("config,c", 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")) {
+ const string cfg = (*conf)["config"].as<string>();
+ cerr << "Configuration file: " << cfg << endl;
+ ifstream config(cfg.c_str());
+ po::store(po::parse_config_file(config, dconfig_options), *conf);
+ }
+ po::notify(*conf);
+
+ if (conf->count("list_feature_functions")) {
+ cerr << "Available feature functions (specify with -F):\n";
+ global_ff_registry->DisplayList();
+ cerr << endl;
+ exit(1);
+ }
+
+ if (conf->count("help") || conf->count("formalism") == 0) {
+ cerr << dcmdline_options << endl;
+ exit(1);
+ }
+
+ const string formalism = LowercaseString((*conf)["formalism"].as<string>());
+ if (formalism != "scfg" && formalism != "fst" && formalism != "lextrans" && formalism != "pb" && formalism != "csplit" && formalism != "tagger" && formalism != "lexalign") {
+ cerr << "Error: --formalism takes only 'scfg', 'fst', 'pb', 'csplit', 'lextrans', 'lexalign', or 'tagger'\n";
+ cerr << dcmdline_options << endl;
+ exit(1);
+ }
+}
+
+// TODO move out of cdec into some sampling decoder file
+void SampleRecurse(const Hypergraph& hg, const vector<SampleSet>& ss, int n, vector<WordID>* out) {
+ const SampleSet& s = ss[n];
+ int i = rng->SelectSample(s);
+ const Hypergraph::Edge& edge = hg.edges_[hg.nodes_[n].in_edges_[i]];
+ vector<vector<WordID> > ants(edge.tail_nodes_.size());
+ for (int j = 0; j < ants.size(); ++j)
+ SampleRecurse(hg, ss, edge.tail_nodes_[j], &ants[j]);
+
+ vector<const vector<WordID>*> pants(ants.size());
+ for (int j = 0; j < ants.size(); ++j) pants[j] = &ants[j];
+ edge.rule_->ESubstitute(pants, out);
+}
+
+struct SampleSort {
+ bool operator()(const pair<int,string>& a, const pair<int,string>& b) const {
+ return a.first > b.first;
+ }
+};
+
+// TODO move out of cdec into some sampling decoder file
+void MaxTranslationSample(Hypergraph* hg, const int samples, const int k) {
+ unordered_map<string, int, boost::hash<string> > m;
+ hg->PushWeightsToGoal();
+ const int num_nodes = hg->nodes_.size();
+ vector<SampleSet> ss(num_nodes);
+ for (int i = 0; i < num_nodes; ++i) {
+ SampleSet& s = ss[i];
+ const vector<int>& in_edges = hg->nodes_[i].in_edges_;
+ for (int j = 0; j < in_edges.size(); ++j) {
+ s.add(hg->edges_[in_edges[j]].edge_prob_);
+ }
+ }
+ for (int i = 0; i < samples; ++i) {
+ vector<WordID> yield;
+ SampleRecurse(*hg, ss, hg->nodes_.size() - 1, &yield);
+ const string trans = TD::GetString(yield);
+ ++m[trans];
+ }
+ vector<pair<int, string> > dist;
+ for (unordered_map<string, int, boost::hash<string> >::iterator i = m.begin();
+ i != m.end(); ++i) {
+ dist.push_back(make_pair(i->second, i->first));
+ }
+ sort(dist.begin(), dist.end(), SampleSort());
+ if (k) {
+ for (int i = 0; i < k; ++i)
+ cout << dist[i].first << " ||| " << dist[i].second << endl;
+ } else {
+ cout << dist[0].second << endl;
+ }
+}
+
+// TODO decoder output should probably be moved to another file
+void DumpKBest(const int sent_id, const Hypergraph& forest, const int k, const bool unique) {
+cerr << "In kbest\n";
+ if (unique) {
+ KBest::KBestDerivations<vector<WordID>, ESentenceTraversal, KBest::FilterUnique> kbest(forest, k);
+ for (int i = 0; i < k; ++i) {
+ const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal, KBest::FilterUnique>::Derivation* d =
+ kbest.LazyKthBest(forest.nodes_.size() - 1, i);
+ if (!d) break;
+ cout << sent_id << " ||| " << TD::GetString(d->yield) << " ||| "
+ << d->feature_values << " ||| " << log(d->score) << endl;
+ }
+ } else {
+ KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, k);
+ for (int i = 0; i < k; ++i) {
+ const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
+ kbest.LazyKthBest(forest.nodes_.size() - 1, i);
+ if (!d) break;
+ cout << sent_id << " ||| " << TD::GetString(d->yield) << " ||| "
+ << d->feature_values << " ||| " << log(d->score) << endl;
+ }
+ }
+}
+
+struct ELengthWeightFunction {
+ double operator()(const Hypergraph::Edge& e) const {
+ return e.rule_->ELength() - e.rule_->Arity();
+ }
+};
+
+
+struct TRPHash {
+ size_t operator()(const TRulePtr& o) const { return reinterpret_cast<size_t>(o.get()); }
+};
+static void ExtractRulesDedupe(const Hypergraph& hg, ostream* os) {
+ static unordered_set<TRulePtr, TRPHash> written;
+ for (int i = 0; i < hg.edges_.size(); ++i) {
+ const TRulePtr& rule = hg.edges_[i].rule_;
+ if (written.insert(rule).second) {
+ (*os) << rule->AsString() << endl;
+ }
+ }
+}
+
+void register_feature_functions();
+
+int main(int argc, char** argv) {
+ global_ff_registry.reset(new FFRegistry);
+ register_feature_functions();
+ ShowBanner();
+ po::variables_map conf;
+ InitCommandLine(argc, argv, &conf);
+ const bool write_gradient = conf.count("cll_gradient");
+ const bool feature_expectations = conf.count("feature_expectations");
+ if (write_gradient && feature_expectations) {
+ cerr << "You can only specify --gradient or --feature_expectations, not both!\n";
+ exit(1);
+ }
+ const bool output_training_vector = (write_gradient || feature_expectations);
+
+ boost::shared_ptr<Translator> translator;
+ const string formalism = LowercaseString(conf["formalism"].as<string>());
+ const bool csplit_preserve_full_word = conf.count("csplit_preserve_full_word");
+ if (csplit_preserve_full_word &&
+ (formalism != "csplit" || !conf.count("beam_prune"))) {
+ cerr << "--csplit_preserve_full_word should only be "
+ << "used with csplit AND --beam_prune!\n";
+ exit(1);
+ }
+ const bool csplit_output_plf = conf.count("csplit_output_plf");
+ if (csplit_output_plf && formalism != "csplit") {
+ cerr << "--csplit_output_plf should only be used with csplit!\n";
+ exit(1);
+ }
+
+ // load feature weights (and possibly freeze feature set)
+ vector<double> feature_weights;
+ Weights w;
+ if (conf.count("weights")) {
+ w.InitFromFile(conf["weights"].as<string>());
+ feature_weights.resize(FD::NumFeats());
+ w.InitVector(&feature_weights);
+ if (!conf.count("no_freeze_feature_set")) {
+ cerr << "Freezing feature set (use --no_freeze_feature_set to change)." << endl;
+ FD::Freeze();
+ }
+ }
+
+ // set up translation back end
+ if (formalism == "scfg")
+ translator.reset(new SCFGTranslator(conf));
+ else if (formalism == "fst")
+ translator.reset(new FSTTranslator(conf));
+ else if (formalism == "pb")
+ translator.reset(new PhraseBasedTranslator(conf));
+ else if (formalism == "csplit")
+ translator.reset(new CompoundSplit(conf));
+ else if (formalism == "lextrans")
+ translator.reset(new LexicalTrans(conf));
+ else if (formalism == "lexalign")
+ translator.reset(new LexicalAlign(conf));
+ else if (formalism == "tagger")
+ translator.reset(new Tagger(conf));
+ else
+ assert(!"error");
+
+ // set up additional scoring features
+ vector<shared_ptr<FeatureFunction> > pffs;
+ vector<const FeatureFunction*> late_ffs;
+ if (conf.count("feature_function") > 0) {
+ const vector<string>& add_ffs = conf["feature_function"].as<vector<string> >();
+ for (int i = 0; i < add_ffs.size(); ++i) {
+ string ff, param;
+ SplitCommandAndParam(add_ffs[i], &ff, &param);
+ cerr << "Feature: " << ff;
+ if (param.size() > 0) cerr << " (with config parameters '" << param << "')\n";
+ else cerr << " (no config parameters)\n";
+ shared_ptr<FeatureFunction> pff = global_ff_registry->Create(ff, param);
+ if (!pff) { exit(1); }
+ // TODO check that multiple features aren't trying to set the same fid
+ pffs.push_back(pff);
+ late_ffs.push_back(pff.get());
+ }
+ }
+ ModelSet late_models(feature_weights, late_ffs);
+ int palg = 1;
+ if (LowercaseString(conf["intersection_strategy"].as<string>()) == "full") {
+ palg = 0;
+ cerr << "Using full intersection (no pruning).\n";
+ }
+ const IntersectionConfiguration inter_conf(palg, conf["cubepruning_pop_limit"].as<int>());
+
+ const int sample_max_trans = conf.count("max_translation_sample") ?
+ conf["max_translation_sample"].as<int>() : 0;
+ if (sample_max_trans)
+ rng.reset(new RandomNumberGenerator<boost::mt19937>);
+ const bool aligner_mode = conf.count("aligner");
+ const bool minimal_forests = conf.count("minimal_forests");
+ const bool graphviz = conf.count("graphviz");
+ const bool encode_b64 = conf["vector_format"].as<string>() == "b64";
+ const bool kbest = conf.count("k_best");
+ const bool unique_kbest = conf.count("unique_k_best");
+ const bool crf_uniform_empirical = conf.count("crf_uniform_empirical");
+ shared_ptr<WriteFile> extract_file;
+ if (conf.count("extract_rules"))
+ extract_file.reset(new WriteFile(conf["extract_rules"].as<string>()));
+
+ int combine_size = conf["combine_size"].as<int>();
+ if (combine_size < 1) combine_size = 1;
+ const string input = conf["input"].as<string>();
+ cerr << "Reading input from " << ((input == "-") ? "STDIN" : input.c_str()) << endl;
+ ReadFile in_read(input);
+ istream *in = in_read.stream();
+ assert(*in);
+
+ SparseVector<prob_t> acc_vec; // accumulate gradient
+ double acc_obj = 0; // accumulate objective
+ int g_count = 0; // number of gradient pieces computed
+ int sent_id = -1; // line counter
+
+ while(*in) {
+ NgramCache::Clear(); // clear ngram cache for remote LM (if used)
+ Timer::Summarize();
+ ++sent_id;
+ string buf;
+ getline(*in, buf);
+ if (buf.empty()) continue;
+ map<string, string> sgml;
+ ProcessAndStripSGML(&buf, &sgml);
+ if (sgml.find("id") != sgml.end())
+ sent_id = atoi(sgml["id"].c_str());
+
+ cerr << "\nINPUT: ";
+ if (buf.size() < 100)
+ cerr << buf << endl;
+ else {
+ size_t x = buf.rfind(" ", 100);
+ if (x == string::npos) x = 100;
+ cerr << buf.substr(0, x) << " ..." << endl;
+ }
+ cerr << " id = " << sent_id << endl;
+ string to_translate;
+ Lattice ref;
+ ParseTranslatorInputLattice(buf, &to_translate, &ref);
+ const bool has_ref = ref.size() > 0;
+ SentenceMetadata smeta(sent_id, ref);
+ const bool hadoop_counters = (write_gradient);
+ Hypergraph forest; // -LM forest
+ translator->ProcessMarkupHints(sgml);
+ Timer t("Translation");
+ const bool translation_successful =
+ translator->Translate(to_translate, &smeta, feature_weights, &forest);
+ translator->SentenceComplete();
+ if (!translation_successful) {
+ cerr << " NO PARSE FOUND.\n";
+ if (hadoop_counters)
+ cerr << "reporter:counter:UserCounters,FParseFailed,1" << endl;
+ cout << endl << flush;
+ continue;
+ }
+ cerr << " -LM forest (nodes/edges): " << forest.nodes_.size() << '/' << forest.edges_.size() << endl;
+ cerr << " -LM forest (paths): " << forest.NumberOfPaths() << endl;
+ if (conf.count("show_expected_length")) {
+ const PRPair<double, double> res =
+ Inside<PRPair<double, double>,
+ PRWeightFunction<double, EdgeProb, double, ELengthWeightFunction> >(forest);
+ cerr << " Expected length (words): " << res.r / res.p << "\t" << res << endl;
+ }
+ if (conf.count("show_partition")) {
+ const prob_t z = Inside<prob_t, EdgeProb>(forest);
+ cerr << " -LM partition log(Z): " << log(z) << endl;
+ }
+ if (extract_file)
+ ExtractRulesDedupe(forest, extract_file->stream());
+ vector<WordID> trans;
+ const prob_t vs = ViterbiESentence(forest, &trans);
+ cerr << " -LM Viterbi: " << TD::GetString(trans) << endl;
+ if (conf.count("show_tree_structure"))
+ cerr << " -LM tree: " << ViterbiETree(forest) << endl;;
+ cerr << " -LM Viterbi: " << log(vs) << endl;
+
+ bool has_late_models = !late_models.empty();
+ if (has_late_models) {
+ forest.Reweight(feature_weights);
+ forest.SortInEdgesByEdgeWeights();
+ Hypergraph lm_forest;
+ ApplyModelSet(forest,
+ smeta,
+ late_models,
+ inter_conf,
+ &lm_forest);
+ forest.swap(lm_forest);
+ forest.Reweight(feature_weights);
+ trans.clear();
+ ViterbiESentence(forest, &trans);
+ cerr << " +LM forest (nodes/edges): " << forest.nodes_.size() << '/' << forest.edges_.size() << endl;
+ cerr << " +LM forest (paths): " << forest.NumberOfPaths() << endl;
+ cerr << " +LM Viterbi: " << TD::GetString(trans) << endl;
+ }
+ if (conf.count("beam_prune")) {
+ vector<bool> preserve_mask(forest.edges_.size(), false);
+ if (csplit_preserve_full_word)
+ preserve_mask[CompoundSplit::GetFullWordEdgeIndex(forest)] = true;
+ forest.BeamPruneInsideOutside(1.0, false, conf["beam_prune"].as<double>(), &preserve_mask);
+ cerr << " Pruned forest (paths): " << forest.NumberOfPaths() << endl;
+ }
+
+ if (conf.count("forest_output") && !has_ref) {
+ ForestWriter writer(conf["forest_output"].as<string>(), sent_id);
+ if (FileExists(writer.fname_)) {
+ cerr << " Unioning...\n";
+ Hypergraph new_hg;
+ {
+ ReadFile rf(writer.fname_);
+ bool succeeded = HypergraphIO::ReadFromJSON(rf.stream(), &new_hg);
+ assert(succeeded);
+ }
+ new_hg.Union(forest);
+ bool succeeded = writer.Write(new_hg, minimal_forests);
+ assert(succeeded);
+ } else {
+ bool succeeded = writer.Write(forest, minimal_forests);
+ assert(succeeded);
+ }
+ }
+
+ if (sample_max_trans) {
+ MaxTranslationSample(&forest, sample_max_trans, conf.count("k_best") ? conf["k_best"].as<int>() : 0);
+ } else {
+ if (kbest) {
+ DumpKBest(sent_id, forest, conf["k_best"].as<int>(), unique_kbest);
+ } else if (csplit_output_plf) {
+ cout << HypergraphIO::AsPLF(forest, false) << endl;
+ } else {
+ if (!graphviz && !has_ref) {
+ cout << TD::GetString(trans) << endl << flush;
+ }
+ }
+ }
+
+ const int max_trans_beam_size = conf.count("max_translation_beam") ?
+ conf["max_translation_beam"].as<int>() : 0;
+ if (max_trans_beam_size) {
+ Hack::MaxTrans(forest, max_trans_beam_size);
+ continue;
+ }
+
+ if (graphviz && !has_ref) forest.PrintGraphviz();
+
+ // the following are only used if write_gradient is true!
+ SparseVector<prob_t> full_exp, ref_exp, gradient;
+ double log_z = 0, log_ref_z = 0;
+ if (write_gradient) {
+ const prob_t z = InsideOutside<prob_t, EdgeProb, SparseVector<prob_t>, EdgeFeaturesAndProbWeightFunction>(forest, &full_exp);
+ log_z = log(z);
+ full_exp /= z;
+ }
+ if (conf.count("show_cfg_search_space"))
+ HypergraphIO::WriteAsCFG(forest);
+ if (has_ref) {
+ if (HG::Intersect(ref, &forest)) {
+ cerr << " Constr. forest (nodes/edges): " << forest.nodes_.size() << '/' << forest.edges_.size() << endl;
+ cerr << " Constr. forest (paths): " << forest.NumberOfPaths() << endl;
+ if (crf_uniform_empirical) {
+ cerr << " USING UNIFORM WEIGHTS\n";
+ for (int i = 0; i < forest.edges_.size(); ++i)
+ forest.edges_[i].edge_prob_=prob_t::One();
+ } else {
+ forest.Reweight(feature_weights);
+ cerr << " Constr. VitTree: " << ViterbiFTree(forest) << endl;
+ }
+ if (hadoop_counters)
+ cerr << "reporter:counter:UserCounters,SentencePairsParsed,1" << endl;
+ if (conf.count("show_partition")) {
+ const prob_t z = Inside<prob_t, EdgeProb>(forest);
+ cerr << " Contst. partition log(Z): " << log(z) << endl;
+ }
+ //DumpKBest(sent_id, forest, 1000);
+ if (conf.count("forest_output")) {
+ ForestWriter writer(conf["forest_output"].as<string>(), sent_id);
+ if (FileExists(writer.fname_)) {
+ cerr << " Unioning...\n";
+ Hypergraph new_hg;
+ {
+ ReadFile rf(writer.fname_);
+ bool succeeded = HypergraphIO::ReadFromJSON(rf.stream(), &new_hg);
+ assert(succeeded);
+ }
+ new_hg.Union(forest);
+ bool succeeded = writer.Write(new_hg, minimal_forests);
+ assert(succeeded);
+ } else {
+ bool succeeded = writer.Write(forest, minimal_forests);
+ assert(succeeded);
+ }
+ }
+ if (aligner_mode && !output_training_vector)
+ AlignerTools::WriteAlignment(smeta.GetSourceLattice(), smeta.GetReference(), forest, &cout);
+ if (write_gradient) {
+ const prob_t ref_z = InsideOutside<prob_t, EdgeProb, SparseVector<prob_t>, EdgeFeaturesAndProbWeightFunction>(forest, &ref_exp);
+ ref_exp /= ref_z;
+ if (crf_uniform_empirical) {
+ log_ref_z = ref_exp.dot(feature_weights);
+ } else {
+ log_ref_z = log(ref_z);
+ }
+ //cerr << " MODEL LOG Z: " << log_z << endl;
+ //cerr << " EMPIRICAL LOG Z: " << log_ref_z << endl;
+ if ((log_z - log_ref_z) < kMINUS_EPSILON) {
+ cerr << "DIFF. ERR! log_z < log_ref_z: " << log_z << " " << log_ref_z << endl;
+ exit(1);
+ }
+ assert(!isnan(log_ref_z));
+ ref_exp -= full_exp;
+ acc_vec += ref_exp;
+ acc_obj += (log_z - log_ref_z);
+ }
+ if (feature_expectations) {
+ const prob_t z =
+ InsideOutside<prob_t, EdgeProb, SparseVector<prob_t>, EdgeFeaturesAndProbWeightFunction>(forest, &ref_exp);
+ ref_exp /= z;
+ acc_obj += log(z);
+ acc_vec += ref_exp;
+ }
+
+ if (output_training_vector) {
+ acc_vec.clear_value(0);
+ ++g_count;
+ if (g_count % combine_size == 0) {
+ if (encode_b64) {
+ cout << "0\t";
+ SparseVector<double> dav; ConvertSV(acc_vec, &dav);
+ B64::Encode(acc_obj, dav, &cout);
+ cout << endl << flush;
+ } else {
+ cout << "0\t**OBJ**=" << acc_obj << ';' << acc_vec << endl << flush;
+ }
+ acc_vec.clear();
+ acc_obj = 0;
+ }
+ }
+ if (conf.count("graphviz")) forest.PrintGraphviz();
+ } else {
+ cerr << " REFERENCE UNREACHABLE.\n";
+ if (write_gradient) {
+ if (hadoop_counters)
+ cerr << "reporter:counter:UserCounters,EFParseFailed,1" << endl;
+ cout << endl << flush;
+ }
+ }
+ }
+ }
+ if (output_training_vector && !acc_vec.empty()) {
+ if (encode_b64) {
+ cout << "0\t";
+ SparseVector<double> dav; ConvertSV(acc_vec, &dav);
+ B64::Encode(acc_obj, dav, &cout);
+ cout << endl << flush;
+ } else {
+ cout << "0\t**OBJ**=" << acc_obj << ';' << acc_vec << endl << flush;
+ }
+ }
+}
+
generated by cgit v1.2.3 (git 2.39.1) at 2024-12-25 19:24:34 +0000