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-rw-r--r--decoder/decoder.cc25
1 files changed, 9 insertions, 16 deletions
diff --git a/decoder/decoder.cc b/decoder/decoder.cc
index 3551b584..e28080aa 100644
--- a/decoder/decoder.cc
+++ b/decoder/decoder.cc
@@ -279,7 +279,6 @@ struct DecoderImpl {
bool encode_b64;
bool kbest;
bool unique_kbest;
- bool crf_uniform_empirical;
bool get_oracle_forest;
shared_ptr<WriteFile> extract_file;
int combine_size;
@@ -379,7 +378,6 @@ DecoderImpl::DecoderImpl(po::variables_map& conf, int argc, char** argv, istream
("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")
("get_oracle_forest,o", "Calculate rescored hypregraph using approximate BLEU scoring of rules")
("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)")
@@ -611,7 +609,6 @@ DecoderImpl::DecoderImpl(po::variables_map& conf, int argc, char** argv, istream
encode_b64 = str("vector_format",conf) == "b64";
kbest = conf.count("k_best");
unique_kbest = conf.count("unique_k_best");
- crf_uniform_empirical = conf.count("crf_uniform_empirical");
get_oracle_forest = conf.count("get_oracle_forest");
cfg_options.Validate();
@@ -842,14 +839,12 @@ bool DecoderImpl::Decode(const string& input, DecoderObserver* o) {
if (has_ref) {
if (HG::Intersect(ref, &forest)) {
if (!SILENT) forest_stats(forest," Constr. forest",show_tree_structure,show_features,feature_weights,oracle.show_derivation);
- if (crf_uniform_empirical) {
- if (!SILENT) 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);
- if (!SILENT) cerr << " Constr. VitTree: " << ViterbiFTree(forest) << endl;
- }
+// if (crf_uniform_empirical) {
+// if (!SILENT) cerr << " USING UNIFORM WEIGHTS\n";
+// for (int i = 0; i < forest.edges_.size(); ++i)
+// forest.edges_[i].edge_prob_=prob_t::One(); }
+ forest.Reweight(feature_weights);
+ if (!SILENT) cerr << " Constr. VitTree: " << ViterbiFTree(forest) << endl;
if (conf.count("show_partition")) {
const prob_t z = Inside<prob_t, EdgeProb>(forest);
cerr << " Contst. partition log(Z): " << log(z) << endl;
@@ -878,11 +873,9 @@ bool DecoderImpl::Decode(const string& input, DecoderObserver* o) {
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);
- }
+// if (crf_uniform_empirical)
+// log_ref_z = ref_exp.dot(feature_weights);
+ 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) {