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| author | Patrick Simianer <simianer@cl.uni-heidelberg.de> | 2012-04-27 16:07:49 +0200 |
|---|---|---|
| committer | Patrick Simianer <simianer@cl.uni-heidelberg.de> | 2012-04-27 16:07:49 +0200 |
| commit | 291e511b8f5d130b94d20caa1097deb74072dc99 (patch) | |
| tree | 6014bd6ab0bc0b43bdf9d03702410047947fe9d6 /rampion/rampion_cccp.cc | |
| parent | c7ac569634c07de169a91c9f4d028ecd3899b4df (diff) | |
| parent | c5f69888943623e80478b6ba9247acc85758bedf (diff) | |
Merge remote-tracking branch 'upstream/master'
Diffstat (limited to 'rampion/rampion_cccp.cc')
| -rw-r--r-- | rampion/rampion_cccp.cc | 8 |
1 files changed, 5 insertions, 3 deletions
diff --git a/rampion/rampion_cccp.cc b/rampion/rampion_cccp.cc index 6eb3ccf3..7a6f1f0c 100644 --- a/rampion/rampion_cccp.cc +++ b/rampion/rampion_cccp.cc @@ -58,6 +58,7 @@ struct HypInfo { SufficientStats ss; scorer.Evaluate(hyp, &ss); g = metric->ComputeScore(ss); + if (!metric->IsErrorMetric()) g = 1 - g; } vector<WordID> hyp; @@ -90,8 +91,7 @@ int main(int argc, char** argv) { EvaluationMetric* metric = EvaluationMetric::Instance(evaluation_metric); DocumentScorer ds(metric, conf["reference"].as<vector<string> >()); cerr << "Loaded " << ds.size() << " references for scoring with " << evaluation_metric << endl; - double goodsign = 1; - if (metric->IsErrorMetric()) goodsign = -goodsign; + double goodsign = -1; double badsign = -goodsign; Hypergraph hg; @@ -121,6 +121,8 @@ int main(int argc, char** argv) { vector<HypInfo>& curkbest = kis.back(); is >> file >> sent_id; ReadFile rf(file); + if (kis.size() % 5 == 0) { cerr << '.'; } + if (kis.size() % 200 == 0) { cerr << " [" << kis.size() << "]\n"; } HypergraphIO::ReadFromJSON(rf.stream(), &hg); hg.Reweight(weights); KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(hg, kbest_size); @@ -132,8 +134,8 @@ int main(int argc, char** argv) { curkbest.push_back(HypInfo(d->yield, d->feature_values, *ds[sent_id], metric)); } } + cerr << "\nHypergraphs loaded.\n"; - cerr << "Hypergraphs loaded.\n"; vector<SparseVector<weight_t> > goals(kis.size()); // f(x_i,y+,h+) SparseVector<weight_t> fear; // f(x,y-,h-) for (unsigned iterp = 1; iterp <= tp; ++iterp) { |