diff options
author | Tetsuo Kiso <tetsuo-s@is.naist.jp> | 2012-03-07 02:19:24 +0400 |
---|---|---|
committer | Tetsuo Kiso <tetsuo-s@is.naist.jp> | 2012-03-07 02:19:24 +0400 |
commit | 851a1835b68deb958956e1d0965db002574d0241 (patch) | |
tree | 02095e5348a02300fc3368fe0dbe2f6e59cd3ff2 /mert/Data.cpp | |
parent | 6ada41576cb9dd8e172505995305de18b9c68292 (diff) |
Remove an unused variable and unnecessary 'std::'.
Diffstat (limited to 'mert/Data.cpp')
-rw-r--r-- | mert/Data.cpp | 27 |
1 files changed, 13 insertions, 14 deletions
diff --git a/mert/Data.cpp b/mert/Data.cpp index 93b193774..c4a35b9b2 100644 --- a/mert/Data.cpp +++ b/mert/Data.cpp @@ -32,7 +32,7 @@ Data::Data(Scorer& ptr) scoredata(new ScoreData(*theScorer)), featdata(new FeatureData) { - TRACE_ERR("Data::score_type " << score_type << std::endl); + TRACE_ERR("Data::score_type " << score_type << endl); TRACE_ERR("Data::Scorer type from Scorer: " << theScorer->getName() << endl); } @@ -50,7 +50,7 @@ void Data::remove_duplicates() { assert(feat_array.size() == score_array.size()); //serves as a hash-map: - std::map<double, std::vector<size_t> > lookup; + map<double, vector<size_t> > lookup; size_t end_pos = feat_array.size() - 1; @@ -65,9 +65,9 @@ void Data::remove_duplicates() { if (lookup.find(sum) != lookup.end()) { - //std::cerr << "hit" << std::endl; + //cerr << "hit" << endl; - std::vector<size_t>& cur_list = lookup[sum]; + vector<size_t>& cur_list = lookup[sum]; size_t l=0; for (l=0; l < cur_list.size(); l++) { @@ -128,17 +128,15 @@ void Data::remove_duplicates() { //END_ADDED -void Data::loadnbest(const std::string &file) +void Data::loadnbest(const string &file) { - TRACE_ERR("loading nbest from " << file << std::endl); + TRACE_ERR("loading nbest from " << file << endl); inputfilestream inp(file); // matches a stream with a file. Opens the file if (!inp.good()) throw runtime_error("Unable to open: " + file); ScoreStats scoreentry; - std::string line; - std::string sentence_index, sentence, feature_str; - std::string::size_type loc; + string line, sentence_index, sentence, feature_str; while (getline(inp, line, '\n')) { if (line.empty()) continue; @@ -206,7 +204,7 @@ void Data::AddFeatures(const string& str, feature_entry.add(ConvertStringToFeatureStatsType(substr)); } else if (substr.find("_") != string::npos) { // sparse feature name? store as well - std::string name = substr; + string name = substr; getNextPound(buf, substr); feature_entry.addSparse(name, atof(substr.c_str())); _sparse_flag = true; @@ -217,12 +215,12 @@ void Data::AddFeatures(const string& str, // TODO void Data::mergeSparseFeatures() { - std::cerr << "ERROR: sparse features can only be trained with pairwise ranked optimizer (PRO), not traditional MERT\n"; + cerr << "ERROR: sparse features can only be trained with pairwise ranked optimizer (PRO), not traditional MERT\n"; exit(1); } void Data::createShards(size_t shard_count, float shard_size, const string& scorerconfig, - std::vector<Data>& shards) + vector<Data>& shards) { CHECK(shard_count); CHECK(shard_size >= 0); @@ -232,13 +230,14 @@ void Data::createShards(size_t shard_count, float shard_size, const string& scor CHECK(data_size == featdata->size()); shard_size *= data_size; + const float coeff = static_cast<float>(data_size) / shard_count; for (size_t shard_id = 0; shard_id < shard_count; ++shard_id) { vector<size_t> shard_contents; if (shard_size == 0) { //split into roughly equal size shards - const size_t shard_start = floor(0.5 + shard_id * static_cast<float>(data_size) / shard_count); - const size_t shard_end = floor(0.5 + (shard_id + 1) * static_cast<float>(data_size) / shard_count); + const size_t shard_start = floor(0.5 + shard_id * coeff); + const size_t shard_end = floor(0.5 + (shard_id + 1) * coeff); for (size_t i = shard_start; i < shard_end; ++i) { shard_contents.push_back(i); } |