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https://github.com/moses-smt/mosesdecoder.git
synced 2024-09-20 07:42:21 +03:00
Change casts to C++ style casts, and delete unnecessary casts.
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380f550cf3
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@ -142,7 +142,7 @@ void BleuScorer::prepareStats(size_t sid, const string& text, ScoreStats& entry)
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for (size_t i = 0; i < _reflengths[sid].size(); ++i) {
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total += _reflengths[sid][i];
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}
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float mean = (float)total/_reflengths[sid].size();
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float mean = static_cast<float>(total) /_reflengths[sid].size();
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stats.push_back(mean);
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} else if (_refLengthStrategy == BLEU_CLOSEST) {
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int min_diff = INT_MAX;
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@ -196,7 +196,7 @@ float BleuScorer::calculateScore(const vector<int>& comps) const
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}
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logbleu /= kLENGTH;
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float brevity = 1.0 - (float)comps[kLENGTH*2]/comps[1];//reflength divided by test length
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const float brevity = 1.0 - static_cast<float>(comps[kLENGTH*2]) / comps[1];//reflength divided by test length
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if (brevity < 0.0) {
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logbleu += brevity;
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}
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@ -50,7 +50,7 @@ float CderScorer::calculateScore(const vector<int>& comps) const
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throw runtime_error("Size of stat vector for CDER is not 2");
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}
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return 1 - (comps[0] / (float) comps[1]);
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return 1 - (comps[0] / static_cast<float>(comps[1]));
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}
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vector<int> CderScorer::computeCD(const sent_t& cand, const sent_t& ref) const
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@ -255,8 +255,8 @@ void Data::createShards(size_t shard_count, float shard_size, const string& scor
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vector<size_t> shard_contents;
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if (shard_size == 0) {
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//split into roughly equal size shards
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size_t shard_start = floor(0.5 + shard_id * (float)data_size / shard_count);
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size_t shard_end = floor(0.5 + (shard_id+1) * (float)data_size / shard_count);
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const size_t shard_start = floor(0.5 + shard_id * static_cast<float>(data_size) / shard_count);
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const size_t shard_end = floor(0.5 + (shard_id + 1) * static_cast<float>(data_size) / shard_count);
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for (size_t i = shard_start; i < shard_end; ++i) {
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shard_contents.push_back(i);
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}
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@ -46,7 +46,7 @@ void SparseVector::set(const string& name, FeatureStatsType value) {
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void SparseVector::write(ostream& out, const string& sep) const {
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for (fvector_t::const_iterator i = fvector_.begin(); i != fvector_.end(); ++i) {
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if (abs((float)(i->second)) < 0.00001) continue;
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if (abs(i->second) < 0.00001) continue;
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string name = id2name_[i->first];
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out << name << sep << i->second << " ";
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}
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@ -54,7 +54,7 @@ void Point::Randomize()
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CHECK(m_max.size()==Point::dim);
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for (unsigned int i=0; i<size(); i++) {
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operator[](i) = m_min[i] +
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(float)random()/(float)RAND_MAX * (float)(m_max[i]-m_min[i]);
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static_cast<float>(random()) / static_cast<float>(RAND_MAX) * (m_max[i] - m_min[i]);
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}
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}
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@ -459,8 +459,8 @@ int main(int argc, char **argv)
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}
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}
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mean /= (float)option.ntry;
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var /= (float)option.ntry;
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mean /= static_cast<float>(option.ntry);
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var /= static_cast<float>(option.ntry);
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var = sqrt(abs(var - mean * mean));
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if (verboselevel() > 1) {
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@ -481,7 +481,7 @@ int main(int argc, char **argv)
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cerr << "bestP: " << finalP << endl;
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// L1-Normalization of the best Point
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if ((int)to_optimize.size() == option.pdim) {
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if (static_cast<int>(to_optimize.size()) == option.pdim) {
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finalP.NormalizeL1();
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}
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@ -76,7 +76,7 @@ static float sentenceLevelBleuPlusOne(const vector<float>& stats) {
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logbleu += log(stats[2*j]+1) - log(stats[2*j+1]+1);
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}
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logbleu /= bleu_order;
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float brevity = 1.0 - (float)stats[(bleu_order*2)]/stats[1];
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const float brevity = 1.0 - static_cast<float>(stats[(bleu_order*2)]) / stats[1];
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if (brevity < 0.0) {
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logbleu += brevity;
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}
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