moses-smt/mosesdecoder
1
1
Fork 0
mirror of https://github.com/moses-smt/mosesdecoder.git synced 2024-09-11 11:25:40 +03:00
Code Issues Packages Projects Releases Wiki Activity

Move 'using namespace std' out from .h.

Browse Source 
Add "std" to size_t, too.
This commit is contained in:
Tetsuo Kiso 2012-05-30 23:11:09 +09:00
parent 01eb60f350
commit beb2256dba
5 changed files with 48 additions and 49 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

45
mert/HypPackEnumerator.h
View File

@ -27,10 +27,10 @@ public:
virtual bool finished() = 0;
virtual void next() = 0;
virtual size_t cur_size() = 0;
virtual size_t num_dense() const = 0;
virtual const FeatureDataItem& featuresAt(size_t i) = 0;
virtual const ScoreDataItem& scoresAt(size_t i) = 0;
virtual std::size_t cur_size() = 0;
virtual std::size_t num_dense() const = 0;
virtual const FeatureDataItem& featuresAt(std::size_t i) = 0;
virtual const ScoreDataItem& scoresAt(std::size_t i) = 0;
};
// Instantiation that streams from disk
@ -38,23 +38,22 @@ public:
class StreamingHypPackEnumerator : public HypPackEnumerator {
public:
StreamingHypPackEnumerator(std::vector<std::string> const& featureFiles,
std::vector<std::string> const& scoreFiles
);
std::vector<std::string> const& scoreFiles);
virtual std::size_t num_dense() const;
virtual size_t num_dense() const;
virtual void reset();
virtual bool finished();
virtual void next();
virtual size_t cur_size();
virtual const FeatureDataItem& featuresAt(size_t i);
virtual const ScoreDataItem& scoresAt(size_t i);
virtual std::size_t cur_size();
virtual const FeatureDataItem& featuresAt(std::size_t i);
virtual const ScoreDataItem& scoresAt(std::size_t i);
private:
void prime();
size_t m_num_lists;
size_t m_sentenceId;
std::size_t m_num_lists;
std::size_t m_sentenceId;
std::vector<std::string> m_featureFiles;
std::vector<std::string> m_scoreFiles;
@ -62,7 +61,7 @@ private:
int m_iNumDense;
std::vector<FeatureDataIterator> m_featureDataIters;
std::vector<ScoreDataIterator> m_scoreDataIters;
std::vector<std::pair<size_t,size_t> > m_current_indexes;
std::vector<std::pair<std::size_t,std::size_t> > m_current_indexes;
};
// Instantiation that reads into memory
@ -74,21 +73,21 @@ public:
std::vector<std::string> const& scoreFiles,
bool no_shuffle);
virtual size_t num_dense() const;
virtual std::size_t num_dense() const;
virtual void reset();
virtual bool finished();
virtual void next();
virtual size_t cur_size();
virtual const FeatureDataItem& featuresAt(size_t i);
virtual const ScoreDataItem& scoresAt(size_t i);
virtual std::size_t cur_size();
virtual const FeatureDataItem& featuresAt(std::size_t i);
virtual const ScoreDataItem& scoresAt(std::size_t i);
private:
bool m_no_shuffle;
size_t m_cur_index;
size_t m_num_dense;
std::vector<size_t> m_indexes;
std::size_t m_cur_index;
std::size_t m_num_dense;
std::vector<std::size_t> m_indexes;
std::vector<std::vector<FeatureDataItem> > m_features;
std::vector<std::vector<ScoreDataItem> > m_scores;
};

6
mert/MiraFeatureVector.cpp
View File

@ -2,6 +2,8 @@
#include "MiraFeatureVector.h"
using namespace std;
MiraFeatureVector::MiraFeatureVector(const FeatureDataItem& vec)
: m_dense(vec.dense)
{
@ -97,7 +99,7 @@ MiraFeatureVector operator-(const MiraFeatureVector& a, const MiraFeatureVector&
vector<ValType> sparseVals;
vector<size_t> sparseFeats;
while(i < a.m_sparseFeats.size() && j < b.m_sparseFeats.size()) {
if(a.m_sparseFeats[i] < b.m_sparseFeats[j]) {
sparseFeats.push_back(a.m_sparseFeats[i]);
sparseVals.push_back(a.m_sparseVals[i]);
@ -136,7 +138,7 @@ MiraFeatureVector operator-(const MiraFeatureVector& a, const MiraFeatureVector&
// Create and return vector
return MiraFeatureVector(dense,sparseFeats,sparseVals);
}
// --Emacs trickery--
// Local Variables:
// mode:c++

18
mert/MiraFeatureVector.h
View File

@ -16,8 +16,6 @@
#include "FeatureDataIterator.h"
using namespace std;
typedef FeatureStatsType ValType;
class MiraFeatureVector {
@ -25,20 +23,20 @@ public:
MiraFeatureVector(const FeatureDataItem& vec);
MiraFeatureVector(const MiraFeatureVector& other);
MiraFeatureVector(const std::vector<ValType>& dense,
const std::vector<size_t>& sparseFeats,
const std::vector<std::size_t>& sparseFeats,
const std::vector<ValType>& sparseVals);
ValType val(size_t index) const;
size_t feat(size_t index) const;
size_t size() const;
ValType val(std::size_t index) const;
std::size_t feat(std::size_t index) const;
std::size_t size() const;
ValType sqrNorm() const;
friend MiraFeatureVector operator-(const MiraFeatureVector& a,
const MiraFeatureVector& b);
private:
std::vector<ValType> m_dense;
std::vector<size_t> m_sparseFeats;
std::vector<std::size_t> m_sparseFeats;
std::vector<ValType> m_sparseVals;
};

2
mert/MiraWeightVector.cpp
View File

@ -1,5 +1,7 @@
#include "MiraWeightVector.h"
using namespace std;
/**
* Constructor, initializes to the zero vector
*/

26
mert/MiraWeightVector.h
View File

@ -4,7 +4,7 @@
*
* A self-averaging weight-vector. Good for
* perceptron learning as well.
*
*
*/
#ifndef MERT_MIRA_WEIGHT_VECTOR_H
@ -14,8 +14,6 @@
#include "MiraFeatureVector.h"
using namespace std;
class AvgWeightVector;
class MiraWeightVector {
@ -29,7 +27,7 @@ public:
* Constructor with provided initial vector
* \param init Initial feature values
*/
MiraWeightVector(const vector<ValType>& init);
MiraWeightVector(const std::vector<ValType>& init);
/**
* Update a the model
@ -60,12 +58,12 @@ public:
AvgWeightVector avg();
friend class AvgWeightVector;
private:
/**
* Updates a weight and lazily updates its total
*/
void update(size_t index, ValType delta);
void update(std::size_t index, ValType delta);
/**
* Make sure everyone's total is up-to-date
@ -75,12 +73,12 @@ private:
/**
* Helper to handle out-of-range weights
*/
ValType weight(size_t index) const;
vector<ValType> m_weights;
vector<ValType> m_totals;
vector<size_t> m_lastUpdated;
size_t m_numUpdates;
ValType weight(std::size_t index) const;
std::vector<ValType> m_weights;
std::vector<ValType> m_totals;
std::vector<std::size_t> m_lastUpdated;
std::size_t m_numUpdates;
};
/**
@ -90,8 +88,8 @@ class AvgWeightVector {
public:
AvgWeightVector(const MiraWeightVector& wv);
ValType score(const MiraFeatureVector& fv) const;
ValType weight(size_t index) const;
size_t size() const;
ValType weight(std::size_t index) const;
std::size_t size() const;
private:
const MiraWeightVector& m_wv;
};