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/*
* FeatureArray.h
* mert - Minimum Error Rate Training
*
* Created by Nicola Bertoldi on 13/05/08.
*
*/
#ifndef MERT_FEATURE_ARRAY_H_
#define MERT_FEATURE_ARRAY_H_
#include <vector>
#include <iosfwd>
#include "FeatureStats.h"
namespace MosesTuning
{
const char FEATURES_TXT_BEGIN[] = "FEATURES_TXT_BEGIN_0";
const char FEATURES_TXT_END[] = "FEATURES_TXT_END_0";
const char FEATURES_BIN_BEGIN[] = "FEATURES_BIN_BEGIN_0";
const char FEATURES_BIN_END[] = "FEATURES_BIN_END_0";
class FeatureArray
{
private:
// idx to identify the utterance. It can differ from
// the index inside the vector.
std::string m_index;
featarray_t m_array;
std::size_t m_num_features;
std::string m_features;
public:
FeatureArray();
~FeatureArray();
void clear() { m_array.clear(); }
std::string getIndex() const { return m_index; }
void setIndex(const std::string& value) { m_index = value; }
FeatureStats& get(std::size_t i) { return m_array.at(i); }
const FeatureStats& get(std::size_t i) const { return m_array.at(i); }
void add(FeatureStats& e) { m_array.push_back(e); }
//ADDED BY TS
void swap(std::size_t i, std::size_t j) {
std::swap(m_array[i], m_array[j]);
}
void resize(std::size_t new_size) {
m_array.resize(std::min(new_size, m_array.size()));
}
//END_ADDED
void merge(FeatureArray& e);
std::size_t size() const { return m_array.size(); }
std::size_t NumberOfFeatures() const { return m_num_features; }
void NumberOfFeatures(std::size_t v) { m_num_features = v; }
std::string Features() const { return m_features; }
void Features(const std::string& f) { m_features = f; }
void savetxt(std::ostream* os);
void savebin(std::ostream* os);
void save(std::ostream* os, bool bin=false);
void save(const std::string &file, bool bin=false);
void save(bool bin=false);
void loadtxt(std::istream* is, const SparseVector& sparseWeights, std::size_t n);
void loadbin(std::istream* is, std::size_t n);
void load(std::istream* is, const SparseVector& sparseWeights);
bool check_consistency() const;
};
}
#endif // MERT_FEATURE_ARRAY_H_
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