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https://github.com/moses-smt/mosesdecoder.git
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8c3b82e596
This change might be useful to avoid duplicating the names. The reason is that although MERT programs are standalone applications, some header files such as data.h and point.h have common guard macro names like "DATA_H" and "POINT_H", and this is not good naming conventions when you want to include external headers. Some files actually include headers in Moses and KenLM's util.
136 lines
3.3 KiB
C++
136 lines
3.3 KiB
C++
#ifndef MERT_OPTIMIZER_H_
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#define MERT_OPTIMIZER_H_
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#include <vector>
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#include <string>
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#include "Data.h"
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#include "FeatureData.h"
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#include "Scorer.h"
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#include "Types.h"
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using namespace std;
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typedef float featurescore;
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class Point;
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/**
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* Abstract optimizer class.
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*/
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class Optimizer
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{
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protected:
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Scorer *scorer; // no accessor for them only child can use them
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FeatureDataHandle FData; // no accessor for them only child can use them
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unsigned int number_of_random_directions;
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public:
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Optimizer(unsigned Pd, vector<unsigned> i2O, vector<parameter_t> start, unsigned int nrandom);
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void SetScorer(Scorer *_scorer);
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void SetFData(FeatureDataHandle _FData);
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virtual ~Optimizer();
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unsigned size() const {
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return FData ? FData->size() : 0;
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}
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/**
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* Generic wrapper around TrueRun to check a few things. Non virtual.
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*/
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statscore_t Run(Point&) const;
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/**
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* Main function that performs an optimization.
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*/
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virtual statscore_t TrueRun(Point&) const = 0;
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/**
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* Given a set of lambdas, get the nbest for each sentence.
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*/
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void Get1bests(const Point& param,vector<unsigned>& bests) const;
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/**
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* Given a set of nbests, get the Statistical score.
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*/
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statscore_t GetStatScore(const vector<unsigned>& nbests) const {
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return scorer->score(nbests);
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}
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statscore_t GetStatScore(const Point& param) const;
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vector<statscore_t> GetIncStatScore(vector<unsigned> ref, vector<vector<pair<unsigned,unsigned> > >) const;
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/**
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* Get the optimal Lambda and the best score in a particular direction from a given Point.
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*/
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statscore_t LineOptimize(const Point& start, const Point& direction, Point& best) const;
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};
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/**
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* Default basic optimizer.
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* This class implements Powell's method.
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*/
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class SimpleOptimizer : public Optimizer
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{
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private:
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const float kEPS;
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public:
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SimpleOptimizer(unsigned dim, vector<unsigned> i2O, vector<parameter_t> start, unsigned int nrandom)
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: Optimizer(dim, i2O, start,nrandom), kEPS(0.0001) {}
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virtual statscore_t TrueRun(Point&) const;
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};
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/**
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* An optimizer with random directions.
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*/
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class RandomDirectionOptimizer : public Optimizer
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{
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private:
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const float kEPS;
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public:
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RandomDirectionOptimizer(unsigned dim, vector<unsigned> i2O, vector<parameter_t> start, unsigned int nrandom)
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: Optimizer(dim, i2O, start, nrandom), kEPS(0.0001) {}
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virtual statscore_t TrueRun(Point&) const;
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};
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/**
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* Dumb baseline optimizer: just picks a random point and quits.
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*/
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class RandomOptimizer : public Optimizer
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{
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public:
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RandomOptimizer(unsigned dim, vector<unsigned> i2O, vector<parameter_t> start, unsigned int nrandom)
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: Optimizer(dim, i2O, start, nrandom) {}
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virtual statscore_t TrueRun(Point&) const;
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};
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class OptimizerFactory
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{
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public:
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static vector<string> GetTypeNames();
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static Optimizer* BuildOptimizer(unsigned dim, vector<unsigned> tooptimize, vector<parameter_t> start, const string& type, unsigned int nrandom);
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private:
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OptimizerFactory() {}
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~OptimizerFactory() {}
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// Add new optimizer here BEFORE NOPTIMZER
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enum OptType {
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POWELL = 0,
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RANDOM_DIRECTION = 1,
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RANDOM,
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NOPTIMIZER
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};
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// Get optimizer type.
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static OptType GetOType(const string& type);
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// Setup optimization types.
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static void SetTypeNames();
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static vector<string> typenames;
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};
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#endif // OPTIMIZER_H
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