LogisticRegression.h

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//
//  LogisticRegression.h
//  hog2 glut
//
//  Created by Nathan Sturtevant on 5/19/14.
//  Copyright (c) 2014 University of Denver. All rights reserved.
//
 
#ifndef __hog2_glut__LogisticRegression__
#define __hog2_glut__LogisticRegression__
 
#include "FPUtil.h"
#include "FunctionApproximator.h"
#include "LinearRegression.h"
 
class LogisticRegression : public FunctionApproximator  {
public:
        LogisticRegression(int _inputs, int _outputs, double learnrate);
        LogisticRegression(LogisticRegression *);
        LogisticRegression(char *);
        LogisticRegression(FunctionApproximator *);
        ~LogisticRegression();
        
        void setUseBinary(bool binary) { useBinary = binary; }
        
        void resizeInputs(int newSize, double newVal);
        void resizeInputs(int newSize);
        
        void save(const char *);
        void save(FILE *);
        void load(const char *);
        void load(FILE *);
        void load(const FunctionApproximator *fa) { load((LogisticRegression*)fa); }
        void load(const LogisticRegression *);
        static bool validSaveFile(char *fname);
        
        double train(std::vector<double> &input, std::vector<double> &output2);
        double *test(const std::vector<double> &input);
        double train(std::vector<unsigned int> &input, std::vector<double> &output2);
        double *test(const std::vector<unsigned int> &input);
        
        void Print();
        
        int getNumInputs() { return inputs; }
        void setInputWeight(double value, unsigned int weightNum, unsigned int whichOutput=0);
        double getInputWeight(int inp, int outp=0) { return weight[outp][inp]; }
        void getWeightUpdateVariance(std::vector<double> &var, unsigned int which=0);
        double getWeightUpdateVariance(unsigned int weightNum, unsigned int whichOutput=0);
        
        void getWeightUpdateAverage(std::vector<double> &var, unsigned int which=0);
        double getWeightUpdateAverage(unsigned int weightNum, unsigned int whichOutput=0);
        
        void getWeightUpdateSum(std::vector<double> &var, unsigned int which=0);
        double getWeightUpdateSum(unsigned int weightNum, unsigned int whichOutput=0);
        void resetWeightVariance(unsigned int weightNum, unsigned int whichOutput=0);
        void resetWeightVariance();
        
        int getWeightFrequency(unsigned int weightNum, unsigned int whichOutput=0);
 
        double GetInput(std::vector<double> &input, const std::vector<double> &target)
        { assert(!"Unimplemented"); }
private:
        void allocateMemory();
        void freeMemory();
        
        double g(double a);
        double dg(double a);
        double outputerr(std::vector<double> &output, std::vector<double> &expected, int which);
        
        double error(double* output);
        std::vector<std::vector<double> > weight;
        std::vector<std::vector<updateData> > updates;
        //std::vector<double > weight;
        std::vector<double> output;
        //double** weight;
        //double* output;
        int inputs, outputs;
        bool useBinary;
};
 
#endif /* defined(__hog2_glut__LogisticRegression__) */