IcaNet.hpp Source File

00001 #ifndef __IcaNet__
00002 #define __IcaNet__
00003 
00028 #include "RealVector.hpp"
00029 
00030 class IcaNet 
00031 {
00032 private:
00033 
00034 bool                    superGaussian;
00035 bool                    combined;
00036 
00037 double          learnRate;
00038 
00039 double          a;              // a >= 1.0, a >> b avoids instabilities
00040 double          b;              // b > 0
00041 double          c;              // avoid w zero, c>a
00042 
00043 RealVector*             w;
00044 double          wNorm2;
00045 RealVector*             dw;
00046 
00047 protected:
00048 
00049 // gMin approximate sigmoids
00050 // tanh ~= a=1.0 and b=1/3
00051 virtual double g(double u){ return (a*u - b*u*u*u); } 
00052 virtual double gMin(double u){ return (a*u - b*u*u*u); } 
00053 virtual double gPlus(double u);
00054 
00055 virtual void learn(RealVector* sample);
00056 
00057 public:
00058 
00059 // default is sub-gaussian
00060 IcaNet(double rate, double ax, double bx, bool superType)
00061         {learnRate=rate; superGaussian= false; w=0; dw=0; a=ax; b=bx; combined= true;}
00062 IcaNet(double rate, double ax, double bx, double cx, bool superType)
00063         {learnRate=rate; superGaussian= false; w=0; dw=0; a=ax; b=bx; c= cx; combined= false;}
00064 virtual ~IcaNet()
00065  {
00066  if( w != 0 )
00067         { 
00068         delete w;
00069         delete dw;
00070         }
00071  }
00072 
00073 virtual RealVector* getW(){return w;}
00074 
00075 // learning
00076 virtual void init(int size);
00077 virtual void learning(RealVector* sample, int nmax=1);
00078 
00079 // out
00080 virtual double forward(RealVector* src);                // maximise abslote value of kurtosis
00081 
00082 virtual void output()
00083         {
00084         printf("Single ICA Net");
00085         }                               
00086 };
00087 
00088 #endif