NormDerivativeLem3.cpp

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00001 /*
00002  * This program is free software; you can redistribute it and/or modify
00003  * it under the terms of the GNU General Public License as published by
00004  * the Free Software Foundation; either version 3 of the License, or
00005  * (at your option) any later version.
00006  *
00007  * Written (W) 1999-2009 Soeren Sonnenburg
00008  * Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
00009  */
00010 
00011 #include "preproc/NormDerivativeLem3.h"
00012 #include "preproc/SimplePreProc.h"
00013 #include "features/Features.h"
00014 #include "features/SimpleFeatures.h"
00015 
00016 CNormDerivativeLem3::CNormDerivativeLem3()
00017 : CSimplePreProc<float64_t>("NormDerivativeLem3", "NDL3")
00018 {
00019 }
00020 
00021 CNormDerivativeLem3::~CNormDerivativeLem3()
00022 {
00023 }
00024 
00026 bool CNormDerivativeLem3::init(CFeatures* f)
00027 {
00028     ASSERT(f->get_feature_class()==C_SIMPLE);
00029     ASSERT(f->get_feature_type()==F_DREAL);
00030 
00031     return true;
00032 }
00033 
00035 void CNormDerivativeLem3::cleanup()
00036 {
00037 }
00038 
00040 bool CNormDerivativeLem3::load(FILE* f)
00041 {
00042     return false;
00043 }
00044 
00046 bool CNormDerivativeLem3::save(FILE* f)
00047 {
00048     return false;
00049 }
00050 
00054 float64_t* CNormDerivativeLem3::apply_to_feature_matrix(CFeatures* f)
00055 {
00056     return NULL;
00057 }
00058 
00061 float64_t* CNormDerivativeLem3::apply_to_feature_vector(
00062     float64_t* f, int32_t len)
00063 {
00064     return NULL;
00065 }
00066 
00067 //#warning TODO implement jahau 
00068 //#ifdef JaaHau
00069 // //this is the normalization used in jaahau
00070 //    int32_t o_p=1;
00071 //    float64_t sum_p=0;
00072 //    float64_t sum_q=0;
00073 //    //first do positive model
00074 //    for (i=0; i<pos->get_N(); i++)
00075 //    {
00076 //  featurevector[p]=exp(pos->model_derivative_p(i, x)-posx);
00077 //  sum_p=exp(pos->get_p(i))*featurevector[p++];
00078 //  featurevector[p]=exp(pos->model_derivative_q(i, x)-posx);
00079 //  sum_q=exp(pos->get_q(i))*featurevector[p++];
00080 //
00081 //  float64_t sum_a=0;
00082 //  for (j=0; j<pos->get_N(); j++)
00083 //  {
00084 //      featurevector[p]=exp(pos->model_derivative_a(i, j, x)-posx);
00085 //      sum_a=exp(pos->get_a(i,j))*featurevector[p++];
00086 //  }
00087 //  p-=pos->get_N();
00088 //  for (j=0; j<pos->get_N(); j++)
00089 //      featurevector[p++]-=sum_a;
00090 //
00091 //  float64_t sum_b=0;
00092 //  for (j=0; j<pos->get_M(); j++)
00093 //  {
00094 //      featurevector[p]=exp(pos->model_derivative_b(i, j, x)-posx);
00095 //      sum_b=exp(pos->get_b(i,j))*featurevector[p++];
00096 //  }
00097 //  p-=pos->get_M();
00098 //  for (j=0; j<pos->get_M(); j++)
00099 //      featurevector[p++]-=sum_b;
00100 //    }
00101 //
00102 //    o_p=p;
00103 //    p=1;
00104 //    for (i=0; i<pos->get_N(); i++)
00105 //    {
00106 //  featurevector[p++]-=sum_p;
00107 //  featurevector[p++]-=sum_q;
00108 //    }
00109 //    p=o_p;
00110 //
00111 //    for (i=0; i<neg->get_N(); i++)
00112 //    {
00113 //  featurevector[p]=-exp(neg->model_derivative_p(i, x)-negx);
00114 //  sum_p=exp(neg->get_p(i))*featurevector[p++];
00115 //  featurevector[p]=-exp(neg->model_derivative_q(i, x)-negx);
00116 //  sum_q=exp(neg->get_q(i))*featurevector[p++];
00117 //
00118 //  float64_t sum_a=0;
00119 //  for (j=0; j<neg->get_N(); j++)
00120 //  {
00121 //      featurevector[p]=-exp(neg->model_derivative_a(i, j, x)-negx);
00122 //      sum_a=exp(neg->get_a(i,j))*featurevector[p++];
00123 //  }
00124 //  p-=neg->get_N();
00125 //  for (j=0; j<neg->get_N(); j++)
00126 //      featurevector[p++]-=sum_a;
00127 //
00128 //  float64_t sum_b=0;
00129 //  for (j=0; j<neg->get_M(); j++)
00130 //  {
00131 //      featurevector[p]=-exp(neg->model_derivative_b(i, j, x)-negx);
00132 //      sum_b=exp(neg->get_b(i,j))*featurevector[p++];
00133 //  }
00134 //  p-=neg->get_M();
00135 //  for (j=0; j<neg->get_M(); j++)
00136 //      featurevector[p++]-=sum_b;
00137 //    }
00138 //
00139 //    p=o_p;
00140 //    for (i=0; i<neg->get_N(); i++)
00141 //    {
00142 //  featurevector[p++]-=sum_p;
00143 //  featurevector[p++]-=sum_q;
00144 //    }
00145 //#endif

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