NormDerivativeLem3.cpp

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/*
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *
 * Written (W) 1999-2009 Soeren Sonnenburg
 * Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
 */

#include "preproc/NormDerivativeLem3.h"
#include "preproc/SimplePreProc.h"
#include "features/Features.h"
#include "features/SimpleFeatures.h"

using namespace shogun;

CNormDerivativeLem3::CNormDerivativeLem3()
: CSimplePreProc<float64_t>("NormDerivativeLem3", "NDL3")
{
}

CNormDerivativeLem3::~CNormDerivativeLem3()
{
}

bool CNormDerivativeLem3::init(CFeatures* f)
{
    ASSERT(f->get_feature_class()==C_SIMPLE);
    ASSERT(f->get_feature_type()==F_DREAL);

    return true;
}

void CNormDerivativeLem3::cleanup()
{
}

bool CNormDerivativeLem3::load(FILE* f)
{
    SG_SET_LOCALE_C;
    SG_RESET_LOCALE;
    return false;
}

bool CNormDerivativeLem3::save(FILE* f)
{
    SG_SET_LOCALE_C;
    SG_RESET_LOCALE;
    return false;
}

float64_t* CNormDerivativeLem3::apply_to_feature_matrix(CFeatures* f)
{
    return NULL;
}

float64_t* CNormDerivativeLem3::apply_to_feature_vector(
    float64_t* f, int32_t& len)
{
    return NULL;
}

//#warning TODO implement jahau 
//#ifdef JaaHau
// //this is the normalization used in jaahau
//    int32_t o_p=1;
//    float64_t sum_p=0;
//    float64_t sum_q=0;
//    //first do positive model
//    for (i=0; i<pos->get_N(); i++)
//    {
//  featurevector[p]=exp(pos->model_derivative_p(i, x)-posx);
//  sum_p=exp(pos->get_p(i))*featurevector[p++];
//  featurevector[p]=exp(pos->model_derivative_q(i, x)-posx);
//  sum_q=exp(pos->get_q(i))*featurevector[p++];
//
//  float64_t sum_a=0;
//  for (j=0; j<pos->get_N(); j++)
//  {
//      featurevector[p]=exp(pos->model_derivative_a(i, j, x)-posx);
//      sum_a=exp(pos->get_a(i,j))*featurevector[p++];
//  }
//  p-=pos->get_N();
//  for (j=0; j<pos->get_N(); j++)
//      featurevector[p++]-=sum_a;
//
//  float64_t sum_b=0;
//  for (j=0; j<pos->get_M(); j++)
//  {
//      featurevector[p]=exp(pos->model_derivative_b(i, j, x)-posx);
//      sum_b=exp(pos->get_b(i,j))*featurevector[p++];
//  }
//  p-=pos->get_M();
//  for (j=0; j<pos->get_M(); j++)
//      featurevector[p++]-=sum_b;
//    }
//
//    o_p=p;
//    p=1;
//    for (i=0; i<pos->get_N(); i++)
//    {
//  featurevector[p++]-=sum_p;
//  featurevector[p++]-=sum_q;
//    }
//    p=o_p;
//
//    for (i=0; i<neg->get_N(); i++)
//    {
//  featurevector[p]=-exp(neg->model_derivative_p(i, x)-negx);
//  sum_p=exp(neg->get_p(i))*featurevector[p++];
//  featurevector[p]=-exp(neg->model_derivative_q(i, x)-negx);
//  sum_q=exp(neg->get_q(i))*featurevector[p++];
//
//  float64_t sum_a=0;
//  for (j=0; j<neg->get_N(); j++)
//  {
//      featurevector[p]=-exp(neg->model_derivative_a(i, j, x)-negx);
//      sum_a=exp(neg->get_a(i,j))*featurevector[p++];
//  }
//  p-=neg->get_N();
//  for (j=0; j<neg->get_N(); j++)
//      featurevector[p++]-=sum_a;
//
//  float64_t sum_b=0;
//  for (j=0; j<neg->get_M(); j++)
//  {
//      featurevector[p]=-exp(neg->model_derivative_b(i, j, x)-negx);
//      sum_b=exp(neg->get_b(i,j))*featurevector[p++];
//  }
//  p-=neg->get_M();
//  for (j=0; j<neg->get_M(); j++)
//      featurevector[p++]-=sum_b;
//    }
//
//    p=o_p;
//    for (i=0; i<neg->get_N(); i++)
//    {
//  featurevector[p++]-=sum_p;
//  featurevector[p++]-=sum_q;
//    }
//#endif