source: trunk/test/kernel_test.cc @ 781

// $Id: kernel_test.cc 781 2007-03-05 19:44:03Z peter $

/*
  Copyright (C) The authors contributing to this file.

  This file is part of the yat library, http://lev.thep.lu.se/trac/yat

  The yat library 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 2 of the
  License, or (at your option) any later version.

  The yat library is distributed in the hope that it will be useful,
  but WITHOUT ANY WARRANTY; without even the implied warranty of
  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
  General Public License for more details.

  You should have received a copy of the GNU General Public License
  along with this program; if not, write to the Free Software
  Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
  02111-1307, USA.
*/

#include "yat/utility/matrix.h"
#include "yat/classifier/KernelFunction.h"
#include "yat/classifier/PolynomialKernelFunction.h"
#include "yat/classifier/GaussianKernelFunction.h"
#include "yat/classifier/KernelLookup.h"
#include "yat/classifier/Kernel_MEV.h"
#include "yat/classifier/Kernel_SEV.h"

#include <cassert>
#include <cmath>
#include <cstdlib>
#include <fstream>
#include <iostream>
#include <vector>

using namespace theplu::yat;

bool test_MEV(const classifier::MatrixLookup& data, 
              const classifier::KernelFunction* kf, 
              const utility::matrix& control, const double error_bound,
              std::ostream* error);

bool test_SEV(const classifier::MatrixLookup& data, 
              const classifier::KernelFunction* kf, 
              const utility::matrix& control, const double error_bound,
              std::ostream* error);


int main(const int argc,const char* argv[])

{  
  std::ostream* error;
  if (argc>1 && argv[1]==std::string("-v"))
    error = &std::cerr;
  else {
    error = new std::ofstream("/dev/null");
    if (argc>1)
      std::cout << "kernel_test -v : for printing extra information\n";
  }
  *error << "testing kernels" << std::endl;
  bool ok = true;

  utility::matrix data2_core(2,3);
  data2_core(0,0)=0;
  data2_core(1,0)=0;
  data2_core(0,1)=0;
  data2_core(1,1)=1;
  data2_core(0,2)=1;
  data2_core(1,2)=0;
  classifier::MatrixLookup data2(data2_core);
  classifier::KernelFunction* kf2 = new classifier::PolynomialKernelFunction(); 
  classifier::Kernel_SEV kernel2(data2,*kf2);
  assert(kernel2.size()==3);
  if(kernel2(0,0) || kernel2(0,1) || kernel2(0,2) || 
     kernel2(1,0) || kernel2(1,1)!=1 || kernel2(1,2) || 
     kernel2(2,0) || kernel2(2,1) || kernel2(2,2)!=1){
    ok = false;
    *error << "Found:\n";
    for (size_t i=0; i<3; i++){
      for (size_t j=0; j<3; j++)
        *error << kernel2(i,j) << " ";
      *error << std::endl;
    }
    *error << "Expected:\n0 0 0\n0 1 0\n0 0 1" << std::endl;
  }
  classifier::KernelLookup kv2(kernel2);
  delete kf2;

  // Peter, the hardcoded number below should be changed.
  double error_bound = 1e-8; 
  std::ifstream is("data/nm_data_centralized.txt");
  utility::matrix data_core(is);
  is.close();

  classifier::MatrixLookup data(data_core);

  is.open("data/nm_kernel.txt");
  utility::matrix kernel_matlab(is);
  is.close();
  classifier::KernelFunction* kf = new classifier::PolynomialKernelFunction(); 
  ok = (ok && test_MEV(data,kf,kernel_matlab,error_bound, error)
        & test_SEV(data,kf,kernel_matlab,error_bound, error));
  delete kf;
  
  is.open("data/nm_kernel2.txt");
  utility::matrix kernel_matlab2(is);
  is.close();
  kf = new classifier::PolynomialKernelFunction(2); 
  ok = (ok && test_MEV(data,kf,kernel_matlab2,error_bound, error)
        & test_SEV(data,kf,kernel_matlab2,error_bound, error));
  delete kf;

  // Checking that a GaussianKernelFunction object can be built.
  kf = new classifier::GaussianKernelFunction(); 
  delete kf;

  data_core = utility::matrix(1,5);
  for (size_t i=0; i<data_core.columns(); i++)
    data_core(0,i)=i;
  data = classifier::MatrixLookup(data_core);
  classifier::PolynomialKernelFunction pkf;
  classifier::Kernel_SEV kernel(data,pkf);
  

  if (error!=&std::cerr)
    delete error;

  if (ok=true)  
    return 0;
  return -1;
}

bool test_MEV(const classifier::MatrixLookup& data, 
              const classifier::KernelFunction* kf, 
              const utility::matrix& control, const double error_bound,
              std::ostream* error)
{
  *error << "testing Kernel_MEV" << std::endl;
  classifier::Kernel_MEV kernel(data,*kf);
  for(u_int i=0;i<control.rows();i++)
    for(u_int j=0;j<control.columns();j++)
      if (fabs(kernel(i,j)-control(i,j))>error_bound)
        return false;

  // checking view
  std::vector<size_t> index(3);
  index[0]=1;
  index[1]=2;
  index[2]=3;
  classifier::KernelLookup kv_raw(kernel);
  classifier::KernelLookup kv(kv_raw,index,index);
  if (kv.rows()!=index.size()){
    *error << "Error: KernelLookup(kernel, index)\n" << std::endl
           << "Size of KernelLookup is " << kv.rows() << std::endl
           << "expected " << index.size() << std::endl;
    
    return false;
  }
  classifier::KernelLookup kv2(kernel);
  if (kv2.rows()!=kernel.size()){
    *error << "Error: KernelLookup(kernel)\n" << std::endl
           << "Size of KernelLookup is " << kv.rows() << std::endl
           << "expected " << kernel.size() << std::endl;
    
    return false;
  }

  return true;
}

bool test_SEV(const classifier::MatrixLookup& data, 
              const classifier::KernelFunction* kf, 
              const utility::matrix& control, const double error_bound,
              std::ostream* error)
{
  *error << "testing Kernel_SEV" << std::endl;
  classifier::Kernel_SEV kernel(data,*kf);
  for(u_int i=0;i<control.rows();i++)
    for(u_int j=0;j<control.columns();j++)
      if (fabs(kernel(i,j)-control(i,j))>error_bound)
        return false;

  // checking view
  std::vector<size_t> index(3);
  index[0]=1;
  index[1]=2;
  index[2]=3;
  classifier::KernelLookup kv_raw(kernel);
  classifier::KernelLookup kv(kv_raw,index, index);
  if (kv.rows()!=index.size()){
    *error << "Error: KernelLookup(kernel, index)\n" << std::endl
           << "Size of KernelLookup is " << kv.rows() << std::endl
           << "expected " << index.size() << std::endl;
    
    return false;
  }
  classifier::KernelLookup kv2(kernel);
  if (kv2.rows()!=kernel.size()){
    *error << "Error: KernelLookup(kernel)\n" << std::endl
           << "Size of KernelLookup is " << kv.rows() << std::endl
           << "expected " << kernel.size() << std::endl;
    
    return false;
  }
  return true;
}