NICE_Optimization/tests/TestGradientDescent.cpp

#ifdef NICE_USELIB_CPPUNIT

#include <string>
#include <exception>
#include <map>

#include "TestGradientDescent.h"

#include "MyCostFunction.h"

using namespace std;
using namespace OPTIMIZATION;

const bool verboseStartEnd = true;
const bool verbose = true;
//const bool verbose = false;

CPPUNIT_TEST_SUITE_REGISTRATION( TestGradientDescent );

void TestGradientDescent::setUp() {
}

void TestGradientDescent::tearDown() {
}



void TestGradientDescent::testGD_1Dim ()
{
  
  if (verboseStartEnd)
    std::cerr << "================== TestGradientDescent::testGD_1Dim ===================== " << std::endl;
  
  int dim (1);
  
  CostFunction *func = new MyCostFunction(dim, verbose);
   
  //initial guess: 2.0
  OPTIMIZATION::matrix_type initialParams (dim, 1);
  initialParams.set(2.0);

  //we use a dimension scale of 1.0
  OPTIMIZATION::matrix_type scales (dim, 1);
  scales.set(1.0);

  //setup the optimization problem
  SimpleOptProblem optProblem ( func, initialParams, scales );
  optProblem.setMaximize(false);

  GradientDescentOptimizer optimizer;
  //we search with step-width of 1.0
  OPTIMIZATION::matrix_type searchSteps (dim, 1);
  searchSteps(0,0) = 1.0f;

  optimizer.setVerbose(true);
  optimizer.setStepSize( searchSteps );
  optimizer.setMaxNumIter(true, 1000);
//  optimizer.setFuncTol(true, 1e-8);
  optimizer.optimizeProb ( optProblem );  
  
  OPTIMIZATION::matrix_type optimizedParams (optProblem.getAllCurrentParams());
  
  double goal(4.2);  
  
  if (verbose)
    std::cerr << "1d optimization -- result " << optimizedParams(0,0) << " -- goal: " << goal << std::endl;

  CPPUNIT_ASSERT_DOUBLES_EQUAL( optimizedParams(0,0), goal, 1e-5 /* tolerance */);

  if (verboseStartEnd)
    std::cerr << "================== TestGradientDescent::testGD_1Dim done ===================== " << std::endl;

}

void TestGradientDescent::testGD_2Dim()
{
  
  if (verboseStartEnd)
    std::cerr << "================== TestGradientDescent::testGD_2Dim ===================== " << std::endl;
  
  int dim (2);  
  
  CostFunction *func = new MyCostFunction(dim, verbose);
   
  //initial guess: 2.0
  OPTIMIZATION::matrix_type initialParams (dim, 1);
  initialParams.set(2.0);

  //we use a dimension scale of 1.0
  OPTIMIZATION::matrix_type scales (dim, 1);
  scales.set(1.0);

  //setup the optimization problem
  SimpleOptProblem optProblem ( func, initialParams, scales );
  optProblem.setMaximize(false);

  GradientDescentOptimizer optimizer;
  //we search with step-width of 1.0
  OPTIMIZATION::matrix_type searchSteps (dim, 1);
  searchSteps(0,0) = 1.0f;
  searchSteps(1,0) = 1.0f;

  optimizer.setVerbose(true);
  optimizer.setStepSize( searchSteps );
  optimizer.setMaxNumIter(true, 1000);
  //optimizer.setMinimalGradientMagnitude(1e-25);
//  optimizer.setFuncTol(true, 1e-8);
  optimizer.optimizeProb ( optProblem );
  
  OPTIMIZATION::matrix_type optimizedParams (optProblem.getAllCurrentParams());

  double goalFirstDim(4.7);
  double goalSecondDim(1.1);
  
  if (verbose)
  {
    std::cerr << "2d optimization  1st dim-- result " << optimizedParams(0,0) << " -- goal: " << goalFirstDim << std::endl;
    std::cerr << "2d optimization  1st dim-- result " << optimizedParams(1,0) << " -- goal: " << goalSecondDim << std::endl;
  }

  CPPUNIT_ASSERT_DOUBLES_EQUAL( optimizedParams(0,0), goalFirstDim, 1e-5 /* tolerance */);
  CPPUNIT_ASSERT_DOUBLES_EQUAL( optimizedParams(1,0), goalSecondDim, 1e-5 /* tolerance */);
  
  
  if (verboseStartEnd)
    std::cerr << "================== TestGradientDescent::testGD_2Dim done ===================== " << std::endl;
}

#endif