/*=========================================================================
 *
 *  Copyright Insight Software Consortium
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *         http://www.apache.org/licenses/LICENSE-2.0.txt
 *
 *  Unless required by applicable law or agreed to in writing, software
 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 *=========================================================================*/
#include "itkMultiGradientOptimizerv4.h"

/**
 *  \class MultiGradientOptimizerv4TestMetric
 *
 *  The objective function is the summation of two quadratics of form:
 *
 *  1/2 x^T A x - b^T x
 *
 *  Where A is a matrix and b is a vector
 *
 *  The systems in this example are:
 *
 *  Metric1
 *
 *     | 3  2 ||x|   | 2|   |0|
 *     | 2  6 ||y| + |-8| = |0|
 *
 *  Metric2
 *
 *     | 3  2 ||x|   | 1|   |0|
 *     | 2  6 ||y| + |-4| = |0|
 *
 *   the weighted optimal solution is the vector | 1.5 -1.5 |
 *
 */
class MultiGradientOptimizerv4TestMetric
  : public itk::ObjectToObjectMetricBase
{
public:

  typedef MultiGradientOptimizerv4TestMetric Self;
  typedef itk::ObjectToObjectMetricBase      Superclass;
  typedef itk::SmartPointer<Self>            Pointer;
  typedef itk::SmartPointer<const Self>      ConstPointer;
  itkNewMacro( Self );
  itkTypeMacro( MultiGradientOptimizerv4TestMetric, ObjectToObjectMetricBase );

  enum { SpaceDimension=2 };

  typedef Superclass::ParametersType        ParametersType;
  typedef Superclass::ParametersType*       ParametersPointer;
  typedef Superclass::ParametersValueType   ParametersValueType;
  typedef Superclass::DerivativeType        DerivativeType;
  typedef Superclass::MeasureType           MeasureType;

  MultiGradientOptimizerv4TestMetric() :
    m_Parameters(ITK_NULLPTR)
  {}

  virtual void Initialize(void) ITK_OVERRIDE {}

  virtual void GetDerivative( DerivativeType & derivative ) const ITK_OVERRIDE
    {
    derivative.Fill( itk::NumericTraits< ParametersValueType >::ZeroValue() );
    }

  void GetValueAndDerivative( MeasureType & value,
                              DerivativeType & derivative ) const ITK_OVERRIDE
  {
    if( derivative.Size() != 2 )
      derivative.SetSize(2);

    double x = (*m_Parameters)[0];
    double y = (*m_Parameters)[1];

    std::cout << "GetValueAndDerivative( ";
    std::cout << x << " ";
    std::cout << y << ") = " << std::endl;

    value = 0.5*(3*x*x+4*x*y+6*y*y) - 2*x + 8*y;

    std::cout << "value: " << value << std::endl;

    /* The optimizer simply takes the derivative from the metric
     * and adds it to the transform after scaling. So instead of
     * setting a 'minimize' option in the gradient, we return
     * a minimizing derivative. */
    derivative[0] = -( 3 * x + 2 * y -2 );
    derivative[1] = -( 2 * x + 6 * y +8 );

    std::cout << "derivative: " << derivative << std::endl;
  }

  virtual MeasureType  GetValue() const ITK_OVERRIDE
  {
    double x = (*m_Parameters)[0];
    double y = (*m_Parameters)[1];
    double metric = 0.5*(3*x*x+4*x*y+6*y*y) - 2*x + 8*y;
    std::cout << (*m_Parameters) <<" metric " << metric << std::endl;
    return metric;
  }

  virtual void UpdateTransformParameters( const DerivativeType & update, ParametersValueType ) ITK_OVERRIDE
  {
    (*m_Parameters) += update;
  }

  virtual unsigned int GetNumberOfParameters(void) const ITK_OVERRIDE
  {
    return SpaceDimension;
  }

  virtual bool HasLocalSupport() const ITK_OVERRIDE
    {
    return false;
    }

  virtual unsigned int GetNumberOfLocalParameters() const ITK_OVERRIDE
  {
    return SpaceDimension;
  }

  /* These Set/Get methods are only needed for this test derivation that
   * isn't using a transform */
  virtual void SetParameters( ParametersType & parameters ) ITK_OVERRIDE
  {
    m_Parameters = &parameters;
  }

  virtual const ParametersType & GetParameters() const ITK_OVERRIDE
  {
    return (*m_Parameters);
  }

private:

  ParametersPointer m_Parameters;
};

/** A second test metric with slightly different optimum */
class MultiGradientOptimizerv4TestMetric2
  : public itk::ObjectToObjectMetricBase
{
public:

  typedef MultiGradientOptimizerv4TestMetric2 Self;
  typedef itk::ObjectToObjectMetricBase       Superclass;
  typedef itk::SmartPointer<Self>             Pointer;
  typedef itk::SmartPointer<const Self>       ConstPointer;
  itkNewMacro( Self );
  itkTypeMacro( MultiGradientOptimizerv4TestMetric2, ObjectToObjectMetricBase );

  enum { SpaceDimension=2 };

  typedef Superclass::ParametersType        ParametersType;
  typedef Superclass::ParametersType*       ParametersPointer;
  typedef Superclass::ParametersValueType   ParametersValueType;
  typedef Superclass::DerivativeType        DerivativeType;
  typedef Superclass::MeasureType           MeasureType;

  MultiGradientOptimizerv4TestMetric2() :
    m_Parameters(ITK_NULLPTR)
  {}

  virtual void Initialize(void) ITK_OVERRIDE {}

  virtual void GetDerivative( DerivativeType & derivative ) const ITK_OVERRIDE
    {
    derivative.Fill( itk::NumericTraits< ParametersValueType >::ZeroValue() );
    }

  void GetValueAndDerivative( MeasureType & value,
                              DerivativeType & derivative ) const ITK_OVERRIDE
  {
    if( derivative.Size() != 2 )
      derivative.SetSize(2);

    double x = (*m_Parameters)[0];
    double y = (*m_Parameters)[1];

    std::cout << "GetValueAndDerivative( ";
    std::cout << x << " ";
    std::cout << y << ") = " << std::endl;

    value = 0.5*(3*x*x+4*x*y+6*y*y) - x + 4*y;

    std::cout << "value: " << value << std::endl;

    /* The optimizer simply takes the derivative from the metric
     * and adds it to the transform after scaling. So instead of
     * setting a 'minimize' option in the gradient, we return
     * a minimizing derivative. */
    derivative[0] = -( 3 * x + 2 * y -1 );
    derivative[1] = -( 2 * x + 6 * y +4 );

    std::cout << "derivative: " << derivative << std::endl;
  }

  virtual MeasureType  GetValue() const ITK_OVERRIDE
  {
    double x = (*m_Parameters)[0];
    double y = (*m_Parameters)[1];
    double metric = 0.5*(3*x*x+4*x*y+6*y*y) - x + 4*y;
    std::cout << (*m_Parameters) <<" metric " << metric << std::endl;
    return metric;
  }

  virtual bool HasLocalSupport() const ITK_OVERRIDE
    {
    return false;
    }

  virtual void UpdateTransformParameters( const DerivativeType & update, ParametersValueType ) ITK_OVERRIDE
  {
    (*m_Parameters) += update;
  }

  virtual unsigned int GetNumberOfParameters(void) const ITK_OVERRIDE
  {
    return SpaceDimension;
  }

  virtual unsigned int GetNumberOfLocalParameters() const ITK_OVERRIDE
  {
    return SpaceDimension;
  }

  /* These Set/Get methods are only needed for this test derivation that
   * isn't using a transform */
  virtual void SetParameters( ParametersType & parameters ) ITK_OVERRIDE
  {
    m_Parameters = &parameters;
  }

  virtual const ParametersType & GetParameters() const ITK_OVERRIDE
  {
    return (*m_Parameters);
  }

private:

  ParametersPointer m_Parameters;
};

///////////////////////////////////////////////////////////
/** This metric has an optimum at (1,-1) and when we
 *  combine its gradient with that of the metric above
 *  we expect an average result with solution @ (1.5,-1.5)
 */
///////////////////////////////////////////////////////////
int MultiGradientOptimizerv4RunTest( itk::MultiGradientOptimizerv4::Pointer & itkOptimizer )
{
  try
    {
    std::cout << "currentPosition before optimization: " << itkOptimizer->GetCurrentPosition() << std::endl;
    itkOptimizer->StartOptimization();
    std::cout << "currentPosition after optimization: " << itkOptimizer->GetCurrentPosition() << std::endl;
    }
  catch( itk::ExceptionObject & e )
    {
    std::cout << "Exception thrown ! " << std::endl;
    std::cout << "An error occurred during Optimization" << std::endl;
    std::cout << "Location    = " << e.GetLocation()    << std::endl;
    std::cout << "Description = " << e.GetDescription() << std::endl;
    return EXIT_FAILURE;
    }

  typedef MultiGradientOptimizerv4TestMetric::ParametersType    ParametersType;
  ParametersType finalPosition = itkOptimizer->GetMetric()->GetParameters();

  std::cout << "Solution        = (";
  std::cout << finalPosition[0] << ",";
  std::cout << finalPosition[1] << ")" << std::endl;

  //
  // check results to see if it is within range
  //
  ParametersType trueParameters(2);
  trueParameters[0] = 1.5;
  trueParameters[1] = -1.5;
  for( itk::SizeValueType j = 0; j < 2; j++ )
    {
    if( fabs( finalPosition[j] - trueParameters[j] ) > 0.01 )
      {
      std::cerr << "Results do not match: " << std::endl
                << "expected: " << trueParameters << std::endl
                << "returned: " << finalPosition << std::endl;
      return EXIT_FAILURE;
      }
    }

  return EXIT_SUCCESS;
}
///////////////////////////////////////////////////////////
int itkMultiGradientOptimizerv4Test(int, char* [] )
{
  std::cout << "MultiGradient descent Optimizer Test ";
  std::cout << std::endl << std::endl;

  typedef  itk::MultiGradientOptimizerv4                     OptimizerType;
  typedef MultiGradientOptimizerv4TestMetric::ParametersType ParametersType;

  // Declaration of a itkOptimizer
  OptimizerType::Pointer  itkOptimizer = OptimizerType::New();

  // Declaration of the Metric
  MultiGradientOptimizerv4TestMetric::Pointer metric = MultiGradientOptimizerv4TestMetric::New();
  MultiGradientOptimizerv4TestMetric2::Pointer metric2 = MultiGradientOptimizerv4TestMetric2::New();
  const unsigned int spaceDimension = 2;
  itkOptimizer->SetMetric( metric );
  itkOptimizer->SetNumberOfIterations( 50 );

  OptimizerType::OptimizersListType optimizersList = itkOptimizer->GetOptimizersList();

  /** Declare the first optimizer for metric 1 */
  OptimizerType::LocalOptimizerPointer locoptimizer = OptimizerType::LocalOptimizerType::New();
  locoptimizer->SetLearningRate( 1.e-1);
  locoptimizer->SetNumberOfIterations( 25 );
  locoptimizer->SetMetric( metric );
  locoptimizer->SetNumberOfIterations( 1 );
  optimizersList.push_back( locoptimizer );

  /** Declare the 2nd optimizer for metric 2 */
  OptimizerType::LocalOptimizerPointer locoptimizer2 = OptimizerType::LocalOptimizerType::New();
  locoptimizer2->SetLearningRate( 1.e-1);
  locoptimizer2->SetNumberOfIterations( 25 );
  locoptimizer2->SetMetric( metric2 );
  locoptimizer->SetNumberOfIterations( 1 );
  optimizersList.push_back( locoptimizer2 );

  /** Pass the list back to the combined optimizer */
  itkOptimizer->SetOptimizersList(optimizersList);

  /*
   * Test 1
   */
  // We start not so far from  | 1.5 -1.5 |
  ParametersType  testPosition( spaceDimension );
  testPosition[0]=(double)7.5;
  testPosition[1]=(double)9.5;
  /** Note: both metrics have the same transforms and parameters */
  /** We need the parameters to be the same object across all metric instances*/
  metric->SetParameters( testPosition );
  metric2->SetParameters( testPosition );
  // test the optimization
  std::cout << "Test optimization with equal weights on each metric:" << std::endl;
  if( MultiGradientOptimizerv4RunTest( itkOptimizer ) == EXIT_FAILURE )
    {
    return EXIT_FAILURE;
    }
  std::cout << "Test 1 passed." << std::endl;
  return EXIT_SUCCESS;
}