/*=========================================================================
 *
 *  Copyright NumFOCUS
 *
 *  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
 *
 *         https://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 "itkLBFGSBOptimizerv4.h"
#include "itkTextOutput.h"
#include "itkMath.h"
#include "itkTestingMacros.h"
#include <iostream>

/**
 *  LBFGSBOptimizerv4TestMetric
 *
 *  The objective function is the quadratic form:
 *
 *  f(x) = 1/2 x^T A x - b^T x  subject to  -1 <= x <= 10
 *
 *  Where A is represented as an itkMatrix and
 *  b is represented as an itkVector
 *
 *  The system in this example is:
 *
 *          | 3  2 |       | 2|
 *      A=  | 2  6 |   b = |-8|
 *
 *   the solution is the vector | 4/3 -1 |
 *
 * \class LBFGSBOptimizerv4TestMetric
 */
class LBFGSBOptimizerv4TestMetric : public itk::ObjectToObjectMetricBase
{
public:
  using Self = LBFGSBOptimizerv4TestMetric;
  using Superclass = itk::ObjectToObjectMetricBase;
  using Pointer = itk::SmartPointer<Self>;
  using ConstPointer = itk::SmartPointer<const Self>;
  itkNewMacro(Self);
  itkOverrideGetNameOfClassMacro(LBFGSBOptimizerv4TestMetric);

  enum
  {
    SpaceDimension = 2
  };

  using ParametersType = Superclass::ParametersType;
  using DerivativeType = Superclass::DerivativeType;
  using MeasureType = Superclass::MeasureType;

  using VectorType = vnl_vector<double>;
  using MatrixType = vnl_matrix<double>;

  LBFGSBOptimizerv4TestMetric()
    : m_Parameters(0)
  {
    m_HasLocalSupport = false;
  }

  void
  Initialize() override
  {
    m_Parameters.SetSize(SpaceDimension);
  }

  Superclass::NumberOfParametersType
  GetNumberOfLocalParameters() const override
  {
    return SpaceDimension;
  }

  Superclass::NumberOfParametersType
  GetNumberOfParameters() const override
  {
    return SpaceDimension;
  }

  void
  SetParameters(ParametersType & params) override
  {
    this->m_Parameters = params;
  }

  const ParametersType &
  GetParameters() const override
  {
    return this->m_Parameters;
  }

  bool
  HasLocalSupport() const override
  {
    return m_HasLocalSupport;
  }

  void
  SetHasLocalSupport(bool hls)
  {
    m_HasLocalSupport = hls;
  }

  void
  UpdateTransformParameters(const DerivativeType &, ParametersValueType) override
  {}

  MeasureType
  GetValue() const override
  {

    double x = m_Parameters[0];
    double y = m_Parameters[1];

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

    std::cout << "GetValue ( " << x << " , " << y << ") = " << val << std::endl;

    return val;
  }

  void
  GetDerivative(DerivativeType & derivative) const override
  {
    double x = m_Parameters[0];
    double y = m_Parameters[1];

    derivative = DerivativeType(SpaceDimension);
    derivative[0] = -(3 * x + 2 * y - 2);
    derivative[1] = -(2 * x + 6 * y + 8);

    std::cout << "GetDerivative ( " << x << " , " << y << ") = " << '(' << -derivative[0] << " , " << -derivative[1]
              << ')' << std::endl;
  }

  void
  GetValueAndDerivative(MeasureType & value, DerivativeType & derivative) const override
  {
    value = GetValue();
    GetDerivative(derivative);
  }

private:
  ParametersType m_Parameters;
  bool           m_HasLocalSupport;
};

/** To ensure the events get fired. */
class EventChecker : public itk::Command
{
public:
  using Self = EventChecker;
  using Superclass = itk::Command;
  using Pointer = itk::SmartPointer<Self>;

  itkNewMacro(Self);

  bool
  GetHadStartEvent() const
  {
    return m_HadStartEvent;
  }
  bool
  GetHadIterationEvent() const
  {
    return m_HadIterationEvent;
  }
  bool
  GetHadEndEvent() const
  {
    return m_HadEndEvent;
  }

  void
  Execute(itk::Object * caller, const itk::EventObject & event) override
  {
    Execute((const itk::Object *)caller, event);
  }

  void
  Execute(const itk::Object * caller, const itk::EventObject & event) override
  {
    if (itk::StartEvent().CheckEvent(&event))
    {
      std::cout << "Received StartEvent." << std::endl;
      m_HadStartEvent = true;
    }
    if (itk::IterationEvent().CheckEvent(&event))
    {
      std::cout << "Received IterationEvent." << std::endl;
      const auto * opt = dynamic_cast<const itk::ObjectToObjectOptimizerBaseTemplate<double> *>(caller);
      if (opt)
      {
        std::cout << " iteration  = " << opt->GetCurrentIteration() << std::endl;
      }
      m_HadIterationEvent = true;
    }
    if (itk::EndEvent().CheckEvent(&event))
    {
      std::cout << "Received EndEvent." << std::endl;
      m_HadEndEvent = true;
    }
  }

protected:
  EventChecker() = default;

private:
  bool m_HadStartEvent{ false };
  bool m_HadIterationEvent{ false };
  bool m_HadEndEvent{ false };
};


int
itkLBFGSBOptimizerv4Test(int, char *[])
{

  itk::OutputWindow::SetInstance(itk::TextOutput::New().GetPointer());

  std::cout << "L-BFGS-B Optimizerv4 Test \n \n";

  using OptimizerType = itk::LBFGSBOptimizerv4;

  // Declaration of an itkOptimizer
  auto itkOptimizer = OptimizerType::New();

  ITK_EXERCISE_BASIC_OBJECT_METHODS(itkOptimizer, LBFGSBOptimizerv4, LBFGSOptimizerBasev4);


  // Declaration of the metric
  auto metric = LBFGSBOptimizerv4TestMetric::New();

  itkOptimizer->SetMetric(metric);

  bool trace = false;
  ITK_TEST_SET_GET_BOOLEAN(itkOptimizer, Trace, trace);

  const double  F_Convergence_Factor = 1e+7;  // Function value tolerance
  const double  Projected_G_Tolerance = 1e-5; // Proj gradient tolerance
  constexpr int Max_Iterations = 25;          // Maximum number of iterations

  itkOptimizer->SetCostFunctionConvergenceFactor(F_Convergence_Factor);
  ITK_TEST_SET_GET_VALUE(F_Convergence_Factor, itkOptimizer->GetCostFunctionConvergenceFactor());

  itkOptimizer->SetGradientConvergenceTolerance(Projected_G_Tolerance);
  ITK_TEST_SET_GET_VALUE(Projected_G_Tolerance, itkOptimizer->GetGradientConvergenceTolerance());

  itkOptimizer->SetNumberOfIterations(Max_Iterations);
  ITK_TEST_SET_GET_VALUE(Max_Iterations, itkOptimizer->GetNumberOfIterations());

  unsigned int maximumNumberOfEvaluations = 100;
  itkOptimizer->SetMaximumNumberOfFunctionEvaluations(maximumNumberOfEvaluations);
  ITK_TEST_SET_GET_VALUE(maximumNumberOfEvaluations, itkOptimizer->GetMaximumNumberOfFunctionEvaluations());

  unsigned int maximumNumberOfCorrections = 5;
  itkOptimizer->SetMaximumNumberOfCorrections(maximumNumberOfCorrections);
  ITK_TEST_SET_GET_VALUE(maximumNumberOfCorrections, itkOptimizer->GetMaximumNumberOfCorrections());

  constexpr unsigned int        SpaceDimension = 2;
  OptimizerType::ParametersType initialValue(SpaceDimension);

  // Starting point
  initialValue[0] = 10;
  initialValue[1] = 10;

  // Set the initial position by setting the metric parameters.
  std::cout << "Set metric parameters." << std::endl;
  metric->SetParameters(initialValue);

  OptimizerType::ParametersType currentValue(2);

  currentValue = initialValue;

  itkOptimizer->SetInitialPosition(currentValue);
  ITK_TEST_SET_GET_VALUE(currentValue, itkOptimizer->GetInitialPosition());

  /**
   * Set the boundary condition for each variable, where
   * select[i] = 0 if x[i] is unbounded,
   *           = 1 if x[i] has only a lower bound,
   *           = 2 if x[i] has both lower and upper bounds, and
   *           = 3 if x[1] has only an upper bound
   */
  OptimizerType::BoundValueType     lower(SpaceDimension);
  OptimizerType::BoundValueType     upper(SpaceDimension);
  OptimizerType::BoundSelectionType select(SpaceDimension);

  lower.Fill(-1);
  upper.Fill(10);
  select.Fill(itk::LBFGSBOptimizerv4::BOTHBOUNDED);

  itkOptimizer->SetLowerBound(lower);
  ITK_TEST_SET_GET_VALUE(lower, itkOptimizer->GetLowerBound());

  itkOptimizer->SetUpperBound(upper);
  ITK_TEST_SET_GET_VALUE(upper, itkOptimizer->GetUpperBound());

  itkOptimizer->SetBoundSelection(select);
  ITK_TEST_SET_GET_VALUE(select, itkOptimizer->GetBoundSelection());

  ITK_TEST_EXPECT_TRUE(!itkOptimizer->CanUseScales());

  auto eventChecker = EventChecker::New();
  itkOptimizer->AddObserver(itk::StartEvent(), eventChecker);
  itkOptimizer->AddObserver(itk::IterationEvent(), eventChecker);
  itkOptimizer->AddObserver(itk::EndEvent(), eventChecker);

  ITK_TRY_EXPECT_NO_EXCEPTION(itkOptimizer->StartOptimization());


  const OptimizerType::ParametersType & finalPosition = itkOptimizer->GetCurrentPosition();

  std::cout << "Solution = (" << finalPosition[0] << ',' << finalPosition[1] << ')' << std::endl;
  std::cout << "Final Function Value = " << itkOptimizer->GetValue() << std::endl;

  std::cout << "Infinity Norm of Projected Gradient = " << itkOptimizer->GetInfinityNormOfProjectedGradient()
            << std::endl;
  std::cout << "End condition   = " << itkOptimizer->GetStopConditionDescription() << std::endl;
  std::cout << "CurrentOfIterations  = " << itkOptimizer->GetCurrentIteration() << std::endl;

  if (!eventChecker->GetHadStartEvent())
  {
    std::cerr << "Did not have StartEvent!" << std::endl;
    return EXIT_FAILURE;
  }
  if (!eventChecker->GetHadIterationEvent())
  {
    std::cerr << "Did not have IterationEvent!" << std::endl;
    return EXIT_FAILURE;
  }
  if (!eventChecker->GetHadEndEvent())
  {
    std::cerr << "Did not have EndEvent!" << std::endl;
    return EXIT_FAILURE;
  }

  //
  // check results to see if it is within range
  //
  bool        pass = true;
  std::string errorIn;

  // true parameters considering bounding constrains -1 <= x <= 10
  double trueParameters[2] = { 4.0 / 3.0, -1.0 };

  for (unsigned int j = 0; j < 2; ++j)
  {
    if (!itk::Math::FloatAlmostEqual(finalPosition[j], trueParameters[j]))
    {
      pass = false;
      errorIn = "solution";
    }
  }

  if (!itk::Math::FloatAlmostEqual(itkOptimizer->GetValue(), -7.66667, 4, 0.01))
  {
    pass = false;
    errorIn = "final function value";
  }

  if (!itk::Math::FloatAlmostEqual(itkOptimizer->GetInfinityNormOfProjectedGradient(), 1.77636e-15, 4, 0.01))
  {
    pass = false;
    errorIn = "infinity norm of projected gradient";
  }

  if (!pass)
  {
    std::cerr << "\nError in " << errorIn << ".\n";
    std::cerr << "Test failed." << std::endl;
    return EXIT_FAILURE;
  }

  //
  // Test stopping when number of iterations reached
  //
  std::cout << "-------------------------------" << std::endl;

  // Testing number of iterations for stopping
  metric->SetParameters(initialValue);
  itkOptimizer->SetNumberOfIterations(1);

  ITK_TRY_EXPECT_NO_EXCEPTION(itkOptimizer->StartOptimization());


  std::cout << "Solution        = (" << finalPosition[0] << ',' << finalPosition[1] << ')' << std::endl;
  std::cout << "NumberOfIterations  = " << itkOptimizer->GetCurrentIteration() << std::endl;

  if (itkOptimizer->GetCurrentIteration() != 1)
  {
    std::cout << "[FAILURE]" << std::endl;
    return EXIT_FAILURE;
  }


  //
  // Test with local-support transform. Should FAIL.
  // Such transforms are not yet supported.
  //
  std::cout << "-------------------------------" << std::endl;
  metric->SetHasLocalSupport(true);
  ITK_TRY_EXPECT_EXCEPTION(itkOptimizer->StartOptimization());

  //
  //  Test in unbounded mode
  //
  std::cout << std::endl << "Test in unbounded mode:" << std::endl;

  auto itkOptimizer2 = OptimizerType::New();

  // Set up boundary conditions
  select.Fill(0);
  itkOptimizer2->SetBoundSelection(select);

  std::cout << "Set metric parameters." << std::endl;
  metric->SetParameters(initialValue);
  metric->SetHasLocalSupport(false);

  itkOptimizer2->SetMetric(metric);
  itkOptimizer2->SetInitialPosition(currentValue);

  itkOptimizer2->SetCostFunctionConvergenceFactor(F_Convergence_Factor);
  itkOptimizer2->SetGradientConvergenceTolerance(Projected_G_Tolerance);
  itkOptimizer2->SetNumberOfIterations(Max_Iterations);
  itkOptimizer2->SetMaximumNumberOfFunctionEvaluations(Max_Iterations);

  itkOptimizer2->AddObserver(itk::StartEvent(), eventChecker);
  itkOptimizer2->AddObserver(itk::IterationEvent(), eventChecker);
  itkOptimizer2->AddObserver(itk::EndEvent(), eventChecker);

  ITK_TRY_EXPECT_NO_EXCEPTION(itkOptimizer2->StartOptimization());


  std::cout << "Boundaries after optimization: " << std::endl;
  std::cout << "Upper bound size: " << itkOptimizer2->GetUpperBound().size() << std::endl;
  std::cout << "Lower bound size: " << itkOptimizer2->GetLowerBound().size() << std::endl;

  const OptimizerType::ParametersType & finalPosition2 = itkOptimizer2->GetCurrentPosition();
  std::cout << "Solution = (" << finalPosition2[0] << ',' << finalPosition2[1] << ')' << std::endl;
  std::cout << "Final Function Value = " << itkOptimizer2->GetValue() << std::endl;

  // check results
  pass = true;

  // true parameters when there is no constrain
  trueParameters[0] = 2.0;
  trueParameters[1] = -2.0;

  for (unsigned int j = 0; j < 2; ++j)
  {
    if (!itk::Math::FloatAlmostEqual(finalPosition2[j], trueParameters[j], 4, 0.01))
    {
      pass = false;
      errorIn = "solution";
    }
  }

  if (!itk::Math::FloatAlmostEqual(itkOptimizer2->GetValue(), -10.0, 4, 0.01))
  {
    pass = false;
    errorIn = "final function value";
  }

  if (!pass)
  {
    std::cerr << "\nError in " << errorIn << ".\n";
    std::cerr << "Test failed." << std::endl;
    return EXIT_FAILURE;
  }

  std::cout << "Test passed." << std::endl;
  return EXIT_SUCCESS;
}