/*=========================================================================
 *
 *  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 "itkAmoebaOptimizerv4.h"
#include "itkTestingMacros.h"
#include "vnl/vnl_vector_fixed.h"
#include "vnl/vnl_vector.h"
#include "vnl/vnl_matrix.h"
#include "itkMath.h"
#include <iostream>

/**
 *  The objective function is the quadratic form:
 *
 *  1/2 x^T A x - b^T x
 *
 *  Where A is represented as an itkMatrix and
 *  b is represented as an itkVector
 *
 *  The system in this example is:
 *
 *     | 3  2 ||x|   | 2|   |0|
 *     | 2  6 ||y| + |-8| = |0|
 *
 *
 *   the solution is the vector | 2 -2 |
 *
 *   and the expected final value of the function is 10.0
 *
 * \class AmoebaOptimizerv4TestMetric1
 */
class itkAmoebaOptimizerv4TestMetric1 : public itk::ObjectToObjectMetricBase
{
public:
  using Self = itkAmoebaOptimizerv4TestMetric1;
  using Superclass = itk::ObjectToObjectMetricBase;
  using Pointer = itk::SmartPointer<Self>;
  using ConstPointer = itk::SmartPointer<const Self>;
  itkNewMacro(Self);

  itkOverrideGetNameOfClassMacro(itkAmoebaOptimizerv4TestMetric1);

  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>;

  itkAmoebaOptimizerv4TestMetric1() { m_HasLocalSupport = false; }

  MeasureType
  GetValue() const override
  {
    double x = this->m_Parameters[0];
    double y = this->m_Parameters[1];
    double val = 0.5 * (3 * x * x + 4 * x * y + 6 * y * y) - 2 * x + 8 * y;
    return val;
  }

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

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

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

  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
  {}

private:
  ParametersType m_Parameters;
  bool           m_HasLocalSupport;
};


/**
 * Function we want to optimize, comprised of two parabolas with C0 continuity
 * at 0:
 * f(x) = if(x<0) x^2+4x; else 2x^2-8x
 *
 * Minima are at -2 and 2 with function values of -4 and -8 respectively.
 *
 * \class AmoebaOptimizerv4TestMetric2
 */
class itkAmoebaOptimizerv4TestMetric2 : public itk::ObjectToObjectMetricBase
{
public:
  using Self = itkAmoebaOptimizerv4TestMetric2;
  using Superclass = itk::ObjectToObjectMetricBase;
  using Pointer = itk::SmartPointer<Self>;
  using ConstPointer = itk::SmartPointer<const Self>;
  itkNewMacro(Self);
  itkOverrideGetNameOfClassMacro(itkAmoebaOptimizerv4TestMetric2);

  enum
  {
    SpaceDimension = 1
  };

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

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

  itkAmoebaOptimizerv4TestMetric2() { m_HasLocalSupport = false; }

  double
  GetValue() const override
  {
    double x = this->m_Parameters[0];
    double val;
    if (x < 0)
    {
      val = x * x + 4 * x;
    }
    else
    {
      val = 2 * x * x - 8 * x;
    }
    return val;
  }

  void
  GetDerivative(DerivativeType & itkNotUsed(derivative)) const override
  {
    throw itk::ExceptionObject(__FILE__, __LINE__, "no derivative available");
  }

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

  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
  {}

private:
  ParametersType m_Parameters;
  bool           m_HasLocalSupport;
};

class CommandIterationUpdateAmoeba : public itk::Command
{
public:
  using Self = CommandIterationUpdateAmoeba;
  using Superclass = itk::Command;
  using Pointer = itk::SmartPointer<Self>;
  itkNewMacro(Self);

  void
  Reset()
  {
    m_IterationNumber = 0;
  }

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

  void
  Execute(const itk::Object * object, const itk::EventObject & event) override
  {
    const auto * optimizer = static_cast<const itk::AmoebaOptimizerv4 *>(object);
    if (dynamic_cast<const itk::FunctionEvaluationIterationEvent *>(&event) != nullptr)
    {
      std::cout << m_IterationNumber++ << ":  ";
      std::cout << "x: " << optimizer->GetCurrentPosition() << "  ";
      std::cout << "f(x): " << optimizer->GetValue() << std::endl;
    }
  }

protected:
  CommandIterationUpdateAmoeba() { m_IterationNumber = 0; }

private:
  unsigned long m_IterationNumber;
};

/**
 * Test Amoeba with a 2D quadratic function - happy day scenario.
 */
int
AmoebaTest1();

/**
 * Test Amoeba and Amoeba with restarts on a function with two minima.
 */
int
AmoebaTest2();

int
itkAmoebaOptimizerv4Test(int, char *[])
{
  int result1 = AmoebaTest1();
  int result2 = AmoebaTest2();

  std::cout << "All Tests Completed." << std::endl;

  if (result1 == EXIT_FAILURE || result2 == EXIT_FAILURE)
  {
    std::cerr << "[FAILURE]\n";
    return EXIT_FAILURE;
  }
  std::cout << "[SUCCESS]\n";
  return EXIT_SUCCESS;
}

int
AmoebaTest1()
{

  std::cout << "Amoeba Optimizer Test 1\n \n";

  using OptimizerType = itk::AmoebaOptimizerv4;

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

  ITK_EXERCISE_BASIC_OBJECT_METHODS(itkOptimizer, AmoebaOptimizerv4, SingleValuedNonLinearVnlOptimizerv4);


  ITK_TEST_EXPECT_TRUE(itkOptimizer->CanUseScales());

  bool doEstimateScales = true;
  ITK_TEST_SET_GET_BOOLEAN(itkOptimizer, DoEstimateScales, doEstimateScales);

  // set optimizer parameters
  itk::SizeValueType numberOfIterations = 10;
  itkOptimizer->SetNumberOfIterations(numberOfIterations);
  ITK_TEST_SET_GET_VALUE(numberOfIterations, itkOptimizer->GetNumberOfIterations());

  auto automaticInitialSimplex = true;
  ITK_TEST_SET_GET_BOOLEAN(itkOptimizer, AutomaticInitialSimplex, automaticInitialSimplex);

  auto optimizeWithRestarts = false;
  ITK_TEST_SET_GET_BOOLEAN(itkOptimizer, OptimizeWithRestarts, optimizeWithRestarts);

  double xTolerance = -0.01;
  itkOptimizer->SetParametersConvergenceTolerance(xTolerance);
  ITK_TEST_SET_GET_VALUE(xTolerance, itkOptimizer->GetParametersConvergenceTolerance());

  double fTolerance = -0.001;
  itkOptimizer->SetFunctionConvergenceTolerance(fTolerance);
  ITK_TEST_SET_GET_VALUE(fTolerance, itkOptimizer->GetFunctionConvergenceTolerance());

  OptimizerType::DerivativeType cachedDerivative{};
  ITK_TEST_EXPECT_EQUAL(cachedDerivative, itkOptimizer->GetCachedDerivative());

  OptimizerType::ParametersType cachedCurrentPos{};
  ITK_TEST_EXPECT_EQUAL(cachedCurrentPos, itkOptimizer->GetCachedCurrentPosition());

  auto metric = itkAmoebaOptimizerv4TestMetric1::New();
  itkOptimizer->SetMetric(metric);
  std::cout << "itkOptimizer->GetMetric(): " << itkOptimizer->GetMetric() << std::endl;

  OptimizerType::ParametersType initialValue(2); // constructor requires vector size

  initialValue[0] = 100; // We start not far from  | 2 -2 |
  initialValue[1] = -100;

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

  // Test exceptions
  ITK_TRY_EXPECT_EXCEPTION(itkOptimizer->StartOptimization());

  xTolerance = 0.01;
  itkOptimizer->SetParametersConvergenceTolerance(xTolerance);
  ITK_TEST_SET_GET_VALUE(xTolerance, itkOptimizer->GetParametersConvergenceTolerance());

  ITK_TRY_EXPECT_EXCEPTION(itkOptimizer->StartOptimization());

  fTolerance = 0.001;
  itkOptimizer->SetFunctionConvergenceTolerance(fTolerance);
  ITK_TEST_SET_GET_VALUE(fTolerance, itkOptimizer->GetFunctionConvergenceTolerance());

  try
  {
    std::cout << "Run for " << itkOptimizer->GetNumberOfIterations();
    std::cout << " iterations or less." << std::endl;

    itkOptimizer->StartOptimization();

    itkOptimizer->SetNumberOfIterations(100);
    std::cout << "Continue for " << itkOptimizer->GetNumberOfIterations();
    std::cout << " iterations or less." << std::endl;

    OptimizerType::ParametersType currentPosition = itkOptimizer->GetCurrentPosition();
    metric->SetParameters(currentPosition);

    itkOptimizer->StartOptimization();
  }
  catch (const itk::ExceptionObject & e)
  {
    std::cerr << "Exception thrown ! " << std::endl;
    std::cerr << "An error occurred during Optimization" << std::endl;
    std::cerr << "Location    = " << e.GetLocation() << std::endl;
    std::cerr << "Description = " << e.GetDescription() << std::endl;
    std::cerr << "[TEST 1 FAILURE]\n";
    return EXIT_FAILURE;
  }

  std::cout << "Optimizer: " << itkOptimizer;

  //
  // check results to see if it is within range
  //
  OptimizerType::ParametersType finalPosition;
  finalPosition = itkOptimizer->GetCurrentPosition();

  double trueParameters[2] = { 2, -2 };
  bool   pass = true;

  std::cout << "Right answer   = " << trueParameters[0] << " , " << trueParameters[1] << std::endl;
  std::cout << "Final position = " << finalPosition << std::endl;

  for (unsigned int j = 0; j < 2; ++j)
  {
    if (itk::Math::abs(finalPosition[j] - trueParameters[j]) > xTolerance)
    {
      pass = false;
    }
  }

  if (!pass)
  {
    std::cerr << "[TEST 1 FAILURE]\n";
    return EXIT_FAILURE;
  }

  // Get the final value of the optimizer
  std::cout << "Testing optimizers GetValue() : ";
  OptimizerType::MeasureType finalValue = itkOptimizer->GetValue();
  if (itk::Math::abs(finalValue + 9.99998) > 0.01)
  {
    std::cerr << "failed\n";
    std::cerr << "[TEST 1 FAILURE]\n";
    return EXIT_FAILURE;
  }
  else
  {
    std::cout << "succeeded\n";
  }

  std::cout << "[TEST 1 SUCCESS]\n";
  return EXIT_SUCCESS;
}

int
AmoebaTest2()
{
  std::cout << "Amoeba Optimizer Test 2\n \n";

  using OptimizerType = itk::AmoebaOptimizerv4;
  auto itkOptimizer = OptimizerType::New();

  // set optimizer parameters
  unsigned int maxIterations = 100;
  itkOptimizer->SetNumberOfIterations(maxIterations);

  double xTolerance = 0.01;
  itkOptimizer->SetParametersConvergenceTolerance(xTolerance);

  double fTolerance = 0.001;
  itkOptimizer->SetFunctionConvergenceTolerance(fTolerance);

  // the initial simplex is constructed as:
  // x,
  // x_i = [x[0], ... , x[i]+initialSimplexDelta[i], ... , x[n]]
  //
  OptimizerType::ParametersType initialSimplexDelta(1);
  initialSimplexDelta[0] = 10;
  itkOptimizer->SetInitialSimplexDelta(initialSimplexDelta);
  ITK_TEST_SET_GET_VALUE(initialSimplexDelta, itkOptimizer->GetInitialSimplexDelta());

  OptimizerType::ParametersType initialParameters(1), finalParameters;
  // starting position
  initialParameters[0] = -100;

  // the function we want to optimize
  auto metric = itkAmoebaOptimizerv4TestMetric2::New();
  itkOptimizer->SetMetric(metric);

  metric->SetParameters(initialParameters);

  // observe the iterations
  auto observer = CommandIterationUpdateAmoeba::New();
  itkOptimizer->AddObserver(itk::IterationEvent(), observer);

  try
  {
    itkOptimizer->StartOptimization();
  }
  catch (const itk::ExceptionObject & e)
  {
    std::cerr << "Exception thrown ! " << std::endl;
    std::cerr << "An error occurred during Optimization" << std::endl;
    std::cerr << "Location    = " << e.GetLocation() << std::endl;
    std::cerr << "Description = " << e.GetDescription() << std::endl;
    std::cerr << "[TEST 2 FAILURE]\n";
    return EXIT_FAILURE;
  }

  std::cout << "StopConditionDescription: " << itkOptimizer->GetStopConditionDescription() << std::endl;

  // we should have converged to the local minimum, -2
  finalParameters = itkOptimizer->GetCurrentPosition();
  double knownParameters = -2.0;
  std::cout << "Standard Amoeba:\n";
  std::cout << "Known parameters   = " << knownParameters << "   ";
  std::cout << "Estimated parameters = " << finalParameters << std::endl;
  std::cout << "Converged to local minimum." << std::endl;
  if (itk::Math::abs(finalParameters[0] - knownParameters) > xTolerance)
  {
    std::cerr << "[TEST 2 FAILURE]\n";
    return EXIT_FAILURE;
  }

  // run again using multiple restarts
  observer->Reset();
  metric->SetParameters(initialParameters);
  itkOptimizer->OptimizeWithRestartsOn();

  try
  {
    itkOptimizer->StartOptimization();
  }
  catch (const itk::ExceptionObject & e)
  {
    std::cerr << "Exception thrown ! " << std::endl;
    std::cerr << "An error occurred during Optimization" << std::endl;
    std::cerr << "Location    = " << e.GetLocation() << std::endl;
    std::cerr << "Description = " << e.GetDescription() << std::endl;
    std::cerr << "[TEST 2 FAILURE]\n";
    return EXIT_FAILURE;
  }

  // we should have converged to the global minimum, 2
  finalParameters = itkOptimizer->GetCurrentPosition();
  knownParameters = 2.0;
  std::cout << "Amoeba with restarts:\n";
  std::cout << "Known parameters   = " << knownParameters << "   ";
  std::cout << "Estimated parameters = " << finalParameters << std::endl;
  std::cout << "Converged to global minimum." << std::endl;

  if (itk::Math::abs(finalParameters[0] - knownParameters) > xTolerance)
  {
    std::cerr << "[TEST 2 FAILURE]\n";
    return EXIT_FAILURE;
  }
  std::cout << "[TEST 1 SUCCESS]\n";
  return EXIT_SUCCESS;
}