/*=========================================================================
 *
 *  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"

namespace itk
{


AmoebaOptimizerv4::AmoebaOptimizerv4()
  : m_InitialSimplexDelta(1)
{
  this->m_NumberOfIterations = 500;
  this->m_ParametersConvergenceTolerance = 1e-8;
  this->m_FunctionConvergenceTolerance = 1e-4;
  this->m_AutomaticInitialSimplex = true;
  this->m_InitialSimplexDelta.Fill(NumericTraits<ParametersType::ValueType>::OneValue());
  this->m_OptimizeWithRestarts = false;
  this->m_VnlOptimizer = nullptr;
}


AmoebaOptimizerv4::~AmoebaOptimizerv4() = default;


const std::string
AmoebaOptimizerv4::GetStopConditionDescription() const
{
  return this->m_StopConditionDescription.str();
}


void
AmoebaOptimizerv4::PrintSelf(std::ostream & os, Indent indent) const
{
  Superclass::PrintSelf(os, indent);
  os << indent << "ParametersConvergenceTolerance: " << this->m_ParametersConvergenceTolerance << std::endl;
  os << indent << "FunctionConvergenceTolerance: " << this->m_FunctionConvergenceTolerance << std::endl;
  os << indent << "AutomaticInitialSimplex: " << (this->m_AutomaticInitialSimplex ? "On" : "Off") << std::endl;
  os << indent << "InitialSimplexDelta: " << this->m_InitialSimplexDelta << std::endl;
}


vnl_amoeba *
AmoebaOptimizerv4::GetOptimizer() const
{
  return this->m_VnlOptimizer.get();
}

void
AmoebaOptimizerv4::SetInitialSimplexDelta(ParametersType initialSimplexDelta, bool automaticInitialSimplex)
{
  this->m_InitialSimplexDelta = initialSimplexDelta;
  this->m_AutomaticInitialSimplex = automaticInitialSimplex;
  this->Modified();
}


void
AmoebaOptimizerv4::SetMetric(MetricType * metric)
{
  this->m_Metric = metric;

  // if cost function is nullptr this will throw an exception when the pointer is dereferenced
  const unsigned int numberOfParameters = metric->GetNumberOfParameters();

  class AmoebaCostFunctionAdaptorv4 : public SingleValuedVnlCostFunctionAdaptorv4
  {
  public:
    using Superclass = SingleValuedVnlCostFunctionAdaptorv4;
    using ITKOptimizerType = AmoebaOptimizerv4;

    AmoebaCostFunctionAdaptorv4(unsigned int spaceDimension, ITKOptimizerType * itkObj)
      : SingleValuedVnlCostFunctionAdaptorv4(spaceDimension)
      , m_ItkObj(itkObj)
    {}

    Superclass::InternalMeasureType
    f(const Superclass::InternalParametersType & inparameters) override
    {
      const Superclass::InternalMeasureType & ret = Superclass::f(inparameters);
      ++m_ItkObj->m_CurrentIteration;
      return ret;
    }

  protected:
    Self * m_ItkObj;
  };

  // assign to vnl cost-function adaptor
  CostFunctionAdaptorType * adaptor = new AmoebaCostFunctionAdaptorv4(numberOfParameters, this);
  adaptor->SetCostFunction(metric);
  this->SetCostFunctionAdaptor(adaptor);
  this->Modified();
}


void
AmoebaOptimizerv4::StartOptimization(bool /* doOnlyInitialization */)
{
  // Perform some verification, check scales,
  // pass settings to cost-function adaptor.
  Superclass::StartOptimization();

  // validate the settings (cost function is initialized, the size of its
  // expected parameter vector matches the one we have etc...)
  this->ValidateSettings();

  ParametersType parameters = this->m_Metric->GetParameters();
  unsigned int   n = parameters.GetSize();

  InternalParametersType delta(m_InitialSimplexDelta);

  // start the actual work
  this->InvokeEvent(StartEvent());

  // configure the vnl optimizer
  CostFunctionAdaptorType * adaptor = GetNonConstCostFunctionAdaptor();
  // get rid of previous instance of the internal optimizer and create a
  // new one
  m_VnlOptimizer = std::make_unique<vnl_amoeba>(*adaptor);
  m_VnlOptimizer->set_max_iterations(static_cast<int>(m_NumberOfIterations));
  m_VnlOptimizer->set_x_tolerance(m_ParametersConvergenceTolerance);
  m_VnlOptimizer->set_f_tolerance(m_FunctionConvergenceTolerance);

  m_StopConditionDescription.str("");
  m_StopConditionDescription << this->GetNameOfClass() << ": Running";

  ParametersType bestPosition = parameters;

  // Scale the initial parameters up if scales are defined.
  // This compensates for later scaling them down in the cost function adaptor
  // and at the end of this function.
  const ScalesType & scales = GetScales();
  if (!this->GetScalesAreIdentity())
  {
    for (unsigned int i = 0; i < n; ++i)
    {
      parameters[i] *= scales[i];
    }
  }
  // copy the automated initialization from vnl so that we have
  // the same control as when the user provides the initial simplex.
  // this also exposes the fact that there is an interaction between
  // the parameter scaling and the initial simplex when using
  // automated initialization - previously hidden inside vnl
  if (this->m_AutomaticInitialSimplex)
  {
    constexpr double       relativeDiameter = 0.05;
    constexpr double       zeroTermDelta = 0.00025;
    InternalParametersType automaticDelta(n);
    for (unsigned int i = 0; i < n; ++i)
    {
      if (itk::Math::abs(parameters[i]) > zeroTermDelta)
      {
        automaticDelta[i] = relativeDiameter * parameters[i];
      }
      else
      {
        automaticDelta[i] = zeroTermDelta;
      }
    }
    delta = automaticDelta;
  }

  this->m_VnlOptimizer->minimize(parameters, delta);
  bestPosition = parameters;
  double bestValue = adaptor->f(bestPosition);
  // multiple restart heuristic
  if (this->m_OptimizeWithRestarts)
  {
    bool         converged = false;
    unsigned int i = 1;
    while (!converged && (this->m_CurrentIteration < m_NumberOfIterations))
    {
      this->m_VnlOptimizer->set_max_iterations(static_cast<int>(this->m_NumberOfIterations - this->m_CurrentIteration));
      parameters = bestPosition;
      delta = delta * (1.0 / pow(2.0, static_cast<double>(i)) * (rand() > RAND_MAX / 2 ? 1 : -1));
      m_VnlOptimizer->minimize(parameters, delta);
      double currentValue = adaptor->f(parameters);
      // be consistent with the underlying vnl amoeba implementation
      double maxAbs = 0.0;
      for (unsigned int j = 0; j < n; ++j)
      {
        if (maxAbs < itk::Math::abs(bestPosition[j] - parameters[j]))
        {
          maxAbs = itk::Math::abs(bestPosition[j] - parameters[j]);
        }
      }
      converged = itk::Math::abs(bestValue - currentValue) < this->m_FunctionConvergenceTolerance &&
                  maxAbs < this->m_ParametersConvergenceTolerance;
      // this comparison is valid because the
      // adaptor is set to always return the function value
      // corresponding to minimization
      if (currentValue < bestValue)
      {
        bestValue = currentValue;
        bestPosition = parameters;
      }
      ++i;
    }
  }
  // get the results, we scale the parameters down if scales are defined
  if (!this->GetScalesAreIdentity())
  {
    for (unsigned int i = 0; i < n; ++i)
    {
      bestPosition[i] /= scales[i];
    }
  }

  this->m_Metric->SetParameters(bestPosition);

  this->m_StopConditionDescription.str("");
  this->m_StopConditionDescription << this->GetNameOfClass() << ": ";
  if (static_cast<unsigned int>(this->m_VnlOptimizer->get_num_evaluations()) < this->m_NumberOfIterations)
  {
    this->m_StopConditionDescription << "Both parameters convergence tolerance ("
                                     << this->m_ParametersConvergenceTolerance
                                     << ") and function convergence tolerance (" << this->m_FunctionConvergenceTolerance
                                     << ") have been met in " << this->m_VnlOptimizer->get_num_evaluations()
                                     << " iterations.";
  }
  else
  {
    this->m_StopConditionDescription << "Maximum number of iterations exceeded."
                                     << " Number of iterations is " << this->m_NumberOfIterations;
  }
  this->InvokeEvent(EndEvent());
}


void
AmoebaOptimizerv4::ValidateSettings()
{
  // if we got here it is safe to get the number of parameters the cost
  // function expects
  ParametersType parameters = this->m_Metric->GetParameters();
  unsigned int   n = parameters.GetSize();

  // the user gave us data to use for the initial simplex, check that it
  // matches the number of parameters (simplex dimension is n+1 - the initial
  // position and n vertices defined by adding m_InitialSimplexDelta[i] to
  // the initial position
  if (!m_AutomaticInitialSimplex)
  {
    if (m_InitialSimplexDelta.size() != n)
    {
      itkExceptionMacro("cost function and simplex delta dimensions mismatch");
    }
  }

  // check that the number of scale factors matches
  if (this->GetScalesInitialized())
  {
    if (this->GetScales().Size() != n)
    {
      itkExceptionMacro("cost function and scaling information dimensions mismatch");
    }
  }

  // parameters' convergence tolerance has to be positive
  if (this->m_ParametersConvergenceTolerance < 0)
  {
    itkExceptionMacro("negative parameters convergence tolerance");
  }
  // function convergence tolerance has to be positive
  if (this->m_FunctionConvergenceTolerance < 0)
  {
    itkExceptionMacro("negative function convergence tolerance");
  }
}

} // end namespace itk
