/*
 * Copyright (c) 2011-2019, The DART development contributors
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 *   without modification, are permitted provided that the following
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 *     notice, this list of conditions and the following disclaimer.
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 */

#include "dart/optimizer/Population.hpp"

#include "dart/common/Console.hpp"
#include "dart/math/Random.hpp"

namespace dart {
namespace optimizer {

namespace {

//==============================================================================
bool isValidX(const MultiObjectiveProblem& problem, const Eigen::VectorXd& x)
{
  if (problem.getSolutionDimension() != static_cast<std::size_t>(x.size()))
  {
    dtwarn << "[Population] Attempting to add an incompatible decision vector. "
           << "The dimension of the decision vector '" << x.size()
           << "' should be '" << problem.getSolutionDimension() << "'.\n";
    return false;
  }

  return true;
}

//==============================================================================
bool isValidF(const MultiObjectiveProblem& problem, const Eigen::VectorXd& f)
{
  if (problem.getFitnessDimension() != static_cast<std::size_t>(f.size()))
  {
    dtwarn << "[Population] Attempting to add an incompatible decision vector. "
           << "The dimension of the decision vector '" << f.size()
           << "' should be '" << problem.getFitnessDimension() << "'.\n";
    return false;
  }

  return true;
}

} // namespace

//==============================================================================
Population::Population(
    std::shared_ptr<MultiObjectiveProblem> problem, std::size_t populationSize)
  : mProblem(std::move(problem))
{
  // TODO(JS): Take the argument type of problem by const reference of
  // MultiObjectiveProblem and clone it once MultiObjectiveProblem::clone()
  // is added.

  assert(mProblem);

  const int xSize = static_cast<int>(mProblem->getSolutionDimension());
  const int fSize = static_cast<int>(mProblem->getFitnessDimension());

  mPopulation.resize(xSize, static_cast<int>(populationSize));
  mFitness.resize(fSize, static_cast<int>(populationSize));

  const Eigen::VectorXd& lb = mProblem->getLowerBounds();
  const Eigen::VectorXd& ub = mProblem->getUpperBounds();

  for (std::size_t i = 0u; i < populationSize; ++i)
    set(i, math::Random::uniform(lb, ub));
}

//==============================================================================
MultiObjectiveProblem* Population::getProblem()
{
  return mProblem.get();
}

//==============================================================================
const MultiObjectiveProblem* Population::getProblem() const
{
  return mProblem.get();
}

//==============================================================================
void Population::pushBack(const Eigen::VectorXd& x)
{
  if (!isValidX(*mProblem, x))
    return;

  const Eigen::VectorXd f = mProblem->evaluateFitness(x);
  pushBack(x, f);
}

//==============================================================================
void Population::pushBack(const Eigen::VectorXd& x, const Eigen::VectorXd& f)
{
  if (!isValidX(*mProblem, x))
    return;

  if (!isValidF(*mProblem, f))
    return;

  mPopulation.conservativeResize(Eigen::NoChange, mPopulation.cols() + 1);
  mPopulation.rightCols(1) = x;

  mFitness.conservativeResize(Eigen::NoChange, mFitness.cols() + 1);
  mFitness.rightCols(1) = f;
}

//==============================================================================
void Population::set(std::size_t index, const Eigen::VectorXd& x)
{
  isValidX(*mProblem, x);

  set(index, x, mProblem->evaluateFitness(x));
}

//==============================================================================
void Population::set(
    std::size_t index, const Eigen::VectorXd& x, const Eigen::VectorXd& f)
{
  if (!isValidX(*mProblem, x))
    return;

  if (!isValidF(*mProblem, f))
    return;

  mPopulation.col(static_cast<int>(index)) = x;
  mFitness.col(static_cast<int>(index)) = f;
}

//==============================================================================
std::size_t Population::getSize() const
{
  assert(mPopulation.cols() == mFitness.cols());
  return static_cast<std::size_t>(mPopulation.cols());
}

//==============================================================================
Eigen::VectorXd Population::getDecisionVector(std::size_t index) const
{
  return mPopulation.col(static_cast<int>(index));
}

//==============================================================================
Eigen::VectorXd Population::getFitnessVector(std::size_t index) const
{
  return mFitness.col(static_cast<int>(index));
}

//==============================================================================
std::ostream& Population::print(std::ostream& os) const
{
  os << getProblem() << "\n";
  os << "Population size: " << getSize() << "\n\n";
  os << "List of individuals: \n";

  for (std::size_t i = 0u; i < getSize(); ++i)
  {
    os << "#" << i << ":\n";
    os << "\tDecision vector:\t"
       << mPopulation.col(static_cast<int>(i)).transpose() << "\n";
    os << "\tDecision vector:\t"
       << mFitness.col(static_cast<int>(i)).transpose() << "\n";
  }

  // TODO(JS): Print champions if the dimension is one.

  return os;
}

} // namespace optimizer
} // namespace dart
