/**
 * @file eve.hpp
 * @author Marcus Edel
 *
 * Eve: a gradient based optimization method with Locally
 * and globally adaptive learning rates.
 *
 * ensmallen is free software; you may redistribute it and/or modify it under
 * the terms of the 3-clause BSD license.  You should have received a copy of
 * the 3-clause BSD license along with ensmallen.  If not, see
 * http://www.opensource.org/licenses/BSD-3-Clause for more information.
 */
#ifndef ENSMALLEN_EVE_EVE_HPP
#define ENSMALLEN_EVE_EVE_HPP

namespace ens {

/**
 * Eve is a stochastic gradient based optimization method with locally and
 * globally adaptive learning rates. Stochastic Gradient Descent is a
 * technique for minimizing a function which can be expressed as a sum of other
 * functions.
 *
 * For more information, see the following.
 *
 * @code
 * @article{Koushik2016,
 *   author  = {Jayanth Koushik and Hiroaki Hayashi},
 *   title   = {Improving Stochastic Gradient Descent with Feedback},
 *   journal = {CoRR},
 *   year    = {2016},
 *   url     = {http://arxiv.org/abs/1611.01505}
 * }
 *
 * For Eve to work, a DecomposableFunctionType template parameter is required.
 * This class must implement the following function:
 *
 *   size_t NumFunctions();
 *   double Evaluate(const arma::mat& coordinates,
 *                   const size_t i,
 *                   const size_t batchSize);
 *   void Gradient(const arma::mat& coordinates,
 *                 const size_t i,
 *                 arma::mat& gradient,
 *                 const size_t batchSize);
 *
 * Eve can optimize differentiable separable functions.  For more details,
 * see the documentation on function types included with this distribution or on
 * the ensmallen website.
 */
class Eve
{
 public:
  /**
   * Construct the Eve optimizer with the given function and parameters. The
   * defaults here are not necessarily good for the given problem, so it is
   * suggested that the values used be tailored to the task at hand.  The
   * maximum number of iterations refers to the maximum number of points that
   * are processed (i.e., one iteration equals one point; one iteration does not
   * equal one pass over the dataset).
   *
   * @param stepSize Step size for each iteration.
   * @param batchSize Number of points to process in a single step.
   * @param beta1 Exponential decay rate for the first moment estimates.
   * @param beta2 Exponential decay rate for the weighted infinity norm
   *        estimates.
   * @param beta3 Exponential decay rate for relative change.
   * @param epsilon Value used to initialise the mean squared gradient parameter.
   * @param clip Clipping range to avoid extreme valus.
   * @param maxIterations Maximum number of iterations allowed (0 means no
   *        limit).
   * @param tolerance Maximum absolute tolerance to terminate algorithm.
   * @param shuffle If true, the function order is shuffled; otherwise, each
   *        function is visited in linear order.
   * @param exactObjective Calculate the exact objective (Default: estimate the
   *        final objective obtained on the last pass over the data).
   */
  Eve(const double stepSize = 0.001,
      const size_t batchSize = 32,
      const double beta1 = 0.9,
      const double beta2 = 0.999,
      const double beta3 = 0.999,
      const double epsilon = 1e-8,
      const double clip = 10,
      const size_t maxIterations = 100000,
      const double tolerance = 1e-5,
      const bool shuffle = true,
      const bool exactObjective = false);

  /**
   * Optimize the given function using stochastic gradient descent.  The given
   * starting point will be modified to store the finishing point of the
   * algorithm, and the final objective value is returned.
   *
   * @tparam DecomposableFunctionType Type of the function to be optimized.
   * @tparam MatType Type of the parameters matrix.
   * @tparam GradType Type of the gradient matrix.
   * @tparam CallbackTypes Types of callback functions.
   * @param function Function to optimize.
   * @param iterate Starting point (will be modified).
   * @param callbacks Callback functions.
   * @return Objective value of the final point.
   */
  template<typename DecomposableFunctionType,
           typename MatType,
           typename GradType,
           typename... CallbackTypes>
  typename std::enable_if<IsArmaType<GradType>::value,
      typename MatType::elem_type>::type
  Optimize(DecomposableFunctionType& function,
           MatType& iterate,
           CallbackTypes&&... callbacks);

  //! Forward the MatType as GradType.
  template<typename DecomposableFunctionType,
           typename MatType,
           typename... CallbackTypes>
  typename MatType::elem_type Optimize(DecomposableFunctionType& function,
                                       MatType& iterate,
                                       CallbackTypes&&... callbacks)
  {
    return Optimize<DecomposableFunctionType, MatType, MatType,
        CallbackTypes...>(function, iterate,
        std::forward<CallbackTypes>(callbacks)...);
  }

  //! Get the step size.
  double StepSize() const { return stepSize; }
  //! Modify the step size.
  double& StepSize() { return stepSize; }

  //! Get the batch size.
  size_t BatchSize() const { return batchSize; }
  //! Modify the batch size.
  size_t& BatchSize() { return batchSize; }

  //! Get the smoothing parameter.
  double Beta1() const { return beta1; }
  //! Modify the smoothing parameter.
  double& Beta1() { return beta1; }

  //! Get the second moment coefficient.
  double Beta2() const { return beta2; }
  //! Modify the second moment coefficient.
  double& Beta2() { return beta2; }

  //! Get the exponential decay rate for relative change.
  double Beta3() const { return beta3; }
  //! Modify the exponential decay rate for relative change.
  double& Beta3() { return beta3; }

  //! Get the value used to initialise the mean squared gradient parameter.
  double Epsilon() const { return epsilon; }
  //! Modify the value used to initialise the mean squared gradient parameter.
  double& Epsilon() { return epsilon; }

  //! Get the clipping range to avoid extreme valus.
  double Clip() const { return clip; }
  //! Modify the clipping range to avoid extreme valus.
  double& Clip() { return clip; }

  //! Get the maximum number of iterations (0 indicates no limit).
  size_t MaxIterations() const { return maxIterations; }
  //! Modify the maximum number of iterations (0 indicates no limit).
  size_t& MaxIterations() { return maxIterations; }

  //! Get the tolerance for termination.
  double Tolerance() const { return tolerance; }
  //! Modify the tolerance for termination.
  double& Tolerance() { return tolerance; }

  //! Get whether or not the individual functions are shuffled.
  bool Shuffle() const { return shuffle; }
  //! Modify whether or not the individual functions are shuffled.
  bool& Shuffle() { return shuffle; }

  //! Get whether or not the actual objective is calculated.
  bool ExactObjective() const { return exactObjective; }
  //! Modify whether or not the actual objective is calculated.
  bool& ExactObjective() { return exactObjective; }

 private:
  //! The step size for each example.
  double stepSize;

  //! The batch size for processing.
  size_t batchSize;

  //! The smoothing parameter.
  double beta1;

  //! The second moment coefficient.
  double beta2;

  //! The third moment coefficient.
  double beta3;

  //! The epsilon value used to initialise the squared gradient parameter.
  double epsilon;

  //! The clip value used to clip the term to avoid extreme values.
  double clip;

  //! The maximum number of allowed iterations.
  size_t maxIterations;

  //! The tolerance for termination.
  double tolerance;

  //! Controls whether or not the individual functions are shuffled when
  //! iterating.
  bool shuffle;

  //! Controls whether or not the actual Objective value is calculated.
  bool exactObjective;
};

} // namespace ens

// Include implementation.
#include "eve_impl.hpp"

#endif
