/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.

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

    http://www.apache.org/licenses/LICENSE-2.0

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.
==============================================================================*/
#ifndef TENSORFLOW_CORE_KERNELS_LOSS_H_
#define TENSORFLOW_CORE_KERNELS_LOSS_H_

#include "tensorflow/core/lib/core/status.h"

namespace tensorflow {

class DualLossUpdater {
 public:
  virtual ~DualLossUpdater() {}

  // Compute update dual (alpha), based on a single example. Various strategies
  // can be employed here, like newton step and/or line search or approximate
  // step that decreases the dual sub-optimality.
  virtual double ComputeUpdatedDual(
      const int num_loss_partitions, const double label,
      const double example_weight, const double current_dual, const double wx,
      const double weighted_example_norm) const = 0;

  // Compute dual loss based on the current dual (alpha), example label (y)
  // and example weight (cost).
  virtual double ComputeDualLoss(const double current_dual,
                                 const double example_label,
                                 const double example_weight) const = 0;

  // Compute the primal loss based on current estimate of log-odds(wx),
  // example label (y) and example weight (cost).
  virtual double ComputePrimalLoss(const double wx, const double example_label,
                                   const double example_weight) const = 0;

  // Primal loss derivative used to compute the dual residue in AdaSDCA
  virtual double PrimalLossDerivative(const double wx,
                                      const double example_label,
                                      const double example_weight) const = 0;

  // This is gamma such that the loss derivative is 1/gamma Lipschitz
  virtual double SmoothnessConstant() const = 0;

  // Converts binary example labels from 0.0 or 1.0 to appropriate range for
  // each loss function.
  virtual Status ConvertLabel(float* const example_label) const = 0;
};

}  // namespace tensorflow
#endif  // TENSORFLOW_CORE_KERNELS_LOSS_H_