// Copyright (c) 2018, ETH Zurich and UNC Chapel Hill.
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
//     * Redistributions of source code must retain the above copyright
//       notice, this list of conditions and the following disclaimer.
//
//     * Redistributions in binary form must reproduce the above copyright
//       notice, this list of conditions and the following disclaimer in the
//       documentation and/or other materials provided with the distribution.
//
//     * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of
//       its contributors may be used to endorse or promote products derived
//       from this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// Author: Johannes L. Schoenberger (jsch at inf.ethz.ch)

#ifndef COLMAP_SRC_OPTIM_SAMPLER_H_
#define COLMAP_SRC_OPTIM_SAMPLER_H_

#include <cstddef>
#include <vector>

#include "util/logging.h"

namespace colmap {

// Abstract base class for sampling methods.
class Sampler {
 public:
  Sampler(){};
  explicit Sampler(const size_t num_samples);

  // Initialize the sampler, before calling the `Sample` method.
  virtual void Initialize(const size_t total_num_samples) = 0;

  // Maximum number of unique samples that can be generated.
  virtual size_t MaxNumSamples() = 0;

  // Sample `num_samples` elements from all samples.
  virtual std::vector<size_t> Sample() = 0;

  // Sample elements from `X` into `X_rand`.
  //
  // Note that `X.size()` should equal `num_total_samples` and `X_rand.size()`
  // should equal `num_samples`.
  template <typename X_t>
  void SampleX(const X_t& X, X_t* X_rand);

  // Sample elements from `X` and `Y` into `X_rand` and `Y_rand`.
  //
  // Note that `X.size()` should equal `num_total_samples` and `X_rand.size()`
  // should equal `num_samples`. The same applies for `Y` and `Y_rand`.
  template <typename X_t, typename Y_t>
  void SampleXY(const X_t& X, const Y_t& Y, X_t* X_rand, Y_t* Y_rand);
};

////////////////////////////////////////////////////////////////////////////////
// Implementation
////////////////////////////////////////////////////////////////////////////////

template <typename X_t>
void Sampler::SampleX(const X_t& X, X_t* X_rand) {
  const auto sample_idxs = Sample();
  for (size_t i = 0; i < X_rand->size(); ++i) {
    (*X_rand)[i] = X[sample_idxs[i]];
  }
}

template <typename X_t, typename Y_t>
void Sampler::SampleXY(const X_t& X, const Y_t& Y, X_t* X_rand, Y_t* Y_rand) {
  CHECK_EQ(X.size(), Y.size());
  CHECK_EQ(X_rand->size(), Y_rand->size());
  const auto sample_idxs = Sample();
  for (size_t i = 0; i < X_rand->size(); ++i) {
    (*X_rand)[i] = X[sample_idxs[i]];
    (*Y_rand)[i] = Y[sample_idxs[i]];
  }
}

}  // namespace colmap

#endif  // COLMAP_SRC_OPTIM_SAMPLER_H_
