// Copyright (c) 2020-2025 Advanced Micro Devices, Inc. All rights reserved.
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in all
// copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
// SOFTWARE.

#include <thrust/device_vector.h>
#include <thrust/functional.h>
#include <thrust/random.h>
#include <thrust/scan.h>
#include <thrust/sequence.h>
#include <thrust/transform.h>

#include <iomanip>
#include <iostream>

#include "include/host_device.h"

// Efficiently computes the simple moving average (SMA) [1] of a data series
// using a parallel prefix-sum or "scan" operation.
//
// Note: additional numerical precision should be used in the cumulative summing
// stage when computing the SMA of large data series.  The most straightforward
// remedy is to replace 'float' with 'double'.   Alternatively a Kahan or
// "compensated" summation algorithm could be applied [2].
//
// [1] http://en.wikipedia.org/wiki/Moving_average#Simple_moving_average
// [2] http://en.wikipedia.org/wiki/Kahan_summation_algorithm

// compute the difference of two positions in the cumumulative sum and
// divide by the SMA window size w.
template <typename T>
struct minus_and_divide
{
  T w;

  minus_and_divide(T w)
      : w(w)
  {}

  __host__ __device__ T operator()(const T& a, const T& b) const
  {
    return (a - b) / w;
  }
};

template <typename InputVector, typename OutputVector>
void simple_moving_average(const InputVector& data, size_t w, OutputVector& output)
{
  using T = typename InputVector::value_type;

  if (data.size() < w)
  {
    return;
  }

  // allocate storage for cumulative sum
  thrust::device_vector<T> temp(data.size() + 1);

  // compute cumulative sum
  thrust::exclusive_scan(data.begin(), data.end(), temp.begin());
  temp[data.size()] = data.back() + temp[data.size() - 1];

  // compute moving averages from cumulative sum
  thrust::transform(temp.begin() + w, temp.end(), temp.begin(), output.begin(), minus_and_divide<T>(T(w)));
}

int main(void)
{
  // length of data series
  size_t n = 30;

  // window size of the moving average
  size_t w = 4;

  // generate random data series
  thrust::device_vector<float> data(n);
  thrust::default_random_engine rng;
  thrust::uniform_int_distribution<int> dist(0, 10);
  for (size_t i = 0; i < n; i++)
  {
    data[i] = static_cast<float>(dist(rng));
  }

  // allocate storage for averages
  thrust::device_vector<float> averages(data.size() - (w - 1));

  // compute SMA using standard summation
  simple_moving_average(data, w, averages);

  // print data series
  std::cout << "data series: [ ";
  for (size_t i = 0; i < data.size(); i++)
  {
    std::cout << data[i] << " ";
  }
  std::cout << "]" << std::endl;

  // print moving averages
  std::cout << "simple moving averages (window = " << w << ")" << std::endl;
  for (size_t i = 0; i < averages.size(); i++)
  {
    std::cout << "  [" << std::setw(2) << i << "," << std::setw(2) << (i + w) << ") = " << averages[i] << std::endl;
  }

  return 0;
}
