// SPDX-License-Identifier: Apache-2.0
//
// Copyright 2008-2016 Conrad Sanderson (http://conradsanderson.id.au)
// Copyright 2008-2016 National ICT Australia (NICTA)
//
// 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.
// ------------------------------------------------------------------------

//! \addtogroup op_mean
//! @{

template <typename T1>
inline void op_mean::apply(Mat<typename T1::elem_type>& out, const Op<T1, op_mean>& in) {
  arma_debug_sigprint();

  typedef typename T1::elem_type eT;

  const uword dim = in.aux_uword_a;
  arma_conform_check((dim > 1), "mean(): parameter 'dim' must be 0 or 1");

  const Proxy<T1> P(in.m);

  if (P.is_alias(out) == false) {
    op_mean::apply_noalias(out, P, dim);
  } else {
    Mat<eT> tmp;

    op_mean::apply_noalias(tmp, P, dim);

    out.steal_mem(tmp);
  }
}

template <typename T1>
inline void op_mean::apply_noalias(Mat<typename T1::elem_type>& out, const Proxy<T1>& P,
                                   const uword dim) {
  arma_debug_sigprint();

  if (is_Mat<typename Proxy<T1>::stored_type>::value) {
    op_mean::apply_noalias_unwrap(out, P, dim);
  } else {
    op_mean::apply_noalias_proxy(out, P, dim);
  }
}

template <typename T1>
inline void op_mean::apply_noalias_unwrap(Mat<typename T1::elem_type>& out,
                                          const Proxy<T1>& P, const uword dim) {
  arma_debug_sigprint();

  typedef typename T1::elem_type eT;
  typedef typename get_pod_type<eT>::result T;

  typedef typename Proxy<T1>::stored_type P_stored_type;

  const unwrap<P_stored_type> tmp(P.Q);

  const typename unwrap<P_stored_type>::stored_type& X = tmp.M;

  const uword X_n_rows = X.n_rows;
  const uword X_n_cols = X.n_cols;

  if (dim == 0) {
    out.set_size((X_n_rows > 0) ? 1 : 0, X_n_cols);

    if (X_n_rows == 0) {
      return;
    }

    eT* out_mem = out.memptr();

    for (uword col = 0; col < X_n_cols; ++col) {
      out_mem[col] = op_mean::direct_mean(X.colptr(col), X_n_rows);
    }
  } else if (dim == 1) {
    out.zeros(X_n_rows, (X_n_cols > 0) ? 1 : 0);

    if (X_n_cols == 0) {
      return;
    }

    eT* out_mem = out.memptr();

    for (uword col = 0; col < X_n_cols; ++col) {
      const eT* col_mem = X.colptr(col);

      for (uword row = 0; row < X_n_rows; ++row) {
        out_mem[row] += col_mem[row];
      }
    }

    out /= T(X_n_cols);

    for (uword row = 0; row < X_n_rows; ++row) {
      if (arma_isfinite(out_mem[row]) == false) {
        out_mem[row] = op_mean::direct_mean_robust(X, row);
      }
    }
  }
}

template <typename T1>
inline void op_mean::apply_noalias_proxy(Mat<typename T1::elem_type>& out,
                                         const Proxy<T1>& P, const uword dim) {
  arma_debug_sigprint();

  typedef typename T1::elem_type eT;
  typedef typename get_pod_type<eT>::result T;

  const uword P_n_rows = P.get_n_rows();
  const uword P_n_cols = P.get_n_cols();

  if (dim == 0) {
    out.set_size((P_n_rows > 0) ? 1 : 0, P_n_cols);

    if (P_n_rows == 0) {
      return;
    }

    eT* out_mem = out.memptr();

    for (uword col = 0; col < P_n_cols; ++col) {
      eT val1 = eT(0);
      eT val2 = eT(0);

      uword i, j;
      for (i = 0, j = 1; j < P_n_rows; i += 2, j += 2) {
        val1 += P.at(i, col);
        val2 += P.at(j, col);
      }

      if (i < P_n_rows) {
        val1 += P.at(i, col);
      }

      out_mem[col] = (val1 + val2) / T(P_n_rows);
    }
  } else if (dim == 1) {
    out.zeros(P_n_rows, (P_n_cols > 0) ? 1 : 0);

    if (P_n_cols == 0) {
      return;
    }

    eT* out_mem = out.memptr();

    for (uword col = 0; col < P_n_cols; ++col)
      for (uword row = 0; row < P_n_rows; ++row) {
        out_mem[row] += P.at(row, col);
      }

    out /= T(P_n_cols);
  }

  if (out.internal_has_nonfinite()) {
    // TODO: replace with dedicated handling to avoid unwrapping
    op_mean::apply_noalias_unwrap(out, P, dim);
  }
}

//
// cubes

template <typename T1>
inline void op_mean::apply(Cube<typename T1::elem_type>& out,
                           const OpCube<T1, op_mean>& in) {
  arma_debug_sigprint();

  typedef typename T1::elem_type eT;

  const uword dim = in.aux_uword_a;
  arma_conform_check((dim > 2), "mean(): parameter 'dim' must be 0 or 1 or 2");

  const ProxyCube<T1> P(in.m);

  if (P.is_alias(out) == false) {
    op_mean::apply_noalias(out, P, dim);
  } else {
    Cube<eT> tmp;

    op_mean::apply_noalias(tmp, P, dim);

    out.steal_mem(tmp);
  }
}

template <typename T1>
inline void op_mean::apply_noalias(Cube<typename T1::elem_type>& out,
                                   const ProxyCube<T1>& P, const uword dim) {
  arma_debug_sigprint();

  if (is_Cube<typename ProxyCube<T1>::stored_type>::value) {
    op_mean::apply_noalias_unwrap(out, P, dim);
  } else {
    op_mean::apply_noalias_proxy(out, P, dim);
  }
}

template <typename T1>
inline void op_mean::apply_noalias_unwrap(Cube<typename T1::elem_type>& out,
                                          const ProxyCube<T1>& P, const uword dim) {
  arma_debug_sigprint();

  typedef typename T1::elem_type eT;
  typedef typename get_pod_type<eT>::result T;

  typedef typename ProxyCube<T1>::stored_type P_stored_type;

  const unwrap_cube<P_stored_type> U(P.Q);

  const Cube<eT>& X = U.M;

  const uword X_n_rows = X.n_rows;
  const uword X_n_cols = X.n_cols;
  const uword X_n_slices = X.n_slices;

  if (dim == 0) {
    out.set_size((X_n_rows > 0) ? 1 : 0, X_n_cols, X_n_slices);

    if (X_n_rows == 0) {
      return;
    }

    for (uword slice = 0; slice < X_n_slices; ++slice) {
      eT* out_mem = out.slice_memptr(slice);

      for (uword col = 0; col < X_n_cols; ++col) {
        out_mem[col] = op_mean::direct_mean(X.slice_colptr(slice, col), X_n_rows);
      }
    }
  } else if (dim == 1) {
    out.zeros(X_n_rows, (X_n_cols > 0) ? 1 : 0, X_n_slices);

    if (X_n_cols == 0) {
      return;
    }

    for (uword slice = 0; slice < X_n_slices; ++slice) {
      eT* out_mem = out.slice_memptr(slice);

      for (uword col = 0; col < X_n_cols; ++col) {
        const eT* col_mem = X.slice_colptr(slice, col);

        for (uword row = 0; row < X_n_rows; ++row) {
          out_mem[row] += col_mem[row];
        }
      }

      const Mat<eT> tmp('j', X.slice_memptr(slice), X_n_rows, X_n_cols);

      for (uword row = 0; row < X_n_rows; ++row) {
        out_mem[row] /= T(X_n_cols);

        if (arma_isfinite(out_mem[row]) == false) {
          out_mem[row] = op_mean::direct_mean_robust(tmp, row);
        }
      }
    }
  } else if (dim == 2) {
    out.zeros(X_n_rows, X_n_cols, (X_n_slices > 0) ? 1 : 0);

    if (X_n_slices == 0) {
      return;
    }

    eT* out_mem = out.memptr();

    for (uword slice = 0; slice < X_n_slices; ++slice) {
      arrayops::inplace_plus(out_mem, X.slice_memptr(slice), X.n_elem_slice);
    }

    out /= T(X_n_slices);

    podarray<eT> tmp(X_n_slices);

    for (uword col = 0; col < X_n_cols; ++col)
      for (uword row = 0; row < X_n_rows; ++row) {
        if (arma_isfinite(out.at(row, col, 0)) == false) {
          for (uword slice = 0; slice < X_n_slices; ++slice) {
            tmp[slice] = X.at(row, col, slice);
          }

          out.at(row, col, 0) = op_mean::direct_mean_robust(tmp.memptr(), X_n_slices);
        }
      }
  }
}

template <typename T1>
inline void op_mean::apply_noalias_proxy(Cube<typename T1::elem_type>& out,
                                         const ProxyCube<T1>& P, const uword dim) {
  arma_debug_sigprint();

  op_mean::apply_noalias_unwrap(out, P, dim);

  // TODO: implement specialised handling
}

//

template <typename eT>
inline eT op_mean::direct_mean(const eT* const X, const uword n_elem) {
  arma_debug_sigprint();

  typedef typename get_pod_type<eT>::result T;

  const eT result = arrayops::accumulate(X, n_elem) / T(n_elem);

  return arma_isfinite(result) ? result : op_mean::direct_mean_robust(X, n_elem);
}

template <typename eT>
inline eT op_mean::direct_mean_robust(const eT* const X, const uword n_elem) {
  arma_debug_sigprint();

  // use an adapted form of the mean finding algorithm from the running_stat class

  typedef typename get_pod_type<eT>::result T;

  uword i, j;

  eT r_mean = eT(0);

  for (i = 0, j = 1; j < n_elem; i += 2, j += 2) {
    const eT Xi = X[i];
    const eT Xj = X[j];

    r_mean =
        r_mean + (Xi - r_mean) / T(j);  // we need i+1, and j is equivalent to i+1 here
    r_mean = r_mean + (Xj - r_mean) / T(j + 1);
  }

  if (i < n_elem) {
    const eT Xi = X[i];

    r_mean = r_mean + (Xi - r_mean) / T(i + 1);
  }

  return r_mean;
}

template <typename eT>
inline eT op_mean::direct_mean(const Mat<eT>& X, const uword row) {
  arma_debug_sigprint();

  typedef typename get_pod_type<eT>::result T;

  const uword X_n_cols = X.n_cols;

  eT val = eT(0);

  uword i, j;
  for (i = 0, j = 1; j < X_n_cols; i += 2, j += 2) {
    val += X.at(row, i);
    val += X.at(row, j);
  }

  if (i < X_n_cols) {
    val += X.at(row, i);
  }

  const eT result = val / T(X_n_cols);

  return arma_isfinite(result) ? result : op_mean::direct_mean_robust(X, row);
}

template <typename eT>
inline eT op_mean::direct_mean_robust(const Mat<eT>& X, const uword row) {
  arma_debug_sigprint();

  typedef typename get_pod_type<eT>::result T;

  const uword X_n_cols = X.n_cols;

  eT r_mean = eT(0);

  for (uword col = 0; col < X_n_cols; ++col) {
    r_mean = r_mean + (X.at(row, col) - r_mean) / T(col + 1);
  }

  return r_mean;
}

template <typename eT>
inline eT op_mean::mean_all(const subview<eT>& X) {
  arma_debug_sigprint();

  typedef typename get_pod_type<eT>::result T;

  const uword X_n_rows = X.n_rows;
  const uword X_n_cols = X.n_cols;
  const uword X_n_elem = X.n_elem;

  if (X_n_elem == 0) {
    arma_conform_check(true, "mean(): object has no elements");

    return Datum<eT>::nan;
  }

  eT val = eT(0);

  if (X_n_rows == 1) {
    const Mat<eT>& A = X.m;

    const uword start_row = X.aux_row1;
    const uword start_col = X.aux_col1;

    const uword end_col_p1 = start_col + X_n_cols;

    uword i, j;
    for (i = start_col, j = start_col + 1; j < end_col_p1; i += 2, j += 2) {
      val += A.at(start_row, i);
      val += A.at(start_row, j);
    }

    if (i < end_col_p1) {
      val += A.at(start_row, i);
    }
  } else {
    for (uword col = 0; col < X_n_cols; ++col) {
      val += arrayops::accumulate(X.colptr(col), X_n_rows);
    }
  }

  const eT result = val / T(X_n_elem);

  return arma_isfinite(result) ? result : op_mean::mean_all_robust(X);
}

template <typename eT>
inline eT op_mean::mean_all_robust(const subview<eT>& X) {
  arma_debug_sigprint();

  typedef typename get_pod_type<eT>::result T;

  const uword X_n_rows = X.n_rows;
  const uword X_n_cols = X.n_cols;

  const uword start_row = X.aux_row1;
  const uword start_col = X.aux_col1;

  const uword end_row_p1 = start_row + X_n_rows;
  const uword end_col_p1 = start_col + X_n_cols;

  const Mat<eT>& A = X.m;

  eT r_mean = eT(0);

  if (X_n_rows == 1) {
    uword i = 0;

    for (uword col = start_col; col < end_col_p1; ++col, ++i) {
      r_mean = r_mean + (A.at(start_row, col) - r_mean) / T(i + 1);
    }
  } else {
    uword i = 0;

    for (uword col = start_col; col < end_col_p1; ++col)
      for (uword row = start_row; row < end_row_p1; ++row, ++i) {
        r_mean = r_mean + (A.at(row, col) - r_mean) / T(i + 1);
      }
  }

  return r_mean;
}

template <typename eT>
inline eT op_mean::mean_all(const diagview<eT>& X) {
  arma_debug_sigprint();

  typedef typename get_pod_type<eT>::result T;

  const uword X_n_elem = X.n_elem;

  if (X_n_elem == 0) {
    arma_conform_check(true, "mean(): object has no elements");

    return Datum<eT>::nan;
  }

  eT val = eT(0);

  for (uword i = 0; i < X_n_elem; ++i) {
    val += X[i];
  }

  const eT result = val / T(X_n_elem);

  return arma_isfinite(result) ? result : op_mean::mean_all_robust(X);
}

template <typename eT>
inline eT op_mean::mean_all_robust(const diagview<eT>& X) {
  arma_debug_sigprint();

  typedef typename get_pod_type<eT>::result T;

  const uword X_n_elem = X.n_elem;

  eT r_mean = eT(0);

  for (uword i = 0; i < X_n_elem; ++i) {
    r_mean = r_mean + (X[i] - r_mean) / T(i + 1);
  }

  return r_mean;
}

template <typename T1>
inline typename T1::elem_type op_mean::mean_all(const Op<T1, op_vectorise_col>& X) {
  arma_debug_sigprint();

  return op_mean::mean_all(X.m);
}

template <typename T1>
inline typename T1::elem_type op_mean::mean_all(
    const Base<typename T1::elem_type, T1>& X) {
  arma_debug_sigprint();

  typedef typename T1::elem_type eT;

  const quasi_unwrap<T1> tmp(X.get_ref());
  const Mat<eT>& A = tmp.M;

  const uword A_n_elem = A.n_elem;

  if (A_n_elem == 0) {
    arma_conform_check(true, "mean(): object has no elements");

    return Datum<eT>::nan;
  }

  return op_mean::direct_mean(A.memptr(), A_n_elem);
}

template <typename eT>
arma_inline eT op_mean::robust_mean(const eT A, const eT B) {
  return A + (B - A) / eT(2);
}

template <typename T>
arma_inline std::complex<T> op_mean::robust_mean(const std::complex<T>& A,
                                                 const std::complex<T>& B) {
  return A + (B - A) / T(2);
}

//! @}
