Fortran 95 source code to compute multiple regression equations.(3 explanatory variables)

regression_multi_3e.f95

!****************************************************
! 重回帰式計算（説明（独立）変数3個限定）
!
!   date          name            version
!   2019.12.08    mk-mode.com     1.00 新規作成
!
! Copyright(C) 2019 mk-mode.com All Rights Reserved.
!****************************************************
!
module const
  ! SP: 単精度(4), DP: 倍精度(8)
  integer,     parameter :: SP = kind(1.0)
  integer(SP), parameter :: DP = selected_real_kind(2 * precision(1.0_SP))
end module const

module comp
  use const
  implicit none
  private
  public :: calc_reg_multi_3e

contains
  ! 重回帰式計算
  ! * 説明変数3個限定
  !
  ! :param(in)  real(8) x(:, 3): 説明変数配列
  ! :param(in)  real(8)    y(:): 目的変数配列
  ! :param(out) real(8)       c: 定数
  ! :param(out) real(8)    v(3): 係数
  subroutine calc_reg_multi_3e(x, y, c, v)
    implicit none
    real(DP), intent(in)  :: x(:, :), y(:)
    real(DP), intent(out) :: c, v(3)
    integer(SP) :: s_x1, s_x2, s_x3, s_y
    real(DP)    :: sum_x1, sum_x1x1, sum_x1x2, sum_x1x3
    real(DP)    :: sum_x2, sum_x2x1, sum_x2x2, sum_x2x3
    real(DP)    :: sum_x3, sum_x3x1, sum_x3x2, sum_x3x3
    real(DP)    :: sum_y, sum_x1y, sum_x2y, sum_x3y
    real(DP)    :: mtx(4, 5)

    s_x1 = size(x(:, 1))
    s_x2 = size(x(:, 2))
    s_x3 = size(x(:, 3))
    s_y  = size(y)
    if (s_x1 == 0 .or. s_x2 == 0 .or. s_x3 == 0 .or. s_y == 0) then
      print *, "[ERROR] array size == 0"
      stop
    end if
    if (s_x1 /= s_y .or. s_x2 /= s_y .or. s_x3 /= s_y) then
      print *, "[ERROR] size(X) != size(Y)"
      stop
    end if

    sum_x1   = sum(x(:, 1))
    sum_x2   = sum(x(:, 2))
    sum_x3   = sum(x(:, 3))
    sum_x1x1 = sum(x(:, 1) * x(:, 1))
    sum_x1x2 = sum(x(:, 1) * x(:, 2))
    sum_x1x3 = sum(x(:, 1) * x(:, 3))
    sum_x2x1 = sum_x1x2
    sum_x2x2 = sum(x(:, 2) * x(:, 2))
    sum_x2x3 = sum(x(:, 2) * x(:, 3))
    sum_x3x1 = sum_x1x3
    sum_x3x2 = sum_x2x3
    sum_x3x3 = sum(x(:, 3) * x(:, 3))
    sum_y    = sum(y)
    sum_x1y  = sum(x(:, 1) * y)
    sum_x2y  = sum(x(:, 2) * y)
    sum_x3y  = sum(x(:, 3) * y)
    mtx(1, :) = (/real(s_x1, DP),   sum_x1,   sum_x2,   sum_x3,   sum_y/)
    mtx(2, :) = (/        sum_x1, sum_x1x1, sum_x1x2, sum_x1x3, sum_x1y/)
    mtx(3, :) = (/        sum_x2, sum_x2x1, sum_x2x2, sum_x2x3, sum_x2y/)
    mtx(4, :) = (/        sum_x3, sum_x3x1, sum_x3x2, sum_x3x3, sum_x3y/)
    call gauss_e(4, mtx)
    c = mtx(1, 5)
    v = mtx(2:4, 5)
  end subroutine calc_reg_multi_3e

  ! Gaussian elimination
  !
  ! :param(in)    integer(4)     n: 元数
  ! :param(inout) real(8) a(n,n+1): 係数配列
  subroutine gauss_e(n, a)
    implicit none
    integer(SP), intent(in)    :: n
    real(DP),    intent(inout) :: a(n, n + 1)
    integer(SP) :: i, j
    real(DP)    :: d

    ! 前進消去
    do j = 1, n - 1
      do i = j + 1, n
        d = a(i, j) / a(j, j)
        a(i, j+1:n+1) = a(i, j+1:n+1) - a(j, j+1:n+1) * d
      end do
    end do

    ! 後退代入
    do i = n, 1, -1
      d = a(i, n + 1)
      do j = i + 1, n
        d = d - a(i, j) * a(j, n + 1)
      end do
      a(i, n + 1) = d / a(i, i)
    end do
  end subroutine gauss_e
end module comp

program regression_multi_3e
  use const
  use comp
  implicit none
  character(9), parameter :: F_INP = "input.txt"
  integer(SP),  parameter :: UID   = 10
  real(DP)    :: c, v(3)
  integer(SP) :: n, i
  character(20) :: f
  real(DP), allocatable :: x(:, :), y(:)

  ! IN ファイル OPEN
  open (UID, file = F_INP, status = "old")

  ! データ数読み込み
  read (UID, *) n

  ! 配列用メモリ確保
  allocate(x(n, 3))
  allocate(y(n))

  ! データ読み込み
  do i = 1, n
    read (UID, *) x(i, :), y(i)
  end do
  write (f, '("(A, ", I0, "F8.2, A)")') n
  print f, "説明変数 X(1) = (", x(:, 1), ")"
  print f, "説明変数 X(2) = (", x(:, 2), ")"
  print f, "説明変数 X(3) = (", x(:, 3), ")"
  print f, "目的変数    Y = (", y, ")"
  print '(A)', "---"

  ! IN ファイル CLOSE
  close (UID)

  call calc_reg_multi_3e(x, y, c, v)
  print '(A, F14.8)', "定数項 = ", c
  print '(A, F14.8)', "係数-1 = ", v(1)
  print '(A, F14.8)', "係数-2 = ", v(2)
  print '(A, F14.8)', "係数-3 = ", v(3)

  ! 配列用メモリ解放
  deallocate(x)
  deallocate(y)
end program regression_multi_3e