Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t
\]
(FPCore (x y z t)
:precision binary64
(+ (- (* (/ 1.0 8.0) x) (/ (* y z) 2.0)) t)) double code(double x, double y, double z, double t) {
return (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t;
}
real(8) function code(x, y, z, t)
real(8), intent (in) :: x
real(8), intent (in) :: y
real(8), intent (in) :: z
real(8), intent (in) :: t
code = (((1.0d0 / 8.0d0) * x) - ((y * z) / 2.0d0)) + t
end function
public static double code(double x, double y, double z, double t) {
return (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t;
}
def code(x, y, z, t):
return (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t
function code(x, y, z, t)
return Float64(Float64(Float64(Float64(1.0 / 8.0) * x) - Float64(Float64(y * z) / 2.0)) + t)
end
function tmp = code(x, y, z, t)
tmp = (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t;
end
code[x_, y_, z_, t_] := N[(N[(N[(N[(1.0 / 8.0), $MachinePrecision] * x), $MachinePrecision] - N[(N[(y * z), $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision] + t), $MachinePrecision]
\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t