Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[x \cdot x - \left(y \cdot 4\right) \cdot z
\]
↓
\[x \cdot x + \left(y \cdot z\right) \cdot -4
\]
(FPCore (x y z) :precision binary64 (- (* x x) (* (* y 4.0) z))) ↓
(FPCore (x y z) :precision binary64 (+ (* x x) (* (* y z) -4.0))) double code(double x, double y, double z) {
return (x * x) - ((y * 4.0) * z);
}
↓
double code(double x, double y, double z) {
return (x * x) + ((y * z) * -4.0);
}
real(8) function code(x, y, z)
real(8), intent (in) :: x
real(8), intent (in) :: y
real(8), intent (in) :: z
code = (x * x) - ((y * 4.0d0) * z)
end function
↓
real(8) function code(x, y, z)
real(8), intent (in) :: x
real(8), intent (in) :: y
real(8), intent (in) :: z
code = (x * x) + ((y * z) * (-4.0d0))
end function
public static double code(double x, double y, double z) {
return (x * x) - ((y * 4.0) * z);
}
↓
public static double code(double x, double y, double z) {
return (x * x) + ((y * z) * -4.0);
}
def code(x, y, z):
return (x * x) - ((y * 4.0) * z)
↓
def code(x, y, z):
return (x * x) + ((y * z) * -4.0)
function code(x, y, z)
return Float64(Float64(x * x) - Float64(Float64(y * 4.0) * z))
end
↓
function code(x, y, z)
return Float64(Float64(x * x) + Float64(Float64(y * z) * -4.0))
end
function tmp = code(x, y, z)
tmp = (x * x) - ((y * 4.0) * z);
end
↓
function tmp = code(x, y, z)
tmp = (x * x) + ((y * z) * -4.0);
end
code[x_, y_, z_] := N[(N[(x * x), $MachinePrecision] - N[(N[(y * 4.0), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]
↓
code[x_, y_, z_] := N[(N[(x * x), $MachinePrecision] + N[(N[(y * z), $MachinePrecision] * -4.0), $MachinePrecision]), $MachinePrecision]
x \cdot x - \left(y \cdot 4\right) \cdot z
↓
x \cdot x + \left(y \cdot z\right) \cdot -4