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