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