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