Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[\frac{x - y}{x + y}
\]
↓
\[\begin{array}{l}
t_0 := \frac{x - y}{x + y}\\
t_0 \cdot \left(t_0 \cdot \frac{1}{t_0}\right)
\end{array}
\]
(FPCore (x y) :precision binary64 (/ (- x y) (+ x y))) ↓
(FPCore (x y)
:precision binary64
(let* ((t_0 (/ (- x y) (+ x y)))) (* t_0 (* t_0 (/ 1.0 t_0))))) double code(double x, double y) {
return (x - y) / (x + y);
}
↓
double code(double x, double y) {
double t_0 = (x - y) / (x + y);
return t_0 * (t_0 * (1.0 / t_0));
}
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
real(8) :: t_0
t_0 = (x - y) / (x + y)
code = t_0 * (t_0 * (1.0d0 / t_0))
end function
public static double code(double x, double y) {
return (x - y) / (x + y);
}
↓
public static double code(double x, double y) {
double t_0 = (x - y) / (x + y);
return t_0 * (t_0 * (1.0 / t_0));
}
def code(x, y):
return (x - y) / (x + y)
↓
def code(x, y):
t_0 = (x - y) / (x + y)
return t_0 * (t_0 * (1.0 / t_0))
function code(x, y)
return Float64(Float64(x - y) / Float64(x + y))
end
↓
function code(x, y)
t_0 = Float64(Float64(x - y) / Float64(x + y))
return Float64(t_0 * Float64(t_0 * Float64(1.0 / t_0)))
end
function tmp = code(x, y)
tmp = (x - y) / (x + y);
end
↓
function tmp = code(x, y)
t_0 = (x - y) / (x + y);
tmp = t_0 * (t_0 * (1.0 / t_0));
end
code[x_, y_] := N[(N[(x - y), $MachinePrecision] / N[(x + y), $MachinePrecision]), $MachinePrecision]
↓
code[x_, y_] := Block[{t$95$0 = N[(N[(x - y), $MachinePrecision] / N[(x + y), $MachinePrecision]), $MachinePrecision]}, N[(t$95$0 * N[(t$95$0 * N[(1.0 / t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]
\frac{x - y}{x + y}
↓
\begin{array}{l}
t_0 := \frac{x - y}{x + y}\\
t_0 \cdot \left(t_0 \cdot \frac{1}{t_0}\right)
\end{array}