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}}
Alternatives Alternative 1 Accuracy 74.6% Cost 712
\[\begin{array}{l}
\mathbf{if}\;x \leq -6.5 \cdot 10^{+32}:\\
\;\;\;\;\frac{x}{x + y}\\
\mathbf{elif}\;x \leq 2.5 \cdot 10^{-50}:\\
\;\;\;\;2 \cdot \frac{x}{y} + -1\\
\mathbf{else}:\\
\;\;\;\;1 - \frac{y}{x}\\
\end{array}
\]
Alternative 2 Accuracy 74.3% Cost 648
\[\begin{array}{l}
\mathbf{if}\;x \leq -8.5 \cdot 10^{+33}:\\
\;\;\;\;\frac{x}{x + y}\\
\mathbf{elif}\;x \leq 3.8 \cdot 10^{-50}:\\
\;\;\;\;\frac{-y}{x + y}\\
\mathbf{else}:\\
\;\;\;\;1 - \frac{y}{x}\\
\end{array}
\]
Alternative 3 Accuracy 74.4% Cost 585
\[\begin{array}{l}
\mathbf{if}\;x \leq -1.85 \cdot 10^{+33} \lor \neg \left(x \leq 3.6 \cdot 10^{-50}\right):\\
\;\;\;\;\frac{x}{x + y}\\
\mathbf{else}:\\
\;\;\;\;\frac{x}{y} + -1\\
\end{array}
\]
Alternative 4 Accuracy 74.0% Cost 584
\[\begin{array}{l}
\mathbf{if}\;x \leq -1.12 \cdot 10^{+33}:\\
\;\;\;\;1\\
\mathbf{elif}\;x \leq 3.8 \cdot 10^{-50}:\\
\;\;\;\;\frac{x}{y} + -1\\
\mathbf{else}:\\
\;\;\;\;1\\
\end{array}
\]
Alternative 5 Accuracy 74.3% Cost 584
\[\begin{array}{l}
\mathbf{if}\;x \leq -2.3 \cdot 10^{+34}:\\
\;\;\;\;\frac{x}{x + y}\\
\mathbf{elif}\;x \leq 3.7 \cdot 10^{-50}:\\
\;\;\;\;\frac{x}{y} + -1\\
\mathbf{else}:\\
\;\;\;\;1 - \frac{y}{x}\\
\end{array}
\]
Alternative 6 Accuracy 100.0% Cost 448
\[\frac{x - y}{x + y}
\]
Alternative 7 Accuracy 73.7% Cost 328
\[\begin{array}{l}
\mathbf{if}\;x \leq -1 \cdot 10^{+33}:\\
\;\;\;\;1\\
\mathbf{elif}\;x \leq 3.6 \cdot 10^{-50}:\\
\;\;\;\;-1\\
\mathbf{else}:\\
\;\;\;\;1\\
\end{array}
\]
Alternative 8 Accuracy 49.5% Cost 64
\[-1
\]