Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[\frac{x \cdot \left(\frac{x}{y} + 1\right)}{x + 1}
\]
↓
\[\frac{x}{\frac{x + 1}{1 + \frac{x}{y}}}
\]
(FPCore (x y) :precision binary64 (/ (* x (+ (/ x y) 1.0)) (+ x 1.0))) ↓
(FPCore (x y) :precision binary64 (/ x (/ (+ x 1.0) (+ 1.0 (/ x y))))) double code(double x, double y) {
return (x * ((x / y) + 1.0)) / (x + 1.0);
}
↓
double code(double x, double y) {
return x / ((x + 1.0) / (1.0 + (x / y)));
}
real(8) function code(x, y)
real(8), intent (in) :: x
real(8), intent (in) :: y
code = (x * ((x / y) + 1.0d0)) / (x + 1.0d0)
end function
↓
real(8) function code(x, y)
real(8), intent (in) :: x
real(8), intent (in) :: y
code = x / ((x + 1.0d0) / (1.0d0 + (x / y)))
end function
public static double code(double x, double y) {
return (x * ((x / y) + 1.0)) / (x + 1.0);
}
↓
public static double code(double x, double y) {
return x / ((x + 1.0) / (1.0 + (x / y)));
}
def code(x, y):
return (x * ((x / y) + 1.0)) / (x + 1.0)
↓
def code(x, y):
return x / ((x + 1.0) / (1.0 + (x / y)))
function code(x, y)
return Float64(Float64(x * Float64(Float64(x / y) + 1.0)) / Float64(x + 1.0))
end
↓
function code(x, y)
return Float64(x / Float64(Float64(x + 1.0) / Float64(1.0 + Float64(x / y))))
end
function tmp = code(x, y)
tmp = (x * ((x / y) + 1.0)) / (x + 1.0);
end
↓
function tmp = code(x, y)
tmp = x / ((x + 1.0) / (1.0 + (x / y)));
end
code[x_, y_] := N[(N[(x * N[(N[(x / y), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]
↓
code[x_, y_] := N[(x / N[(N[(x + 1.0), $MachinePrecision] / N[(1.0 + N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\frac{x \cdot \left(\frac{x}{y} + 1\right)}{x + 1}
↓
\frac{x}{\frac{x + 1}{1 + \frac{x}{y}}}
Alternatives Alternative 1 Accuracy 99.9% Cost 704
\[\frac{x}{\frac{x + 1}{1 + \frac{x}{y}}}
\]
Alternative 2 Accuracy 73.5% Cost 720
\[\begin{array}{l}
\mathbf{if}\;x \leq -2.7 \cdot 10^{+49}:\\
\;\;\;\;\frac{x}{y}\\
\mathbf{elif}\;x \leq -4.9 \cdot 10^{+36}:\\
\;\;\;\;1\\
\mathbf{elif}\;x \leq -1:\\
\;\;\;\;\frac{x}{y}\\
\mathbf{elif}\;x \leq 2.4 \cdot 10^{-11}:\\
\;\;\;\;x\\
\mathbf{else}:\\
\;\;\;\;\frac{x}{y}\\
\end{array}
\]
Alternative 3 Accuracy 97.7% Cost 713
\[\begin{array}{l}
\mathbf{if}\;x \leq -1 \lor \neg \left(x \leq 1\right):\\
\;\;\;\;1 + \frac{x}{y}\\
\mathbf{else}:\\
\;\;\;\;x + x \cdot \frac{x}{y}\\
\end{array}
\]
Alternative 4 Accuracy 98.0% Cost 713
\[\begin{array}{l}
\mathbf{if}\;x \leq -1 \lor \neg \left(x \leq 1.25\right):\\
\;\;\;\;1 + \frac{x + -1}{y}\\
\mathbf{else}:\\
\;\;\;\;x + x \cdot \frac{x}{y}\\
\end{array}
\]
Alternative 5 Accuracy 85.3% Cost 585
\[\begin{array}{l}
\mathbf{if}\;x \leq -1 \lor \neg \left(x \leq 2.4 \cdot 10^{-11}\right):\\
\;\;\;\;1 + \frac{x}{y}\\
\mathbf{else}:\\
\;\;\;\;x\\
\end{array}
\]
Alternative 6 Accuracy 85.4% Cost 585
\[\begin{array}{l}
\mathbf{if}\;x \leq -1 \lor \neg \left(x \leq 2.4 \cdot 10^{-11}\right):\\
\;\;\;\;1 + \frac{x}{y}\\
\mathbf{else}:\\
\;\;\;\;x - x \cdot x\\
\end{array}
\]
Alternative 7 Accuracy 85.6% Cost 585
\[\begin{array}{l}
\mathbf{if}\;x \leq -1520000 \lor \neg \left(x \leq 2.4 \cdot 10^{-11}\right):\\
\;\;\;\;1 + \frac{x}{y}\\
\mathbf{else}:\\
\;\;\;\;\frac{x}{x + 1}\\
\end{array}
\]
Alternative 8 Accuracy 48.8% Cost 328
\[\begin{array}{l}
\mathbf{if}\;x \leq -1:\\
\;\;\;\;1\\
\mathbf{elif}\;x \leq 210000000:\\
\;\;\;\;x\\
\mathbf{else}:\\
\;\;\;\;1\\
\end{array}
\]
Alternative 9 Accuracy 14.1% Cost 64
\[1
\]