Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[\frac{x - y}{z - y}
\]
↓
\[\frac{x - y}{z - y}
\]
(FPCore (x y z) :precision binary64 (/ (- x y) (- z y))) ↓
(FPCore (x y z) :precision binary64 (/ (- x y) (- z y))) double code(double x, double y, double z) {
return (x - y) / (z - y);
}
↓
double code(double x, double y, double z) {
return (x - y) / (z - y);
}
real(8) function code(x, y, z)
real(8), intent (in) :: x
real(8), intent (in) :: y
real(8), intent (in) :: z
code = (x - y) / (z - y)
end function
↓
real(8) function code(x, y, z)
real(8), intent (in) :: x
real(8), intent (in) :: y
real(8), intent (in) :: z
code = (x - y) / (z - y)
end function
public static double code(double x, double y, double z) {
return (x - y) / (z - y);
}
↓
public static double code(double x, double y, double z) {
return (x - y) / (z - y);
}
def code(x, y, z):
return (x - y) / (z - y)
↓
def code(x, y, z):
return (x - y) / (z - y)
function code(x, y, z)
return Float64(Float64(x - y) / Float64(z - y))
end
↓
function code(x, y, z)
return Float64(Float64(x - y) / Float64(z - y))
end
function tmp = code(x, y, z)
tmp = (x - y) / (z - y);
end
↓
function tmp = code(x, y, z)
tmp = (x - y) / (z - y);
end
code[x_, y_, z_] := N[(N[(x - y), $MachinePrecision] / N[(z - y), $MachinePrecision]), $MachinePrecision]
↓
code[x_, y_, z_] := N[(N[(x - y), $MachinePrecision] / N[(z - y), $MachinePrecision]), $MachinePrecision]
\frac{x - y}{z - y}
↓
\frac{x - y}{z - y}
Alternatives Alternative 1 Accuracy 72.2% Cost 1505
\[\begin{array}{l}
t_0 := \frac{x - y}{z}\\
t_1 := 1 + \frac{z - x}{y}\\
\mathbf{if}\;z \leq -1.15 \cdot 10^{+35}:\\
\;\;\;\;t_0\\
\mathbf{elif}\;z \leq 7.2 \cdot 10^{-196}:\\
\;\;\;\;t_1\\
\mathbf{elif}\;z \leq 2.1 \cdot 10^{-173}:\\
\;\;\;\;t_0\\
\mathbf{elif}\;z \leq 4.6 \cdot 10^{-116}:\\
\;\;\;\;t_1\\
\mathbf{elif}\;z \leq 1.65 \cdot 10^{-49}:\\
\;\;\;\;\frac{x}{z - y}\\
\mathbf{elif}\;z \leq 4.7 \cdot 10^{-11} \lor \neg \left(z \leq 6.5 \cdot 10^{+112}\right) \land z \leq 7 \cdot 10^{+134}:\\
\;\;\;\;t_1\\
\mathbf{else}:\\
\;\;\;\;t_0\\
\end{array}
\]
Alternative 2 Accuracy 67.4% Cost 980
\[\begin{array}{l}
t_0 := 1 + \frac{z}{y}\\
t_1 := \frac{x}{z - y}\\
\mathbf{if}\;y \leq -1.6 \cdot 10^{+163}:\\
\;\;\;\;t_0\\
\mathbf{elif}\;y \leq 6.8 \cdot 10^{-11}:\\
\;\;\;\;t_1\\
\mathbf{elif}\;y \leq 3.5 \cdot 10^{+39}:\\
\;\;\;\;1\\
\mathbf{elif}\;y \leq 1.04 \cdot 10^{+102}:\\
\;\;\;\;t_1\\
\mathbf{elif}\;y \leq 4 \cdot 10^{+114}:\\
\;\;\;\;t_0\\
\mathbf{else}:\\
\;\;\;\;1\\
\end{array}
\]
Alternative 3 Accuracy 58.9% Cost 588
\[\begin{array}{l}
\mathbf{if}\;y \leq -1.6 \cdot 10^{+163}:\\
\;\;\;\;1\\
\mathbf{elif}\;y \leq -8 \cdot 10^{-49}:\\
\;\;\;\;\frac{-x}{y}\\
\mathbf{elif}\;y \leq 6.8 \cdot 10^{-11}:\\
\;\;\;\;\frac{x}{z}\\
\mathbf{else}:\\
\;\;\;\;1\\
\end{array}
\]
Alternative 4 Accuracy 58.9% Cost 588
\[\begin{array}{l}
\mathbf{if}\;y \leq -1.6 \cdot 10^{+163}:\\
\;\;\;\;1 + \frac{z}{y}\\
\mathbf{elif}\;y \leq -1.4 \cdot 10^{-46}:\\
\;\;\;\;\frac{-x}{y}\\
\mathbf{elif}\;y \leq 6.8 \cdot 10^{-11}:\\
\;\;\;\;\frac{x}{z}\\
\mathbf{else}:\\
\;\;\;\;1\\
\end{array}
\]
Alternative 5 Accuracy 76.8% Cost 585
\[\begin{array}{l}
\mathbf{if}\;x \leq -9 \lor \neg \left(x \leq 8.5 \cdot 10^{-6}\right):\\
\;\;\;\;\frac{x}{z - y}\\
\mathbf{else}:\\
\;\;\;\;\frac{y}{y - z}\\
\end{array}
\]
Alternative 6 Accuracy 61.1% Cost 456
\[\begin{array}{l}
\mathbf{if}\;y \leq -30:\\
\;\;\;\;1\\
\mathbf{elif}\;y \leq 6.5 \cdot 10^{-11}:\\
\;\;\;\;\frac{x}{z}\\
\mathbf{else}:\\
\;\;\;\;1\\
\end{array}
\]
Alternative 7 Accuracy 36.3% Cost 64
\[1
\]