\[x + \frac{\left|y - x\right|}{2}
\]
↓
\[x + \frac{\left|y - x\right|}{2}
\]
(FPCore (x y) :precision binary64 (+ x (/ (fabs (- y x)) 2.0)))
↓
(FPCore (x y) :precision binary64 (+ x (/ (fabs (- y x)) 2.0)))
double code(double x, double y) {
return x + (fabs((y - x)) / 2.0);
}
↓
double code(double x, double y) {
return x + (fabs((y - x)) / 2.0);
}
real(8) function code(x, y)
real(8), intent (in) :: x
real(8), intent (in) :: y
code = x + (abs((y - x)) / 2.0d0)
end function
↓
real(8) function code(x, y)
real(8), intent (in) :: x
real(8), intent (in) :: y
code = x + (abs((y - x)) / 2.0d0)
end function
public static double code(double x, double y) {
return x + (Math.abs((y - x)) / 2.0);
}
↓
public static double code(double x, double y) {
return x + (Math.abs((y - x)) / 2.0);
}
def code(x, y):
return x + (math.fabs((y - x)) / 2.0)
↓
def code(x, y):
return x + (math.fabs((y - x)) / 2.0)
function code(x, y)
return Float64(x + Float64(abs(Float64(y - x)) / 2.0))
end
↓
function code(x, y)
return Float64(x + Float64(abs(Float64(y - x)) / 2.0))
end
function tmp = code(x, y)
tmp = x + (abs((y - x)) / 2.0);
end
↓
function tmp = code(x, y)
tmp = x + (abs((y - x)) / 2.0);
end
code[x_, y_] := N[(x + N[(N[Abs[N[(y - x), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]
↓
code[x_, y_] := N[(x + N[(N[Abs[N[(y - x), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]
x + \frac{\left|y - x\right|}{2}
↓
x + \frac{\left|y - x\right|}{2}
Alternatives
| Alternative 1 |
|---|
| Accuracy | 79.3% |
|---|
| Cost | 7512 |
|---|
\[\begin{array}{l}
t_0 := 0.5 \cdot \left(x + y\right)\\
t_1 := \left|y - x\right| \cdot 0.5\\
\mathbf{if}\;x \leq -2.7 \cdot 10^{+55}:\\
\;\;\;\;t_0\\
\mathbf{elif}\;x \leq -0.11:\\
\;\;\;\;t_1\\
\mathbf{elif}\;x \leq -7.2 \cdot 10^{-33}:\\
\;\;\;\;t_0\\
\mathbf{elif}\;x \leq -6.5 \cdot 10^{-102}:\\
\;\;\;\;t_1\\
\mathbf{elif}\;x \leq -3.2 \cdot 10^{-203}:\\
\;\;\;\;t_0\\
\mathbf{elif}\;x \leq 12500:\\
\;\;\;\;t_1\\
\mathbf{else}:\\
\;\;\;\;x + t_0\\
\end{array}
\]
| Alternative 2 |
|---|
| Accuracy | 68.9% |
|---|
| Cost | 977 |
|---|
\[\begin{array}{l}
t_0 := 0.5 \cdot \left(x + y\right)\\
\mathbf{if}\;y \leq -1.32 \cdot 10^{-45}:\\
\;\;\;\;\frac{-0.25}{\frac{0.5}{y}}\\
\mathbf{elif}\;y \leq -2.35 \cdot 10^{-260} \lor \neg \left(y \leq 4.7 \cdot 10^{-261}\right) \land y \leq 2.25 \cdot 10^{-11}:\\
\;\;\;\;x + t_0\\
\mathbf{else}:\\
\;\;\;\;t_0\\
\end{array}
\]
| Alternative 3 |
|---|
| Accuracy | 43.3% |
|---|
| Cost | 589 |
|---|
\[\begin{array}{l}
\mathbf{if}\;y \leq 8 \cdot 10^{-215} \lor \neg \left(y \leq 5.8 \cdot 10^{-120}\right) \land y \leq 4.4 \cdot 10^{+18}:\\
\;\;\;\;x \cdot 0.5\\
\mathbf{else}:\\
\;\;\;\;y \cdot 0.5\\
\end{array}
\]
| Alternative 4 |
|---|
| Accuracy | 69.6% |
|---|
| Cost | 452 |
|---|
\[\begin{array}{l}
\mathbf{if}\;y \leq -1.85 \cdot 10^{-174}:\\
\;\;\;\;\frac{-0.25}{\frac{0.5}{y}}\\
\mathbf{else}:\\
\;\;\;\;0.5 \cdot \left(x + y\right)\\
\end{array}
\]
| Alternative 5 |
|---|
| Accuracy | 31.4% |
|---|
| Cost | 324 |
|---|
\[\begin{array}{l}
\mathbf{if}\;y \leq 3.2 \cdot 10^{-268}:\\
\;\;\;\;x\\
\mathbf{else}:\\
\;\;\;\;y \cdot 0.5\\
\end{array}
\]
| Alternative 6 |
|---|
| Accuracy | 55.2% |
|---|
| Cost | 320 |
|---|
\[0.5 \cdot \left(x + y\right)
\]
| Alternative 7 |
|---|
| Accuracy | 11.4% |
|---|
| Cost | 64 |
|---|
\[x
\]