Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[\left(\frac{x}{2} + y \cdot x\right) + z
\]
↓
\[z + \left(0.5 + y\right) \cdot x
\]
(FPCore (x y z) :precision binary64 (+ (+ (/ x 2.0) (* y x)) z)) ↓
(FPCore (x y z) :precision binary64 (+ z (* (+ 0.5 y) x))) double code(double x, double y, double z) {
return ((x / 2.0) + (y * x)) + z;
}
↓
double code(double x, double y, double z) {
return z + ((0.5 + y) * x);
}
real(8) function code(x, y, z)
real(8), intent (in) :: x
real(8), intent (in) :: y
real(8), intent (in) :: z
code = ((x / 2.0d0) + (y * x)) + z
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 = z + ((0.5d0 + y) * x)
end function
public static double code(double x, double y, double z) {
return ((x / 2.0) + (y * x)) + z;
}
↓
public static double code(double x, double y, double z) {
return z + ((0.5 + y) * x);
}
def code(x, y, z):
return ((x / 2.0) + (y * x)) + z
↓
def code(x, y, z):
return z + ((0.5 + y) * x)
function code(x, y, z)
return Float64(Float64(Float64(x / 2.0) + Float64(y * x)) + z)
end
↓
function code(x, y, z)
return Float64(z + Float64(Float64(0.5 + y) * x))
end
function tmp = code(x, y, z)
tmp = ((x / 2.0) + (y * x)) + z;
end
↓
function tmp = code(x, y, z)
tmp = z + ((0.5 + y) * x);
end
code[x_, y_, z_] := N[(N[(N[(x / 2.0), $MachinePrecision] + N[(y * x), $MachinePrecision]), $MachinePrecision] + z), $MachinePrecision]
↓
code[x_, y_, z_] := N[(z + N[(N[(0.5 + y), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision]
\left(\frac{x}{2} + y \cdot x\right) + z
↓
z + \left(0.5 + y\right) \cdot x
Alternatives Alternative 1 Accuracy 52.5% Cost 1116
\[\begin{array}{l}
\mathbf{if}\;y \leq -0.5:\\
\;\;\;\;y \cdot x\\
\mathbf{elif}\;y \leq -1.45 \cdot 10^{-223}:\\
\;\;\;\;0.5 \cdot x\\
\mathbf{elif}\;y \leq -1.7 \cdot 10^{-246}:\\
\;\;\;\;z\\
\mathbf{elif}\;y \leq 5.9 \cdot 10^{-207}:\\
\;\;\;\;0.5 \cdot x\\
\mathbf{elif}\;y \leq 1.15 \cdot 10^{-113}:\\
\;\;\;\;z\\
\mathbf{elif}\;y \leq 5.2 \cdot 10^{-17}:\\
\;\;\;\;0.5 \cdot x\\
\mathbf{elif}\;y \leq 2.4 \cdot 10^{+181}:\\
\;\;\;\;z\\
\mathbf{else}:\\
\;\;\;\;y \cdot x\\
\end{array}
\]
Alternative 2 Accuracy 83.7% Cost 585
\[\begin{array}{l}
\mathbf{if}\;x \leq -4.5 \cdot 10^{+56} \lor \neg \left(x \leq 1.2 \cdot 10^{+48}\right):\\
\;\;\;\;\left(0.5 + y\right) \cdot x\\
\mathbf{else}:\\
\;\;\;\;z + y \cdot x\\
\end{array}
\]
Alternative 3 Accuracy 98.7% Cost 585
\[\begin{array}{l}
\mathbf{if}\;y \leq -0.5 \lor \neg \left(y \leq 0.0115\right):\\
\;\;\;\;z + y \cdot x\\
\mathbf{else}:\\
\;\;\;\;z + \frac{x}{2}\\
\end{array}
\]
Alternative 4 Accuracy 76.0% Cost 584
\[\begin{array}{l}
\mathbf{if}\;z \leq -5.5 \cdot 10^{+33}:\\
\;\;\;\;z\\
\mathbf{elif}\;z \leq 5 \cdot 10^{+99}:\\
\;\;\;\;\left(0.5 + y\right) \cdot x\\
\mathbf{else}:\\
\;\;\;\;z\\
\end{array}
\]
Alternative 5 Accuracy 58.2% Cost 456
\[\begin{array}{l}
\mathbf{if}\;x \leq -7.6 \cdot 10^{+55}:\\
\;\;\;\;0.5 \cdot x\\
\mathbf{elif}\;x \leq 2.7 \cdot 10^{+46}:\\
\;\;\;\;z\\
\mathbf{else}:\\
\;\;\;\;0.5 \cdot x\\
\end{array}
\]
Alternative 6 Accuracy 46.2% Cost 64
\[z
\]