Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[\left(x + y\right) \cdot \left(z + 1\right)
\]
↓
\[\left(x + y\right) \cdot \left(z + 1\right)
\]
(FPCore (x y z) :precision binary64 (* (+ x y) (+ z 1.0))) ↓
(FPCore (x y z) :precision binary64 (* (+ x y) (+ z 1.0))) double code(double x, double y, double z) {
return (x + y) * (z + 1.0);
}
↓
double code(double x, double y, double z) {
return (x + y) * (z + 1.0);
}
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 + 1.0d0)
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 + 1.0d0)
end function
public static double code(double x, double y, double z) {
return (x + y) * (z + 1.0);
}
↓
public static double code(double x, double y, double z) {
return (x + y) * (z + 1.0);
}
def code(x, y, z):
return (x + y) * (z + 1.0)
↓
def code(x, y, z):
return (x + y) * (z + 1.0)
function code(x, y, z)
return Float64(Float64(x + y) * Float64(z + 1.0))
end
↓
function code(x, y, z)
return Float64(Float64(x + y) * Float64(z + 1.0))
end
function tmp = code(x, y, z)
tmp = (x + y) * (z + 1.0);
end
↓
function tmp = code(x, y, z)
tmp = (x + y) * (z + 1.0);
end
code[x_, y_, z_] := N[(N[(x + y), $MachinePrecision] * N[(z + 1.0), $MachinePrecision]), $MachinePrecision]
↓
code[x_, y_, z_] := N[(N[(x + y), $MachinePrecision] * N[(z + 1.0), $MachinePrecision]), $MachinePrecision]
\left(x + y\right) \cdot \left(z + 1\right)
↓
\left(x + y\right) \cdot \left(z + 1\right)
Alternatives Alternative 1 Accuracy 100.0% Cost 448
\[\left(x + y\right) \cdot \left(z + 1\right)
\]
Alternative 2 Accuracy 50.1% Cost 1116
\[\begin{array}{l}
\mathbf{if}\;z \leq -4.7 \cdot 10^{+123}:\\
\;\;\;\;x \cdot z\\
\mathbf{elif}\;z \leq -1:\\
\;\;\;\;y \cdot z\\
\mathbf{elif}\;z \leq -1.65 \cdot 10^{-267}:\\
\;\;\;\;x\\
\mathbf{elif}\;z \leq 9.5 \cdot 10^{-259}:\\
\;\;\;\;y\\
\mathbf{elif}\;z \leq 2.6 \cdot 10^{-9}:\\
\;\;\;\;x\\
\mathbf{elif}\;z \leq 1.04 \cdot 10^{+173}:\\
\;\;\;\;y \cdot z\\
\mathbf{elif}\;z \leq 5.6 \cdot 10^{+236}:\\
\;\;\;\;x \cdot z\\
\mathbf{else}:\\
\;\;\;\;y \cdot z\\
\end{array}
\]
Alternative 3 Accuracy 75.7% Cost 981
\[\begin{array}{l}
t_0 := y \cdot \left(z + 1\right)\\
\mathbf{if}\;z \leq -8.6 \cdot 10^{+123}:\\
\;\;\;\;x \cdot z\\
\mathbf{elif}\;z \leq -2.6 \cdot 10^{-9}:\\
\;\;\;\;t_0\\
\mathbf{elif}\;z \leq 2.5 \cdot 10^{-9}:\\
\;\;\;\;x + y\\
\mathbf{elif}\;z \leq 3.3 \cdot 10^{+176} \lor \neg \left(z \leq 6.3 \cdot 10^{+227}\right):\\
\;\;\;\;t_0\\
\mathbf{else}:\\
\;\;\;\;x \cdot z\\
\end{array}
\]
Alternative 4 Accuracy 75.0% Cost 852
\[\begin{array}{l}
\mathbf{if}\;z \leq -1.02 \cdot 10^{+124}:\\
\;\;\;\;x \cdot z\\
\mathbf{elif}\;z \leq -1:\\
\;\;\;\;y \cdot z\\
\mathbf{elif}\;z \leq 51000:\\
\;\;\;\;x + y\\
\mathbf{elif}\;z \leq 3.15 \cdot 10^{+177}:\\
\;\;\;\;y \cdot z\\
\mathbf{elif}\;z \leq 7 \cdot 10^{+229}:\\
\;\;\;\;x \cdot z\\
\mathbf{else}:\\
\;\;\;\;y \cdot z\\
\end{array}
\]
Alternative 5 Accuracy 50.3% Cost 720
\[\begin{array}{l}
\mathbf{if}\;z \leq -1:\\
\;\;\;\;x \cdot z\\
\mathbf{elif}\;z \leq -1.22 \cdot 10^{-272}:\\
\;\;\;\;x\\
\mathbf{elif}\;z \leq 1.9 \cdot 10^{-258}:\\
\;\;\;\;y\\
\mathbf{elif}\;z \leq 1:\\
\;\;\;\;x\\
\mathbf{else}:\\
\;\;\;\;x \cdot z\\
\end{array}
\]
Alternative 6 Accuracy 98.0% Cost 585
\[\begin{array}{l}
\mathbf{if}\;z \leq -1 \lor \neg \left(z \leq 1\right):\\
\;\;\;\;z \cdot \left(x + y\right)\\
\mathbf{else}:\\
\;\;\;\;x + y\\
\end{array}
\]
Alternative 7 Accuracy 62.4% Cost 452
\[\begin{array}{l}
\mathbf{if}\;x \leq -2 \cdot 10^{-106}:\\
\;\;\;\;x \cdot \left(z + 1\right)\\
\mathbf{else}:\\
\;\;\;\;y \cdot \left(z + 1\right)\\
\end{array}
\]
Alternative 8 Accuracy 30.6% Cost 196
\[\begin{array}{l}
\mathbf{if}\;x \leq -6.4 \cdot 10^{-165}:\\
\;\;\;\;x\\
\mathbf{else}:\\
\;\;\;\;y\\
\end{array}
\]
Alternative 9 Accuracy 26.1% Cost 64
\[x
\]