Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[x \cdot y + \left(x - 1\right) \cdot z
\]
↓
\[x \cdot \left(y + z\right) - z
\]
(FPCore (x y z) :precision binary64 (+ (* x y) (* (- x 1.0) z))) ↓
(FPCore (x y z) :precision binary64 (- (* x (+ y z)) z)) double code(double x, double y, double z) {
return (x * y) + ((x - 1.0) * z);
}
↓
double code(double x, double y, double z) {
return (x * (y + z)) - z;
}
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) + ((x - 1.0d0) * 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 = (x * (y + z)) - z
end function
public static double code(double x, double y, double z) {
return (x * y) + ((x - 1.0) * z);
}
↓
public static double code(double x, double y, double z) {
return (x * (y + z)) - z;
}
def code(x, y, z):
return (x * y) + ((x - 1.0) * z)
↓
def code(x, y, z):
return (x * (y + z)) - z
function code(x, y, z)
return Float64(Float64(x * y) + Float64(Float64(x - 1.0) * z))
end
↓
function code(x, y, z)
return Float64(Float64(x * Float64(y + z)) - z)
end
function tmp = code(x, y, z)
tmp = (x * y) + ((x - 1.0) * z);
end
↓
function tmp = code(x, y, z)
tmp = (x * (y + z)) - z;
end
code[x_, y_, z_] := N[(N[(x * y), $MachinePrecision] + N[(N[(x - 1.0), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]
↓
code[x_, y_, z_] := N[(N[(x * N[(y + z), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]
x \cdot y + \left(x - 1\right) \cdot z
↓
x \cdot \left(y + z\right) - z
Alternatives Alternative 1 Accuracy 63.3% Cost 848
\[\begin{array}{l}
t_0 := x \cdot \left(y + z\right)\\
\mathbf{if}\;x \leq -1.95 \cdot 10^{-50}:\\
\;\;\;\;t_0\\
\mathbf{elif}\;x \leq 3.1 \cdot 10^{-106}:\\
\;\;\;\;-z\\
\mathbf{elif}\;x \leq 2.4 \cdot 10^{-77}:\\
\;\;\;\;x \cdot y\\
\mathbf{elif}\;x \leq 1.3 \cdot 10^{-6}:\\
\;\;\;\;-z\\
\mathbf{else}:\\
\;\;\;\;t_0\\
\end{array}
\]
Alternative 2 Accuracy 47.7% Cost 720
\[\begin{array}{l}
\mathbf{if}\;z \leq -7.2 \cdot 10^{-62}:\\
\;\;\;\;-z\\
\mathbf{elif}\;z \leq 9.5 \cdot 10^{-91}:\\
\;\;\;\;x \cdot y\\
\mathbf{elif}\;z \leq 2.35 \cdot 10^{+73}:\\
\;\;\;\;-z\\
\mathbf{elif}\;z \leq 4.7 \cdot 10^{+123}:\\
\;\;\;\;x \cdot z\\
\mathbf{else}:\\
\;\;\;\;-z\\
\end{array}
\]
Alternative 3 Accuracy 63.1% Cost 585
\[\begin{array}{l}
\mathbf{if}\;z \leq -2.25 \cdot 10^{-60} \lor \neg \left(z \leq 4.2 \cdot 10^{-60}\right):\\
\;\;\;\;z \cdot \left(x + -1\right)\\
\mathbf{else}:\\
\;\;\;\;x \cdot \left(y + z\right)\\
\end{array}
\]
Alternative 4 Accuracy 63.2% Cost 584
\[\begin{array}{l}
\mathbf{if}\;z \leq -2.95 \cdot 10^{-59}:\\
\;\;\;\;x \cdot z - z\\
\mathbf{elif}\;z \leq 9.5 \cdot 10^{-58}:\\
\;\;\;\;x \cdot \left(y + z\right)\\
\mathbf{else}:\\
\;\;\;\;z \cdot \left(x + -1\right)\\
\end{array}
\]
Alternative 5 Accuracy 48.5% Cost 456
\[\begin{array}{l}
\mathbf{if}\;z \leq -7 \cdot 10^{-62}:\\
\;\;\;\;-z\\
\mathbf{elif}\;z \leq 4.7 \cdot 10^{-91}:\\
\;\;\;\;x \cdot y\\
\mathbf{else}:\\
\;\;\;\;-z\\
\end{array}
\]
Alternative 6 Accuracy 36.1% Cost 128
\[-z
\]