Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[\left(x \cdot 2 + x \cdot x\right) + y \cdot y
\]
↓
\[\left(x \cdot 2 + x \cdot x\right) + y \cdot y
\]
(FPCore (x y) :precision binary64 (+ (+ (* x 2.0) (* x x)) (* y y))) ↓
(FPCore (x y) :precision binary64 (+ (+ (* x 2.0) (* x x)) (* y y))) double code(double x, double y) {
return ((x * 2.0) + (x * x)) + (y * y);
}
↓
double code(double x, double y) {
return ((x * 2.0) + (x * x)) + (y * y);
}
real(8) function code(x, y)
real(8), intent (in) :: x
real(8), intent (in) :: y
code = ((x * 2.0d0) + (x * x)) + (y * y)
end function
↓
real(8) function code(x, y)
real(8), intent (in) :: x
real(8), intent (in) :: y
code = ((x * 2.0d0) + (x * x)) + (y * y)
end function
public static double code(double x, double y) {
return ((x * 2.0) + (x * x)) + (y * y);
}
↓
public static double code(double x, double y) {
return ((x * 2.0) + (x * x)) + (y * y);
}
def code(x, y):
return ((x * 2.0) + (x * x)) + (y * y)
↓
def code(x, y):
return ((x * 2.0) + (x * x)) + (y * y)
function code(x, y)
return Float64(Float64(Float64(x * 2.0) + Float64(x * x)) + Float64(y * y))
end
↓
function code(x, y)
return Float64(Float64(Float64(x * 2.0) + Float64(x * x)) + Float64(y * y))
end
function tmp = code(x, y)
tmp = ((x * 2.0) + (x * x)) + (y * y);
end
↓
function tmp = code(x, y)
tmp = ((x * 2.0) + (x * x)) + (y * y);
end
code[x_, y_] := N[(N[(N[(x * 2.0), $MachinePrecision] + N[(x * x), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision]
↓
code[x_, y_] := N[(N[(N[(x * 2.0), $MachinePrecision] + N[(x * x), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision]
\left(x \cdot 2 + x \cdot x\right) + y \cdot y
↓
\left(x \cdot 2 + x \cdot x\right) + y \cdot y
Alternatives Alternative 1 Accuracy 100.0% Cost 704
\[\left(x \cdot 2 + x \cdot x\right) + y \cdot y
\]
Alternative 2 Accuracy 70.6% Cost 984
\[\begin{array}{l}
\mathbf{if}\;x \leq -2.4 \cdot 10^{+64}:\\
\;\;\;\;x \cdot x\\
\mathbf{elif}\;x \leq -8.5 \cdot 10^{-6}:\\
\;\;\;\;y \cdot y\\
\mathbf{elif}\;x \leq -1.1 \cdot 10^{-36}:\\
\;\;\;\;x + x\\
\mathbf{elif}\;x \leq 2.9 \cdot 10^{-197}:\\
\;\;\;\;y \cdot y\\
\mathbf{elif}\;x \leq 1.4 \cdot 10^{-99}:\\
\;\;\;\;x + x\\
\mathbf{elif}\;x \leq 7.3 \cdot 10^{+33}:\\
\;\;\;\;y \cdot y\\
\mathbf{else}:\\
\;\;\;\;x \cdot x\\
\end{array}
\]
Alternative 3 Accuracy 98.8% Cost 713
\[\begin{array}{l}
\mathbf{if}\;x \leq -6600000000 \lor \neg \left(x \leq 0.96\right):\\
\;\;\;\;x \cdot x + y \cdot y\\
\mathbf{else}:\\
\;\;\;\;y \cdot y + \left(x + x\right)\\
\end{array}
\]
Alternative 4 Accuracy 84.3% Cost 708
\[\begin{array}{l}
\mathbf{if}\;y \cdot y \leq 4 \cdot 10^{+24}:\\
\;\;\;\;x \cdot 2 + x \cdot x\\
\mathbf{else}:\\
\;\;\;\;y \cdot y\\
\end{array}
\]
Alternative 5 Accuracy 96.5% Cost 708
\[\begin{array}{l}
\mathbf{if}\;y \cdot y \leq 2.1 \cdot 10^{-165}:\\
\;\;\;\;x \cdot 2 + x \cdot x\\
\mathbf{else}:\\
\;\;\;\;x \cdot x + y \cdot y\\
\end{array}
\]
Alternative 6 Accuracy 84.3% Cost 580
\[\begin{array}{l}
\mathbf{if}\;y \cdot y \leq 6.5 \cdot 10^{+24}:\\
\;\;\;\;x \cdot \left(x + 2\right)\\
\mathbf{else}:\\
\;\;\;\;y \cdot y\\
\end{array}
\]
Alternative 7 Accuracy 100.0% Cost 576
\[y \cdot y + x \cdot \left(x + 2\right)
\]
Alternative 8 Accuracy 72.7% Cost 456
\[\begin{array}{l}
\mathbf{if}\;x \leq -1.35 \cdot 10^{+65}:\\
\;\;\;\;x \cdot x\\
\mathbf{elif}\;x \leq 7.3 \cdot 10^{+33}:\\
\;\;\;\;y \cdot y\\
\mathbf{else}:\\
\;\;\;\;x \cdot x\\
\end{array}
\]
Alternative 9 Accuracy 42.5% Cost 192
\[x \cdot x
\]