Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[\left(x \cdot y + z\right) \cdot y + t
\]
↓
\[\left(y \cdot \left(x \cdot y\right) + y \cdot z\right) + t
\]
(FPCore (x y z t) :precision binary64 (+ (* (+ (* x y) z) y) t)) ↓
(FPCore (x y z t) :precision binary64 (+ (+ (* y (* x y)) (* y z)) t)) double code(double x, double y, double z, double t) {
return (((x * y) + z) * y) + t;
}
↓
double code(double x, double y, double z, double t) {
return ((y * (x * y)) + (y * z)) + t;
}
real(8) function code(x, y, z, t)
real(8), intent (in) :: x
real(8), intent (in) :: y
real(8), intent (in) :: z
real(8), intent (in) :: t
code = (((x * y) + z) * y) + t
end function
↓
real(8) function code(x, y, z, t)
real(8), intent (in) :: x
real(8), intent (in) :: y
real(8), intent (in) :: z
real(8), intent (in) :: t
code = ((y * (x * y)) + (y * z)) + t
end function
public static double code(double x, double y, double z, double t) {
return (((x * y) + z) * y) + t;
}
↓
public static double code(double x, double y, double z, double t) {
return ((y * (x * y)) + (y * z)) + t;
}
def code(x, y, z, t):
return (((x * y) + z) * y) + t
↓
def code(x, y, z, t):
return ((y * (x * y)) + (y * z)) + t
function code(x, y, z, t)
return Float64(Float64(Float64(Float64(x * y) + z) * y) + t)
end
↓
function code(x, y, z, t)
return Float64(Float64(Float64(y * Float64(x * y)) + Float64(y * z)) + t)
end
function tmp = code(x, y, z, t)
tmp = (((x * y) + z) * y) + t;
end
↓
function tmp = code(x, y, z, t)
tmp = ((y * (x * y)) + (y * z)) + t;
end
code[x_, y_, z_, t_] := N[(N[(N[(N[(x * y), $MachinePrecision] + z), $MachinePrecision] * y), $MachinePrecision] + t), $MachinePrecision]
↓
code[x_, y_, z_, t_] := N[(N[(N[(y * N[(x * y), $MachinePrecision]), $MachinePrecision] + N[(y * z), $MachinePrecision]), $MachinePrecision] + t), $MachinePrecision]
\left(x \cdot y + z\right) \cdot y + t
↓
\left(y \cdot \left(x \cdot y\right) + y \cdot z\right) + t
Alternatives Alternative 1 Accuracy 77.7% Cost 1243
\[\begin{array}{l}
\mathbf{if}\;t \leq -8.5 \cdot 10^{+182} \lor \neg \left(t \leq -1.55 \cdot 10^{+110}\right) \land \left(t \leq -1.75 \cdot 10^{-139} \lor \neg \left(t \leq 8 \cdot 10^{-238}\right) \land \left(t \leq 5 \cdot 10^{-154} \lor \neg \left(t \leq 4.3 \cdot 10^{-38}\right)\right)\right):\\
\;\;\;\;y \cdot z + t\\
\mathbf{else}:\\
\;\;\;\;y \cdot \left(x \cdot y + z\right)\\
\end{array}
\]
Alternative 2 Accuracy 53.1% Cost 720
\[\begin{array}{l}
\mathbf{if}\;t \leq -8.5 \cdot 10^{+182}:\\
\;\;\;\;t\\
\mathbf{elif}\;t \leq -1.55 \cdot 10^{+110}:\\
\;\;\;\;y \cdot \left(x \cdot y\right)\\
\mathbf{elif}\;t \leq -1.25 \cdot 10^{-54}:\\
\;\;\;\;t\\
\mathbf{elif}\;t \leq 1.1 \cdot 10^{+113}:\\
\;\;\;\;y \cdot z\\
\mathbf{else}:\\
\;\;\;\;t\\
\end{array}
\]
Alternative 3 Accuracy 88.0% Cost 713
\[\begin{array}{l}
\mathbf{if}\;z \leq -6.8 \cdot 10^{-43} \lor \neg \left(z \leq 6 \cdot 10^{-76}\right):\\
\;\;\;\;y \cdot z + t\\
\mathbf{else}:\\
\;\;\;\;t + x \cdot \left(y \cdot y\right)\\
\end{array}
\]
Alternative 4 Accuracy 99.9% Cost 576
\[t + y \cdot \left(x \cdot y + z\right)
\]
Alternative 5 Accuracy 56.7% Cost 456
\[\begin{array}{l}
\mathbf{if}\;t \leq -5.8 \cdot 10^{-55}:\\
\;\;\;\;t\\
\mathbf{elif}\;t \leq 1.1 \cdot 10^{+113}:\\
\;\;\;\;y \cdot z\\
\mathbf{else}:\\
\;\;\;\;t\\
\end{array}
\]
Alternative 6 Accuracy 80.7% Cost 452
\[\begin{array}{l}
\mathbf{if}\;y \leq -3.7 \cdot 10^{+128}:\\
\;\;\;\;y \cdot \left(x \cdot y\right)\\
\mathbf{else}:\\
\;\;\;\;y \cdot z + t\\
\end{array}
\]
Alternative 7 Accuracy 54.2% Cost 64
\[t
\]