Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[\frac{x \cdot 2}{y \cdot z - t \cdot z}
\]
↓
\[\begin{array}{l}
\mathbf{if}\;x \cdot 2 \leq -5 \cdot 10^{-97} \lor \neg \left(x \cdot 2 \leq 2 \cdot 10^{-43}\right):\\
\;\;\;\;\frac{\frac{x}{y - t}}{z \cdot 0.5}\\
\mathbf{else}:\\
\;\;\;\;\frac{\frac{x \cdot 2}{z}}{y - t}\\
\end{array}
\]
(FPCore (x y z t) :precision binary64 (/ (* x 2.0) (- (* y z) (* t z)))) ↓
(FPCore (x y z t)
:precision binary64
(if (or (<= (* x 2.0) -5e-97) (not (<= (* x 2.0) 2e-43)))
(/ (/ x (- y t)) (* z 0.5))
(/ (/ (* x 2.0) z) (- y t)))) double code(double x, double y, double z, double t) {
return (x * 2.0) / ((y * z) - (t * z));
}
↓
double code(double x, double y, double z, double t) {
double tmp;
if (((x * 2.0) <= -5e-97) || !((x * 2.0) <= 2e-43)) {
tmp = (x / (y - t)) / (z * 0.5);
} else {
tmp = ((x * 2.0) / z) / (y - t);
}
return tmp;
}
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 * 2.0d0) / ((y * z) - (t * z))
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
real(8) :: tmp
if (((x * 2.0d0) <= (-5d-97)) .or. (.not. ((x * 2.0d0) <= 2d-43))) then
tmp = (x / (y - t)) / (z * 0.5d0)
else
tmp = ((x * 2.0d0) / z) / (y - t)
end if
code = tmp
end function
public static double code(double x, double y, double z, double t) {
return (x * 2.0) / ((y * z) - (t * z));
}
↓
public static double code(double x, double y, double z, double t) {
double tmp;
if (((x * 2.0) <= -5e-97) || !((x * 2.0) <= 2e-43)) {
tmp = (x / (y - t)) / (z * 0.5);
} else {
tmp = ((x * 2.0) / z) / (y - t);
}
return tmp;
}
def code(x, y, z, t):
return (x * 2.0) / ((y * z) - (t * z))
↓
def code(x, y, z, t):
tmp = 0
if ((x * 2.0) <= -5e-97) or not ((x * 2.0) <= 2e-43):
tmp = (x / (y - t)) / (z * 0.5)
else:
tmp = ((x * 2.0) / z) / (y - t)
return tmp
function code(x, y, z, t)
return Float64(Float64(x * 2.0) / Float64(Float64(y * z) - Float64(t * z)))
end
↓
function code(x, y, z, t)
tmp = 0.0
if ((Float64(x * 2.0) <= -5e-97) || !(Float64(x * 2.0) <= 2e-43))
tmp = Float64(Float64(x / Float64(y - t)) / Float64(z * 0.5));
else
tmp = Float64(Float64(Float64(x * 2.0) / z) / Float64(y - t));
end
return tmp
end
function tmp = code(x, y, z, t)
tmp = (x * 2.0) / ((y * z) - (t * z));
end
↓
function tmp_2 = code(x, y, z, t)
tmp = 0.0;
if (((x * 2.0) <= -5e-97) || ~(((x * 2.0) <= 2e-43)))
tmp = (x / (y - t)) / (z * 0.5);
else
tmp = ((x * 2.0) / z) / (y - t);
end
tmp_2 = tmp;
end
code[x_, y_, z_, t_] := N[(N[(x * 2.0), $MachinePrecision] / N[(N[(y * z), $MachinePrecision] - N[(t * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
↓
code[x_, y_, z_, t_] := If[Or[LessEqual[N[(x * 2.0), $MachinePrecision], -5e-97], N[Not[LessEqual[N[(x * 2.0), $MachinePrecision], 2e-43]], $MachinePrecision]], N[(N[(x / N[(y - t), $MachinePrecision]), $MachinePrecision] / N[(z * 0.5), $MachinePrecision]), $MachinePrecision], N[(N[(N[(x * 2.0), $MachinePrecision] / z), $MachinePrecision] / N[(y - t), $MachinePrecision]), $MachinePrecision]]
\frac{x \cdot 2}{y \cdot z - t \cdot z}
↓
\begin{array}{l}
\mathbf{if}\;x \cdot 2 \leq -5 \cdot 10^{-97} \lor \neg \left(x \cdot 2 \leq 2 \cdot 10^{-43}\right):\\
\;\;\;\;\frac{\frac{x}{y - t}}{z \cdot 0.5}\\
\mathbf{else}:\\
\;\;\;\;\frac{\frac{x \cdot 2}{z}}{y - t}\\
\end{array}
Alternatives Alternative 1 Accuracy 96.4% Cost 841
\[\begin{array}{l}
\mathbf{if}\;z \leq -8.8 \cdot 10^{+82} \lor \neg \left(z \leq 7 \cdot 10^{-20}\right):\\
\;\;\;\;\frac{\frac{2}{z}}{\frac{y - t}{x}}\\
\mathbf{else}:\\
\;\;\;\;\frac{x}{\frac{\left(y - t\right) \cdot z}{2}}\\
\end{array}
\]
Alternative 2 Accuracy 96.2% Cost 841
\[\begin{array}{l}
\mathbf{if}\;x \leq -9.6 \cdot 10^{-44} \lor \neg \left(x \leq 2.2 \cdot 10^{+34}\right):\\
\;\;\;\;\frac{\frac{x}{y - t}}{z \cdot 0.5}\\
\mathbf{else}:\\
\;\;\;\;x \cdot \frac{\frac{2}{z}}{y - t}\\
\end{array}
\]
Alternative 3 Accuracy 73.1% Cost 713
\[\begin{array}{l}
\mathbf{if}\;y \leq -8.5 \cdot 10^{-48} \lor \neg \left(y \leq 1.78 \cdot 10^{+19}\right):\\
\;\;\;\;x \cdot \frac{2}{y \cdot z}\\
\mathbf{else}:\\
\;\;\;\;x \cdot \frac{-2}{t \cdot z}\\
\end{array}
\]
Alternative 4 Accuracy 73.2% Cost 713
\[\begin{array}{l}
\mathbf{if}\;y \leq -2.45 \cdot 10^{-48} \lor \neg \left(y \leq 2.95 \cdot 10^{+20}\right):\\
\;\;\;\;x \cdot \frac{2}{y \cdot z}\\
\mathbf{else}:\\
\;\;\;\;x \cdot \frac{\frac{-2}{z}}{t}\\
\end{array}
\]
Alternative 5 Accuracy 73.2% Cost 712
\[\begin{array}{l}
\mathbf{if}\;y \leq -1.2 \cdot 10^{-49}:\\
\;\;\;\;x \cdot \frac{2}{y \cdot z}\\
\mathbf{elif}\;y \leq 3.7 \cdot 10^{+27}:\\
\;\;\;\;x \cdot \frac{\frac{-2}{z}}{t}\\
\mathbf{else}:\\
\;\;\;\;x \cdot \frac{\frac{2}{y}}{z}\\
\end{array}
\]
Alternative 6 Accuracy 73.3% Cost 712
\[\begin{array}{l}
\mathbf{if}\;y \leq -2.55 \cdot 10^{-49}:\\
\;\;\;\;x \cdot \frac{2}{y \cdot z}\\
\mathbf{elif}\;y \leq 1.9 \cdot 10^{+23}:\\
\;\;\;\;\frac{-2}{t \cdot \frac{z}{x}}\\
\mathbf{else}:\\
\;\;\;\;x \cdot \frac{\frac{2}{y}}{z}\\
\end{array}
\]
Alternative 7 Accuracy 73.2% Cost 712
\[\begin{array}{l}
\mathbf{if}\;y \leq -4 \cdot 10^{-48}:\\
\;\;\;\;\frac{2}{y \cdot \frac{z}{x}}\\
\mathbf{elif}\;y \leq 4.3 \cdot 10^{+22}:\\
\;\;\;\;\frac{-2}{t \cdot \frac{z}{x}}\\
\mathbf{else}:\\
\;\;\;\;x \cdot \frac{\frac{2}{y}}{z}\\
\end{array}
\]
Alternative 8 Accuracy 73.4% Cost 712
\[\begin{array}{l}
\mathbf{if}\;y \leq -1.62 \cdot 10^{-49}:\\
\;\;\;\;\frac{2}{y \cdot \frac{z}{x}}\\
\mathbf{elif}\;y \leq 1.52 \cdot 10^{+25}:\\
\;\;\;\;\frac{\frac{x \cdot -2}{z}}{t}\\
\mathbf{else}:\\
\;\;\;\;x \cdot \frac{\frac{2}{y}}{z}\\
\end{array}
\]
Alternative 9 Accuracy 91.9% Cost 576
\[x \cdot \frac{\frac{2}{z}}{y - t}
\]
Alternative 10 Accuracy 50.5% Cost 448
\[x \cdot \frac{-2}{t \cdot z}
\]