Math FPCore C Java Python Julia MATLAB Wolfram TeX \[\frac{x \cdot \left(y + z\right)}{z}
\]
↓
\[\begin{array}{l}
t_0 := \frac{x \cdot \left(y + z\right)}{z}\\
\mathbf{if}\;t_0 \leq -\infty \lor \neg \left(t_0 \leq -2 \cdot 10^{+57}\right):\\
\;\;\;\;\frac{x}{\frac{z}{y + z}}\\
\mathbf{else}:\\
\;\;\;\;t_0\\
\end{array}
\]
(FPCore (x y z) :precision binary64 (/ (* x (+ y z)) z)) ↓
(FPCore (x y z)
:precision binary64
(let* ((t_0 (/ (* x (+ y z)) z)))
(if (or (<= t_0 (- INFINITY)) (not (<= t_0 -2e+57)))
(/ x (/ z (+ y z)))
t_0))) double code(double x, double y, double z) {
return (x * (y + z)) / z;
}
↓
double code(double x, double y, double z) {
double t_0 = (x * (y + z)) / z;
double tmp;
if ((t_0 <= -((double) INFINITY)) || !(t_0 <= -2e+57)) {
tmp = x / (z / (y + z));
} else {
tmp = t_0;
}
return tmp;
}
public static double code(double x, double y, double z) {
return (x * (y + z)) / z;
}
↓
public static double code(double x, double y, double z) {
double t_0 = (x * (y + z)) / z;
double tmp;
if ((t_0 <= -Double.POSITIVE_INFINITY) || !(t_0 <= -2e+57)) {
tmp = x / (z / (y + z));
} else {
tmp = t_0;
}
return tmp;
}
def code(x, y, z):
return (x * (y + z)) / z
↓
def code(x, y, z):
t_0 = (x * (y + z)) / z
tmp = 0
if (t_0 <= -math.inf) or not (t_0 <= -2e+57):
tmp = x / (z / (y + z))
else:
tmp = t_0
return tmp
function code(x, y, z)
return Float64(Float64(x * Float64(y + z)) / z)
end
↓
function code(x, y, z)
t_0 = Float64(Float64(x * Float64(y + z)) / z)
tmp = 0.0
if ((t_0 <= Float64(-Inf)) || !(t_0 <= -2e+57))
tmp = Float64(x / Float64(z / Float64(y + z)));
else
tmp = t_0;
end
return tmp
end
function tmp = code(x, y, z)
tmp = (x * (y + z)) / z;
end
↓
function tmp_2 = code(x, y, z)
t_0 = (x * (y + z)) / z;
tmp = 0.0;
if ((t_0 <= -Inf) || ~((t_0 <= -2e+57)))
tmp = x / (z / (y + z));
else
tmp = t_0;
end
tmp_2 = tmp;
end
code[x_, y_, z_] := N[(N[(x * N[(y + z), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]
↓
code[x_, y_, z_] := Block[{t$95$0 = N[(N[(x * N[(y + z), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]}, If[Or[LessEqual[t$95$0, (-Infinity)], N[Not[LessEqual[t$95$0, -2e+57]], $MachinePrecision]], N[(x / N[(z / N[(y + z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]
\frac{x \cdot \left(y + z\right)}{z}
↓
\begin{array}{l}
t_0 := \frac{x \cdot \left(y + z\right)}{z}\\
\mathbf{if}\;t_0 \leq -\infty \lor \neg \left(t_0 \leq -2 \cdot 10^{+57}\right):\\
\;\;\;\;\frac{x}{\frac{z}{y + z}}\\
\mathbf{else}:\\
\;\;\;\;t_0\\
\end{array}
Alternatives Alternative 1 Error 20.7 Cost 849
\[\begin{array}{l}
\mathbf{if}\;z \leq -1.56 \cdot 10^{-51}:\\
\;\;\;\;x\\
\mathbf{elif}\;z \leq 5.5 \cdot 10^{-196} \lor \neg \left(z \leq 2.3 \cdot 10^{-166}\right) \land z \leq 2.6 \cdot 10^{-118}:\\
\;\;\;\;x \cdot \frac{y}{z}\\
\mathbf{else}:\\
\;\;\;\;x\\
\end{array}
\]
Alternative 2 Error 19.2 Cost 849
\[\begin{array}{l}
\mathbf{if}\;z \leq -2 \cdot 10^{-55}:\\
\;\;\;\;x\\
\mathbf{elif}\;z \leq 7.3 \cdot 10^{-196} \lor \neg \left(z \leq 2.3 \cdot 10^{-166}\right) \land z \leq 1.06 \cdot 10^{-106}:\\
\;\;\;\;y \cdot \frac{x}{z}\\
\mathbf{else}:\\
\;\;\;\;x\\
\end{array}
\]
Alternative 3 Error 19.2 Cost 849
\[\begin{array}{l}
\mathbf{if}\;z \leq -6.1 \cdot 10^{-49}:\\
\;\;\;\;x\\
\mathbf{elif}\;z \leq 6.6 \cdot 10^{-196} \lor \neg \left(z \leq 2.35 \cdot 10^{-166}\right) \land z \leq 3.8 \cdot 10^{-109}:\\
\;\;\;\;\frac{y}{\frac{z}{x}}\\
\mathbf{else}:\\
\;\;\;\;x\\
\end{array}
\]
Alternative 4 Error 7.8 Cost 712
\[\begin{array}{l}
\mathbf{if}\;z \leq -2.5 \cdot 10^{+126}:\\
\;\;\;\;x\\
\mathbf{elif}\;z \leq 1.26 \cdot 10^{+169}:\\
\;\;\;\;\left(y + z\right) \cdot \frac{x}{z}\\
\mathbf{else}:\\
\;\;\;\;x\\
\end{array}
\]
Alternative 5 Error 3.2 Cost 448
\[\frac{x}{\frac{z}{y + z}}
\]
Alternative 6 Error 25.9 Cost 64
\[x
\]