Math FPCore C Java Python Julia MATLAB Wolfram TeX \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)
\]
↓
\[\begin{array}{l}
t_1 := \tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\\
\mathbf{if}\;x + \left(y \cdot z\right) \cdot t_1 \leq \infty:\\
\;\;\;\;x + z \cdot \left(y \cdot t_1\right)\\
\mathbf{else}:\\
\;\;\;\;x + 2 \cdot \frac{z}{\frac{2}{t - x}}\\
\end{array}
\]
(FPCore (x y z t)
:precision binary64
(+ x (* (* y z) (- (tanh (/ t y)) (tanh (/ x y)))))) ↓
(FPCore (x y z t)
:precision binary64
(let* ((t_1 (- (tanh (/ t y)) (tanh (/ x y)))))
(if (<= (+ x (* (* y z) t_1)) INFINITY)
(+ x (* z (* y t_1)))
(+ x (* 2.0 (/ z (/ 2.0 (- t x)))))))) double code(double x, double y, double z, double t) {
return x + ((y * z) * (tanh((t / y)) - tanh((x / y))));
}
↓
double code(double x, double y, double z, double t) {
double t_1 = tanh((t / y)) - tanh((x / y));
double tmp;
if ((x + ((y * z) * t_1)) <= ((double) INFINITY)) {
tmp = x + (z * (y * t_1));
} else {
tmp = x + (2.0 * (z / (2.0 / (t - x))));
}
return tmp;
}
public static double code(double x, double y, double z, double t) {
return x + ((y * z) * (Math.tanh((t / y)) - Math.tanh((x / y))));
}
↓
public static double code(double x, double y, double z, double t) {
double t_1 = Math.tanh((t / y)) - Math.tanh((x / y));
double tmp;
if ((x + ((y * z) * t_1)) <= Double.POSITIVE_INFINITY) {
tmp = x + (z * (y * t_1));
} else {
tmp = x + (2.0 * (z / (2.0 / (t - x))));
}
return tmp;
}
def code(x, y, z, t):
return x + ((y * z) * (math.tanh((t / y)) - math.tanh((x / y))))
↓
def code(x, y, z, t):
t_1 = math.tanh((t / y)) - math.tanh((x / y))
tmp = 0
if (x + ((y * z) * t_1)) <= math.inf:
tmp = x + (z * (y * t_1))
else:
tmp = x + (2.0 * (z / (2.0 / (t - x))))
return tmp
function code(x, y, z, t)
return Float64(x + Float64(Float64(y * z) * Float64(tanh(Float64(t / y)) - tanh(Float64(x / y)))))
end
↓
function code(x, y, z, t)
t_1 = Float64(tanh(Float64(t / y)) - tanh(Float64(x / y)))
tmp = 0.0
if (Float64(x + Float64(Float64(y * z) * t_1)) <= Inf)
tmp = Float64(x + Float64(z * Float64(y * t_1)));
else
tmp = Float64(x + Float64(2.0 * Float64(z / Float64(2.0 / Float64(t - x)))));
end
return tmp
end
function tmp = code(x, y, z, t)
tmp = x + ((y * z) * (tanh((t / y)) - tanh((x / y))));
end
↓
function tmp_2 = code(x, y, z, t)
t_1 = tanh((t / y)) - tanh((x / y));
tmp = 0.0;
if ((x + ((y * z) * t_1)) <= Inf)
tmp = x + (z * (y * t_1));
else
tmp = x + (2.0 * (z / (2.0 / (t - x))));
end
tmp_2 = tmp;
end
code[x_, y_, z_, t_] := N[(x + N[(N[(y * z), $MachinePrecision] * N[(N[Tanh[N[(t / y), $MachinePrecision]], $MachinePrecision] - N[Tanh[N[(x / y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
↓
code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[Tanh[N[(t / y), $MachinePrecision]], $MachinePrecision] - N[Tanh[N[(x / y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(x + N[(N[(y * z), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision], Infinity], N[(x + N[(z * N[(y * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(2.0 * N[(z / N[(2.0 / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]
x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)
↓
\begin{array}{l}
t_1 := \tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\\
\mathbf{if}\;x + \left(y \cdot z\right) \cdot t_1 \leq \infty:\\
\;\;\;\;x + z \cdot \left(y \cdot t_1\right)\\
\mathbf{else}:\\
\;\;\;\;x + 2 \cdot \frac{z}{\frac{2}{t - x}}\\
\end{array}
Alternatives Alternative 1 Error 1.7 Cost 13764
\[\begin{array}{l}
\mathbf{if}\;y \leq 2.25 \cdot 10^{+185}:\\
\;\;\;\;x + y \cdot \left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)\right)\\
\mathbf{else}:\\
\;\;\;\;x + \left(t - x\right) \cdot z\\
\end{array}
\]
Alternative 2 Error 12.4 Cost 7628
\[\begin{array}{l}
t_1 := x + y \cdot \left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \frac{x}{y}\right)\right)\\
\mathbf{if}\;y \leq -4.6 \cdot 10^{-23}:\\
\;\;\;\;t_1\\
\mathbf{elif}\;y \leq 1.2 \cdot 10^{-72}:\\
\;\;\;\;x\\
\mathbf{elif}\;y \leq 1.55 \cdot 10^{+153}:\\
\;\;\;\;t_1\\
\mathbf{else}:\\
\;\;\;\;x + \left(t - x\right) \cdot z\\
\end{array}
\]
Alternative 3 Error 21.7 Cost 848
\[\begin{array}{l}
t_1 := \left(t - x\right) \cdot z\\
\mathbf{if}\;x \leq -2.3 \cdot 10^{-120}:\\
\;\;\;\;x\\
\mathbf{elif}\;x \leq -5.1 \cdot 10^{-162}:\\
\;\;\;\;t_1\\
\mathbf{elif}\;x \leq -5.2 \cdot 10^{-263}:\\
\;\;\;\;x\\
\mathbf{elif}\;x \leq 8.5 \cdot 10^{-233}:\\
\;\;\;\;t_1\\
\mathbf{else}:\\
\;\;\;\;x\\
\end{array}
\]
Alternative 4 Error 22.0 Cost 720
\[\begin{array}{l}
\mathbf{if}\;x \leq -3.05 \cdot 10^{-120}:\\
\;\;\;\;x\\
\mathbf{elif}\;x \leq -2.9 \cdot 10^{-146}:\\
\;\;\;\;t \cdot z\\
\mathbf{elif}\;x \leq -2.4 \cdot 10^{-263}:\\
\;\;\;\;x\\
\mathbf{elif}\;x \leq 10^{-235}:\\
\;\;\;\;t \cdot z\\
\mathbf{else}:\\
\;\;\;\;x\\
\end{array}
\]
Alternative 5 Error 22.7 Cost 720
\[\begin{array}{l}
t_1 := x \cdot \left(1 - z\right)\\
\mathbf{if}\;y \leq -3.3 \cdot 10^{+213}:\\
\;\;\;\;t_1\\
\mathbf{elif}\;y \leq -1.85 \cdot 10^{+202}:\\
\;\;\;\;t \cdot z\\
\mathbf{elif}\;y \leq -1.5 \cdot 10^{+37}:\\
\;\;\;\;t_1\\
\mathbf{elif}\;y \leq 1.05 \cdot 10^{+198}:\\
\;\;\;\;x\\
\mathbf{else}:\\
\;\;\;\;t \cdot z\\
\end{array}
\]
Alternative 6 Error 15.1 Cost 712
\[\begin{array}{l}
t_1 := x + \left(t - x\right) \cdot z\\
\mathbf{if}\;y \leq -5 \cdot 10^{+37}:\\
\;\;\;\;t_1\\
\mathbf{elif}\;y \leq 2.4 \cdot 10^{+96}:\\
\;\;\;\;x\\
\mathbf{else}:\\
\;\;\;\;t_1\\
\end{array}
\]
Alternative 7 Error 18.4 Cost 584
\[\begin{array}{l}
t_1 := t \cdot z + x\\
\mathbf{if}\;y \leq -4.8 \cdot 10^{+125}:\\
\;\;\;\;t_1\\
\mathbf{elif}\;y \leq 6.4 \cdot 10^{+180}:\\
\;\;\;\;x\\
\mathbf{else}:\\
\;\;\;\;t_1\\
\end{array}
\]
Alternative 8 Error 22.4 Cost 64
\[x
\]