\[\frac{\frac{\frac{\left(\alpha + \beta\right) \cdot \left(\beta - \alpha\right)}{\left(\alpha + \beta\right) + 2 \cdot i}}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) + 2} + 1}{2}
\]
↓
\[\begin{array}{l}
t_0 := \beta + \left(i \cdot -2 - \beta\right)\\
t_1 := \left(\alpha + \beta\right) + 2 \cdot i\\
t_2 := 2 + t_1\\
t_3 := \beta + \mathsf{fma}\left(2, i, 2\right)\\
t_4 := \beta + t_3\\
\mathbf{if}\;\frac{\frac{\left(\alpha + \beta\right) \cdot \left(\beta - \alpha\right)}{t_1}}{t_2} \leq -0.5:\\
\;\;\;\;\frac{\mathsf{fma}\left(-1, \frac{\mathsf{fma}\left(-1, \beta, t_0\right) - t_3}{\alpha}, \frac{\left(\beta + 2 \cdot i\right) \cdot t_0}{\alpha \cdot \alpha} - \left(\frac{t_4}{\frac{\alpha}{\frac{t_3}{\alpha}}} + \frac{t_4}{\frac{\frac{\alpha \cdot \alpha}{-1}}{t_0}}\right)\right)}{2}\\
\mathbf{else}:\\
\;\;\;\;\frac{1 + \frac{\left(\beta - \alpha\right) \cdot \left(\left(\alpha + \beta\right) \cdot \frac{1}{t_1}\right)}{t_2}}{2}\\
\end{array}
\]
double code(double alpha, double beta, double i) {
return (((((alpha + beta) * (beta - alpha)) / ((alpha + beta) + (2.0 * i))) / (((alpha + beta) + (2.0 * i)) + 2.0)) + 1.0) / 2.0;
}
↓
double code(double alpha, double beta, double i) {
double t_0 = beta + ((i * -2.0) - beta);
double t_1 = (alpha + beta) + (2.0 * i);
double t_2 = 2.0 + t_1;
double t_3 = beta + fma(2.0, i, 2.0);
double t_4 = beta + t_3;
double tmp;
if (((((alpha + beta) * (beta - alpha)) / t_1) / t_2) <= -0.5) {
tmp = fma(-1.0, ((fma(-1.0, beta, t_0) - t_3) / alpha), ((((beta + (2.0 * i)) * t_0) / (alpha * alpha)) - ((t_4 / (alpha / (t_3 / alpha))) + (t_4 / (((alpha * alpha) / -1.0) / t_0))))) / 2.0;
} else {
tmp = (1.0 + (((beta - alpha) * ((alpha + beta) * (1.0 / t_1))) / t_2)) / 2.0;
}
return tmp;
}
function code(alpha, beta, i)
return Float64(Float64(Float64(Float64(Float64(Float64(alpha + beta) * Float64(beta - alpha)) / Float64(Float64(alpha + beta) + Float64(2.0 * i))) / Float64(Float64(Float64(alpha + beta) + Float64(2.0 * i)) + 2.0)) + 1.0) / 2.0)
end
↓
function code(alpha, beta, i)
t_0 = Float64(beta + Float64(Float64(i * -2.0) - beta))
t_1 = Float64(Float64(alpha + beta) + Float64(2.0 * i))
t_2 = Float64(2.0 + t_1)
t_3 = Float64(beta + fma(2.0, i, 2.0))
t_4 = Float64(beta + t_3)
tmp = 0.0
if (Float64(Float64(Float64(Float64(alpha + beta) * Float64(beta - alpha)) / t_1) / t_2) <= -0.5)
tmp = Float64(fma(-1.0, Float64(Float64(fma(-1.0, beta, t_0) - t_3) / alpha), Float64(Float64(Float64(Float64(beta + Float64(2.0 * i)) * t_0) / Float64(alpha * alpha)) - Float64(Float64(t_4 / Float64(alpha / Float64(t_3 / alpha))) + Float64(t_4 / Float64(Float64(Float64(alpha * alpha) / -1.0) / t_0))))) / 2.0);
else
tmp = Float64(Float64(1.0 + Float64(Float64(Float64(beta - alpha) * Float64(Float64(alpha + beta) * Float64(1.0 / t_1))) / t_2)) / 2.0);
end
return tmp
end
code[alpha_, beta_, i_] := N[(N[(N[(N[(N[(N[(alpha + beta), $MachinePrecision] * N[(beta - alpha), $MachinePrecision]), $MachinePrecision] / N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * i), $MachinePrecision]), $MachinePrecision] + 2.0), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision] / 2.0), $MachinePrecision]
↓
code[alpha_, beta_, i_] := Block[{t$95$0 = N[(beta + N[(N[(i * -2.0), $MachinePrecision] - beta), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * i), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(2.0 + t$95$1), $MachinePrecision]}, Block[{t$95$3 = N[(beta + N[(2.0 * i + 2.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(beta + t$95$3), $MachinePrecision]}, If[LessEqual[N[(N[(N[(N[(alpha + beta), $MachinePrecision] * N[(beta - alpha), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision] / t$95$2), $MachinePrecision], -0.5], N[(N[(-1.0 * N[(N[(N[(-1.0 * beta + t$95$0), $MachinePrecision] - t$95$3), $MachinePrecision] / alpha), $MachinePrecision] + N[(N[(N[(N[(beta + N[(2.0 * i), $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision] / N[(alpha * alpha), $MachinePrecision]), $MachinePrecision] - N[(N[(t$95$4 / N[(alpha / N[(t$95$3 / alpha), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t$95$4 / N[(N[(N[(alpha * alpha), $MachinePrecision] / -1.0), $MachinePrecision] / t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[(1.0 + N[(N[(N[(beta - alpha), $MachinePrecision] * N[(N[(alpha + beta), $MachinePrecision] * N[(1.0 / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t$95$2), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]]]]]]]
\frac{\frac{\frac{\left(\alpha + \beta\right) \cdot \left(\beta - \alpha\right)}{\left(\alpha + \beta\right) + 2 \cdot i}}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) + 2} + 1}{2}
↓
\begin{array}{l}
t_0 := \beta + \left(i \cdot -2 - \beta\right)\\
t_1 := \left(\alpha + \beta\right) + 2 \cdot i\\
t_2 := 2 + t_1\\
t_3 := \beta + \mathsf{fma}\left(2, i, 2\right)\\
t_4 := \beta + t_3\\
\mathbf{if}\;\frac{\frac{\left(\alpha + \beta\right) \cdot \left(\beta - \alpha\right)}{t_1}}{t_2} \leq -0.5:\\
\;\;\;\;\frac{\mathsf{fma}\left(-1, \frac{\mathsf{fma}\left(-1, \beta, t_0\right) - t_3}{\alpha}, \frac{\left(\beta + 2 \cdot i\right) \cdot t_0}{\alpha \cdot \alpha} - \left(\frac{t_4}{\frac{\alpha}{\frac{t_3}{\alpha}}} + \frac{t_4}{\frac{\frac{\alpha \cdot \alpha}{-1}}{t_0}}\right)\right)}{2}\\
\mathbf{else}:\\
\;\;\;\;\frac{1 + \frac{\left(\beta - \alpha\right) \cdot \left(\left(\alpha + \beta\right) \cdot \frac{1}{t_1}\right)}{t_2}}{2}\\
\end{array}