\[1 - \frac{1}{2 + \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right) \cdot \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right)}
\]
↓
\[\begin{array}{l}
t_1 := -2 - {\left(2 + \frac{\frac{2}{t}}{\frac{-1}{t} + -1}\right)}^{2}\\
\frac{t_1 + 1}{t_1}
\end{array}
\]
(FPCore (t)
:precision binary64
(-
1.0
(/
1.0
(+
2.0
(*
(- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t))))
(- 2.0 (/ (/ 2.0 t) (+ 1.0 (/ 1.0 t)))))))))↓
(FPCore (t)
:precision binary64
(let* ((t_1 (- -2.0 (pow (+ 2.0 (/ (/ 2.0 t) (+ (/ -1.0 t) -1.0))) 2.0))))
(/ (+ t_1 1.0) t_1)))
double code(double t) {
return 1.0 - (1.0 / (2.0 + ((2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))) * (2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))))));
}
↓
double code(double t) {
double t_1 = -2.0 - pow((2.0 + ((2.0 / t) / ((-1.0 / t) + -1.0))), 2.0);
return (t_1 + 1.0) / t_1;
}
real(8) function code(t)
real(8), intent (in) :: t
code = 1.0d0 - (1.0d0 / (2.0d0 + ((2.0d0 - ((2.0d0 / t) / (1.0d0 + (1.0d0 / t)))) * (2.0d0 - ((2.0d0 / t) / (1.0d0 + (1.0d0 / t)))))))
end function
↓
real(8) function code(t)
real(8), intent (in) :: t
real(8) :: t_1
t_1 = (-2.0d0) - ((2.0d0 + ((2.0d0 / t) / (((-1.0d0) / t) + (-1.0d0)))) ** 2.0d0)
code = (t_1 + 1.0d0) / t_1
end function
public static double code(double t) {
return 1.0 - (1.0 / (2.0 + ((2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))) * (2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))))));
}
↓
public static double code(double t) {
double t_1 = -2.0 - Math.pow((2.0 + ((2.0 / t) / ((-1.0 / t) + -1.0))), 2.0);
return (t_1 + 1.0) / t_1;
}
def code(t):
return 1.0 - (1.0 / (2.0 + ((2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))) * (2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))))))
↓
def code(t):
t_1 = -2.0 - math.pow((2.0 + ((2.0 / t) / ((-1.0 / t) + -1.0))), 2.0)
return (t_1 + 1.0) / t_1
function code(t)
return Float64(1.0 - Float64(1.0 / Float64(2.0 + Float64(Float64(2.0 - Float64(Float64(2.0 / t) / Float64(1.0 + Float64(1.0 / t)))) * Float64(2.0 - Float64(Float64(2.0 / t) / Float64(1.0 + Float64(1.0 / t))))))))
end
↓
function code(t)
t_1 = Float64(-2.0 - (Float64(2.0 + Float64(Float64(2.0 / t) / Float64(Float64(-1.0 / t) + -1.0))) ^ 2.0))
return Float64(Float64(t_1 + 1.0) / t_1)
end
function tmp = code(t)
tmp = 1.0 - (1.0 / (2.0 + ((2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))) * (2.0 - ((2.0 / t) / (1.0 + (1.0 / t)))))));
end
↓
function tmp = code(t)
t_1 = -2.0 - ((2.0 + ((2.0 / t) / ((-1.0 / t) + -1.0))) ^ 2.0);
tmp = (t_1 + 1.0) / t_1;
end
code[t_] := N[(1.0 - N[(1.0 / N[(2.0 + N[(N[(2.0 - N[(N[(2.0 / t), $MachinePrecision] / N[(1.0 + N[(1.0 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(2.0 - N[(N[(2.0 / t), $MachinePrecision] / N[(1.0 + N[(1.0 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
↓
code[t_] := Block[{t$95$1 = N[(-2.0 - N[Power[N[(2.0 + N[(N[(2.0 / t), $MachinePrecision] / N[(N[(-1.0 / t), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]}, N[(N[(t$95$1 + 1.0), $MachinePrecision] / t$95$1), $MachinePrecision]]
1 - \frac{1}{2 + \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right) \cdot \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right)}
↓
\begin{array}{l}
t_1 := -2 - {\left(2 + \frac{\frac{2}{t}}{\frac{-1}{t} + -1}\right)}^{2}\\
\frac{t_1 + 1}{t_1}
\end{array}
Alternatives
| Alternative 1 |
|---|
| Error | 0.0 |
|---|
| Cost | 2880 |
|---|
\[\begin{array}{l}
t_1 := \frac{2}{t + 1} + -2\\
t_2 := \frac{\frac{2}{t}}{\frac{t + 1}{t}} + -2\\
\frac{\left(-2 - t_1 \cdot t_1\right) + 1}{-2 - t_2 \cdot t_2}
\end{array}
\]
| Alternative 2 |
|---|
| Error | 0.0 |
|---|
| Cost | 1856 |
|---|
\[\begin{array}{l}
t_1 := 2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\\
1 - \frac{1}{2 + t_1 \cdot t_1}
\end{array}
\]
| Alternative 3 |
|---|
| Error | 0.5 |
|---|
| Cost | 1608 |
|---|
\[\begin{array}{l}
\mathbf{if}\;t \leq -9.434969598260269:\\
\;\;\;\;\left(-0.16666666666666666 + \frac{-0.2222222222222222}{t}\right) + \left(1 + \frac{0.037037037037037035}{t \cdot t}\right)\\
\mathbf{elif}\;t \leq 0.12462912159661302:\\
\;\;\;\;1 - \frac{1}{2 + \left(t \cdot 2\right) \cdot \left(2 - \frac{\frac{2}{t}}{1 + \frac{1}{t}}\right)}\\
\mathbf{else}:\\
\;\;\;\;0.8333333333333334 + \frac{-0.2222222222222222}{t}\\
\end{array}
\]
| Alternative 4 |
|---|
| Error | 0.5 |
|---|
| Cost | 964 |
|---|
\[\begin{array}{l}
\mathbf{if}\;t \leq -9.434969598260269:\\
\;\;\;\;\left(-0.16666666666666666 + \frac{-0.2222222222222222}{t}\right) + \left(1 + \frac{0.037037037037037035}{t \cdot t}\right)\\
\mathbf{elif}\;t \leq 0.12462912159661302:\\
\;\;\;\;0.5 + t \cdot t\\
\mathbf{else}:\\
\;\;\;\;0.8333333333333334 + \frac{-0.2222222222222222}{t}\\
\end{array}
\]
| Alternative 5 |
|---|
| Error | 0.5 |
|---|
| Cost | 708 |
|---|
\[\begin{array}{l}
\mathbf{if}\;t \leq -9.434969598260269:\\
\;\;\;\;0.8333333333333334 + \frac{\frac{0.037037037037037035}{t} - 0.2222222222222222}{t}\\
\mathbf{elif}\;t \leq 0.12462912159661302:\\
\;\;\;\;0.5 + t \cdot t\\
\mathbf{else}:\\
\;\;\;\;0.8333333333333334 + \frac{-0.2222222222222222}{t}\\
\end{array}
\]
| Alternative 6 |
|---|
| Error | 0.8 |
|---|
| Cost | 584 |
|---|
\[\begin{array}{l}
\mathbf{if}\;t \leq -9.434969598260269:\\
\;\;\;\;0.8333333333333334\\
\mathbf{elif}\;t \leq 0.12462912159661302:\\
\;\;\;\;0.5 + t \cdot t\\
\mathbf{else}:\\
\;\;\;\;0.8333333333333334\\
\end{array}
\]
| Alternative 7 |
|---|
| Error | 0.5 |
|---|
| Cost | 584 |
|---|
\[\begin{array}{l}
t_1 := 0.8333333333333334 + \frac{-0.2222222222222222}{t}\\
\mathbf{if}\;t \leq -9.434969598260269:\\
\;\;\;\;t_1\\
\mathbf{elif}\;t \leq 0.12462912159661302:\\
\;\;\;\;0.5 + t \cdot t\\
\mathbf{else}:\\
\;\;\;\;t_1\\
\end{array}
\]
| Alternative 8 |
|---|
| Error | 0.9 |
|---|
| Cost | 328 |
|---|
\[\begin{array}{l}
\mathbf{if}\;t \leq -9.434969598260269:\\
\;\;\;\;0.8333333333333334\\
\mathbf{elif}\;t \leq 0.12462912159661302:\\
\;\;\;\;0.5\\
\mathbf{else}:\\
\;\;\;\;0.8333333333333334\\
\end{array}
\]
| Alternative 9 |
|---|
| Error | 26.4 |
|---|
| Cost | 64 |
|---|
\[0.8333333333333334
\]