Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right)
\]
↓
\[\begin{array}{l}
t_0 := e^{-im} - e^{im}\\
\mathbf{if}\;t_0 \leq -4 \cdot 10^{+23} \lor \neg \left(t_0 \leq 0.1\right):\\
\;\;\;\;t_0 \cdot \left(0.5 \cdot \sin re\right)\\
\mathbf{else}:\\
\;\;\;\;\sin re \cdot \left({im}^{5} \cdot -0.008333333333333333 + \left({im}^{7} \cdot -0.0001984126984126984 + \left({im}^{3} \cdot -0.16666666666666666 - im\right)\right)\right)\\
\end{array}
\]
(FPCore (re im)
:precision binary64
(* (* 0.5 (sin re)) (- (exp (- im)) (exp im)))) ↓
(FPCore (re im)
:precision binary64
(let* ((t_0 (- (exp (- im)) (exp im))))
(if (or (<= t_0 -4e+23) (not (<= t_0 0.1)))
(* t_0 (* 0.5 (sin re)))
(*
(sin re)
(+
(* (pow im 5.0) -0.008333333333333333)
(+
(* (pow im 7.0) -0.0001984126984126984)
(- (* (pow im 3.0) -0.16666666666666666) im))))))) double code(double re, double im) {
return (0.5 * sin(re)) * (exp(-im) - exp(im));
}
↓
double code(double re, double im) {
double t_0 = exp(-im) - exp(im);
double tmp;
if ((t_0 <= -4e+23) || !(t_0 <= 0.1)) {
tmp = t_0 * (0.5 * sin(re));
} else {
tmp = sin(re) * ((pow(im, 5.0) * -0.008333333333333333) + ((pow(im, 7.0) * -0.0001984126984126984) + ((pow(im, 3.0) * -0.16666666666666666) - im)));
}
return tmp;
}
real(8) function code(re, im)
real(8), intent (in) :: re
real(8), intent (in) :: im
code = (0.5d0 * sin(re)) * (exp(-im) - exp(im))
end function
↓
real(8) function code(re, im)
real(8), intent (in) :: re
real(8), intent (in) :: im
real(8) :: t_0
real(8) :: tmp
t_0 = exp(-im) - exp(im)
if ((t_0 <= (-4d+23)) .or. (.not. (t_0 <= 0.1d0))) then
tmp = t_0 * (0.5d0 * sin(re))
else
tmp = sin(re) * (((im ** 5.0d0) * (-0.008333333333333333d0)) + (((im ** 7.0d0) * (-0.0001984126984126984d0)) + (((im ** 3.0d0) * (-0.16666666666666666d0)) - im)))
end if
code = tmp
end function
public static double code(double re, double im) {
return (0.5 * Math.sin(re)) * (Math.exp(-im) - Math.exp(im));
}
↓
public static double code(double re, double im) {
double t_0 = Math.exp(-im) - Math.exp(im);
double tmp;
if ((t_0 <= -4e+23) || !(t_0 <= 0.1)) {
tmp = t_0 * (0.5 * Math.sin(re));
} else {
tmp = Math.sin(re) * ((Math.pow(im, 5.0) * -0.008333333333333333) + ((Math.pow(im, 7.0) * -0.0001984126984126984) + ((Math.pow(im, 3.0) * -0.16666666666666666) - im)));
}
return tmp;
}
def code(re, im):
return (0.5 * math.sin(re)) * (math.exp(-im) - math.exp(im))
↓
def code(re, im):
t_0 = math.exp(-im) - math.exp(im)
tmp = 0
if (t_0 <= -4e+23) or not (t_0 <= 0.1):
tmp = t_0 * (0.5 * math.sin(re))
else:
tmp = math.sin(re) * ((math.pow(im, 5.0) * -0.008333333333333333) + ((math.pow(im, 7.0) * -0.0001984126984126984) + ((math.pow(im, 3.0) * -0.16666666666666666) - im)))
return tmp
function code(re, im)
return Float64(Float64(0.5 * sin(re)) * Float64(exp(Float64(-im)) - exp(im)))
end
↓
function code(re, im)
t_0 = Float64(exp(Float64(-im)) - exp(im))
tmp = 0.0
if ((t_0 <= -4e+23) || !(t_0 <= 0.1))
tmp = Float64(t_0 * Float64(0.5 * sin(re)));
else
tmp = Float64(sin(re) * Float64(Float64((im ^ 5.0) * -0.008333333333333333) + Float64(Float64((im ^ 7.0) * -0.0001984126984126984) + Float64(Float64((im ^ 3.0) * -0.16666666666666666) - im))));
end
return tmp
end
function tmp = code(re, im)
tmp = (0.5 * sin(re)) * (exp(-im) - exp(im));
end
↓
function tmp_2 = code(re, im)
t_0 = exp(-im) - exp(im);
tmp = 0.0;
if ((t_0 <= -4e+23) || ~((t_0 <= 0.1)))
tmp = t_0 * (0.5 * sin(re));
else
tmp = sin(re) * (((im ^ 5.0) * -0.008333333333333333) + (((im ^ 7.0) * -0.0001984126984126984) + (((im ^ 3.0) * -0.16666666666666666) - im)));
end
tmp_2 = tmp;
end
code[re_, im_] := N[(N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision] * N[(N[Exp[(-im)], $MachinePrecision] - N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
↓
code[re_, im_] := Block[{t$95$0 = N[(N[Exp[(-im)], $MachinePrecision] - N[Exp[im], $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$0, -4e+23], N[Not[LessEqual[t$95$0, 0.1]], $MachinePrecision]], N[(t$95$0 * N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[Sin[re], $MachinePrecision] * N[(N[(N[Power[im, 5.0], $MachinePrecision] * -0.008333333333333333), $MachinePrecision] + N[(N[(N[Power[im, 7.0], $MachinePrecision] * -0.0001984126984126984), $MachinePrecision] + N[(N[(N[Power[im, 3.0], $MachinePrecision] * -0.16666666666666666), $MachinePrecision] - im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]
\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right)
↓
\begin{array}{l}
t_0 := e^{-im} - e^{im}\\
\mathbf{if}\;t_0 \leq -4 \cdot 10^{+23} \lor \neg \left(t_0 \leq 0.1\right):\\
\;\;\;\;t_0 \cdot \left(0.5 \cdot \sin re\right)\\
\mathbf{else}:\\
\;\;\;\;\sin re \cdot \left({im}^{5} \cdot -0.008333333333333333 + \left({im}^{7} \cdot -0.0001984126984126984 + \left({im}^{3} \cdot -0.16666666666666666 - im\right)\right)\right)\\
\end{array}
Alternatives Alternative 1 Accuracy 99.8% Cost 53001
\[\begin{array}{l}
t_0 := e^{-im} - e^{im}\\
\mathbf{if}\;t_0 \leq -4 \cdot 10^{+23} \lor \neg \left(t_0 \leq 0.1\right):\\
\;\;\;\;t_0 \cdot \left(0.5 \cdot \sin re\right)\\
\mathbf{else}:\\
\;\;\;\;\sin re \cdot \left({im}^{5} \cdot -0.008333333333333333 + \left({im}^{7} \cdot -0.0001984126984126984 + \left({im}^{3} \cdot -0.16666666666666666 - im\right)\right)\right)\\
\end{array}
\]
Alternative 2 Accuracy 99.5% Cost 45961
\[\begin{array}{l}
t_0 := e^{-im} - e^{im}\\
\mathbf{if}\;t_0 \leq -4 \cdot 10^{+23} \lor \neg \left(t_0 \leq 2 \cdot 10^{-9}\right):\\
\;\;\;\;t_0 \cdot \left(0.5 \cdot \sin re\right)\\
\mathbf{else}:\\
\;\;\;\;\left(-im\right) \cdot \sin re\\
\end{array}
\]
Alternative 3 Accuracy 94.6% Cost 13708
\[\begin{array}{l}
t_0 := {im}^{7} \cdot \left(\sin re \cdot -0.0001984126984126984\right)\\
\mathbf{if}\;im \leq -2.45 \cdot 10^{+67}:\\
\;\;\;\;t_0\\
\mathbf{elif}\;im \leq -19.5:\\
\;\;\;\;0.5 \cdot \left(\left(e^{-im} - e^{im}\right) \cdot re\right)\\
\mathbf{elif}\;im \leq 5.6:\\
\;\;\;\;\sin re \cdot \left({im}^{3} \cdot -0.16666666666666666 - im\right)\\
\mathbf{else}:\\
\;\;\;\;t_0\\
\end{array}
\]
Alternative 4 Accuracy 94.3% Cost 13580
\[\begin{array}{l}
t_0 := {im}^{7} \cdot \left(\sin re \cdot -0.0001984126984126984\right)\\
\mathbf{if}\;im \leq -2.45 \cdot 10^{+67}:\\
\;\;\;\;t_0\\
\mathbf{elif}\;im \leq -19.5:\\
\;\;\;\;0.5 \cdot \left(\left(e^{-im} - e^{im}\right) \cdot re\right)\\
\mathbf{elif}\;im \leq 4.2:\\
\;\;\;\;\left(-im\right) \cdot \sin re\\
\mathbf{else}:\\
\;\;\;\;t_0\\
\end{array}
\]
Alternative 5 Accuracy 92.2% Cost 13449
\[\begin{array}{l}
\mathbf{if}\;im \leq -4.2 \lor \neg \left(im \leq 4.2\right):\\
\;\;\;\;{im}^{7} \cdot \left(\sin re \cdot -0.0001984126984126984\right)\\
\mathbf{else}:\\
\;\;\;\;\left(-im\right) \cdot \sin re\\
\end{array}
\]
Alternative 6 Accuracy 59.6% Cost 7180
\[\begin{array}{l}
t_0 := 0.16666666666666666 \cdot \left(im \cdot {re}^{3}\right)\\
\mathbf{if}\;im \leq -3.4 \cdot 10^{+14}:\\
\;\;\;\;t_0\\
\mathbf{elif}\;im \leq 580:\\
\;\;\;\;\left(-im\right) \cdot \sin re\\
\mathbf{elif}\;im \leq 6.4 \cdot 10^{+125}:\\
\;\;\;\;t_0\\
\mathbf{else}:\\
\;\;\;\;im \cdot \left(-re\right)\\
\end{array}
\]
Alternative 7 Accuracy 81.3% Cost 7049
\[\begin{array}{l}
\mathbf{if}\;im \leq -22000000000000 \lor \neg \left(im \leq 9.4 \cdot 10^{+29}\right):\\
\;\;\;\;{im}^{7} \cdot \left(re \cdot -0.0001984126984126984\right)\\
\mathbf{else}:\\
\;\;\;\;\left(-im\right) \cdot \sin re\\
\end{array}
\]
Alternative 8 Accuracy 55.7% Cost 6921
\[\begin{array}{l}
\mathbf{if}\;im \leq -6.4 \cdot 10^{+103} \lor \neg \left(im \leq 5.9 \cdot 10^{+134}\right):\\
\;\;\;\;im \cdot \left(-re\right)\\
\mathbf{else}:\\
\;\;\;\;\left(-im\right) \cdot \sin re\\
\end{array}
\]
Alternative 9 Accuracy 32.7% Cost 256
\[im \cdot \left(-re\right)
\]
Alternative 10 Accuracy 2.7% Cost 64
\[-1.5
\]
Alternative 11 Accuracy 2.7% Cost 64
\[-0.004166666666666667
\]
Alternative 12 Accuracy 2.8% Cost 64
\[-3.9055157630365495 \cdot 10^{-12}
\]
Alternative 13 Accuracy 2.9% Cost 64
\[-2.382841615671091 \cdot 10^{-34}
\]
Alternative 14 Accuracy 14.8% Cost 64
\[0
\]