Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[e^{re} \cdot \sin im
\]
↓
\[e^{re} \cdot \sin im
\]
(FPCore (re im) :precision binary64 (* (exp re) (sin im))) ↓
(FPCore (re im) :precision binary64 (* (exp re) (sin im))) double code(double re, double im) {
return exp(re) * sin(im);
}
↓
double code(double re, double im) {
return exp(re) * sin(im);
}
real(8) function code(re, im)
real(8), intent (in) :: re
real(8), intent (in) :: im
code = exp(re) * sin(im)
end function
↓
real(8) function code(re, im)
real(8), intent (in) :: re
real(8), intent (in) :: im
code = exp(re) * sin(im)
end function
public static double code(double re, double im) {
return Math.exp(re) * Math.sin(im);
}
↓
public static double code(double re, double im) {
return Math.exp(re) * Math.sin(im);
}
def code(re, im):
return math.exp(re) * math.sin(im)
↓
def code(re, im):
return math.exp(re) * math.sin(im)
function code(re, im)
return Float64(exp(re) * sin(im))
end
↓
function code(re, im)
return Float64(exp(re) * sin(im))
end
function tmp = code(re, im)
tmp = exp(re) * sin(im);
end
↓
function tmp = code(re, im)
tmp = exp(re) * sin(im);
end
code[re_, im_] := N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision]
↓
code[re_, im_] := N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision]
e^{re} \cdot \sin im
↓
e^{re} \cdot \sin im
Alternatives Alternative 1 Accuracy 100.0% Cost 12992
\[e^{re} \cdot \sin im
\]
Alternative 2 Accuracy 92.6% Cost 19657
\[\begin{array}{l}
\mathbf{if}\;e^{re} \leq 0 \lor \neg \left(e^{re} \leq 1.00000000002\right):\\
\;\;\;\;e^{re} \cdot im\\
\mathbf{else}:\\
\;\;\;\;\sin im\\
\end{array}
\]
Alternative 3 Accuracy 97.2% Cost 7757
\[\begin{array}{l}
\mathbf{if}\;re \leq -0.0285 \lor \neg \left(re \leq 2.4 \cdot 10^{-11}\right) \land re \leq 10^{+103}:\\
\;\;\;\;e^{re} \cdot im\\
\mathbf{else}:\\
\;\;\;\;\sin im \cdot \left(\left(re + 1\right) + \left(re \cdot re\right) \cdot \left(re \cdot 0.16666666666666666 + 0.5\right)\right)\\
\end{array}
\]
Alternative 4 Accuracy 95.7% Cost 7501
\[\begin{array}{l}
\mathbf{if}\;re \leq -2.05 \lor \neg \left(re \leq 2.4 \cdot 10^{-11}\right) \land re \leq 6.8 \cdot 10^{+142}:\\
\;\;\;\;e^{re} \cdot im\\
\mathbf{else}:\\
\;\;\;\;\sin im \cdot \left(\left(re + 1\right) + re \cdot \left(re \cdot 0.5\right)\right)\\
\end{array}
\]
Alternative 5 Accuracy 95.6% Cost 7244
\[\begin{array}{l}
t_0 := e^{re} \cdot im\\
\mathbf{if}\;re \leq -0.00046:\\
\;\;\;\;t_0\\
\mathbf{elif}\;re \leq 2.4 \cdot 10^{-11}:\\
\;\;\;\;\sin im \cdot \left(re + 1\right)\\
\mathbf{elif}\;re \leq 6.8 \cdot 10^{+142}:\\
\;\;\;\;t_0\\
\mathbf{else}:\\
\;\;\;\;\sin im \cdot \left(re \cdot \left(re \cdot 0.5\right)\right)\\
\end{array}
\]
Alternative 6 Accuracy 93.1% Cost 6985
\[\begin{array}{l}
\mathbf{if}\;re \leq -0.00084 \lor \neg \left(re \leq 2.4 \cdot 10^{-11}\right):\\
\;\;\;\;e^{re} \cdot im\\
\mathbf{else}:\\
\;\;\;\;\sin im \cdot \left(re + 1\right)\\
\end{array}
\]
Alternative 7 Accuracy 61.7% Cost 6728
\[\begin{array}{l}
\mathbf{if}\;re \leq -3.5 \cdot 10^{+47}:\\
\;\;\;\;\frac{im}{1 - re} \cdot \left(1 - re \cdot re\right)\\
\mathbf{elif}\;re \leq 1.65 \cdot 10^{+23}:\\
\;\;\;\;\sin im\\
\mathbf{else}:\\
\;\;\;\;0.5 \cdot \left(im \cdot \left(re \cdot re\right)\right)\\
\end{array}
\]
Alternative 8 Accuracy 38.5% Cost 836
\[\begin{array}{l}
\mathbf{if}\;re \leq 1.1 \cdot 10^{+23}:\\
\;\;\;\;\frac{im}{1 - re} \cdot \left(1 - re \cdot re\right)\\
\mathbf{else}:\\
\;\;\;\;0.5 \cdot \left(im \cdot \left(re \cdot re\right)\right)\\
\end{array}
\]
Alternative 9 Accuracy 37.5% Cost 580
\[\begin{array}{l}
\mathbf{if}\;re \leq 1.1 \cdot 10^{+23}:\\
\;\;\;\;im\\
\mathbf{else}:\\
\;\;\;\;0.5 \cdot \left(im \cdot \left(re \cdot re\right)\right)\\
\end{array}
\]
Alternative 10 Accuracy 30.0% Cost 324
\[\begin{array}{l}
\mathbf{if}\;re \leq 1.1 \cdot 10^{+23}:\\
\;\;\;\;im\\
\mathbf{else}:\\
\;\;\;\;re \cdot im\\
\end{array}
\]
Alternative 11 Accuracy 30.0% Cost 320
\[im + re \cdot im
\]
Alternative 12 Accuracy 27.1% Cost 64
\[im
\]