Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[\frac{\sin x \cdot \sinh y}{x}
\]
↓
\[\begin{array}{l}
\\
\frac{\sin x}{x} \cdot \sinh y
\end{array}
\]
(FPCore (x y) :precision binary64 (/ (* (sin x) (sinh y)) x)) ↓
(FPCore (x y) :precision binary64 (* (/ (sin x) x) (sinh y))) double code(double x, double y) {
return (sin(x) * sinh(y)) / x;
}
↓
double code(double x, double y) {
return (sin(x) / x) * sinh(y);
}
real(8) function code(x, y)
real(8), intent (in) :: x
real(8), intent (in) :: y
code = (sin(x) * sinh(y)) / x
end function
↓
real(8) function code(x, y)
real(8), intent (in) :: x
real(8), intent (in) :: y
code = (sin(x) / x) * sinh(y)
end function
public static double code(double x, double y) {
return (Math.sin(x) * Math.sinh(y)) / x;
}
↓
public static double code(double x, double y) {
return (Math.sin(x) / x) * Math.sinh(y);
}
def code(x, y):
return (math.sin(x) * math.sinh(y)) / x
↓
def code(x, y):
return (math.sin(x) / x) * math.sinh(y)
function code(x, y)
return Float64(Float64(sin(x) * sinh(y)) / x)
end
↓
function code(x, y)
return Float64(Float64(sin(x) / x) * sinh(y))
end
function tmp = code(x, y)
tmp = (sin(x) * sinh(y)) / x;
end
↓
function tmp = code(x, y)
tmp = (sin(x) / x) * sinh(y);
end
code[x_, y_] := N[(N[(N[Sin[x], $MachinePrecision] * N[Sinh[y], $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]
↓
code[x_, y_] := N[(N[(N[Sin[x], $MachinePrecision] / x), $MachinePrecision] * N[Sinh[y], $MachinePrecision]), $MachinePrecision]
\frac{\sin x \cdot \sinh y}{x}
↓
\begin{array}{l}
\\
\frac{\sin x}{x} \cdot \sinh y
\end{array}
Alternatives Alternative 1 Accuracy 99.9% Cost 13120
\[\frac{\sin x}{x} \cdot \sinh y
\]
Alternative 2 Accuracy 86.3% Cost 20040
\[\begin{array}{l}
\mathbf{if}\;\sinh y \leq -\infty:\\
\;\;\;\;\sinh y\\
\mathbf{elif}\;\sinh y \leq 5 \cdot 10^{+104}:\\
\;\;\;\;\frac{\sin x}{x} \cdot y\\
\mathbf{else}:\\
\;\;\;\;\sinh y \cdot \left(1 + \left(x \cdot x\right) \cdot -0.16666666666666666\right)\\
\end{array}
\]
Alternative 3 Accuracy 99.9% Cost 13120
\[\sin x \cdot \frac{\sinh y}{x}
\]
Alternative 4 Accuracy 85.9% Cost 6984
\[\begin{array}{l}
\mathbf{if}\;y \leq -4.8 \cdot 10^{-5}:\\
\;\;\;\;\sinh y\\
\mathbf{elif}\;y \leq 2400000000:\\
\;\;\;\;\sin x \cdot \frac{y}{x}\\
\mathbf{else}:\\
\;\;\;\;\sinh y\\
\end{array}
\]
Alternative 5 Accuracy 86.0% Cost 6984
\[\begin{array}{l}
\mathbf{if}\;y \leq -0.000106:\\
\;\;\;\;\sinh y\\
\mathbf{elif}\;y \leq 2400000000:\\
\;\;\;\;\frac{\sin x}{x} \cdot y\\
\mathbf{else}:\\
\;\;\;\;\sinh y\\
\end{array}
\]
Alternative 6 Accuracy 74.1% Cost 6728
\[\begin{array}{l}
\mathbf{if}\;y \leq -4 \cdot 10^{-8}:\\
\;\;\;\;\sinh y\\
\mathbf{elif}\;y \leq 2400000000:\\
\;\;\;\;\frac{1}{\frac{1}{y} + 0.16666666666666666 \cdot \frac{x \cdot x}{y}}\\
\mathbf{else}:\\
\;\;\;\;\sinh y\\
\end{array}
\]
Alternative 7 Accuracy 46.6% Cost 1096
\[\begin{array}{l}
\mathbf{if}\;y \leq -660:\\
\;\;\;\;\frac{x \cdot y}{x}\\
\mathbf{elif}\;y \leq 0.0052:\\
\;\;\;\;\frac{1}{\frac{1}{y} + 0.16666666666666666 \cdot \frac{x \cdot x}{y}}\\
\mathbf{else}:\\
\;\;\;\;y + -0.16666666666666666 \cdot \left(y \cdot \left(x \cdot x\right)\right)\\
\end{array}
\]
Alternative 8 Accuracy 34.5% Cost 712
\[\begin{array}{l}
\mathbf{if}\;y \leq -1 \cdot 10^{+94}:\\
\;\;\;\;\frac{x \cdot y}{x}\\
\mathbf{elif}\;y \leq 0.0052:\\
\;\;\;\;y\\
\mathbf{else}:\\
\;\;\;\;-0.16666666666666666 \cdot \left(y \cdot \left(x \cdot x\right)\right)\\
\end{array}
\]
Alternative 9 Accuracy 32.2% Cost 585
\[\begin{array}{l}
\mathbf{if}\;x \leq -3.3 \cdot 10^{+193} \lor \neg \left(x \leq 5 \cdot 10^{+96}\right):\\
\;\;\;\;\frac{x \cdot y}{x}\\
\mathbf{else}:\\
\;\;\;\;y\\
\end{array}
\]
Alternative 10 Accuracy 36.3% Cost 576
\[y + -0.16666666666666666 \cdot \left(y \cdot \left(x \cdot x\right)\right)
\]
Alternative 11 Accuracy 27.8% Cost 64
\[y
\]