Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[\frac{x + y \cdot \left(z - x\right)}{z}
\]
↓
\[\frac{x}{z} + \left(1 - \frac{x}{z}\right) \cdot y
\]
(FPCore (x y z) :precision binary64 (/ (+ x (* y (- z x))) z)) ↓
(FPCore (x y z) :precision binary64 (+ (/ x z) (* (- 1.0 (/ x z)) y))) double code(double x, double y, double z) {
return (x + (y * (z - x))) / z;
}
↓
double code(double x, double y, double z) {
return (x / z) + ((1.0 - (x / z)) * y);
}
real(8) function code(x, y, z)
real(8), intent (in) :: x
real(8), intent (in) :: y
real(8), intent (in) :: z
code = (x + (y * (z - x))) / z
end function
↓
real(8) function code(x, y, z)
real(8), intent (in) :: x
real(8), intent (in) :: y
real(8), intent (in) :: z
code = (x / z) + ((1.0d0 - (x / z)) * y)
end function
public static double code(double x, double y, double z) {
return (x + (y * (z - x))) / z;
}
↓
public static double code(double x, double y, double z) {
return (x / z) + ((1.0 - (x / z)) * y);
}
def code(x, y, z):
return (x + (y * (z - x))) / z
↓
def code(x, y, z):
return (x / z) + ((1.0 - (x / z)) * y)
function code(x, y, z)
return Float64(Float64(x + Float64(y * Float64(z - x))) / z)
end
↓
function code(x, y, z)
return Float64(Float64(x / z) + Float64(Float64(1.0 - Float64(x / z)) * y))
end
function tmp = code(x, y, z)
tmp = (x + (y * (z - x))) / z;
end
↓
function tmp = code(x, y, z)
tmp = (x / z) + ((1.0 - (x / z)) * y);
end
code[x_, y_, z_] := N[(N[(x + N[(y * N[(z - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]
↓
code[x_, y_, z_] := N[(N[(x / z), $MachinePrecision] + N[(N[(1.0 - N[(x / z), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]
\frac{x + y \cdot \left(z - x\right)}{z}
↓
\frac{x}{z} + \left(1 - \frac{x}{z}\right) \cdot y
Alternatives Alternative 1 Error 0.5 Cost 1864
\[\begin{array}{l}
t_0 := \frac{x + y \cdot \left(z - x\right)}{z}\\
t_1 := \left(1 - \frac{x}{z}\right) \cdot y\\
\mathbf{if}\;t_0 \leq -\infty:\\
\;\;\;\;t_1\\
\mathbf{elif}\;t_0 \leq 2 \cdot 10^{+291}:\\
\;\;\;\;t_0\\
\mathbf{else}:\\
\;\;\;\;t_1\\
\end{array}
\]
Alternative 2 Error 9.9 Cost 1108
\[\begin{array}{l}
t_0 := \frac{x}{z} + y\\
t_1 := \frac{x}{\frac{z}{1 - y}}\\
\mathbf{if}\;x \leq -4.6 \cdot 10^{+173}:\\
\;\;\;\;t_1\\
\mathbf{elif}\;x \leq 2.4941530778943612 \cdot 10^{-138}:\\
\;\;\;\;t_0\\
\mathbf{elif}\;x \leq 5.930112861205471 \cdot 10^{-94}:\\
\;\;\;\;\frac{y \cdot \left(z - x\right)}{z}\\
\mathbf{elif}\;x \leq 3.5984261973140385 \cdot 10^{-15}:\\
\;\;\;\;t_0\\
\mathbf{elif}\;x \leq 1.65 \cdot 10^{+52}:\\
\;\;\;\;t_1\\
\mathbf{else}:\\
\;\;\;\;t_0\\
\end{array}
\]
Alternative 3 Error 9.2 Cost 844
\[\begin{array}{l}
t_0 := \frac{x}{z} + y\\
t_1 := \frac{x}{\frac{z}{1 - y}}\\
\mathbf{if}\;x \leq -4.6 \cdot 10^{+173}:\\
\;\;\;\;t_1\\
\mathbf{elif}\;x \leq 3.5984261973140385 \cdot 10^{-15}:\\
\;\;\;\;t_0\\
\mathbf{elif}\;x \leq 1.65 \cdot 10^{+52}:\\
\;\;\;\;t_1\\
\mathbf{else}:\\
\;\;\;\;t_0\\
\end{array}
\]
Alternative 4 Error 1.0 Cost 712
\[\begin{array}{l}
t_0 := \left(1 - \frac{x}{z}\right) \cdot y\\
\mathbf{if}\;y \leq -1296949.680851089:\\
\;\;\;\;t_0\\
\mathbf{elif}\;y \leq 2.9570055291872127 \cdot 10^{-18}:\\
\;\;\;\;\frac{x}{z} + y\\
\mathbf{else}:\\
\;\;\;\;t_0\\
\end{array}
\]
Alternative 5 Error 9.4 Cost 648
\[\begin{array}{l}
t_0 := \frac{x}{z} + y\\
\mathbf{if}\;y \leq -2.2322738979989566 \cdot 10^{+61}:\\
\;\;\;\;t_0\\
\mathbf{elif}\;y \leq -1.3729984763004903 \cdot 10^{+34}:\\
\;\;\;\;\frac{x}{z} \cdot \left(-y\right)\\
\mathbf{else}:\\
\;\;\;\;t_0\\
\end{array}
\]
Alternative 6 Error 19.7 Cost 456
\[\begin{array}{l}
\mathbf{if}\;y \leq -5.8547347651076085 \cdot 10^{-43}:\\
\;\;\;\;y\\
\mathbf{elif}\;y \leq 2.8119025787597107 \cdot 10^{-50}:\\
\;\;\;\;\frac{x}{z}\\
\mathbf{else}:\\
\;\;\;\;y\\
\end{array}
\]
Alternative 7 Error 9.0 Cost 320
\[\frac{x}{z} + y
\]
Alternative 8 Error 32.0 Cost 64
\[y
\]