\[lo < -1 \cdot 10^{+308} \land hi > 10^{+308}\]
Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[\frac{x - lo}{hi - lo}
\]
↓
\[\begin{array}{l}
t_0 := \frac{x - lo}{hi}\\
\frac{{t_0}^{3}}{{\left(lo \cdot \frac{x - lo}{hi \cdot hi}\right)}^{2} + \left(1 - \frac{lo}{hi}\right) \cdot {t_0}^{2}}
\end{array}
\]
(FPCore (lo hi x) :precision binary64 (/ (- x lo) (- hi lo))) ↓
(FPCore (lo hi x)
:precision binary64
(let* ((t_0 (/ (- x lo) hi)))
(/
(pow t_0 3.0)
(+
(pow (* lo (/ (- x lo) (* hi hi))) 2.0)
(* (- 1.0 (/ lo hi)) (pow t_0 2.0)))))) double code(double lo, double hi, double x) {
return (x - lo) / (hi - lo);
}
↓
double code(double lo, double hi, double x) {
double t_0 = (x - lo) / hi;
return pow(t_0, 3.0) / (pow((lo * ((x - lo) / (hi * hi))), 2.0) + ((1.0 - (lo / hi)) * pow(t_0, 2.0)));
}
real(8) function code(lo, hi, x)
real(8), intent (in) :: lo
real(8), intent (in) :: hi
real(8), intent (in) :: x
code = (x - lo) / (hi - lo)
end function
↓
real(8) function code(lo, hi, x)
real(8), intent (in) :: lo
real(8), intent (in) :: hi
real(8), intent (in) :: x
real(8) :: t_0
t_0 = (x - lo) / hi
code = (t_0 ** 3.0d0) / (((lo * ((x - lo) / (hi * hi))) ** 2.0d0) + ((1.0d0 - (lo / hi)) * (t_0 ** 2.0d0)))
end function
public static double code(double lo, double hi, double x) {
return (x - lo) / (hi - lo);
}
↓
public static double code(double lo, double hi, double x) {
double t_0 = (x - lo) / hi;
return Math.pow(t_0, 3.0) / (Math.pow((lo * ((x - lo) / (hi * hi))), 2.0) + ((1.0 - (lo / hi)) * Math.pow(t_0, 2.0)));
}
def code(lo, hi, x):
return (x - lo) / (hi - lo)
↓
def code(lo, hi, x):
t_0 = (x - lo) / hi
return math.pow(t_0, 3.0) / (math.pow((lo * ((x - lo) / (hi * hi))), 2.0) + ((1.0 - (lo / hi)) * math.pow(t_0, 2.0)))
function code(lo, hi, x)
return Float64(Float64(x - lo) / Float64(hi - lo))
end
↓
function code(lo, hi, x)
t_0 = Float64(Float64(x - lo) / hi)
return Float64((t_0 ^ 3.0) / Float64((Float64(lo * Float64(Float64(x - lo) / Float64(hi * hi))) ^ 2.0) + Float64(Float64(1.0 - Float64(lo / hi)) * (t_0 ^ 2.0))))
end
function tmp = code(lo, hi, x)
tmp = (x - lo) / (hi - lo);
end
↓
function tmp = code(lo, hi, x)
t_0 = (x - lo) / hi;
tmp = (t_0 ^ 3.0) / (((lo * ((x - lo) / (hi * hi))) ^ 2.0) + ((1.0 - (lo / hi)) * (t_0 ^ 2.0)));
end
code[lo_, hi_, x_] := N[(N[(x - lo), $MachinePrecision] / N[(hi - lo), $MachinePrecision]), $MachinePrecision]
↓
code[lo_, hi_, x_] := Block[{t$95$0 = N[(N[(x - lo), $MachinePrecision] / hi), $MachinePrecision]}, N[(N[Power[t$95$0, 3.0], $MachinePrecision] / N[(N[Power[N[(lo * N[(N[(x - lo), $MachinePrecision] / N[(hi * hi), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + N[(N[(1.0 - N[(lo / hi), $MachinePrecision]), $MachinePrecision] * N[Power[t$95$0, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]
\frac{x - lo}{hi - lo}
↓
\begin{array}{l}
t_0 := \frac{x - lo}{hi}\\
\frac{{t_0}^{3}}{{\left(lo \cdot \frac{x - lo}{hi \cdot hi}\right)}^{2} + \left(1 - \frac{lo}{hi}\right) \cdot {t_0}^{2}}
\end{array}