Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[\frac{x + y}{x - y}
\]
↓
\[\log \left(e^{\frac{x + y}{x - y}}\right)
\]
(FPCore (x y) :precision binary64 (/ (+ x y) (- x y))) ↓
(FPCore (x y) :precision binary64 (log (exp (/ (+ x y) (- x y))))) double code(double x, double y) {
return (x + y) / (x - y);
}
↓
double code(double x, double y) {
return log(exp(((x + y) / (x - y))));
}
real(8) function code(x, y)
real(8), intent (in) :: x
real(8), intent (in) :: y
code = (x + y) / (x - y)
end function
↓
real(8) function code(x, y)
real(8), intent (in) :: x
real(8), intent (in) :: y
code = log(exp(((x + y) / (x - y))))
end function
public static double code(double x, double y) {
return (x + y) / (x - y);
}
↓
public static double code(double x, double y) {
return Math.log(Math.exp(((x + y) / (x - y))));
}
def code(x, y):
return (x + y) / (x - y)
↓
def code(x, y):
return math.log(math.exp(((x + y) / (x - y))))
function code(x, y)
return Float64(Float64(x + y) / Float64(x - y))
end
↓
function code(x, y)
return log(exp(Float64(Float64(x + y) / Float64(x - y))))
end
function tmp = code(x, y)
tmp = (x + y) / (x - y);
end
↓
function tmp = code(x, y)
tmp = log(exp(((x + y) / (x - y))));
end
code[x_, y_] := N[(N[(x + y), $MachinePrecision] / N[(x - y), $MachinePrecision]), $MachinePrecision]
↓
code[x_, y_] := N[Log[N[Exp[N[(N[(x + y), $MachinePrecision] / N[(x - y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]
\frac{x + y}{x - y}
↓
\log \left(e^{\frac{x + y}{x - y}}\right)