Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[\frac{x - y}{z - y}
\]
↓
\[\frac{x - y}{z - y}
\]
(FPCore (x y z) :precision binary64 (/ (- x y) (- z y))) ↓
(FPCore (x y z) :precision binary64 (/ (- x y) (- z y))) double code(double x, double y, double z) {
return (x - y) / (z - y);
}
↓
double code(double x, double y, double z) {
return (x - y) / (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 - y)
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 - y) / (z - y)
end function
public static double code(double x, double y, double z) {
return (x - y) / (z - y);
}
↓
public static double code(double x, double y, double z) {
return (x - y) / (z - y);
}
def code(x, y, z):
return (x - y) / (z - y)
↓
def code(x, y, z):
return (x - y) / (z - y)
function code(x, y, z)
return Float64(Float64(x - y) / Float64(z - y))
end
↓
function code(x, y, z)
return Float64(Float64(x - y) / Float64(z - y))
end
function tmp = code(x, y, z)
tmp = (x - y) / (z - y);
end
↓
function tmp = code(x, y, z)
tmp = (x - y) / (z - y);
end
code[x_, y_, z_] := N[(N[(x - y), $MachinePrecision] / N[(z - y), $MachinePrecision]), $MachinePrecision]
↓
code[x_, y_, z_] := N[(N[(x - y), $MachinePrecision] / N[(z - y), $MachinePrecision]), $MachinePrecision]
\frac{x - y}{z - y}
↓
\frac{x - y}{z - y}