Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[x \cdot y + \left(1 - x\right) \cdot z
\]
↓
\[z + x \cdot \left(y - z\right)
\]
(FPCore (x y z) :precision binary64 (+ (* x y) (* (- 1.0 x) z))) ↓
(FPCore (x y z) :precision binary64 (+ z (* x (- y z)))) double code(double x, double y, double z) {
return (x * y) + ((1.0 - x) * z);
}
↓
double code(double x, double y, double z) {
return z + (x * (y - z));
}
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) + ((1.0d0 - 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 = z + (x * (y - z))
end function
public static double code(double x, double y, double z) {
return (x * y) + ((1.0 - x) * z);
}
↓
public static double code(double x, double y, double z) {
return z + (x * (y - z));
}
def code(x, y, z):
return (x * y) + ((1.0 - x) * z)
↓
def code(x, y, z):
return z + (x * (y - z))
function code(x, y, z)
return Float64(Float64(x * y) + Float64(Float64(1.0 - x) * z))
end
↓
function code(x, y, z)
return Float64(z + Float64(x * Float64(y - z)))
end
function tmp = code(x, y, z)
tmp = (x * y) + ((1.0 - x) * z);
end
↓
function tmp = code(x, y, z)
tmp = z + (x * (y - z));
end
code[x_, y_, z_] := N[(N[(x * y), $MachinePrecision] + N[(N[(1.0 - x), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]
↓
code[x_, y_, z_] := N[(z + N[(x * N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
x \cdot y + \left(1 - x\right) \cdot z
↓
z + x \cdot \left(y - z\right)