Math FPCore C Fortran Java Python Julia MATLAB Wolfram TeX \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y}
\]
↓
\[4 + 4 \cdot \frac{x - z}{y}
\]
(FPCore (x y z)
:precision binary64
(+ 1.0 (/ (* 4.0 (- (+ x (* y 0.75)) z)) y))) ↓
(FPCore (x y z) :precision binary64 (+ 4.0 (* 4.0 (/ (- x z) y)))) double code(double x, double y, double z) {
return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
}
↓
double code(double x, double y, double z) {
return 4.0 + (4.0 * ((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 = 1.0d0 + ((4.0d0 * ((x + (y * 0.75d0)) - 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 = 4.0d0 + (4.0d0 * ((x - z) / y))
end function
public static double code(double x, double y, double z) {
return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
}
↓
public static double code(double x, double y, double z) {
return 4.0 + (4.0 * ((x - z) / y));
}
def code(x, y, z):
return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y)
↓
def code(x, y, z):
return 4.0 + (4.0 * ((x - z) / y))
function code(x, y, z)
return Float64(1.0 + Float64(Float64(4.0 * Float64(Float64(x + Float64(y * 0.75)) - z)) / y))
end
↓
function code(x, y, z)
return Float64(4.0 + Float64(4.0 * Float64(Float64(x - z) / y)))
end
function tmp = code(x, y, z)
tmp = 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
end
↓
function tmp = code(x, y, z)
tmp = 4.0 + (4.0 * ((x - z) / y));
end
code[x_, y_, z_] := N[(1.0 + N[(N[(4.0 * N[(N[(x + N[(y * 0.75), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]
↓
code[x_, y_, z_] := N[(4.0 + N[(4.0 * N[(N[(x - z), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y}
↓
4 + 4 \cdot \frac{x - z}{y}