Data.Colour.RGB:hslsv from colour-2.3.3, D

Percentage Accurate: 100.0% → 100.0%

Time: 6.8s

Alternatives: 6

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \frac{x - y}{x + y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (- x y) (+ x y)))

C

double code(double x, double y) {
	return (x - y) / (x + y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x - y) / (x + y)
end function

Java

public static double code(double x, double y) {
	return (x - y) / (x + y);
}

Python

def code(x, y):
	return (x - y) / (x + y)

Julia

function code(x, y)
	return Float64(Float64(x - y) / Float64(x + y))
end

MATLAB

function tmp = code(x, y)
	tmp = (x - y) / (x + y);
end

Wolfram

code[x_, y_] := N[(N[(x - y), $MachinePrecision] / N[(x + y), $MachinePrecision]), $MachinePrecision]

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x - y}{x + y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (- x y) (+ x y)))

C

double code(double x, double y) {
	return (x - y) / (x + y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x - y) / (x + y)
end function

Java

public static double code(double x, double y) {
	return (x - y) / (x + y);
}

Python

def code(x, y):
	return (x - y) / (x + y)

Julia

function code(x, y)
	return Float64(Float64(x - y) / Float64(x + y))
end

MATLAB

function tmp = code(x, y)
	tmp = (x - y) / (x + y);
end

Wolfram

code[x_, y_] := N[(N[(x - y), $MachinePrecision] / N[(x + y), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ 1 - 2 \cdot \frac{y}{y + x} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- 1.0 (* 2.0 (/ y (+ y x)))))

C

double code(double x, double y) {
	return 1.0 - (2.0 * (y / (y + x)));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 1.0d0 - (2.0d0 * (y / (y + x)))
end function

Java

public static double code(double x, double y) {
	return 1.0 - (2.0 * (y / (y + x)));
}

Python

def code(x, y):
	return 1.0 - (2.0 * (y / (y + x)))

Julia

function code(x, y)
	return Float64(1.0 - Float64(2.0 * Float64(y / Float64(y + x))))
end

MATLAB

function tmp = code(x, y)
	tmp = 1.0 - (2.0 * (y / (y + x)));
end

Wolfram

code[x_, y_] := N[(1.0 - N[(2.0 * N[(y / N[(y + x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\frac{x - y}{x + y} \]
2. Step-by-step derivation

   1. div-sub 100.0%

      \[\leadsto \color{blue}{\frac{x}{x + y} - \frac{y}{x + y}} \]

3. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\frac{x}{x + y} - \frac{y}{x + y}} \]
4. Step-by-step derivation

   1. div-inv 99.8%

      \[\leadsto \color{blue}{x \cdot \frac{1}{x + y}} - \frac{y}{x + y} \]

   2. div-inv 99.7%

      \[\leadsto x \cdot \frac{1}{x + y} - \color{blue}{y \cdot \frac{1}{x + y}} \]

   3. prod-diff 99.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, \frac{1}{x + y}, -\frac{1}{x + y} \cdot y\right) + \mathsf{fma}\left(-\frac{1}{x + y}, y, \frac{1}{x + y} \cdot y\right)} \]

   4. *-commutative 99.7%

      \[\leadsto \mathsf{fma}\left(x, \frac{1}{x + y}, -\color{blue}{y \cdot \frac{1}{x + y}}\right) + \mathsf{fma}\left(-\frac{1}{x + y}, y, \frac{1}{x + y} \cdot y\right) \]

   5. div-inv 99.8%

      \[\leadsto \mathsf{fma}\left(x, \frac{1}{x + y}, -\color{blue}{\frac{y}{x + y}}\right) + \mathsf{fma}\left(-\frac{1}{x + y}, y, \frac{1}{x + y} \cdot y\right) \]

   6. fma-def 99.8%

      \[\leadsto \color{blue}{\left(x \cdot \frac{1}{x + y} + \left(-\frac{y}{x + y}\right)\right)} + \mathsf{fma}\left(-\frac{1}{x + y}, y, \frac{1}{x + y} \cdot y\right) \]

   7. div-inv 100.0%

      \[\leadsto \left(\color{blue}{\frac{x}{x + y}} + \left(-\frac{y}{x + y}\right)\right) + \mathsf{fma}\left(-\frac{1}{x + y}, y, \frac{1}{x + y} \cdot y\right) \]

   8. associate-+l+ 100.0%

      \[\leadsto \color{blue}{\frac{x}{x + y} + \left(\left(-\frac{y}{x + y}\right) + \mathsf{fma}\left(-\frac{1}{x + y}, y, \frac{1}{x + y} \cdot y\right)\right)} \]

   9. +-commutative 100.0%

      \[\leadsto \frac{x}{\color{blue}{y + x}} + \left(\left(-\frac{y}{x + y}\right) + \mathsf{fma}\left(-\frac{1}{x + y}, y, \frac{1}{x + y} \cdot y\right)\right) \]

   10. distribute-neg-frac 100.0%

       \[\leadsto \frac{x}{y + x} + \left(\color{blue}{\frac{-y}{x + y}} + \mathsf{fma}\left(-\frac{1}{x + y}, y, \frac{1}{x + y} \cdot y\right)\right) \]

   11. +-commutative 100.0%

       \[\leadsto \frac{x}{y + x} + \left(\frac{-y}{\color{blue}{y + x}} + \mathsf{fma}\left(-\frac{1}{x + y}, y, \frac{1}{x + y} \cdot y\right)\right) \]

   12. +-commutative 100.0%

       \[\leadsto \frac{x}{y + x} + \left(\frac{-y}{y + x} + \mathsf{fma}\left(-\frac{1}{\color{blue}{y + x}}, y, \frac{1}{x + y} \cdot y\right)\right) \]

   13. *-commutative 100.0%

       \[\leadsto \frac{x}{y + x} + \left(\frac{-y}{y + x} + \mathsf{fma}\left(-\frac{1}{y + x}, y, \color{blue}{y \cdot \frac{1}{x + y}}\right)\right) \]

   14. div-inv 99.9%

       \[\leadsto \frac{x}{y + x} + \left(\frac{-y}{y + x} + \mathsf{fma}\left(-\frac{1}{y + x}, y, \color{blue}{\frac{y}{x + y}}\right)\right) \]

5. Applied egg-rr 99.9%

   \[\leadsto \color{blue}{\frac{x}{y + x} + \left(\frac{-y}{y + x} + \mathsf{fma}\left(-\frac{1}{y + x}, y, \frac{y}{y + x}\right)\right)} \]
6. Step-by-step derivation

   1. +-commutative 99.9%

      \[\leadsto \frac{x}{y + x} + \color{blue}{\left(\mathsf{fma}\left(-\frac{1}{y + x}, y, \frac{y}{y + x}\right) + \frac{-y}{y + x}\right)} \]

   2. associate-+r+ 99.9%

      \[\leadsto \color{blue}{\left(\frac{x}{y + x} + \mathsf{fma}\left(-\frac{1}{y + x}, y, \frac{y}{y + x}\right)\right) + \frac{-y}{y + x}} \]

   3. +-commutative 99.9%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-\frac{1}{y + x}, y, \frac{y}{y + x}\right) + \frac{x}{y + x}\right)} + \frac{-y}{y + x} \]

   4. associate-+r+ 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-\frac{1}{y + x}, y, \frac{y}{y + x}\right) + \left(\frac{x}{y + x} + \frac{-y}{y + x}\right)} \]

   5. distribute-frac-neg 99.9%

      \[\leadsto \mathsf{fma}\left(-\frac{1}{y + x}, y, \frac{y}{y + x}\right) + \left(\frac{x}{y + x} + \color{blue}{\left(-\frac{y}{y + x}\right)}\right) \]

   6. sub-neg 99.9%

      \[\leadsto \mathsf{fma}\left(-\frac{1}{y + x}, y, \frac{y}{y + x}\right) + \color{blue}{\left(\frac{x}{y + x} - \frac{y}{y + x}\right)} \]

   7. associate-+r- 99.9%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-\frac{1}{y + x}, y, \frac{y}{y + x}\right) + \frac{x}{y + x}\right) - \frac{y}{y + x}} \]

7. Simplified 100.0%

   \[\leadsto \color{blue}{\left(\frac{-y}{y + x} + 1\right) - \frac{y}{y + x}} \]
8. Step-by-step derivation

   1. +-commutative 100.0%

      \[\leadsto \color{blue}{\left(1 + \frac{-y}{y + x}\right)} - \frac{y}{y + x} \]

   2. distribute-frac-neg 100.0%

      \[\leadsto \left(1 + \color{blue}{\left(-\frac{y}{y + x}\right)}\right) - \frac{y}{y + x} \]

   3. sub-neg 100.0%

      \[\leadsto \color{blue}{\left(1 - \frac{y}{y + x}\right)} - \frac{y}{y + x} \]

9. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\left(1 - \frac{y}{y + x}\right)} - \frac{y}{y + x} \]
10. Step-by-step derivation

    1. sub-neg 100.0%

       \[\leadsto \color{blue}{\left(1 - \frac{y}{y + x}\right) + \left(-\frac{y}{y + x}\right)} \]

    2. sub-neg 100.0%

       \[\leadsto \color{blue}{\left(1 + \left(-\frac{y}{y + x}\right)\right)} + \left(-\frac{y}{y + x}\right) \]

    3. associate-+l+ 100.0%

       \[\leadsto \color{blue}{1 + \left(\left(-\frac{y}{y + x}\right) + \left(-\frac{y}{y + x}\right)\right)} \]

    4. +-commutative 100.0%

       \[\leadsto \color{blue}{\left(\left(-\frac{y}{y + x}\right) + \left(-\frac{y}{y + x}\right)\right) + 1} \]

    5. count-2 100.0%

       \[\leadsto \color{blue}{2 \cdot \left(-\frac{y}{y + x}\right)} + 1 \]

    6. distribute-neg-frac 100.0%

       \[\leadsto 2 \cdot \color{blue}{\frac{-y}{y + x}} + 1 \]

11. Applied egg-rr 100.0%

    \[\leadsto \color{blue}{2 \cdot \frac{-y}{y + x} + 1} \]

12. Final simplification 100.0%

    \[\leadsto 1 - 2 \cdot \frac{y}{y + x} \]

Alternative 2: 72.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.12 \cdot 10^{+135} \lor \neg \left(x \leq 7.8 \cdot 10^{-91}\right):\\ \;\;\;\;1 + -2 \cdot \frac{y}{x}\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -1.12e+135) (not (<= x 7.8e-91)))
   (+ 1.0 (* -2.0 (/ y x)))
   -1.0))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -1.12e+135) || !(x <= 7.8e-91)) {
		tmp = 1.0 + (-2.0 * (y / x));
	} else {
		tmp = -1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((x <= (-1.12d+135)) .or. (.not. (x <= 7.8d-91))) then
        tmp = 1.0d0 + ((-2.0d0) * (y / x))
    else
        tmp = -1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -1.12e+135) || !(x <= 7.8e-91)) {
		tmp = 1.0 + (-2.0 * (y / x));
	} else {
		tmp = -1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -1.12e+135) or not (x <= 7.8e-91):
		tmp = 1.0 + (-2.0 * (y / x))
	else:
		tmp = -1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -1.12e+135) || !(x <= 7.8e-91))
		tmp = Float64(1.0 + Float64(-2.0 * Float64(y / x)));
	else
		tmp = -1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -1.12e+135) || ~((x <= 7.8e-91)))
		tmp = 1.0 + (-2.0 * (y / x));
	else
		tmp = -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -1.12e+135], N[Not[LessEqual[x, 7.8e-91]], $MachinePrecision]], N[(1.0 + N[(-2.0 * N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -1.0]

Derivation

1. Split input into 2 regimes

2. if x < -1.1199999999999999e135 or 7.79999999999999987e-91 < x

   1. Initial program 100.0%

      \[\frac{x - y}{x + y} \]

   2. Taylor expanded in y around 0 84.2%

      \[\leadsto \color{blue}{1 + -2 \cdot \frac{y}{x}} \]

   if -1.1199999999999999e135 < x < 7.79999999999999987e-91

   1. Initial program 99.9%

      \[\frac{x - y}{x + y} \]

   2. Taylor expanded in x around 0 73.0%

      \[\leadsto \color{blue}{-1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 78.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.12 \cdot 10^{+135} \lor \neg \left(x \leq 7.8 \cdot 10^{-91}\right):\\ \;\;\;\;1 + -2 \cdot \frac{y}{x}\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \]

Alternative 3: 72.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.12 \cdot 10^{+135} \lor \neg \left(x \leq 1.22 \cdot 10^{-90}\right):\\ \;\;\;\;1 + -2 \cdot \frac{y}{x}\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \frac{x}{y} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -1.12e+135) (not (<= x 1.22e-90)))
   (+ 1.0 (* -2.0 (/ y x)))
   (+ (* 2.0 (/ x y)) -1.0)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -1.12e+135) || !(x <= 1.22e-90)) {
		tmp = 1.0 + (-2.0 * (y / x));
	} else {
		tmp = (2.0 * (x / y)) + -1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((x <= (-1.12d+135)) .or. (.not. (x <= 1.22d-90))) then
        tmp = 1.0d0 + ((-2.0d0) * (y / x))
    else
        tmp = (2.0d0 * (x / y)) + (-1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -1.12e+135) || !(x <= 1.22e-90)) {
		tmp = 1.0 + (-2.0 * (y / x));
	} else {
		tmp = (2.0 * (x / y)) + -1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -1.12e+135) or not (x <= 1.22e-90):
		tmp = 1.0 + (-2.0 * (y / x))
	else:
		tmp = (2.0 * (x / y)) + -1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -1.12e+135) || !(x <= 1.22e-90))
		tmp = Float64(1.0 + Float64(-2.0 * Float64(y / x)));
	else
		tmp = Float64(Float64(2.0 * Float64(x / y)) + -1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -1.12e+135) || ~((x <= 1.22e-90)))
		tmp = 1.0 + (-2.0 * (y / x));
	else
		tmp = (2.0 * (x / y)) + -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -1.12e+135], N[Not[LessEqual[x, 1.22e-90]], $MachinePrecision]], N[(1.0 + N[(-2.0 * N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(2.0 * N[(x / y), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1.1199999999999999e135 or 1.2199999999999999e-90 < x

   1. Initial program 100.0%

      \[\frac{x - y}{x + y} \]

   2. Taylor expanded in y around 0 84.2%

      \[\leadsto \color{blue}{1 + -2 \cdot \frac{y}{x}} \]

   if -1.1199999999999999e135 < x < 1.2199999999999999e-90

   1. Initial program 99.9%

      \[\frac{x - y}{x + y} \]

   2. Taylor expanded in x around 0 74.6%

      \[\leadsto \color{blue}{2 \cdot \frac{x}{y} - 1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 79.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.12 \cdot 10^{+135} \lor \neg \left(x \leq 1.22 \cdot 10^{-90}\right):\\ \;\;\;\;1 + -2 \cdot \frac{y}{x}\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \frac{x}{y} + -1\\ \end{array} \]

Alternative 4: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x - y}{y + x} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (- x y) (+ y x)))

C

double code(double x, double y) {
	return (x - y) / (y + x);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x - y) / (y + x)
end function

Java

public static double code(double x, double y) {
	return (x - y) / (y + x);
}

Python

def code(x, y):
	return (x - y) / (y + x)

Julia

function code(x, y)
	return Float64(Float64(x - y) / Float64(y + x))
end

MATLAB

function tmp = code(x, y)
	tmp = (x - y) / (y + x);
end

Wolfram

code[x_, y_] := N[(N[(x - y), $MachinePrecision] / N[(y + x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\frac{x - y}{x + y} \]

2. Final simplification 100.0%

   \[\leadsto \frac{x - y}{y + x} \]

Alternative 5: 71.6% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.12 \cdot 10^{+135}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 4.8 \cdot 10^{-90}:\\ \;\;\;\;-1\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -1.12e+135) 1.0 (if (<= x 4.8e-90) -1.0 1.0)))

C

double code(double x, double y) {
	double tmp;
	if (x <= -1.12e+135) {
		tmp = 1.0;
	} else if (x <= 4.8e-90) {
		tmp = -1.0;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-1.12d+135)) then
        tmp = 1.0d0
    else if (x <= 4.8d-90) then
        tmp = -1.0d0
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -1.12e+135) {
		tmp = 1.0;
	} else if (x <= 4.8e-90) {
		tmp = -1.0;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -1.12e+135:
		tmp = 1.0
	elif x <= 4.8e-90:
		tmp = -1.0
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -1.12e+135)
		tmp = 1.0;
	elseif (x <= 4.8e-90)
		tmp = -1.0;
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -1.12e+135)
		tmp = 1.0;
	elseif (x <= 4.8e-90)
		tmp = -1.0;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -1.12e+135], 1.0, If[LessEqual[x, 4.8e-90], -1.0, 1.0]]

Derivation

1. Split input into 2 regimes

2. if x < -1.1199999999999999e135 or 4.8000000000000003e-90 < x

   1. Initial program 100.0%

      \[\frac{x - y}{x + y} \]

   2. Taylor expanded in x around inf 83.6%

      \[\leadsto \color{blue}{1} \]

   if -1.1199999999999999e135 < x < 4.8000000000000003e-90

   1. Initial program 99.9%

      \[\frac{x - y}{x + y} \]

   2. Taylor expanded in x around 0 73.0%

      \[\leadsto \color{blue}{-1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 78.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.12 \cdot 10^{+135}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 4.8 \cdot 10^{-90}:\\ \;\;\;\;-1\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]

Alternative 6: 50.3% accurate, 7.0× speedup

Math

\[\begin{array}{l} \\ -1 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 -1.0)

C

double code(double x, double y) {
	return -1.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = -1.0d0
end function

Java

public static double code(double x, double y) {
	return -1.0;
}

Python

def code(x, y):
	return -1.0

Julia

function code(x, y)
	return -1.0
end

MATLAB

function tmp = code(x, y)
	tmp = -1.0;
end

Wolfram

code[x_, y_] := -1.0

Derivation

1. Initial program 100.0%

   \[\frac{x - y}{x + y} \]

2. Taylor expanded in x around 0 45.5%

   \[\leadsto \color{blue}{-1} \]

3. Final simplification 45.5%

   \[\leadsto -1 \]

Developer target: 100.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \frac{x}{x + y} - \frac{y}{x + y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- (/ x (+ x y)) (/ y (+ x y))))

C

double code(double x, double y) {
	return (x / (x + y)) - (y / (x + y));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x / (x + y)) - (y / (x + y))
end function

Java

public static double code(double x, double y) {
	return (x / (x + y)) - (y / (x + y));
}

Python

def code(x, y):
	return (x / (x + y)) - (y / (x + y))

Julia

function code(x, y)
	return Float64(Float64(x / Float64(x + y)) - Float64(y / Float64(x + y)))
end

MATLAB

function tmp = code(x, y)
	tmp = (x / (x + y)) - (y / (x + y));
end

Wolfram

code[x_, y_] := N[(N[(x / N[(x + y), $MachinePrecision]), $MachinePrecision] - N[(y / N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2023301 
(FPCore (x y)
  :name "Data.Colour.RGB:hslsv from colour-2.3.3, D"
  :precision binary64

  :herbie-target
  (- (/ x (+ x y)) (/ y (+ x y)))

  (/ (- x y) (+ x y)))