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

Percentage Accurate: 100.0% → 100.0%

Time: 6.6s

Alternatives: 7

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, 0.1× speedup

Math

\[\begin{array}{l} \\ {\left(\frac{1}{x - y} \cdot \left(x + y\right)\right)}^{-1} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return math.pow(((1.0 / (x - y)) * (x + y)), -1.0)

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

   1. clear-num 100.0%

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

   2. inv-pow 100.0%

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

4. Applied egg-rr 100.0%

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

   1. clear-num 100.0%

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

   2. associate-/r/ 100.0%

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

6. Applied egg-rr 100.0%

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

7. Final simplification 100.0%

   \[\leadsto {\left(\frac{1}{x - y} \cdot \left(x + y\right)\right)}^{-1} \]
8. Add Preprocessing

Alternative 2: 75.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{+64} \lor \neg \left(x \leq -2.95 \cdot 10^{+39} \lor \neg \left(x \leq -15500000000\right) \land x \leq 1.05 \cdot 10^{+52}\right):\\ \;\;\;\;1 + -2 \cdot \frac{y}{x}\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -5e+64)
         (not
          (or (<= x -2.95e+39)
              (and (not (<= x -15500000000.0)) (<= x 1.05e+52)))))
   (+ 1.0 (* -2.0 (/ y x)))
   -1.0))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -5e+64) || !((x <= -2.95e+39) || (!(x <= -15500000000.0) && (x <= 1.05e+52)))) {
		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 <= (-5d+64)) .or. (.not. (x <= (-2.95d+39)) .or. (.not. (x <= (-15500000000.0d0))) .and. (x <= 1.05d+52))) 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 <= -5e+64) || !((x <= -2.95e+39) || (!(x <= -15500000000.0) && (x <= 1.05e+52)))) {
		tmp = 1.0 + (-2.0 * (y / x));
	} else {
		tmp = -1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -5e+64) or not ((x <= -2.95e+39) or (not (x <= -15500000000.0) and (x <= 1.05e+52))):
		tmp = 1.0 + (-2.0 * (y / x))
	else:
		tmp = -1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -5e+64) || !((x <= -2.95e+39) || (!(x <= -15500000000.0) && (x <= 1.05e+52))))
		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 <= -5e+64) || ~(((x <= -2.95e+39) || (~((x <= -15500000000.0)) && (x <= 1.05e+52)))))
		tmp = 1.0 + (-2.0 * (y / x));
	else
		tmp = -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -5e+64], N[Not[Or[LessEqual[x, -2.95e+39], And[N[Not[LessEqual[x, -15500000000.0]], $MachinePrecision], LessEqual[x, 1.05e+52]]]], $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 < -5e64 or -2.94999999999999983e39 < x < -1.55e10 or 1.05e52 < x

   1. Initial program 99.9%

      \[\frac{x - y}{x + y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 87.9%

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

   if -5e64 < x < -2.94999999999999983e39 or -1.55e10 < x < 1.05e52

   1. Initial program 100.0%

      \[\frac{x - y}{x + y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 80.2%

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

4. Final simplification 83.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{+64} \lor \neg \left(x \leq -2.95 \cdot 10^{+39} \lor \neg \left(x \leq -15500000000\right) \land x \leq 1.05 \cdot 10^{+52}\right):\\ \;\;\;\;1 + -2 \cdot \frac{y}{x}\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 75.6% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.6 \cdot 10^{+65} \lor \neg \left(x \leq -2 \cdot 10^{+32} \lor \neg \left(x \leq -880000000\right) \land x \leq 1.5 \cdot 10^{+51}\right):\\ \;\;\;\;1 + -2 \cdot \frac{y}{x}\\ \mathbf{else}:\\ \;\;\;\;-1 + 2 \cdot \frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -1.6e+65)
         (not
          (or (<= x -2e+32) (and (not (<= x -880000000.0)) (<= x 1.5e+51)))))
   (+ 1.0 (* -2.0 (/ y x)))
   (+ -1.0 (* 2.0 (/ x y)))))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -1.6e+65) || !((x <= -2e+32) || (!(x <= -880000000.0) && (x <= 1.5e+51)))) {
		tmp = 1.0 + (-2.0 * (y / x));
	} else {
		tmp = -1.0 + (2.0 * (x / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((x <= (-1.6d+65)) .or. (.not. (x <= (-2d+32)) .or. (.not. (x <= (-880000000.0d0))) .and. (x <= 1.5d+51))) then
        tmp = 1.0d0 + ((-2.0d0) * (y / x))
    else
        tmp = (-1.0d0) + (2.0d0 * (x / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -1.6e+65) || !((x <= -2e+32) || (!(x <= -880000000.0) && (x <= 1.5e+51)))) {
		tmp = 1.0 + (-2.0 * (y / x));
	} else {
		tmp = -1.0 + (2.0 * (x / y));
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -1.6e+65) or not ((x <= -2e+32) or (not (x <= -880000000.0) and (x <= 1.5e+51))):
		tmp = 1.0 + (-2.0 * (y / x))
	else:
		tmp = -1.0 + (2.0 * (x / y))
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -1.6e+65) || !((x <= -2e+32) || (!(x <= -880000000.0) && (x <= 1.5e+51))))
		tmp = Float64(1.0 + Float64(-2.0 * Float64(y / x)));
	else
		tmp = Float64(-1.0 + Float64(2.0 * Float64(x / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -1.6e+65) || ~(((x <= -2e+32) || (~((x <= -880000000.0)) && (x <= 1.5e+51)))))
		tmp = 1.0 + (-2.0 * (y / x));
	else
		tmp = -1.0 + (2.0 * (x / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -1.6e+65], N[Not[Or[LessEqual[x, -2e+32], And[N[Not[LessEqual[x, -880000000.0]], $MachinePrecision], LessEqual[x, 1.5e+51]]]], $MachinePrecision]], N[(1.0 + N[(-2.0 * N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(-1.0 + N[(2.0 * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1.60000000000000003e65 or -2.00000000000000011e32 < x < -8.8e8 or 1.5e51 < x

   1. Initial program 99.9%

      \[\frac{x - y}{x + y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 87.9%

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

   if -1.60000000000000003e65 < x < -2.00000000000000011e32 or -8.8e8 < x < 1.5e51

   1. Initial program 100.0%

      \[\frac{x - y}{x + y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 81.3%

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

4. Final simplification 84.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.6 \cdot 10^{+65} \lor \neg \left(x \leq -2 \cdot 10^{+32} \lor \neg \left(x \leq -880000000\right) \land x \leq 1.5 \cdot 10^{+51}\right):\\ \;\;\;\;1 + -2 \cdot \frac{y}{x}\\ \mathbf{else}:\\ \;\;\;\;-1 + 2 \cdot \frac{x}{y}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 74.7% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.2 \cdot 10^{+64}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq -2.05 \cdot 10^{+49}:\\ \;\;\;\;-1\\ \mathbf{elif}\;x \leq -8 \cdot 10^{+14}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 2 \cdot 10^{+51}:\\ \;\;\;\;-1\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -3.2e+64)
   1.0
   (if (<= x -2.05e+49)
     -1.0
     (if (<= x -8e+14) 1.0 (if (<= x 2e+51) -1.0 1.0)))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -3.2e+64) {
		tmp = 1.0;
	} else if (x <= -2.05e+49) {
		tmp = -1.0;
	} else if (x <= -8e+14) {
		tmp = 1.0;
	} else if (x <= 2e+51) {
		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 <= (-3.2d+64)) then
        tmp = 1.0d0
    else if (x <= (-2.05d+49)) then
        tmp = -1.0d0
    else if (x <= (-8d+14)) then
        tmp = 1.0d0
    else if (x <= 2d+51) 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 <= -3.2e+64) {
		tmp = 1.0;
	} else if (x <= -2.05e+49) {
		tmp = -1.0;
	} else if (x <= -8e+14) {
		tmp = 1.0;
	} else if (x <= 2e+51) {
		tmp = -1.0;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -3.2e+64:
		tmp = 1.0
	elif x <= -2.05e+49:
		tmp = -1.0
	elif x <= -8e+14:
		tmp = 1.0
	elif x <= 2e+51:
		tmp = -1.0
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -3.2e+64)
		tmp = 1.0;
	elseif (x <= -2.05e+49)
		tmp = -1.0;
	elseif (x <= -8e+14)
		tmp = 1.0;
	elseif (x <= 2e+51)
		tmp = -1.0;
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -3.2e+64)
		tmp = 1.0;
	elseif (x <= -2.05e+49)
		tmp = -1.0;
	elseif (x <= -8e+14)
		tmp = 1.0;
	elseif (x <= 2e+51)
		tmp = -1.0;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -3.2e+64], 1.0, If[LessEqual[x, -2.05e+49], -1.0, If[LessEqual[x, -8e+14], 1.0, If[LessEqual[x, 2e+51], -1.0, 1.0]]]]

Derivation

1. Split input into 2 regimes

2. if x < -3.20000000000000019e64 or -2.05e49 < x < -8e14 or 2e51 < x

   1. Initial program 99.9%

      \[\frac{x - y}{x + y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 86.4%

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

   if -3.20000000000000019e64 < x < -2.05e49 or -8e14 < x < 2e51

   1. Initial program 100.0%

      \[\frac{x - y}{x + y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 80.2%

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

4. Final simplification 82.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.2 \cdot 10^{+64}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq -2.05 \cdot 10^{+49}:\\ \;\;\;\;-1\\ \mathbf{elif}\;x \leq -8 \cdot 10^{+14}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 2 \cdot 10^{+51}:\\ \;\;\;\;-1\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 100.0% accurate, 0.8× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

   1. clear-num 100.0%

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

   2. inv-pow 100.0%

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

4. Applied egg-rr 100.0%

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

   1. unpow-1 100.0%

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

6. Applied egg-rr 100.0%

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

7. Final simplification 100.0%

   \[\leadsto \frac{1}{\frac{x + y}{x - y}} \]
8. Add Preprocessing

Alternative 6: 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]

Derivation

1. Initial program 100.0%

   \[\frac{x - y}{x + y} \]
2. Add Preprocessing

3. Final simplification 100.0%

   \[\leadsto \frac{x - y}{x + y} \]
4. Add Preprocessing

Alternative 7: 49.8% 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. Add Preprocessing

3. Taylor expanded in x around 0 50.1%

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

4. Final simplification 50.1%

   \[\leadsto -1 \]
5. Add Preprocessing

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 2024075 
(FPCore (x y)
  :name "Data.Colour.RGB:hslsv from colour-2.3.3, D"
  :precision binary64

  :alt
  (- (/ x (+ x y)) (/ y (+ x y)))

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