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

Percentage Accurate: 100.0% → 100.0%

Time: 10.1s

Alternatives: 10

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \frac{x - y}{2 - \left(x + y\right)} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x - y}{2 - \left(x + y\right)} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x - y}{2 - \left(x + y\right)} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 2: 73.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -9.8 \cdot 10^{+64}:\\ \;\;\;\;-1 + \frac{y}{x}\\ \mathbf{elif}\;x \leq 1.9 \cdot 10^{-100}:\\ \;\;\;\;\frac{y}{y + -2}\\ \mathbf{elif}\;x \leq 4.6 \cdot 10^{-18}:\\ \;\;\;\;\frac{x}{2 - x}\\ \mathbf{elif}\;x \leq 4.55 \cdot 10^{+24}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\frac{x - y}{-x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -9.8e+64)
   (+ -1.0 (/ y x))
   (if (<= x 1.9e-100)
     (/ y (+ y -2.0))
     (if (<= x 4.6e-18)
       (/ x (- 2.0 x))
       (if (<= x 4.55e+24) 1.0 (/ (- x y) (- x)))))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -9.8e+64) {
		tmp = -1.0 + (y / x);
	} else if (x <= 1.9e-100) {
		tmp = y / (y + -2.0);
	} else if (x <= 4.6e-18) {
		tmp = x / (2.0 - x);
	} else if (x <= 4.55e+24) {
		tmp = 1.0;
	} else {
		tmp = (x - y) / -x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-9.8d+64)) then
        tmp = (-1.0d0) + (y / x)
    else if (x <= 1.9d-100) then
        tmp = y / (y + (-2.0d0))
    else if (x <= 4.6d-18) then
        tmp = x / (2.0d0 - x)
    else if (x <= 4.55d+24) then
        tmp = 1.0d0
    else
        tmp = (x - y) / -x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -9.8e+64) {
		tmp = -1.0 + (y / x);
	} else if (x <= 1.9e-100) {
		tmp = y / (y + -2.0);
	} else if (x <= 4.6e-18) {
		tmp = x / (2.0 - x);
	} else if (x <= 4.55e+24) {
		tmp = 1.0;
	} else {
		tmp = (x - y) / -x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -9.8e+64:
		tmp = -1.0 + (y / x)
	elif x <= 1.9e-100:
		tmp = y / (y + -2.0)
	elif x <= 4.6e-18:
		tmp = x / (2.0 - x)
	elif x <= 4.55e+24:
		tmp = 1.0
	else:
		tmp = (x - y) / -x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -9.8e+64)
		tmp = Float64(-1.0 + Float64(y / x));
	elseif (x <= 1.9e-100)
		tmp = Float64(y / Float64(y + -2.0));
	elseif (x <= 4.6e-18)
		tmp = Float64(x / Float64(2.0 - x));
	elseif (x <= 4.55e+24)
		tmp = 1.0;
	else
		tmp = Float64(Float64(x - y) / Float64(-x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -9.8e+64)
		tmp = -1.0 + (y / x);
	elseif (x <= 1.9e-100)
		tmp = y / (y + -2.0);
	elseif (x <= 4.6e-18)
		tmp = x / (2.0 - x);
	elseif (x <= 4.55e+24)
		tmp = 1.0;
	else
		tmp = (x - y) / -x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -9.8e+64], N[(-1.0 + N[(y / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.9e-100], N[(y / N[(y + -2.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 4.6e-18], N[(x / N[(2.0 - x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 4.55e+24], 1.0, N[(N[(x - y), $MachinePrecision] / (-x)), $MachinePrecision]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 3: 72.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - \frac{x}{y}\\ t_1 := \frac{x}{2 - x}\\ \mathbf{if}\;y \leq -9 \cdot 10^{-20}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -1.55 \cdot 10^{-89}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -1.9 \cdot 10^{-133}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{elif}\;y \leq 2.2 \cdot 10^{+30}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- 1.0 (/ x y))) (t_1 (/ x (- 2.0 x))))
   (if (<= y -9e-20)
     t_0
     (if (<= y -1.55e-89)
       t_1
       (if (<= y -1.9e-133) (* y -0.5) (if (<= y 2.2e+30) t_1 t_0))))))

C

double code(double x, double y) {
	double t_0 = 1.0 - (x / y);
	double t_1 = x / (2.0 - x);
	double tmp;
	if (y <= -9e-20) {
		tmp = t_0;
	} else if (y <= -1.55e-89) {
		tmp = t_1;
	} else if (y <= -1.9e-133) {
		tmp = y * -0.5;
	} else if (y <= 2.2e+30) {
		tmp = t_1;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = 1.0d0 - (x / y)
    t_1 = x / (2.0d0 - x)
    if (y <= (-9d-20)) then
        tmp = t_0
    else if (y <= (-1.55d-89)) then
        tmp = t_1
    else if (y <= (-1.9d-133)) then
        tmp = y * (-0.5d0)
    else if (y <= 2.2d+30) then
        tmp = t_1
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = 1.0 - (x / y);
	double t_1 = x / (2.0 - x);
	double tmp;
	if (y <= -9e-20) {
		tmp = t_0;
	} else if (y <= -1.55e-89) {
		tmp = t_1;
	} else if (y <= -1.9e-133) {
		tmp = y * -0.5;
	} else if (y <= 2.2e+30) {
		tmp = t_1;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = 1.0 - (x / y)
	t_1 = x / (2.0 - x)
	tmp = 0
	if y <= -9e-20:
		tmp = t_0
	elif y <= -1.55e-89:
		tmp = t_1
	elif y <= -1.9e-133:
		tmp = y * -0.5
	elif y <= 2.2e+30:
		tmp = t_1
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(1.0 - Float64(x / y))
	t_1 = Float64(x / Float64(2.0 - x))
	tmp = 0.0
	if (y <= -9e-20)
		tmp = t_0;
	elseif (y <= -1.55e-89)
		tmp = t_1;
	elseif (y <= -1.9e-133)
		tmp = Float64(y * -0.5);
	elseif (y <= 2.2e+30)
		tmp = t_1;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = 1.0 - (x / y);
	t_1 = x / (2.0 - x);
	tmp = 0.0;
	if (y <= -9e-20)
		tmp = t_0;
	elseif (y <= -1.55e-89)
		tmp = t_1;
	elseif (y <= -1.9e-133)
		tmp = y * -0.5;
	elseif (y <= 2.2e+30)
		tmp = t_1;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(x / N[(2.0 - x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -9e-20], t$95$0, If[LessEqual[y, -1.55e-89], t$95$1, If[LessEqual[y, -1.9e-133], N[(y * -0.5), $MachinePrecision], If[LessEqual[y, 2.2e+30], t$95$1, t$95$0]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 4: 73.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := -1 + \frac{y}{x}\\ \mathbf{if}\;x \leq -1.55 \cdot 10^{+62}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 6 \cdot 10^{-103}:\\ \;\;\;\;\frac{y}{y + -2}\\ \mathbf{elif}\;x \leq 7 \cdot 10^{-19}:\\ \;\;\;\;\frac{x}{2 - x}\\ \mathbf{elif}\;x \leq 4.8 \cdot 10^{+24}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (+ -1.0 (/ y x))))
   (if (<= x -1.55e+62)
     t_0
     (if (<= x 6e-103)
       (/ y (+ y -2.0))
       (if (<= x 7e-19) (/ x (- 2.0 x)) (if (<= x 4.8e+24) 1.0 t_0))))))

C

double code(double x, double y) {
	double t_0 = -1.0 + (y / x);
	double tmp;
	if (x <= -1.55e+62) {
		tmp = t_0;
	} else if (x <= 6e-103) {
		tmp = y / (y + -2.0);
	} else if (x <= 7e-19) {
		tmp = x / (2.0 - x);
	} else if (x <= 4.8e+24) {
		tmp = 1.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (-1.0d0) + (y / x)
    if (x <= (-1.55d+62)) then
        tmp = t_0
    else if (x <= 6d-103) then
        tmp = y / (y + (-2.0d0))
    else if (x <= 7d-19) then
        tmp = x / (2.0d0 - x)
    else if (x <= 4.8d+24) then
        tmp = 1.0d0
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = -1.0 + (y / x);
	double tmp;
	if (x <= -1.55e+62) {
		tmp = t_0;
	} else if (x <= 6e-103) {
		tmp = y / (y + -2.0);
	} else if (x <= 7e-19) {
		tmp = x / (2.0 - x);
	} else if (x <= 4.8e+24) {
		tmp = 1.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = -1.0 + (y / x)
	tmp = 0
	if x <= -1.55e+62:
		tmp = t_0
	elif x <= 6e-103:
		tmp = y / (y + -2.0)
	elif x <= 7e-19:
		tmp = x / (2.0 - x)
	elif x <= 4.8e+24:
		tmp = 1.0
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(-1.0 + Float64(y / x))
	tmp = 0.0
	if (x <= -1.55e+62)
		tmp = t_0;
	elseif (x <= 6e-103)
		tmp = Float64(y / Float64(y + -2.0));
	elseif (x <= 7e-19)
		tmp = Float64(x / Float64(2.0 - x));
	elseif (x <= 4.8e+24)
		tmp = 1.0;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = -1.0 + (y / x);
	tmp = 0.0;
	if (x <= -1.55e+62)
		tmp = t_0;
	elseif (x <= 6e-103)
		tmp = y / (y + -2.0);
	elseif (x <= 7e-19)
		tmp = x / (2.0 - x);
	elseif (x <= 4.8e+24)
		tmp = 1.0;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(-1.0 + N[(y / x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.55e+62], t$95$0, If[LessEqual[x, 6e-103], N[(y / N[(y + -2.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 7e-19], N[(x / N[(2.0 - x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 4.8e+24], 1.0, t$95$0]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 5: 87.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -6.2 \cdot 10^{+62} \lor \neg \left(x \leq 4.7 \cdot 10^{+24}\right):\\ \;\;\;\;-1 - \frac{2 + y \cdot -2}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{x - y}{2 - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -6.2e+62) (not (<= x 4.7e+24)))
   (- -1.0 (/ (+ 2.0 (* y -2.0)) x))
   (/ (- x y) (- 2.0 y))))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -6.2e+62) || !(x <= 4.7e+24)) {
		tmp = -1.0 - ((2.0 + (y * -2.0)) / x);
	} else {
		tmp = (x - y) / (2.0 - 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 <= (-6.2d+62)) .or. (.not. (x <= 4.7d+24))) then
        tmp = (-1.0d0) - ((2.0d0 + (y * (-2.0d0))) / x)
    else
        tmp = (x - y) / (2.0d0 - y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -6.2e+62) || !(x <= 4.7e+24)) {
		tmp = -1.0 - ((2.0 + (y * -2.0)) / x);
	} else {
		tmp = (x - y) / (2.0 - y);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -6.2e+62) or not (x <= 4.7e+24):
		tmp = -1.0 - ((2.0 + (y * -2.0)) / x)
	else:
		tmp = (x - y) / (2.0 - y)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -6.2e+62) || !(x <= 4.7e+24))
		tmp = Float64(-1.0 - Float64(Float64(2.0 + Float64(y * -2.0)) / x));
	else
		tmp = Float64(Float64(x - y) / Float64(2.0 - y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -6.2e+62) || ~((x <= 4.7e+24)))
		tmp = -1.0 - ((2.0 + (y * -2.0)) / x);
	else
		tmp = (x - y) / (2.0 - y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -6.2e+62], N[Not[LessEqual[x, 4.7e+24]], $MachinePrecision]], N[(-1.0 - N[(N[(2.0 + N[(y * -2.0), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision], N[(N[(x - y), $MachinePrecision] / N[(2.0 - y), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 6: 60.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - \frac{x}{y}\\ \mathbf{if}\;y \leq -1.8:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -2.05 \cdot 10^{-134}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{elif}\;y \leq 4.8 \cdot 10^{+31}:\\ \;\;\;\;-1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- 1.0 (/ x y))))
   (if (<= y -1.8)
     t_0
     (if (<= y -2.05e-134) (* y -0.5) (if (<= y 4.8e+31) -1.0 t_0)))))

C

double code(double x, double y) {
	double t_0 = 1.0 - (x / y);
	double tmp;
	if (y <= -1.8) {
		tmp = t_0;
	} else if (y <= -2.05e-134) {
		tmp = y * -0.5;
	} else if (y <= 4.8e+31) {
		tmp = -1.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = 1.0d0 - (x / y)
    if (y <= (-1.8d0)) then
        tmp = t_0
    else if (y <= (-2.05d-134)) then
        tmp = y * (-0.5d0)
    else if (y <= 4.8d+31) then
        tmp = -1.0d0
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = 1.0 - (x / y);
	double tmp;
	if (y <= -1.8) {
		tmp = t_0;
	} else if (y <= -2.05e-134) {
		tmp = y * -0.5;
	} else if (y <= 4.8e+31) {
		tmp = -1.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = 1.0 - (x / y)
	tmp = 0
	if y <= -1.8:
		tmp = t_0
	elif y <= -2.05e-134:
		tmp = y * -0.5
	elif y <= 4.8e+31:
		tmp = -1.0
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(1.0 - Float64(x / y))
	tmp = 0.0
	if (y <= -1.8)
		tmp = t_0;
	elseif (y <= -2.05e-134)
		tmp = Float64(y * -0.5);
	elseif (y <= 4.8e+31)
		tmp = -1.0;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = 1.0 - (x / y);
	tmp = 0.0;
	if (y <= -1.8)
		tmp = t_0;
	elseif (y <= -2.05e-134)
		tmp = y * -0.5;
	elseif (y <= 4.8e+31)
		tmp = -1.0;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.8], t$95$0, If[LessEqual[y, -2.05e-134], N[(y * -0.5), $MachinePrecision], If[LessEqual[y, 4.8e+31], -1.0, t$95$0]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 7: 86.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.55 \cdot 10^{+62}:\\ \;\;\;\;-1 + \frac{y}{x}\\ \mathbf{elif}\;x \leq 5.2 \cdot 10^{+29}:\\ \;\;\;\;\frac{x - y}{2 - y}\\ \mathbf{else}:\\ \;\;\;\;\frac{x - y}{-x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -1.55e+62)
   (+ -1.0 (/ y x))
   (if (<= x 5.2e+29) (/ (- x y) (- 2.0 y)) (/ (- x y) (- x)))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -1.55e+62) {
		tmp = -1.0 + (y / x);
	} else if (x <= 5.2e+29) {
		tmp = (x - y) / (2.0 - y);
	} else {
		tmp = (x - y) / -x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-1.55d+62)) then
        tmp = (-1.0d0) + (y / x)
    else if (x <= 5.2d+29) then
        tmp = (x - y) / (2.0d0 - y)
    else
        tmp = (x - y) / -x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -1.55e+62) {
		tmp = -1.0 + (y / x);
	} else if (x <= 5.2e+29) {
		tmp = (x - y) / (2.0 - y);
	} else {
		tmp = (x - y) / -x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -1.55e+62:
		tmp = -1.0 + (y / x)
	elif x <= 5.2e+29:
		tmp = (x - y) / (2.0 - y)
	else:
		tmp = (x - y) / -x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -1.55e+62)
		tmp = Float64(-1.0 + Float64(y / x));
	elseif (x <= 5.2e+29)
		tmp = Float64(Float64(x - y) / Float64(2.0 - y));
	else
		tmp = Float64(Float64(x - y) / Float64(-x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -1.55e+62)
		tmp = -1.0 + (y / x);
	elseif (x <= 5.2e+29)
		tmp = (x - y) / (2.0 - y);
	else
		tmp = (x - y) / -x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -1.55e+62], N[(-1.0 + N[(y / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 5.2e+29], N[(N[(x - y), $MachinePrecision] / N[(2.0 - y), $MachinePrecision]), $MachinePrecision], N[(N[(x - y), $MachinePrecision] / (-x)), $MachinePrecision]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 8: 60.7% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq -2.1 \cdot 10^{-133}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{elif}\;y \leq 8.5 \cdot 10^{+29}:\\ \;\;\;\;-1\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -2.0)
   1.0
   (if (<= y -2.1e-133) (* y -0.5) (if (<= y 8.5e+29) -1.0 1.0))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -2.0) {
		tmp = 1.0;
	} else if (y <= -2.1e-133) {
		tmp = y * -0.5;
	} else if (y <= 8.5e+29) {
		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 (y <= (-2.0d0)) then
        tmp = 1.0d0
    else if (y <= (-2.1d-133)) then
        tmp = y * (-0.5d0)
    else if (y <= 8.5d+29) 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 (y <= -2.0) {
		tmp = 1.0;
	} else if (y <= -2.1e-133) {
		tmp = y * -0.5;
	} else if (y <= 8.5e+29) {
		tmp = -1.0;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -2.0:
		tmp = 1.0
	elif y <= -2.1e-133:
		tmp = y * -0.5
	elif y <= 8.5e+29:
		tmp = -1.0
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -2.0)
		tmp = 1.0;
	elseif (y <= -2.1e-133)
		tmp = Float64(y * -0.5);
	elseif (y <= 8.5e+29)
		tmp = -1.0;
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -2.0)
		tmp = 1.0;
	elseif (y <= -2.1e-133)
		tmp = y * -0.5;
	elseif (y <= 8.5e+29)
		tmp = -1.0;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -2.0], 1.0, If[LessEqual[y, -2.1e-133], N[(y * -0.5), $MachinePrecision], If[LessEqual[y, 8.5e+29], -1.0, 1.0]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 9: 61.5% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9 \cdot 10^{-20}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 1.2 \cdot 10^{+30}:\\ \;\;\;\;-1\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -9e-20) 1.0 (if (<= y 1.2e+30) -1.0 1.0)))

C

double code(double x, double y) {
	double tmp;
	if (y <= -9e-20) {
		tmp = 1.0;
	} else if (y <= 1.2e+30) {
		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 (y <= (-9d-20)) then
        tmp = 1.0d0
    else if (y <= 1.2d+30) 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 (y <= -9e-20) {
		tmp = 1.0;
	} else if (y <= 1.2e+30) {
		tmp = -1.0;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -9e-20:
		tmp = 1.0
	elif y <= 1.2e+30:
		tmp = -1.0
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -9e-20)
		tmp = 1.0;
	elseif (y <= 1.2e+30)
		tmp = -1.0;
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -9e-20)
		tmp = 1.0;
	elseif (y <= 1.2e+30)
		tmp = -1.0;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -9e-20], 1.0, If[LessEqual[y, 1.2e+30], -1.0, 1.0]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 10: 38.5% accurate, 9.0× 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

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Developer target: 100.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 2 - \left(x + y\right)\\ \frac{x}{t_0} - \frac{y}{t_0} \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- 2.0 (+ x y)))) (- (/ x t_0) (/ y t_0))))

C

double code(double x, double y) {
	double t_0 = 2.0 - (x + y);
	return (x / t_0) - (y / t_0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    t_0 = 2.0d0 - (x + y)
    code = (x / t_0) - (y / t_0)
end function

Java

public static double code(double x, double y) {
	double t_0 = 2.0 - (x + y);
	return (x / t_0) - (y / t_0);
}

Python

def code(x, y):
	t_0 = 2.0 - (x + y)
	return (x / t_0) - (y / t_0)

Julia

function code(x, y)
	t_0 = Float64(2.0 - Float64(x + y))
	return Float64(Float64(x / t_0) - Float64(y / t_0))
end

MATLAB

function tmp = code(x, y)
	t_0 = 2.0 - (x + y);
	tmp = (x / t_0) - (y / t_0);
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(2.0 - N[(x + y), $MachinePrecision]), $MachinePrecision]}, N[(N[(x / t$95$0), $MachinePrecision] - N[(y / t$95$0), $MachinePrecision]), $MachinePrecision]]

Reproduce

herbie shell --seed 2023348 
(FPCore (x y)
  :name "Data.Colour.RGB:hslsv from colour-2.3.3, C"
  :precision binary64

  :herbie-target
  (- (/ x (- 2.0 (+ x y))) (/ y (- 2.0 (+ x y))))

  (/ (- x y) (- 2.0 (+ x y))))