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

Percentage Accurate: 100.0% → 100.0%

Time: 7.5s

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: 59.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - \frac{x}{y}\\ \mathbf{if}\;y \leq -5.7 \cdot 10^{-13}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -3.8 \cdot 10^{-231}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq -2.4 \cdot 10^{-288}:\\ \;\;\;\;x \cdot 0.5\\ \mathbf{elif}\;y \leq 2.7 \cdot 10^{-147}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq 7.2:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- 1.0 (/ x y))))
   (if (<= y -5.7e-13)
     t_0
     (if (<= y -3.8e-231)
       -1.0
       (if (<= y -2.4e-288)
         (* x 0.5)
         (if (<= y 2.7e-147) -1.0 (if (<= y 7.2) (* y -0.5) t_0)))))))

C

double code(double x, double y) {
	double t_0 = 1.0 - (x / y);
	double tmp;
	if (y <= -5.7e-13) {
		tmp = t_0;
	} else if (y <= -3.8e-231) {
		tmp = -1.0;
	} else if (y <= -2.4e-288) {
		tmp = x * 0.5;
	} else if (y <= 2.7e-147) {
		tmp = -1.0;
	} else if (y <= 7.2) {
		tmp = y * -0.5;
	} 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 <= (-5.7d-13)) then
        tmp = t_0
    else if (y <= (-3.8d-231)) then
        tmp = -1.0d0
    else if (y <= (-2.4d-288)) then
        tmp = x * 0.5d0
    else if (y <= 2.7d-147) then
        tmp = -1.0d0
    else if (y <= 7.2d0) then
        tmp = y * (-0.5d0)
    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 <= -5.7e-13) {
		tmp = t_0;
	} else if (y <= -3.8e-231) {
		tmp = -1.0;
	} else if (y <= -2.4e-288) {
		tmp = x * 0.5;
	} else if (y <= 2.7e-147) {
		tmp = -1.0;
	} else if (y <= 7.2) {
		tmp = y * -0.5;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = 1.0 - (x / y)
	tmp = 0
	if y <= -5.7e-13:
		tmp = t_0
	elif y <= -3.8e-231:
		tmp = -1.0
	elif y <= -2.4e-288:
		tmp = x * 0.5
	elif y <= 2.7e-147:
		tmp = -1.0
	elif y <= 7.2:
		tmp = y * -0.5
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(1.0 - Float64(x / y))
	tmp = 0.0
	if (y <= -5.7e-13)
		tmp = t_0;
	elseif (y <= -3.8e-231)
		tmp = -1.0;
	elseif (y <= -2.4e-288)
		tmp = Float64(x * 0.5);
	elseif (y <= 2.7e-147)
		tmp = -1.0;
	elseif (y <= 7.2)
		tmp = Float64(y * -0.5);
	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 <= -5.7e-13)
		tmp = t_0;
	elseif (y <= -3.8e-231)
		tmp = -1.0;
	elseif (y <= -2.4e-288)
		tmp = x * 0.5;
	elseif (y <= 2.7e-147)
		tmp = -1.0;
	elseif (y <= 7.2)
		tmp = y * -0.5;
	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, -5.7e-13], t$95$0, If[LessEqual[y, -3.8e-231], -1.0, If[LessEqual[y, -2.4e-288], N[(x * 0.5), $MachinePrecision], If[LessEqual[y, 2.7e-147], -1.0, If[LessEqual[y, 7.2], N[(y * -0.5), $MachinePrecision], t$95$0]]]]]]

Derivation

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

Alternative 3: 59.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - \frac{x}{y}\\ \mathbf{if}\;y \leq -5.7 \cdot 10^{-13}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -2.1 \cdot 10^{-230}:\\ \;\;\;\;\frac{y}{x} + -1\\ \mathbf{elif}\;y \leq -1.28 \cdot 10^{-288}:\\ \;\;\;\;x \cdot 0.5\\ \mathbf{elif}\;y \leq 2.7 \cdot 10^{-147}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq 2.4:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- 1.0 (/ x y))))
   (if (<= y -5.7e-13)
     t_0
     (if (<= y -2.1e-230)
       (+ (/ y x) -1.0)
       (if (<= y -1.28e-288)
         (* x 0.5)
         (if (<= y 2.7e-147) -1.0 (if (<= y 2.4) (* y -0.5) t_0)))))))

C

double code(double x, double y) {
	double t_0 = 1.0 - (x / y);
	double tmp;
	if (y <= -5.7e-13) {
		tmp = t_0;
	} else if (y <= -2.1e-230) {
		tmp = (y / x) + -1.0;
	} else if (y <= -1.28e-288) {
		tmp = x * 0.5;
	} else if (y <= 2.7e-147) {
		tmp = -1.0;
	} else if (y <= 2.4) {
		tmp = y * -0.5;
	} 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 <= (-5.7d-13)) then
        tmp = t_0
    else if (y <= (-2.1d-230)) then
        tmp = (y / x) + (-1.0d0)
    else if (y <= (-1.28d-288)) then
        tmp = x * 0.5d0
    else if (y <= 2.7d-147) then
        tmp = -1.0d0
    else if (y <= 2.4d0) then
        tmp = y * (-0.5d0)
    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 <= -5.7e-13) {
		tmp = t_0;
	} else if (y <= -2.1e-230) {
		tmp = (y / x) + -1.0;
	} else if (y <= -1.28e-288) {
		tmp = x * 0.5;
	} else if (y <= 2.7e-147) {
		tmp = -1.0;
	} else if (y <= 2.4) {
		tmp = y * -0.5;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = 1.0 - (x / y)
	tmp = 0
	if y <= -5.7e-13:
		tmp = t_0
	elif y <= -2.1e-230:
		tmp = (y / x) + -1.0
	elif y <= -1.28e-288:
		tmp = x * 0.5
	elif y <= 2.7e-147:
		tmp = -1.0
	elif y <= 2.4:
		tmp = y * -0.5
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(1.0 - Float64(x / y))
	tmp = 0.0
	if (y <= -5.7e-13)
		tmp = t_0;
	elseif (y <= -2.1e-230)
		tmp = Float64(Float64(y / x) + -1.0);
	elseif (y <= -1.28e-288)
		tmp = Float64(x * 0.5);
	elseif (y <= 2.7e-147)
		tmp = -1.0;
	elseif (y <= 2.4)
		tmp = Float64(y * -0.5);
	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 <= -5.7e-13)
		tmp = t_0;
	elseif (y <= -2.1e-230)
		tmp = (y / x) + -1.0;
	elseif (y <= -1.28e-288)
		tmp = x * 0.5;
	elseif (y <= 2.7e-147)
		tmp = -1.0;
	elseif (y <= 2.4)
		tmp = y * -0.5;
	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, -5.7e-13], t$95$0, If[LessEqual[y, -2.1e-230], N[(N[(y / x), $MachinePrecision] + -1.0), $MachinePrecision], If[LessEqual[y, -1.28e-288], N[(x * 0.5), $MachinePrecision], If[LessEqual[y, 2.7e-147], -1.0, If[LessEqual[y, 2.4], N[(y * -0.5), $MachinePrecision], t$95$0]]]]]]

Derivation

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

Alternative 4: 71.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - \frac{x}{y}\\ t_1 := \frac{x}{2 - x}\\ \mathbf{if}\;y \leq -5.7 \cdot 10^{-13}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 2.7 \cdot 10^{-147}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 3.65 \cdot 10^{-121}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{elif}\;y \leq 4.8 \cdot 10^{-92}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 2.85:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- 1.0 (/ x y))) (t_1 (/ x (- 2.0 x))))
   (if (<= y -5.7e-13)
     t_0
     (if (<= y 2.7e-147)
       t_1
       (if (<= y 3.65e-121)
         (* y -0.5)
         (if (<= y 4.8e-92) t_1 (if (<= y 2.85) (* y -0.5) 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 <= -5.7e-13) {
		tmp = t_0;
	} else if (y <= 2.7e-147) {
		tmp = t_1;
	} else if (y <= 3.65e-121) {
		tmp = y * -0.5;
	} else if (y <= 4.8e-92) {
		tmp = t_1;
	} else if (y <= 2.85) {
		tmp = y * -0.5;
	} 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 <= (-5.7d-13)) then
        tmp = t_0
    else if (y <= 2.7d-147) then
        tmp = t_1
    else if (y <= 3.65d-121) then
        tmp = y * (-0.5d0)
    else if (y <= 4.8d-92) then
        tmp = t_1
    else if (y <= 2.85d0) then
        tmp = y * (-0.5d0)
    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 <= -5.7e-13) {
		tmp = t_0;
	} else if (y <= 2.7e-147) {
		tmp = t_1;
	} else if (y <= 3.65e-121) {
		tmp = y * -0.5;
	} else if (y <= 4.8e-92) {
		tmp = t_1;
	} else if (y <= 2.85) {
		tmp = y * -0.5;
	} 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 <= -5.7e-13:
		tmp = t_0
	elif y <= 2.7e-147:
		tmp = t_1
	elif y <= 3.65e-121:
		tmp = y * -0.5
	elif y <= 4.8e-92:
		tmp = t_1
	elif y <= 2.85:
		tmp = y * -0.5
	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 <= -5.7e-13)
		tmp = t_0;
	elseif (y <= 2.7e-147)
		tmp = t_1;
	elseif (y <= 3.65e-121)
		tmp = Float64(y * -0.5);
	elseif (y <= 4.8e-92)
		tmp = t_1;
	elseif (y <= 2.85)
		tmp = Float64(y * -0.5);
	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 <= -5.7e-13)
		tmp = t_0;
	elseif (y <= 2.7e-147)
		tmp = t_1;
	elseif (y <= 3.65e-121)
		tmp = y * -0.5;
	elseif (y <= 4.8e-92)
		tmp = t_1;
	elseif (y <= 2.85)
		tmp = y * -0.5;
	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, -5.7e-13], t$95$0, If[LessEqual[y, 2.7e-147], t$95$1, If[LessEqual[y, 3.65e-121], N[(y * -0.5), $MachinePrecision], If[LessEqual[y, 4.8e-92], t$95$1, If[LessEqual[y, 2.85], N[(y * -0.5), $MachinePrecision], t$95$0]]]]]]]

Derivation

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

Alternative 5: 60.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -6.6 \cdot 10^{+16}:\\ \;\;\;\;-1\\ \mathbf{elif}\;x \leq 9.6 \cdot 10^{-102}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 8.2 \cdot 10^{-23}:\\ \;\;\;\;x \cdot 0.5\\ \mathbf{elif}\;x \leq 460000:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 1.1 \cdot 10^{+96}:\\ \;\;\;\;-1\\ \mathbf{elif}\;x \leq 1.35 \cdot 10^{+118}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -6.6e+16)
   -1.0
   (if (<= x 9.6e-102)
     1.0
     (if (<= x 8.2e-23)
       (* x 0.5)
       (if (<= x 460000.0)
         1.0
         (if (<= x 1.1e+96) -1.0 (if (<= x 1.35e+118) 1.0 -1.0)))))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -6.6e+16) {
		tmp = -1.0;
	} else if (x <= 9.6e-102) {
		tmp = 1.0;
	} else if (x <= 8.2e-23) {
		tmp = x * 0.5;
	} else if (x <= 460000.0) {
		tmp = 1.0;
	} else if (x <= 1.1e+96) {
		tmp = -1.0;
	} else if (x <= 1.35e+118) {
		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 <= (-6.6d+16)) then
        tmp = -1.0d0
    else if (x <= 9.6d-102) then
        tmp = 1.0d0
    else if (x <= 8.2d-23) then
        tmp = x * 0.5d0
    else if (x <= 460000.0d0) then
        tmp = 1.0d0
    else if (x <= 1.1d+96) then
        tmp = -1.0d0
    else if (x <= 1.35d+118) 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 <= -6.6e+16) {
		tmp = -1.0;
	} else if (x <= 9.6e-102) {
		tmp = 1.0;
	} else if (x <= 8.2e-23) {
		tmp = x * 0.5;
	} else if (x <= 460000.0) {
		tmp = 1.0;
	} else if (x <= 1.1e+96) {
		tmp = -1.0;
	} else if (x <= 1.35e+118) {
		tmp = 1.0;
	} else {
		tmp = -1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -6.6e+16:
		tmp = -1.0
	elif x <= 9.6e-102:
		tmp = 1.0
	elif x <= 8.2e-23:
		tmp = x * 0.5
	elif x <= 460000.0:
		tmp = 1.0
	elif x <= 1.1e+96:
		tmp = -1.0
	elif x <= 1.35e+118:
		tmp = 1.0
	else:
		tmp = -1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -6.6e+16)
		tmp = -1.0;
	elseif (x <= 9.6e-102)
		tmp = 1.0;
	elseif (x <= 8.2e-23)
		tmp = Float64(x * 0.5);
	elseif (x <= 460000.0)
		tmp = 1.0;
	elseif (x <= 1.1e+96)
		tmp = -1.0;
	elseif (x <= 1.35e+118)
		tmp = 1.0;
	else
		tmp = -1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -6.6e+16)
		tmp = -1.0;
	elseif (x <= 9.6e-102)
		tmp = 1.0;
	elseif (x <= 8.2e-23)
		tmp = x * 0.5;
	elseif (x <= 460000.0)
		tmp = 1.0;
	elseif (x <= 1.1e+96)
		tmp = -1.0;
	elseif (x <= 1.35e+118)
		tmp = 1.0;
	else
		tmp = -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -6.6e+16], -1.0, If[LessEqual[x, 9.6e-102], 1.0, If[LessEqual[x, 8.2e-23], N[(x * 0.5), $MachinePrecision], If[LessEqual[x, 460000.0], 1.0, If[LessEqual[x, 1.1e+96], -1.0, If[LessEqual[x, 1.35e+118], 1.0, -1.0]]]]]]

Derivation

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

Alternative 6: 59.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5.7 \cdot 10^{-13}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq -1.4 \cdot 10^{-231}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq -2 \cdot 10^{-288}:\\ \;\;\;\;x \cdot 0.5\\ \mathbf{elif}\;y \leq 4.5 \cdot 10^{-148}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq 4.8:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -5.7e-13)
   1.0
   (if (<= y -1.4e-231)
     -1.0
     (if (<= y -2e-288)
       (* x 0.5)
       (if (<= y 4.5e-148) -1.0 (if (<= y 4.8) (* y -0.5) 1.0))))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -5.7e-13) {
		tmp = 1.0;
	} else if (y <= -1.4e-231) {
		tmp = -1.0;
	} else if (y <= -2e-288) {
		tmp = x * 0.5;
	} else if (y <= 4.5e-148) {
		tmp = -1.0;
	} else if (y <= 4.8) {
		tmp = y * -0.5;
	} 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 <= (-5.7d-13)) then
        tmp = 1.0d0
    else if (y <= (-1.4d-231)) then
        tmp = -1.0d0
    else if (y <= (-2d-288)) then
        tmp = x * 0.5d0
    else if (y <= 4.5d-148) then
        tmp = -1.0d0
    else if (y <= 4.8d0) then
        tmp = y * (-0.5d0)
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -5.7e-13) {
		tmp = 1.0;
	} else if (y <= -1.4e-231) {
		tmp = -1.0;
	} else if (y <= -2e-288) {
		tmp = x * 0.5;
	} else if (y <= 4.5e-148) {
		tmp = -1.0;
	} else if (y <= 4.8) {
		tmp = y * -0.5;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -5.7e-13:
		tmp = 1.0
	elif y <= -1.4e-231:
		tmp = -1.0
	elif y <= -2e-288:
		tmp = x * 0.5
	elif y <= 4.5e-148:
		tmp = -1.0
	elif y <= 4.8:
		tmp = y * -0.5
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -5.7e-13)
		tmp = 1.0;
	elseif (y <= -1.4e-231)
		tmp = -1.0;
	elseif (y <= -2e-288)
		tmp = Float64(x * 0.5);
	elseif (y <= 4.5e-148)
		tmp = -1.0;
	elseif (y <= 4.8)
		tmp = Float64(y * -0.5);
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -5.7e-13)
		tmp = 1.0;
	elseif (y <= -1.4e-231)
		tmp = -1.0;
	elseif (y <= -2e-288)
		tmp = x * 0.5;
	elseif (y <= 4.5e-148)
		tmp = -1.0;
	elseif (y <= 4.8)
		tmp = y * -0.5;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -5.7e-13], 1.0, If[LessEqual[y, -1.4e-231], -1.0, If[LessEqual[y, -2e-288], N[(x * 0.5), $MachinePrecision], If[LessEqual[y, 4.5e-148], -1.0, If[LessEqual[y, 4.8], N[(y * -0.5), $MachinePrecision], 1.0]]]]]

Derivation

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

Alternative 7: 74.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{y}{x} + -1\\ \mathbf{if}\;x \leq -320000000000:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 2900:\\ \;\;\;\;\frac{y}{y + -2}\\ \mathbf{elif}\;x \leq 8.8 \cdot 10^{+95}:\\ \;\;\;\;\frac{x}{2 - x}\\ \mathbf{elif}\;x \leq 1.35 \cdot 10^{+118}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (+ (/ y x) -1.0)))
   (if (<= x -320000000000.0)
     t_0
     (if (<= x 2900.0)
       (/ y (+ y -2.0))
       (if (<= x 8.8e+95) (/ x (- 2.0 x)) (if (<= x 1.35e+118) 1.0 t_0))))))

C

double code(double x, double y) {
	double t_0 = (y / x) + -1.0;
	double tmp;
	if (x <= -320000000000.0) {
		tmp = t_0;
	} else if (x <= 2900.0) {
		tmp = y / (y + -2.0);
	} else if (x <= 8.8e+95) {
		tmp = x / (2.0 - x);
	} else if (x <= 1.35e+118) {
		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 = (y / x) + (-1.0d0)
    if (x <= (-320000000000.0d0)) then
        tmp = t_0
    else if (x <= 2900.0d0) then
        tmp = y / (y + (-2.0d0))
    else if (x <= 8.8d+95) then
        tmp = x / (2.0d0 - x)
    else if (x <= 1.35d+118) 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 = (y / x) + -1.0;
	double tmp;
	if (x <= -320000000000.0) {
		tmp = t_0;
	} else if (x <= 2900.0) {
		tmp = y / (y + -2.0);
	} else if (x <= 8.8e+95) {
		tmp = x / (2.0 - x);
	} else if (x <= 1.35e+118) {
		tmp = 1.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (y / x) + -1.0
	tmp = 0
	if x <= -320000000000.0:
		tmp = t_0
	elif x <= 2900.0:
		tmp = y / (y + -2.0)
	elif x <= 8.8e+95:
		tmp = x / (2.0 - x)
	elif x <= 1.35e+118:
		tmp = 1.0
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(y / x) + -1.0)
	tmp = 0.0
	if (x <= -320000000000.0)
		tmp = t_0;
	elseif (x <= 2900.0)
		tmp = Float64(y / Float64(y + -2.0));
	elseif (x <= 8.8e+95)
		tmp = Float64(x / Float64(2.0 - x));
	elseif (x <= 1.35e+118)
		tmp = 1.0;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (y / x) + -1.0;
	tmp = 0.0;
	if (x <= -320000000000.0)
		tmp = t_0;
	elseif (x <= 2900.0)
		tmp = y / (y + -2.0);
	elseif (x <= 8.8e+95)
		tmp = x / (2.0 - x);
	elseif (x <= 1.35e+118)
		tmp = 1.0;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(y / x), $MachinePrecision] + -1.0), $MachinePrecision]}, If[LessEqual[x, -320000000000.0], t$95$0, If[LessEqual[x, 2900.0], N[(y / N[(y + -2.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 8.8e+95], N[(x / N[(2.0 - x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.35e+118], 1.0, t$95$0]]]]]

Derivation

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

Alternative 8: 74.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.4 \cdot 10^{+15}:\\ \;\;\;\;\frac{x - y}{-x}\\ \mathbf{elif}\;x \leq 13.2:\\ \;\;\;\;\frac{y}{y + -2}\\ \mathbf{elif}\;x \leq 1.15 \cdot 10^{+96}:\\ \;\;\;\;\frac{x}{2 - x}\\ \mathbf{elif}\;x \leq 1.35 \cdot 10^{+118}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{x} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -3.4e+15)
   (/ (- x y) (- x))
   (if (<= x 13.2)
     (/ y (+ y -2.0))
     (if (<= x 1.15e+96)
       (/ x (- 2.0 x))
       (if (<= x 1.35e+118) 1.0 (+ (/ y x) -1.0))))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -3.4e+15) {
		tmp = (x - y) / -x;
	} else if (x <= 13.2) {
		tmp = y / (y + -2.0);
	} else if (x <= 1.15e+96) {
		tmp = x / (2.0 - x);
	} else if (x <= 1.35e+118) {
		tmp = 1.0;
	} else {
		tmp = (y / x) + -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 <= (-3.4d+15)) then
        tmp = (x - y) / -x
    else if (x <= 13.2d0) then
        tmp = y / (y + (-2.0d0))
    else if (x <= 1.15d+96) then
        tmp = x / (2.0d0 - x)
    else if (x <= 1.35d+118) then
        tmp = 1.0d0
    else
        tmp = (y / x) + (-1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -3.4e+15) {
		tmp = (x - y) / -x;
	} else if (x <= 13.2) {
		tmp = y / (y + -2.0);
	} else if (x <= 1.15e+96) {
		tmp = x / (2.0 - x);
	} else if (x <= 1.35e+118) {
		tmp = 1.0;
	} else {
		tmp = (y / x) + -1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -3.4e+15:
		tmp = (x - y) / -x
	elif x <= 13.2:
		tmp = y / (y + -2.0)
	elif x <= 1.15e+96:
		tmp = x / (2.0 - x)
	elif x <= 1.35e+118:
		tmp = 1.0
	else:
		tmp = (y / x) + -1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -3.4e+15)
		tmp = Float64(Float64(x - y) / Float64(-x));
	elseif (x <= 13.2)
		tmp = Float64(y / Float64(y + -2.0));
	elseif (x <= 1.15e+96)
		tmp = Float64(x / Float64(2.0 - x));
	elseif (x <= 1.35e+118)
		tmp = 1.0;
	else
		tmp = Float64(Float64(y / x) + -1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -3.4e+15)
		tmp = (x - y) / -x;
	elseif (x <= 13.2)
		tmp = y / (y + -2.0);
	elseif (x <= 1.15e+96)
		tmp = x / (2.0 - x);
	elseif (x <= 1.35e+118)
		tmp = 1.0;
	else
		tmp = (y / x) + -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -3.4e+15], N[(N[(x - y), $MachinePrecision] / (-x)), $MachinePrecision], If[LessEqual[x, 13.2], N[(y / N[(y + -2.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.15e+96], N[(x / N[(2.0 - x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.35e+118], 1.0, N[(N[(y / x), $MachinePrecision] + -1.0), $MachinePrecision]]]]]

Derivation

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

Alternative 9: 61.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -410000000000:\\ \;\;\;\;-1\\ \mathbf{elif}\;x \leq 460000:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 1.15 \cdot 10^{+96}:\\ \;\;\;\;-1\\ \mathbf{elif}\;x \leq 1.35 \cdot 10^{+118}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -410000000000.0)
   -1.0
   (if (<= x 460000.0)
     1.0
     (if (<= x 1.15e+96) -1.0 (if (<= x 1.35e+118) 1.0 -1.0)))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -410000000000.0) {
		tmp = -1.0;
	} else if (x <= 460000.0) {
		tmp = 1.0;
	} else if (x <= 1.15e+96) {
		tmp = -1.0;
	} else if (x <= 1.35e+118) {
		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 <= (-410000000000.0d0)) then
        tmp = -1.0d0
    else if (x <= 460000.0d0) then
        tmp = 1.0d0
    else if (x <= 1.15d+96) then
        tmp = -1.0d0
    else if (x <= 1.35d+118) 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 <= -410000000000.0) {
		tmp = -1.0;
	} else if (x <= 460000.0) {
		tmp = 1.0;
	} else if (x <= 1.15e+96) {
		tmp = -1.0;
	} else if (x <= 1.35e+118) {
		tmp = 1.0;
	} else {
		tmp = -1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -410000000000.0:
		tmp = -1.0
	elif x <= 460000.0:
		tmp = 1.0
	elif x <= 1.15e+96:
		tmp = -1.0
	elif x <= 1.35e+118:
		tmp = 1.0
	else:
		tmp = -1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -410000000000.0)
		tmp = -1.0;
	elseif (x <= 460000.0)
		tmp = 1.0;
	elseif (x <= 1.15e+96)
		tmp = -1.0;
	elseif (x <= 1.35e+118)
		tmp = 1.0;
	else
		tmp = -1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -410000000000.0)
		tmp = -1.0;
	elseif (x <= 460000.0)
		tmp = 1.0;
	elseif (x <= 1.15e+96)
		tmp = -1.0;
	elseif (x <= 1.35e+118)
		tmp = 1.0;
	else
		tmp = -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -410000000000.0], -1.0, If[LessEqual[x, 460000.0], 1.0, If[LessEqual[x, 1.15e+96], -1.0, If[LessEqual[x, 1.35e+118], 1.0, -1.0]]]]

Derivation

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

Alternative 10: 38.0% 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 2023343 
(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))))