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

Percentage Accurate: 100.0% → 100.0%

Time: 4.5s

Alternatives: 5

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, 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

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

Alternative 2: 73.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.35 \cdot 10^{-40}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq -2.1 \cdot 10^{-88} \lor \neg \left(y \leq -8 \cdot 10^{-108}\right) \land \left(y \leq 1.8 \cdot 10^{-75} \lor \neg \left(y \leq 1.5 \cdot 10^{-50}\right) \land y \leq 2.4 \cdot 10^{+25}\right):\\ \;\;\;\;1 + -2 \cdot \frac{y}{x}\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.35e-40)
   -1.0
   (if (or (<= y -2.1e-88)
           (and (not (<= y -8e-108))
                (or (<= y 1.8e-75) (and (not (<= y 1.5e-50)) (<= y 2.4e+25)))))
     (+ 1.0 (* -2.0 (/ y x)))
     -1.0)))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.35e-40) {
		tmp = -1.0;
	} else if ((y <= -2.1e-88) || (!(y <= -8e-108) && ((y <= 1.8e-75) || (!(y <= 1.5e-50) && (y <= 2.4e+25))))) {
		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 (y <= (-1.35d-40)) then
        tmp = -1.0d0
    else if ((y <= (-2.1d-88)) .or. (.not. (y <= (-8d-108))) .and. (y <= 1.8d-75) .or. (.not. (y <= 1.5d-50)) .and. (y <= 2.4d+25)) 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 (y <= -1.35e-40) {
		tmp = -1.0;
	} else if ((y <= -2.1e-88) || (!(y <= -8e-108) && ((y <= 1.8e-75) || (!(y <= 1.5e-50) && (y <= 2.4e+25))))) {
		tmp = 1.0 + (-2.0 * (y / x));
	} else {
		tmp = -1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -1.35e-40:
		tmp = -1.0
	elif (y <= -2.1e-88) or (not (y <= -8e-108) and ((y <= 1.8e-75) or (not (y <= 1.5e-50) and (y <= 2.4e+25)))):
		tmp = 1.0 + (-2.0 * (y / x))
	else:
		tmp = -1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1.35e-40)
		tmp = -1.0;
	elseif ((y <= -2.1e-88) || (!(y <= -8e-108) && ((y <= 1.8e-75) || (!(y <= 1.5e-50) && (y <= 2.4e+25)))))
		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 (y <= -1.35e-40)
		tmp = -1.0;
	elseif ((y <= -2.1e-88) || (~((y <= -8e-108)) && ((y <= 1.8e-75) || (~((y <= 1.5e-50)) && (y <= 2.4e+25)))))
		tmp = 1.0 + (-2.0 * (y / x));
	else
		tmp = -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1.35e-40], -1.0, If[Or[LessEqual[y, -2.1e-88], And[N[Not[LessEqual[y, -8e-108]], $MachinePrecision], Or[LessEqual[y, 1.8e-75], And[N[Not[LessEqual[y, 1.5e-50]], $MachinePrecision], LessEqual[y, 2.4e+25]]]]], N[(1.0 + N[(-2.0 * N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -1.0]]

Derivation

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

Alternative 3: 73.9% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 2 \cdot \frac{x}{y} + -1\\ t_1 := 1 + -2 \cdot \frac{y}{x}\\ \mathbf{if}\;y \leq -1 \cdot 10^{-40}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -2.6 \cdot 10^{-88}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -9.2 \cdot 10^{-108}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq 1.5 \cdot 10^{-75}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 4.8 \cdot 10^{-51}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq 3 \cdot 10^{+25}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (+ (* 2.0 (/ x y)) -1.0)) (t_1 (+ 1.0 (* -2.0 (/ y x)))))
   (if (<= y -1e-40)
     t_0
     (if (<= y -2.6e-88)
       t_1
       (if (<= y -9.2e-108)
         -1.0
         (if (<= y 1.5e-75)
           t_1
           (if (<= y 4.8e-51) -1.0 (if (<= y 3e+25) t_1 t_0))))))))

C

double code(double x, double y) {
	double t_0 = (2.0 * (x / y)) + -1.0;
	double t_1 = 1.0 + (-2.0 * (y / x));
	double tmp;
	if (y <= -1e-40) {
		tmp = t_0;
	} else if (y <= -2.6e-88) {
		tmp = t_1;
	} else if (y <= -9.2e-108) {
		tmp = -1.0;
	} else if (y <= 1.5e-75) {
		tmp = t_1;
	} else if (y <= 4.8e-51) {
		tmp = -1.0;
	} else if (y <= 3e+25) {
		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 = (2.0d0 * (x / y)) + (-1.0d0)
    t_1 = 1.0d0 + ((-2.0d0) * (y / x))
    if (y <= (-1d-40)) then
        tmp = t_0
    else if (y <= (-2.6d-88)) then
        tmp = t_1
    else if (y <= (-9.2d-108)) then
        tmp = -1.0d0
    else if (y <= 1.5d-75) then
        tmp = t_1
    else if (y <= 4.8d-51) then
        tmp = -1.0d0
    else if (y <= 3d+25) 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 = (2.0 * (x / y)) + -1.0;
	double t_1 = 1.0 + (-2.0 * (y / x));
	double tmp;
	if (y <= -1e-40) {
		tmp = t_0;
	} else if (y <= -2.6e-88) {
		tmp = t_1;
	} else if (y <= -9.2e-108) {
		tmp = -1.0;
	} else if (y <= 1.5e-75) {
		tmp = t_1;
	} else if (y <= 4.8e-51) {
		tmp = -1.0;
	} else if (y <= 3e+25) {
		tmp = t_1;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (2.0 * (x / y)) + -1.0
	t_1 = 1.0 + (-2.0 * (y / x))
	tmp = 0
	if y <= -1e-40:
		tmp = t_0
	elif y <= -2.6e-88:
		tmp = t_1
	elif y <= -9.2e-108:
		tmp = -1.0
	elif y <= 1.5e-75:
		tmp = t_1
	elif y <= 4.8e-51:
		tmp = -1.0
	elif y <= 3e+25:
		tmp = t_1
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(2.0 * Float64(x / y)) + -1.0)
	t_1 = Float64(1.0 + Float64(-2.0 * Float64(y / x)))
	tmp = 0.0
	if (y <= -1e-40)
		tmp = t_0;
	elseif (y <= -2.6e-88)
		tmp = t_1;
	elseif (y <= -9.2e-108)
		tmp = -1.0;
	elseif (y <= 1.5e-75)
		tmp = t_1;
	elseif (y <= 4.8e-51)
		tmp = -1.0;
	elseif (y <= 3e+25)
		tmp = t_1;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (2.0 * (x / y)) + -1.0;
	t_1 = 1.0 + (-2.0 * (y / x));
	tmp = 0.0;
	if (y <= -1e-40)
		tmp = t_0;
	elseif (y <= -2.6e-88)
		tmp = t_1;
	elseif (y <= -9.2e-108)
		tmp = -1.0;
	elseif (y <= 1.5e-75)
		tmp = t_1;
	elseif (y <= 4.8e-51)
		tmp = -1.0;
	elseif (y <= 3e+25)
		tmp = t_1;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(2.0 * N[(x / y), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]}, Block[{t$95$1 = N[(1.0 + N[(-2.0 * N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1e-40], t$95$0, If[LessEqual[y, -2.6e-88], t$95$1, If[LessEqual[y, -9.2e-108], -1.0, If[LessEqual[y, 1.5e-75], t$95$1, If[LessEqual[y, 4.8e-51], -1.0, If[LessEqual[y, 3e+25], t$95$1, t$95$0]]]]]]]]

Derivation

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

Alternative 4: 71.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.45 \cdot 10^{-40}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq -3.6 \cdot 10^{-88}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq -5.8 \cdot 10^{-148}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq 2.7 \cdot 10^{-76}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 8 \cdot 10^{-51}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq 2.3 \cdot 10^{+25}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.45e-40)
   -1.0
   (if (<= y -3.6e-88)
     1.0
     (if (<= y -5.8e-148)
       -1.0
       (if (<= y 2.7e-76)
         1.0
         (if (<= y 8e-51) -1.0 (if (<= y 2.3e+25) 1.0 -1.0)))))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.45e-40) {
		tmp = -1.0;
	} else if (y <= -3.6e-88) {
		tmp = 1.0;
	} else if (y <= -5.8e-148) {
		tmp = -1.0;
	} else if (y <= 2.7e-76) {
		tmp = 1.0;
	} else if (y <= 8e-51) {
		tmp = -1.0;
	} else if (y <= 2.3e+25) {
		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 <= (-1.45d-40)) then
        tmp = -1.0d0
    else if (y <= (-3.6d-88)) then
        tmp = 1.0d0
    else if (y <= (-5.8d-148)) then
        tmp = -1.0d0
    else if (y <= 2.7d-76) then
        tmp = 1.0d0
    else if (y <= 8d-51) then
        tmp = -1.0d0
    else if (y <= 2.3d+25) 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 <= -1.45e-40) {
		tmp = -1.0;
	} else if (y <= -3.6e-88) {
		tmp = 1.0;
	} else if (y <= -5.8e-148) {
		tmp = -1.0;
	} else if (y <= 2.7e-76) {
		tmp = 1.0;
	} else if (y <= 8e-51) {
		tmp = -1.0;
	} else if (y <= 2.3e+25) {
		tmp = 1.0;
	} else {
		tmp = -1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -1.45e-40:
		tmp = -1.0
	elif y <= -3.6e-88:
		tmp = 1.0
	elif y <= -5.8e-148:
		tmp = -1.0
	elif y <= 2.7e-76:
		tmp = 1.0
	elif y <= 8e-51:
		tmp = -1.0
	elif y <= 2.3e+25:
		tmp = 1.0
	else:
		tmp = -1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1.45e-40)
		tmp = -1.0;
	elseif (y <= -3.6e-88)
		tmp = 1.0;
	elseif (y <= -5.8e-148)
		tmp = -1.0;
	elseif (y <= 2.7e-76)
		tmp = 1.0;
	elseif (y <= 8e-51)
		tmp = -1.0;
	elseif (y <= 2.3e+25)
		tmp = 1.0;
	else
		tmp = -1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -1.45e-40)
		tmp = -1.0;
	elseif (y <= -3.6e-88)
		tmp = 1.0;
	elseif (y <= -5.8e-148)
		tmp = -1.0;
	elseif (y <= 2.7e-76)
		tmp = 1.0;
	elseif (y <= 8e-51)
		tmp = -1.0;
	elseif (y <= 2.3e+25)
		tmp = 1.0;
	else
		tmp = -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1.45e-40], -1.0, If[LessEqual[y, -3.6e-88], 1.0, If[LessEqual[y, -5.8e-148], -1.0, If[LessEqual[y, 2.7e-76], 1.0, If[LessEqual[y, 8e-51], -1.0, If[LessEqual[y, 2.3e+25], 1.0, -1.0]]]]]]

Derivation

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

Alternative 5: 48.8% 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

&prev;&pcontext;&pcontext2;&ctx;
1. 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 2023350 
(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)))