Data.Colour.RGBSpace.HSL:hsl from colour-2.3.3, D

Percentage Accurate: 99.5% → 99.8%

Time: 10.6s

Alternatives: 16

Speedup: 1.2×

• 21.3% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (+ x (* (* (- y x) 6.0) (- (/ 2.0 3.0) z))))

C

double code(double x, double y, double z) {
	return x + (((y - x) * 6.0) * ((2.0 / 3.0) - z));
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = x + (((y - x) * 6.0d0) * ((2.0d0 / 3.0d0) - z))
end function

Java

public static double code(double x, double y, double z) {
	return x + (((y - x) * 6.0) * ((2.0 / 3.0) - z));
}

Python

def code(x, y, z):
	return x + (((y - x) * 6.0) * ((2.0 / 3.0) - z))

Julia

function code(x, y, z)
	return Float64(x + Float64(Float64(Float64(y - x) * 6.0) * Float64(Float64(2.0 / 3.0) - z)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = x + (((y - x) * 6.0) * ((2.0 / 3.0) - z));
end

Wolfram

code[x_, y_, z_] := N[(x + N[(N[(N[(y - x), $MachinePrecision] * 6.0), $MachinePrecision] * N[(N[(2.0 / 3.0), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (+ x (* (* (- y x) 6.0) (- (/ 2.0 3.0) z))))

C

double code(double x, double y, double z) {
	return x + (((y - x) * 6.0) * ((2.0 / 3.0) - z));
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = x + (((y - x) * 6.0d0) * ((2.0d0 / 3.0d0) - z))
end function

Java

public static double code(double x, double y, double z) {
	return x + (((y - x) * 6.0) * ((2.0 / 3.0) - z));
}

Python

def code(x, y, z):
	return x + (((y - x) * 6.0) * ((2.0 / 3.0) - z))

Julia

function code(x, y, z)
	return Float64(x + Float64(Float64(Float64(y - x) * 6.0) * Float64(Float64(2.0 / 3.0) - z)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = x + (((y - x) * 6.0) * ((2.0 / 3.0) - z));
end

Wolfram

code[x_, y_, z_] := N[(x + N[(N[(N[(y - x), $MachinePrecision] * 6.0), $MachinePrecision] * N[(N[(2.0 / 3.0), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.8% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(y - x, 4 + z \cdot -6, x\right) \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (fma (- y x) (+ 4.0 (* z -6.0)) x))

C

double code(double x, double y, double z) {
	return fma((y - x), (4.0 + (z * -6.0)), x);
}

Julia

function code(x, y, z)
	return fma(Float64(y - x), Float64(4.0 + Float64(z * -6.0)), x)
end

Wolfram

code[x_, y_, z_] := N[(N[(y - x), $MachinePrecision] * N[(4.0 + N[(z * -6.0), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision]

Derivation

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

Alternative 2: 50.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := -6 \cdot \left(y \cdot z\right)\\ t_1 := 6 \cdot \left(x \cdot z\right)\\ \mathbf{if}\;z \leq -4.8 \cdot 10^{+183}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -220000000:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -9.2 \cdot 10^{-122}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq -4.1 \cdot 10^{-308}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 2 \cdot 10^{-176}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 240:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 5.5 \cdot 10^{+73} \lor \neg \left(z \leq 2.6 \cdot 10^{+214}\right) \land z \leq 6 \cdot 10^{+253}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* -6.0 (* y z))) (t_1 (* 6.0 (* x z))))
   (if (<= z -4.8e+183)
     t_0
     (if (<= z -220000000.0)
       t_1
       (if (<= z -9.2e-122)
         (* y 4.0)
         (if (<= z -4.1e-308)
           (* x -3.0)
           (if (<= z 2e-176)
             (* y 4.0)
             (if (<= z 240.0)
               (* x -3.0)
               (if (or (<= z 5.5e+73)
                       (and (not (<= z 2.6e+214)) (<= z 6e+253)))
                 t_1
                 t_0)))))))))

C

double code(double x, double y, double z) {
	double t_0 = -6.0 * (y * z);
	double t_1 = 6.0 * (x * z);
	double tmp;
	if (z <= -4.8e+183) {
		tmp = t_0;
	} else if (z <= -220000000.0) {
		tmp = t_1;
	} else if (z <= -9.2e-122) {
		tmp = y * 4.0;
	} else if (z <= -4.1e-308) {
		tmp = x * -3.0;
	} else if (z <= 2e-176) {
		tmp = y * 4.0;
	} else if (z <= 240.0) {
		tmp = x * -3.0;
	} else if ((z <= 5.5e+73) || (!(z <= 2.6e+214) && (z <= 6e+253))) {
		tmp = t_1;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = (-6.0d0) * (y * z)
    t_1 = 6.0d0 * (x * z)
    if (z <= (-4.8d+183)) then
        tmp = t_0
    else if (z <= (-220000000.0d0)) then
        tmp = t_1
    else if (z <= (-9.2d-122)) then
        tmp = y * 4.0d0
    else if (z <= (-4.1d-308)) then
        tmp = x * (-3.0d0)
    else if (z <= 2d-176) then
        tmp = y * 4.0d0
    else if (z <= 240.0d0) then
        tmp = x * (-3.0d0)
    else if ((z <= 5.5d+73) .or. (.not. (z <= 2.6d+214)) .and. (z <= 6d+253)) then
        tmp = t_1
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = -6.0 * (y * z);
	double t_1 = 6.0 * (x * z);
	double tmp;
	if (z <= -4.8e+183) {
		tmp = t_0;
	} else if (z <= -220000000.0) {
		tmp = t_1;
	} else if (z <= -9.2e-122) {
		tmp = y * 4.0;
	} else if (z <= -4.1e-308) {
		tmp = x * -3.0;
	} else if (z <= 2e-176) {
		tmp = y * 4.0;
	} else if (z <= 240.0) {
		tmp = x * -3.0;
	} else if ((z <= 5.5e+73) || (!(z <= 2.6e+214) && (z <= 6e+253))) {
		tmp = t_1;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = -6.0 * (y * z)
	t_1 = 6.0 * (x * z)
	tmp = 0
	if z <= -4.8e+183:
		tmp = t_0
	elif z <= -220000000.0:
		tmp = t_1
	elif z <= -9.2e-122:
		tmp = y * 4.0
	elif z <= -4.1e-308:
		tmp = x * -3.0
	elif z <= 2e-176:
		tmp = y * 4.0
	elif z <= 240.0:
		tmp = x * -3.0
	elif (z <= 5.5e+73) or (not (z <= 2.6e+214) and (z <= 6e+253)):
		tmp = t_1
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(-6.0 * Float64(y * z))
	t_1 = Float64(6.0 * Float64(x * z))
	tmp = 0.0
	if (z <= -4.8e+183)
		tmp = t_0;
	elseif (z <= -220000000.0)
		tmp = t_1;
	elseif (z <= -9.2e-122)
		tmp = Float64(y * 4.0);
	elseif (z <= -4.1e-308)
		tmp = Float64(x * -3.0);
	elseif (z <= 2e-176)
		tmp = Float64(y * 4.0);
	elseif (z <= 240.0)
		tmp = Float64(x * -3.0);
	elseif ((z <= 5.5e+73) || (!(z <= 2.6e+214) && (z <= 6e+253)))
		tmp = t_1;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = -6.0 * (y * z);
	t_1 = 6.0 * (x * z);
	tmp = 0.0;
	if (z <= -4.8e+183)
		tmp = t_0;
	elseif (z <= -220000000.0)
		tmp = t_1;
	elseif (z <= -9.2e-122)
		tmp = y * 4.0;
	elseif (z <= -4.1e-308)
		tmp = x * -3.0;
	elseif (z <= 2e-176)
		tmp = y * 4.0;
	elseif (z <= 240.0)
		tmp = x * -3.0;
	elseif ((z <= 5.5e+73) || (~((z <= 2.6e+214)) && (z <= 6e+253)))
		tmp = t_1;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(-6.0 * N[(y * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(6.0 * N[(x * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -4.8e+183], t$95$0, If[LessEqual[z, -220000000.0], t$95$1, If[LessEqual[z, -9.2e-122], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, -4.1e-308], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 2e-176], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 240.0], N[(x * -3.0), $MachinePrecision], If[Or[LessEqual[z, 5.5e+73], And[N[Not[LessEqual[z, 2.6e+214]], $MachinePrecision], LessEqual[z, 6e+253]]], t$95$1, t$95$0]]]]]]]]]

Derivation

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

Alternative 3: 50.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := -6 \cdot \left(y \cdot z\right)\\ t_1 := x \cdot \left(z \cdot 6\right)\\ \mathbf{if}\;z \leq -5.4 \cdot 10^{+184}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -220000000:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -1.2 \cdot 10^{-121}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 3.3 \cdot 10^{-308}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 1.3 \cdot 10^{-177}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 240:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 2.5 \cdot 10^{+74}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.35 \cdot 10^{+214} \lor \neg \left(z \leq 2.3 \cdot 10^{+255}\right):\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;6 \cdot \left(x \cdot z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* -6.0 (* y z))) (t_1 (* x (* z 6.0))))
   (if (<= z -5.4e+184)
     t_0
     (if (<= z -220000000.0)
       t_1
       (if (<= z -1.2e-121)
         (* y 4.0)
         (if (<= z 3.3e-308)
           (* x -3.0)
           (if (<= z 1.3e-177)
             (* y 4.0)
             (if (<= z 240.0)
               (* x -3.0)
               (if (<= z 2.5e+74)
                 t_1
                 (if (or (<= z 1.35e+214) (not (<= z 2.3e+255)))
                   t_0
                   (* 6.0 (* x z))))))))))))

C

double code(double x, double y, double z) {
	double t_0 = -6.0 * (y * z);
	double t_1 = x * (z * 6.0);
	double tmp;
	if (z <= -5.4e+184) {
		tmp = t_0;
	} else if (z <= -220000000.0) {
		tmp = t_1;
	} else if (z <= -1.2e-121) {
		tmp = y * 4.0;
	} else if (z <= 3.3e-308) {
		tmp = x * -3.0;
	} else if (z <= 1.3e-177) {
		tmp = y * 4.0;
	} else if (z <= 240.0) {
		tmp = x * -3.0;
	} else if (z <= 2.5e+74) {
		tmp = t_1;
	} else if ((z <= 1.35e+214) || !(z <= 2.3e+255)) {
		tmp = t_0;
	} else {
		tmp = 6.0 * (x * z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = (-6.0d0) * (y * z)
    t_1 = x * (z * 6.0d0)
    if (z <= (-5.4d+184)) then
        tmp = t_0
    else if (z <= (-220000000.0d0)) then
        tmp = t_1
    else if (z <= (-1.2d-121)) then
        tmp = y * 4.0d0
    else if (z <= 3.3d-308) then
        tmp = x * (-3.0d0)
    else if (z <= 1.3d-177) then
        tmp = y * 4.0d0
    else if (z <= 240.0d0) then
        tmp = x * (-3.0d0)
    else if (z <= 2.5d+74) then
        tmp = t_1
    else if ((z <= 1.35d+214) .or. (.not. (z <= 2.3d+255))) then
        tmp = t_0
    else
        tmp = 6.0d0 * (x * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = -6.0 * (y * z);
	double t_1 = x * (z * 6.0);
	double tmp;
	if (z <= -5.4e+184) {
		tmp = t_0;
	} else if (z <= -220000000.0) {
		tmp = t_1;
	} else if (z <= -1.2e-121) {
		tmp = y * 4.0;
	} else if (z <= 3.3e-308) {
		tmp = x * -3.0;
	} else if (z <= 1.3e-177) {
		tmp = y * 4.0;
	} else if (z <= 240.0) {
		tmp = x * -3.0;
	} else if (z <= 2.5e+74) {
		tmp = t_1;
	} else if ((z <= 1.35e+214) || !(z <= 2.3e+255)) {
		tmp = t_0;
	} else {
		tmp = 6.0 * (x * z);
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = -6.0 * (y * z)
	t_1 = x * (z * 6.0)
	tmp = 0
	if z <= -5.4e+184:
		tmp = t_0
	elif z <= -220000000.0:
		tmp = t_1
	elif z <= -1.2e-121:
		tmp = y * 4.0
	elif z <= 3.3e-308:
		tmp = x * -3.0
	elif z <= 1.3e-177:
		tmp = y * 4.0
	elif z <= 240.0:
		tmp = x * -3.0
	elif z <= 2.5e+74:
		tmp = t_1
	elif (z <= 1.35e+214) or not (z <= 2.3e+255):
		tmp = t_0
	else:
		tmp = 6.0 * (x * z)
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(-6.0 * Float64(y * z))
	t_1 = Float64(x * Float64(z * 6.0))
	tmp = 0.0
	if (z <= -5.4e+184)
		tmp = t_0;
	elseif (z <= -220000000.0)
		tmp = t_1;
	elseif (z <= -1.2e-121)
		tmp = Float64(y * 4.0);
	elseif (z <= 3.3e-308)
		tmp = Float64(x * -3.0);
	elseif (z <= 1.3e-177)
		tmp = Float64(y * 4.0);
	elseif (z <= 240.0)
		tmp = Float64(x * -3.0);
	elseif (z <= 2.5e+74)
		tmp = t_1;
	elseif ((z <= 1.35e+214) || !(z <= 2.3e+255))
		tmp = t_0;
	else
		tmp = Float64(6.0 * Float64(x * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = -6.0 * (y * z);
	t_1 = x * (z * 6.0);
	tmp = 0.0;
	if (z <= -5.4e+184)
		tmp = t_0;
	elseif (z <= -220000000.0)
		tmp = t_1;
	elseif (z <= -1.2e-121)
		tmp = y * 4.0;
	elseif (z <= 3.3e-308)
		tmp = x * -3.0;
	elseif (z <= 1.3e-177)
		tmp = y * 4.0;
	elseif (z <= 240.0)
		tmp = x * -3.0;
	elseif (z <= 2.5e+74)
		tmp = t_1;
	elseif ((z <= 1.35e+214) || ~((z <= 2.3e+255)))
		tmp = t_0;
	else
		tmp = 6.0 * (x * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(-6.0 * N[(y * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(x * N[(z * 6.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -5.4e+184], t$95$0, If[LessEqual[z, -220000000.0], t$95$1, If[LessEqual[z, -1.2e-121], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 3.3e-308], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 1.3e-177], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 240.0], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 2.5e+74], t$95$1, If[Or[LessEqual[z, 1.35e+214], N[Not[LessEqual[z, 2.3e+255]], $MachinePrecision]], t$95$0, N[(6.0 * N[(x * z), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]

Derivation

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

Alternative 4: 50.8% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := -6 \cdot \left(y \cdot z\right)\\ t_1 := x \cdot \left(z \cdot 6\right)\\ \mathbf{if}\;z \leq -3.05 \cdot 10^{+184}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -220000000:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -2.45 \cdot 10^{-116}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq -3.3 \cdot 10^{-308}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 1.2 \cdot 10^{-166}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 240:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 1.25 \cdot 10^{+75}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 6.2 \cdot 10^{+214}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 4.4 \cdot 10^{+257}:\\ \;\;\;\;6 \cdot \left(x \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(y \cdot -6\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* -6.0 (* y z))) (t_1 (* x (* z 6.0))))
   (if (<= z -3.05e+184)
     t_0
     (if (<= z -220000000.0)
       t_1
       (if (<= z -2.45e-116)
         (* y 4.0)
         (if (<= z -3.3e-308)
           (* x -3.0)
           (if (<= z 1.2e-166)
             (* y 4.0)
             (if (<= z 240.0)
               (* x -3.0)
               (if (<= z 1.25e+75)
                 t_1
                 (if (<= z 6.2e+214)
                   t_0
                   (if (<= z 4.4e+257)
                     (* 6.0 (* x z))
                     (* z (* y -6.0)))))))))))))

C

double code(double x, double y, double z) {
	double t_0 = -6.0 * (y * z);
	double t_1 = x * (z * 6.0);
	double tmp;
	if (z <= -3.05e+184) {
		tmp = t_0;
	} else if (z <= -220000000.0) {
		tmp = t_1;
	} else if (z <= -2.45e-116) {
		tmp = y * 4.0;
	} else if (z <= -3.3e-308) {
		tmp = x * -3.0;
	} else if (z <= 1.2e-166) {
		tmp = y * 4.0;
	} else if (z <= 240.0) {
		tmp = x * -3.0;
	} else if (z <= 1.25e+75) {
		tmp = t_1;
	} else if (z <= 6.2e+214) {
		tmp = t_0;
	} else if (z <= 4.4e+257) {
		tmp = 6.0 * (x * z);
	} else {
		tmp = z * (y * -6.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = (-6.0d0) * (y * z)
    t_1 = x * (z * 6.0d0)
    if (z <= (-3.05d+184)) then
        tmp = t_0
    else if (z <= (-220000000.0d0)) then
        tmp = t_1
    else if (z <= (-2.45d-116)) then
        tmp = y * 4.0d0
    else if (z <= (-3.3d-308)) then
        tmp = x * (-3.0d0)
    else if (z <= 1.2d-166) then
        tmp = y * 4.0d0
    else if (z <= 240.0d0) then
        tmp = x * (-3.0d0)
    else if (z <= 1.25d+75) then
        tmp = t_1
    else if (z <= 6.2d+214) then
        tmp = t_0
    else if (z <= 4.4d+257) then
        tmp = 6.0d0 * (x * z)
    else
        tmp = z * (y * (-6.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = -6.0 * (y * z);
	double t_1 = x * (z * 6.0);
	double tmp;
	if (z <= -3.05e+184) {
		tmp = t_0;
	} else if (z <= -220000000.0) {
		tmp = t_1;
	} else if (z <= -2.45e-116) {
		tmp = y * 4.0;
	} else if (z <= -3.3e-308) {
		tmp = x * -3.0;
	} else if (z <= 1.2e-166) {
		tmp = y * 4.0;
	} else if (z <= 240.0) {
		tmp = x * -3.0;
	} else if (z <= 1.25e+75) {
		tmp = t_1;
	} else if (z <= 6.2e+214) {
		tmp = t_0;
	} else if (z <= 4.4e+257) {
		tmp = 6.0 * (x * z);
	} else {
		tmp = z * (y * -6.0);
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = -6.0 * (y * z)
	t_1 = x * (z * 6.0)
	tmp = 0
	if z <= -3.05e+184:
		tmp = t_0
	elif z <= -220000000.0:
		tmp = t_1
	elif z <= -2.45e-116:
		tmp = y * 4.0
	elif z <= -3.3e-308:
		tmp = x * -3.0
	elif z <= 1.2e-166:
		tmp = y * 4.0
	elif z <= 240.0:
		tmp = x * -3.0
	elif z <= 1.25e+75:
		tmp = t_1
	elif z <= 6.2e+214:
		tmp = t_0
	elif z <= 4.4e+257:
		tmp = 6.0 * (x * z)
	else:
		tmp = z * (y * -6.0)
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(-6.0 * Float64(y * z))
	t_1 = Float64(x * Float64(z * 6.0))
	tmp = 0.0
	if (z <= -3.05e+184)
		tmp = t_0;
	elseif (z <= -220000000.0)
		tmp = t_1;
	elseif (z <= -2.45e-116)
		tmp = Float64(y * 4.0);
	elseif (z <= -3.3e-308)
		tmp = Float64(x * -3.0);
	elseif (z <= 1.2e-166)
		tmp = Float64(y * 4.0);
	elseif (z <= 240.0)
		tmp = Float64(x * -3.0);
	elseif (z <= 1.25e+75)
		tmp = t_1;
	elseif (z <= 6.2e+214)
		tmp = t_0;
	elseif (z <= 4.4e+257)
		tmp = Float64(6.0 * Float64(x * z));
	else
		tmp = Float64(z * Float64(y * -6.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = -6.0 * (y * z);
	t_1 = x * (z * 6.0);
	tmp = 0.0;
	if (z <= -3.05e+184)
		tmp = t_0;
	elseif (z <= -220000000.0)
		tmp = t_1;
	elseif (z <= -2.45e-116)
		tmp = y * 4.0;
	elseif (z <= -3.3e-308)
		tmp = x * -3.0;
	elseif (z <= 1.2e-166)
		tmp = y * 4.0;
	elseif (z <= 240.0)
		tmp = x * -3.0;
	elseif (z <= 1.25e+75)
		tmp = t_1;
	elseif (z <= 6.2e+214)
		tmp = t_0;
	elseif (z <= 4.4e+257)
		tmp = 6.0 * (x * z);
	else
		tmp = z * (y * -6.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(-6.0 * N[(y * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(x * N[(z * 6.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -3.05e+184], t$95$0, If[LessEqual[z, -220000000.0], t$95$1, If[LessEqual[z, -2.45e-116], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, -3.3e-308], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 1.2e-166], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 240.0], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 1.25e+75], t$95$1, If[LessEqual[z, 6.2e+214], t$95$0, If[LessEqual[z, 4.4e+257], N[(6.0 * N[(x * z), $MachinePrecision]), $MachinePrecision], N[(z * N[(y * -6.0), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]

Derivation

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

Alternative 5: 73.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 6 \cdot \left(y \cdot \left(0.6666666666666666 - z\right)\right)\\ t_1 := -6 \cdot \left(\left(y - x\right) \cdot z\right)\\ \mathbf{if}\;z \leq -860000000:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -1.02 \cdot 10^{-116}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -3.9 \cdot 10^{-308}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 4.5 \cdot 10^{-172}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 3.3 \cdot 10^{-27}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 115000:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* 6.0 (* y (- 0.6666666666666666 z))))
        (t_1 (* -6.0 (* (- y x) z))))
   (if (<= z -860000000.0)
     t_1
     (if (<= z -1.02e-116)
       t_0
       (if (<= z -3.9e-308)
         (* x -3.0)
         (if (<= z 4.5e-172)
           (* y 4.0)
           (if (<= z 3.3e-27) (* x -3.0) (if (<= z 115000.0) t_0 t_1))))))))

C

double code(double x, double y, double z) {
	double t_0 = 6.0 * (y * (0.6666666666666666 - z));
	double t_1 = -6.0 * ((y - x) * z);
	double tmp;
	if (z <= -860000000.0) {
		tmp = t_1;
	} else if (z <= -1.02e-116) {
		tmp = t_0;
	} else if (z <= -3.9e-308) {
		tmp = x * -3.0;
	} else if (z <= 4.5e-172) {
		tmp = y * 4.0;
	} else if (z <= 3.3e-27) {
		tmp = x * -3.0;
	} else if (z <= 115000.0) {
		tmp = t_0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = 6.0d0 * (y * (0.6666666666666666d0 - z))
    t_1 = (-6.0d0) * ((y - x) * z)
    if (z <= (-860000000.0d0)) then
        tmp = t_1
    else if (z <= (-1.02d-116)) then
        tmp = t_0
    else if (z <= (-3.9d-308)) then
        tmp = x * (-3.0d0)
    else if (z <= 4.5d-172) then
        tmp = y * 4.0d0
    else if (z <= 3.3d-27) then
        tmp = x * (-3.0d0)
    else if (z <= 115000.0d0) then
        tmp = t_0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = 6.0 * (y * (0.6666666666666666 - z));
	double t_1 = -6.0 * ((y - x) * z);
	double tmp;
	if (z <= -860000000.0) {
		tmp = t_1;
	} else if (z <= -1.02e-116) {
		tmp = t_0;
	} else if (z <= -3.9e-308) {
		tmp = x * -3.0;
	} else if (z <= 4.5e-172) {
		tmp = y * 4.0;
	} else if (z <= 3.3e-27) {
		tmp = x * -3.0;
	} else if (z <= 115000.0) {
		tmp = t_0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 6.0 * (y * (0.6666666666666666 - z))
	t_1 = -6.0 * ((y - x) * z)
	tmp = 0
	if z <= -860000000.0:
		tmp = t_1
	elif z <= -1.02e-116:
		tmp = t_0
	elif z <= -3.9e-308:
		tmp = x * -3.0
	elif z <= 4.5e-172:
		tmp = y * 4.0
	elif z <= 3.3e-27:
		tmp = x * -3.0
	elif z <= 115000.0:
		tmp = t_0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(6.0 * Float64(y * Float64(0.6666666666666666 - z)))
	t_1 = Float64(-6.0 * Float64(Float64(y - x) * z))
	tmp = 0.0
	if (z <= -860000000.0)
		tmp = t_1;
	elseif (z <= -1.02e-116)
		tmp = t_0;
	elseif (z <= -3.9e-308)
		tmp = Float64(x * -3.0);
	elseif (z <= 4.5e-172)
		tmp = Float64(y * 4.0);
	elseif (z <= 3.3e-27)
		tmp = Float64(x * -3.0);
	elseif (z <= 115000.0)
		tmp = t_0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = 6.0 * (y * (0.6666666666666666 - z));
	t_1 = -6.0 * ((y - x) * z);
	tmp = 0.0;
	if (z <= -860000000.0)
		tmp = t_1;
	elseif (z <= -1.02e-116)
		tmp = t_0;
	elseif (z <= -3.9e-308)
		tmp = x * -3.0;
	elseif (z <= 4.5e-172)
		tmp = y * 4.0;
	elseif (z <= 3.3e-27)
		tmp = x * -3.0;
	elseif (z <= 115000.0)
		tmp = t_0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(6.0 * N[(y * N[(0.6666666666666666 - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(-6.0 * N[(N[(y - x), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -860000000.0], t$95$1, If[LessEqual[z, -1.02e-116], t$95$0, If[LessEqual[z, -3.9e-308], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 4.5e-172], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 3.3e-27], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 115000.0], t$95$0, t$95$1]]]]]]]]

Derivation

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

Alternative 6: 73.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := -6 \cdot \left(\left(y - x\right) \cdot z\right)\\ \mathbf{if}\;z \leq -1.3 \cdot 10^{-9}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -2.3 \cdot 10^{-117}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 1.35 \cdot 10^{-306}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 1.75 \cdot 10^{-169}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 0.5:\\ \;\;\;\;x \cdot -3\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* -6.0 (* (- y x) z))))
   (if (<= z -1.3e-9)
     t_0
     (if (<= z -2.3e-117)
       (* y 4.0)
       (if (<= z 1.35e-306)
         (* x -3.0)
         (if (<= z 1.75e-169) (* y 4.0) (if (<= z 0.5) (* x -3.0) t_0)))))))

C

double code(double x, double y, double z) {
	double t_0 = -6.0 * ((y - x) * z);
	double tmp;
	if (z <= -1.3e-9) {
		tmp = t_0;
	} else if (z <= -2.3e-117) {
		tmp = y * 4.0;
	} else if (z <= 1.35e-306) {
		tmp = x * -3.0;
	} else if (z <= 1.75e-169) {
		tmp = y * 4.0;
	} else if (z <= 0.5) {
		tmp = x * -3.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (-6.0d0) * ((y - x) * z)
    if (z <= (-1.3d-9)) then
        tmp = t_0
    else if (z <= (-2.3d-117)) then
        tmp = y * 4.0d0
    else if (z <= 1.35d-306) then
        tmp = x * (-3.0d0)
    else if (z <= 1.75d-169) then
        tmp = y * 4.0d0
    else if (z <= 0.5d0) then
        tmp = x * (-3.0d0)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = -6.0 * ((y - x) * z);
	double tmp;
	if (z <= -1.3e-9) {
		tmp = t_0;
	} else if (z <= -2.3e-117) {
		tmp = y * 4.0;
	} else if (z <= 1.35e-306) {
		tmp = x * -3.0;
	} else if (z <= 1.75e-169) {
		tmp = y * 4.0;
	} else if (z <= 0.5) {
		tmp = x * -3.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = -6.0 * ((y - x) * z)
	tmp = 0
	if z <= -1.3e-9:
		tmp = t_0
	elif z <= -2.3e-117:
		tmp = y * 4.0
	elif z <= 1.35e-306:
		tmp = x * -3.0
	elif z <= 1.75e-169:
		tmp = y * 4.0
	elif z <= 0.5:
		tmp = x * -3.0
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(-6.0 * Float64(Float64(y - x) * z))
	tmp = 0.0
	if (z <= -1.3e-9)
		tmp = t_0;
	elseif (z <= -2.3e-117)
		tmp = Float64(y * 4.0);
	elseif (z <= 1.35e-306)
		tmp = Float64(x * -3.0);
	elseif (z <= 1.75e-169)
		tmp = Float64(y * 4.0);
	elseif (z <= 0.5)
		tmp = Float64(x * -3.0);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = -6.0 * ((y - x) * z);
	tmp = 0.0;
	if (z <= -1.3e-9)
		tmp = t_0;
	elseif (z <= -2.3e-117)
		tmp = y * 4.0;
	elseif (z <= 1.35e-306)
		tmp = x * -3.0;
	elseif (z <= 1.75e-169)
		tmp = y * 4.0;
	elseif (z <= 0.5)
		tmp = x * -3.0;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(-6.0 * N[(N[(y - x), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.3e-9], t$95$0, If[LessEqual[z, -2.3e-117], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 1.35e-306], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 1.75e-169], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 0.5], N[(x * -3.0), $MachinePrecision], t$95$0]]]]]]

Derivation

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

Alternative 7: 49.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := -6 \cdot \left(y \cdot z\right)\\ \mathbf{if}\;z \leq -0.68:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -2.95 \cdot 10^{-121}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 2.7 \cdot 10^{-307}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 4.4 \cdot 10^{-171}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 0.65:\\ \;\;\;\;x \cdot -3\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* -6.0 (* y z))))
   (if (<= z -0.68)
     t_0
     (if (<= z -2.95e-121)
       (* y 4.0)
       (if (<= z 2.7e-307)
         (* x -3.0)
         (if (<= z 4.4e-171) (* y 4.0) (if (<= z 0.65) (* x -3.0) t_0)))))))

C

double code(double x, double y, double z) {
	double t_0 = -6.0 * (y * z);
	double tmp;
	if (z <= -0.68) {
		tmp = t_0;
	} else if (z <= -2.95e-121) {
		tmp = y * 4.0;
	} else if (z <= 2.7e-307) {
		tmp = x * -3.0;
	} else if (z <= 4.4e-171) {
		tmp = y * 4.0;
	} else if (z <= 0.65) {
		tmp = x * -3.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (-6.0d0) * (y * z)
    if (z <= (-0.68d0)) then
        tmp = t_0
    else if (z <= (-2.95d-121)) then
        tmp = y * 4.0d0
    else if (z <= 2.7d-307) then
        tmp = x * (-3.0d0)
    else if (z <= 4.4d-171) then
        tmp = y * 4.0d0
    else if (z <= 0.65d0) then
        tmp = x * (-3.0d0)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = -6.0 * (y * z);
	double tmp;
	if (z <= -0.68) {
		tmp = t_0;
	} else if (z <= -2.95e-121) {
		tmp = y * 4.0;
	} else if (z <= 2.7e-307) {
		tmp = x * -3.0;
	} else if (z <= 4.4e-171) {
		tmp = y * 4.0;
	} else if (z <= 0.65) {
		tmp = x * -3.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = -6.0 * (y * z)
	tmp = 0
	if z <= -0.68:
		tmp = t_0
	elif z <= -2.95e-121:
		tmp = y * 4.0
	elif z <= 2.7e-307:
		tmp = x * -3.0
	elif z <= 4.4e-171:
		tmp = y * 4.0
	elif z <= 0.65:
		tmp = x * -3.0
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(-6.0 * Float64(y * z))
	tmp = 0.0
	if (z <= -0.68)
		tmp = t_0;
	elseif (z <= -2.95e-121)
		tmp = Float64(y * 4.0);
	elseif (z <= 2.7e-307)
		tmp = Float64(x * -3.0);
	elseif (z <= 4.4e-171)
		tmp = Float64(y * 4.0);
	elseif (z <= 0.65)
		tmp = Float64(x * -3.0);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = -6.0 * (y * z);
	tmp = 0.0;
	if (z <= -0.68)
		tmp = t_0;
	elseif (z <= -2.95e-121)
		tmp = y * 4.0;
	elseif (z <= 2.7e-307)
		tmp = x * -3.0;
	elseif (z <= 4.4e-171)
		tmp = y * 4.0;
	elseif (z <= 0.65)
		tmp = x * -3.0;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(-6.0 * N[(y * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -0.68], t$95$0, If[LessEqual[z, -2.95e-121], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 2.7e-307], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 4.4e-171], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 0.65], N[(x * -3.0), $MachinePrecision], t$95$0]]]]]]

Derivation

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

Alternative 8: 75.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -9.8 \cdot 10^{-20} \lor \neg \left(x \leq 10^{-12} \lor \neg \left(x \leq 69000000000000\right) \land x \leq 1.4 \cdot 10^{+53}\right):\\ \;\;\;\;x \cdot \left(-3 + z \cdot 6\right)\\ \mathbf{else}:\\ \;\;\;\;6 \cdot \left(y \cdot \left(0.6666666666666666 - z\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -9.8e-20)
         (not
          (or (<= x 1e-12)
              (and (not (<= x 69000000000000.0)) (<= x 1.4e+53)))))
   (* x (+ -3.0 (* z 6.0)))
   (* 6.0 (* y (- 0.6666666666666666 z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -9.8e-20) || !((x <= 1e-12) || (!(x <= 69000000000000.0) && (x <= 1.4e+53)))) {
		tmp = x * (-3.0 + (z * 6.0));
	} else {
		tmp = 6.0 * (y * (0.6666666666666666 - z));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((x <= (-9.8d-20)) .or. (.not. (x <= 1d-12) .or. (.not. (x <= 69000000000000.0d0)) .and. (x <= 1.4d+53))) then
        tmp = x * ((-3.0d0) + (z * 6.0d0))
    else
        tmp = 6.0d0 * (y * (0.6666666666666666d0 - z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -9.8e-20) || !((x <= 1e-12) || (!(x <= 69000000000000.0) && (x <= 1.4e+53)))) {
		tmp = x * (-3.0 + (z * 6.0));
	} else {
		tmp = 6.0 * (y * (0.6666666666666666 - z));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -9.8e-20) or not ((x <= 1e-12) or (not (x <= 69000000000000.0) and (x <= 1.4e+53))):
		tmp = x * (-3.0 + (z * 6.0))
	else:
		tmp = 6.0 * (y * (0.6666666666666666 - z))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -9.8e-20) || !((x <= 1e-12) || (!(x <= 69000000000000.0) && (x <= 1.4e+53))))
		tmp = Float64(x * Float64(-3.0 + Float64(z * 6.0)));
	else
		tmp = Float64(6.0 * Float64(y * Float64(0.6666666666666666 - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -9.8e-20) || ~(((x <= 1e-12) || (~((x <= 69000000000000.0)) && (x <= 1.4e+53)))))
		tmp = x * (-3.0 + (z * 6.0));
	else
		tmp = 6.0 * (y * (0.6666666666666666 - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -9.8e-20], N[Not[Or[LessEqual[x, 1e-12], And[N[Not[LessEqual[x, 69000000000000.0]], $MachinePrecision], LessEqual[x, 1.4e+53]]]], $MachinePrecision]], N[(x * N[(-3.0 + N[(z * 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(6.0 * N[(y * N[(0.6666666666666666 - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 9: 75.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.05 \cdot 10^{-20} \lor \neg \left(x \leq 1.12 \cdot 10^{-15}\right) \land \left(x \leq 4.15 \cdot 10^{+21} \lor \neg \left(x \leq 2.75 \cdot 10^{+54}\right)\right):\\ \;\;\;\;x \cdot \left(-3 + z \cdot 6\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(6 \cdot \left(0.6666666666666666 - z\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -2.05e-20)
         (and (not (<= x 1.12e-15))
              (or (<= x 4.15e+21) (not (<= x 2.75e+54)))))
   (* x (+ -3.0 (* z 6.0)))
   (* y (* 6.0 (- 0.6666666666666666 z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -2.05e-20) || (!(x <= 1.12e-15) && ((x <= 4.15e+21) || !(x <= 2.75e+54)))) {
		tmp = x * (-3.0 + (z * 6.0));
	} else {
		tmp = y * (6.0 * (0.6666666666666666 - z));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((x <= (-2.05d-20)) .or. (.not. (x <= 1.12d-15)) .and. (x <= 4.15d+21) .or. (.not. (x <= 2.75d+54))) then
        tmp = x * ((-3.0d0) + (z * 6.0d0))
    else
        tmp = y * (6.0d0 * (0.6666666666666666d0 - z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -2.05e-20) || (!(x <= 1.12e-15) && ((x <= 4.15e+21) || !(x <= 2.75e+54)))) {
		tmp = x * (-3.0 + (z * 6.0));
	} else {
		tmp = y * (6.0 * (0.6666666666666666 - z));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -2.05e-20) or (not (x <= 1.12e-15) and ((x <= 4.15e+21) or not (x <= 2.75e+54))):
		tmp = x * (-3.0 + (z * 6.0))
	else:
		tmp = y * (6.0 * (0.6666666666666666 - z))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -2.05e-20) || (!(x <= 1.12e-15) && ((x <= 4.15e+21) || !(x <= 2.75e+54))))
		tmp = Float64(x * Float64(-3.0 + Float64(z * 6.0)));
	else
		tmp = Float64(y * Float64(6.0 * Float64(0.6666666666666666 - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -2.05e-20) || (~((x <= 1.12e-15)) && ((x <= 4.15e+21) || ~((x <= 2.75e+54)))))
		tmp = x * (-3.0 + (z * 6.0));
	else
		tmp = y * (6.0 * (0.6666666666666666 - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -2.05e-20], And[N[Not[LessEqual[x, 1.12e-15]], $MachinePrecision], Or[LessEqual[x, 4.15e+21], N[Not[LessEqual[x, 2.75e+54]], $MachinePrecision]]]], N[(x * N[(-3.0 + N[(z * 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y * N[(6.0 * N[(0.6666666666666666 - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 10: 97.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -0.58 \lor \neg \left(z \leq 0.6\right):\\ \;\;\;\;-6 \cdot \left(\left(y - x\right) \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;x + \left(y - x\right) \cdot 4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -0.58) (not (<= z 0.6)))
   (* -6.0 (* (- y x) z))
   (+ x (* (- y x) 4.0))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -0.58) || !(z <= 0.6)) {
		tmp = -6.0 * ((y - x) * z);
	} else {
		tmp = x + ((y - x) * 4.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((z <= (-0.58d0)) .or. (.not. (z <= 0.6d0))) then
        tmp = (-6.0d0) * ((y - x) * z)
    else
        tmp = x + ((y - x) * 4.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -0.58) || !(z <= 0.6)) {
		tmp = -6.0 * ((y - x) * z);
	} else {
		tmp = x + ((y - x) * 4.0);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -0.58) or not (z <= 0.6):
		tmp = -6.0 * ((y - x) * z)
	else:
		tmp = x + ((y - x) * 4.0)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -0.58) || !(z <= 0.6))
		tmp = Float64(-6.0 * Float64(Float64(y - x) * z));
	else
		tmp = Float64(x + Float64(Float64(y - x) * 4.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -0.58) || ~((z <= 0.6)))
		tmp = -6.0 * ((y - x) * z);
	else
		tmp = x + ((y - x) * 4.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -0.58], N[Not[LessEqual[z, 0.6]], $MachinePrecision]], N[(-6.0 * N[(N[(y - x), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(y - x), $MachinePrecision] * 4.0), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 11: 97.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -0.58 \lor \neg \left(z \leq 0.6\right):\\ \;\;\;\;-6 \cdot \left(\left(y - x\right) \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot 4 + x \cdot -3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -0.58) (not (<= z 0.6)))
   (* -6.0 (* (- y x) z))
   (+ (* y 4.0) (* x -3.0))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -0.58) || !(z <= 0.6)) {
		tmp = -6.0 * ((y - x) * z);
	} else {
		tmp = (y * 4.0) + (x * -3.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((z <= (-0.58d0)) .or. (.not. (z <= 0.6d0))) then
        tmp = (-6.0d0) * ((y - x) * z)
    else
        tmp = (y * 4.0d0) + (x * (-3.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -0.58) || !(z <= 0.6)) {
		tmp = -6.0 * ((y - x) * z);
	} else {
		tmp = (y * 4.0) + (x * -3.0);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -0.58) or not (z <= 0.6):
		tmp = -6.0 * ((y - x) * z)
	else:
		tmp = (y * 4.0) + (x * -3.0)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -0.58) || !(z <= 0.6))
		tmp = Float64(-6.0 * Float64(Float64(y - x) * z));
	else
		tmp = Float64(Float64(y * 4.0) + Float64(x * -3.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -0.58) || ~((z <= 0.6)))
		tmp = -6.0 * ((y - x) * z);
	else
		tmp = (y * 4.0) + (x * -3.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -0.58], N[Not[LessEqual[z, 0.6]], $MachinePrecision]], N[(-6.0 * N[(N[(y - x), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision], N[(N[(y * 4.0), $MachinePrecision] + N[(x * -3.0), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 12: 99.5% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(0.6666666666666666 - z\right) \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (+ x (* (* (- y x) 6.0) (- 0.6666666666666666 z))))

C

double code(double x, double y, double z) {
	return x + (((y - x) * 6.0) * (0.6666666666666666 - z));
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = x + (((y - x) * 6.0d0) * (0.6666666666666666d0 - z))
end function

Java

public static double code(double x, double y, double z) {
	return x + (((y - x) * 6.0) * (0.6666666666666666 - z));
}

Python

def code(x, y, z):
	return x + (((y - x) * 6.0) * (0.6666666666666666 - z))

Julia

function code(x, y, z)
	return Float64(x + Float64(Float64(Float64(y - x) * 6.0) * Float64(0.6666666666666666 - z)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = x + (((y - x) * 6.0) * (0.6666666666666666 - z));
end

Wolfram

code[x_, y_, z_] := N[(x + N[(N[(N[(y - x), $MachinePrecision] * 6.0), $MachinePrecision] * N[(0.6666666666666666 - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

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

Alternative 13: 37.3% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.8 \cdot 10^{+38} \lor \neg \left(x \leq 1.6 \cdot 10^{+90}\right):\\ \;\;\;\;x \cdot -3\\ \mathbf{else}:\\ \;\;\;\;y \cdot 4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -2.8e+38) (not (<= x 1.6e+90))) (* x -3.0) (* y 4.0)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -2.8e+38) || !(x <= 1.6e+90)) {
		tmp = x * -3.0;
	} else {
		tmp = y * 4.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((x <= (-2.8d+38)) .or. (.not. (x <= 1.6d+90))) then
        tmp = x * (-3.0d0)
    else
        tmp = y * 4.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -2.8e+38) || !(x <= 1.6e+90)) {
		tmp = x * -3.0;
	} else {
		tmp = y * 4.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -2.8e+38) or not (x <= 1.6e+90):
		tmp = x * -3.0
	else:
		tmp = y * 4.0
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -2.8e+38) || !(x <= 1.6e+90))
		tmp = Float64(x * -3.0);
	else
		tmp = Float64(y * 4.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -2.8e+38) || ~((x <= 1.6e+90)))
		tmp = x * -3.0;
	else
		tmp = y * 4.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -2.8e+38], N[Not[LessEqual[x, 1.6e+90]], $MachinePrecision]], N[(x * -3.0), $MachinePrecision], N[(y * 4.0), $MachinePrecision]]

Derivation

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

Alternative 14: 25.9% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.55 \cdot 10^{+100}:\\ \;\;\;\;x \cdot -4\\ \mathbf{else}:\\ \;\;\;\;y \cdot 4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -1.55e+100) (* x -4.0) (* y 4.0)))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -1.55e+100) {
		tmp = x * -4.0;
	} else {
		tmp = y * 4.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-1.55d+100)) then
        tmp = x * (-4.0d0)
    else
        tmp = y * 4.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -1.55e+100) {
		tmp = x * -4.0;
	} else {
		tmp = y * 4.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -1.55e+100:
		tmp = x * -4.0
	else:
		tmp = y * 4.0
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -1.55e+100)
		tmp = Float64(x * -4.0);
	else
		tmp = Float64(y * 4.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -1.55e+100)
		tmp = x * -4.0;
	else
		tmp = y * 4.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -1.55e+100], N[(x * -4.0), $MachinePrecision], N[(y * 4.0), $MachinePrecision]]

Derivation

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

Alternative 15: 7.6% accurate, 4.3× speedup

Math

\[\begin{array}{l} \\ x \cdot -4 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (* x -4.0))

C

double code(double x, double y, double z) {
	return x * -4.0;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = x * (-4.0d0)
end function

Java

public static double code(double x, double y, double z) {
	return x * -4.0;
}

Python

def code(x, y, z):
	return x * -4.0

Julia

function code(x, y, z)
	return Float64(x * -4.0)
end

MATLAB

function tmp = code(x, y, z)
	tmp = x * -4.0;
end

Wolfram

code[x_, y_, z_] := N[(x * -4.0), $MachinePrecision]

Derivation

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

Alternative 16: 2.6% accurate, 13.0× speedup

Math

\[\begin{array}{l} \\ x \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 x)

C

double code(double x, double y, double z) {
	return x;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = x
end function

Java

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

Python

def code(x, y, z):
	return x

Julia

function code(x, y, z)
	return x
end

MATLAB

function tmp = code(x, y, z)
	tmp = x;
end

Wolfram

code[x_, y_, z_] := x

Derivation

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

Reproduce

herbie shell --seed 2023350 
(FPCore (x y z)
  :name "Data.Colour.RGBSpace.HSL:hsl from colour-2.3.3, D"
  :precision binary64
  (+ x (* (* (- y x) 6.0) (- (/ 2.0 3.0) z))))