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

Percentage Accurate: 99.5% → 99.8%

Time: 11.2s

Alternatives: 18

Speedup: 1.2×

• 21.5% 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: 50.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 6 \cdot \left(x \cdot z\right)\\ t_1 := -6 \cdot \left(y \cdot z\right)\\ \mathbf{if}\;z \leq -4.6 \cdot 10^{+256}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -7.6 \cdot 10^{+227}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -4.5 \cdot 10^{+189}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -3.85:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -2.45 \cdot 10^{-228}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 1.8 \cdot 10^{-275}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 2.65 \cdot 10^{-220}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 2.95 \cdot 10^{-37}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 7.2 \cdot 10^{-21}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 0.58:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 3.3 \cdot 10^{+121} \lor \neg \left(z \leq 3.35 \cdot 10^{+214}\right) \land z \leq 1.05 \cdot 10^{+242}:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* 6.0 (* x z))) (t_1 (* -6.0 (* y z))))
   (if (<= z -4.6e+256)
     t_0
     (if (<= z -7.6e+227)
       t_1
       (if (<= z -4.5e+189)
         t_0
         (if (<= z -3.85)
           t_1
           (if (<= z -2.45e-228)
             (* x -3.0)
             (if (<= z 1.8e-275)
               (* y 4.0)
               (if (<= z 2.65e-220)
                 (* x -3.0)
                 (if (<= z 2.95e-37)
                   (* y 4.0)
                   (if (<= z 7.2e-21)
                     (* x -3.0)
                     (if (<= z 0.58)
                       (* y 4.0)
                       (if (or (<= z 3.3e+121)
                               (and (not (<= z 3.35e+214)) (<= z 1.05e+242)))
                         t_0
                         t_1)))))))))))))

C

double code(double x, double y, double z) {
	double t_0 = 6.0 * (x * z);
	double t_1 = -6.0 * (y * z);
	double tmp;
	if (z <= -4.6e+256) {
		tmp = t_0;
	} else if (z <= -7.6e+227) {
		tmp = t_1;
	} else if (z <= -4.5e+189) {
		tmp = t_0;
	} else if (z <= -3.85) {
		tmp = t_1;
	} else if (z <= -2.45e-228) {
		tmp = x * -3.0;
	} else if (z <= 1.8e-275) {
		tmp = y * 4.0;
	} else if (z <= 2.65e-220) {
		tmp = x * -3.0;
	} else if (z <= 2.95e-37) {
		tmp = y * 4.0;
	} else if (z <= 7.2e-21) {
		tmp = x * -3.0;
	} else if (z <= 0.58) {
		tmp = y * 4.0;
	} else if ((z <= 3.3e+121) || (!(z <= 3.35e+214) && (z <= 1.05e+242))) {
		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 * (x * z)
    t_1 = (-6.0d0) * (y * z)
    if (z <= (-4.6d+256)) then
        tmp = t_0
    else if (z <= (-7.6d+227)) then
        tmp = t_1
    else if (z <= (-4.5d+189)) then
        tmp = t_0
    else if (z <= (-3.85d0)) then
        tmp = t_1
    else if (z <= (-2.45d-228)) then
        tmp = x * (-3.0d0)
    else if (z <= 1.8d-275) then
        tmp = y * 4.0d0
    else if (z <= 2.65d-220) then
        tmp = x * (-3.0d0)
    else if (z <= 2.95d-37) then
        tmp = y * 4.0d0
    else if (z <= 7.2d-21) then
        tmp = x * (-3.0d0)
    else if (z <= 0.58d0) then
        tmp = y * 4.0d0
    else if ((z <= 3.3d+121) .or. (.not. (z <= 3.35d+214)) .and. (z <= 1.05d+242)) 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 * (x * z);
	double t_1 = -6.0 * (y * z);
	double tmp;
	if (z <= -4.6e+256) {
		tmp = t_0;
	} else if (z <= -7.6e+227) {
		tmp = t_1;
	} else if (z <= -4.5e+189) {
		tmp = t_0;
	} else if (z <= -3.85) {
		tmp = t_1;
	} else if (z <= -2.45e-228) {
		tmp = x * -3.0;
	} else if (z <= 1.8e-275) {
		tmp = y * 4.0;
	} else if (z <= 2.65e-220) {
		tmp = x * -3.0;
	} else if (z <= 2.95e-37) {
		tmp = y * 4.0;
	} else if (z <= 7.2e-21) {
		tmp = x * -3.0;
	} else if (z <= 0.58) {
		tmp = y * 4.0;
	} else if ((z <= 3.3e+121) || (!(z <= 3.35e+214) && (z <= 1.05e+242))) {
		tmp = t_0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 6.0 * (x * z)
	t_1 = -6.0 * (y * z)
	tmp = 0
	if z <= -4.6e+256:
		tmp = t_0
	elif z <= -7.6e+227:
		tmp = t_1
	elif z <= -4.5e+189:
		tmp = t_0
	elif z <= -3.85:
		tmp = t_1
	elif z <= -2.45e-228:
		tmp = x * -3.0
	elif z <= 1.8e-275:
		tmp = y * 4.0
	elif z <= 2.65e-220:
		tmp = x * -3.0
	elif z <= 2.95e-37:
		tmp = y * 4.0
	elif z <= 7.2e-21:
		tmp = x * -3.0
	elif z <= 0.58:
		tmp = y * 4.0
	elif (z <= 3.3e+121) or (not (z <= 3.35e+214) and (z <= 1.05e+242)):
		tmp = t_0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(6.0 * Float64(x * z))
	t_1 = Float64(-6.0 * Float64(y * z))
	tmp = 0.0
	if (z <= -4.6e+256)
		tmp = t_0;
	elseif (z <= -7.6e+227)
		tmp = t_1;
	elseif (z <= -4.5e+189)
		tmp = t_0;
	elseif (z <= -3.85)
		tmp = t_1;
	elseif (z <= -2.45e-228)
		tmp = Float64(x * -3.0);
	elseif (z <= 1.8e-275)
		tmp = Float64(y * 4.0);
	elseif (z <= 2.65e-220)
		tmp = Float64(x * -3.0);
	elseif (z <= 2.95e-37)
		tmp = Float64(y * 4.0);
	elseif (z <= 7.2e-21)
		tmp = Float64(x * -3.0);
	elseif (z <= 0.58)
		tmp = Float64(y * 4.0);
	elseif ((z <= 3.3e+121) || (!(z <= 3.35e+214) && (z <= 1.05e+242)))
		tmp = t_0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = 6.0 * (x * z);
	t_1 = -6.0 * (y * z);
	tmp = 0.0;
	if (z <= -4.6e+256)
		tmp = t_0;
	elseif (z <= -7.6e+227)
		tmp = t_1;
	elseif (z <= -4.5e+189)
		tmp = t_0;
	elseif (z <= -3.85)
		tmp = t_1;
	elseif (z <= -2.45e-228)
		tmp = x * -3.0;
	elseif (z <= 1.8e-275)
		tmp = y * 4.0;
	elseif (z <= 2.65e-220)
		tmp = x * -3.0;
	elseif (z <= 2.95e-37)
		tmp = y * 4.0;
	elseif (z <= 7.2e-21)
		tmp = x * -3.0;
	elseif (z <= 0.58)
		tmp = y * 4.0;
	elseif ((z <= 3.3e+121) || (~((z <= 3.35e+214)) && (z <= 1.05e+242)))
		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[(x * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(-6.0 * N[(y * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -4.6e+256], t$95$0, If[LessEqual[z, -7.6e+227], t$95$1, If[LessEqual[z, -4.5e+189], t$95$0, If[LessEqual[z, -3.85], t$95$1, If[LessEqual[z, -2.45e-228], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 1.8e-275], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 2.65e-220], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 2.95e-37], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 7.2e-21], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 0.58], N[(y * 4.0), $MachinePrecision], If[Or[LessEqual[z, 3.3e+121], And[N[Not[LessEqual[z, 3.35e+214]], $MachinePrecision], LessEqual[z, 1.05e+242]]], t$95$0, t$95$1]]]]]]]]]]]]]

Derivation

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

Alternative 3: 50.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(z \cdot 6\right)\\ t_1 := -6 \cdot \left(y \cdot z\right)\\ \mathbf{if}\;z \leq -4.5 \cdot 10^{+256}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -4.5 \cdot 10^{+226}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -6.8 \cdot 10^{+189}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -115:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -1.45 \cdot 10^{-228}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 10^{-279}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 3 \cdot 10^{-219}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 9 \cdot 10^{-38}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 1.3 \cdot 10^{-19}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 0.62:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 2.95 \cdot 10^{+119}:\\ \;\;\;\;6 \cdot \left(x \cdot z\right)\\ \mathbf{elif}\;z \leq 1.56 \cdot 10^{+214} \lor \neg \left(z \leq 9.6 \cdot 10^{+241}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* x (* z 6.0))) (t_1 (* -6.0 (* y z))))
   (if (<= z -4.5e+256)
     t_0
     (if (<= z -4.5e+226)
       t_1
       (if (<= z -6.8e+189)
         t_0
         (if (<= z -115.0)
           t_1
           (if (<= z -1.45e-228)
             (* x -3.0)
             (if (<= z 1e-279)
               (* y 4.0)
               (if (<= z 3e-219)
                 (* x -3.0)
                 (if (<= z 9e-38)
                   (* y 4.0)
                   (if (<= z 1.3e-19)
                     (* x -3.0)
                     (if (<= z 0.62)
                       (* y 4.0)
                       (if (<= z 2.95e+119)
                         (* 6.0 (* x z))
                         (if (or (<= z 1.56e+214) (not (<= z 9.6e+241)))
                           t_1
                           t_0))))))))))))))

C

double code(double x, double y, double z) {
	double t_0 = x * (z * 6.0);
	double t_1 = -6.0 * (y * z);
	double tmp;
	if (z <= -4.5e+256) {
		tmp = t_0;
	} else if (z <= -4.5e+226) {
		tmp = t_1;
	} else if (z <= -6.8e+189) {
		tmp = t_0;
	} else if (z <= -115.0) {
		tmp = t_1;
	} else if (z <= -1.45e-228) {
		tmp = x * -3.0;
	} else if (z <= 1e-279) {
		tmp = y * 4.0;
	} else if (z <= 3e-219) {
		tmp = x * -3.0;
	} else if (z <= 9e-38) {
		tmp = y * 4.0;
	} else if (z <= 1.3e-19) {
		tmp = x * -3.0;
	} else if (z <= 0.62) {
		tmp = y * 4.0;
	} else if (z <= 2.95e+119) {
		tmp = 6.0 * (x * z);
	} else if ((z <= 1.56e+214) || !(z <= 9.6e+241)) {
		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 = x * (z * 6.0d0)
    t_1 = (-6.0d0) * (y * z)
    if (z <= (-4.5d+256)) then
        tmp = t_0
    else if (z <= (-4.5d+226)) then
        tmp = t_1
    else if (z <= (-6.8d+189)) then
        tmp = t_0
    else if (z <= (-115.0d0)) then
        tmp = t_1
    else if (z <= (-1.45d-228)) then
        tmp = x * (-3.0d0)
    else if (z <= 1d-279) then
        tmp = y * 4.0d0
    else if (z <= 3d-219) then
        tmp = x * (-3.0d0)
    else if (z <= 9d-38) then
        tmp = y * 4.0d0
    else if (z <= 1.3d-19) then
        tmp = x * (-3.0d0)
    else if (z <= 0.62d0) then
        tmp = y * 4.0d0
    else if (z <= 2.95d+119) then
        tmp = 6.0d0 * (x * z)
    else if ((z <= 1.56d+214) .or. (.not. (z <= 9.6d+241))) 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 = x * (z * 6.0);
	double t_1 = -6.0 * (y * z);
	double tmp;
	if (z <= -4.5e+256) {
		tmp = t_0;
	} else if (z <= -4.5e+226) {
		tmp = t_1;
	} else if (z <= -6.8e+189) {
		tmp = t_0;
	} else if (z <= -115.0) {
		tmp = t_1;
	} else if (z <= -1.45e-228) {
		tmp = x * -3.0;
	} else if (z <= 1e-279) {
		tmp = y * 4.0;
	} else if (z <= 3e-219) {
		tmp = x * -3.0;
	} else if (z <= 9e-38) {
		tmp = y * 4.0;
	} else if (z <= 1.3e-19) {
		tmp = x * -3.0;
	} else if (z <= 0.62) {
		tmp = y * 4.0;
	} else if (z <= 2.95e+119) {
		tmp = 6.0 * (x * z);
	} else if ((z <= 1.56e+214) || !(z <= 9.6e+241)) {
		tmp = t_1;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x * (z * 6.0)
	t_1 = -6.0 * (y * z)
	tmp = 0
	if z <= -4.5e+256:
		tmp = t_0
	elif z <= -4.5e+226:
		tmp = t_1
	elif z <= -6.8e+189:
		tmp = t_0
	elif z <= -115.0:
		tmp = t_1
	elif z <= -1.45e-228:
		tmp = x * -3.0
	elif z <= 1e-279:
		tmp = y * 4.0
	elif z <= 3e-219:
		tmp = x * -3.0
	elif z <= 9e-38:
		tmp = y * 4.0
	elif z <= 1.3e-19:
		tmp = x * -3.0
	elif z <= 0.62:
		tmp = y * 4.0
	elif z <= 2.95e+119:
		tmp = 6.0 * (x * z)
	elif (z <= 1.56e+214) or not (z <= 9.6e+241):
		tmp = t_1
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(z * 6.0))
	t_1 = Float64(-6.0 * Float64(y * z))
	tmp = 0.0
	if (z <= -4.5e+256)
		tmp = t_0;
	elseif (z <= -4.5e+226)
		tmp = t_1;
	elseif (z <= -6.8e+189)
		tmp = t_0;
	elseif (z <= -115.0)
		tmp = t_1;
	elseif (z <= -1.45e-228)
		tmp = Float64(x * -3.0);
	elseif (z <= 1e-279)
		tmp = Float64(y * 4.0);
	elseif (z <= 3e-219)
		tmp = Float64(x * -3.0);
	elseif (z <= 9e-38)
		tmp = Float64(y * 4.0);
	elseif (z <= 1.3e-19)
		tmp = Float64(x * -3.0);
	elseif (z <= 0.62)
		tmp = Float64(y * 4.0);
	elseif (z <= 2.95e+119)
		tmp = Float64(6.0 * Float64(x * z));
	elseif ((z <= 1.56e+214) || !(z <= 9.6e+241))
		tmp = t_1;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x * (z * 6.0);
	t_1 = -6.0 * (y * z);
	tmp = 0.0;
	if (z <= -4.5e+256)
		tmp = t_0;
	elseif (z <= -4.5e+226)
		tmp = t_1;
	elseif (z <= -6.8e+189)
		tmp = t_0;
	elseif (z <= -115.0)
		tmp = t_1;
	elseif (z <= -1.45e-228)
		tmp = x * -3.0;
	elseif (z <= 1e-279)
		tmp = y * 4.0;
	elseif (z <= 3e-219)
		tmp = x * -3.0;
	elseif (z <= 9e-38)
		tmp = y * 4.0;
	elseif (z <= 1.3e-19)
		tmp = x * -3.0;
	elseif (z <= 0.62)
		tmp = y * 4.0;
	elseif (z <= 2.95e+119)
		tmp = 6.0 * (x * z);
	elseif ((z <= 1.56e+214) || ~((z <= 9.6e+241)))
		tmp = t_1;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * N[(z * 6.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(-6.0 * N[(y * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -4.5e+256], t$95$0, If[LessEqual[z, -4.5e+226], t$95$1, If[LessEqual[z, -6.8e+189], t$95$0, If[LessEqual[z, -115.0], t$95$1, If[LessEqual[z, -1.45e-228], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 1e-279], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 3e-219], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 9e-38], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 1.3e-19], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 0.62], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 2.95e+119], N[(6.0 * N[(x * z), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[z, 1.56e+214], N[Not[LessEqual[z, 9.6e+241]], $MachinePrecision]], t$95$1, t$95$0]]]]]]]]]]]]]]

Derivation

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

Alternative 4: 50.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(z \cdot 6\right)\\ t_1 := -6 \cdot \left(y \cdot z\right)\\ \mathbf{if}\;z \leq -4.6 \cdot 10^{+256}:\\ \;\;\;\;z \cdot \left(x \cdot 6\right)\\ \mathbf{elif}\;z \leq -2.3 \cdot 10^{+228}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -6.2 \cdot 10^{+189}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -3.2:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -2.2 \cdot 10^{-228}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 5.2 \cdot 10^{-280}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 1.45 \cdot 10^{-219}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 8.5 \cdot 10^{-38}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 1.6 \cdot 10^{-21}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 0.58:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 8.5 \cdot 10^{+121}:\\ \;\;\;\;6 \cdot \left(x \cdot z\right)\\ \mathbf{elif}\;z \leq 1.1 \cdot 10^{+215} \lor \neg \left(z \leq 6.5 \cdot 10^{+241}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* x (* z 6.0))) (t_1 (* -6.0 (* y z))))
   (if (<= z -4.6e+256)
     (* z (* x 6.0))
     (if (<= z -2.3e+228)
       t_1
       (if (<= z -6.2e+189)
         t_0
         (if (<= z -3.2)
           t_1
           (if (<= z -2.2e-228)
             (* x -3.0)
             (if (<= z 5.2e-280)
               (* y 4.0)
               (if (<= z 1.45e-219)
                 (* x -3.0)
                 (if (<= z 8.5e-38)
                   (* y 4.0)
                   (if (<= z 1.6e-21)
                     (* x -3.0)
                     (if (<= z 0.58)
                       (* y 4.0)
                       (if (<= z 8.5e+121)
                         (* 6.0 (* x z))
                         (if (or (<= z 1.1e+215) (not (<= z 6.5e+241)))
                           t_1
                           t_0))))))))))))))

C

double code(double x, double y, double z) {
	double t_0 = x * (z * 6.0);
	double t_1 = -6.0 * (y * z);
	double tmp;
	if (z <= -4.6e+256) {
		tmp = z * (x * 6.0);
	} else if (z <= -2.3e+228) {
		tmp = t_1;
	} else if (z <= -6.2e+189) {
		tmp = t_0;
	} else if (z <= -3.2) {
		tmp = t_1;
	} else if (z <= -2.2e-228) {
		tmp = x * -3.0;
	} else if (z <= 5.2e-280) {
		tmp = y * 4.0;
	} else if (z <= 1.45e-219) {
		tmp = x * -3.0;
	} else if (z <= 8.5e-38) {
		tmp = y * 4.0;
	} else if (z <= 1.6e-21) {
		tmp = x * -3.0;
	} else if (z <= 0.58) {
		tmp = y * 4.0;
	} else if (z <= 8.5e+121) {
		tmp = 6.0 * (x * z);
	} else if ((z <= 1.1e+215) || !(z <= 6.5e+241)) {
		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 = x * (z * 6.0d0)
    t_1 = (-6.0d0) * (y * z)
    if (z <= (-4.6d+256)) then
        tmp = z * (x * 6.0d0)
    else if (z <= (-2.3d+228)) then
        tmp = t_1
    else if (z <= (-6.2d+189)) then
        tmp = t_0
    else if (z <= (-3.2d0)) then
        tmp = t_1
    else if (z <= (-2.2d-228)) then
        tmp = x * (-3.0d0)
    else if (z <= 5.2d-280) then
        tmp = y * 4.0d0
    else if (z <= 1.45d-219) then
        tmp = x * (-3.0d0)
    else if (z <= 8.5d-38) then
        tmp = y * 4.0d0
    else if (z <= 1.6d-21) then
        tmp = x * (-3.0d0)
    else if (z <= 0.58d0) then
        tmp = y * 4.0d0
    else if (z <= 8.5d+121) then
        tmp = 6.0d0 * (x * z)
    else if ((z <= 1.1d+215) .or. (.not. (z <= 6.5d+241))) 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 = x * (z * 6.0);
	double t_1 = -6.0 * (y * z);
	double tmp;
	if (z <= -4.6e+256) {
		tmp = z * (x * 6.0);
	} else if (z <= -2.3e+228) {
		tmp = t_1;
	} else if (z <= -6.2e+189) {
		tmp = t_0;
	} else if (z <= -3.2) {
		tmp = t_1;
	} else if (z <= -2.2e-228) {
		tmp = x * -3.0;
	} else if (z <= 5.2e-280) {
		tmp = y * 4.0;
	} else if (z <= 1.45e-219) {
		tmp = x * -3.0;
	} else if (z <= 8.5e-38) {
		tmp = y * 4.0;
	} else if (z <= 1.6e-21) {
		tmp = x * -3.0;
	} else if (z <= 0.58) {
		tmp = y * 4.0;
	} else if (z <= 8.5e+121) {
		tmp = 6.0 * (x * z);
	} else if ((z <= 1.1e+215) || !(z <= 6.5e+241)) {
		tmp = t_1;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x * (z * 6.0)
	t_1 = -6.0 * (y * z)
	tmp = 0
	if z <= -4.6e+256:
		tmp = z * (x * 6.0)
	elif z <= -2.3e+228:
		tmp = t_1
	elif z <= -6.2e+189:
		tmp = t_0
	elif z <= -3.2:
		tmp = t_1
	elif z <= -2.2e-228:
		tmp = x * -3.0
	elif z <= 5.2e-280:
		tmp = y * 4.0
	elif z <= 1.45e-219:
		tmp = x * -3.0
	elif z <= 8.5e-38:
		tmp = y * 4.0
	elif z <= 1.6e-21:
		tmp = x * -3.0
	elif z <= 0.58:
		tmp = y * 4.0
	elif z <= 8.5e+121:
		tmp = 6.0 * (x * z)
	elif (z <= 1.1e+215) or not (z <= 6.5e+241):
		tmp = t_1
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(z * 6.0))
	t_1 = Float64(-6.0 * Float64(y * z))
	tmp = 0.0
	if (z <= -4.6e+256)
		tmp = Float64(z * Float64(x * 6.0));
	elseif (z <= -2.3e+228)
		tmp = t_1;
	elseif (z <= -6.2e+189)
		tmp = t_0;
	elseif (z <= -3.2)
		tmp = t_1;
	elseif (z <= -2.2e-228)
		tmp = Float64(x * -3.0);
	elseif (z <= 5.2e-280)
		tmp = Float64(y * 4.0);
	elseif (z <= 1.45e-219)
		tmp = Float64(x * -3.0);
	elseif (z <= 8.5e-38)
		tmp = Float64(y * 4.0);
	elseif (z <= 1.6e-21)
		tmp = Float64(x * -3.0);
	elseif (z <= 0.58)
		tmp = Float64(y * 4.0);
	elseif (z <= 8.5e+121)
		tmp = Float64(6.0 * Float64(x * z));
	elseif ((z <= 1.1e+215) || !(z <= 6.5e+241))
		tmp = t_1;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x * (z * 6.0);
	t_1 = -6.0 * (y * z);
	tmp = 0.0;
	if (z <= -4.6e+256)
		tmp = z * (x * 6.0);
	elseif (z <= -2.3e+228)
		tmp = t_1;
	elseif (z <= -6.2e+189)
		tmp = t_0;
	elseif (z <= -3.2)
		tmp = t_1;
	elseif (z <= -2.2e-228)
		tmp = x * -3.0;
	elseif (z <= 5.2e-280)
		tmp = y * 4.0;
	elseif (z <= 1.45e-219)
		tmp = x * -3.0;
	elseif (z <= 8.5e-38)
		tmp = y * 4.0;
	elseif (z <= 1.6e-21)
		tmp = x * -3.0;
	elseif (z <= 0.58)
		tmp = y * 4.0;
	elseif (z <= 8.5e+121)
		tmp = 6.0 * (x * z);
	elseif ((z <= 1.1e+215) || ~((z <= 6.5e+241)))
		tmp = t_1;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * N[(z * 6.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(-6.0 * N[(y * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -4.6e+256], N[(z * N[(x * 6.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -2.3e+228], t$95$1, If[LessEqual[z, -6.2e+189], t$95$0, If[LessEqual[z, -3.2], t$95$1, If[LessEqual[z, -2.2e-228], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 5.2e-280], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 1.45e-219], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 8.5e-38], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 1.6e-21], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 0.58], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 8.5e+121], N[(6.0 * N[(x * z), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[z, 1.1e+215], N[Not[LessEqual[z, 6.5e+241]], $MachinePrecision]], t$95$1, t$95$0]]]]]]]]]]]]]]

Derivation

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

Alternative 5: 50.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(z \cdot 6\right)\\ t_1 := -6 \cdot \left(y \cdot z\right)\\ \mathbf{if}\;z \leq -4.7 \cdot 10^{+256}:\\ \;\;\;\;z \cdot \left(x \cdot 6\right)\\ \mathbf{elif}\;z \leq -7.5 \cdot 10^{+227}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -1.8 \cdot 10^{+189}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -7.5:\\ \;\;\;\;z \cdot \left(y \cdot -6\right)\\ \mathbf{elif}\;z \leq -2.05 \cdot 10^{-228}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 3.4 \cdot 10^{-276}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 2.7 \cdot 10^{-220}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 1.26 \cdot 10^{-36}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 4.95 \cdot 10^{-20}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 0.65:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 1.3 \cdot 10^{+120}:\\ \;\;\;\;6 \cdot \left(x \cdot z\right)\\ \mathbf{elif}\;z \leq 1.3 \cdot 10^{+215} \lor \neg \left(z \leq 2.7 \cdot 10^{+241}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* x (* z 6.0))) (t_1 (* -6.0 (* y z))))
   (if (<= z -4.7e+256)
     (* z (* x 6.0))
     (if (<= z -7.5e+227)
       t_1
       (if (<= z -1.8e+189)
         t_0
         (if (<= z -7.5)
           (* z (* y -6.0))
           (if (<= z -2.05e-228)
             (* x -3.0)
             (if (<= z 3.4e-276)
               (* y 4.0)
               (if (<= z 2.7e-220)
                 (* x -3.0)
                 (if (<= z 1.26e-36)
                   (* y 4.0)
                   (if (<= z 4.95e-20)
                     (* x -3.0)
                     (if (<= z 0.65)
                       (* y 4.0)
                       (if (<= z 1.3e+120)
                         (* 6.0 (* x z))
                         (if (or (<= z 1.3e+215) (not (<= z 2.7e+241)))
                           t_1
                           t_0))))))))))))))

C

double code(double x, double y, double z) {
	double t_0 = x * (z * 6.0);
	double t_1 = -6.0 * (y * z);
	double tmp;
	if (z <= -4.7e+256) {
		tmp = z * (x * 6.0);
	} else if (z <= -7.5e+227) {
		tmp = t_1;
	} else if (z <= -1.8e+189) {
		tmp = t_0;
	} else if (z <= -7.5) {
		tmp = z * (y * -6.0);
	} else if (z <= -2.05e-228) {
		tmp = x * -3.0;
	} else if (z <= 3.4e-276) {
		tmp = y * 4.0;
	} else if (z <= 2.7e-220) {
		tmp = x * -3.0;
	} else if (z <= 1.26e-36) {
		tmp = y * 4.0;
	} else if (z <= 4.95e-20) {
		tmp = x * -3.0;
	} else if (z <= 0.65) {
		tmp = y * 4.0;
	} else if (z <= 1.3e+120) {
		tmp = 6.0 * (x * z);
	} else if ((z <= 1.3e+215) || !(z <= 2.7e+241)) {
		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 = x * (z * 6.0d0)
    t_1 = (-6.0d0) * (y * z)
    if (z <= (-4.7d+256)) then
        tmp = z * (x * 6.0d0)
    else if (z <= (-7.5d+227)) then
        tmp = t_1
    else if (z <= (-1.8d+189)) then
        tmp = t_0
    else if (z <= (-7.5d0)) then
        tmp = z * (y * (-6.0d0))
    else if (z <= (-2.05d-228)) then
        tmp = x * (-3.0d0)
    else if (z <= 3.4d-276) then
        tmp = y * 4.0d0
    else if (z <= 2.7d-220) then
        tmp = x * (-3.0d0)
    else if (z <= 1.26d-36) then
        tmp = y * 4.0d0
    else if (z <= 4.95d-20) then
        tmp = x * (-3.0d0)
    else if (z <= 0.65d0) then
        tmp = y * 4.0d0
    else if (z <= 1.3d+120) then
        tmp = 6.0d0 * (x * z)
    else if ((z <= 1.3d+215) .or. (.not. (z <= 2.7d+241))) 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 = x * (z * 6.0);
	double t_1 = -6.0 * (y * z);
	double tmp;
	if (z <= -4.7e+256) {
		tmp = z * (x * 6.0);
	} else if (z <= -7.5e+227) {
		tmp = t_1;
	} else if (z <= -1.8e+189) {
		tmp = t_0;
	} else if (z <= -7.5) {
		tmp = z * (y * -6.0);
	} else if (z <= -2.05e-228) {
		tmp = x * -3.0;
	} else if (z <= 3.4e-276) {
		tmp = y * 4.0;
	} else if (z <= 2.7e-220) {
		tmp = x * -3.0;
	} else if (z <= 1.26e-36) {
		tmp = y * 4.0;
	} else if (z <= 4.95e-20) {
		tmp = x * -3.0;
	} else if (z <= 0.65) {
		tmp = y * 4.0;
	} else if (z <= 1.3e+120) {
		tmp = 6.0 * (x * z);
	} else if ((z <= 1.3e+215) || !(z <= 2.7e+241)) {
		tmp = t_1;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x * (z * 6.0)
	t_1 = -6.0 * (y * z)
	tmp = 0
	if z <= -4.7e+256:
		tmp = z * (x * 6.0)
	elif z <= -7.5e+227:
		tmp = t_1
	elif z <= -1.8e+189:
		tmp = t_0
	elif z <= -7.5:
		tmp = z * (y * -6.0)
	elif z <= -2.05e-228:
		tmp = x * -3.0
	elif z <= 3.4e-276:
		tmp = y * 4.0
	elif z <= 2.7e-220:
		tmp = x * -3.0
	elif z <= 1.26e-36:
		tmp = y * 4.0
	elif z <= 4.95e-20:
		tmp = x * -3.0
	elif z <= 0.65:
		tmp = y * 4.0
	elif z <= 1.3e+120:
		tmp = 6.0 * (x * z)
	elif (z <= 1.3e+215) or not (z <= 2.7e+241):
		tmp = t_1
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(z * 6.0))
	t_1 = Float64(-6.0 * Float64(y * z))
	tmp = 0.0
	if (z <= -4.7e+256)
		tmp = Float64(z * Float64(x * 6.0));
	elseif (z <= -7.5e+227)
		tmp = t_1;
	elseif (z <= -1.8e+189)
		tmp = t_0;
	elseif (z <= -7.5)
		tmp = Float64(z * Float64(y * -6.0));
	elseif (z <= -2.05e-228)
		tmp = Float64(x * -3.0);
	elseif (z <= 3.4e-276)
		tmp = Float64(y * 4.0);
	elseif (z <= 2.7e-220)
		tmp = Float64(x * -3.0);
	elseif (z <= 1.26e-36)
		tmp = Float64(y * 4.0);
	elseif (z <= 4.95e-20)
		tmp = Float64(x * -3.0);
	elseif (z <= 0.65)
		tmp = Float64(y * 4.0);
	elseif (z <= 1.3e+120)
		tmp = Float64(6.0 * Float64(x * z));
	elseif ((z <= 1.3e+215) || !(z <= 2.7e+241))
		tmp = t_1;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x * (z * 6.0);
	t_1 = -6.0 * (y * z);
	tmp = 0.0;
	if (z <= -4.7e+256)
		tmp = z * (x * 6.0);
	elseif (z <= -7.5e+227)
		tmp = t_1;
	elseif (z <= -1.8e+189)
		tmp = t_0;
	elseif (z <= -7.5)
		tmp = z * (y * -6.0);
	elseif (z <= -2.05e-228)
		tmp = x * -3.0;
	elseif (z <= 3.4e-276)
		tmp = y * 4.0;
	elseif (z <= 2.7e-220)
		tmp = x * -3.0;
	elseif (z <= 1.26e-36)
		tmp = y * 4.0;
	elseif (z <= 4.95e-20)
		tmp = x * -3.0;
	elseif (z <= 0.65)
		tmp = y * 4.0;
	elseif (z <= 1.3e+120)
		tmp = 6.0 * (x * z);
	elseif ((z <= 1.3e+215) || ~((z <= 2.7e+241)))
		tmp = t_1;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * N[(z * 6.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(-6.0 * N[(y * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -4.7e+256], N[(z * N[(x * 6.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -7.5e+227], t$95$1, If[LessEqual[z, -1.8e+189], t$95$0, If[LessEqual[z, -7.5], N[(z * N[(y * -6.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -2.05e-228], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 3.4e-276], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 2.7e-220], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 1.26e-36], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 4.95e-20], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 0.65], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 1.3e+120], N[(6.0 * N[(x * z), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[z, 1.3e+215], N[Not[LessEqual[z, 2.7e+241]], $MachinePrecision]], t$95$1, t$95$0]]]]]]]]]]]]]]

Derivation

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

Alternative 6: 74.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := -6 \cdot \left(\left(y - x\right) \cdot z\right)\\ \mathbf{if}\;z \leq -0.0062:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -2 \cdot 10^{-228}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 8 \cdot 10^{-277}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 1.45 \cdot 10^{-220}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 5 \cdot 10^{-37}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 2.15 \cdot 10^{-21}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 0.65:\\ \;\;\;\;y \cdot 4\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* -6.0 (* (- y x) z))))
   (if (<= z -0.0062)
     t_0
     (if (<= z -2e-228)
       (* x -3.0)
       (if (<= z 8e-277)
         (* y 4.0)
         (if (<= z 1.45e-220)
           (* x -3.0)
           (if (<= z 5e-37)
             (* y 4.0)
             (if (<= z 2.15e-21)
               (* x -3.0)
               (if (<= z 0.65) (* y 4.0) t_0)))))))))

C

double code(double x, double y, double z) {
	double t_0 = -6.0 * ((y - x) * z);
	double tmp;
	if (z <= -0.0062) {
		tmp = t_0;
	} else if (z <= -2e-228) {
		tmp = x * -3.0;
	} else if (z <= 8e-277) {
		tmp = y * 4.0;
	} else if (z <= 1.45e-220) {
		tmp = x * -3.0;
	} else if (z <= 5e-37) {
		tmp = y * 4.0;
	} else if (z <= 2.15e-21) {
		tmp = x * -3.0;
	} else if (z <= 0.65) {
		tmp = y * 4.0;
	} 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 <= (-0.0062d0)) then
        tmp = t_0
    else if (z <= (-2d-228)) then
        tmp = x * (-3.0d0)
    else if (z <= 8d-277) then
        tmp = y * 4.0d0
    else if (z <= 1.45d-220) then
        tmp = x * (-3.0d0)
    else if (z <= 5d-37) then
        tmp = y * 4.0d0
    else if (z <= 2.15d-21) then
        tmp = x * (-3.0d0)
    else if (z <= 0.65d0) then
        tmp = y * 4.0d0
    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 <= -0.0062) {
		tmp = t_0;
	} else if (z <= -2e-228) {
		tmp = x * -3.0;
	} else if (z <= 8e-277) {
		tmp = y * 4.0;
	} else if (z <= 1.45e-220) {
		tmp = x * -3.0;
	} else if (z <= 5e-37) {
		tmp = y * 4.0;
	} else if (z <= 2.15e-21) {
		tmp = x * -3.0;
	} else if (z <= 0.65) {
		tmp = y * 4.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = -6.0 * ((y - x) * z)
	tmp = 0
	if z <= -0.0062:
		tmp = t_0
	elif z <= -2e-228:
		tmp = x * -3.0
	elif z <= 8e-277:
		tmp = y * 4.0
	elif z <= 1.45e-220:
		tmp = x * -3.0
	elif z <= 5e-37:
		tmp = y * 4.0
	elif z <= 2.15e-21:
		tmp = x * -3.0
	elif z <= 0.65:
		tmp = y * 4.0
	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 <= -0.0062)
		tmp = t_0;
	elseif (z <= -2e-228)
		tmp = Float64(x * -3.0);
	elseif (z <= 8e-277)
		tmp = Float64(y * 4.0);
	elseif (z <= 1.45e-220)
		tmp = Float64(x * -3.0);
	elseif (z <= 5e-37)
		tmp = Float64(y * 4.0);
	elseif (z <= 2.15e-21)
		tmp = Float64(x * -3.0);
	elseif (z <= 0.65)
		tmp = Float64(y * 4.0);
	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 <= -0.0062)
		tmp = t_0;
	elseif (z <= -2e-228)
		tmp = x * -3.0;
	elseif (z <= 8e-277)
		tmp = y * 4.0;
	elseif (z <= 1.45e-220)
		tmp = x * -3.0;
	elseif (z <= 5e-37)
		tmp = y * 4.0;
	elseif (z <= 2.15e-21)
		tmp = x * -3.0;
	elseif (z <= 0.65)
		tmp = y * 4.0;
	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, -0.0062], t$95$0, If[LessEqual[z, -2e-228], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 8e-277], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 1.45e-220], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 5e-37], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 2.15e-21], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 0.65], N[(y * 4.0), $MachinePrecision], t$95$0]]]]]]]]

Derivation

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

Alternative 7: 74.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := -6 \cdot \left(\left(y - x\right) \cdot z\right)\\ \mathbf{if}\;z \leq -11000000:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -1.32 \cdot 10^{-228}:\\ \;\;\;\;x \cdot \left(-3 + z \cdot 6\right)\\ \mathbf{elif}\;z \leq 6.4 \cdot 10^{-276}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 1.18 \cdot 10^{-219}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 1.68 \cdot 10^{-37}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 3.7 \cdot 10^{-21}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 0.66:\\ \;\;\;\;y \cdot 4\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* -6.0 (* (- y x) z))))
   (if (<= z -11000000.0)
     t_0
     (if (<= z -1.32e-228)
       (* x (+ -3.0 (* z 6.0)))
       (if (<= z 6.4e-276)
         (* y 4.0)
         (if (<= z 1.18e-219)
           (* x -3.0)
           (if (<= z 1.68e-37)
             (* y 4.0)
             (if (<= z 3.7e-21)
               (* x -3.0)
               (if (<= z 0.66) (* y 4.0) t_0)))))))))

C

double code(double x, double y, double z) {
	double t_0 = -6.0 * ((y - x) * z);
	double tmp;
	if (z <= -11000000.0) {
		tmp = t_0;
	} else if (z <= -1.32e-228) {
		tmp = x * (-3.0 + (z * 6.0));
	} else if (z <= 6.4e-276) {
		tmp = y * 4.0;
	} else if (z <= 1.18e-219) {
		tmp = x * -3.0;
	} else if (z <= 1.68e-37) {
		tmp = y * 4.0;
	} else if (z <= 3.7e-21) {
		tmp = x * -3.0;
	} else if (z <= 0.66) {
		tmp = y * 4.0;
	} 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 <= (-11000000.0d0)) then
        tmp = t_0
    else if (z <= (-1.32d-228)) then
        tmp = x * ((-3.0d0) + (z * 6.0d0))
    else if (z <= 6.4d-276) then
        tmp = y * 4.0d0
    else if (z <= 1.18d-219) then
        tmp = x * (-3.0d0)
    else if (z <= 1.68d-37) then
        tmp = y * 4.0d0
    else if (z <= 3.7d-21) then
        tmp = x * (-3.0d0)
    else if (z <= 0.66d0) then
        tmp = y * 4.0d0
    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 <= -11000000.0) {
		tmp = t_0;
	} else if (z <= -1.32e-228) {
		tmp = x * (-3.0 + (z * 6.0));
	} else if (z <= 6.4e-276) {
		tmp = y * 4.0;
	} else if (z <= 1.18e-219) {
		tmp = x * -3.0;
	} else if (z <= 1.68e-37) {
		tmp = y * 4.0;
	} else if (z <= 3.7e-21) {
		tmp = x * -3.0;
	} else if (z <= 0.66) {
		tmp = y * 4.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = -6.0 * ((y - x) * z)
	tmp = 0
	if z <= -11000000.0:
		tmp = t_0
	elif z <= -1.32e-228:
		tmp = x * (-3.0 + (z * 6.0))
	elif z <= 6.4e-276:
		tmp = y * 4.0
	elif z <= 1.18e-219:
		tmp = x * -3.0
	elif z <= 1.68e-37:
		tmp = y * 4.0
	elif z <= 3.7e-21:
		tmp = x * -3.0
	elif z <= 0.66:
		tmp = y * 4.0
	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 <= -11000000.0)
		tmp = t_0;
	elseif (z <= -1.32e-228)
		tmp = Float64(x * Float64(-3.0 + Float64(z * 6.0)));
	elseif (z <= 6.4e-276)
		tmp = Float64(y * 4.0);
	elseif (z <= 1.18e-219)
		tmp = Float64(x * -3.0);
	elseif (z <= 1.68e-37)
		tmp = Float64(y * 4.0);
	elseif (z <= 3.7e-21)
		tmp = Float64(x * -3.0);
	elseif (z <= 0.66)
		tmp = Float64(y * 4.0);
	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 <= -11000000.0)
		tmp = t_0;
	elseif (z <= -1.32e-228)
		tmp = x * (-3.0 + (z * 6.0));
	elseif (z <= 6.4e-276)
		tmp = y * 4.0;
	elseif (z <= 1.18e-219)
		tmp = x * -3.0;
	elseif (z <= 1.68e-37)
		tmp = y * 4.0;
	elseif (z <= 3.7e-21)
		tmp = x * -3.0;
	elseif (z <= 0.66)
		tmp = y * 4.0;
	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, -11000000.0], t$95$0, If[LessEqual[z, -1.32e-228], N[(x * N[(-3.0 + N[(z * 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 6.4e-276], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 1.18e-219], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 1.68e-37], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 3.7e-21], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 0.66], N[(y * 4.0), $MachinePrecision], t$95$0]]]]]]]]

Derivation

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

Alternative 8: 50.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := -6 \cdot \left(y \cdot z\right)\\ \mathbf{if}\;z \leq -5.8:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -6 \cdot 10^{-228}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 10^{-278}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 1.85 \cdot 10^{-219}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 1.9 \cdot 10^{-36}:\\ \;\;\;\;y \cdot 4\\ \mathbf{elif}\;z \leq 1.8 \cdot 10^{-19}:\\ \;\;\;\;x \cdot -3\\ \mathbf{elif}\;z \leq 0.66:\\ \;\;\;\;y \cdot 4\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* -6.0 (* y z))))
   (if (<= z -5.8)
     t_0
     (if (<= z -6e-228)
       (* x -3.0)
       (if (<= z 1e-278)
         (* y 4.0)
         (if (<= z 1.85e-219)
           (* x -3.0)
           (if (<= z 1.9e-36)
             (* y 4.0)
             (if (<= z 1.8e-19)
               (* x -3.0)
               (if (<= z 0.66) (* y 4.0) t_0)))))))))

C

double code(double x, double y, double z) {
	double t_0 = -6.0 * (y * z);
	double tmp;
	if (z <= -5.8) {
		tmp = t_0;
	} else if (z <= -6e-228) {
		tmp = x * -3.0;
	} else if (z <= 1e-278) {
		tmp = y * 4.0;
	} else if (z <= 1.85e-219) {
		tmp = x * -3.0;
	} else if (z <= 1.9e-36) {
		tmp = y * 4.0;
	} else if (z <= 1.8e-19) {
		tmp = x * -3.0;
	} else if (z <= 0.66) {
		tmp = y * 4.0;
	} 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 <= (-5.8d0)) then
        tmp = t_0
    else if (z <= (-6d-228)) then
        tmp = x * (-3.0d0)
    else if (z <= 1d-278) then
        tmp = y * 4.0d0
    else if (z <= 1.85d-219) then
        tmp = x * (-3.0d0)
    else if (z <= 1.9d-36) then
        tmp = y * 4.0d0
    else if (z <= 1.8d-19) then
        tmp = x * (-3.0d0)
    else if (z <= 0.66d0) then
        tmp = y * 4.0d0
    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 <= -5.8) {
		tmp = t_0;
	} else if (z <= -6e-228) {
		tmp = x * -3.0;
	} else if (z <= 1e-278) {
		tmp = y * 4.0;
	} else if (z <= 1.85e-219) {
		tmp = x * -3.0;
	} else if (z <= 1.9e-36) {
		tmp = y * 4.0;
	} else if (z <= 1.8e-19) {
		tmp = x * -3.0;
	} else if (z <= 0.66) {
		tmp = y * 4.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = -6.0 * (y * z)
	tmp = 0
	if z <= -5.8:
		tmp = t_0
	elif z <= -6e-228:
		tmp = x * -3.0
	elif z <= 1e-278:
		tmp = y * 4.0
	elif z <= 1.85e-219:
		tmp = x * -3.0
	elif z <= 1.9e-36:
		tmp = y * 4.0
	elif z <= 1.8e-19:
		tmp = x * -3.0
	elif z <= 0.66:
		tmp = y * 4.0
	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 <= -5.8)
		tmp = t_0;
	elseif (z <= -6e-228)
		tmp = Float64(x * -3.0);
	elseif (z <= 1e-278)
		tmp = Float64(y * 4.0);
	elseif (z <= 1.85e-219)
		tmp = Float64(x * -3.0);
	elseif (z <= 1.9e-36)
		tmp = Float64(y * 4.0);
	elseif (z <= 1.8e-19)
		tmp = Float64(x * -3.0);
	elseif (z <= 0.66)
		tmp = Float64(y * 4.0);
	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 <= -5.8)
		tmp = t_0;
	elseif (z <= -6e-228)
		tmp = x * -3.0;
	elseif (z <= 1e-278)
		tmp = y * 4.0;
	elseif (z <= 1.85e-219)
		tmp = x * -3.0;
	elseif (z <= 1.9e-36)
		tmp = y * 4.0;
	elseif (z <= 1.8e-19)
		tmp = x * -3.0;
	elseif (z <= 0.66)
		tmp = y * 4.0;
	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, -5.8], t$95$0, If[LessEqual[z, -6e-228], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 1e-278], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 1.85e-219], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 1.9e-36], N[(y * 4.0), $MachinePrecision], If[LessEqual[z, 1.8e-19], N[(x * -3.0), $MachinePrecision], If[LessEqual[z, 0.66], N[(y * 4.0), $MachinePrecision], t$95$0]]]]]]]]

Derivation

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

Alternative 9: 99.7% accurate, 0.9× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return x + ((-6.0 * ((y - x) * z)) + ((y - 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 + (((-6.0d0) * ((y - x) * z)) + ((y - x) * 4.0d0))
end function

Java

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

Python

def code(x, y, z):
	return x + ((-6.0 * ((y - x) * z)) + ((y - x) * 4.0))

Julia

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

MATLAB

function tmp = code(x, y, z)
	tmp = x + ((-6.0 * ((y - x) * z)) + ((y - x) * 4.0));
end

Wolfram

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

Derivation

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

Alternative 10: 97.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -0.6 \lor \neg \left(z \leq 0.5\right):\\ \;\;\;\;z \cdot \left(y \cdot -6 + x \cdot 6\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot 4 + x \cdot -3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -0.6) (not (<= z 0.5)))
   (* z (+ (* y -6.0) (* x 6.0)))
   (+ (* y 4.0) (* x -3.0))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -0.6) || !(z <= 0.5)) {
		tmp = z * ((y * -6.0) + (x * 6.0));
	} 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 <= (-0.6d0)) .or. (.not. (z <= 0.5d0))) then
        tmp = z * ((y * (-6.0d0)) + (x * 6.0d0))
    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 <= -0.6) || !(z <= 0.5)) {
		tmp = z * ((y * -6.0) + (x * 6.0));
	} else {
		tmp = (y * 4.0) + (x * -3.0);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -0.6) or not (z <= 0.5):
		tmp = z * ((y * -6.0) + (x * 6.0))
	else:
		tmp = (y * 4.0) + (x * -3.0)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -0.6) || !(z <= 0.5))
		tmp = Float64(z * Float64(Float64(y * -6.0) + Float64(x * 6.0)));
	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 <= -0.6) || ~((z <= 0.5)))
		tmp = z * ((y * -6.0) + (x * 6.0));
	else
		tmp = (y * 4.0) + (x * -3.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -0.6], N[Not[LessEqual[z, 0.5]], $MachinePrecision]], N[(z * N[(N[(y * -6.0), $MachinePrecision] + N[(x * 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(y * 4.0), $MachinePrecision] + N[(x * -3.0), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 11: 99.5% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return x + (((x * -6.0) + (y * 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 + (((x * (-6.0d0)) + (y * 6.0d0)) * (0.6666666666666666d0 - z))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 12: 73.5% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.8 \cdot 10^{+81} \lor \neg \left(x \leq 4.6 \cdot 10^{-82}\right):\\ \;\;\;\;x \cdot \left(-3 + z \cdot 6\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(4 + z \cdot -6\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -2.8e+81) (not (<= x 4.6e-82)))
   (* x (+ -3.0 (* z 6.0)))
   (* y (+ 4.0 (* z -6.0)))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -2.8e+81) || !(x <= 4.6e-82)) {
		tmp = x * (-3.0 + (z * 6.0));
	} else {
		tmp = y * (4.0 + (z * -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) :: tmp
    if ((x <= (-2.8d+81)) .or. (.not. (x <= 4.6d-82))) then
        tmp = x * ((-3.0d0) + (z * 6.0d0))
    else
        tmp = y * (4.0d0 + (z * (-6.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -2.8e+81) || !(x <= 4.6e-82)) {
		tmp = x * (-3.0 + (z * 6.0));
	} else {
		tmp = y * (4.0 + (z * -6.0));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -2.8e+81) or not (x <= 4.6e-82):
		tmp = x * (-3.0 + (z * 6.0))
	else:
		tmp = y * (4.0 + (z * -6.0))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -2.8e+81) || !(x <= 4.6e-82))
		tmp = Float64(x * Float64(-3.0 + Float64(z * 6.0)));
	else
		tmp = Float64(y * Float64(4.0 + Float64(z * -6.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -2.8e+81) || ~((x <= 4.6e-82)))
		tmp = x * (-3.0 + (z * 6.0));
	else
		tmp = y * (4.0 + (z * -6.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -2.8e+81], N[Not[LessEqual[x, 4.6e-82]], $MachinePrecision]], N[(x * N[(-3.0 + N[(z * 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y * N[(4.0 + N[(z * -6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 13: 97.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -0.6 \lor \neg \left(z \leq 0.5\right):\\ \;\;\;\;\left(y - x\right) \cdot \left(z \cdot -6\right)\\ \mathbf{else}:\\ \;\;\;\;x + \left(y - x\right) \cdot 4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -0.6) (not (<= z 0.5)))
   (* (- y x) (* z -6.0))
   (+ x (* (- y x) 4.0))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -0.6) || !(z <= 0.5)) {
		tmp = (y - x) * (z * -6.0);
	} 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 <= (-0.6d0)) .or. (.not. (z <= 0.5d0))) then
        tmp = (y - x) * (z * (-6.0d0))
    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 <= -0.6) || !(z <= 0.5)) {
		tmp = (y - x) * (z * -6.0);
	} else {
		tmp = x + ((y - x) * 4.0);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -0.6) or not (z <= 0.5):
		tmp = (y - x) * (z * -6.0)
	else:
		tmp = x + ((y - x) * 4.0)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -0.6) || !(z <= 0.5))
		tmp = Float64(Float64(y - x) * Float64(z * -6.0));
	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 <= -0.6) || ~((z <= 0.5)))
		tmp = (y - x) * (z * -6.0);
	else
		tmp = x + ((y - x) * 4.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -0.6], N[Not[LessEqual[z, 0.5]], $MachinePrecision]], N[(N[(y - x), $MachinePrecision] * N[(z * -6.0), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(y - x), $MachinePrecision] * 4.0), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 14: 97.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -0.6 \lor \neg \left(z \leq 0.53\right):\\ \;\;\;\;\left(y - x\right) \cdot \left(z \cdot -6\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot 4 + x \cdot -3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -0.6) (not (<= z 0.53)))
   (* (- y x) (* z -6.0))
   (+ (* y 4.0) (* x -3.0))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -0.6) || !(z <= 0.53)) {
		tmp = (y - x) * (z * -6.0);
	} 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 <= (-0.6d0)) .or. (.not. (z <= 0.53d0))) then
        tmp = (y - x) * (z * (-6.0d0))
    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 <= -0.6) || !(z <= 0.53)) {
		tmp = (y - x) * (z * -6.0);
	} else {
		tmp = (y * 4.0) + (x * -3.0);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -0.6) or not (z <= 0.53):
		tmp = (y - x) * (z * -6.0)
	else:
		tmp = (y * 4.0) + (x * -3.0)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -0.6) || !(z <= 0.53))
		tmp = Float64(Float64(y - x) * Float64(z * -6.0));
	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 <= -0.6) || ~((z <= 0.53)))
		tmp = (y - x) * (z * -6.0);
	else
		tmp = (y * 4.0) + (x * -3.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -0.6], N[Not[LessEqual[z, 0.53]], $MachinePrecision]], N[(N[(y - x), $MachinePrecision] * N[(z * -6.0), $MachinePrecision]), $MachinePrecision], N[(N[(y * 4.0), $MachinePrecision] + N[(x * -3.0), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 15: 99.5% accurate, 1.2× speedup

Math

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

FPCore

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

C

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

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 + ((0.6666666666666666d0 - z) * ((y - x) * 6.0d0))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 16: 38.3% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9 \cdot 10^{-57} \lor \neg \left(y \leq 4.5 \cdot 10^{+27}\right):\\ \;\;\;\;y \cdot 4\\ \mathbf{else}:\\ \;\;\;\;x \cdot -3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -9e-57) (not (<= y 4.5e+27))) (* y 4.0) (* x -3.0)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -9e-57) || !(y <= 4.5e+27)) {
		tmp = y * 4.0;
	} else {
		tmp = 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 ((y <= (-9d-57)) .or. (.not. (y <= 4.5d+27))) then
        tmp = y * 4.0d0
    else
        tmp = x * (-3.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -9e-57) || !(y <= 4.5e+27)) {
		tmp = y * 4.0;
	} else {
		tmp = x * -3.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -9e-57) or not (y <= 4.5e+27):
		tmp = y * 4.0
	else:
		tmp = x * -3.0
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -9e-57) || !(y <= 4.5e+27))
		tmp = Float64(y * 4.0);
	else
		tmp = Float64(x * -3.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -9e-57) || ~((y <= 4.5e+27)))
		tmp = y * 4.0;
	else
		tmp = x * -3.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -9e-57], N[Not[LessEqual[y, 4.5e+27]], $MachinePrecision]], N[(y * 4.0), $MachinePrecision], N[(x * -3.0), $MachinePrecision]]

Derivation

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

Alternative 17: 25.7% accurate, 4.3× speedup

Math

\[\begin{array}{l} \\ x \cdot -3 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (* x -3.0))

C

double code(double x, double y, double z) {
	return x * -3.0;
}

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 * (-3.0d0)
end function

Java

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

Python

def code(x, y, z):
	return x * -3.0

Julia

function code(x, y, z)
	return Float64(x * -3.0)
end

MATLAB

function tmp = code(x, y, z)
	tmp = x * -3.0;
end

Wolfram

code[x_, y_, z_] := N[(x * -3.0), $MachinePrecision]

Derivation

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

Alternative 18: 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 2023343 
(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))))