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

Percentage Accurate: 99.8% → 99.8%

Time: 7.9s

Alternatives: 15

Speedup: 1.0×

• 21.5% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.8% accurate, 0.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

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

Alternative 2: 58.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 6 \cdot \left(y \cdot z\right)\\ t_1 := -6 \cdot \left(x \cdot z\right)\\ \mathbf{if}\;z \leq -7.5 \cdot 10^{+152}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -9 \cdot 10^{+95}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -6.3 \cdot 10^{-165}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 1.16 \cdot 10^{-6}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 1.75 \cdot 10^{+56}:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* 6.0 (* y z))) (t_1 (* -6.0 (* x z))))
   (if (<= z -7.5e+152)
     t_0
     (if (<= z -9e+95)
       t_1
       (if (<= z -6.3e-165)
         t_0
         (if (<= z 1.16e-6) x (if (<= z 1.75e+56) t_0 t_1)))))))

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 <= -7.5e+152) {
		tmp = t_0;
	} else if (z <= -9e+95) {
		tmp = t_1;
	} else if (z <= -6.3e-165) {
		tmp = t_0;
	} else if (z <= 1.16e-6) {
		tmp = x;
	} else if (z <= 1.75e+56) {
		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 * z)
    t_1 = (-6.0d0) * (x * z)
    if (z <= (-7.5d+152)) then
        tmp = t_0
    else if (z <= (-9d+95)) then
        tmp = t_1
    else if (z <= (-6.3d-165)) then
        tmp = t_0
    else if (z <= 1.16d-6) then
        tmp = x
    else if (z <= 1.75d+56) 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 * z);
	double t_1 = -6.0 * (x * z);
	double tmp;
	if (z <= -7.5e+152) {
		tmp = t_0;
	} else if (z <= -9e+95) {
		tmp = t_1;
	} else if (z <= -6.3e-165) {
		tmp = t_0;
	} else if (z <= 1.16e-6) {
		tmp = x;
	} else if (z <= 1.75e+56) {
		tmp = t_0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 6.0 * (y * z)
	t_1 = -6.0 * (x * z)
	tmp = 0
	if z <= -7.5e+152:
		tmp = t_0
	elif z <= -9e+95:
		tmp = t_1
	elif z <= -6.3e-165:
		tmp = t_0
	elif z <= 1.16e-6:
		tmp = x
	elif z <= 1.75e+56:
		tmp = t_0
	else:
		tmp = t_1
	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 <= -7.5e+152)
		tmp = t_0;
	elseif (z <= -9e+95)
		tmp = t_1;
	elseif (z <= -6.3e-165)
		tmp = t_0;
	elseif (z <= 1.16e-6)
		tmp = x;
	elseif (z <= 1.75e+56)
		tmp = t_0;
	else
		tmp = t_1;
	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 <= -7.5e+152)
		tmp = t_0;
	elseif (z <= -9e+95)
		tmp = t_1;
	elseif (z <= -6.3e-165)
		tmp = t_0;
	elseif (z <= 1.16e-6)
		tmp = x;
	elseif (z <= 1.75e+56)
		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 * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(-6.0 * N[(x * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -7.5e+152], t$95$0, If[LessEqual[z, -9e+95], t$95$1, If[LessEqual[z, -6.3e-165], t$95$0, If[LessEqual[z, 1.16e-6], x, If[LessEqual[z, 1.75e+56], t$95$0, t$95$1]]]]]]]

Derivation

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

Alternative 3: 58.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 6 \cdot \left(y \cdot z\right)\\ \mathbf{if}\;z \leq -3.35 \cdot 10^{+153}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -3.8 \cdot 10^{+100}:\\ \;\;\;\;x \cdot \left(z \cdot -6\right)\\ \mathbf{elif}\;z \leq -6.7 \cdot 10^{-165}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 1.16 \cdot 10^{-6}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 3.2 \cdot 10^{+57}:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;-6 \cdot \left(x \cdot z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* 6.0 (* y z))))
   (if (<= z -3.35e+153)
     t_0
     (if (<= z -3.8e+100)
       (* x (* z -6.0))
       (if (<= z -6.7e-165)
         t_0
         (if (<= z 1.16e-6) x (if (<= z 3.2e+57) t_0 (* -6.0 (* x z)))))))))

C

double code(double x, double y, double z) {
	double t_0 = 6.0 * (y * z);
	double tmp;
	if (z <= -3.35e+153) {
		tmp = t_0;
	} else if (z <= -3.8e+100) {
		tmp = x * (z * -6.0);
	} else if (z <= -6.7e-165) {
		tmp = t_0;
	} else if (z <= 1.16e-6) {
		tmp = x;
	} else if (z <= 3.2e+57) {
		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) :: tmp
    t_0 = 6.0d0 * (y * z)
    if (z <= (-3.35d+153)) then
        tmp = t_0
    else if (z <= (-3.8d+100)) then
        tmp = x * (z * (-6.0d0))
    else if (z <= (-6.7d-165)) then
        tmp = t_0
    else if (z <= 1.16d-6) then
        tmp = x
    else if (z <= 3.2d+57) 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 tmp;
	if (z <= -3.35e+153) {
		tmp = t_0;
	} else if (z <= -3.8e+100) {
		tmp = x * (z * -6.0);
	} else if (z <= -6.7e-165) {
		tmp = t_0;
	} else if (z <= 1.16e-6) {
		tmp = x;
	} else if (z <= 3.2e+57) {
		tmp = t_0;
	} else {
		tmp = -6.0 * (x * z);
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 6.0 * (y * z)
	tmp = 0
	if z <= -3.35e+153:
		tmp = t_0
	elif z <= -3.8e+100:
		tmp = x * (z * -6.0)
	elif z <= -6.7e-165:
		tmp = t_0
	elif z <= 1.16e-6:
		tmp = x
	elif z <= 3.2e+57:
		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))
	tmp = 0.0
	if (z <= -3.35e+153)
		tmp = t_0;
	elseif (z <= -3.8e+100)
		tmp = Float64(x * Float64(z * -6.0));
	elseif (z <= -6.7e-165)
		tmp = t_0;
	elseif (z <= 1.16e-6)
		tmp = x;
	elseif (z <= 3.2e+57)
		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);
	tmp = 0.0;
	if (z <= -3.35e+153)
		tmp = t_0;
	elseif (z <= -3.8e+100)
		tmp = x * (z * -6.0);
	elseif (z <= -6.7e-165)
		tmp = t_0;
	elseif (z <= 1.16e-6)
		tmp = x;
	elseif (z <= 3.2e+57)
		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]}, If[LessEqual[z, -3.35e+153], t$95$0, If[LessEqual[z, -3.8e+100], N[(x * N[(z * -6.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -6.7e-165], t$95$0, If[LessEqual[z, 1.16e-6], x, If[LessEqual[z, 3.2e+57], t$95$0, N[(-6.0 * N[(x * z), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

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

Alternative 4: 58.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 6 \cdot \left(y \cdot z\right)\\ \mathbf{if}\;z \leq -6.8 \cdot 10^{+152}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -4.5 \cdot 10^{+101}:\\ \;\;\;\;x \cdot \left(z \cdot -6\right)\\ \mathbf{elif}\;z \leq -6.7 \cdot 10^{-165}:\\ \;\;\;\;y \cdot \left(6 \cdot z\right)\\ \mathbf{elif}\;z \leq 1.2 \cdot 10^{-6}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 2.22 \cdot 10^{+55}:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;-6 \cdot \left(x \cdot z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* 6.0 (* y z))))
   (if (<= z -6.8e+152)
     t_0
     (if (<= z -4.5e+101)
       (* x (* z -6.0))
       (if (<= z -6.7e-165)
         (* y (* 6.0 z))
         (if (<= z 1.2e-6) x (if (<= z 2.22e+55) t_0 (* -6.0 (* x z)))))))))

C

double code(double x, double y, double z) {
	double t_0 = 6.0 * (y * z);
	double tmp;
	if (z <= -6.8e+152) {
		tmp = t_0;
	} else if (z <= -4.5e+101) {
		tmp = x * (z * -6.0);
	} else if (z <= -6.7e-165) {
		tmp = y * (6.0 * z);
	} else if (z <= 1.2e-6) {
		tmp = x;
	} else if (z <= 2.22e+55) {
		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) :: tmp
    t_0 = 6.0d0 * (y * z)
    if (z <= (-6.8d+152)) then
        tmp = t_0
    else if (z <= (-4.5d+101)) then
        tmp = x * (z * (-6.0d0))
    else if (z <= (-6.7d-165)) then
        tmp = y * (6.0d0 * z)
    else if (z <= 1.2d-6) then
        tmp = x
    else if (z <= 2.22d+55) 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 tmp;
	if (z <= -6.8e+152) {
		tmp = t_0;
	} else if (z <= -4.5e+101) {
		tmp = x * (z * -6.0);
	} else if (z <= -6.7e-165) {
		tmp = y * (6.0 * z);
	} else if (z <= 1.2e-6) {
		tmp = x;
	} else if (z <= 2.22e+55) {
		tmp = t_0;
	} else {
		tmp = -6.0 * (x * z);
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 6.0 * (y * z)
	tmp = 0
	if z <= -6.8e+152:
		tmp = t_0
	elif z <= -4.5e+101:
		tmp = x * (z * -6.0)
	elif z <= -6.7e-165:
		tmp = y * (6.0 * z)
	elif z <= 1.2e-6:
		tmp = x
	elif z <= 2.22e+55:
		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))
	tmp = 0.0
	if (z <= -6.8e+152)
		tmp = t_0;
	elseif (z <= -4.5e+101)
		tmp = Float64(x * Float64(z * -6.0));
	elseif (z <= -6.7e-165)
		tmp = Float64(y * Float64(6.0 * z));
	elseif (z <= 1.2e-6)
		tmp = x;
	elseif (z <= 2.22e+55)
		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);
	tmp = 0.0;
	if (z <= -6.8e+152)
		tmp = t_0;
	elseif (z <= -4.5e+101)
		tmp = x * (z * -6.0);
	elseif (z <= -6.7e-165)
		tmp = y * (6.0 * z);
	elseif (z <= 1.2e-6)
		tmp = x;
	elseif (z <= 2.22e+55)
		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]}, If[LessEqual[z, -6.8e+152], t$95$0, If[LessEqual[z, -4.5e+101], N[(x * N[(z * -6.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -6.7e-165], N[(y * N[(6.0 * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.2e-6], x, If[LessEqual[z, 2.22e+55], t$95$0, N[(-6.0 * N[(x * z), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

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

Alternative 5: 58.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.2 \cdot 10^{+153}:\\ \;\;\;\;z \cdot \left(y \cdot 6\right)\\ \mathbf{elif}\;z \leq -3.7 \cdot 10^{+102}:\\ \;\;\;\;x \cdot \left(z \cdot -6\right)\\ \mathbf{elif}\;z \leq -6.7 \cdot 10^{-165}:\\ \;\;\;\;y \cdot \left(6 \cdot z\right)\\ \mathbf{elif}\;z \leq 1.16 \cdot 10^{-6}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 5.7 \cdot 10^{+54}:\\ \;\;\;\;6 \cdot \left(y \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;-6 \cdot \left(x \cdot z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -3.2e+153)
   (* z (* y 6.0))
   (if (<= z -3.7e+102)
     (* x (* z -6.0))
     (if (<= z -6.7e-165)
       (* y (* 6.0 z))
       (if (<= z 1.16e-6)
         x
         (if (<= z 5.7e+54) (* 6.0 (* y z)) (* -6.0 (* x z))))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -3.2e+153) {
		tmp = z * (y * 6.0);
	} else if (z <= -3.7e+102) {
		tmp = x * (z * -6.0);
	} else if (z <= -6.7e-165) {
		tmp = y * (6.0 * z);
	} else if (z <= 1.16e-6) {
		tmp = x;
	} else if (z <= 5.7e+54) {
		tmp = 6.0 * (y * z);
	} 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) :: tmp
    if (z <= (-3.2d+153)) then
        tmp = z * (y * 6.0d0)
    else if (z <= (-3.7d+102)) then
        tmp = x * (z * (-6.0d0))
    else if (z <= (-6.7d-165)) then
        tmp = y * (6.0d0 * z)
    else if (z <= 1.16d-6) then
        tmp = x
    else if (z <= 5.7d+54) then
        tmp = 6.0d0 * (y * z)
    else
        tmp = (-6.0d0) * (x * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -3.2e+153) {
		tmp = z * (y * 6.0);
	} else if (z <= -3.7e+102) {
		tmp = x * (z * -6.0);
	} else if (z <= -6.7e-165) {
		tmp = y * (6.0 * z);
	} else if (z <= 1.16e-6) {
		tmp = x;
	} else if (z <= 5.7e+54) {
		tmp = 6.0 * (y * z);
	} else {
		tmp = -6.0 * (x * z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -3.2e+153:
		tmp = z * (y * 6.0)
	elif z <= -3.7e+102:
		tmp = x * (z * -6.0)
	elif z <= -6.7e-165:
		tmp = y * (6.0 * z)
	elif z <= 1.16e-6:
		tmp = x
	elif z <= 5.7e+54:
		tmp = 6.0 * (y * z)
	else:
		tmp = -6.0 * (x * z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -3.2e+153)
		tmp = Float64(z * Float64(y * 6.0));
	elseif (z <= -3.7e+102)
		tmp = Float64(x * Float64(z * -6.0));
	elseif (z <= -6.7e-165)
		tmp = Float64(y * Float64(6.0 * z));
	elseif (z <= 1.16e-6)
		tmp = x;
	elseif (z <= 5.7e+54)
		tmp = Float64(6.0 * Float64(y * z));
	else
		tmp = Float64(-6.0 * Float64(x * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -3.2e+153)
		tmp = z * (y * 6.0);
	elseif (z <= -3.7e+102)
		tmp = x * (z * -6.0);
	elseif (z <= -6.7e-165)
		tmp = y * (6.0 * z);
	elseif (z <= 1.16e-6)
		tmp = x;
	elseif (z <= 5.7e+54)
		tmp = 6.0 * (y * z);
	else
		tmp = -6.0 * (x * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -3.2e+153], N[(z * N[(y * 6.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -3.7e+102], N[(x * N[(z * -6.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -6.7e-165], N[(y * N[(6.0 * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.16e-6], x, If[LessEqual[z, 5.7e+54], N[(6.0 * N[(y * z), $MachinePrecision]), $MachinePrecision], N[(-6.0 * N[(x * z), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 6: 58.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.4 \cdot 10^{+152}:\\ \;\;\;\;z \cdot \left(y \cdot 6\right)\\ \mathbf{elif}\;z \leq -2.2 \cdot 10^{+103}:\\ \;\;\;\;x \cdot \left(z \cdot -6\right)\\ \mathbf{elif}\;z \leq -6.7 \cdot 10^{-165}:\\ \;\;\;\;y \cdot \left(6 \cdot z\right)\\ \mathbf{elif}\;z \leq 1.16 \cdot 10^{-6}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 1.05 \cdot 10^{+56}:\\ \;\;\;\;6 \cdot \left(y \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(x \cdot -6\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -1.4e+152)
   (* z (* y 6.0))
   (if (<= z -2.2e+103)
     (* x (* z -6.0))
     (if (<= z -6.7e-165)
       (* y (* 6.0 z))
       (if (<= z 1.16e-6)
         x
         (if (<= z 1.05e+56) (* 6.0 (* y z)) (* z (* x -6.0))))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -1.4e+152) {
		tmp = z * (y * 6.0);
	} else if (z <= -2.2e+103) {
		tmp = x * (z * -6.0);
	} else if (z <= -6.7e-165) {
		tmp = y * (6.0 * z);
	} else if (z <= 1.16e-6) {
		tmp = x;
	} else if (z <= 1.05e+56) {
		tmp = 6.0 * (y * z);
	} else {
		tmp = z * (x * -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 (z <= (-1.4d+152)) then
        tmp = z * (y * 6.0d0)
    else if (z <= (-2.2d+103)) then
        tmp = x * (z * (-6.0d0))
    else if (z <= (-6.7d-165)) then
        tmp = y * (6.0d0 * z)
    else if (z <= 1.16d-6) then
        tmp = x
    else if (z <= 1.05d+56) then
        tmp = 6.0d0 * (y * z)
    else
        tmp = z * (x * (-6.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -1.4e+152) {
		tmp = z * (y * 6.0);
	} else if (z <= -2.2e+103) {
		tmp = x * (z * -6.0);
	} else if (z <= -6.7e-165) {
		tmp = y * (6.0 * z);
	} else if (z <= 1.16e-6) {
		tmp = x;
	} else if (z <= 1.05e+56) {
		tmp = 6.0 * (y * z);
	} else {
		tmp = z * (x * -6.0);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -1.4e+152:
		tmp = z * (y * 6.0)
	elif z <= -2.2e+103:
		tmp = x * (z * -6.0)
	elif z <= -6.7e-165:
		tmp = y * (6.0 * z)
	elif z <= 1.16e-6:
		tmp = x
	elif z <= 1.05e+56:
		tmp = 6.0 * (y * z)
	else:
		tmp = z * (x * -6.0)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -1.4e+152)
		tmp = Float64(z * Float64(y * 6.0));
	elseif (z <= -2.2e+103)
		tmp = Float64(x * Float64(z * -6.0));
	elseif (z <= -6.7e-165)
		tmp = Float64(y * Float64(6.0 * z));
	elseif (z <= 1.16e-6)
		tmp = x;
	elseif (z <= 1.05e+56)
		tmp = Float64(6.0 * Float64(y * z));
	else
		tmp = Float64(z * Float64(x * -6.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -1.4e+152)
		tmp = z * (y * 6.0);
	elseif (z <= -2.2e+103)
		tmp = x * (z * -6.0);
	elseif (z <= -6.7e-165)
		tmp = y * (6.0 * z);
	elseif (z <= 1.16e-6)
		tmp = x;
	elseif (z <= 1.05e+56)
		tmp = 6.0 * (y * z);
	else
		tmp = z * (x * -6.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -1.4e+152], N[(z * N[(y * 6.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -2.2e+103], N[(x * N[(z * -6.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -6.7e-165], N[(y * N[(6.0 * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.16e-6], x, If[LessEqual[z, 1.05e+56], N[(6.0 * N[(y * z), $MachinePrecision]), $MachinePrecision], N[(z * N[(x * -6.0), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 7: 82.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.65 \cdot 10^{-166} \lor \neg \left(z \leq 1.25 \cdot 10^{-6}\right):\\ \;\;\;\;6 \cdot \left(\left(y - x\right) \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -2.65e-166) (not (<= z 1.25e-6))) (* 6.0 (* (- y x) z)) x))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -2.65e-166) || !(z <= 1.25e-6)) {
		tmp = 6.0 * ((y - x) * z);
	} else {
		tmp = x;
	}
	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 <= (-2.65d-166)) .or. (.not. (z <= 1.25d-6))) then
        tmp = 6.0d0 * ((y - x) * z)
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -2.65e-166) || !(z <= 1.25e-6)) {
		tmp = 6.0 * ((y - x) * z);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -2.65e-166) or not (z <= 1.25e-6):
		tmp = 6.0 * ((y - x) * z)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -2.65e-166) || !(z <= 1.25e-6))
		tmp = Float64(6.0 * Float64(Float64(y - x) * z));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -2.65e-166) || ~((z <= 1.25e-6)))
		tmp = 6.0 * ((y - x) * z);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -2.65e-166], N[Not[LessEqual[z, 1.25e-6]], $MachinePrecision]], N[(6.0 * N[(N[(y - x), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision], x]

Derivation

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

Alternative 8: 82.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.65 \cdot 10^{-166}:\\ \;\;\;\;\left(y - x\right) \cdot \left(6 \cdot z\right)\\ \mathbf{elif}\;z \leq 1.16 \cdot 10^{-6}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;6 \cdot \left(\left(y - x\right) \cdot z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -2.65e-166)
   (* (- y x) (* 6.0 z))
   (if (<= z 1.16e-6) x (* 6.0 (* (- y x) z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.65e-166) {
		tmp = (y - x) * (6.0 * z);
	} else if (z <= 1.16e-6) {
		tmp = x;
	} else {
		tmp = 6.0 * ((y - 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) :: tmp
    if (z <= (-2.65d-166)) then
        tmp = (y - x) * (6.0d0 * z)
    else if (z <= 1.16d-6) then
        tmp = x
    else
        tmp = 6.0d0 * ((y - x) * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.65e-166) {
		tmp = (y - x) * (6.0 * z);
	} else if (z <= 1.16e-6) {
		tmp = x;
	} else {
		tmp = 6.0 * ((y - x) * z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -2.65e-166:
		tmp = (y - x) * (6.0 * z)
	elif z <= 1.16e-6:
		tmp = x
	else:
		tmp = 6.0 * ((y - x) * z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -2.65e-166)
		tmp = Float64(Float64(y - x) * Float64(6.0 * z));
	elseif (z <= 1.16e-6)
		tmp = x;
	else
		tmp = Float64(6.0 * Float64(Float64(y - x) * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -2.65e-166)
		tmp = (y - x) * (6.0 * z);
	elseif (z <= 1.16e-6)
		tmp = x;
	else
		tmp = 6.0 * ((y - x) * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -2.65e-166], N[(N[(y - x), $MachinePrecision] * N[(6.0 * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.16e-6], x, N[(6.0 * N[(N[(y - x), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 9: 82.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.3 \cdot 10^{-166}:\\ \;\;\;\;\left(y - x\right) \cdot \left(6 \cdot z\right)\\ \mathbf{elif}\;z \leq 1.16 \cdot 10^{-6}:\\ \;\;\;\;x + -6 \cdot \left(x \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;6 \cdot \left(\left(y - x\right) \cdot z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -2.3e-166)
   (* (- y x) (* 6.0 z))
   (if (<= z 1.16e-6) (+ x (* -6.0 (* x z))) (* 6.0 (* (- y x) z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.3e-166) {
		tmp = (y - x) * (6.0 * z);
	} else if (z <= 1.16e-6) {
		tmp = x + (-6.0 * (x * z));
	} else {
		tmp = 6.0 * ((y - 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) :: tmp
    if (z <= (-2.3d-166)) then
        tmp = (y - x) * (6.0d0 * z)
    else if (z <= 1.16d-6) then
        tmp = x + ((-6.0d0) * (x * z))
    else
        tmp = 6.0d0 * ((y - x) * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.3e-166) {
		tmp = (y - x) * (6.0 * z);
	} else if (z <= 1.16e-6) {
		tmp = x + (-6.0 * (x * z));
	} else {
		tmp = 6.0 * ((y - x) * z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -2.3e-166:
		tmp = (y - x) * (6.0 * z)
	elif z <= 1.16e-6:
		tmp = x + (-6.0 * (x * z))
	else:
		tmp = 6.0 * ((y - x) * z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -2.3e-166)
		tmp = Float64(Float64(y - x) * Float64(6.0 * z));
	elseif (z <= 1.16e-6)
		tmp = Float64(x + Float64(-6.0 * Float64(x * z)));
	else
		tmp = Float64(6.0 * Float64(Float64(y - x) * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -2.3e-166)
		tmp = (y - x) * (6.0 * z);
	elseif (z <= 1.16e-6)
		tmp = x + (-6.0 * (x * z));
	else
		tmp = 6.0 * ((y - x) * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -2.3e-166], N[(N[(y - x), $MachinePrecision] * N[(6.0 * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.16e-6], N[(x + N[(-6.0 * N[(x * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(6.0 * N[(N[(y - x), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 10: 98.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -0.182:\\ \;\;\;\;\left(y - x\right) \cdot \left(6 \cdot z\right)\\ \mathbf{elif}\;z \leq 1.3 \cdot 10^{-6}:\\ \;\;\;\;x + 6 \cdot \left(y \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;6 \cdot \left(\left(y - x\right) \cdot z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -0.182)
   (* (- y x) (* 6.0 z))
   (if (<= z 1.3e-6) (+ x (* 6.0 (* y z))) (* 6.0 (* (- y x) z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -0.182) {
		tmp = (y - x) * (6.0 * z);
	} else if (z <= 1.3e-6) {
		tmp = x + (6.0 * (y * z));
	} else {
		tmp = 6.0 * ((y - 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) :: tmp
    if (z <= (-0.182d0)) then
        tmp = (y - x) * (6.0d0 * z)
    else if (z <= 1.3d-6) then
        tmp = x + (6.0d0 * (y * z))
    else
        tmp = 6.0d0 * ((y - x) * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -0.182) {
		tmp = (y - x) * (6.0 * z);
	} else if (z <= 1.3e-6) {
		tmp = x + (6.0 * (y * z));
	} else {
		tmp = 6.0 * ((y - x) * z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -0.182:
		tmp = (y - x) * (6.0 * z)
	elif z <= 1.3e-6:
		tmp = x + (6.0 * (y * z))
	else:
		tmp = 6.0 * ((y - x) * z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -0.182)
		tmp = Float64(Float64(y - x) * Float64(6.0 * z));
	elseif (z <= 1.3e-6)
		tmp = Float64(x + Float64(6.0 * Float64(y * z)));
	else
		tmp = Float64(6.0 * Float64(Float64(y - x) * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -0.182)
		tmp = (y - x) * (6.0 * z);
	elseif (z <= 1.3e-6)
		tmp = x + (6.0 * (y * z));
	else
		tmp = 6.0 * ((y - x) * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -0.182], N[(N[(y - x), $MachinePrecision] * N[(6.0 * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.3e-6], N[(x + N[(6.0 * N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(6.0 * N[(N[(y - x), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 11: 98.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1 \cdot 10^{+18}:\\ \;\;\;\;\left(y - x\right) \cdot \left(6 \cdot z\right)\\ \mathbf{elif}\;z \leq 3.2 \cdot 10^{-5}:\\ \;\;\;\;x + z \cdot \left(y \cdot 6\right)\\ \mathbf{else}:\\ \;\;\;\;6 \cdot \left(\left(y - x\right) \cdot z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -1e+18)
   (* (- y x) (* 6.0 z))
   (if (<= z 3.2e-5) (+ x (* z (* y 6.0))) (* 6.0 (* (- y x) z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -1e+18) {
		tmp = (y - x) * (6.0 * z);
	} else if (z <= 3.2e-5) {
		tmp = x + (z * (y * 6.0));
	} else {
		tmp = 6.0 * ((y - 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) :: tmp
    if (z <= (-1d+18)) then
        tmp = (y - x) * (6.0d0 * z)
    else if (z <= 3.2d-5) then
        tmp = x + (z * (y * 6.0d0))
    else
        tmp = 6.0d0 * ((y - x) * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -1e+18) {
		tmp = (y - x) * (6.0 * z);
	} else if (z <= 3.2e-5) {
		tmp = x + (z * (y * 6.0));
	} else {
		tmp = 6.0 * ((y - x) * z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -1e+18:
		tmp = (y - x) * (6.0 * z)
	elif z <= 3.2e-5:
		tmp = x + (z * (y * 6.0))
	else:
		tmp = 6.0 * ((y - x) * z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -1e+18)
		tmp = Float64(Float64(y - x) * Float64(6.0 * z));
	elseif (z <= 3.2e-5)
		tmp = Float64(x + Float64(z * Float64(y * 6.0)));
	else
		tmp = Float64(6.0 * Float64(Float64(y - x) * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -1e+18)
		tmp = (y - x) * (6.0 * z);
	elseif (z <= 3.2e-5)
		tmp = x + (z * (y * 6.0));
	else
		tmp = 6.0 * ((y - x) * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -1e+18], N[(N[(y - x), $MachinePrecision] * N[(6.0 * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 3.2e-5], N[(x + N[(z * N[(y * 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(6.0 * N[(N[(y - x), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 12: 60.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -0.38 \lor \neg \left(z \leq 0.165\right):\\ \;\;\;\;-6 \cdot \left(x \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -0.38) (not (<= z 0.165))) (* -6.0 (* x z)) x))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -0.38) || !(z <= 0.165)) {
		tmp = -6.0 * (x * z);
	} else {
		tmp = x;
	}
	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 <= (-0.38d0)) .or. (.not. (z <= 0.165d0))) then
        tmp = (-6.0d0) * (x * z)
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -0.38) || !(z <= 0.165)) {
		tmp = -6.0 * (x * z);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -0.38) or not (z <= 0.165):
		tmp = -6.0 * (x * z)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -0.38) || !(z <= 0.165))
		tmp = Float64(-6.0 * Float64(x * z));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -0.38) || ~((z <= 0.165)))
		tmp = -6.0 * (x * z);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -0.38], N[Not[LessEqual[z, 0.165]], $MachinePrecision]], N[(-6.0 * N[(x * z), $MachinePrecision]), $MachinePrecision], x]

Derivation

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

Alternative 13: 99.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 14: 99.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 15: 35.6% accurate, 9.0× 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

Developer target: 99.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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 * z) * (x - y))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2023364 
(FPCore (x y z)
  :name "Data.Colour.RGBSpace.HSL:hsl from colour-2.3.3, E"
  :precision binary64

  :herbie-target
  (- x (* (* 6.0 z) (- x y)))

  (+ x (* (* (- y x) 6.0) z)))