Data.Colour.RGBSpace.HSV:hsv from colour-2.3.3, I

Percentage Accurate: 95.7% → 99.6%

Time: 4.5s

Alternatives: 5

Speedup: 0.5×

Specification

Math

\[\begin{array}{l} \\ x \cdot \left(1 - y \cdot z\right) \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (* x (- 1.0 (* y z))))

C

double code(double x, double y, double z) {
	return x * (1.0 - (y * 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 * (1.0d0 - (y * z))
end function

Java

public static double code(double x, double y, double z) {
	return x * (1.0 - (y * z));
}

Python

def code(x, y, z):
	return x * (1.0 - (y * z))

Julia

function code(x, y, z)
	return Float64(x * Float64(1.0 - Float64(y * z)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = x * (1.0 - (y * z));
end

Wolfram

code[x_, y_, z_] := N[(x * N[(1.0 - N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 95.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(1 - y \cdot z\right) \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (* x (- 1.0 (* y z))))

C

double code(double x, double y, double z) {
	return x * (1.0 - (y * 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 * (1.0d0 - (y * z))
end function

Java

public static double code(double x, double y, double z) {
	return x * (1.0 - (y * z));
}

Python

def code(x, y, z):
	return x * (1.0 - (y * z))

Julia

function code(x, y, z)
	return Float64(x * Float64(1.0 - Float64(y * z)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = x * (1.0 - (y * z));
end

Wolfram

code[x_, y_, z_] := N[(x * N[(1.0 - N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} [y, z] = \mathsf{sort}([y, z])\\ \\ \begin{array}{l} \mathbf{if}\;y \cdot z \leq -2 \cdot 10^{+158}:\\ \;\;\;\;y \cdot \left(z \cdot \left(-x\right)\right)\\ \mathbf{elif}\;y \cdot z \leq 4 \cdot 10^{+192}:\\ \;\;\;\;x \cdot \left(1 - y \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;x - z \cdot \left(y \cdot x\right)\\ \end{array} \end{array} \]

FPCore

NOTE: y and z should be sorted in increasing order before calling this function.
(FPCore (x y z)
 :precision binary64
 (if (<= (* y z) -2e+158)
   (* y (* z (- x)))
   (if (<= (* y z) 4e+192) (* x (- 1.0 (* y z))) (- x (* z (* y x))))))

C

assert(y < z);
double code(double x, double y, double z) {
	double tmp;
	if ((y * z) <= -2e+158) {
		tmp = y * (z * -x);
	} else if ((y * z) <= 4e+192) {
		tmp = x * (1.0 - (y * z));
	} else {
		tmp = x - (z * (y * x));
	}
	return tmp;
}

Fortran

NOTE: y and z should be sorted in increasing order before calling this function.
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 * z) <= (-2d+158)) then
        tmp = y * (z * -x)
    else if ((y * z) <= 4d+192) then
        tmp = x * (1.0d0 - (y * z))
    else
        tmp = x - (z * (y * x))
    end if
    code = tmp
end function

Java

assert y < z;
public static double code(double x, double y, double z) {
	double tmp;
	if ((y * z) <= -2e+158) {
		tmp = y * (z * -x);
	} else if ((y * z) <= 4e+192) {
		tmp = x * (1.0 - (y * z));
	} else {
		tmp = x - (z * (y * x));
	}
	return tmp;
}

Python

[y, z] = sort([y, z])
def code(x, y, z):
	tmp = 0
	if (y * z) <= -2e+158:
		tmp = y * (z * -x)
	elif (y * z) <= 4e+192:
		tmp = x * (1.0 - (y * z))
	else:
		tmp = x - (z * (y * x))
	return tmp

Julia

y, z = sort([y, z])
function code(x, y, z)
	tmp = 0.0
	if (Float64(y * z) <= -2e+158)
		tmp = Float64(y * Float64(z * Float64(-x)));
	elseif (Float64(y * z) <= 4e+192)
		tmp = Float64(x * Float64(1.0 - Float64(y * z)));
	else
		tmp = Float64(x - Float64(z * Float64(y * x)));
	end
	return tmp
end

MATLAB

y, z = num2cell(sort([y, z])){:}
function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y * z) <= -2e+158)
		tmp = y * (z * -x);
	elseif ((y * z) <= 4e+192)
		tmp = x * (1.0 - (y * z));
	else
		tmp = x - (z * (y * x));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: y and z should be sorted in increasing order before calling this function.
code[x_, y_, z_] := If[LessEqual[N[(y * z), $MachinePrecision], -2e+158], N[(y * N[(z * (-x)), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(y * z), $MachinePrecision], 4e+192], N[(x * N[(1.0 - N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(z * N[(y * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 y z) < -1.99999999999999991e158

   1. Initial program 77.6%

      \[x \cdot \left(1 - y \cdot z\right) \]

   2. Taylor expanded in y around inf 99.8%

      \[\leadsto \color{blue}{-1 \cdot \left(y \cdot \left(z \cdot x\right)\right)} \]
   3. Step-by-step derivation

      1. mul-1-neg 99.8%

         \[\leadsto \color{blue}{-y \cdot \left(z \cdot x\right)} \]

      2. distribute-rgt-neg-in 99.8%

         \[\leadsto \color{blue}{y \cdot \left(-z \cdot x\right)} \]

      3. distribute-rgt-neg-in 99.8%

         \[\leadsto y \cdot \color{blue}{\left(z \cdot \left(-x\right)\right)} \]

   4. Simplified 99.8%

      \[\leadsto \color{blue}{y \cdot \left(z \cdot \left(-x\right)\right)} \]

   if -1.99999999999999991e158 < (*.f64 y z) < 4.00000000000000016e192

   1. Initial program 99.9%

      \[x \cdot \left(1 - y \cdot z\right) \]

   if 4.00000000000000016e192 < (*.f64 y z)

   1. Initial program 72.8%

      \[x \cdot \left(1 - y \cdot z\right) \]

   2. Taylor expanded in x around 0 72.8%

      \[\leadsto \color{blue}{\left(1 - y \cdot z\right) \cdot x} \]
   3. Step-by-step derivation

      1. sub-neg 72.8%

         \[\leadsto \color{blue}{\left(1 + \left(-y \cdot z\right)\right)} \cdot x \]

      2. distribute-rgt-neg-out 72.8%

         \[\leadsto \left(1 + \color{blue}{y \cdot \left(-z\right)}\right) \cdot x \]

      3. +-commutative 72.8%

         \[\leadsto \color{blue}{\left(y \cdot \left(-z\right) + 1\right)} \cdot x \]

      4. distribute-lft1-in 72.8%

         \[\leadsto \color{blue}{\left(y \cdot \left(-z\right)\right) \cdot x + x} \]

      5. associate-*l* 99.9%

         \[\leadsto \color{blue}{y \cdot \left(\left(-z\right) \cdot x\right)} + x \]

      6. fma-def 99.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \left(-z\right) \cdot x, x\right)} \]

      7. *-commutative 99.9%

         \[\leadsto \mathsf{fma}\left(y, \color{blue}{x \cdot \left(-z\right)}, x\right) \]

   4. Simplified 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, x \cdot \left(-z\right), x\right)} \]
   5. Step-by-step derivation

      1. fma-udef 99.9%

         \[\leadsto \color{blue}{y \cdot \left(x \cdot \left(-z\right)\right) + x} \]

      2. *-commutative 99.9%

         \[\leadsto y \cdot \color{blue}{\left(\left(-z\right) \cdot x\right)} + x \]

      3. associate-*l* 72.8%

         \[\leadsto \color{blue}{\left(y \cdot \left(-z\right)\right) \cdot x} + x \]

      4. +-commutative 72.8%

         \[\leadsto \color{blue}{x + \left(y \cdot \left(-z\right)\right) \cdot x} \]

      5. distribute-rgt-neg-out 72.8%

         \[\leadsto x + \color{blue}{\left(-y \cdot z\right)} \cdot x \]

      6. distribute-lft-neg-out 72.8%

         \[\leadsto x + \color{blue}{\left(-\left(y \cdot z\right) \cdot x\right)} \]

      7. distribute-rgt-neg-out 72.8%

         \[\leadsto x + \color{blue}{\left(y \cdot z\right) \cdot \left(-x\right)} \]

      8. add-sqr-sqrt 48.3%

         \[\leadsto x + \left(y \cdot z\right) \cdot \color{blue}{\left(\sqrt{-x} \cdot \sqrt{-x}\right)} \]

      9. sqrt-unprod 42.4%

         \[\leadsto x + \left(y \cdot z\right) \cdot \color{blue}{\sqrt{\left(-x\right) \cdot \left(-x\right)}} \]

      10. sqr-neg 42.4%

          \[\leadsto x + \left(y \cdot z\right) \cdot \sqrt{\color{blue}{x \cdot x}} \]

      11. sqrt-unprod 0.0%

          \[\leadsto x + \left(y \cdot z\right) \cdot \color{blue}{\left(\sqrt{x} \cdot \sqrt{x}\right)} \]

      12. add-sqr-sqrt 0.2%

          \[\leadsto x + \left(y \cdot z\right) \cdot \color{blue}{x} \]

      13. cancel-sign-sub 0.2%

          \[\leadsto \color{blue}{x - \left(-y \cdot z\right) \cdot x} \]

      14. distribute-rgt-neg-out 0.2%

          \[\leadsto x - \color{blue}{\left(y \cdot \left(-z\right)\right)} \cdot x \]

      15. *-commutative 0.2%

          \[\leadsto x - \color{blue}{x \cdot \left(y \cdot \left(-z\right)\right)} \]

      16. associate-*r* 0.3%

          \[\leadsto x - \color{blue}{\left(x \cdot y\right) \cdot \left(-z\right)} \]

      17. cancel-sign-sub-inv 0.3%

          \[\leadsto \color{blue}{x + \left(-x \cdot y\right) \cdot \left(-z\right)} \]

      18. *-commutative 0.3%

          \[\leadsto x + \left(-\color{blue}{y \cdot x}\right) \cdot \left(-z\right) \]

      19. add-sqr-sqrt 0.1%

          \[\leadsto x + \left(-y \cdot x\right) \cdot \color{blue}{\left(\sqrt{-z} \cdot \sqrt{-z}\right)} \]

      20. sqrt-unprod 35.3%

          \[\leadsto x + \left(-y \cdot x\right) \cdot \color{blue}{\sqrt{\left(-z\right) \cdot \left(-z\right)}} \]

      21. sqr-neg 35.3%

          \[\leadsto x + \left(-y \cdot x\right) \cdot \sqrt{\color{blue}{z \cdot z}} \]

      22. sqrt-unprod 49.8%

          \[\leadsto x + \left(-y \cdot x\right) \cdot \color{blue}{\left(\sqrt{z} \cdot \sqrt{z}\right)} \]

      23. add-sqr-sqrt 99.9%

          \[\leadsto x + \left(-y \cdot x\right) \cdot \color{blue}{z} \]

   6. Applied egg-rr 99.9%

      \[\leadsto \color{blue}{x + \left(-y \cdot x\right) \cdot z} \]
3. Recombined 3 regimes into one program.

4. Final simplification 99.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \cdot z \leq -2 \cdot 10^{+158}:\\ \;\;\;\;y \cdot \left(z \cdot \left(-x\right)\right)\\ \mathbf{elif}\;y \cdot z \leq 4 \cdot 10^{+192}:\\ \;\;\;\;x \cdot \left(1 - y \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;x - z \cdot \left(y \cdot x\right)\\ \end{array} \]

Alternative 2: 99.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} [y, z] = \mathsf{sort}([y, z])\\ \\ \begin{array}{l} \mathbf{if}\;y \cdot z \leq -2 \cdot 10^{+158} \lor \neg \left(y \cdot z \leq 4 \cdot 10^{+276}\right):\\ \;\;\;\;y \cdot \left(z \cdot \left(-x\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(1 - y \cdot z\right)\\ \end{array} \end{array} \]

FPCore

NOTE: y and z should be sorted in increasing order before calling this function.
(FPCore (x y z)
 :precision binary64
 (if (or (<= (* y z) -2e+158) (not (<= (* y z) 4e+276)))
   (* y (* z (- x)))
   (* x (- 1.0 (* y z)))))

C

assert(y < z);
double code(double x, double y, double z) {
	double tmp;
	if (((y * z) <= -2e+158) || !((y * z) <= 4e+276)) {
		tmp = y * (z * -x);
	} else {
		tmp = x * (1.0 - (y * z));
	}
	return tmp;
}

Fortran

NOTE: y and z should be sorted in increasing order before calling this function.
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 * z) <= (-2d+158)) .or. (.not. ((y * z) <= 4d+276))) then
        tmp = y * (z * -x)
    else
        tmp = x * (1.0d0 - (y * z))
    end if
    code = tmp
end function

Java

assert y < z;
public static double code(double x, double y, double z) {
	double tmp;
	if (((y * z) <= -2e+158) || !((y * z) <= 4e+276)) {
		tmp = y * (z * -x);
	} else {
		tmp = x * (1.0 - (y * z));
	}
	return tmp;
}

Python

[y, z] = sort([y, z])
def code(x, y, z):
	tmp = 0
	if ((y * z) <= -2e+158) or not ((y * z) <= 4e+276):
		tmp = y * (z * -x)
	else:
		tmp = x * (1.0 - (y * z))
	return tmp

Julia

y, z = sort([y, z])
function code(x, y, z)
	tmp = 0.0
	if ((Float64(y * z) <= -2e+158) || !(Float64(y * z) <= 4e+276))
		tmp = Float64(y * Float64(z * Float64(-x)));
	else
		tmp = Float64(x * Float64(1.0 - Float64(y * z)));
	end
	return tmp
end

MATLAB

y, z = num2cell(sort([y, z])){:}
function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (((y * z) <= -2e+158) || ~(((y * z) <= 4e+276)))
		tmp = y * (z * -x);
	else
		tmp = x * (1.0 - (y * z));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: y and z should be sorted in increasing order before calling this function.
code[x_, y_, z_] := If[Or[LessEqual[N[(y * z), $MachinePrecision], -2e+158], N[Not[LessEqual[N[(y * z), $MachinePrecision], 4e+276]], $MachinePrecision]], N[(y * N[(z * (-x)), $MachinePrecision]), $MachinePrecision], N[(x * N[(1.0 - N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 y z) < -1.99999999999999991e158 or 4.0000000000000002e276 < (*.f64 y z)

   1. Initial program 69.2%

      \[x \cdot \left(1 - y \cdot z\right) \]

   2. Taylor expanded in y around inf 99.8%

      \[\leadsto \color{blue}{-1 \cdot \left(y \cdot \left(z \cdot x\right)\right)} \]
   3. Step-by-step derivation

      1. mul-1-neg 99.8%

         \[\leadsto \color{blue}{-y \cdot \left(z \cdot x\right)} \]

      2. distribute-rgt-neg-in 99.8%

         \[\leadsto \color{blue}{y \cdot \left(-z \cdot x\right)} \]

      3. distribute-rgt-neg-in 99.8%

         \[\leadsto y \cdot \color{blue}{\left(z \cdot \left(-x\right)\right)} \]

   4. Simplified 99.8%

      \[\leadsto \color{blue}{y \cdot \left(z \cdot \left(-x\right)\right)} \]

   if -1.99999999999999991e158 < (*.f64 y z) < 4.0000000000000002e276

   1. Initial program 99.9%

      \[x \cdot \left(1 - y \cdot z\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \cdot z \leq -2 \cdot 10^{+158} \lor \neg \left(y \cdot z \leq 4 \cdot 10^{+276}\right):\\ \;\;\;\;y \cdot \left(z \cdot \left(-x\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(1 - y \cdot z\right)\\ \end{array} \]

Alternative 3: 74.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} [y, z] = \mathsf{sort}([y, z])\\ \\ \begin{array}{l} \mathbf{if}\;y \leq -1.55 \cdot 10^{+76} \lor \neg \left(y \leq 1.15 \cdot 10^{-124}\right):\\ \;\;\;\;\left(y \cdot z\right) \cdot \left(-x\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

NOTE: y and z should be sorted in increasing order before calling this function.
(FPCore (x y z)
 :precision binary64
 (if (or (<= y -1.55e+76) (not (<= y 1.15e-124))) (* (* y z) (- x)) x))

C

assert(y < z);
double code(double x, double y, double z) {
	double tmp;
	if ((y <= -1.55e+76) || !(y <= 1.15e-124)) {
		tmp = (y * z) * -x;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

NOTE: y and z should be sorted in increasing order before calling this function.
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 <= (-1.55d+76)) .or. (.not. (y <= 1.15d-124))) then
        tmp = (y * z) * -x
    else
        tmp = x
    end if
    code = tmp
end function

Java

assert y < z;
public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -1.55e+76) || !(y <= 1.15e-124)) {
		tmp = (y * z) * -x;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

[y, z] = sort([y, z])
def code(x, y, z):
	tmp = 0
	if (y <= -1.55e+76) or not (y <= 1.15e-124):
		tmp = (y * z) * -x
	else:
		tmp = x
	return tmp

Julia

y, z = sort([y, z])
function code(x, y, z)
	tmp = 0.0
	if ((y <= -1.55e+76) || !(y <= 1.15e-124))
		tmp = Float64(Float64(y * z) * Float64(-x));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

y, z = num2cell(sort([y, z])){:}
function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -1.55e+76) || ~((y <= 1.15e-124)))
		tmp = (y * z) * -x;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: y and z should be sorted in increasing order before calling this function.
code[x_, y_, z_] := If[Or[LessEqual[y, -1.55e+76], N[Not[LessEqual[y, 1.15e-124]], $MachinePrecision]], N[(N[(y * z), $MachinePrecision] * (-x)), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if y < -1.55000000000000006e76 or 1.15000000000000006e-124 < y

   1. Initial program 86.5%

      \[x \cdot \left(1 - y \cdot z\right) \]

   2. Taylor expanded in y around inf 63.6%

      \[\leadsto x \cdot \color{blue}{\left(-1 \cdot \left(y \cdot z\right)\right)} \]
   3. Step-by-step derivation

      1. mul-1-neg 63.6%

         \[\leadsto x \cdot \color{blue}{\left(-y \cdot z\right)} \]

      2. distribute-rgt-neg-out 63.6%

         \[\leadsto x \cdot \color{blue}{\left(y \cdot \left(-z\right)\right)} \]

   4. Simplified 63.6%

      \[\leadsto x \cdot \color{blue}{\left(y \cdot \left(-z\right)\right)} \]

   if -1.55000000000000006e76 < y < 1.15000000000000006e-124

   1. Initial program 99.2%

      \[x \cdot \left(1 - y \cdot z\right) \]

   2. Taylor expanded in y around 0 76.6%

      \[\leadsto \color{blue}{x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 69.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.55 \cdot 10^{+76} \lor \neg \left(y \leq 1.15 \cdot 10^{-124}\right):\\ \;\;\;\;\left(y \cdot z\right) \cdot \left(-x\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 4: 76.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} [y, z] = \mathsf{sort}([y, z])\\ \\ \begin{array}{l} \mathbf{if}\;y \leq -2.15 \cdot 10^{+76} \lor \neg \left(y \leq 1.15 \cdot 10^{-124}\right):\\ \;\;\;\;y \cdot \left(z \cdot \left(-x\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

NOTE: y and z should be sorted in increasing order before calling this function.
(FPCore (x y z)
 :precision binary64
 (if (or (<= y -2.15e+76) (not (<= y 1.15e-124))) (* y (* z (- x))) x))

C

assert(y < z);
double code(double x, double y, double z) {
	double tmp;
	if ((y <= -2.15e+76) || !(y <= 1.15e-124)) {
		tmp = y * (z * -x);
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

NOTE: y and z should be sorted in increasing order before calling this function.
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 <= (-2.15d+76)) .or. (.not. (y <= 1.15d-124))) then
        tmp = y * (z * -x)
    else
        tmp = x
    end if
    code = tmp
end function

Java

assert y < z;
public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -2.15e+76) || !(y <= 1.15e-124)) {
		tmp = y * (z * -x);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

[y, z] = sort([y, z])
def code(x, y, z):
	tmp = 0
	if (y <= -2.15e+76) or not (y <= 1.15e-124):
		tmp = y * (z * -x)
	else:
		tmp = x
	return tmp

Julia

y, z = sort([y, z])
function code(x, y, z)
	tmp = 0.0
	if ((y <= -2.15e+76) || !(y <= 1.15e-124))
		tmp = Float64(y * Float64(z * Float64(-x)));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

y, z = num2cell(sort([y, z])){:}
function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -2.15e+76) || ~((y <= 1.15e-124)))
		tmp = y * (z * -x);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: y and z should be sorted in increasing order before calling this function.
code[x_, y_, z_] := If[Or[LessEqual[y, -2.15e+76], N[Not[LessEqual[y, 1.15e-124]], $MachinePrecision]], N[(y * N[(z * (-x)), $MachinePrecision]), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if y < -2.14999999999999989e76 or 1.15000000000000006e-124 < y

   1. Initial program 86.5%

      \[x \cdot \left(1 - y \cdot z\right) \]

   2. Taylor expanded in y around inf 74.1%

      \[\leadsto \color{blue}{-1 \cdot \left(y \cdot \left(z \cdot x\right)\right)} \]
   3. Step-by-step derivation

      1. mul-1-neg 74.1%

         \[\leadsto \color{blue}{-y \cdot \left(z \cdot x\right)} \]

      2. distribute-rgt-neg-in 74.1%

         \[\leadsto \color{blue}{y \cdot \left(-z \cdot x\right)} \]

      3. distribute-rgt-neg-in 74.1%

         \[\leadsto y \cdot \color{blue}{\left(z \cdot \left(-x\right)\right)} \]

   4. Simplified 74.1%

      \[\leadsto \color{blue}{y \cdot \left(z \cdot \left(-x\right)\right)} \]

   if -2.14999999999999989e76 < y < 1.15000000000000006e-124

   1. Initial program 99.2%

      \[x \cdot \left(1 - y \cdot z\right) \]

   2. Taylor expanded in y around 0 76.6%

      \[\leadsto \color{blue}{x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 75.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.15 \cdot 10^{+76} \lor \neg \left(y \leq 1.15 \cdot 10^{-124}\right):\\ \;\;\;\;y \cdot \left(z \cdot \left(-x\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 5: 50.1% accurate, 7.0× speedup

Math

\[\begin{array}{l} [y, z] = \mathsf{sort}([y, z])\\ \\ x \end{array} \]

FPCore

NOTE: y and z should be sorted in increasing order before calling this function.
(FPCore (x y z) :precision binary64 x)

C

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

Fortran

NOTE: y and z should be sorted in increasing order before calling this function.
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

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

Python

[y, z] = sort([y, z])
def code(x, y, z):
	return x

Julia

y, z = sort([y, z])
function code(x, y, z)
	return x
end

MATLAB

y, z = num2cell(sort([y, z])){:}
function tmp = code(x, y, z)
	tmp = x;
end

Wolfram

NOTE: y and z should be sorted in increasing order before calling this function.
code[x_, y_, z_] := x

Derivation

1. Initial program 92.6%

   \[x \cdot \left(1 - y \cdot z\right) \]

2. Taylor expanded in y around 0 48.9%

   \[\leadsto \color{blue}{x} \]

3. Final simplification 48.9%

   \[\leadsto x \]

Reproduce

herbie shell --seed 2023202 
(FPCore (x y z)
  :name "Data.Colour.RGBSpace.HSV:hsv from colour-2.3.3, I"
  :precision binary64
  (* x (- 1.0 (* y z))))