Data.Colour.CIE:cieLAB from colour-2.3.3, C

Percentage Accurate: 100.0% → 100.0%

Time: 3.1s

Alternatives: 5

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ x + \frac{y}{500} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x (/ y 500.0)))

C

double code(double x, double y) {
	return x + (y / 500.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x + (y / 500.0d0)
end function

Java

public static double code(double x, double y) {
	return x + (y / 500.0);
}

Python

def code(x, y):
	return x + (y / 500.0)

Julia

function code(x, y)
	return Float64(x + Float64(y / 500.0))
end

MATLAB

function tmp = code(x, y)
	tmp = x + (y / 500.0);
end

Wolfram

code[x_, y_] := N[(x + N[(y / 500.0), $MachinePrecision]), $MachinePrecision]

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \frac{y}{500} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x (/ y 500.0)))

C

double code(double x, double y) {
	return x + (y / 500.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x + (y / 500.0d0)
end function

Java

public static double code(double x, double y) {
	return x + (y / 500.0);
}

Python

def code(x, y):
	return x + (y / 500.0)

Julia

function code(x, y)
	return Float64(x + Float64(y / 500.0))
end

MATLAB

function tmp = code(x, y)
	tmp = x + (y / 500.0);
end

Wolfram

code[x_, y_] := N[(x + N[(y / 500.0), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \frac{y}{500} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x (/ y 500.0)))

C

double code(double x, double y) {
	return x + (y / 500.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x + (y / 500.0d0)
end function

Java

public static double code(double x, double y) {
	return x + (y / 500.0);
}

Python

def code(x, y):
	return x + (y / 500.0)

Julia

function code(x, y)
	return Float64(x + Float64(y / 500.0))
end

MATLAB

function tmp = code(x, y)
	tmp = x + (y / 500.0);
end

Wolfram

code[x_, y_] := N[(x + N[(y / 500.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x + \frac{y}{500} \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 2: 73.1% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{y}{500} \leq -5 \cdot 10^{+79}:\\ \;\;\;\;\frac{y}{500}\\ \mathbf{elif}\;\frac{y}{500} \leq -2 \cdot 10^{+42}:\\ \;\;\;\;x\\ \mathbf{elif}\;\frac{y}{500} \leq -2 \cdot 10^{-106}:\\ \;\;\;\;y \cdot 0.002\\ \mathbf{elif}\;\frac{y}{500} \leq 2 \cdot 10^{-17}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{500}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (/ y 500.0) -5e+79)
   (/ y 500.0)
   (if (<= (/ y 500.0) -2e+42)
     x
     (if (<= (/ y 500.0) -2e-106)
       (* y 0.002)
       (if (<= (/ y 500.0) 2e-17) x (/ y 500.0))))))

C

double code(double x, double y) {
	double tmp;
	if ((y / 500.0) <= -5e+79) {
		tmp = y / 500.0;
	} else if ((y / 500.0) <= -2e+42) {
		tmp = x;
	} else if ((y / 500.0) <= -2e-106) {
		tmp = y * 0.002;
	} else if ((y / 500.0) <= 2e-17) {
		tmp = x;
	} else {
		tmp = y / 500.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y / 500.0d0) <= (-5d+79)) then
        tmp = y / 500.0d0
    else if ((y / 500.0d0) <= (-2d+42)) then
        tmp = x
    else if ((y / 500.0d0) <= (-2d-106)) then
        tmp = y * 0.002d0
    else if ((y / 500.0d0) <= 2d-17) then
        tmp = x
    else
        tmp = y / 500.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y / 500.0) <= -5e+79) {
		tmp = y / 500.0;
	} else if ((y / 500.0) <= -2e+42) {
		tmp = x;
	} else if ((y / 500.0) <= -2e-106) {
		tmp = y * 0.002;
	} else if ((y / 500.0) <= 2e-17) {
		tmp = x;
	} else {
		tmp = y / 500.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y / 500.0) <= -5e+79:
		tmp = y / 500.0
	elif (y / 500.0) <= -2e+42:
		tmp = x
	elif (y / 500.0) <= -2e-106:
		tmp = y * 0.002
	elif (y / 500.0) <= 2e-17:
		tmp = x
	else:
		tmp = y / 500.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(y / 500.0) <= -5e+79)
		tmp = Float64(y / 500.0);
	elseif (Float64(y / 500.0) <= -2e+42)
		tmp = x;
	elseif (Float64(y / 500.0) <= -2e-106)
		tmp = Float64(y * 0.002);
	elseif (Float64(y / 500.0) <= 2e-17)
		tmp = x;
	else
		tmp = Float64(y / 500.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y / 500.0) <= -5e+79)
		tmp = y / 500.0;
	elseif ((y / 500.0) <= -2e+42)
		tmp = x;
	elseif ((y / 500.0) <= -2e-106)
		tmp = y * 0.002;
	elseif ((y / 500.0) <= 2e-17)
		tmp = x;
	else
		tmp = y / 500.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(y / 500.0), $MachinePrecision], -5e+79], N[(y / 500.0), $MachinePrecision], If[LessEqual[N[(y / 500.0), $MachinePrecision], -2e+42], x, If[LessEqual[N[(y / 500.0), $MachinePrecision], -2e-106], N[(y * 0.002), $MachinePrecision], If[LessEqual[N[(y / 500.0), $MachinePrecision], 2e-17], x, N[(y / 500.0), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 y #s(literal 500 binary64)) < -5e79 or 2.00000000000000014e-17 < (/.f64 y #s(literal 500 binary64))

   1. Initial program 100.0%

      \[x + \frac{y}{500} \]
   2. Step-by-step derivation

      1. +-commutative 100.0%

         \[\leadsto \color{blue}{\frac{y}{500} + x} \]

      2. remove-double-neg 100.0%

         \[\leadsto \color{blue}{\left(-\left(-\frac{y}{500}\right)\right)} + x \]

      3. distribute-frac-neg 100.0%

         \[\leadsto \left(-\color{blue}{\frac{-y}{500}}\right) + x \]

      4. distribute-neg-frac2 100.0%

         \[\leadsto \color{blue}{\frac{-y}{-500}} + x \]

      5. neg-mul-1 100.0%

         \[\leadsto \frac{\color{blue}{-1 \cdot y}}{-500} + x \]

      6. *-commutative 100.0%

         \[\leadsto \frac{\color{blue}{y \cdot -1}}{-500} + x \]

      7. associate-/l* 99.8%

         \[\leadsto \color{blue}{y \cdot \frac{-1}{-500}} + x \]

      8. fma-define 99.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{-1}{-500}, x\right)} \]

      9. metadata-eval 99.9%

         \[\leadsto \mathsf{fma}\left(y, \frac{-1}{\color{blue}{-500}}, x\right) \]

      10. metadata-eval 99.9%

          \[\leadsto \mathsf{fma}\left(y, \color{blue}{0.002}, x\right) \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, 0.002, x\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 99.8%

      \[\leadsto \color{blue}{y \cdot \left(0.002 + \frac{x}{y}\right)} \]

   6. Taylor expanded in x around 0 82.2%

      \[\leadsto y \cdot \color{blue}{0.002} \]
   7. Step-by-step derivation

      1. metadata-eval 82.2%

         \[\leadsto y \cdot \color{blue}{\frac{1}{500}} \]

      2. div-inv 82.3%

         \[\leadsto \color{blue}{\frac{y}{500}} \]

   8. Applied egg-rr 82.3%

      \[\leadsto \color{blue}{\frac{y}{500}} \]

   if -5e79 < (/.f64 y #s(literal 500 binary64)) < -2.00000000000000009e42 or -1.99999999999999988e-106 < (/.f64 y #s(literal 500 binary64)) < 2.00000000000000014e-17

   1. Initial program 100.0%

      \[x + \frac{y}{500} \]
   2. Step-by-step derivation

      1. +-commutative 100.0%

         \[\leadsto \color{blue}{\frac{y}{500} + x} \]

      2. remove-double-neg 100.0%

         \[\leadsto \color{blue}{\left(-\left(-\frac{y}{500}\right)\right)} + x \]

      3. distribute-frac-neg 100.0%

         \[\leadsto \left(-\color{blue}{\frac{-y}{500}}\right) + x \]

      4. distribute-neg-frac2 100.0%

         \[\leadsto \color{blue}{\frac{-y}{-500}} + x \]

      5. neg-mul-1 100.0%

         \[\leadsto \frac{\color{blue}{-1 \cdot y}}{-500} + x \]

      6. *-commutative 100.0%

         \[\leadsto \frac{\color{blue}{y \cdot -1}}{-500} + x \]

      7. associate-/l* 99.9%

         \[\leadsto \color{blue}{y \cdot \frac{-1}{-500}} + x \]

      8. fma-define 99.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{-1}{-500}, x\right)} \]

      9. metadata-eval 99.9%

         \[\leadsto \mathsf{fma}\left(y, \frac{-1}{\color{blue}{-500}}, x\right) \]

      10. metadata-eval 99.9%

          \[\leadsto \mathsf{fma}\left(y, \color{blue}{0.002}, x\right) \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, 0.002, x\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 86.1%

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

   if -2.00000000000000009e42 < (/.f64 y #s(literal 500 binary64)) < -1.99999999999999988e-106

   1. Initial program 99.9%

      \[x + \frac{y}{500} \]
   2. Step-by-step derivation

      1. +-commutative 99.9%

         \[\leadsto \color{blue}{\frac{y}{500} + x} \]

      2. remove-double-neg 99.9%

         \[\leadsto \color{blue}{\left(-\left(-\frac{y}{500}\right)\right)} + x \]

      3. distribute-frac-neg 99.9%

         \[\leadsto \left(-\color{blue}{\frac{-y}{500}}\right) + x \]

      4. distribute-neg-frac2 99.9%

         \[\leadsto \color{blue}{\frac{-y}{-500}} + x \]

      5. neg-mul-1 99.9%

         \[\leadsto \frac{\color{blue}{-1 \cdot y}}{-500} + x \]

      6. *-commutative 99.9%

         \[\leadsto \frac{\color{blue}{y \cdot -1}}{-500} + x \]

      7. associate-/l* 99.9%

         \[\leadsto \color{blue}{y \cdot \frac{-1}{-500}} + x \]

      8. fma-define 100.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{-1}{-500}, x\right)} \]

      9. metadata-eval 100.0%

         \[\leadsto \mathsf{fma}\left(y, \frac{-1}{\color{blue}{-500}}, x\right) \]

      10. metadata-eval 100.0%

          \[\leadsto \mathsf{fma}\left(y, \color{blue}{0.002}, x\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, 0.002, x\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 99.8%

      \[\leadsto \color{blue}{y \cdot \left(0.002 + \frac{x}{y}\right)} \]

   6. Taylor expanded in x around 0 68.3%

      \[\leadsto y \cdot \color{blue}{0.002} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 73.2% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.7 \cdot 10^{+76} \lor \neg \left(y \leq -1 \cdot 10^{+45}\right) \land \left(y \leq -1.09 \cdot 10^{-103} \lor \neg \left(y \leq 1.5 \cdot 10^{-10}\right)\right):\\ \;\;\;\;y \cdot 0.002\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -4.7e+76)
         (and (not (<= y -1e+45)) (or (<= y -1.09e-103) (not (<= y 1.5e-10)))))
   (* y 0.002)
   x))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -4.7e+76) || (!(y <= -1e+45) && ((y <= -1.09e-103) || !(y <= 1.5e-10)))) {
		tmp = y * 0.002;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y <= (-4.7d+76)) .or. (.not. (y <= (-1d+45))) .and. (y <= (-1.09d-103)) .or. (.not. (y <= 1.5d-10))) then
        tmp = y * 0.002d0
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -4.7e+76) || (!(y <= -1e+45) && ((y <= -1.09e-103) || !(y <= 1.5e-10)))) {
		tmp = y * 0.002;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -4.7e+76) or (not (y <= -1e+45) and ((y <= -1.09e-103) or not (y <= 1.5e-10))):
		tmp = y * 0.002
	else:
		tmp = x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -4.7e+76) || (!(y <= -1e+45) && ((y <= -1.09e-103) || !(y <= 1.5e-10))))
		tmp = Float64(y * 0.002);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -4.7e+76) || (~((y <= -1e+45)) && ((y <= -1.09e-103) || ~((y <= 1.5e-10)))))
		tmp = y * 0.002;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -4.7e+76], And[N[Not[LessEqual[y, -1e+45]], $MachinePrecision], Or[LessEqual[y, -1.09e-103], N[Not[LessEqual[y, 1.5e-10]], $MachinePrecision]]]], N[(y * 0.002), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if y < -4.7000000000000003e76 or -9.9999999999999993e44 < y < -1.09e-103 or 1.5e-10 < y

   1. Initial program 100.0%

      \[x + \frac{y}{500} \]
   2. Step-by-step derivation

      1. +-commutative 100.0%

         \[\leadsto \color{blue}{\frac{y}{500} + x} \]

      2. remove-double-neg 100.0%

         \[\leadsto \color{blue}{\left(-\left(-\frac{y}{500}\right)\right)} + x \]

      3. distribute-frac-neg 100.0%

         \[\leadsto \left(-\color{blue}{\frac{-y}{500}}\right) + x \]

      4. distribute-neg-frac2 100.0%

         \[\leadsto \color{blue}{\frac{-y}{-500}} + x \]

      5. neg-mul-1 100.0%

         \[\leadsto \frac{\color{blue}{-1 \cdot y}}{-500} + x \]

      6. *-commutative 100.0%

         \[\leadsto \frac{\color{blue}{y \cdot -1}}{-500} + x \]

      7. associate-/l* 99.8%

         \[\leadsto \color{blue}{y \cdot \frac{-1}{-500}} + x \]

      8. fma-define 99.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{-1}{-500}, x\right)} \]

      9. metadata-eval 99.9%

         \[\leadsto \mathsf{fma}\left(y, \frac{-1}{\color{blue}{-500}}, x\right) \]

      10. metadata-eval 99.9%

          \[\leadsto \mathsf{fma}\left(y, \color{blue}{0.002}, x\right) \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, 0.002, x\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 99.8%

      \[\leadsto \color{blue}{y \cdot \left(0.002 + \frac{x}{y}\right)} \]

   6. Taylor expanded in x around 0 79.3%

      \[\leadsto y \cdot \color{blue}{0.002} \]

   if -4.7000000000000003e76 < y < -9.9999999999999993e44 or -1.09e-103 < y < 1.5e-10

   1. Initial program 100.0%

      \[x + \frac{y}{500} \]
   2. Step-by-step derivation

      1. +-commutative 100.0%

         \[\leadsto \color{blue}{\frac{y}{500} + x} \]

      2. remove-double-neg 100.0%

         \[\leadsto \color{blue}{\left(-\left(-\frac{y}{500}\right)\right)} + x \]

      3. distribute-frac-neg 100.0%

         \[\leadsto \left(-\color{blue}{\frac{-y}{500}}\right) + x \]

      4. distribute-neg-frac2 100.0%

         \[\leadsto \color{blue}{\frac{-y}{-500}} + x \]

      5. neg-mul-1 100.0%

         \[\leadsto \frac{\color{blue}{-1 \cdot y}}{-500} + x \]

      6. *-commutative 100.0%

         \[\leadsto \frac{\color{blue}{y \cdot -1}}{-500} + x \]

      7. associate-/l* 99.9%

         \[\leadsto \color{blue}{y \cdot \frac{-1}{-500}} + x \]

      8. fma-define 99.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{-1}{-500}, x\right)} \]

      9. metadata-eval 99.9%

         \[\leadsto \mathsf{fma}\left(y, \frac{-1}{\color{blue}{-500}}, x\right) \]

      10. metadata-eval 99.9%

          \[\leadsto \mathsf{fma}\left(y, \color{blue}{0.002}, x\right) \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, 0.002, x\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 86.1%

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

4. Final simplification 82.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4.7 \cdot 10^{+76} \lor \neg \left(y \leq -1 \cdot 10^{+45}\right) \land \left(y \leq -1.09 \cdot 10^{-103} \lor \neg \left(y \leq 1.5 \cdot 10^{-10}\right)\right):\\ \;\;\;\;y \cdot 0.002\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + y \cdot 0.002 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x (* y 0.002)))

C

double code(double x, double y) {
	return x + (y * 0.002);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x + (y * 0.002d0)
end function

Java

public static double code(double x, double y) {
	return x + (y * 0.002);
}

Python

def code(x, y):
	return x + (y * 0.002)

Julia

function code(x, y)
	return Float64(x + Float64(y * 0.002))
end

MATLAB

function tmp = code(x, y)
	tmp = x + (y * 0.002);
end

Wolfram

code[x_, y_] := N[(x + N[(y * 0.002), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x + \frac{y}{500} \]
2. Step-by-step derivation

   1. *-rgt-identity 100.0%

      \[\leadsto x + \color{blue}{\frac{y}{500} \cdot 1} \]

   2. metadata-eval 100.0%

      \[\leadsto x + \frac{y}{500} \cdot \color{blue}{\left(--1\right)} \]

   3. associate-*l/ 100.0%

      \[\leadsto x + \color{blue}{\frac{y \cdot \left(--1\right)}{500}} \]

   4. associate-/l* 99.9%

      \[\leadsto x + \color{blue}{y \cdot \frac{--1}{500}} \]

   5. metadata-eval 99.9%

      \[\leadsto x + y \cdot \frac{\color{blue}{1}}{500} \]

   6. metadata-eval 99.9%

      \[\leadsto x + y \cdot \color{blue}{0.002} \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{x + y \cdot 0.002} \]
4. Add Preprocessing
5. Add Preprocessing

Alternative 5: 50.9% accurate, 5.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 x)

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return x

Julia

function code(x, y)
	return x
end

MATLAB

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

Wolfram

code[x_, y_] := x

Derivation

1. Initial program 100.0%

   \[x + \frac{y}{500} \]
2. Step-by-step derivation

   1. +-commutative 100.0%

      \[\leadsto \color{blue}{\frac{y}{500} + x} \]

   2. remove-double-neg 100.0%

      \[\leadsto \color{blue}{\left(-\left(-\frac{y}{500}\right)\right)} + x \]

   3. distribute-frac-neg 100.0%

      \[\leadsto \left(-\color{blue}{\frac{-y}{500}}\right) + x \]

   4. distribute-neg-frac2 100.0%

      \[\leadsto \color{blue}{\frac{-y}{-500}} + x \]

   5. neg-mul-1 100.0%

      \[\leadsto \frac{\color{blue}{-1 \cdot y}}{-500} + x \]

   6. *-commutative 100.0%

      \[\leadsto \frac{\color{blue}{y \cdot -1}}{-500} + x \]

   7. associate-/l* 99.9%

      \[\leadsto \color{blue}{y \cdot \frac{-1}{-500}} + x \]

   8. fma-define 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{-1}{-500}, x\right)} \]

   9. metadata-eval 99.9%

      \[\leadsto \mathsf{fma}\left(y, \frac{-1}{\color{blue}{-500}}, x\right) \]

   10. metadata-eval 99.9%

       \[\leadsto \mathsf{fma}\left(y, \color{blue}{0.002}, x\right) \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y, 0.002, x\right)} \]
4. Add Preprocessing

5. Taylor expanded in y around 0 49.4%

   \[\leadsto \color{blue}{x} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024111 
(FPCore (x y)
  :name "Data.Colour.CIE:cieLAB from colour-2.3.3, C"
  :precision binary64
  (+ x (/ y 500.0)))