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

Percentage Accurate: 100.0% → 100.0%

Time: 2.3s

Alternatives: 5

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \frac{x + 16}{116} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (+ x 16.0) 116.0))

C

double code(double x) {
	return (x + 16.0) / 116.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x + 16.0d0) / 116.0d0
end function

Java

public static double code(double x) {
	return (x + 16.0) / 116.0;
}

Python

def code(x):
	return (x + 16.0) / 116.0

Julia

function code(x)
	return Float64(Float64(x + 16.0) / 116.0)
end

MATLAB

function tmp = code(x)
	tmp = (x + 16.0) / 116.0;
end

Wolfram

code[x_] := N[(N[(x + 16.0), $MachinePrecision] / 116.0), $MachinePrecision]

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x + 16}{116} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (+ x 16.0) 116.0))

C

double code(double x) {
	return (x + 16.0) / 116.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x + 16.0d0) / 116.0d0
end function

Java

public static double code(double x) {
	return (x + 16.0) / 116.0;
}

Python

def code(x):
	return (x + 16.0) / 116.0

Julia

function code(x)
	return Float64(Float64(x + 16.0) / 116.0)
end

MATLAB

function tmp = code(x)
	tmp = (x + 16.0) / 116.0;
end

Wolfram

code[x_] := N[(N[(x + 16.0), $MachinePrecision] / 116.0), $MachinePrecision]

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x + 16}{116} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (+ x 16.0) 116.0))

C

double code(double x) {
	return (x + 16.0) / 116.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x + 16.0d0) / 116.0d0
end function

Java

public static double code(double x) {
	return (x + 16.0) / 116.0;
}

Python

def code(x):
	return (x + 16.0) / 116.0

Julia

function code(x)
	return Float64(Float64(x + 16.0) / 116.0)
end

MATLAB

function tmp = code(x)
	tmp = (x + 16.0) / 116.0;
end

Wolfram

code[x_] := N[(N[(x + 16.0), $MachinePrecision] / 116.0), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\frac{x + 16}{116} \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 2: 97.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -16 \lor \neg \left(x \leq 16\right):\\ \;\;\;\;0.008620689655172414 \cdot x\\ \mathbf{else}:\\ \;\;\;\;0.13793103448275862\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (or (<= x -16.0) (not (<= x 16.0)))
   (* 0.008620689655172414 x)
   0.13793103448275862))

C

double code(double x) {
	double tmp;
	if ((x <= -16.0) || !(x <= 16.0)) {
		tmp = 0.008620689655172414 * x;
	} else {
		tmp = 0.13793103448275862;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if ((x <= (-16.0d0)) .or. (.not. (x <= 16.0d0))) then
        tmp = 0.008620689655172414d0 * x
    else
        tmp = 0.13793103448275862d0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if ((x <= -16.0) || !(x <= 16.0)) {
		tmp = 0.008620689655172414 * x;
	} else {
		tmp = 0.13793103448275862;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if (x <= -16.0) or not (x <= 16.0):
		tmp = 0.008620689655172414 * x
	else:
		tmp = 0.13793103448275862
	return tmp

Julia

function code(x)
	tmp = 0.0
	if ((x <= -16.0) || !(x <= 16.0))
		tmp = Float64(0.008620689655172414 * x);
	else
		tmp = 0.13793103448275862;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if ((x <= -16.0) || ~((x <= 16.0)))
		tmp = 0.008620689655172414 * x;
	else
		tmp = 0.13793103448275862;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[Or[LessEqual[x, -16.0], N[Not[LessEqual[x, 16.0]], $MachinePrecision]], N[(0.008620689655172414 * x), $MachinePrecision], 0.13793103448275862]

Derivation

1. Split input into 2 regimes

2. if x < -16 or 16 < x

   1. Initial program 100.0%

      \[\frac{x + 16}{116} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{1}{116} \cdot x} \]
   4. Step-by-step derivation

      1. lower-*.f64 98.0

         \[\leadsto \color{blue}{0.008620689655172414 \cdot x} \]

   5. Applied rewrites 98.0%

      \[\leadsto \color{blue}{0.008620689655172414 \cdot x} \]

   if -16 < x < 16

   1. Initial program 100.0%

      \[\frac{x + 16}{116} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{4}{29}} \]
   4. Step-by-step derivation

   5. Applied rewrites 98.5%

      \[\leadsto \color{blue}{0.13793103448275862} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -16 \lor \neg \left(x \leq 16\right):\\ \;\;\;\;0.008620689655172414 \cdot x\\ \mathbf{else}:\\ \;\;\;\;0.13793103448275862\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.9% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ 0.13793103448275862 - -0.008620689655172414 \cdot x \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (- 0.13793103448275862 (* -0.008620689655172414 x)))

C

double code(double x) {
	return 0.13793103448275862 - (-0.008620689655172414 * x);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 0.13793103448275862d0 - ((-0.008620689655172414d0) * x)
end function

Java

public static double code(double x) {
	return 0.13793103448275862 - (-0.008620689655172414 * x);
}

Python

def code(x):
	return 0.13793103448275862 - (-0.008620689655172414 * x)

Julia

function code(x)
	return Float64(0.13793103448275862 - Float64(-0.008620689655172414 * x))
end

MATLAB

function tmp = code(x)
	tmp = 0.13793103448275862 - (-0.008620689655172414 * x);
end

Wolfram

code[x_] := N[(0.13793103448275862 - N[(-0.008620689655172414 * x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\frac{x + 16}{116} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\frac{4}{29} + \frac{1}{116} \cdot x} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \color{blue}{\frac{1}{116} \cdot x + \frac{4}{29}} \]

   2. lower-fma.f64 99.9

      \[\leadsto \color{blue}{\mathsf{fma}\left(0.008620689655172414, x, 0.13793103448275862\right)} \]

5. Applied rewrites 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(0.008620689655172414, x, 0.13793103448275862\right)} \]
6. Step-by-step derivation

7. Applied rewrites 99.9%

   \[\leadsto 0.13793103448275862 - \color{blue}{-0.008620689655172414 \cdot x} \]
8. Add Preprocessing

Alternative 4: 99.9% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(0.008620689655172414, x, 0.13793103448275862\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (fma 0.008620689655172414 x 0.13793103448275862))

C

double code(double x) {
	return fma(0.008620689655172414, x, 0.13793103448275862);
}

Julia

function code(x)
	return fma(0.008620689655172414, x, 0.13793103448275862)
end

Wolfram

code[x_] := N[(0.008620689655172414 * x + 0.13793103448275862), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\frac{x + 16}{116} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\frac{4}{29} + \frac{1}{116} \cdot x} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \color{blue}{\frac{1}{116} \cdot x + \frac{4}{29}} \]

   2. lower-fma.f64 99.9

      \[\leadsto \color{blue}{\mathsf{fma}\left(0.008620689655172414, x, 0.13793103448275862\right)} \]

5. Applied rewrites 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(0.008620689655172414, x, 0.13793103448275862\right)} \]
6. Add Preprocessing

Alternative 5: 50.5% accurate, 15.0× speedup

Math

\[\begin{array}{l} \\ 0.13793103448275862 \end{array} \]

FPCore

(FPCore (x) :precision binary64 0.13793103448275862)

C

double code(double x) {
	return 0.13793103448275862;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 0.13793103448275862d0
end function

Java

public static double code(double x) {
	return 0.13793103448275862;
}

Python

def code(x):
	return 0.13793103448275862

Julia

function code(x)
	return 0.13793103448275862
end

MATLAB

function tmp = code(x)
	tmp = 0.13793103448275862;
end

Wolfram

code[x_] := 0.13793103448275862

Derivation

1. Initial program 100.0%

   \[\frac{x + 16}{116} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\frac{4}{29}} \]
4. Step-by-step derivation

5. Applied rewrites 53.2%

   \[\leadsto \color{blue}{0.13793103448275862} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024339 
(FPCore (x)
  :name "Data.Colour.CIE:cieLAB from colour-2.3.3, B"
  :precision binary64
  (/ (+ x 16.0) 116.0))