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

Percentage Accurate: 100.0% → 100.0%

Time: 3.1s

Alternatives: 4

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: 74.2% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{y}{500} \leq -1 \cdot 10^{+40}:\\ \;\;\;\;\frac{y}{500}\\ \mathbf{elif}\;\frac{y}{500} \leq 10^{+17}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{500}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (/ y 500.0) -1e+40)
   (/ y 500.0)
   (if (<= (/ y 500.0) 1e+17) x (/ y 500.0))))

C

double code(double x, double y) {
	double tmp;
	if ((y / 500.0) <= -1e+40) {
		tmp = y / 500.0;
	} else if ((y / 500.0) <= 1e+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) <= (-1d+40)) then
        tmp = y / 500.0d0
    else if ((y / 500.0d0) <= 1d+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) <= -1e+40) {
		tmp = y / 500.0;
	} else if ((y / 500.0) <= 1e+17) {
		tmp = x;
	} else {
		tmp = y / 500.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y / 500.0) <= -1e+40:
		tmp = y / 500.0
	elif (y / 500.0) <= 1e+17:
		tmp = x
	else:
		tmp = y / 500.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(y / 500.0) <= -1e+40)
		tmp = Float64(y / 500.0);
	elseif (Float64(y / 500.0) <= 1e+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) <= -1e+40)
		tmp = y / 500.0;
	elseif ((y / 500.0) <= 1e+17)
		tmp = x;
	else
		tmp = y / 500.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(y / 500.0), $MachinePrecision], -1e+40], N[(y / 500.0), $MachinePrecision], If[LessEqual[N[(y / 500.0), $MachinePrecision], 1e+17], x, N[(y / 500.0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 y #s(literal 500 binary64)) < -1.00000000000000003e40 or 1e17 < (/.f64 y #s(literal 500 binary64))

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{500} \cdot y} \]
   4. Step-by-step derivation

      1. *-lowering-*.f64 77.8%

         \[\leadsto \mathsf{*.f64}\left(\frac{1}{500}, \color{blue}{y}\right) \]

   5. Simplified 77.8%

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

      1. metadata-eval N/A

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

      2. associate-/r/ N/A

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

      3. clear-num N/A

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

      4. div-inv N/A

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

      5. associate-/r* N/A

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

      6. remove-double-div N/A

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

      7. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(y, \color{blue}{\left(\frac{1}{\frac{1}{500}}\right)}\right) \]

      8. metadata-eval 77.9%

         \[\leadsto \mathsf{/.f64}\left(y, 500\right) \]

   7. Applied egg-rr 77.9%

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

   if -1.00000000000000003e40 < (/.f64 y #s(literal 500 binary64)) < 1e17

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf

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

   5. Simplified 78.1%

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

Alternative 3: 74.0% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5.8 \cdot 10^{+41}:\\ \;\;\;\;y \cdot 0.002\\ \mathbf{elif}\;y \leq 6 \cdot 10^{+29}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot 0.002\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -5.8e+41) (* y 0.002) (if (<= y 6e+29) x (* y 0.002))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -5.8e+41) {
		tmp = y * 0.002;
	} else if (y <= 6e+29) {
		tmp = x;
	} else {
		tmp = y * 0.002;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-5.8d+41)) then
        tmp = y * 0.002d0
    else if (y <= 6d+29) then
        tmp = x
    else
        tmp = y * 0.002d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -5.8e+41) {
		tmp = y * 0.002;
	} else if (y <= 6e+29) {
		tmp = x;
	} else {
		tmp = y * 0.002;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -5.8e+41:
		tmp = y * 0.002
	elif y <= 6e+29:
		tmp = x
	else:
		tmp = y * 0.002
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -5.8e+41)
		tmp = Float64(y * 0.002);
	elseif (y <= 6e+29)
		tmp = x;
	else
		tmp = Float64(y * 0.002);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -5.8e+41)
		tmp = y * 0.002;
	elseif (y <= 6e+29)
		tmp = x;
	else
		tmp = y * 0.002;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -5.8e+41], N[(y * 0.002), $MachinePrecision], If[LessEqual[y, 6e+29], x, N[(y * 0.002), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -5.79999999999999977e41 or 5.9999999999999998e29 < y

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{500} \cdot y} \]
   4. Step-by-step derivation

      1. *-lowering-*.f64 77.8%

         \[\leadsto \mathsf{*.f64}\left(\frac{1}{500}, \color{blue}{y}\right) \]

   5. Simplified 77.8%

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

   if -5.79999999999999977e41 < y < 5.9999999999999998e29

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf

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

   5. Simplified 78.1%

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

4. Final simplification 78.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -5.8 \cdot 10^{+41}:\\ \;\;\;\;y \cdot 0.002\\ \mathbf{elif}\;y \leq 6 \cdot 10^{+29}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot 0.002\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 51.1% 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. Add Preprocessing

3. Taylor expanded in x around inf

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

5. Simplified 51.8%

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

Reproduce

herbie shell --seed 2024138 
(FPCore (x y)
  :name "Data.Colour.CIE:cieLAB from colour-2.3.3, C"
  :precision binary64
  (+ x (/ y 500.0)))