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

Percentage Accurate: 100.0% → 100.0%

Time: 1.1s

Alternatives: 5

Speedup: 1.0×

Specification

Math

\[x + \frac{y}{500} \]

FPCore

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

C

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

Fortran

real(8) function code(x, y)
use fmin_fmax_functions
    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]

ReFlow

f(x, y):
	x in [-inf, +inf],
	y in [-inf, +inf]
code: THEORY
BEGIN
f(x, y: real): real =
	x + (y / (500))
END code

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[x + \frac{y}{500} \]

FPCore

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

C

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

Fortran

real(8) function code(x, y)
use fmin_fmax_functions
    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]

ReFlow

f(x, y):
	x in [-inf, +inf],
	y in [-inf, +inf]
code: THEORY
BEGIN
f(x, y: real): real =
	x + (y / (500))
END code

Alternative 1: 99.9% accurate, 1.2× speedup

Math

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

FPCore

(FPCore (x y)
  :precision binary64
  :pre TRUE
  (fma 0.002 y x))

C

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

Julia

function code(x, y)
	return fma(0.002, y, x)
end

Wolfram

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

ReFlow

f(x, y):
	x in [-inf, +inf],
	y in [-inf, +inf]
code: THEORY
BEGIN
f(x, y: real): real =
	((200000000000000004163336342344337026588618755340576171875e-59) * y) + x
END code

Derivation

1. Initial program 100.0%

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

3. Applied rewrites 99.9%

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

Alternative 2: 74.9% accurate, 0.4× speedup

Math

\[\begin{array}{l} \mathbf{if}\;\frac{y}{500} \leq -5 \cdot 10^{+30}:\\ \;\;\;\;\frac{y}{500}\\ \mathbf{elif}\;\frac{y}{500} \leq 5 \cdot 10^{+37}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{500}\\ \end{array} \]

FPCore

(FPCore (x y)
  :precision binary64
  :pre TRUE
  (if (<= (/ y 500.0) -5e+30)
  (/ y 500.0)
  (if (<= (/ y 500.0) 5e+37) x (/ y 500.0))))

C

double code(double x, double y) {
	double tmp;
	if ((y / 500.0) <= -5e+30) {
		tmp = y / 500.0;
	} else if ((y / 500.0) <= 5e+37) {
		tmp = x;
	} else {
		tmp = y / 500.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y / 500.0d0) <= (-5d+30)) then
        tmp = y / 500.0d0
    else if ((y / 500.0d0) <= 5d+37) 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+30) {
		tmp = y / 500.0;
	} else if ((y / 500.0) <= 5e+37) {
		tmp = x;
	} else {
		tmp = y / 500.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y / 500.0) <= -5e+30:
		tmp = y / 500.0
	elif (y / 500.0) <= 5e+37:
		tmp = x
	else:
		tmp = y / 500.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(y / 500.0) <= -5e+30)
		tmp = Float64(y / 500.0);
	elseif (Float64(y / 500.0) <= 5e+37)
		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+30)
		tmp = y / 500.0;
	elseif ((y / 500.0) <= 5e+37)
		tmp = x;
	else
		tmp = y / 500.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(y / 500.0), $MachinePrecision], -5e+30], N[(y / 500.0), $MachinePrecision], If[LessEqual[N[(y / 500.0), $MachinePrecision], 5e+37], x, N[(y / 500.0), $MachinePrecision]]]

ReFlow

f(x, y):
	x in [-inf, +inf],
	y in [-inf, +inf]
code: THEORY
BEGIN
f(x, y: real): real =
	LET tmp_1 = IF ((y / (500)) <= (49999999999999998874404911728017014784)) THEN x ELSE (y / (500)) ENDIF IN
	LET tmp = IF ((y / (500)) <= (-4999999999999999817948147482624)) THEN (y / (500)) ELSE tmp_1 ENDIF IN
	tmp
END code

Derivation

1. Split input into 2 regimes

2. if (/.f64 y #s(literal 500 binary64)) < -4.9999999999999998e30 or 4.9999999999999999e37 < (/.f64 y #s(literal 500 binary64))

   1. Initial program 100.0%

      \[x + \frac{y}{500} \]

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 49.7%

      \[\leadsto 0.002 \cdot y \]
   5. Step-by-step derivation

   6. Applied rewrites 49.8%

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

   if -4.9999999999999998e30 < (/.f64 y #s(literal 500 binary64)) < 4.9999999999999999e37

   1. Initial program 100.0%

      \[x + \frac{y}{500} \]

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 49.7%

      \[\leadsto 0.002 \cdot y \]
   5. Step-by-step derivation

   6. Applied rewrites 49.8%

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

   7. Taylor expanded in x around inf

      \[\leadsto x \]
   8. Step-by-step derivation

   9. Applied rewrites 51.7%

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

Alternative 3: 74.9% accurate, 0.4× speedup

Math

\[\begin{array}{l} \mathbf{if}\;\frac{y}{500} \leq -5 \cdot 10^{+30}:\\ \;\;\;\;0.002 \cdot y\\ \mathbf{elif}\;\frac{y}{500} \leq 5 \cdot 10^{+37}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;0.002 \cdot y\\ \end{array} \]

FPCore

(FPCore (x y)
  :precision binary64
  :pre TRUE
  (if (<= (/ y 500.0) -5e+30)
  (* 0.002 y)
  (if (<= (/ y 500.0) 5e+37) x (* 0.002 y))))

C

double code(double x, double y) {
	double tmp;
	if ((y / 500.0) <= -5e+30) {
		tmp = 0.002 * y;
	} else if ((y / 500.0) <= 5e+37) {
		tmp = x;
	} else {
		tmp = 0.002 * y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y / 500.0d0) <= (-5d+30)) then
        tmp = 0.002d0 * y
    else if ((y / 500.0d0) <= 5d+37) then
        tmp = x
    else
        tmp = 0.002d0 * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y / 500.0) <= -5e+30) {
		tmp = 0.002 * y;
	} else if ((y / 500.0) <= 5e+37) {
		tmp = x;
	} else {
		tmp = 0.002 * y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y / 500.0) <= -5e+30:
		tmp = 0.002 * y
	elif (y / 500.0) <= 5e+37:
		tmp = x
	else:
		tmp = 0.002 * y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(y / 500.0) <= -5e+30)
		tmp = Float64(0.002 * y);
	elseif (Float64(y / 500.0) <= 5e+37)
		tmp = x;
	else
		tmp = Float64(0.002 * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y / 500.0) <= -5e+30)
		tmp = 0.002 * y;
	elseif ((y / 500.0) <= 5e+37)
		tmp = x;
	else
		tmp = 0.002 * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(y / 500.0), $MachinePrecision], -5e+30], N[(0.002 * y), $MachinePrecision], If[LessEqual[N[(y / 500.0), $MachinePrecision], 5e+37], x, N[(0.002 * y), $MachinePrecision]]]

ReFlow

f(x, y):
	x in [-inf, +inf],
	y in [-inf, +inf]
code: THEORY
BEGIN
f(x, y: real): real =
	LET tmp_1 = IF ((y / (500)) <= (49999999999999998874404911728017014784)) THEN x ELSE ((200000000000000004163336342344337026588618755340576171875e-59) * y) ENDIF IN
	LET tmp = IF ((y / (500)) <= (-4999999999999999817948147482624)) THEN ((200000000000000004163336342344337026588618755340576171875e-59) * y) ELSE tmp_1 ENDIF IN
	tmp
END code

Derivation

1. Split input into 2 regimes

2. if (/.f64 y #s(literal 500 binary64)) < -4.9999999999999998e30 or 4.9999999999999999e37 < (/.f64 y #s(literal 500 binary64))

   1. Initial program 100.0%

      \[x + \frac{y}{500} \]

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 49.7%

      \[\leadsto 0.002 \cdot y \]

   if -4.9999999999999998e30 < (/.f64 y #s(literal 500 binary64)) < 4.9999999999999999e37

   1. Initial program 100.0%

      \[x + \frac{y}{500} \]

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 49.7%

      \[\leadsto 0.002 \cdot y \]
   5. Step-by-step derivation

   6. Applied rewrites 49.8%

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

   7. Taylor expanded in x around inf

      \[\leadsto x \]
   8. Step-by-step derivation

   9. Applied rewrites 51.7%

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

Alternative 4: 51.7% accurate, 7.4× speedup

Math

\[x \]

FPCore

(FPCore (x y)
  :precision binary64
  :pre TRUE
  x)

C

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

Fortran

real(8) function code(x, y)
use fmin_fmax_functions
    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

ReFlow

f(x, y):
	x in [-inf, +inf],
	y in [-inf, +inf]
code: THEORY
BEGIN
f(x, y: real): real =
	x
END code

Derivation

1. Initial program 100.0%

   \[x + \frac{y}{500} \]

2. Taylor expanded in x around 0

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

4. Applied rewrites 49.7%

   \[\leadsto 0.002 \cdot y \]
5. Step-by-step derivation

6. Applied rewrites 49.8%

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

7. Taylor expanded in x around inf

   \[\leadsto x \]
8. Step-by-step derivation

9. Applied rewrites 51.7%

   \[\leadsto x \]
10. Add Preprocessing

Reproduce

herbie shell --seed 2026092 
(FPCore (x y)
  :name "Data.Colour.CIE:cieLAB from colour-2.3.3, C"
  :precision binary64
  (+ x (/ y 500.0)))