Data.Colour.RGBSpace.HSL:hsl from colour-2.3.3, C

Percentage Accurate: 100.0% → 100.0%

Time: 1.3s

Alternatives: 3

Speedup: 1.1×

Specification

Math

\[x \cdot 2 - y \]

FPCore

(FPCore (x y)
  :precision binary64
  :pre TRUE
  (- (* x 2.0) y))

C

double code(double x, double y) {
	return (x * 2.0) - y;
}

Fortran

real(8) function code(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x * 2.0d0) - y
end function

Java

public static double code(double x, double y) {
	return (x * 2.0) - y;
}

Python

def code(x, y):
	return (x * 2.0) - y

Julia

function code(x, y)
	return Float64(Float64(x * 2.0) - y)
end

MATLAB

function tmp = code(x, y)
	tmp = (x * 2.0) - y;
end

Wolfram

code[x_, y_] := N[(N[(x * 2.0), $MachinePrecision] - y), $MachinePrecision]

ReFlow

f(x, y):
	x in [-inf, +inf],
	y in [-inf, +inf]
code: THEORY
BEGIN
f(x, y: real): real =
	(x * (2)) - y
END code

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[x \cdot 2 - y \]

FPCore

(FPCore (x y)
  :precision binary64
  :pre TRUE
  (- (* x 2.0) y))

C

double code(double x, double y) {
	return (x * 2.0) - y;
}

Fortran

real(8) function code(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x * 2.0d0) - y
end function

Java

public static double code(double x, double y) {
	return (x * 2.0) - y;
}

Python

def code(x, y):
	return (x * 2.0) - y

Julia

function code(x, y)
	return Float64(Float64(x * 2.0) - y)
end

MATLAB

function tmp = code(x, y)
	tmp = (x * 2.0) - y;
end

Wolfram

code[x_, y_] := N[(N[(x * 2.0), $MachinePrecision] - y), $MachinePrecision]

ReFlow

f(x, y):
	x in [-inf, +inf],
	y in [-inf, +inf]
code: THEORY
BEGIN
f(x, y: real): real =
	(x * (2)) - y
END code

Alternative 1: 100.0% accurate, 1.1× speedup

Math

\[\left(x + x\right) - y \]

FPCore

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

C

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

Fortran

real(8) function code(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x + x) - y
end function

Java

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

Python

def code(x, y):
	return (x + x) - y

Julia

function code(x, y)
	return Float64(Float64(x + x) - y)
end

MATLAB

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

Wolfram

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

ReFlow

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

Derivation

1. Initial program 100.0%

   \[x \cdot 2 - y \]
2. Step-by-step derivation

3. Applied rewrites 100.0%

   \[\leadsto \left(x + x\right) - y \]
4. Add Preprocessing

Alternative 2: 74.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \mathbf{if}\;y \leq -4.155509726251822 \cdot 10^{+46}:\\ \;\;\;\;-y\\ \mathbf{elif}\;y \leq 2.426229654520916 \cdot 10^{+48}:\\ \;\;\;\;x + x\\ \mathbf{else}:\\ \;\;\;\;-y\\ \end{array} \]

FPCore

(FPCore (x y)
  :precision binary64
  :pre TRUE
  (if (<= y -4.155509726251822e+46)
  (- y)
  (if (<= y 2.426229654520916e+48) (+ x x) (- y))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -4.155509726251822e+46) {
		tmp = -y;
	} else if (y <= 2.426229654520916e+48) {
		tmp = x + x;
	} else {
		tmp = -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 <= (-4.155509726251822d+46)) then
        tmp = -y
    else if (y <= 2.426229654520916d+48) then
        tmp = x + x
    else
        tmp = -y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -4.155509726251822e+46) {
		tmp = -y;
	} else if (y <= 2.426229654520916e+48) {
		tmp = x + x;
	} else {
		tmp = -y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -4.155509726251822e+46:
		tmp = -y
	elif y <= 2.426229654520916e+48:
		tmp = x + x
	else:
		tmp = -y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -4.155509726251822e+46)
		tmp = Float64(-y);
	elseif (y <= 2.426229654520916e+48)
		tmp = Float64(x + x);
	else
		tmp = Float64(-y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -4.155509726251822e+46)
		tmp = -y;
	elseif (y <= 2.426229654520916e+48)
		tmp = x + x;
	else
		tmp = -y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -4.155509726251822e+46], (-y), If[LessEqual[y, 2.426229654520916e+48], N[(x + x), $MachinePrecision], (-y)]]

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 <= (2426229654520915891643607200729785382101422440448)) THEN (x + x) ELSE (- y) ENDIF IN
	LET tmp = IF (y <= (-41555097262518219140230899091581622926874509312)) THEN (- y) ELSE tmp_1 ENDIF IN
	tmp
END code

Derivation

1. Split input into 2 regimes

2. if y < -4.1555097262518219e46 or 2.4262296545209159e48 < y

   1. Initial program 100.0%

      \[x \cdot 2 - y \]

   2. Taylor expanded in x around 0

      \[\leadsto -1 \cdot y \]
   3. Step-by-step derivation

   4. Applied rewrites 50.1%

      \[\leadsto -1 \cdot y \]
   5. Step-by-step derivation

   6. Applied rewrites 50.1%

      \[\leadsto -y \]

   if -4.1555097262518219e46 < y < 2.4262296545209159e48

   1. Initial program 100.0%

      \[x \cdot 2 - y \]

   2. Taylor expanded in x around 0

      \[\leadsto -1 \cdot y \]
   3. Step-by-step derivation

   4. Applied rewrites 50.1%

      \[\leadsto -1 \cdot y \]
   5. Step-by-step derivation

   6. Applied rewrites 50.1%

      \[\leadsto -y \]

   7. Taylor expanded in x around inf

      \[\leadsto 2 \cdot x \]
   8. Step-by-step derivation

   9. Applied rewrites 51.3%

      \[\leadsto 2 \cdot x \]
   10. Step-by-step derivation

   11. Applied rewrites 51.3%

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

Alternative 3: 50.1% accurate, 3.3× speedup

Math

\[-y \]

FPCore

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

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return -y

Julia

function code(x, y)
	return Float64(-y)
end

MATLAB

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

Wolfram

code[x_, y_] := (-y)

ReFlow

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

Derivation

1. Initial program 100.0%

   \[x \cdot 2 - y \]

2. Taylor expanded in x around 0

   \[\leadsto -1 \cdot y \]
3. Step-by-step derivation

4. Applied rewrites 50.1%

   \[\leadsto -1 \cdot y \]
5. Step-by-step derivation

6. Applied rewrites 50.1%

   \[\leadsto -y \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2026092 
(FPCore (x y)
  :name "Data.Colour.RGBSpace.HSL:hsl from colour-2.3.3, C"
  :precision binary64
  (- (* x 2.0) y))