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

Percentage Accurate: 100.0% → 100.0%

Time: 2.7s

Alternatives: 3

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ x \cdot 2 - y \end{array} \]

FPCore

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

C

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

Fortran

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot 2 - y \end{array} \]

FPCore

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

C

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

Fortran

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot 2 - y \end{array} \]

FPCore

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

C

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

Fortran

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

Derivation

1. Initial program 100.0%

   \[x \cdot 2 - y \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 2: 73.6% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.7 \cdot 10^{+55}:\\ \;\;\;\;x \cdot 2\\ \mathbf{elif}\;x \leq 1.25 \cdot 10^{+82}:\\ \;\;\;\;0 - y\\ \mathbf{else}:\\ \;\;\;\;x \cdot 2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -4.7e+55) (* x 2.0) (if (<= x 1.25e+82) (- 0.0 y) (* x 2.0))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -4.7e+55) {
		tmp = x * 2.0;
	} else if (x <= 1.25e+82) {
		tmp = 0.0 - y;
	} else {
		tmp = x * 2.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-4.7d+55)) then
        tmp = x * 2.0d0
    else if (x <= 1.25d+82) then
        tmp = 0.0d0 - y
    else
        tmp = x * 2.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -4.7e+55) {
		tmp = x * 2.0;
	} else if (x <= 1.25e+82) {
		tmp = 0.0 - y;
	} else {
		tmp = x * 2.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -4.7e+55:
		tmp = x * 2.0
	elif x <= 1.25e+82:
		tmp = 0.0 - y
	else:
		tmp = x * 2.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -4.7e+55)
		tmp = Float64(x * 2.0);
	elseif (x <= 1.25e+82)
		tmp = Float64(0.0 - y);
	else
		tmp = Float64(x * 2.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -4.7e+55)
		tmp = x * 2.0;
	elseif (x <= 1.25e+82)
		tmp = 0.0 - y;
	else
		tmp = x * 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -4.7e+55], N[(x * 2.0), $MachinePrecision], If[LessEqual[x, 1.25e+82], N[(0.0 - y), $MachinePrecision], N[(x * 2.0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -4.7000000000000001e55 or 1.25000000000000004e82 < x

   1. Initial program 100.0%

      \[x \cdot 2 - y \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{2 \cdot x} \]
   4. Step-by-step derivation

      1. *-lowering-*.f64 82.8%

         \[\leadsto \mathsf{*.f64}\left(2, \color{blue}{x}\right) \]

   5. Simplified 82.8%

      \[\leadsto \color{blue}{2 \cdot x} \]

   if -4.7000000000000001e55 < x < 1.25000000000000004e82

   1. Initial program 100.0%

      \[x \cdot 2 - y \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. mul-1-neg N/A

         \[\leadsto \mathsf{neg}\left(y\right) \]

      2. neg-sub0 N/A

         \[\leadsto 0 - \color{blue}{y} \]

      3. --lowering--.f64 76.2%

         \[\leadsto \mathsf{\_.f64}\left(0, \color{blue}{y}\right) \]

   5. Simplified 76.2%

      \[\leadsto \color{blue}{0 - y} \]
   6. Step-by-step derivation

      1. sub0-neg N/A

         \[\leadsto \mathsf{neg}\left(y\right) \]

      2. neg-lowering-neg.f64 76.2%

         \[\leadsto \mathsf{neg.f64}\left(y\right) \]

   7. Applied egg-rr 76.2%

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

4. Final simplification 78.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -4.7 \cdot 10^{+55}:\\ \;\;\;\;x \cdot 2\\ \mathbf{elif}\;x \leq 1.25 \cdot 10^{+82}:\\ \;\;\;\;0 - y\\ \mathbf{else}:\\ \;\;\;\;x \cdot 2\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 51.5% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ 0 - y \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- 0.0 y))

C

double code(double x, double y) {
	return 0.0 - y;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 0.0d0 - y
end function

Java

public static double code(double x, double y) {
	return 0.0 - y;
}

Python

def code(x, y):
	return 0.0 - y

Julia

function code(x, y)
	return Float64(0.0 - y)
end

MATLAB

function tmp = code(x, y)
	tmp = 0.0 - y;
end

Wolfram

code[x_, y_] := N[(0.0 - y), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x \cdot 2 - y \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

   1. mul-1-neg N/A

      \[\leadsto \mathsf{neg}\left(y\right) \]

   2. neg-sub0 N/A

      \[\leadsto 0 - \color{blue}{y} \]

   3. --lowering--.f64 54.3%

      \[\leadsto \mathsf{\_.f64}\left(0, \color{blue}{y}\right) \]

5. Simplified 54.3%

   \[\leadsto \color{blue}{0 - y} \]
6. Step-by-step derivation

   1. sub0-neg N/A

      \[\leadsto \mathsf{neg}\left(y\right) \]

   2. neg-lowering-neg.f64 54.3%

      \[\leadsto \mathsf{neg.f64}\left(y\right) \]

7. Applied egg-rr 54.3%

   \[\leadsto \color{blue}{-y} \]

8. Final simplification 54.3%

   \[\leadsto 0 - y \]
9. Add Preprocessing

Reproduce

herbie shell --seed 2024158 
(FPCore (x y)
  :name "Data.Colour.RGBSpace.HSL:hsl from colour-2.3.3, C"
  :precision binary64
  (- (* x 2.0) y))