Data.Colour.RGB:hslsv from colour-2.3.3, A

Percentage Accurate: 100.0% → 100.0%

Time: 2.2s

Alternatives: 3

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \frac{x + y}{2} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (+ x y) 2.0))

C

double code(double x, double y) {
	return (x + y) / 2.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x + y) / 2.0d0
end function

Java

public static double code(double x, double y) {
	return (x + y) / 2.0;
}

Python

def code(x, y):
	return (x + y) / 2.0

Julia

function code(x, y)
	return Float64(Float64(x + y) / 2.0)
end

MATLAB

function tmp = code(x, y)
	tmp = (x + y) / 2.0;
end

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x + y}{2} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (+ x y) 2.0))

C

double code(double x, double y) {
	return (x + y) / 2.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x + y) / 2.0d0
end function

Java

public static double code(double x, double y) {
	return (x + y) / 2.0;
}

Python

def code(x, y):
	return (x + y) / 2.0

Julia

function code(x, y)
	return Float64(Float64(x + y) / 2.0)
end

MATLAB

function tmp = code(x, y)
	tmp = (x + y) / 2.0;
end

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x + y}{2} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (+ x y) 2.0))

C

double code(double x, double y) {
	return (x + y) / 2.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x + y) / 2.0d0
end function

Java

public static double code(double x, double y) {
	return (x + y) / 2.0;
}

Python

def code(x, y):
	return (x + y) / 2.0

Julia

function code(x, y)
	return Float64(Float64(x + y) / 2.0)
end

MATLAB

function tmp = code(x, y)
	tmp = (x + y) / 2.0;
end

Wolfram

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

Derivation

1. Initial program 100.0%

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

Alternative 2: 62.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 5.2 \cdot 10^{-93} \lor \neg \left(y \leq 9 \cdot 10^{-72}\right) \land y \leq 7.2 \cdot 10^{-33}:\\ \;\;\;\;\frac{x}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{2}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y 5.2e-93) (and (not (<= y 9e-72)) (<= y 7.2e-33)))
   (/ x 2.0)
   (/ y 2.0)))

C

double code(double x, double y) {
	double tmp;
	if ((y <= 5.2e-93) || (!(y <= 9e-72) && (y <= 7.2e-33))) {
		tmp = x / 2.0;
	} else {
		tmp = y / 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 ((y <= 5.2d-93) .or. (.not. (y <= 9d-72)) .and. (y <= 7.2d-33)) then
        tmp = x / 2.0d0
    else
        tmp = y / 2.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= 5.2e-93) || (!(y <= 9e-72) && (y <= 7.2e-33))) {
		tmp = x / 2.0;
	} else {
		tmp = y / 2.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= 5.2e-93) or (not (y <= 9e-72) and (y <= 7.2e-33)):
		tmp = x / 2.0
	else:
		tmp = y / 2.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= 5.2e-93) || (!(y <= 9e-72) && (y <= 7.2e-33)))
		tmp = Float64(x / 2.0);
	else
		tmp = Float64(y / 2.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= 5.2e-93) || (~((y <= 9e-72)) && (y <= 7.2e-33)))
		tmp = x / 2.0;
	else
		tmp = y / 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, 5.2e-93], And[N[Not[LessEqual[y, 9e-72]], $MachinePrecision], LessEqual[y, 7.2e-33]]], N[(x / 2.0), $MachinePrecision], N[(y / 2.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 5.1999999999999997e-93 or 9e-72 < y < 7.20000000000000068e-33

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf 62.2%

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

   if 5.1999999999999997e-93 < y < 9e-72 or 7.20000000000000068e-33 < y

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 74.0%

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

4. Final simplification 65.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 5.2 \cdot 10^{-93} \lor \neg \left(y \leq 9 \cdot 10^{-72}\right) \land y \leq 7.2 \cdot 10^{-33}:\\ \;\;\;\;\frac{x}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 51.2% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \frac{x}{2} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ x 2.0))

C

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

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x / 2.0d0
end function

Java

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

Python

def code(x, y):
	return x / 2.0

Julia

function code(x, y)
	return Float64(x / 2.0)
end

MATLAB

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

Wolfram

code[x_, y_] := N[(x / 2.0), $MachinePrecision]

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in x around inf 52.7%

   \[\leadsto \frac{\color{blue}{x}}{2} \]
4. Add Preprocessing

Reproduce

herbie shell --seed 2024110 
(FPCore (x y)
  :name "Data.Colour.RGB:hslsv from colour-2.3.3, A"
  :precision binary64
  (/ (+ x y) 2.0))