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

Percentage Accurate: 100.0% → 100.0%

Time: 2.0s

Alternatives: 6

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ 200 \cdot \left(x - y\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 200.0 (- x y)))

C

double code(double x, double y) {
	return 200.0 * (x - y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 200.0d0 * (x - y)
end function

Java

public static double code(double x, double y) {
	return 200.0 * (x - y);
}

Python

def code(x, y):
	return 200.0 * (x - y)

Julia

function code(x, y)
	return Float64(200.0 * Float64(x - y))
end

MATLAB

function tmp = code(x, y)
	tmp = 200.0 * (x - y);
end

Wolfram

code[x_, y_] := N[(200.0 * N[(x - y), $MachinePrecision]), $MachinePrecision]

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 200 \cdot \left(x - y\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 200.0 (- x y)))

C

double code(double x, double y) {
	return 200.0 * (x - y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 200.0d0 * (x - y)
end function

Java

public static double code(double x, double y) {
	return 200.0 * (x - y);
}

Python

def code(x, y):
	return 200.0 * (x - y)

Julia

function code(x, y)
	return Float64(200.0 * Float64(x - y))
end

MATLAB

function tmp = code(x, y)
	tmp = 200.0 * (x - y);
end

Wolfram

code[x_, y_] := N[(200.0 * N[(x - y), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 0.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, 200, 200 \cdot \left(-y\right)\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (fma x 200.0 (* 200.0 (- y))))

C

double code(double x, double y) {
	return fma(x, 200.0, (200.0 * -y));
}

Julia

function code(x, y)
	return fma(x, 200.0, Float64(200.0 * Float64(-y)))
end

Wolfram

code[x_, y_] := N[(x * 200.0 + N[(200.0 * (-y)), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[200 \cdot \left(x - y\right) \]
2. Step-by-step derivation

   1. sub-neg 100.0%

      \[\leadsto 200 \cdot \color{blue}{\left(x + \left(-y\right)\right)} \]

   2. distribute-rgt-in 100.0%

      \[\leadsto \color{blue}{x \cdot 200 + \left(-y\right) \cdot 200} \]

   3. fma-def 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, 200, \left(-y\right) \cdot 200\right)} \]

3. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, 200, \left(-y\right) \cdot 200\right)} \]

4. Final simplification 100.0%

   \[\leadsto \mathsf{fma}\left(x, 200, 200 \cdot \left(-y\right)\right) \]

Alternative 2: 100.0% accurate, 0.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(-200, y, x \cdot 200\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (fma -200.0 y (* x 200.0)))

C

double code(double x, double y) {
	return fma(-200.0, y, (x * 200.0));
}

Julia

function code(x, y)
	return fma(-200.0, y, Float64(x * 200.0))
end

Wolfram

code[x_, y_] := N[(-200.0 * y + N[(x * 200.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[200 \cdot \left(x - y\right) \]

2. Taylor expanded in x around 0 100.0%

   \[\leadsto \color{blue}{-200 \cdot y + 200 \cdot x} \]
3. Step-by-step derivation

   1. fma-def 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-200, y, 200 \cdot x\right)} \]

4. Simplified 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(-200, y, 200 \cdot x\right)} \]

5. Final simplification 100.0%

   \[\leadsto \mathsf{fma}\left(-200, y, x \cdot 200\right) \]

Alternative 3: 74.9% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.12 \cdot 10^{+36} \lor \neg \left(x \leq -0.095\right) \land \left(x \leq -3.5 \cdot 10^{-24} \lor \neg \left(x \leq 7 \cdot 10^{+42}\right)\right):\\ \;\;\;\;x \cdot 200\\ \mathbf{else}:\\ \;\;\;\;y \cdot -200\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -1.12e+36)
         (and (not (<= x -0.095)) (or (<= x -3.5e-24) (not (<= x 7e+42)))))
   (* x 200.0)
   (* y -200.0)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -1.12e+36) || (!(x <= -0.095) && ((x <= -3.5e-24) || !(x <= 7e+42)))) {
		tmp = x * 200.0;
	} else {
		tmp = y * -200.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((x <= (-1.12d+36)) .or. (.not. (x <= (-0.095d0))) .and. (x <= (-3.5d-24)) .or. (.not. (x <= 7d+42))) then
        tmp = x * 200.0d0
    else
        tmp = y * (-200.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -1.12e+36) || (!(x <= -0.095) && ((x <= -3.5e-24) || !(x <= 7e+42)))) {
		tmp = x * 200.0;
	} else {
		tmp = y * -200.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -1.12e+36) or (not (x <= -0.095) and ((x <= -3.5e-24) or not (x <= 7e+42))):
		tmp = x * 200.0
	else:
		tmp = y * -200.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -1.12e+36) || (!(x <= -0.095) && ((x <= -3.5e-24) || !(x <= 7e+42))))
		tmp = Float64(x * 200.0);
	else
		tmp = Float64(y * -200.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -1.12e+36) || (~((x <= -0.095)) && ((x <= -3.5e-24) || ~((x <= 7e+42)))))
		tmp = x * 200.0;
	else
		tmp = y * -200.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -1.12e+36], And[N[Not[LessEqual[x, -0.095]], $MachinePrecision], Or[LessEqual[x, -3.5e-24], N[Not[LessEqual[x, 7e+42]], $MachinePrecision]]]], N[(x * 200.0), $MachinePrecision], N[(y * -200.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1.11999999999999999e36 or -0.095000000000000001 < x < -3.4999999999999996e-24 or 7.00000000000000047e42 < x

   1. Initial program 99.9%

      \[200 \cdot \left(x - y\right) \]

   2. Taylor expanded in x around inf 81.8%

      \[\leadsto \color{blue}{200 \cdot x} \]

   if -1.11999999999999999e36 < x < -0.095000000000000001 or -3.4999999999999996e-24 < x < 7.00000000000000047e42

   1. Initial program 100.0%

      \[200 \cdot \left(x - y\right) \]

   2. Taylor expanded in x around 0 78.1%

      \[\leadsto \color{blue}{-200 \cdot y} \]
3. Recombined 2 regimes into one program.

4. Final simplification 79.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.12 \cdot 10^{+36} \lor \neg \left(x \leq -0.095\right) \land \left(x \leq -3.5 \cdot 10^{-24} \lor \neg \left(x \leq 7 \cdot 10^{+42}\right)\right):\\ \;\;\;\;x \cdot 200\\ \mathbf{else}:\\ \;\;\;\;y \cdot -200\\ \end{array} \]

Alternative 4: 99.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ x \cdot 200 + y \cdot -200 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ (* x 200.0) (* y -200.0)))

C

double code(double x, double y) {
	return (x * 200.0) + (y * -200.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x * 200.0d0) + (y * (-200.0d0))
end function

Java

public static double code(double x, double y) {
	return (x * 200.0) + (y * -200.0);
}

Python

def code(x, y):
	return (x * 200.0) + (y * -200.0)

Julia

function code(x, y)
	return Float64(Float64(x * 200.0) + Float64(y * -200.0))
end

MATLAB

function tmp = code(x, y)
	tmp = (x * 200.0) + (y * -200.0);
end

Wolfram

code[x_, y_] := N[(N[(x * 200.0), $MachinePrecision] + N[(y * -200.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[200 \cdot \left(x - y\right) \]

2. Taylor expanded in x around 0 100.0%

   \[\leadsto \color{blue}{-200 \cdot y + 200 \cdot x} \]

3. Final simplification 100.0%

   \[\leadsto x \cdot 200 + y \cdot -200 \]

Alternative 5: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 200 \cdot \left(x - y\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 200.0 (- x y)))

C

double code(double x, double y) {
	return 200.0 * (x - y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 200.0d0 * (x - y)
end function

Java

public static double code(double x, double y) {
	return 200.0 * (x - y);
}

Python

def code(x, y):
	return 200.0 * (x - y)

Julia

function code(x, y)
	return Float64(200.0 * Float64(x - y))
end

MATLAB

function tmp = code(x, y)
	tmp = 200.0 * (x - y);
end

Wolfram

code[x_, y_] := N[(200.0 * N[(x - y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[200 \cdot \left(x - y\right) \]

2. Final simplification 100.0%

   \[\leadsto 200 \cdot \left(x - y\right) \]

Alternative 6: 51.2% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ y \cdot -200 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* y -200.0))

C

double code(double x, double y) {
	return y * -200.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = y * (-200.0d0)
end function

Java

public static double code(double x, double y) {
	return y * -200.0;
}

Python

def code(x, y):
	return y * -200.0

Julia

function code(x, y)
	return Float64(y * -200.0)
end

MATLAB

function tmp = code(x, y)
	tmp = y * -200.0;
end

Wolfram

code[x_, y_] := N[(y * -200.0), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[200 \cdot \left(x - y\right) \]

2. Taylor expanded in x around 0 49.7%

   \[\leadsto \color{blue}{-200 \cdot y} \]

3. Final simplification 49.7%

   \[\leadsto y \cdot -200 \]

Reproduce

herbie shell --seed 2023338 
(FPCore (x y)
  :name "Data.Colour.CIE:cieLABView from colour-2.3.3, C"
  :precision binary64
  (* 200.0 (- x y)))