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

Percentage Accurate: 100.0% → 100.0%

Time: 2.8s

Alternatives: 4

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[x - \frac{y}{200} \]

2. Final simplification 100.0%

   \[\leadsto x - \frac{y}{200} \]

Alternative 2: 73.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.2 \cdot 10^{+29} \lor \neg \left(y \leq 4.3 \cdot 10^{+73}\right):\\ \;\;\;\;y \cdot -0.005\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -1.2e+29) (not (<= y 4.3e+73))) (* y -0.005) x))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -1.2e+29) || !(y <= 4.3e+73)) {
		tmp = y * -0.005;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y <= (-1.2d+29)) .or. (.not. (y <= 4.3d+73))) then
        tmp = y * (-0.005d0)
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -1.2e+29) || !(y <= 4.3e+73)) {
		tmp = y * -0.005;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -1.2e+29) or not (y <= 4.3e+73):
		tmp = y * -0.005
	else:
		tmp = x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -1.2e+29) || !(y <= 4.3e+73))
		tmp = Float64(y * -0.005);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -1.2e+29) || ~((y <= 4.3e+73)))
		tmp = y * -0.005;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -1.2e+29], N[Not[LessEqual[y, 4.3e+73]], $MachinePrecision]], N[(y * -0.005), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if y < -1.2e29 or 4.30000000000000013e73 < y

   1. Initial program 100.0%

      \[x - \frac{y}{200} \]

   2. Taylor expanded in x around 0 80.2%

      \[\leadsto \color{blue}{-0.005 \cdot y} \]

   if -1.2e29 < y < 4.30000000000000013e73

   1. Initial program 100.0%

      \[x - \frac{y}{200} \]

   2. Taylor expanded in x around inf 78.2%

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

4. Final simplification 79.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.2 \cdot 10^{+29} \lor \neg \left(y \leq 4.3 \cdot 10^{+73}\right):\\ \;\;\;\;y \cdot -0.005\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 3: 2.7% accurate, 5.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 0.0)

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return 0.0

Julia

function code(x, y)
	return 0.0
end

MATLAB

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

Wolfram

code[x_, y_] := 0.0

Derivation

1. Initial program 100.0%

   \[x - \frac{y}{200} \]

2. Taylor expanded in x around 0 51.5%

   \[\leadsto \color{blue}{-0.005 \cdot y} \]
3. Step-by-step derivation

   1. expm1-log1p-u 30.5%

      \[\leadsto \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(-0.005 \cdot y\right)\right)} \]

   2. expm1-udef 22.2%

      \[\leadsto \color{blue}{e^{\mathsf{log1p}\left(-0.005 \cdot y\right)} - 1} \]

   3. sub-neg 22.2%

      \[\leadsto \color{blue}{e^{\mathsf{log1p}\left(-0.005 \cdot y\right)} + \left(-1\right)} \]

   4. log1p-udef 22.2%

      \[\leadsto e^{\color{blue}{\log \left(1 + -0.005 \cdot y\right)}} + \left(-1\right) \]

   5. rem-exp-log 43.2%

      \[\leadsto \color{blue}{\left(1 + -0.005 \cdot y\right)} + \left(-1\right) \]

   6. +-commutative 43.2%

      \[\leadsto \color{blue}{\left(-0.005 \cdot y + 1\right)} + \left(-1\right) \]

   7. metadata-eval 43.2%

      \[\leadsto \left(-0.005 \cdot y + 1\right) + \color{blue}{-1} \]

4. Applied egg-rr 43.2%

   \[\leadsto \color{blue}{\left(-0.005 \cdot y + 1\right) + -1} \]

6. Simplified 2.5%

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

7. Final simplification 2.5%

   \[\leadsto 0 \]

Alternative 4: 50.8% accurate, 5.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 x)

C

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

Fortran

real(8) function code(x, y)
    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

Derivation

1. Initial program 100.0%

   \[x - \frac{y}{200} \]

2. Taylor expanded in x around inf 49.3%

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

3. Final simplification 49.3%

   \[\leadsto x \]

Reproduce

herbie shell --seed 2023310 
(FPCore (x y)
  :name "Data.Colour.CIE:cieLAB from colour-2.3.3, D"
  :precision binary64
  (- x (/ y 200.0)))