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

Percentage Accurate: 100.0% → 100.0%

Time: 2.9s

Alternatives: 5

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: 73.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9.5 \cdot 10^{-34} \lor \neg \left(y \leq 4.5 \cdot 10^{-11} \lor \neg \left(y \leq 4 \cdot 10^{+100}\right) \land y \leq 1.3 \cdot 10^{+126}\right):\\ \;\;\;\;\frac{-y}{200}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -9.5e-34)
         (not (or (<= y 4.5e-11) (and (not (<= y 4e+100)) (<= y 1.3e+126)))))
   (/ (- y) 200.0)
   x))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -9.5e-34) || !((y <= 4.5e-11) || (!(y <= 4e+100) && (y <= 1.3e+126)))) {
		tmp = -y / 200.0;
	} 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 <= (-9.5d-34)) .or. (.not. (y <= 4.5d-11) .or. (.not. (y <= 4d+100)) .and. (y <= 1.3d+126))) then
        tmp = -y / 200.0d0
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -9.5e-34) || !((y <= 4.5e-11) || (!(y <= 4e+100) && (y <= 1.3e+126)))) {
		tmp = -y / 200.0;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -9.5e-34) or not ((y <= 4.5e-11) or (not (y <= 4e+100) and (y <= 1.3e+126))):
		tmp = -y / 200.0
	else:
		tmp = x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -9.5e-34) || !((y <= 4.5e-11) || (!(y <= 4e+100) && (y <= 1.3e+126))))
		tmp = Float64(Float64(-y) / 200.0);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -9.5e-34) || ~(((y <= 4.5e-11) || (~((y <= 4e+100)) && (y <= 1.3e+126)))))
		tmp = -y / 200.0;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -9.5e-34], N[Not[Or[LessEqual[y, 4.5e-11], And[N[Not[LessEqual[y, 4e+100]], $MachinePrecision], LessEqual[y, 1.3e+126]]]], $MachinePrecision]], N[((-y) / 200.0), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if y < -9.49999999999999985e-34 or 4.5e-11 < y < 4.00000000000000006e100 or 1.3e126 < y

   1. Initial program 100.0%

      \[x - \frac{y}{200} \]
   2. Step-by-step derivation

      1. sub-neg 100.0%

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

      2. distribute-neg-frac 100.0%

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

      3. neg-mul-1 100.0%

         \[\leadsto x + \frac{\color{blue}{-1 \cdot y}}{200} \]

      4. associate-/l* 99.7%

         \[\leadsto x + \color{blue}{\frac{-1}{\frac{200}{y}}} \]

      5. associate-/r/ 99.9%

         \[\leadsto x + \color{blue}{\frac{-1}{200} \cdot y} \]

      6. metadata-eval 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in x around 0 79.1%

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

      1. *-commutative 79.1%

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

      2. metadata-eval 79.1%

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

      3. metadata-eval 79.1%

         \[\leadsto y \cdot \left(-\color{blue}{\frac{1}{200}}\right) \]

      4. distribute-rgt-neg-in 79.1%

         \[\leadsto \color{blue}{-y \cdot \frac{1}{200}} \]

      5. div-inv 79.2%

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

      6. distribute-neg-frac 79.2%

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

   6. Applied egg-rr 79.2%

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

   if -9.49999999999999985e-34 < y < 4.5e-11 or 4.00000000000000006e100 < y < 1.3e126

   1. Initial program 100.0%

      \[x - \frac{y}{200} \]
   2. Step-by-step derivation

      1. sub-neg 100.0%

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

      2. distribute-neg-frac 100.0%

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

      3. neg-mul-1 100.0%

         \[\leadsto x + \frac{\color{blue}{-1 \cdot y}}{200} \]

      4. associate-/l* 99.9%

         \[\leadsto x + \color{blue}{\frac{-1}{\frac{200}{y}}} \]

      5. associate-/r/ 99.9%

         \[\leadsto x + \color{blue}{\frac{-1}{200} \cdot y} \]

      6. metadata-eval 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in x around inf 78.1%

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

4. Final simplification 78.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -9.5 \cdot 10^{-34} \lor \neg \left(y \leq 4.5 \cdot 10^{-11} \lor \neg \left(y \leq 4 \cdot 10^{+100}\right) \land y \leq 1.3 \cdot 10^{+126}\right):\\ \;\;\;\;\frac{-y}{200}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 3: 73.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.1 \cdot 10^{-34}:\\ \;\;\;\;y \cdot -0.005\\ \mathbf{elif}\;y \leq 2.8 \cdot 10^{-8}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 4.4 \cdot 10^{+100}:\\ \;\;\;\;y \cdot -0.005\\ \mathbf{elif}\;y \leq 1.3 \cdot 10^{+126}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot -0.005\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -3.1e-34)
   (* y -0.005)
   (if (<= y 2.8e-8)
     x
     (if (<= y 4.4e+100) (* y -0.005) (if (<= y 1.3e+126) x (* y -0.005))))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -3.1e-34) {
		tmp = y * -0.005;
	} else if (y <= 2.8e-8) {
		tmp = x;
	} else if (y <= 4.4e+100) {
		tmp = y * -0.005;
	} else if (y <= 1.3e+126) {
		tmp = x;
	} else {
		tmp = y * -0.005;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-3.1d-34)) then
        tmp = y * (-0.005d0)
    else if (y <= 2.8d-8) then
        tmp = x
    else if (y <= 4.4d+100) then
        tmp = y * (-0.005d0)
    else if (y <= 1.3d+126) then
        tmp = x
    else
        tmp = y * (-0.005d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -3.1e-34) {
		tmp = y * -0.005;
	} else if (y <= 2.8e-8) {
		tmp = x;
	} else if (y <= 4.4e+100) {
		tmp = y * -0.005;
	} else if (y <= 1.3e+126) {
		tmp = x;
	} else {
		tmp = y * -0.005;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -3.1e-34:
		tmp = y * -0.005
	elif y <= 2.8e-8:
		tmp = x
	elif y <= 4.4e+100:
		tmp = y * -0.005
	elif y <= 1.3e+126:
		tmp = x
	else:
		tmp = y * -0.005
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -3.1e-34)
		tmp = Float64(y * -0.005);
	elseif (y <= 2.8e-8)
		tmp = x;
	elseif (y <= 4.4e+100)
		tmp = Float64(y * -0.005);
	elseif (y <= 1.3e+126)
		tmp = x;
	else
		tmp = Float64(y * -0.005);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -3.1e-34)
		tmp = y * -0.005;
	elseif (y <= 2.8e-8)
		tmp = x;
	elseif (y <= 4.4e+100)
		tmp = y * -0.005;
	elseif (y <= 1.3e+126)
		tmp = x;
	else
		tmp = y * -0.005;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -3.1e-34], N[(y * -0.005), $MachinePrecision], If[LessEqual[y, 2.8e-8], x, If[LessEqual[y, 4.4e+100], N[(y * -0.005), $MachinePrecision], If[LessEqual[y, 1.3e+126], x, N[(y * -0.005), $MachinePrecision]]]]]

Derivation

1. Split input into 2 regimes

2. if y < -3.0999999999999998e-34 or 2.7999999999999999e-8 < y < 4.4000000000000001e100 or 1.3e126 < y

   1. Initial program 100.0%

      \[x - \frac{y}{200} \]
   2. Step-by-step derivation

      1. sub-neg 100.0%

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

      2. distribute-neg-frac 100.0%

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

      3. neg-mul-1 100.0%

         \[\leadsto x + \frac{\color{blue}{-1 \cdot y}}{200} \]

      4. associate-/l* 99.7%

         \[\leadsto x + \color{blue}{\frac{-1}{\frac{200}{y}}} \]

      5. associate-/r/ 99.9%

         \[\leadsto x + \color{blue}{\frac{-1}{200} \cdot y} \]

      6. metadata-eval 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in x around 0 79.1%

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

   if -3.0999999999999998e-34 < y < 2.7999999999999999e-8 or 4.4000000000000001e100 < y < 1.3e126

   1. Initial program 100.0%

      \[x - \frac{y}{200} \]
   2. Step-by-step derivation

      1. sub-neg 100.0%

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

      2. distribute-neg-frac 100.0%

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

      3. neg-mul-1 100.0%

         \[\leadsto x + \frac{\color{blue}{-1 \cdot y}}{200} \]

      4. associate-/l* 99.9%

         \[\leadsto x + \color{blue}{\frac{-1}{\frac{200}{y}}} \]

      5. associate-/r/ 99.9%

         \[\leadsto x + \color{blue}{\frac{-1}{200} \cdot y} \]

      6. metadata-eval 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in x around inf 78.1%

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

4. Final simplification 78.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -3.1 \cdot 10^{-34}:\\ \;\;\;\;y \cdot -0.005\\ \mathbf{elif}\;y \leq 2.8 \cdot 10^{-8}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 4.4 \cdot 10^{+100}:\\ \;\;\;\;y \cdot -0.005\\ \mathbf{elif}\;y \leq 1.3 \cdot 10^{+126}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot -0.005\\ \end{array} \]

Alternative 4: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + y \cdot -0.005 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x (* y -0.005)))

C

double code(double x, double y) {
	return x + (y * -0.005);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x + (y * (-0.005d0))
end function

Java

public static double code(double x, double y) {
	return x + (y * -0.005);
}

Python

def code(x, y):
	return x + (y * -0.005)

Julia

function code(x, y)
	return Float64(x + Float64(y * -0.005))
end

MATLAB

function tmp = code(x, y)
	tmp = x + (y * -0.005);
end

Wolfram

code[x_, y_] := N[(x + N[(y * -0.005), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x - \frac{y}{200} \]
2. Step-by-step derivation

   1. sub-neg 100.0%

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

   2. distribute-neg-frac 100.0%

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

   3. neg-mul-1 100.0%

      \[\leadsto x + \frac{\color{blue}{-1 \cdot y}}{200} \]

   4. associate-/l* 99.8%

      \[\leadsto x + \color{blue}{\frac{-1}{\frac{200}{y}}} \]

   5. associate-/r/ 99.9%

      \[\leadsto x + \color{blue}{\frac{-1}{200} \cdot y} \]

   6. metadata-eval 99.9%

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

3. Simplified 99.9%

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

4. Final simplification 99.9%

   \[\leadsto x + y \cdot -0.005 \]

Alternative 5: 50.6% 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. Step-by-step derivation

   1. sub-neg 100.0%

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

   2. distribute-neg-frac 100.0%

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

   3. neg-mul-1 100.0%

      \[\leadsto x + \frac{\color{blue}{-1 \cdot y}}{200} \]

   4. associate-/l* 99.8%

      \[\leadsto x + \color{blue}{\frac{-1}{\frac{200}{y}}} \]

   5. associate-/r/ 99.9%

      \[\leadsto x + \color{blue}{\frac{-1}{200} \cdot y} \]

   6. metadata-eval 99.9%

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

3. Simplified 99.9%

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

4. Taylor expanded in x around inf 47.1%

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

5. Final simplification 47.1%

   \[\leadsto x \]

Reproduce

herbie shell --seed 2023200 
(FPCore (x y)
  :name "Data.Colour.CIE:cieLAB from colour-2.3.3, D"
  :precision binary64
  (- x (/ y 200.0)))