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

Percentage Accurate: 100.0% → 100.0%

Time: 3.7s

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. Add Preprocessing
3. Add Preprocessing

Alternative 2: 73.7% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.8 \cdot 10^{+79}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq -2.5 \cdot 10^{+28}:\\ \;\;\;\;\frac{y}{-200}\\ \mathbf{elif}\;x \leq -1.15 \cdot 10^{-46}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 6 \cdot 10^{-35}:\\ \;\;\;\;\frac{y}{-200}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -3.8e+79)
   x
   (if (<= x -2.5e+28)
     (/ y -200.0)
     (if (<= x -1.15e-46) x (if (<= x 6e-35) (/ y -200.0) x)))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -3.8e+79) {
		tmp = x;
	} else if (x <= -2.5e+28) {
		tmp = y / -200.0;
	} else if (x <= -1.15e-46) {
		tmp = x;
	} else if (x <= 6e-35) {
		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 (x <= (-3.8d+79)) then
        tmp = x
    else if (x <= (-2.5d+28)) then
        tmp = y / (-200.0d0)
    else if (x <= (-1.15d-46)) then
        tmp = x
    else if (x <= 6d-35) 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 (x <= -3.8e+79) {
		tmp = x;
	} else if (x <= -2.5e+28) {
		tmp = y / -200.0;
	} else if (x <= -1.15e-46) {
		tmp = x;
	} else if (x <= 6e-35) {
		tmp = y / -200.0;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -3.8e+79:
		tmp = x
	elif x <= -2.5e+28:
		tmp = y / -200.0
	elif x <= -1.15e-46:
		tmp = x
	elif x <= 6e-35:
		tmp = y / -200.0
	else:
		tmp = x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -3.8e+79)
		tmp = x;
	elseif (x <= -2.5e+28)
		tmp = Float64(y / -200.0);
	elseif (x <= -1.15e-46)
		tmp = x;
	elseif (x <= 6e-35)
		tmp = Float64(y / -200.0);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -3.8e+79)
		tmp = x;
	elseif (x <= -2.5e+28)
		tmp = y / -200.0;
	elseif (x <= -1.15e-46)
		tmp = x;
	elseif (x <= 6e-35)
		tmp = y / -200.0;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -3.8e+79], x, If[LessEqual[x, -2.5e+28], N[(y / -200.0), $MachinePrecision], If[LessEqual[x, -1.15e-46], x, If[LessEqual[x, 6e-35], N[(y / -200.0), $MachinePrecision], x]]]]

Derivation

1. Split input into 2 regimes

2. if x < -3.8000000000000002e79 or -2.49999999999999979e28 < x < -1.15e-46 or 5.99999999999999978e-35 < x

   1. Initial program 100.0%

      \[x - \frac{y}{200} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -3.8000000000000002e79 < x < -2.49999999999999979e28 or -1.15e-46 < x < 5.99999999999999978e-35

   1. Initial program 100.0%

      \[x - \frac{y}{200} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   7. Applied egg-rr 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 73.7% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.8 \cdot 10^{+79}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq -2.45 \cdot 10^{+30}:\\ \;\;\;\;-0.005 \cdot y\\ \mathbf{elif}\;x \leq -2.1 \cdot 10^{-48}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 8.8 \cdot 10^{-36}:\\ \;\;\;\;-0.005 \cdot y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -3.8e+79)
   x
   (if (<= x -2.45e+30)
     (* -0.005 y)
     (if (<= x -2.1e-48) x (if (<= x 8.8e-36) (* -0.005 y) x)))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -3.8e+79) {
		tmp = x;
	} else if (x <= -2.45e+30) {
		tmp = -0.005 * y;
	} else if (x <= -2.1e-48) {
		tmp = x;
	} else if (x <= 8.8e-36) {
		tmp = -0.005 * y;
	} 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 (x <= (-3.8d+79)) then
        tmp = x
    else if (x <= (-2.45d+30)) then
        tmp = (-0.005d0) * y
    else if (x <= (-2.1d-48)) then
        tmp = x
    else if (x <= 8.8d-36) then
        tmp = (-0.005d0) * y
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -3.8e+79) {
		tmp = x;
	} else if (x <= -2.45e+30) {
		tmp = -0.005 * y;
	} else if (x <= -2.1e-48) {
		tmp = x;
	} else if (x <= 8.8e-36) {
		tmp = -0.005 * y;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -3.8e+79:
		tmp = x
	elif x <= -2.45e+30:
		tmp = -0.005 * y
	elif x <= -2.1e-48:
		tmp = x
	elif x <= 8.8e-36:
		tmp = -0.005 * y
	else:
		tmp = x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -3.8e+79)
		tmp = x;
	elseif (x <= -2.45e+30)
		tmp = Float64(-0.005 * y);
	elseif (x <= -2.1e-48)
		tmp = x;
	elseif (x <= 8.8e-36)
		tmp = Float64(-0.005 * y);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -3.8e+79)
		tmp = x;
	elseif (x <= -2.45e+30)
		tmp = -0.005 * y;
	elseif (x <= -2.1e-48)
		tmp = x;
	elseif (x <= 8.8e-36)
		tmp = -0.005 * y;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -3.8e+79], x, If[LessEqual[x, -2.45e+30], N[(-0.005 * y), $MachinePrecision], If[LessEqual[x, -2.1e-48], x, If[LessEqual[x, 8.8e-36], N[(-0.005 * y), $MachinePrecision], x]]]]

Derivation

1. Split input into 2 regimes

2. if x < -3.8000000000000002e79 or -2.44999999999999992e30 < x < -2.09999999999999989e-48 or 8.7999999999999997e-36 < x

   1. Initial program 100.0%

      \[x - \frac{y}{200} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -3.8000000000000002e79 < x < -2.44999999999999992e30 or -2.09999999999999989e-48 < x < 8.7999999999999997e-36

   1. Initial program 100.0%

      \[x - \frac{y}{200} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 51.5% 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. Add Preprocessing

3. Taylor expanded in x around inf 0

   \[\leadsto expr\]

5. Simplified 0

   \[\leadsto expr\]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024110 
(FPCore (x y)
  :name "Data.Colour.CIE:cieLAB from colour-2.3.3, D"
  :precision binary64
  (- x (/ y 200.0)))