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

Percentage Accurate: 100.0% → 100.0%

Time: 3.7s

Alternatives: 5

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ x + \frac{y}{500} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x (/ y 500.0)))

C

double code(double x, double y) {
	return x + (y / 500.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x + (y / 500.0d0)
end function

Java

public static double code(double x, double y) {
	return x + (y / 500.0);
}

Python

def code(x, y):
	return x + (y / 500.0)

Julia

function code(x, y)
	return Float64(x + Float64(y / 500.0))
end

MATLAB

function tmp = code(x, y)
	tmp = x + (y / 500.0);
end

Wolfram

code[x_, y_] := N[(x + N[(y / 500.0), $MachinePrecision]), $MachinePrecision]

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \frac{y}{500} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x (/ y 500.0)))

C

double code(double x, double y) {
	return x + (y / 500.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x + (y / 500.0d0)
end function

Java

public static double code(double x, double y) {
	return x + (y / 500.0);
}

Python

def code(x, y):
	return x + (y / 500.0)

Julia

function code(x, y)
	return Float64(x + Float64(y / 500.0))
end

MATLAB

function tmp = code(x, y)
	tmp = x + (y / 500.0);
end

Wolfram

code[x_, y_] := N[(x + N[(y / 500.0), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \frac{y}{500} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x (/ y 500.0)))

C

double code(double x, double y) {
	return x + (y / 500.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x + (y / 500.0d0)
end function

Java

public static double code(double x, double y) {
	return x + (y / 500.0);
}

Python

def code(x, y):
	return x + (y / 500.0)

Julia

function code(x, y)
	return Float64(x + Float64(y / 500.0))
end

MATLAB

function tmp = code(x, y)
	tmp = x + (y / 500.0);
end

Wolfram

code[x_, y_] := N[(x + N[(y / 500.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x + \frac{y}{500} \]

2. Final simplification 100.0%

   \[\leadsto x + \frac{y}{500} \]

Alternative 2: 75.2% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{y}{500} \leq -2 \cdot 10^{+54}:\\ \;\;\;\;\frac{y}{500}\\ \mathbf{elif}\;\frac{y}{500} \leq 2.5 \cdot 10^{-27}:\\ \;\;\;\;x\\ \mathbf{elif}\;\frac{y}{500} \leq 20:\\ \;\;\;\;\frac{y}{500}\\ \mathbf{elif}\;\frac{y}{500} \leq 10^{+28}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{500}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (/ y 500.0) -2e+54)
   (/ y 500.0)
   (if (<= (/ y 500.0) 2.5e-27)
     x
     (if (<= (/ y 500.0) 20.0)
       (/ y 500.0)
       (if (<= (/ y 500.0) 1e+28) x (/ y 500.0))))))

C

double code(double x, double y) {
	double tmp;
	if ((y / 500.0) <= -2e+54) {
		tmp = y / 500.0;
	} else if ((y / 500.0) <= 2.5e-27) {
		tmp = x;
	} else if ((y / 500.0) <= 20.0) {
		tmp = y / 500.0;
	} else if ((y / 500.0) <= 1e+28) {
		tmp = x;
	} else {
		tmp = y / 500.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y / 500.0d0) <= (-2d+54)) then
        tmp = y / 500.0d0
    else if ((y / 500.0d0) <= 2.5d-27) then
        tmp = x
    else if ((y / 500.0d0) <= 20.0d0) then
        tmp = y / 500.0d0
    else if ((y / 500.0d0) <= 1d+28) then
        tmp = x
    else
        tmp = y / 500.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y / 500.0) <= -2e+54) {
		tmp = y / 500.0;
	} else if ((y / 500.0) <= 2.5e-27) {
		tmp = x;
	} else if ((y / 500.0) <= 20.0) {
		tmp = y / 500.0;
	} else if ((y / 500.0) <= 1e+28) {
		tmp = x;
	} else {
		tmp = y / 500.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y / 500.0) <= -2e+54:
		tmp = y / 500.0
	elif (y / 500.0) <= 2.5e-27:
		tmp = x
	elif (y / 500.0) <= 20.0:
		tmp = y / 500.0
	elif (y / 500.0) <= 1e+28:
		tmp = x
	else:
		tmp = y / 500.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(y / 500.0) <= -2e+54)
		tmp = Float64(y / 500.0);
	elseif (Float64(y / 500.0) <= 2.5e-27)
		tmp = x;
	elseif (Float64(y / 500.0) <= 20.0)
		tmp = Float64(y / 500.0);
	elseif (Float64(y / 500.0) <= 1e+28)
		tmp = x;
	else
		tmp = Float64(y / 500.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y / 500.0) <= -2e+54)
		tmp = y / 500.0;
	elseif ((y / 500.0) <= 2.5e-27)
		tmp = x;
	elseif ((y / 500.0) <= 20.0)
		tmp = y / 500.0;
	elseif ((y / 500.0) <= 1e+28)
		tmp = x;
	else
		tmp = y / 500.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(y / 500.0), $MachinePrecision], -2e+54], N[(y / 500.0), $MachinePrecision], If[LessEqual[N[(y / 500.0), $MachinePrecision], 2.5e-27], x, If[LessEqual[N[(y / 500.0), $MachinePrecision], 20.0], N[(y / 500.0), $MachinePrecision], If[LessEqual[N[(y / 500.0), $MachinePrecision], 1e+28], x, N[(y / 500.0), $MachinePrecision]]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 y 500) < -2.0000000000000002e54 or 2.5000000000000001e-27 < (/.f64 y 500) < 20 or 9.99999999999999958e27 < (/.f64 y 500)

   1. Initial program 100.0%

      \[x + \frac{y}{500} \]
   2. Step-by-step derivation

      1. *-lft-identity 100.0%

         \[\leadsto x + \frac{\color{blue}{1 \cdot y}}{500} \]

      2. metadata-eval 100.0%

         \[\leadsto x + \frac{\color{blue}{\left(--1\right)} \cdot y}{500} \]

      3. associate-/l* 99.7%

         \[\leadsto x + \color{blue}{\frac{--1}{\frac{500}{y}}} \]

      4. distribute-neg-frac 99.7%

         \[\leadsto x + \color{blue}{\left(-\frac{-1}{\frac{500}{y}}\right)} \]

      5. associate-/r/ 99.8%

         \[\leadsto x + \left(-\color{blue}{\frac{-1}{500} \cdot y}\right) \]

      6. distribute-lft-neg-in 99.8%

         \[\leadsto x + \color{blue}{\left(-\frac{-1}{500}\right) \cdot y} \]

      7. *-commutative 99.8%

         \[\leadsto x + \color{blue}{y \cdot \left(-\frac{-1}{500}\right)} \]

      8. metadata-eval 99.8%

         \[\leadsto x + y \cdot \left(-\color{blue}{-0.002}\right) \]

      9. metadata-eval 99.8%

         \[\leadsto x + y \cdot \color{blue}{0.002} \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x + y \cdot 0.002} \]
   4. Step-by-step derivation

      1. +-commutative 99.8%

         \[\leadsto \color{blue}{y \cdot 0.002 + x} \]

      2. flip-+ 42.4%

         \[\leadsto \color{blue}{\frac{\left(y \cdot 0.002\right) \cdot \left(y \cdot 0.002\right) - x \cdot x}{y \cdot 0.002 - x}} \]

      3. swap-sqr 42.1%

         \[\leadsto \frac{\color{blue}{\left(y \cdot y\right) \cdot \left(0.002 \cdot 0.002\right)} - x \cdot x}{y \cdot 0.002 - x} \]

      4. metadata-eval 42.1%

         \[\leadsto \frac{\left(y \cdot y\right) \cdot \color{blue}{4 \cdot 10^{-6}} - x \cdot x}{y \cdot 0.002 - x} \]

   5. Applied egg-rr 42.1%

      \[\leadsto \color{blue}{\frac{\left(y \cdot y\right) \cdot 4 \cdot 10^{-6} - x \cdot x}{y \cdot 0.002 - x}} \]

   6. Taylor expanded in y around inf 39.8%

      \[\leadsto \frac{\color{blue}{4 \cdot 10^{-6} \cdot {y}^{2}}}{y \cdot 0.002 - x} \]
   7. Step-by-step derivation

      1. unpow2 39.8%

         \[\leadsto \frac{4 \cdot 10^{-6} \cdot \color{blue}{\left(y \cdot y\right)}}{y \cdot 0.002 - x} \]

      2. *-commutative 39.8%

         \[\leadsto \frac{\color{blue}{\left(y \cdot y\right) \cdot 4 \cdot 10^{-6}}}{y \cdot 0.002 - x} \]

      3. associate-*r* 39.9%

         \[\leadsto \frac{\color{blue}{y \cdot \left(y \cdot 4 \cdot 10^{-6}\right)}}{y \cdot 0.002 - x} \]

   8. Simplified 39.9%

      \[\leadsto \frac{\color{blue}{y \cdot \left(y \cdot 4 \cdot 10^{-6}\right)}}{y \cdot 0.002 - x} \]

   9. Taylor expanded in y around inf 86.1%

      \[\leadsto \color{blue}{0.002 \cdot y} \]
   10. Step-by-step derivation

       1. *-commutative 86.1%

          \[\leadsto \color{blue}{y \cdot 0.002} \]

   11. Simplified 86.1%

       \[\leadsto \color{blue}{y \cdot 0.002} \]
   12. Step-by-step derivation

       1. metadata-eval 86.1%

          \[\leadsto y \cdot \color{blue}{\frac{1}{500}} \]

       2. div-inv 86.2%

          \[\leadsto \color{blue}{\frac{y}{500}} \]

   13. Applied egg-rr 86.2%

       \[\leadsto \color{blue}{\frac{y}{500}} \]

   if -2.0000000000000002e54 < (/.f64 y 500) < 2.5000000000000001e-27 or 20 < (/.f64 y 500) < 9.99999999999999958e27

   1. Initial program 100.0%

      \[x + \frac{y}{500} \]
   2. Step-by-step derivation

      1. *-lft-identity 100.0%

         \[\leadsto x + \frac{\color{blue}{1 \cdot y}}{500} \]

      2. metadata-eval 100.0%

         \[\leadsto x + \frac{\color{blue}{\left(--1\right)} \cdot y}{500} \]

      3. associate-/l* 99.8%

         \[\leadsto x + \color{blue}{\frac{--1}{\frac{500}{y}}} \]

      4. distribute-neg-frac 99.8%

         \[\leadsto x + \color{blue}{\left(-\frac{-1}{\frac{500}{y}}\right)} \]

      5. associate-/r/ 100.0%

         \[\leadsto x + \left(-\color{blue}{\frac{-1}{500} \cdot y}\right) \]

      6. distribute-lft-neg-in 100.0%

         \[\leadsto x + \color{blue}{\left(-\frac{-1}{500}\right) \cdot y} \]

      7. *-commutative 100.0%

         \[\leadsto x + \color{blue}{y \cdot \left(-\frac{-1}{500}\right)} \]

      8. metadata-eval 100.0%

         \[\leadsto x + y \cdot \left(-\color{blue}{-0.002}\right) \]

      9. metadata-eval 100.0%

         \[\leadsto x + y \cdot \color{blue}{0.002} \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{x + y \cdot 0.002} \]

   4. Taylor expanded in x around inf 78.4%

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

4. Final simplification 82.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{y}{500} \leq -2 \cdot 10^{+54}:\\ \;\;\;\;\frac{y}{500}\\ \mathbf{elif}\;\frac{y}{500} \leq 2.5 \cdot 10^{-27}:\\ \;\;\;\;x\\ \mathbf{elif}\;\frac{y}{500} \leq 20:\\ \;\;\;\;\frac{y}{500}\\ \mathbf{elif}\;\frac{y}{500} \leq 10^{+28}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{500}\\ \end{array} \]

Alternative 3: 75.2% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9.2 \cdot 10^{+56}:\\ \;\;\;\;y \cdot 0.002\\ \mathbf{elif}\;y \leq 1.3 \cdot 10^{-24}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 4100000000:\\ \;\;\;\;y \cdot 0.002\\ \mathbf{elif}\;y \leq 4.5 \cdot 10^{+30}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot 0.002\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -9.2e+56)
   (* y 0.002)
   (if (<= y 1.3e-24)
     x
     (if (<= y 4100000000.0) (* y 0.002) (if (<= y 4.5e+30) x (* y 0.002))))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -9.2e+56) {
		tmp = y * 0.002;
	} else if (y <= 1.3e-24) {
		tmp = x;
	} else if (y <= 4100000000.0) {
		tmp = y * 0.002;
	} else if (y <= 4.5e+30) {
		tmp = x;
	} else {
		tmp = y * 0.002;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-9.2d+56)) then
        tmp = y * 0.002d0
    else if (y <= 1.3d-24) then
        tmp = x
    else if (y <= 4100000000.0d0) then
        tmp = y * 0.002d0
    else if (y <= 4.5d+30) then
        tmp = x
    else
        tmp = y * 0.002d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -9.2e+56) {
		tmp = y * 0.002;
	} else if (y <= 1.3e-24) {
		tmp = x;
	} else if (y <= 4100000000.0) {
		tmp = y * 0.002;
	} else if (y <= 4.5e+30) {
		tmp = x;
	} else {
		tmp = y * 0.002;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -9.2e+56:
		tmp = y * 0.002
	elif y <= 1.3e-24:
		tmp = x
	elif y <= 4100000000.0:
		tmp = y * 0.002
	elif y <= 4.5e+30:
		tmp = x
	else:
		tmp = y * 0.002
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -9.2e+56)
		tmp = Float64(y * 0.002);
	elseif (y <= 1.3e-24)
		tmp = x;
	elseif (y <= 4100000000.0)
		tmp = Float64(y * 0.002);
	elseif (y <= 4.5e+30)
		tmp = x;
	else
		tmp = Float64(y * 0.002);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -9.2e+56)
		tmp = y * 0.002;
	elseif (y <= 1.3e-24)
		tmp = x;
	elseif (y <= 4100000000.0)
		tmp = y * 0.002;
	elseif (y <= 4.5e+30)
		tmp = x;
	else
		tmp = y * 0.002;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -9.2e+56], N[(y * 0.002), $MachinePrecision], If[LessEqual[y, 1.3e-24], x, If[LessEqual[y, 4100000000.0], N[(y * 0.002), $MachinePrecision], If[LessEqual[y, 4.5e+30], x, N[(y * 0.002), $MachinePrecision]]]]]

Derivation

1. Split input into 2 regimes

2. if y < -9.20000000000000058e56 or 1.3e-24 < y < 4.1e9 or 4.49999999999999995e30 < y

   1. Initial program 100.0%

      \[x + \frac{y}{500} \]
   2. Step-by-step derivation

      1. *-lft-identity 100.0%

         \[\leadsto x + \frac{\color{blue}{1 \cdot y}}{500} \]

      2. metadata-eval 100.0%

         \[\leadsto x + \frac{\color{blue}{\left(--1\right)} \cdot y}{500} \]

      3. associate-/l* 99.7%

         \[\leadsto x + \color{blue}{\frac{--1}{\frac{500}{y}}} \]

      4. distribute-neg-frac 99.7%

         \[\leadsto x + \color{blue}{\left(-\frac{-1}{\frac{500}{y}}\right)} \]

      5. associate-/r/ 99.8%

         \[\leadsto x + \left(-\color{blue}{\frac{-1}{500} \cdot y}\right) \]

      6. distribute-lft-neg-in 99.8%

         \[\leadsto x + \color{blue}{\left(-\frac{-1}{500}\right) \cdot y} \]

      7. *-commutative 99.8%

         \[\leadsto x + \color{blue}{y \cdot \left(-\frac{-1}{500}\right)} \]

      8. metadata-eval 99.8%

         \[\leadsto x + y \cdot \left(-\color{blue}{-0.002}\right) \]

      9. metadata-eval 99.8%

         \[\leadsto x + y \cdot \color{blue}{0.002} \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x + y \cdot 0.002} \]
   4. Step-by-step derivation

      1. +-commutative 99.8%

         \[\leadsto \color{blue}{y \cdot 0.002 + x} \]

      2. flip-+ 42.4%

         \[\leadsto \color{blue}{\frac{\left(y \cdot 0.002\right) \cdot \left(y \cdot 0.002\right) - x \cdot x}{y \cdot 0.002 - x}} \]

      3. swap-sqr 42.1%

         \[\leadsto \frac{\color{blue}{\left(y \cdot y\right) \cdot \left(0.002 \cdot 0.002\right)} - x \cdot x}{y \cdot 0.002 - x} \]

      4. metadata-eval 42.1%

         \[\leadsto \frac{\left(y \cdot y\right) \cdot \color{blue}{4 \cdot 10^{-6}} - x \cdot x}{y \cdot 0.002 - x} \]

   5. Applied egg-rr 42.1%

      \[\leadsto \color{blue}{\frac{\left(y \cdot y\right) \cdot 4 \cdot 10^{-6} - x \cdot x}{y \cdot 0.002 - x}} \]

   6. Taylor expanded in y around inf 39.8%

      \[\leadsto \frac{\color{blue}{4 \cdot 10^{-6} \cdot {y}^{2}}}{y \cdot 0.002 - x} \]
   7. Step-by-step derivation

      1. unpow2 39.8%

         \[\leadsto \frac{4 \cdot 10^{-6} \cdot \color{blue}{\left(y \cdot y\right)}}{y \cdot 0.002 - x} \]

      2. *-commutative 39.8%

         \[\leadsto \frac{\color{blue}{\left(y \cdot y\right) \cdot 4 \cdot 10^{-6}}}{y \cdot 0.002 - x} \]

      3. associate-*r* 39.9%

         \[\leadsto \frac{\color{blue}{y \cdot \left(y \cdot 4 \cdot 10^{-6}\right)}}{y \cdot 0.002 - x} \]

   8. Simplified 39.9%

      \[\leadsto \frac{\color{blue}{y \cdot \left(y \cdot 4 \cdot 10^{-6}\right)}}{y \cdot 0.002 - x} \]

   9. Taylor expanded in y around inf 86.1%

      \[\leadsto \color{blue}{0.002 \cdot y} \]
   10. Step-by-step derivation

       1. *-commutative 86.1%

          \[\leadsto \color{blue}{y \cdot 0.002} \]

   11. Simplified 86.1%

       \[\leadsto \color{blue}{y \cdot 0.002} \]

   if -9.20000000000000058e56 < y < 1.3e-24 or 4.1e9 < y < 4.49999999999999995e30

   1. Initial program 100.0%

      \[x + \frac{y}{500} \]
   2. Step-by-step derivation

      1. *-lft-identity 100.0%

         \[\leadsto x + \frac{\color{blue}{1 \cdot y}}{500} \]

      2. metadata-eval 100.0%

         \[\leadsto x + \frac{\color{blue}{\left(--1\right)} \cdot y}{500} \]

      3. associate-/l* 99.8%

         \[\leadsto x + \color{blue}{\frac{--1}{\frac{500}{y}}} \]

      4. distribute-neg-frac 99.8%

         \[\leadsto x + \color{blue}{\left(-\frac{-1}{\frac{500}{y}}\right)} \]

      5. associate-/r/ 100.0%

         \[\leadsto x + \left(-\color{blue}{\frac{-1}{500} \cdot y}\right) \]

      6. distribute-lft-neg-in 100.0%

         \[\leadsto x + \color{blue}{\left(-\frac{-1}{500}\right) \cdot y} \]

      7. *-commutative 100.0%

         \[\leadsto x + \color{blue}{y \cdot \left(-\frac{-1}{500}\right)} \]

      8. metadata-eval 100.0%

         \[\leadsto x + y \cdot \left(-\color{blue}{-0.002}\right) \]

      9. metadata-eval 100.0%

         \[\leadsto x + y \cdot \color{blue}{0.002} \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{x + y \cdot 0.002} \]

   4. Taylor expanded in x around inf 78.4%

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

4. Final simplification 82.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -9.2 \cdot 10^{+56}:\\ \;\;\;\;y \cdot 0.002\\ \mathbf{elif}\;y \leq 1.3 \cdot 10^{-24}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 4100000000:\\ \;\;\;\;y \cdot 0.002\\ \mathbf{elif}\;y \leq 4.5 \cdot 10^{+30}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot 0.002\\ \end{array} \]

Alternative 4: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + y \cdot 0.002 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x (* y 0.002)))

C

double code(double x, double y) {
	return x + (y * 0.002);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x + (y * 0.002d0)
end function

Java

public static double code(double x, double y) {
	return x + (y * 0.002);
}

Python

def code(x, y):
	return x + (y * 0.002)

Julia

function code(x, y)
	return Float64(x + Float64(y * 0.002))
end

MATLAB

function tmp = code(x, y)
	tmp = x + (y * 0.002);
end

Wolfram

code[x_, y_] := N[(x + N[(y * 0.002), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x + \frac{y}{500} \]
2. Step-by-step derivation

   1. *-lft-identity 100.0%

      \[\leadsto x + \frac{\color{blue}{1 \cdot y}}{500} \]

   2. metadata-eval 100.0%

      \[\leadsto x + \frac{\color{blue}{\left(--1\right)} \cdot y}{500} \]

   3. associate-/l* 99.8%

      \[\leadsto x + \color{blue}{\frac{--1}{\frac{500}{y}}} \]

   4. distribute-neg-frac 99.8%

      \[\leadsto x + \color{blue}{\left(-\frac{-1}{\frac{500}{y}}\right)} \]

   5. associate-/r/ 99.9%

      \[\leadsto x + \left(-\color{blue}{\frac{-1}{500} \cdot y}\right) \]

   6. distribute-lft-neg-in 99.9%

      \[\leadsto x + \color{blue}{\left(-\frac{-1}{500}\right) \cdot y} \]

   7. *-commutative 99.9%

      \[\leadsto x + \color{blue}{y \cdot \left(-\frac{-1}{500}\right)} \]

   8. metadata-eval 99.9%

      \[\leadsto x + y \cdot \left(-\color{blue}{-0.002}\right) \]

   9. metadata-eval 99.9%

      \[\leadsto x + y \cdot \color{blue}{0.002} \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{x + y \cdot 0.002} \]

4. Final simplification 99.9%

   \[\leadsto x + y \cdot 0.002 \]

Alternative 5: 52.1% 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%

   \[x + \frac{y}{500} \]
2. Step-by-step derivation

   1. *-lft-identity 100.0%

      \[\leadsto x + \frac{\color{blue}{1 \cdot y}}{500} \]

   2. metadata-eval 100.0%

      \[\leadsto x + \frac{\color{blue}{\left(--1\right)} \cdot y}{500} \]

   3. associate-/l* 99.8%

      \[\leadsto x + \color{blue}{\frac{--1}{\frac{500}{y}}} \]

   4. distribute-neg-frac 99.8%

      \[\leadsto x + \color{blue}{\left(-\frac{-1}{\frac{500}{y}}\right)} \]

   5. associate-/r/ 99.9%

      \[\leadsto x + \left(-\color{blue}{\frac{-1}{500} \cdot y}\right) \]

   6. distribute-lft-neg-in 99.9%

      \[\leadsto x + \color{blue}{\left(-\frac{-1}{500}\right) \cdot y} \]

   7. *-commutative 99.9%

      \[\leadsto x + \color{blue}{y \cdot \left(-\frac{-1}{500}\right)} \]

   8. metadata-eval 99.9%

      \[\leadsto x + y \cdot \left(-\color{blue}{-0.002}\right) \]

   9. metadata-eval 99.9%

      \[\leadsto x + y \cdot \color{blue}{0.002} \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{x + y \cdot 0.002} \]

4. Taylor expanded in x around inf 48.0%

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

5. Final simplification 48.0%

   \[\leadsto x \]

Reproduce

herbie shell --seed 2023293 
(FPCore (x y)
  :name "Data.Colour.CIE:cieLAB from colour-2.3.3, C"
  :precision binary64
  (+ x (/ y 500.0)))