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

Percentage Accurate: 100.0% → 100.0%

Time: 2.3s

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. Add Preprocessing

3. Final simplification 100.0%

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

Alternative 2: 74.9% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{y}{200} \leq -1 \cdot 10^{+112} \lor \neg \left(\frac{y}{200} \leq -5 \cdot 10^{+65} \lor \neg \left(\frac{y}{200} \leq -0.0005\right) \land \frac{y}{200} \leq 4\right):\\ \;\;\;\;\frac{-y}{200}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= (/ y 200.0) -1e+112)
         (not
          (or (<= (/ y 200.0) -5e+65)
              (and (not (<= (/ y 200.0) -0.0005)) (<= (/ y 200.0) 4.0)))))
   (/ (- y) 200.0)
   x))

C

double code(double x, double y) {
	double tmp;
	if (((y / 200.0) <= -1e+112) || !(((y / 200.0) <= -5e+65) || (!((y / 200.0) <= -0.0005) && ((y / 200.0) <= 4.0)))) {
		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 / 200.0d0) <= (-1d+112)) .or. (.not. ((y / 200.0d0) <= (-5d+65)) .or. (.not. ((y / 200.0d0) <= (-0.0005d0))) .and. ((y / 200.0d0) <= 4.0d0))) 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 / 200.0) <= -1e+112) || !(((y / 200.0) <= -5e+65) || (!((y / 200.0) <= -0.0005) && ((y / 200.0) <= 4.0)))) {
		tmp = -y / 200.0;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if ((y / 200.0) <= -1e+112) or not (((y / 200.0) <= -5e+65) or (not ((y / 200.0) <= -0.0005) and ((y / 200.0) <= 4.0))):
		tmp = -y / 200.0
	else:
		tmp = x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((Float64(y / 200.0) <= -1e+112) || !((Float64(y / 200.0) <= -5e+65) || (!(Float64(y / 200.0) <= -0.0005) && (Float64(y / 200.0) <= 4.0))))
		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 / 200.0) <= -1e+112) || ~((((y / 200.0) <= -5e+65) || (~(((y / 200.0) <= -0.0005)) && ((y / 200.0) <= 4.0)))))
		tmp = -y / 200.0;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[N[(y / 200.0), $MachinePrecision], -1e+112], N[Not[Or[LessEqual[N[(y / 200.0), $MachinePrecision], -5e+65], And[N[Not[LessEqual[N[(y / 200.0), $MachinePrecision], -0.0005]], $MachinePrecision], LessEqual[N[(y / 200.0), $MachinePrecision], 4.0]]]], $MachinePrecision]], N[((-y) / 200.0), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if (/.f64 y 200) < -9.9999999999999993e111 or -4.99999999999999973e65 < (/.f64 y 200) < -5.0000000000000001e-4 or 4 < (/.f64 y 200)

   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-frac-neg 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* 99.6%

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

      5. associate-/r/ 99.8%

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

      6. metadata-eval 99.8%

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

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x + -0.005 \cdot y} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 81.4%

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

      1. *-commutative 81.4%

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

      2. metadata-eval 81.4%

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

      3. metadata-eval 81.4%

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

      4. distribute-rgt-neg-in 81.4%

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

      5. div-inv 81.6%

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

      6. distribute-neg-frac 81.6%

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

   7. Applied egg-rr 81.6%

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

   if -9.9999999999999993e111 < (/.f64 y 200) < -4.99999999999999973e65 or -5.0000000000000001e-4 < (/.f64 y 200) < 4

   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-frac-neg 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/ 100.0%

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

      6. metadata-eval 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{x + -0.005 \cdot y} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 80.0%

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

4. Final simplification 80.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{y}{200} \leq -1 \cdot 10^{+112} \lor \neg \left(\frac{y}{200} \leq -5 \cdot 10^{+65} \lor \neg \left(\frac{y}{200} \leq -0.0005\right) \land \frac{y}{200} \leq 4\right):\\ \;\;\;\;\frac{-y}{200}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 74.9% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{y}{200} \leq -1 \cdot 10^{+112} \lor \neg \left(\frac{y}{200} \leq -5 \cdot 10^{+65} \lor \neg \left(\frac{y}{200} \leq -0.0005\right) \land \frac{y}{200} \leq 4\right):\\ \;\;\;\;y \cdot -0.005\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= (/ y 200.0) -1e+112)
         (not
          (or (<= (/ y 200.0) -5e+65)
              (and (not (<= (/ y 200.0) -0.0005)) (<= (/ y 200.0) 4.0)))))
   (* y -0.005)
   x))

C

double code(double x, double y) {
	double tmp;
	if (((y / 200.0) <= -1e+112) || !(((y / 200.0) <= -5e+65) || (!((y / 200.0) <= -0.0005) && ((y / 200.0) <= 4.0)))) {
		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 / 200.0d0) <= (-1d+112)) .or. (.not. ((y / 200.0d0) <= (-5d+65)) .or. (.not. ((y / 200.0d0) <= (-0.0005d0))) .and. ((y / 200.0d0) <= 4.0d0))) 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 / 200.0) <= -1e+112) || !(((y / 200.0) <= -5e+65) || (!((y / 200.0) <= -0.0005) && ((y / 200.0) <= 4.0)))) {
		tmp = y * -0.005;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if ((y / 200.0) <= -1e+112) or not (((y / 200.0) <= -5e+65) or (not ((y / 200.0) <= -0.0005) and ((y / 200.0) <= 4.0))):
		tmp = y * -0.005
	else:
		tmp = x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((Float64(y / 200.0) <= -1e+112) || !((Float64(y / 200.0) <= -5e+65) || (!(Float64(y / 200.0) <= -0.0005) && (Float64(y / 200.0) <= 4.0))))
		tmp = Float64(y * -0.005);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (((y / 200.0) <= -1e+112) || ~((((y / 200.0) <= -5e+65) || (~(((y / 200.0) <= -0.0005)) && ((y / 200.0) <= 4.0)))))
		tmp = y * -0.005;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[N[(y / 200.0), $MachinePrecision], -1e+112], N[Not[Or[LessEqual[N[(y / 200.0), $MachinePrecision], -5e+65], And[N[Not[LessEqual[N[(y / 200.0), $MachinePrecision], -0.0005]], $MachinePrecision], LessEqual[N[(y / 200.0), $MachinePrecision], 4.0]]]], $MachinePrecision]], N[(y * -0.005), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if (/.f64 y 200) < -9.9999999999999993e111 or -4.99999999999999973e65 < (/.f64 y 200) < -5.0000000000000001e-4 or 4 < (/.f64 y 200)

   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-frac-neg 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* 99.6%

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

      5. associate-/r/ 99.8%

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

      6. metadata-eval 99.8%

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

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x + -0.005 \cdot y} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 81.4%

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

   if -9.9999999999999993e111 < (/.f64 y 200) < -4.99999999999999973e65 or -5.0000000000000001e-4 < (/.f64 y 200) < 4

   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-frac-neg 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/ 100.0%

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

      6. metadata-eval 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{x + -0.005 \cdot y} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 80.0%

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

4. Final simplification 80.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{y}{200} \leq -1 \cdot 10^{+112} \lor \neg \left(\frac{y}{200} \leq -5 \cdot 10^{+65} \lor \neg \left(\frac{y}{200} \leq -0.0005\right) \land \frac{y}{200} \leq 4\right):\\ \;\;\;\;y \cdot -0.005\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

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-frac-neg 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. Add Preprocessing

5. Final simplification 99.9%

   \[\leadsto x + y \cdot -0.005 \]
6. Add Preprocessing

Alternative 5: 51.1% 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%

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

   1. sub-neg 100.0%

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

   2. distribute-frac-neg 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. Add Preprocessing

5. Taylor expanded in x around inf 53.5%

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

6. Final simplification 53.5%

   \[\leadsto x \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2024026 
(FPCore (x y)
  :name "Data.Colour.CIE:cieLAB from colour-2.3.3, D"
  :precision binary64
  (- x (/ y 200.0)))