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

Percentage Accurate: 100.0% → 100.0%

Time: 2.5s

Alternatives: 4

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ 200 \cdot \left(x - y\right) \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 200 \cdot \left(x - y\right) \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 0.7× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 (+ (* -200.0 y) (* 200.0 x)))

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return (-200.0 * y) + (200.0 * x)

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[200 \cdot \left(x - y\right) \]
2. Add Preprocessing

3. Taylor expanded in x around 0 100.0%

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

4. Final simplification 100.0%

   \[\leadsto -200 \cdot y + 200 \cdot x \]
5. Add Preprocessing

Alternative 2: 71.8% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.2 \cdot 10^{+79} \lor \neg \left(x \leq -1.85 \cdot 10^{-12}\right) \land \left(x \leq -6.8 \cdot 10^{-66} \lor \neg \left(x \leq 4 \cdot 10^{-54}\right) \land \left(x \leq 8 \cdot 10^{-26} \lor \neg \left(x \leq 5.8 \cdot 10^{+52}\right)\right)\right):\\ \;\;\;\;200 \cdot x\\ \mathbf{else}:\\ \;\;\;\;-200 \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -1.2e+79)
         (and (not (<= x -1.85e-12))
              (or (<= x -6.8e-66)
                  (and (not (<= x 4e-54))
                       (or (<= x 8e-26) (not (<= x 5.8e+52)))))))
   (* 200.0 x)
   (* -200.0 y)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -1.2e+79) || (!(x <= -1.85e-12) && ((x <= -6.8e-66) || (!(x <= 4e-54) && ((x <= 8e-26) || !(x <= 5.8e+52)))))) {
		tmp = 200.0 * x;
	} else {
		tmp = -200.0 * y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((x <= (-1.2d+79)) .or. (.not. (x <= (-1.85d-12))) .and. (x <= (-6.8d-66)) .or. (.not. (x <= 4d-54)) .and. (x <= 8d-26) .or. (.not. (x <= 5.8d+52))) then
        tmp = 200.0d0 * x
    else
        tmp = (-200.0d0) * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -1.2e+79) || (!(x <= -1.85e-12) && ((x <= -6.8e-66) || (!(x <= 4e-54) && ((x <= 8e-26) || !(x <= 5.8e+52)))))) {
		tmp = 200.0 * x;
	} else {
		tmp = -200.0 * y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -1.2e+79) or (not (x <= -1.85e-12) and ((x <= -6.8e-66) or (not (x <= 4e-54) and ((x <= 8e-26) or not (x <= 5.8e+52))))):
		tmp = 200.0 * x
	else:
		tmp = -200.0 * y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -1.2e+79) || (!(x <= -1.85e-12) && ((x <= -6.8e-66) || (!(x <= 4e-54) && ((x <= 8e-26) || !(x <= 5.8e+52))))))
		tmp = Float64(200.0 * x);
	else
		tmp = Float64(-200.0 * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -1.2e+79) || (~((x <= -1.85e-12)) && ((x <= -6.8e-66) || (~((x <= 4e-54)) && ((x <= 8e-26) || ~((x <= 5.8e+52)))))))
		tmp = 200.0 * x;
	else
		tmp = -200.0 * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -1.2e+79], And[N[Not[LessEqual[x, -1.85e-12]], $MachinePrecision], Or[LessEqual[x, -6.8e-66], And[N[Not[LessEqual[x, 4e-54]], $MachinePrecision], Or[LessEqual[x, 8e-26], N[Not[LessEqual[x, 5.8e+52]], $MachinePrecision]]]]]], N[(200.0 * x), $MachinePrecision], N[(-200.0 * y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1.19999999999999993e79 or -1.84999999999999999e-12 < x < -6.79999999999999994e-66 or 4.0000000000000001e-54 < x < 8.0000000000000003e-26 or 5.8e52 < x

   1. Initial program 100.0%

      \[200 \cdot \left(x - y\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 85.5%

      \[\leadsto \color{blue}{200 \cdot x} \]

   if -1.19999999999999993e79 < x < -1.84999999999999999e-12 or -6.79999999999999994e-66 < x < 4.0000000000000001e-54 or 8.0000000000000003e-26 < x < 5.8e52

   1. Initial program 100.0%

      \[200 \cdot \left(x - y\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 76.7%

      \[\leadsto \color{blue}{-200 \cdot y} \]
3. Recombined 2 regimes into one program.

4. Final simplification 80.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.2 \cdot 10^{+79} \lor \neg \left(x \leq -1.85 \cdot 10^{-12}\right) \land \left(x \leq -6.8 \cdot 10^{-66} \lor \neg \left(x \leq 4 \cdot 10^{-54}\right) \land \left(x \leq 8 \cdot 10^{-26} \lor \neg \left(x \leq 5.8 \cdot 10^{+52}\right)\right)\right):\\ \;\;\;\;200 \cdot x\\ \mathbf{else}:\\ \;\;\;\;-200 \cdot y\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 200 \cdot \left(x - y\right) \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[200 \cdot \left(x - y\right) \]
2. Add Preprocessing

3. Final simplification 100.0%

   \[\leadsto 200 \cdot \left(x - y\right) \]
4. Add Preprocessing

Alternative 4: 50.4% accurate, 1.7× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[200 \cdot \left(x - y\right) \]
2. Add Preprocessing

3. Taylor expanded in x around 0 48.9%

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

4. Final simplification 48.9%

   \[\leadsto -200 \cdot y \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2024078 
(FPCore (x y)
  :name "Data.Colour.CIE:cieLABView from colour-2.3.3, C"
  :precision binary64
  (* 200.0 (- x y)))