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

Percentage Accurate: 100.0% → 100.0%
Time: 647.0ms
Alternatives: 4
Speedup: 1.0×

Specification

?
\[200 \cdot \left(x - y\right) \]
(FPCore (x y)
  :precision binary64
  :pre TRUE
  (* 200.0 (- x y)))
double code(double x, double y) {
	return 200.0 * (x - y);
}

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

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

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

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

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

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

f(x, y):
	x in [-inf, +inf],
	y in [-inf, +inf]
code: THEORY
BEGIN
f(x, y: real): real =
	(200) * (x - y)
END code
200 \cdot \left(x - y\right)

Local Percentage Accuracy vs ?

The average percentage accuracy by input value. Horizontal axis shows value of an input variable; the variable is choosen in the title. Vertical axis is accuracy; higher is better. Red represent the original program, while blue represents Herbie's suggestion. These can be toggled with buttons below the plot. The line is an average while dots represent individual samples.

Accuracy vs Speed?

Herbie found 4 alternatives:

AlternativeAccuracySpeedup
The accuracy (vertical axis) and speed (horizontal axis) of each alternatives. Up and to the right is better. The red square shows the initial program, and each blue circle shows an alternative.The line shows the best available speed-accuracy tradeoffs.

Initial Program: 100.0% accurate, 1.0× speedup?

\[200 \cdot \left(x - y\right) \]
(FPCore (x y)
  :precision binary64
  :pre TRUE
  (* 200.0 (- x y)))
double code(double x, double y) {
	return 200.0 * (x - y);
}

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

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

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

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

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

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

f(x, y):
	x in [-inf, +inf],
	y in [-inf, +inf]
code: THEORY
BEGIN
f(x, y: real): real =
	(200) * (x - y)
END code
200 \cdot \left(x - y\right)

Alternative 1: 100.0% accurate, 0.7× speedup?

\[\mathsf{fma}\left(-200, y, 200 \cdot x\right) \]
(FPCore (x y)
  :precision binary64
  :pre TRUE
  (fma -200.0 y (* 200.0 x)))
double code(double x, double y) {
	return fma(-200.0, y, (200.0 * x));
}

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

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

f(x, y):
	x in [-inf, +inf],
	y in [-inf, +inf]
code: THEORY
BEGIN
f(x, y: real): real =
	((-200) * y) + ((200) * x)
END code
\mathsf{fma}\left(-200, y, 200 \cdot x\right)
Derivation

  1. Initial program 100.0%

    \[200 \cdot \left(x - y\right) \]

  2. Taylor expanded in x around 0

    \[\leadsto -200 \cdot y + 200 \cdot x \]
  3. Step-by-step derivation

    1. Applied rewrites100.0%

      \[\leadsto \mathsf{fma}\left(-200, y, 200 \cdot x\right) \]
    2. Add Preprocessing

    Alternative 2: 74.7% accurate, 0.6× speedup?

    \[\begin{array}{l} \mathbf{if}\;y \leq -4.155509726251822 \cdot 10^{+46}:\\ \;\;\;\;-200 \cdot y\\ \mathbf{elif}\;y \leq 2.426229654520916 \cdot 10^{+48}:\\ \;\;\;\;200 \cdot x\\ \mathbf{else}:\\ \;\;\;\;-200 \cdot y\\ \end{array} \]
    (FPCore (x y)
  :precision binary64
  :pre TRUE
  (if (<= y -4.155509726251822e+46)
  (* -200.0 y)
  (if (<= y 2.426229654520916e+48) (* 200.0 x) (* -200.0 y))))
    double code(double x, double y) {
	double tmp;
	if (y <= -4.155509726251822e+46) {
		tmp = -200.0 * y;
	} else if (y <= 2.426229654520916e+48) {
		tmp = 200.0 * x;
	} else {
		tmp = -200.0 * y;
	}
	return tmp;
}

    real(8) function code(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-4.155509726251822d+46)) then
        tmp = (-200.0d0) * y
    else if (y <= 2.426229654520916d+48) then
        tmp = 200.0d0 * x
    else
        tmp = (-200.0d0) * y
    end if
    code = tmp
end function

    public static double code(double x, double y) {
	double tmp;
	if (y <= -4.155509726251822e+46) {
		tmp = -200.0 * y;
	} else if (y <= 2.426229654520916e+48) {
		tmp = 200.0 * x;
	} else {
		tmp = -200.0 * y;
	}
	return tmp;
}

    def code(x, y):
	tmp = 0
	if y <= -4.155509726251822e+46:
		tmp = -200.0 * y
	elif y <= 2.426229654520916e+48:
		tmp = 200.0 * x
	else:
		tmp = -200.0 * y
	return tmp

    function code(x, y)
	tmp = 0.0
	if (y <= -4.155509726251822e+46)
		tmp = Float64(-200.0 * y);
	elseif (y <= 2.426229654520916e+48)
		tmp = Float64(200.0 * x);
	else
		tmp = Float64(-200.0 * y);
	end
	return tmp
end

    function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -4.155509726251822e+46)
		tmp = -200.0 * y;
	elseif (y <= 2.426229654520916e+48)
		tmp = 200.0 * x;
	else
		tmp = -200.0 * y;
	end
	tmp_2 = tmp;
end

    code[x_, y_] := If[LessEqual[y, -4.155509726251822e+46], N[(-200.0 * y), $MachinePrecision], If[LessEqual[y, 2.426229654520916e+48], N[(200.0 * x), $MachinePrecision], N[(-200.0 * y), $MachinePrecision]]]

    f(x, y):
	x in [-inf, +inf],
	y in [-inf, +inf]
code: THEORY
BEGIN
f(x, y: real): real =
	LET tmp_1 = IF (y <= (2426229654520915891643607200729785382101422440448)) THEN ((200) * x) ELSE ((-200) * y) ENDIF IN
	LET tmp = IF (y <= (-41555097262518219140230899091581622926874509312)) THEN ((-200) * y) ELSE tmp_1 ENDIF IN
	tmp
END code
    \begin{array}{l}
\mathbf{if}\;y \leq -4.155509726251822 \cdot 10^{+46}:\\
\;\;\;\;-200 \cdot y\\

\mathbf{elif}\;y \leq 2.426229654520916 \cdot 10^{+48}:\\
\;\;\;\;200 \cdot x\\

\mathbf{else}:\\
\;\;\;\;-200 \cdot y\\


\end{array}
    Derivation
    1. Split input into 2 regimes

    2. if y < -4.1555097262518219e46 or 2.4262296545209159e48 < y

      1. Initial program 100.0%

        \[200 \cdot \left(x - y\right) \]

      2. Taylor expanded in x around 0

        \[\leadsto -200 \cdot y \]
      3. Step-by-step derivation

        1. Applied rewrites50.2%

          \[\leadsto -200 \cdot y \]

        if -4.1555097262518219e46 < y < 2.4262296545209159e48

        1. Initial program 100.0%

          \[200 \cdot \left(x - y\right) \]

        2. Taylor expanded in x around inf

          \[\leadsto 200 \cdot x \]
        3. Step-by-step derivation

          1. Applied rewrites51.2%

            \[\leadsto 200 \cdot x \]
        4. Recombined 2 regimes into one program.
        5. Add Preprocessing

        Alternative 3: 50.2% accurate, 1.6× speedup?

        \[-200 \cdot y \]
        (FPCore (x y)
  :precision binary64
  :pre TRUE
  (* -200.0 y))
        double code(double x, double y) {
	return -200.0 * y;
}

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

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

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

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

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

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

        f(x, y):
	x in [-inf, +inf],
	y in [-inf, +inf]
code: THEORY
BEGIN
f(x, y: real): real =
	(-200) * y
END code
        -200 \cdot y
        Derivation

        1. Initial program 100.0%

          \[200 \cdot \left(x - y\right) \]

        2. Taylor expanded in x around 0

          \[\leadsto -200 \cdot y \]
        3. Step-by-step derivation

          1. Applied rewrites50.2%

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

          Reproduce

          ?
          herbie shell --seed 2026092 
(FPCore (x y)
  :name "Data.Colour.CIE:cieLABView from colour-2.3.3, C"
  :precision binary64
  (* 200.0 (- x y)))