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

Average Error: 0.01% → 0.01%

Time: 1.2s

Precision: binary64

Cost: 320

Math

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

↓

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

FPCore

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

↓

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

C

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

↓

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

↓

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);
}

↓

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

Python

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

↓

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

Julia

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

↓

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

MATLAB

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

↓

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

Wolfram

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

↓

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

Derivation

1. Initial program 0.01

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

2. Final simplification 0.01

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

Alternatives

Alternative 1
Error	26.26%
Cost	456

\[\begin{array}{l} \mathbf{if}\;y \leq -1.08 \cdot 10^{-51}:\\ \;\;\;\;y \cdot 0.002\\ \mathbf{elif}\;y \leq 1.45 \cdot 10^{-37}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot 0.002\\ \end{array} \]

Alternative 2
Error	47.88%
Cost	64

\[x \]

Reproduce

herbie shell --seed 2023102 
(FPCore (x y)
  :name "Data.Colour.CIE:cieLAB from colour-2.3.3, C"
  :precision binary64
  (+ x (/ y 500.0)))