human
I wrote the below code

    def safeLog(d):
        if (d <= 0.0):
            return 0.0
        else:
            return np.log(d)
    def logL(p, k, n):
        return ((k * safeLog(p)) + ((n - k) * safeLog((1.0 - p))))
    def twoLogLambda(k1, k2, n1, n2):
        p = ((k1 + k2) / (n1 + n2))
        return (2.0 * (((logL((k1 / n1), k1, n1) + logL((k2 / n2), k2, n2)) - logL(p, k1, n1)) - logL(p, k2, n2)))
    if (X is Y):
        X = Y = np.asanyarray(X)
    else:
        X = np.asanyarray(X)
        Y = np.asanyarray(Y)
    result = []
    i = 0
    for arrayX in X:
        result.append([])
        for arrayY in Y:
            XY = np.intersect1d(arrayX, arrayY)
            if (XY.size == 0):
                result[i].append(0.0)
            else:
                nX = arrayX.size
                nY = arrayY.size
                if (((nX - XY.size) == 0) or ((n_items - nY) == 0)):
                    result[i].append(1.0)
                else:
                    logLikelihood = twoLogLambda(float(XY.size), float((nX - XY.size)), float(nY), float((n_items - nY)))
                    result[i].append((1.0 - (1.0 / (1.0 + float(logLikelihood)))))
        result[i] = np.asanyarray(result[i])
        i += 1
    return np.asanyarray(result)

What's a good function header?

=====
Here's a good function header?
assistant
#Here's a good function header.

def loglikehood_coefficient(n_items, X, Y):