def robotsrulesetOfDomain(self, domain):
		'''检查domain是否在数据库中,
			否 --> (False, None)
			是 --> (True, 数据库中存储的robots.txt内容)
		'''
		exist = False
		cur = self.conn.cursor()
		# 是否存在
		cur.execute("SELECT 1 FROM Domains WHERE domain=?", (domain,))
		if cur.fetchone() :
			exist = True
		# 存在的话,结果是什么
		cur.execute("SELECT rules FROM Domains,Rulesets "
			"WHERE domain=? AND Domains.did=Rulesets.did"
			,(domain,) )
		ruleset = cur.fetchone()
		return (exist, ruleset)
def gerrity_score(self):
        """
        Gerrity Score, which weights each cell in the contingency table by its observed relative frequency.
        :return:
        """
        k = self.table.shape[0]
        n = float(self.table.sum())
        p_o = self.table.sum(axis=0) / n
        p_sum = np.cumsum(p_o)[:-1]
        a = (1.0 - p_sum) / p_sum
        s = np.zeros(self.table.shape, dtype=float)
        for (i, j) in np.ndindex(*s.shape):
            if i == j:
                s[i, j] = 1.0 / (k - 1.0) * (np.sum(1.0 / a[0:j]) + np.sum(a[j:k - 1]))
            elif i < j:
                s[i, j] = 1.0 / (k - 1.0) * (np.sum(1.0 / a[0:i]) - (j - i) + np.sum(a[j:k - 1]))
            else:
                s[i, j] = s[j, i]
        return np.sum(self.table / float(self.table.sum()) * s)