当且仅当我的remoteHost变量不是十进制点格式的字符串时，以下语句是否会抛出异常？
boost::asio::ip::address::from_string(remoteHost)
我真的想在我的方案中检查remoteHost变量的有效性，而不是运行时。
感谢。
答案 0 :(得分：0)
如果您使用address.ipp中的两个参数版本跟address_v6.ipp和address_v4.ipp中的版本，您会看到他们都调用了boost::asio::detail::socket_ops::inet_pton中定义的def auc(y_true, y_val, plot=False):
#check input
if len(y_true) != len(y_val):
raise ValueError('Label vector (y_true) and corresponding value vector (y_val) must have the same length.\n')
#empty arrays, true positive and false positive numbers
tp = []
fp = []
#count 1's and -1's in y_true
cond_positive = list(y_true).count(1)
cond_negative = list(y_true).count(-1)
#all possibly relevant bias parameters stored in a list
bias_set = sorted(list(set(y_val)), key=float, reverse=True)
bias_set.append(min(bias_set)*0.9)
#initialize y_pred array full of negative predictions (-1)
y_pred = np.ones(len(y_true))*(-1)
#the computation time is mainly influenced by this for loop
#for a contamination rate of 1% it already takes ~8s to terminate
for bias in bias_set:
#"lower values tend to correspond to label −1"
#indices of values which exceed the bias
posIdx = np.where(y_val > bias)
#set predicted values to 1
y_pred[posIdx] = 1
#the following function simply calculates results which enable a distinction
#between the cases of true positive and false positive
results = np.asarray(y_true) + 2*np.asarray(y_pred)
#append the amount of tp's and fp's
tp.append(float(list(results).count(3)))
fp.append(float(list(results).count(1)))
#calculate false positive/negative rate
tpr = np.asarray(tp)/cond_positive
fpr = np.asarray(fp)/cond_negative
#optional scatterplot
if plot == True:
plt.scatter(fpr,tpr)
plt.show()
#calculate AUC
AUC = np.trapz(tpr,fpr)
return AUC
3}}从第2029行开始。
这是一个很长的功能，所以我会把它留给你看看它究竟需要什么。