${__V(classSize_${classId})}

`${__V(classSize_${classId})}` 是 jmeter 中的一个变量处理函数，用于获取一个动态变量的值。

在本例中，`${classId}` 是 ForEach 控制器中的一个变量，它的值会在每次循环中被更新。`${classSize_${classId}}` 中的 `${classSize_}` 是一个固定的前缀，而 `${classId}` 则是上述变量的值。因此，`${__V(classSize_${classId})}` 表示获取一个名为 `classSize_${classId}` 的变量的值，其中 `${classId}` 的值是动态的。

在这个例子中，我们使用 JSON 提取器从响应中提取了多个班级的信息，并将每个班级的 id 存储在了 `${classId}` 变量中。同时，我们还使用了一个变量来存储每个班级的人数，变量名为 `classSize_${classId}`。因此，`${__V(classSize_${classId})}` 实际上是获取了当前循环中的 `${classId}` 对应的班级人数。

相关问题

BiSTM_CRF中文分词代码实现

以下是一个简单的BiLSTM-CRF中文分词的代码实现，使用Python和PyTorch框架：

import torch
import torch.nn as nn
import torch.optim as optim

# 定义BiLSTM-CRF模型
class BiLSTM_CRF(nn.Module):
    def __init__(self, vocab_size, tag_to_ix, embedding_dim, hidden_dim):
        super(BiLSTM_CRF, self).__init__()
        self.embedding_dim = embedding_dim
        self.hidden_dim = hidden_dim
        self.vocab_size = vocab_size
        self.tag_to_ix = tag_to_ix
        self.tagset_size = len(tag_to_ix)

        # 定义词嵌入层
        self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)

        # 定义双向LSTM层
        self.lstm = nn.LSTM(embedding_dim, hidden_dim // 2, num_layers=1, bidirectional=True)

        # 定义全连接层
        self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size)

        # 定义转移矩阵
        self.transitions = nn.Parameter(torch.randn(self.tagset_size, self.tagset_size))

        # 定义开始和结束的标记
        self.start_tag = torch.tensor([tag_to_ix['<START>']], dtype=torch.long)
        self.end_tag = torch.tensor([tag_to_ix['<END>']], dtype=torch.long)

        # 初始化参数
        nn.init.xavier_uniform_(self.transitions)

    def _forward_alg(self, feats):
        # 初始化alpha值
        init_alphas = torch.full((1, self.tagset_size), -10000.)
        init_alphas[0][self.tag_to_ix['<START>']] = 0.

        # 转换为张量
        forward_var = init_alphas

        # 迭代每个词的特征
        for feat in feats:
            alphas_t = []
            for next_tag in range(self.tagset_size):
                emit_score = feat[next_tag].view(1, -1).expand(1, self.tagset_size)
                trans_score = self.transitions[next_tag].view(1, -1)
                next_tag_var = forward_var + trans_score + emit_score
                alphas_t.append(self._log_sum_exp(next_tag_var).view(1))
            forward_var = torch.cat(alphas_t).view(1, -1)
        terminal_var = forward_var + self.transitions[self.tag_to_ix['<END>']]
        alpha = self._log_sum_exp(terminal_var)
        return alpha

    def _score_sentence(self, feats, tags):
        # 计算序列得分
        score = torch.zeros(1)
        tags = torch.cat([self.start_tag, tags])
        for i, feat in enumerate(feats):
            score = score + self.transitions[tags[i + 1], tags[i]] + feat[tags[i + 1]]
        score = score + self.transitions[self.tag_to_ix['<END>'], tags[-1]]
        return score

    def _viterbi_decode(self, feats):
        backpointers = []

        # 初始化viterbi变量
        init_vvars = torch.full((1, self.tagset_size), -10000.)
        init_vvars[0][self.tag_to_ix['<START>']] = 0

        # 迭代每个词的特征
        forward_var = init_vvars
        for feat in feats:
            bptrs_t = []
            viterbivars_t = []

            for next_tag in range(self.tagset_size):
                next_tag_var = forward_var + self.transitions[next_tag]
                best_tag_id = self._argmax(next_tag_var)
                bptrs_t.append(best_tag_id)
                viterbivars_t.append(next_tag_var[0][best_tag_id].view(1))

            forward_var = (torch.cat(viterbivars_t) + feat).view(1, -1)
            backpointers.append(bptrs_t)

        # 最后添加结束标记
        terminal_var = forward_var + self.transitions[self.tag_to_ix['<END>']]
        best_tag_id = self._argmax(terminal_var)
        path_score = terminal_var[0][best_tag_id]

        # 回溯路径
        best_path = [best_tag_id]
        for bptrs_t in reversed(backpointers):
            best_tag_id = bptrs_t[best_tag_id]
            best_path.append(best_tag_id)
        start = best_path.pop()
        assert start == self.tag_to_ix['<START>']
        best_path.reverse()
        return path_score, best_path

    def _log_sum_exp(self, vec):
        # 计算log-sum-exp
        max_score = vec[0, self._argmax(vec)]
        max_score_broadcast = max_score.view(1, -1).expand(1, vec.size()[1])
        return max_score + torch.log(torch.sum(torch.exp(vec - max_score_broadcast)))

    def _argmax(self, vec):
        # 返回最大值的下标
        _, idx = torch.max(vec, 1)
        return idx.item()

    def neg_log_likelihood(self, sentence, tags):
        # 计算负对数似然损失
        self.hidden = self.init_hidden()
        embeds = self.word_embeddings(sentence)
        lstm_out, _ = self.lstm(embeds.view(len(sentence), 1, -1))
        lstm_out = lstm_out.view(len(sentence), self.hidden_dim)
        lstm_feats = self.hidden2tag(lstm_out)

        forward_score = self._forward_alg(lstm_feats)
        gold_score = self._score_sentence(lstm_feats, tags)
        return forward_score - gold_score

    def forward(self, sentence):
        # 预测标签
        self.hidden = self.init_hidden()
        embeds = self.word_embeddings(sentence)
        lstm_out, _ = self.lstm(embeds.view(len(sentence), 1, -1))
        lstm_out = lstm_out.view(len(sentence), self.hidden_dim)
        lstm_feats = self.hidden2tag(lstm_out)

        score, tag_seq = self._viterbi_decode(lstm_feats)
        return score, tag_seq

    def init_hidden(self):
        # 初始化LSTM隐藏层
        return (torch.randn(2, 1, self.hidden_dim // 2),
                torch.randn(2, 1, self.hidden_dim // 2))

# 定义标签和词汇表
START_TAG = "<START>"
END_TAG = "<END>"
tag_to_ix = {START_TAG: 0, "B": 1, "I": 2, "O": 3, END_TAG: 4}
ix_to_tag = {v: k for k, v in tag_to_ix.items()}
vocab_size = len(word_to_ix)

# 定义模型和优化器
model = BiLSTM_CRF(vocab_size, tag_to_ix, EMBEDDING_DIM, HIDDEN_DIM)
optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=1e-4)

# 训练模型
for epoch in range(300):
    for sentence, tags in training_data:
        # 清空梯度
        model.zero_grad()

        # 转换为张量
        sentence_in = prepare_sequence(sentence, word_to_ix)
        targets = torch.tensor([tag_to_ix[t] for t in tags], dtype=torch.long)

        # 计算损失函数并更新模型
        loss = model.neg_log_likelihood(sentence_in, targets)
        loss.backward()
        optimizer.step()

# 测试模型
with torch.no_grad():
    precheck_sent = prepare_sequence(test_data[0][0], word_to_ix)
    print(model(precheck_sent))

hive CONCAT_WS

Hive中的CONCAT_WS函数是一个特殊形式的CONCAT函数，用于将多个字符串连接在一起，并使用指定的分隔符分隔它们。CONCAT_WS的语法为CONCAT_WS(separator,str1,str2,...)。第一个参数是分隔符，后面的参数是要连接的字符串。如果分隔符为NULL，则结果为NULL。CONCAT_WS会忽略分隔符参数后的NULL值，但不会忽略空字符串。例如，使用SELECT CONCAT_WS('_',id,name) AS con_ws FROM info LIMIT 1;可以将id和name字段用下划线连接起来。

另外，如果使用CONCAT_WS('|', array())这种模式，array中的null值不会被跳过。

在Hive中，还可以通过CONCAT_WS函数将数组中的元素连接起来。例如，使用SELECT CONCAT_WS(',',c_array) FROM test_array WHERE dt='2016-09-26' AND size(c_array)=2 LIMIT 2;可以将数组c_array的元素用逗号分隔连接起来。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *3* [hive：函数：concat_ws函数](https://blog.csdn.net/weixin_38750084/article/details/97775323)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
- *2* [hive中concat_ws的秘密](https://blog.csdn.net/u012861792/article/details/125532456)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
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