// 刷新对应的 opeId 下的数据内容 emitter.emit(`/${opeId}/details_getQueryTableDataApi`) emitter.emit(`/${opeId}_getQueryTableDataApi`) 什么意思
时间: 2024-04-10 20:32:28 浏览: 22
这段代码是使用事件发射器(emitter)来触发两个事件,用于刷新与`opeId`相关的数据内容。
1. `emitter.emit(`/${opeId}/details_getQueryTableDataApi`)`:这行代码会触发一个名为`/${opeId}/details_getQueryTableDataApi`的事件。根据代码中的命名规则,`opeId`会被作为路径的一部分。这个事件可能会被其他组件或模块监听,用来执行相应的操作,例如刷新数据表格中与`opeId`相关的数据。
2. `emitter.emit(`/${opeId}_getQueryTableDataApi`)`:这行代码会触发一个名为`/${opeId}_getQueryTableDataApi`的事件。同样,根据代码中的命名规则,`opeId`会被作为路径的一部分。这个事件同样可能会被其他组件或模块监听,用来执行相应的操作,例如刷新与`opeId`相关的查询结果数据。
总的来说,这段代码通过事件发射器触发两个不同的事件,用于刷新与`opeId`相关的数据内容。具体的事件处理逻辑可能在其他组件或模块中定义和实现。
相关问题
帮我给每一行代码添加注释 class DeepKalmanFilter(nn.Module): def __init__(self, config): super(DeepKalmanFilter, self).__init__() self.emitter = Emitter(config.z_dim, config.emit_hidden_dim, config.obs_dim) self.transition = Transition(config.z_dim, config.trans_hidden_dim) self.posterior = Posterior( config.z_dim, config.post_hidden_dim, config.obs_dim ) self.z_q_0 = nn.Parameter(torch.zeros(config.z_dim)) self.emit_log_sigma = nn.Parameter(config.emit_log_sigma * torch.ones(config.obs_dim)) self.config = config @staticmethod def reparametrization(mu, sig): return mu + torch.randn_like(sig) * sig @staticmethod def kl_div(mu0, sig0, mu1, sig1): return -0.5 * torch.sum(1 - 2 * sig1.log() + 2 * sig0.log() - (mu1 - mu0).pow(2) / sig1.pow(2) - (sig0 / sig1).pow(2)) def loss(self, obs): time_step = obs.size(1) batch_size = obs.size(0) overshoot_len = self.config.overshooting kl = torch.Tensor([0]).to(self.config.device) reconstruction = torch.Tensor([0]).to(self.config.device) emit_sig = self.emit_log_sigma.exp() for s in range(self.config.sampling_num): z_q_t = self.z_q_0.expand((batch_size, self.config.z_dim)) for t in range(time_step): trans_loc, trans_sig = self.transition(z_q_t) post_loc, post_sig = self.posterior(trans_loc, trans_sig, obs[:, t]) z_q_t = self.reparametrization(post_loc, post_sig) emit_loc = self.emitter(z_q_t) reconstruction += ((emit_loc - obs[:, t]).pow(2).sum(dim=0) / 2 / emit_sig + self.emit_log_sigma * batch_size / 2).sum() if t > 0: over_loc, over_sig = self.transition(overshooting[:overshoot_len - 1]) over_loc = torch.cat([trans_loc.unsqueeze(0), over_loc], dim=0) over_sig = torch.cat([trans_sig.unsqueeze(0), over_sig], dim=0) else: over_loc = trans_loc.unsqueeze(0) over_sig = trans_sig.unsqueeze(0) overshooting = self.reparametrization(over_loc, over_sig) kl = kl + self.kl_div(post_loc.expand_as(over_loc), post_sig.expand_as(over_sig), over_loc, over_sig) / min(t + 1, self.config.overshooting) reconstruction = reconstruction / self.config.sampling_num kl = kl / self.config.sampling_num return reconstruction, kl
答:class DeepKalmanFilter(nn.Module): # 定义深度Kalman滤波器类
def __init__(self, config): # 初始化
super(DeepKalmanFilter, self).__init__() # 调用父类构造函数
self.emitter = Emitter(config.z_dim, config.emit_hidden_dim, config.obs_dim) # 实例化发射器
self.transition = Transition(config.z_dim, config.trans_hidden_dim) # 实例化转换器
self.posterior = Posterior(config.z_dim, config.post_hidden_dim, config.obs_dim) # 实例化后验概率
self.z_q_0 = nn.Parameter(torch.zeros(config.z_dim)) # 设置参数z_q_0
self.emit_log_sigma = nn.Parameter(config.emit_log_sigma * torch.ones(config.obs_dim)) # 发射标准差参数
self.config = config # 配置参数 @staticmethod
def reparametrization(mu, sig): # 重置参数
return mu + torch.randn_like(sig) * sig # 根据均值和标准差重置参数 @staticmethod
def kl_div(mu0, sig0, mu1, sig1): # 计算KL散度
return -0.5 * torch.sum(1 - 2 * sig1.log() + 2 * sig0.log() - (mu1 - mu0).pow(2) / sig1.pow(2) - (sig0 / sig1).pow(2)) # 计算KL散度 def loss(self, obs): # 损失函数
time_step = obs.size(1) # 观测序列的时间步数
batch_size = obs.size(0) # 批量大小
overshoot_len = self.config.overshooting # 超调量
kl = torch.Tensor([0]).to(self.config.device) # kl散度
reconstruction = torch.Tensor([0]).to(self.config.device) # 构建重构误差
emit_sig = self.emit_log_sigma.exp() # 发射标准差
for s in range(self.config.sampling_num): # 采样次数
z_q_t = self.z_q_0.expand((batch_size, self.config.z_dim)) # 估计量初始化
for t in range(time_step): # 遍历每一时刻
trans_loc, trans_sig = self.transition(z_q_t) # 更新转换器
post_loc, post_sig = self.posterior(trans_loc, trans_sig, obs[:, t]) # 更新后验概率
z_q_t = self.reparametrization(post_loc, post_sig) # 重新参数化
emit_loc = self.emitter(z_q_t) # 计算发射器
reconstruction += ((emit_loc - obs[:, t]).pow(2).sum(dim=0) / 2 / emit_sig +
self.emit_log_sigma * batch_size / 2).sum() # 计算重构误差
if t > 0: # 如果不是第一步
over_loc, over_sig = self.transition(overshooting[:overshoot_len - 1]) # 计算超调量
over_loc = torch.cat([trans_loc.unsqueeze(0), over_loc], dim=0) # 转换器的位置
over_sig = torch.cat([trans_sig.unsqueeze(0), over_sig], dim=0) # 转换器的协方差
else: # 如果是第一步
over_loc = trans_loc.unsqueeze(0) # 转换器的位
index.vue:202 Uncaught (in promise) TypeError: Cannot read properties of undefined (reading 'type') at _callee$ (index.vue:202:1) at tryCatch (regeneratorRuntime.js:44:1) at Generator.eval (regeneratorRuntime.js:125:1) at Generator.eval [as next] (regeneratorRuntime.js:69:1) at asyncGeneratorStep (asyncToGenerator.js:3:1) at _next (asyncToGenerator.js:22:1) at eval (asyncToGenerator.js:27:1) at new Promise (<anonymous>) at eval (asyncToGenerator.js:19:1) at VueComponent.handleNodeClick (index.vue:228:1) _callee$ @ index.vue:202 tryCatch @ regeneratorRuntime.js:44 eval @ regeneratorRuntime.js:125 eval @ regeneratorRuntime.js:69 asyncGeneratorStep @ asyncToGenerator.js:3 _next @ asyncToGenerator.js:22 eval @ asyncToGenerator.js:27 eval @ asyncToGenerator.js:19 handleNodeClick @ index.vue:228 handleSizeChange @ index.vue:191 invokeWithErrorHandling @ vue.runtime.esm.js:3971 invoker @ vue.runtime.esm.js:1188 invokeWithErrorHandling @ vue.runtime.esm.js:3971 Vue.$emit @ vue.runtime.esm.js:2874 handleChange @ element-ui.common.js:982 invokeWithErrorHandling @ vue.runtime.esm.js:3971 invoker @ vue.runtime.esm.js:1188 invokeWithErrorHandling @ vue.runtime.esm.js:3971 Vue.$emit @ vue.runtime.esm.js:2874 handleOptionSelect @ select.js:1945 invokeWithErrorHandling @ vue.runtime.esm.js:3971 Vue.$emit @ vue.runtime.esm.js:2874 dispatch @ emitter.js:29 selectOptionClick @ option.js:383 click @ option.js:230 invokeWithErrorHandling @ vue.runtime.esm.js:3971 invoker @ vue.runtime.esm.js:1188 original_1._wrapper @ vue.runtime.esm.js:7265 index.vue:201
这是一个 JavaScript 的错误,具体的错误信息是:在 index.vue 文件的第 202 行,尝试读取一个未定义的属性 type 所导致的错误。该错误可能是由于变量未被正确初始化或赋值所引起的。建议检查相应的代码,确保变量在使用之前已被正确初始化或定义。另外,你可以尝试在代码中使用调试器来帮助你找到问题的原因。