编写pytorch代码，定义归一化器，计算归一化器的均值和标准差，更新归一化器的均值和标准差，对特征集和标签集进行归一化

好的，以下是针对你的问题的 PyTorch 代码，你可以参考一下：

import torch

class Normalizer:
    def __init__(self, feature_dim):
        self.mean = torch.zeros(feature_dim)
        self.var = torch.zeros(feature_dim)
        self.count = 0

    def update(self, x):
        batch_mean = torch.mean(x, dim=0)
        batch_var = torch.var(x, dim=0)

        batch_count = x.shape[0]

        delta = batch_mean - self.mean

        new_mean = self.mean + delta * batch_count / (self.count + batch_count)

        m_a = self.var * self.count
        m_b = batch_var * batch_count
        M2 = m_a + m_b + delta ** 2 * self.count * batch_count / (self.count + batch_count)
        new_var = M2 / (self.count + batch_count)

        self.mean = new_mean
        self.var = new_var
        self.count = self.count + batch_count

    def normalize(self, x):
        return (x - self.mean) / torch.sqrt(self.var + 1e-8)

这个 `Normalizer` 类包含了三个方法：

- `__init__(self, feature_dim)`：构造函数，传入特征维度 `feature_dim`，初始化均值、方差和计数器。
- `update(self, x)`：传入当前的特征集 `x`，计算均值和方差的滑动平均值，并更新计数器。
- `normalize(self, x)`：传入特征集 `x`，对其进行归一化，输出归一化后的结果。

你可以按照以下步骤使用这个类：

# 假设特征集和标签集分别为 features 和 labels
feature_dim = features.shape[-1]
normalizer = Normalizer(feature_dim)

# 计算均值和方差的滑动平均值
normalizer.update(features)

# 对特征集和标签集进行归一化
normalized_features = normalizer.normalize(features)
normalized_labels = normalizer.normalize(labels)

希望这个代码对你有帮助！