[np.random.normal(0.0, 0.9), np.random.normal(0.0, 0.9)] 这个是什么意思

这个是用来生成符合正态分布的随机数的函数。具体来说，`np.random.normal(mean, stddev, size)`会生成一个大小为`size`的数组，其中的元素符合均值为`mean`，标准差为`stddev`的正态分布。在这个例子中，`np.random.normal(0.0, 0.9)`会生成一个均值为0，标准差为0.9的正态分布随机数，这个函数被用来生成样本数据。因为在这个例子中，我们想要生成两个不同的簇，所以我们在样本数据生成的时候，使用了两个不同的均值和标准差来产生两个不同的正态分布随机数，分别对应于两个簇的数据。

相关问题

用pytorch写一段代码：以西储大学CWRU轴承故障的振动信号数据库作为模型的训练集，故障类型标签包括：正常、内圈故障、外圈故障和滚动体故障四类。首先将一维故障信号转换为二维灰度图作为CNN模型的输...

以下是一个简单的示例代码，用于将一维信号转换为二维灰度图，并使用PyTorch中的CNN模型进行训练。

首先，需要导入必要的Python库和CWRU轴承故障的振动信号数据库。

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader

from sklearn.model_selection import train_test_split

# Load the CWRU bearing dataset
data = pd.read_csv("cwru_data.csv")
labels = pd.read_csv("cwru_labels.csv")

然后，我们将一维信号转换为二维灰度图。我们可以使用Python的OpenCV库进行这个过程，也可以手动编写代码来完成这个过程。这里，我们演示手动编写代码的过程。

# Convert 1D signals to 2D grayscale images
def signal_to_image(signal):
    image = np.zeros((256, 256))
    for i in range(256):
        for j in range(256):
            image[i][j] = signal[i*256+j]
    return image

# Convert all signals to images
images = []
for i in range(len(data)):
    images.append(signal_to_image(data.iloc[i]))

# Convert images to PyTorch tensors
images = torch.tensor(images).unsqueeze(1)

接下来，我们需要将故障类型标签转换为数字标签。

# Convert labels to numerical labels
label_map = {"Normal": 0, "Inner": 1, "Outer": 2, "Rolling": 3}
labels = labels.replace({"Fault": label_map})

然后，我们可以将数据集拆分为训练集和测试集。

# Split the dataset into train and test sets
train_images, test_images, train_labels, test_labels = train_test_split(images, labels, test_size=0.2, random_state=42)

最后，我们可以定义CNN模型并进行训练。

# Define the CNN model
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.fc1 = nn.Linear(64 * 64 * 64, 128)
        self.fc2 = nn.Linear(128, 4)

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = x.view(-1, 64 * 64 * 64)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# Initialize the model and loss function
model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# Train the model
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 200 == 199:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 200))
            running_loss = 0.0

# Test the model
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the test images: %d %%' % (100 * correct / total))

这里，我们使用了一个简单的CNN模型，并在训练集上进行了10个epoch的训练。最后，我们在测试集上评估了模型的准确性。

我用的是新版gym，请修改这份代码，使之能运行

为了使您的代码能够兼容新版Gym（例如 Gymnasium），您需要对一些API调用进行调整。以下是主要的改动点：

1. **环境创建**：`gym.make` 返回的对象不再直接提供 `reset()` 方法，而是返回一个 `EnvSpec` 对象。您需要使用 `reset()` 方法时传递 `seed` 参数。
2. **步进方法**：`step()` 方法现在返回五个元素，而不是四个。具体来说，它返回 `(observation, reward, terminated, truncated, info)` 而不是 `(observation, reward, done, info)`。
3. **动作空间和观测空间**：`action_space` 和 `observation_space` 的属性和方法可能有所变化，但通常情况下不需要修改。

下面是修改后的代码：

import numpy as np
from scipy.stats import truncnorm
import gymnasium as gym
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
import collections
import matplotlib.pyplot as plt

class CEM:
    def __init__(self, n_sequence, elite_ratio, fake_env, upper_bound, lower_bound):
        self.n_sequence = n_sequence
        self.elite_ratio = elite_ratio
        self.upper_bound = upper_bound
        self.lower_bound = lower_bound
        self.fake_env = fake_env

    def optimize(self, state, init_mean, init_var):
        mean, var = init_mean, init_var
        X = truncnorm(-2, 2, loc=np.zeros_like(mean), scale=np.ones_like(var))
        state = np.tile(state, (self.n_sequence, 1))
        
        for _ in range(5):
            lb_dist, ub_dist = mean - self.lower_bound, self.upper_bound - mean
            constrained_var = np.minimum(
                np.minimum(np.square(lb_dist / 2), np.square(ub_dist / 2)),
                var
            )
            action_sequences = [X.rvs() for _ in range(self.n_sequence)] * np.sqrt(constrained_var) + mean
            returns = self.fake_env.propagate(state, action_sequences)[:, 0]
            elites = action_sequences[np.argsort(returns)][-int(self.elite_ratio * self.n_sequence):]
            new_mean = np.mean(elites, axis=0)
            new_var = np.var(elites, axis=0)
            mean = 0.1 * mean + 0.9 * new_mean
            var = 0.1 * var + 0.9 * new_var
        
        return mean

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

class Swish(nn.Module):
    def __init__(self):
        super(Swish, self).__init__()
    
    def forward(self, x):
        return x * torch.sigmoid(x)

def init_weights(m):
    def truncated_normal_init(t, mean=0.0, std=0.01):
        torch.nn.init.normal_(t, mean=mean, std=std)
        while True:
            cond = (t < mean - 2 * std) | (t > mean + 2 * std)
            if not torch.sum(cond):
                break
            t = torch.where(
                cond,
                torch.nn.init.normal_(torch.ones(t.shape, device=device), mean=mean, std=std),
                t
            )
        return t
    
    if type(m) == nn.Linear or isinstance(m, FCLayer):
        truncated_normal_init(m.weight, std=1 / (2 * np.sqrt(m._input_dim)))
        m.bias.data.fill_(0.0)

class FCLayer(nn.Module):
    def __init__(self, input_dim, output_dim, ensemble_size, activation):
        super(FCLayer, self).__init__()
        self._input_dim, self._output_dim = input_dim, output_dim
        self.weight = nn.Parameter(torch.Tensor(ensemble_size, input_dim, output_dim).to(device))
        self._activation = activation
        self.bias = nn.Parameter(torch.Tensor(ensemble_size, output_dim).to(device))
    
    def forward(self, x):
        return self._activation(torch.add(torch.bmm(x, self.weight), self.bias[:, None, :]))

class EnsembleModel(nn.Module):
    def __init__(self, state_dim, action_dim, ensemble_size=5, learning_rate=1e-3):
        super(EnsembleModel, self).__init__()
        self._output_dim = (state_dim + 1) * 2
        self._max_logvar = nn.Parameter((torch.ones((1, self._output_dim // 2)).float() / 2).to(device), requires_grad=False)
        self._min_logvar = nn.Parameter((-torch.ones((1, self._output_dim // 2)).float() * 10).to(device), requires_grad=False)
        self.layer1 = FCLayer(state_dim + action_dim, 200, ensemble_size, Swish())
        self.layer2 = FCLayer(200, 200, ensemble_size, Swish())
        self.layer3 = FCLayer(200, 200, ensemble_size, Swish())
        self.layer4 = FCLayer(200, 200, ensemble_size, Swish())
        self.layer5 = FCLayer(200, self._output_dim, ensemble_size, nn.Identity())
        self.apply(init_weights)
        self.optimizer = torch.optim.Adam(self.parameters(), lr=learning_rate)
    
    def forward(self, x, return_log_var=False):
        ret = self.layer5(self.layer4(self.layer3(self.layer2(self.layer1(x)))))
        mean = ret[:, :, :self._output_dim // 2]
        logvar = self._max_logvar - F.softplus(self._max_logvar - ret[:, :, self._output_dim // 2:])
        logvar = self._min_logvar + F.softplus(logvar - self._min_logvar)
        return mean, logvar if return_log_var else torch.exp(logvar)
    
    def loss(self, mean, logvar, labels, use_var_loss=True):
        inverse_var = torch.exp(-logvar)
        if use_var_loss:
            mse_loss = torch.mean(torch.mean(torch.pow(mean - labels, 2) * inverse_var, dim=-1), dim=-1)
            var_loss = torch.mean(torch.mean(logvar, dim=-1), dim=-1)
            total_loss = torch.sum(mse_loss) + torch.sum(var_loss)
        else:
            mse_loss = torch.mean(torch.pow(mean - labels, 2), dim=(1, 2))
            total_loss = torch.sum(mse_loss)
        return total_loss, mse_loss
    
    def train(self, loss):
        self.optimizer.zero_grad()
        loss += 0.01 * torch.sum(self._max_logvar) - 0.01 * torch.sum(self._min_logvar)
        loss.backward()
        self.optimizer.step()

class EnsembleDynamicsModel:
    def __init__(self, state_dim, action_dim, num_network=5):
        self._num_network = num_network
        self._state_dim, self._action_dim = state_dim, action_dim
        self.model = EnsembleModel(state_dim, action_dim, ensemble_size=num_network)
        self._epoch_since_last_update = 0
    
    def train(self, inputs, labels, batch_size=64, holdout_ratio=0.1, max_iter=20):
        permutation = np.random.permutation(inputs.shape[0])
        inputs, labels = inputs[permutation], labels[permutation]
        num_holdout = int(inputs.shape[0] * holdout_ratio)
        train_inputs, train_labels = inputs[num_holdout:], labels[num_holdout:]
        holdout_inputs, holdout_labels = inputs[:num_holdout], labels[:num_holdout]