基于CNN_A_LSTM的小时天气预测

    label_encoder = LabelEncoder()
    df['weather'] = label_encoder.fit_transform(df['weather'])
    category_mapping = dict(zip(label_encoder.classes_, label_encoder.transform(label_encoder.classes_)))

def create_sliding_window(data, window_size):
    window_data = []
    for i in range(len(data) - window_size):
        window = data[i:i + window_size]
        target = data[i + window_size][2]
        window_data.append((window, target))
    return window_data

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(LSTMModel, self).__init__()
        self.hidden_size = hidden_size
        self.lstm = nn.RNN(input_size, hidden_size)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        lstm_out, _ = self.lstm(x)
        output = self.fc(lstm_out[:, -1, :])
        return output

class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(LSTMModel, self).__init__()
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(input_size, hidden_size)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        lstm_out, _ = self.lstm(x)
        output = self.fc(lstm_out[:, -1, :])
        return output

class CNNLSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(CNNLSTMModel, self).__init__()
        self.hidden_size = hidden_size
        self.conv1 = nn.Conv1d(input_size, hidden_size, kernel_size=3, padding=1)
        self.lstm = nn.LSTM(hidden_size, hidden_size)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = x.permute(0, 2, 1)  # 转换输入的维度，使其适合卷积层的输入
        x = F.relu(self.conv1(x))
        x = x.permute(2, 0, 1)  # 将维度转换回原始维度
        lstm_out, _ = self.lstm(x)
        output = self.fc(lstm_out[-1, :, :])  # 只使用最后一个时间步的输出
        return output