小波神经网络预测python代码举例

小波神经网络（Wavelet Neural Network，WNN）是一种结合了小波分析和神经网络技术的模型，常用于信号处理、时间序列预测等领域。以下是使用Python和PyTorch库实现小波神经网络的一个简单示例：

# 首先，需要安装必要的库
import numpy as np
import torch
from torch import nn
from pywt import wavedec, waverec

class WaveletLayer(nn.Module):
    def __init__(self, wavelet='db4', level=4):
        super(WaveletLayer, self).__init__()
        self.wavelet = wavelet
        self.level = level

    def forward(self, x):
        # 使用pywt工具包的小波分解
        coeffs = wavedec(x, wavelet=self.wavelet, level=self.level)
        return coeffs

class WNN(nn.Module):
    def __init__(self, input_size, hidden_layers, output_size):
        super(WNN, self).__init__()
        # 添加小波层
        self.wavelet_layer = WaveletLayer()
        
        # 创建隐藏层
        self.hidden_layers = nn.ModuleList([nn.Linear(input_size + len(coeffs), hidden_layers) for coeffs in range(0, len(hidden_layers))])
        self.output_layer = nn.Linear(hidden_layers[-1], output_size)

    def forward(self, x):
        # 分解输入并传递给隐藏层
        coeffs = self.wavelet_layer(x)
        coefficients = [coeffs]
        for layer in self.hidden_layers:
            flattened_coeffs = torch.cat((coeffs[0], *coeffs[1:]), dim=-1)
            coeffs = [layer(flattened_coeffs)]
        
        # 合成预测
        output = self.output_layer(torch.flatten(coeffs[-1], start_dim=1))
        return output

# 示例使用
input_size = 100  # 输入数据维度
hidden_layers = [64, 32]  # 每个隐藏层的节点数
output_size = 1  # 输出的预测值维度
model = WNN(input_size, hidden_layers, output_size)

# 假设我们有训练数据x_train和对应的标签y_train
# train_loader是一个数据加载器
for x_batch, y_batch in train_loader:
    # 预测
    pred = model(x_batch)
    # 训练模型...

注意，这只是一个简化示例，实际应用中还需要配合损失函数、优化器以及训练循环。此外，对于时间序列预测，可能还需处理窗口滑动的问题。