小波神经网络预测模型代码

小波神经网络（Wavelet Neural Network，WNN）是一种结合小波分析和人工神经网络的预测模型，可以用于时间序列预测等方面。以下是一个简单的小波神经网络预测模型的Python代码示例：

import numpy as np
import pywt
from keras.models import Sequential
from keras.layers import Dense

# 定义小波函数，这里使用 Daubechies 4 小波
wavelet = pywt.Wavelet('db4')

# 小波分解函数
def wavelet_decomposition(data, level):
    coeffs = pywt.wavedec(data, wavelet, level=level)
    return np.concatenate(coeffs)

# 小波重构函数
def wavelet_reconstruction(coeffs):
    return pywt.waverec(coeffs, wavelet)

# 生成训练数据
def generate_data(data, window_size, level):
    X, y = [], []
    for i in range(len(data)-window_size-1):
        window = data[i:i+window_size]
        X.append(wavelet_decomposition(window, level))
        y.append(data[i+window_size])
    return np.array(X), np.array(y)

# 构建小波神经网络模型
def build_model(input_shape):
    model = Sequential()
    model.add(Dense(32, input_shape=input_shape, activation='relu'))
    model.add(Dense(1))
    model.compile(optimizer='adam', loss='mse')
    return model

# 训练模型
def train_model(X_train, y_train, epochs):
    input_shape = X_train[0].shape
    model = build_model(input_shape)
    model.fit(X_train, y_train, epochs=epochs, batch_size=32, verbose=0)
    return model

# 预测函数
def predict(model, data, window_size, level):
    result = []
    for i in range(len(data)-window_size-1):
        window = data[i:i+window_size]
        X = wavelet_decomposition(window, level)
        yhat = model.predict(X.reshape(1, -1))[0][0]
        result.append(yhat)
    return result

# 示例使用
if __name__ == '__main__':
    # 生成示例数据
    data = np.sin(np.arange(1000) / 10.0)
    # 设置小波分解层数和滑动窗口大小
    level = 3
    window_size = 32
    # 生成训练数据和测试数据
    X_train, y_train = generate_data(data[:800], window_size, level)
    X_test, y_test = generate_data(data[800:], window_size, level)
    # 训练模型
    model = train_model(X_train, y_train, epochs=100)
    # 预测并计算误差
    yhat_test = predict(model, data[800:], window_size, level)
    mse = np.mean((yhat_test - y_test) ** 2)
    print('MSE:', mse)

这是一个简单的小波神经网络预测模型的代码示例，可以根据自己的需求进行修改和优化。