小波神经网络的python代码

小波神经网络的Python代码如下：

import numpy as np
import pywt
import matplotlib.pyplot as plt

class WaveletNeuralNetwork:
    
    def __init__(self, num_inputs, num_hidden, num_outputs, wavelet_name='db4'):
        self.num_inputs = num_inputs
        self.num_hidden = num_hidden
        self.num_outputs = num_outputs
        self.wavelet_name = wavelet_name
        
        # initialize weights and biases
        self.hidden_weights = np.random.randn(num_inputs, num_hidden)
        self.hidden_bias = np.zeros(num_hidden)
        self.output_weights = np.random.randn(num_hidden, num_outputs)
        self.output_bias = np.zeros(num_outputs)
        
    def train(self, X, y, num_epochs=1000, learning_rate=0.1):
        for epoch in range(num_epochs):
            # forward pass
            hidden_inputs = np.dot(X, self.hidden_weights) + self.hidden_bias
            hidden_outputs, _ = pywt.dwt(hidden_inputs, self.wavelet_name)
            output_inputs = np.dot(hidden_outputs, self.output_weights) + self.output_bias
            output = sigmoid(output_inputs)
            
            # backward pass
            output_error = (y - output) * sigmoid_derivative(output)
            hidden_error = pywt.idwt(np.dot(output_error, self.output_weights.T), None, self.wavelet_name)
            hidden_error *= sigmoid_derivative(hidden_inputs)
            
            # update weights and biases
            self.output_weights += learning_rate * np.dot(hidden_outputs.T, output_error)
            self.output_bias += learning_rate * np.sum(output_error, axis=0)
            self.hidden_weights += learning_rate * np.dot(X.T, hidden_error)
            self.hidden_bias += learning_rate * np.sum(hidden_error, axis=0)
        
    def predict(self, X):
        hidden_inputs = np.dot(X, self.hidden_weights) + self.hidden_bias
        hidden_outputs, _ = pywt.dwt(hidden_inputs, self.wavelet_name)
        output_inputs = np.dot(hidden_outputs, self.output_weights) + self.output_bias
        output = sigmoid(output_inputs)
        return output
    
    def plot_wavelet(self, X):
        hidden_inputs = np.dot(X, self.hidden_weights) + self.hidden_bias
        hidden_outputs, _ = pywt.dwt(hidden_inputs, self.wavelet_name)
        
        plt.subplot(2, 1, 1)
        plt.plot(hidden_inputs)
        plt.title('Hidden Layer Inputs')
        
        plt.subplot(2, 1, 2)
        plt.plot(hidden_outputs)
        plt.title('Hidden Layer Outputs')
        
        plt.tight_layout()
        plt.show()
        
def sigmoid(x):
    return 1 / (1 + np.exp(-x))

def sigmoid_derivative(x):
    return sigmoid(x) * (1 - sigmoid(x))

这是一个简单的小波神经网络实现，包含了训练、预测和绘制小波变换图像的功能。在训练过程中，使用了小波变换来对输入进行降维和特征提取，然后使用标准的反向传播算法更新权重和偏置。