用python写一个小波神经网络并进行测试

好的，让我们来编写一个小波神经网络，并对其进行测试。

首先，我们需要导入必要的库：

import numpy as np
import pywt
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

然后我们加载鸢尾花数据集：

# 加载鸢尾花数据集
iris = load_iris()
X = iris.data
y = iris.target

接着，我们将数据集分为训练集和测试集：

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

下一步，我们将定义一个 `WaveletNeuralNetwork` 类来实现小波神经网络：

# 定义小波神经网络类
class WaveletNeuralNetwork:
    def __init__(self, input_size, hidden_size, output_size):
        # 初始化网络参数
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        
        # 随机初始化权重和偏置
        self.weights_ih = np.random.randn(hidden_size, input_size)
        self.bias_ih = np.random.randn(hidden_size, 1)
        self.weights_ho = np.random.randn(output_size, hidden_size)
        self.bias_ho = np.random.randn(output_size, 1)
        
        # 定义激活函数
        self.activation_function = lambda x: 1 / (1 + np.exp(-x))
        
    # 前向传播
    def forward(self, inputs):
        # 小波变换
        coeffs = pywt.dwt(inputs, 'haar')
        inputs = coeffs[0]
        
        # 输入层到隐层
        hidden = np.dot(self.weights_ih, inputs) + self.bias_ih
        hidden = self.activation_function(hidden)
        
        # 隐层到输出层
        outputs = np.dot(self.weights_ho, hidden) + self.bias_ho
        
        return outputs
    
    # 训练网络
    def train(self, inputs, targets, learning_rate):
        # 前向传播
        outputs = self.forward(inputs)
        
        # 反向传播
        error = targets - outputs
        
        # 输出层到隐层的权重和偏置调整
        gradient = error * learning_rate
        hidden_T = np.transpose(hidden)
        weights_ho_deltas = np.dot(gradient, hidden_T)
        self.weights_ho += weights_ho_deltas
        self.bias_ho += gradient
        
        # 隐层到输入层的权重和偏置调整
        who_T = np.transpose(self.weights_ho)
        hidden_errors = np.dot(who_T, error)
        hidden_gradient = hidden * (1 - hidden) * hidden_errors
        inputs_T = np.transpose(inputs)
        weights_ih_deltas = np.dot(hidden_gradient, inputs_T)
        self.weights_ih += weights_ih_deltas
        self.bias_ih += hidden_gradient
        
    # 预测
    def predict(self, inputs):
        # 前向传播
        outputs = self.forward(inputs)
        
        # 取最大值作为预测结果
        prediction = np.argmax(outputs)
        
        return prediction

现在我们可以实例化一个小波神经网络，并对其进行训练和测试：

# 定义网络结构
input_size = X_train.shape[1]
hidden_size = 10
output_size = len(np.unique(y_train))

# 实例化小波神经网络
wnn = WaveletNeuralNetwork(input_size, hidden_size, output_size)

# 训练网络
learning_rate = 0.1
epochs = 1000
for i in range(epochs):
    for j in range(len(X_train)):
        inputs = X_train[j]
        targets = np.zeros(output_size)
        targets[y_train[j]] = 1
        wnn.train(inputs, targets, learning_rate)

# 测试网络
correct = 0
for i in range(len(X_test)):
    inputs = X_test[i]
    target = y_test[i]
    prediction = wnn.predict(inputs)
    if prediction == target:
        correct += 1
        
accuracy = correct / len(X_test)
print('Accuracy:', accuracy)

这里我们使用了小波神经网络对鸢尾花数据集进行分类，并取得了不错的准确率。你可以尝试使用其他数据集进行测试，并对网络结构和超参数进行调整，以获得更好的性能。