上面这串代码报错AttributeError: 'NeuralNetwork' object has no attribute 'predict'

抱歉，我的回答有误。你需要在 `NeuralNetwork` 类中添加 `predict` 方法，以便在训练模型后进行预测。下面是一个简单的实现：

class NeuralNetwork:
    def __init__(self, input_dim, hidden_dim, output_dim):
        # 初始化权重和偏置
        self.W1 = np.random.randn(input_dim, hidden_dim)
        self.b1 = np.zeros((1, hidden_dim))
        self.W2 = np.random.randn(hidden_dim, output_dim)
        self.b2 = np.zeros((1, output_dim))

    def sigmoid(self, z):
        return 1 / (1 + np.exp(-z))

    def forward(self, X):
        # 前向传播
        self.z1 = np.dot(X, self.W1) + self.b1
        self.a1 = self.sigmoid(self.z1)
        self.z2 = np.dot(self.a1, self.W2) + self.b2
        y_pred = self.z2
        return y_pred

    def mse_loss(self, y_pred, y_true):
        # 计算均方误差
        mse = np.mean((y_pred - y_true)**2)
        return mse

    def mse_loss_derivative(self, y_pred, y_true):
        # 计算均方误差的导数
        mse_derivative = 2 * (y_pred - y_true) / y_pred.size
        return mse_derivative

    def backward(self, X, y_true, y_pred, learning_rate):
        # 反向传播
        mse_derivative = self.mse_loss_derivative(y_pred, y_true)
        dW2 = np.dot(self.a1.T, mse_derivative)
        db2 = np.sum(mse_derivative, axis=0, keepdims=True)
        da1 = np.dot(mse_derivative, self.W2.T)
        dz1 = da1 * self.a1 * (1 - self.a1)
        dW1 = np.dot(X.T, dz1)
        db1 = np.sum(dz1, axis=0, keepdims=True)
        
        # 更新参数
        self.W2 -= learning_rate * dW2
        self.b2 -= learning_rate * db2
        self.W1 -= learning_rate * dW1
        self.b1 -= learning_rate * db1

    def train(self, X_train, y_train, learning_rate, num_epochs):
        loss_history = []
        for i in range(num_epochs):
            # 前向传播
            y_pred = self.forward(X_train)

            # 计算损失
            loss = self.mse_loss(y_pred, y_train)

            # 反向传播更新参数
            self.backward(X_train, y_train, y_pred, learning_rate)

            # 记录损失历史
            loss_history.append(loss)

            # 打印损失
            if i % 100 == 0:
                print('Epoch: {}, Loss: {:.4f}'.format(i, loss))

        return loss_history
    
    def predict(self, X):
        # 前向传播预测结果
        y_pred = self.forward(X)
        return y_pred

在上面的 `NeuralNetwork` 类中添加了 `predict` 方法，用于进行预测。你可以使用该方法进行预测，如下所示：

# 预测
y_pred = nn.predict(X_test)

# 可视化预测结果和真实结果
plt.plot(y_test, label='true')
plt.plot(y_pred, label='pred')
plt.legend()

# 输出模型评估指标
mse = mean_squared_error(y_test, y_pred)
mae = mean_absolute_error(y_test, y_pred)
print('MSE:', mse)
print('MAE:', mae)