写一个python代码实现可应用于多分类问题的朴素贝叶斯算法数据集划分为训练集、验证集、测试集算法包含学习率

下面是一个使用朴素贝叶斯算法进行多分类问题的示例代码：

import numpy as np

class NaiveBayes:
    def __init__(self, alpha=1.0):
        self.alpha = alpha  # 学习率，用于平滑概率
    
    def fit(self, X, y):
        self.classes = np.unique(y)
        self.num_classes = len(self.classes)
        self.num_features = X.shape[1]
        
        # 计算每个类别的先验概率
        self.priors = np.zeros(self.num_classes)
        for i, c in enumerate(self.classes):
            self.priors[i] = np.sum(y == c) / len(y)
        
        # 计算每个特征的条件概率
        self.likelihoods = np.zeros((self.num_classes, self.num_features, 256))
        for i, c in enumerate(self.classes):
            X_c = X[y == c]
            for feature in range(self.num_features):
                for value in range(256):
                    self.likelihoods[i, feature, value] = (np.sum(X_c[:, feature] == value) + self.alpha) / (len(X_c) + self.alpha * 256)
    
    def predict(self, X):
        y_pred = []
        for x in X:
            posteriors = []
            for i, c in enumerate(self.classes):
                likelihood = 1.0
                for feature, value in enumerate(x):
                    likelihood *= self.likelihoods[i, feature, value]
                posterior = self.priors[i] * likelihood
                posteriors.append(posterior)
            y_pred.append(self.classes[np.argmax(posteriors)])
        return y_pred


# 生成随机训练集和标签
np.random.seed(0)
X_train = np.random.randint(0, 256, (100, 5))
y_train = np.random.randint(0, 3, 100)

# 生成随机验证集和标签
X_val = np.random.randint(0, 256, (30, 5))
y_val = np.random.randint(0, 3, 30)

# 生成随机测试集和标签
X_test = np.random.randint(0, 256, (20, 5))
y_test = np.random.randint(0, 3, 20)

# 创建朴素贝叶斯分类器
naive_bayes = NaiveBayes(alpha=1.0)

# 训练模型
naive_bayes.fit(X_train, y_train)

# 预测验证集
y_val_pred = naive_bayes.predict(X_val)
print("Validation Accuracy:", np.mean(y_val_pred == y_val))

# 预测测试集
y_test_pred = naive_bayes.predict(X_test)
print("Test Accuracy:", np.mean(y_test_pred == y_test))

在上面的示例代码中，首先定义了一个NaiveBayes类，其构造函数接收一个学习率参数alpha。fit方法用于训练模型，接收训练集X和对应的标签y，并计算每个类别的先验概率和每个特征的条件概率。predict方法用于预测新样本的类别。然后，使用随机生成的训练集、验证集和测试集进行模型训练和预测，并输出准确率