实现拉普拉斯修正的朴素贝叶斯分类器

编码实现拉普拉斯修正的朴素贝叶斯分类器，基于给定的训练数据，对测试样本进行判别。

import numpy as np
def loaddata():
    X = np.array([[1,'S'],[1,'M'],[1,'M'],[1,'S'],
         [1, 'S'], [2, 'S'], [2, 'M'], [2, 'M'],
         [2, 'L'], [2, 'L'], [3, 'L'], [3, 'M'],
         [3, 'M'], [3, 'L'], [3, 'L']])
    y = np.array([-1,-1,1,1,-1,-1,-1,1,1,1,1,1,1,1,-1])
return X, y

def Train(trainset,train_labels):
    m = trainset.shape[0] 
    n = trainset.shape[1] 
    prior_probability = {}# 先验概率 key是类别值，value是类别的概率值
    conditional_probability ={}# 条件概率 key的构造：类别，特征,特征值
    #类别的可能取值
    labels = set(train_labels)
    # 计算先验概率(此时没有除以总数据量m)
    for label in labels:
        prior_probability[label] = len(train_labels[train_labels == label])+1
    #计算条件概率
    for i in range(m):
        for j in range(n):
            # key的构造：类别，特征,特征值
            #补充计算条件概率的代码-1；
            key = str(train_labels[i])+','+str(j)+','+str(trainset[i][j])
            conditional_probability[key] = (conditional_probability[key]+1 if (key in conditional_probability) else 1)

    conditional_probability_final = {} 
    for key in conditional_probability:
        #补充计算条件概率的代码-2；
label = key.split(',')[0]
        conditional_probability[key]+=1
        key1 = int(key.split(',')[1])
        Ni = len(set(trainset[:,key1]))
      
        conditional_probability_final[key] =conditional_probability[key]/(prior_probability[int(label)]+Ni)
   
    # 最终的先验概率(此时除以总数据量m)
    for label in labels:
        prior_probability[label] = prior_probability[label]/ (m+len(labels))
    return prior_probability,conditional_probability_final,labels

def predict(data):
    result={}
    for label in train_labels_set:
        temp=1.0
#补充预测代码；
print('result=',result)
#排序返回标签值
result[label] = temp*prior_probability[label]
for i in range (len(data)):
    key = str(label)+ ','+str(i)+','+str(data[i])
    result[label]*=conditional_probability_final[key]
    print('result=',result)
    #排序返回标签值
    return sorted(result.items(), key=lambda x: x[1],reverse=True)[0][0]

X,y = loaddata()
prior_probability,conditional_probability,train_labels_set = Train(X,y)
r_label = predict([2,'S'])
print(' r_label =', r_label)