朴素贝叶斯算法---统计学习方法例4.1计算（MATLAB）

最新推荐文章于 2025-05-15 20:23:32 发布

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最新推荐文章于 2025-05-15 20:23:32 发布

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分类专栏：机器学习文章标签：朴素贝叶斯机器学习统计学习方法分类

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个人学习记录，只进行了书上例题的结果验证，没有改写成函数。

%%%%%%    朴素贝叶斯算法     %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

clc
clear

X = {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 = [-1,-1,1,1,-1,-1,-1,1,1,1,1,1,1,1,-1]';
x = {2,'S'};  
%%%%%%%  输入参数X：训练数据的特征集   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%          Y：训练数据的目标集   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%          x：待分类数据的特征   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%  输出参数y：待分类数据的类别   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Num = length(Y); %训练样本总数
%计算正负类的先验概率
PositiveIndex = find(Y == 1);
NegativeIndex = find(Y == -1);
PositiveNum = length(PositiveIndex);  %训练集中正类的个数
NegativeNum = length(NegativeIndex); %训练集中负类的个数

format rat

PositiveP = PositiveNum/Num; %正类的先验概率        
NegativeP = NegativeNum/Num; %负类的先验概率

%计算条件概率，即类别确定的条件下，训练集中不同特征取值的概率
PositiveX1 = X(PositiveIndex,1); %正类对应的特征1
PositiveX2 = X(PositiveIndex,2); %正类对应的特征2
NegativeX1 = X(NegativeIndex,1); %负类对应的