R语言-朴素贝叶斯分类器（1）

最新推荐文章于 2024-05-14 10:25:58 发布

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最新推荐文章于 2024-05-14 10:25:58 发布

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文章标签：人工智能 r语言

原文链接：http://www.cnblogs.com/sklww/p/3507811.html

本文介绍了一种使用R语言进行简单预测的方法。通过给定的天气数据集，包括天气状况、温度、湿度和风力等特征，计算了在特定条件下（晴朗、凉爽、湿度高、强风）是否适合打球的概率。

利用给定的数据预测某天（"Sunny","Cool","High","Strong"）是否打球……

数据：

NO Outlook Temperature Humidity Wind Play
1 Sunny Hot High Weak No
2 Sunny Hot High Strong No
3 Overcast Hot High Weak Yes
4 Rain Mild High Weak Yes
5 Rain Cool Normal Weak Yes
6 Rain Cool Normal Strong No
7 Overcast Cool Normal Strong Yes
8 Sunny Mild High Weak No
9 Sunny Cool Normal Weak Yes
10 Rain Mild Normal Weak Yes
11 Sunny Mild Normal Strong Yes
12 Overcast Mild High Strong Yes
13 Overcast Hot Normal Weak Yes
14 Rain Mild High Strong No

代码：

data=read.table("C:\\code\\R\\playTennis.txt",header=T)
pre=c("Sunny","Cool","High","Strong","xx")
sum_Yes=length(which(data$Play=="Yes"))
sum_No=length(which(data$Play=="No"))
sum=sum_Yes+sum_No
#计算yes的概率
p_O_y=length(which(data$Outlook==pre[1]&data$Play=="Yes"))/sum_Yes
p_T_y=length(which(data$Temperature==pre[2]&data$Play=="Yes"))/sum_Yes
p_H_y=length(which(data$Humidity==pre[3]&data$Play=="Yes"))/sum_Yes
p_W_y=length(which(data$Wind==pre[4]&data$Play=="Yes"))/sum_Yes
p_y=(sum_Yes/sum)*p_O_y*p_T_y*p_H_y*p_W_y
#计算No的概率
p_O_n=length(which(data$Outlook==pre[1]&data$Play=="No"))/sum_No
p_T_n=length(which(data$Temperature==pre[2]&data$Play=="No"))/sum_No
p_H_n=length(which(data$Humidity==pre[3]&data$Play=="No"))/sum_No
p_W_n=length(which(data$Wind==pre[4]&data$Play=="No"))/sum_No
p_n=(sum_No/sum)*p_O_n*p_T_n*p_H_n*p_W_n
#结果
print(p_y)
print(p_n)