对于某些类解决方案，熵输出为NaN，而对于其他类解决方案则不是

Question

我正在R中运行潜在类分析并使用熵函数。我想了解为什么在输出中，它为低nclass生成结果，然后为高nclass生成NaN。

我是该软件的初学者！

作为参考，是输出和代码：

> entropy<-function (p) sum(-p*log(p))
> error_prior <- entropy(France_2class$P) # Class proportions
> error_post <- mean(apply(France_2class$posterior, 1, entropy))
> R2_entropy <- (error_prior - error_post) / error_prior
> R2_entropy
[1] 0.8121263
> 
> entropy<-function (p) sum(-p*log(p))
> error_prior <- entropy(France_3class$P) # Class proportions
> error_post <- mean(apply(France_3class$posterior, 1, entropy))
> R2_entropy <- (error_prior - error_post) / error_prior
> R2_entropy
[1] 0.8139903
> 
> entropy<-function (p) sum(-p*log(p))
> error_prior <- entropy(France_4class$P) # Class proportions
> error_post <- mean(apply(France_4class$posterior, 1, entropy))
> R2_entropy <- (error_prior - error_post) / error_prior
> R2_entropy
[1] NaN
> 
> entropy<-function (p) sum(-p*log(p))
> error_prior <- entropy(France_5class$P) # Class proportions
> error_post <- mean(apply(France_5class$posterior, 1, entropy))
> R2_entropy <- (error_prior - error_post) / error_prior
> R2_entropy
[1] NaN
> 
> entropy<-function (p) sum(-p*log(p))
> error_prior <- entropy(France_6class$P) # Class proportions
> error_post <- mean(apply(France_6class$posterior, 1, entropy))
> R2_entropy <- (error_prior - error_post) / error_prior
> R2_entropy
[1] NaN

有人可以帮忙吗？谢谢

Answer 1

我想问题出在entropy的定义上。更准确地说，如果0中包含p，那么您将获得NaN，例如，

> entropy(p1)
[1] 1.279854

> entropy(p2)
[1] NaN

> entropy(p3)
[1] 0.5004024

---

要解决此问题，您可以像下面这样向功能na.omit添加entropy

entropy<-function(p) sum(na.omit(-p*log(p)))

然后您可以看到

> entropy(p1)
[1] 1.279854

> entropy(p2)
[1] 0.5004024

> entropy(p3)
[1] 0.5004024

数据

p1 <- c(0.1,0.2,0.3,0.4)
p2 <- c(0,0.2,0.8)
p3 <- c(0.2,0.8)