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I don't understand this part of output from lda.collapsed.gibbs.sampler command. What I don't understand is why the numbers of the same word in different topics are different? For example, why for the word "test" there is 4 of them in second topics when topic 8 get 37 of them. Shouldn't number of same word in different topic be the same integer or 0?

Or Do I misunderstood something and these numbers don't stand for number of word in the topic? 

$topics
      tests-loc fail  test testmultisendcookieget
 [1,]         0    0     0                      0
 [2,]         0    0     4                      0
 [3,]         0    0     0                      0
 [4,]         0    1     0                      0
 [5,]         0    0     0                      0
 [6,]         0    0     0                      0
 [7,]         0    0     0                      0
 [8,]         0    0    37                      0
 [9,]         0    0     0                      0
[10,]         0    0     0                      0
[11,]         0    0     0                      0
[12,]         0    2     0                      0
[13,]         0    0     0                      0
[14,]         0    0     0                      0
[15,]         0    0     0                      0
[16,]         0    0     0                      0
[17,]         0    0     0                      0
[18,]         0    0     0                      0
[19,]         0    0     0                      0
[20,]         0    0     0                      0
[21,]         0    0     0                      0
[22,]         0  361  1000                      0
[23,]         0    0     0                      0
[24,]         0    0     0                      0
[25,]         0    0     0                      0
[26,]         0    0     0                      0
[27,]         0    0     0                      0
[28,]         0 1904 12617                      0
[29,]         0    0     0                      0
[30,]         0    0     0                      0
[31,]         0    0     0                      0
[32,]         0 1255  3158                      0
[33,]         0    0     0                      0
[34,]         0    0     0                      0
[35,]         0    0     0                      0
[36,]         1    0     0                      1
[37,]         0    1     0                      0
[38,]         0    0     0                      0
[39,]         0    0     0                      0
[40,]         0    0     0                      0
[41,]         0    0     0                      0
[42,]         0    0     0                      0
[43,]         0    0     0                      0
[44,]         0    0     0                      0
[45,]         0    2     0                      0
[46,]         0    0     0                      0
[47,]         0    0     0                      0
[48,]         0    0     4                      0
[49,]         0    0     0                      0
[50,]         0    1     0                      0

Here is the code that I run.

library(lda)
data=read.documents(filename = "data.ldac")
vocab=read.vocab(filename = "words.csv")

K=100
num.iterations=100
alpha=1
eta=1


result = lda.collapsed.gibbs.sampler(data, K,vocab, num.iterations, alpha,eta, initial = NULL, burnin = NULL, compute.log.likelihood = FALSE,trace = 0L, freeze.topics = FALSE)

options(max.print=100000000) 
result

PS. Sorry for the long post and my bad english.

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1 回答 1

3

LDA 中的主题分布就是：多项分布。这些对应于您上面的矩阵行。在任何给定主题中看到单词的概率不限于任何主题的固定值（或零）。也就是说，“测试”这个词在一个主题中出现的几率为 3%，在另一个主题中出现的几率为 1%。

nb 如果要将矩阵转换为概率，只需行归一化并添加先前的平滑常数。这里的函数只返回最后一次 Gibbs 采样扫描中分配的原始数量。

于 2014-01-24T06:33:30.210 回答