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我有兴趣使用Spark MLlib应用LDA主题建模.我已经检查了这里的代码和解释,但我找不到如何使用模型然后在一个新的看不见的文档中找到主题分布.

我正在尝试在Spark中编写一个progor来执行Latent Dirichlet分配(LDA).此Spark文档页面提供了一个很好的示例,用于在示例数据上执行LDA.以下是该计划

from pyspark.mllib.clustering import LDA, LDAModel
from pyspark.mllib.linalg import Vectors

# Load and parse the data
data = sc.textFile("data/mllib/sample_lda_data.txt")
parsedData = data.map(lambda line: Vectors.dense([float(x) for x in line.strip().split(' ')]))
# Index documents with unique IDs
corpus = parsedData.zipWithIndex().map(lambda x: [x[1], x[0]]).cache()

# Cluster the documents into three topics using LDA
ldaModel = LDA.train(corpus, k=3)

# Output topics. Each is a distribution over words (matching word count vectors)
print("Learned topics (as distributions over vocab of " + str(ldaModel.vocabSize()) …