From 02d037387f32adcddd98858176813f3a66991a38 Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Thu, 12 Mar 2015 10:43:03 +0800 Subject: [PATCH] fix style in comment --- .../src/main/scala/org/apache/spark/mllib/clustering/LDA.scala | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala index 7e902333ebc70..dc4e8f9701837 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala @@ -250,7 +250,6 @@ class LDA private ( /** * Learn an LDA model using the given dataset, using online variational Bayes (VB) algorithm. - * Hoffman, Blei and Bach, “Online Learning for Latent Dirichlet Allocation.” NIPS, 2010. * * @param documents RDD of documents, which are term (word) count vectors paired with IDs. * The term count vectors are "bags of words" with a fixed-size vocabulary @@ -444,7 +443,7 @@ private[clustering] object LDA { private val D = documents.count().toInt val actualBatchNumber = Math.ceil(D.toDouble / batchSize).toInt - //Initialize the variational distribution q(beta|lambda) + // Initialize the variational distribution q(beta|lambda) var lambda = getGammaMatrix(k, vocabSize) // K * V private var Elogbeta = dirichlet_expectation(lambda) // K * V private var expElogbeta = exp(Elogbeta) // K * V