Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/4047#discussion_r23501379
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
---
@@ -0,0 +1,472 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.clustering
+
+import java.util.Random
+
+import breeze.linalg.{DenseVector => BDV, sum => brzSum, normalize, axpy
=> brzAxpy}
+
+import org.apache.spark.Logging
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.graphx._
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.rdd.RDD
+import org.apache.spark.util.Utils
+
+
+/**
+ * :: DeveloperApi ::
+ *
+ * Latent Dirichlet Allocation (LDA), a topic model designed for text
documents.
+ *
+ * Terminology:
+ * - "word" = "term": an element of the vocabulary
+ * - "token": instance of a term appearing in a document
+ * - "topic": multinomial distribution over words representing some
concept
+ *
+ * Currently, the underlying implementation uses Expectation-Maximization
(EM), implemented
+ * according to the Asuncion et al. (2009) paper referenced below.
+ *
+ * References:
+ * - Original LDA paper (journal version):
+ * Blei, Ng, and Jordan. "Latent Dirichlet Allocation." JMLR, 2003.
+ * - This class implements their "smoothed" LDA model.
+ * - Paper which clearly explains several algorithms, including EM:
+ * Asuncion, Welling, Smyth, and Teh.
+ * "On Smoothing and Inference for Topic Models." UAI, 2009.
+ *
+ * NOTE: This is currently marked DeveloperApi since it is under active
development and may undergo
+ * API changes.
+ */
+@DeveloperApi
+class LDA private (
+ private var k: Int,
+ private var maxIterations: Int,
--- End diff --
I like having "smoothing" in the names since that's easier to understand
for people who have not read LDA papers. I agree the difference between
smoothing "topics" and "terms" is hard to understand. (If it were not for the
precedent set by the Stanford NLP toolbox, I would actually vote to relabel
"termSmoothing" --> "topicSmoothing" and "topicSmoothing" --> "docSmoothing"
since we are smoothing distributions, not smoothing elements of distributions.)
One other issue with alpha/beta is that papers are not consistent about
parameter names. ("beta" is more often called "eta" in what I've read.)
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