Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/6880#discussion_r38953312
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/DpMeansModel.scala ---
@@ -0,0 +1,67 @@
+/*
+ * 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 scala.collection.JavaConverters._
+import scala.collection.mutable
+
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.rdd.RDD
+
+/**
+ * A clustering model for DP means. Each point belongs to the cluster with
the closest center.
+ */
+class DpMeansModel
+ (val clusterCenters: Array[Vector]) extends Serializable {
+
+ /** A Java-friendly constructor that takes an Iterable of Vectors. */
+ def this(centers: java.lang.Iterable[Vector]) =
this(centers.asScala.toArray)
+
+ /** Total number of clusters obtained. */
+ def k: Int = clusterCenters.length
+
+ /** Returns the cluster index that a given point belongs to. */
+ def predict(point: Vector): Int = {
+ val centersWithNorm = clusterCentersWithNorm
+ DpMeans.assignCluster(centersWithNorm.to[mutable.ArrayBuffer], new
VectorWithNorm(point))._1
+ }
+
+ /** Maps the points in the given RDD to their closest cluster indices. */
+ def predict(points: RDD[Vector]): RDD[Int] = {
+ val centersWithNorm = clusterCentersWithNorm
+ val bcCentersWithNorm = points.context.broadcast(centersWithNorm)
+ points.map(p =>
DpMeans.assignCluster(bcCentersWithNorm.value.to[mutable.ArrayBuffer],
+ new VectorWithNorm(p))._1)
--- End diff --
fix indentation and break lines to make it easier to read, e.g.
~~~scala
points.map { p =>
DpMeans.assignCluster(
bcCentersWithNorm.value.to[mutable.ArrayBuffer], new
VectorWithNorm(p)
)._1
}
~~~
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