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/*
* 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 org.apache.spark.annotation.{Experimental, Since}
import org.apache.spark.api.java.JavaRDD
import org.apache.spark.internal.Logging
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.rdd.RDD
/**
* Clustering model produced by [[BisectingKMeans]].
* The prediction is done level-by-level from the root node to a leaf node, and at each node among
* its children the closest to the input point is selected.
*
* @param root the root node of the clustering tree
*/
@Since("1.6.0")
@Experimental
class BisectingKMeansModel private[clustering] (
private[clustering] val root: ClusteringTreeNode
) extends Serializable with Logging {
/**
* Leaf cluster centers.
*/
@Since("1.6.0")
def clusterCenters: Array[Vector] = root.leafNodes.map(_.center)
/**
* Number of leaf clusters.
*/
lazy val k: Int = clusterCenters.length
/**
* Predicts the index of the cluster that the input point belongs to.
*/
@Since("1.6.0")
def predict(point: Vector): Int = {
root.predict(point)
}
/**
* Predicts the indices of the clusters that the input points belong to.
*/
@Since("1.6.0")
def predict(points: RDD[Vector]): RDD[Int] = {
points.map { p => root.predict(p) }
}
/**
* Java-friendly version of [[predict()]].
*/
@Since("1.6.0")
def predict(points: JavaRDD[Vector]): JavaRDD[java.lang.Integer] =
predict(points.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Integer]]
/**
* Computes the squared distance between the input point and the cluster center it belongs to.
*/
@Since("1.6.0")
def computeCost(point: Vector): Double = {
root.computeCost(point)
}
/**
* Computes the sum of squared distances between the input points and their corresponding cluster
* centers.
*/
@Since("1.6.0")
def computeCost(data: RDD[Vector]): Double = {
data.map(root.computeCost).sum()
}
/**
* Java-friendly version of [[computeCost()]].
*/
@Since("1.6.0")
def computeCost(data: JavaRDD[Vector]): Double = this.computeCost(data.rdd)
}
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