本文整理汇总了Scala中org.apache.spark.ml.clustering.KMeansModel类的典型用法代码示例。如果您正苦于以下问题：Scala KMeansModel类的具体用法？Scala KMeansModel怎么用？Scala KMeansModel使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了KMeansModel类的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Scala代码示例。
示例1: LocalKMeansModel
//设置package包名称以及导入依赖的类
package io.hydrosphere.spark_ml_serving.clustering
import io.hydrosphere.spark_ml_serving._
import org.apache.spark.ml.clustering.KMeansModel
import org.apache.spark.mllib.clustering.{KMeansModel => OldKMeansModel}
import org.apache.spark.mllib.clustering.{KMeansModel => MLlibKMeans}
import org.apache.spark.mllib.linalg.{Vectors, Vector => MLlibVec}
import scala.collection.immutable.ListMap
import scala.reflect.runtime.universe
class LocalKMeansModel(override val sparkTransformer: KMeansModel) extends LocalTransformer[KMeansModel] {
lazy val parent: OldKMeansModel = {
val mirror = universe.runtimeMirror(sparkTransformer.getClass.getClassLoader)
val parentTerm = universe.typeOf[KMeansModel].decl(universe.TermName("parentModel")).asTerm
mirror.reflect(sparkTransformer).reflectField(parentTerm).get.asInstanceOf[OldKMeansModel]
}
override def transform(localData: LocalData): LocalData = {
localData.column(sparkTransformer.getFeaturesCol) match {
case Some(column) =>
val newColumn = LocalDataColumn(sparkTransformer.getPredictionCol, column.data.map(f => Vectors.dense(f.asInstanceOf[Array[Double]])).map { vector =>
parent.predict(vector)
})
localData.withColumn(newColumn)
case None => localData
}
}
}
object LocalKMeansModel extends LocalModel[KMeansModel] {
override def load(metadata: Metadata, data: Map[String, Any]): KMeansModel = {
val sorted = ListMap(data.toSeq.sortBy { case (key: String, _: Any) => key.toInt}: _*)
val centers = sorted map {
case (_: String, value: Any) =>
val center = value.asInstanceOf[Map[String, Any]]
Vectors.dense(center("values").asInstanceOf[List[Double]].to[Array])
}
val parentConstructor = classOf[MLlibKMeans].getDeclaredConstructor(classOf[Array[MLlibVec]])
parentConstructor.setAccessible(true)
val mlk = parentConstructor.newInstance(centers.toArray)
val constructor = classOf[KMeansModel].getDeclaredConstructor(classOf[String], classOf[MLlibKMeans])
constructor.setAccessible(true)
var inst = constructor
.newInstance(metadata.uid, mlk)
.setFeaturesCol(metadata.paramMap("featuresCol").asInstanceOf[String])
.setPredictionCol(metadata.paramMap("predictionCol").asInstanceOf[String])
inst = inst.set(inst.k, metadata.paramMap("k").asInstanceOf[Number].intValue())
inst = inst.set(inst.initMode, metadata.paramMap("initMode").asInstanceOf[String])
inst = inst.set(inst.maxIter, metadata.paramMap("maxIter").asInstanceOf[Number].intValue())
inst = inst.set(inst.initSteps, metadata.paramMap("initSteps").asInstanceOf[Number].intValue())
inst = inst.set(inst.seed, metadata.paramMap("seed").toString.toLong)
inst = inst.set(inst.tol, metadata.paramMap("tol").asInstanceOf[Double])
inst
}
override implicit def getTransformer(transformer: KMeansModel): LocalTransformer[KMeansModel] = new LocalKMeansModel(transformer)
} 示例2: LocalKMeansModel
//设置package包名称以及导入依赖的类
package io.hydrosphere.mist.api.ml.clustering
import io.hydrosphere.mist.api.ml._
import org.apache.spark.ml.clustering.KMeansModel
import org.apache.spark.mllib.clustering.{KMeansModel => OldKMeansModel}
import org.apache.spark.mllib.clustering.{KMeansModel => MLlibKMeans}
import org.apache.spark.mllib.linalg.{Vectors, Vector => MLlibVec}
import scala.collection.immutable.ListMap
import scala.reflect.runtime.universe
class LocalKMeansModel(override val sparkTransformer: KMeansModel) extends LocalTransformer[KMeansModel] {
lazy val parent: OldKMeansModel = {
val mirror = universe.runtimeMirror(sparkTransformer.getClass.getClassLoader)
val parentTerm = universe.typeOf[KMeansModel].decl(universe.TermName("parentModel")).asTerm
mirror.reflect(sparkTransformer).reflectField(parentTerm).get.asInstanceOf[OldKMeansModel]
}
override def transform(localData: LocalData): LocalData = {
localData.column(sparkTransformer.getFeaturesCol) match {
case Some(column) =>
val newColumn = LocalDataColumn(sparkTransformer.getPredictionCol, column.data.map(f => Vectors.dense(f.asInstanceOf[Array[Double]])).map { vector =>
parent.predict(vector)
})
localData.withColumn(newColumn)
case None => localData
}
}
}
object LocalKMeansModel extends LocalModel[KMeansModel] {
override def load(metadata: Metadata, data: Map[String, Any]): KMeansModel = {
val sorted = ListMap(data.toSeq.sortBy { case (key: String, _: Any) => key.toInt}: _*)
val centers = sorted map {
case (_: String, value: Any) =>
val center = value.asInstanceOf[Map[String, Any]]
Vectors.dense(center("values").asInstanceOf[List[Double]].to[Array])
}
val parentConstructor = classOf[MLlibKMeans].getDeclaredConstructor(classOf[Array[MLlibVec]])
parentConstructor.setAccessible(true)
val mlk = parentConstructor.newInstance(centers.toArray)
val constructor = classOf[KMeansModel].getDeclaredConstructor(classOf[String], classOf[MLlibKMeans])
constructor.setAccessible(true)
var inst = constructor
.newInstance(metadata.uid, mlk)
.setFeaturesCol(metadata.paramMap("featuresCol").asInstanceOf[String])
.setPredictionCol(metadata.paramMap("predictionCol").asInstanceOf[String])
inst = inst.set(inst.k, metadata.paramMap("k").asInstanceOf[Number].intValue())
inst = inst.set(inst.initMode, metadata.paramMap("initMode").asInstanceOf[String])
inst = inst.set(inst.maxIter, metadata.paramMap("maxIter").asInstanceOf[Number].intValue())
inst = inst.set(inst.initSteps, metadata.paramMap("initSteps").asInstanceOf[Number].intValue())
inst = inst.set(inst.seed, metadata.paramMap("seed").toString.toLong)
inst = inst.set(inst.tol, metadata.paramMap("tol").asInstanceOf[Double])
inst
}
override implicit def getTransformer(transformer: KMeansModel): LocalTransformer[KMeansModel] = new LocalKMeansModel(transformer)
}