本文整理汇总了Scala中org.apache.spark.ml.stat.distribution.MultivariateGaussian类的典型用法代码示例。如果您正苦于以下问题：Scala MultivariateGaussian类的具体用法？Scala MultivariateGaussian怎么用？Scala MultivariateGaussian使用的例子？那么, 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MultivariateGaussian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: LocalGaussianMixtureModel
//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.GaussianMixtureModel
import org.apache.spark.ml.linalg.{Matrix, Vector}
import org.apache.spark.ml.stat.distribution.MultivariateGaussian
class LocalGaussianMixtureModel(override val sparkTransformer: GaussianMixtureModel) extends LocalTransformer[GaussianMixtureModel] {
override def transform(localData: LocalData): LocalData = {
localData.column(sparkTransformer.getFeaturesCol) match {
case Some(column) =>
val predictMethod = classOf[GaussianMixtureModel].getMethod("predict", classOf[Vector])
predictMethod.setAccessible(true)
val newColumn = LocalDataColumn(sparkTransformer.getPredictionCol, column.data map { feature =>
predictMethod.invoke(sparkTransformer, feature.asInstanceOf[Vector]).asInstanceOf[Int]
})
localData.withColumn(newColumn)
case None => localData
}
}
}
object LocalGaussianMixtureModel extends LocalModel[GaussianMixtureModel] {
override def load(metadata: Metadata, data: Map[String, Any]): GaussianMixtureModel = {
val weights = data("weights").asInstanceOf[List[Double]].toArray
val mus = data("mus").asInstanceOf[List[Vector]].toArray
val sigmas = data("sigmas").asInstanceOf[List[Matrix]].toArray
val gaussians = mus zip sigmas map {
case (mu, sigma) => new MultivariateGaussian(mu, sigma)
}
val constructor = classOf[GaussianMixtureModel].getDeclaredConstructor(
classOf[String],
classOf[Array[Double]],
classOf[Array[MultivariateGaussian]]
)
constructor.setAccessible(true)
var inst = constructor.newInstance(metadata.uid, weights, gaussians)
inst = inst.set(inst.probabilityCol, metadata.paramMap("probabilityCol").asInstanceOf[String])
inst = inst.set(inst.featuresCol, metadata.paramMap("featuresCol").asInstanceOf[String])
inst = inst.set(inst.predictionCol, metadata.paramMap("predictionCol").asInstanceOf[String])
inst
}
override implicit def getTransformer(transformer: GaussianMixtureModel): LocalTransformer[GaussianMixtureModel] = new LocalGaussianMixtureModel(transformer)
} 示例2: LocalGaussianMixtureModel
//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.GaussianMixtureModel
import org.apache.spark.ml.linalg.{Matrix, Vector}
import org.apache.spark.ml.stat.distribution.MultivariateGaussian
class LocalGaussianMixtureModel(override val sparkTransformer: GaussianMixtureModel) extends LocalTransformer[GaussianMixtureModel] {
override def transform(localData: LocalData): LocalData = {
localData.column(sparkTransformer.getFeaturesCol) match {
case Some(column) =>
val predictMethod = classOf[GaussianMixtureModel].getMethod("predict", classOf[Vector])
predictMethod.setAccessible(true)
val newColumn = LocalDataColumn(sparkTransformer.getPredictionCol, column.data map { feature =>
predictMethod.invoke(sparkTransformer, feature.asInstanceOf[Vector]).asInstanceOf[Int]
})
localData.withColumn(newColumn)
case None => localData
}
}
}
object LocalGaussianMixtureModel extends LocalModel[GaussianMixtureModel] {
override def load(metadata: Metadata, data: Map[String, Any]): GaussianMixtureModel = {
val weights = data("weights").asInstanceOf[List[Double]].toArray
val mus = data("mus").asInstanceOf[List[Vector]].toArray
val sigmas = data("sigmas").asInstanceOf[List[Matrix]].toArray
val gaussians = mus zip sigmas map {
case (mu, sigma) => new MultivariateGaussian(mu, sigma)
}
val constructor = classOf[GaussianMixtureModel].getDeclaredConstructor(
classOf[String],
classOf[Array[Double]],
classOf[Array[MultivariateGaussian]]
)
constructor.setAccessible(true)
var inst = constructor.newInstance(metadata.uid, weights, gaussians)
inst = inst.set(inst.probabilityCol, metadata.paramMap("probabilityCol").asInstanceOf[String])
inst = inst.set(inst.featuresCol, metadata.paramMap("featuresCol").asInstanceOf[String])
inst = inst.set(inst.predictionCol, metadata.paramMap("predictionCol").asInstanceOf[String])
inst
}
override implicit def getTransformer(transformer: GaussianMixtureModel): LocalTransformer[GaussianMixtureModel] = new LocalGaussianMixtureModel(transformer)
}