本文整理汇总了Scala中org.apache.spark.ml.feature.MinMaxScaler类的典型用法代码示例。如果您正苦于以下问题：Scala MinMaxScaler类的具体用法？Scala MinMaxScaler怎么用？Scala MinMaxScaler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MinMaxScaler类的1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: MinMaxScalerJob
//设置package包名称以及导入依赖的类
import io.hydrosphere.mist.api._
import io.hydrosphere.mist.api.ml._
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.feature.MinMaxScaler
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.sql.SparkSession
object MinMaxScalerJob extends MLMistJob {
def session: SparkSession = SparkSession
.builder()
.appName(context.appName)
.config(context.getConf)
.getOrCreate()
def train(savePath: String): Map[String, Any] = {
val dataFrame = session.createDataFrame(Seq(
(0, Vectors.dense(1.0, 0.1, -1.0)),
(1, Vectors.dense(2.0, 1.1, 1.0)),
(2, Vectors.dense(3.0, 10.1, 3.0))
)).toDF("id", "features")
val scaler = new MinMaxScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures")
val pipeline = new Pipeline().setStages(Array(scaler))
val model = pipeline.fit(dataFrame)
model.write.overwrite().save(savePath)
Map.empty[String, Any]
}
def serve(modelPath: String, features: List[Array[Double]]): Map[String, Any] = {
import LocalPipelineModel._
val pipeline = PipelineLoader.load(modelPath)
val data = LocalData(
LocalDataColumn("features", features)
)
val result: LocalData = pipeline.transform(data)
Map("result" -> result.select("scaledFeatures").toMapList)
}
}