spark StandardScalerExample 源码
spark StandardScalerExample 代码
文件路径：/examples/src/main/scala/org/apache/spark/examples/mllib/StandardScalerExample.scala
/*
* 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.
*/
// scalastyle:off println
package org.apache.spark.examples.mllib
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
// $example on$
import org.apache.spark.mllib.feature.{StandardScaler, StandardScalerModel}
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.util.MLUtils
// $example off$
object StandardScalerExample {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("StandardScalerExample")
val sc = new SparkContext(conf)
// $example on$
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
val scaler1 = new StandardScaler().fit(data.map(x => x.features))
val scaler2 = new StandardScaler(withMean = true, withStd = true).fit(data.map(x => x.features))
// scaler3 is an identical model to scaler2, and will produce identical transformations
val scaler3 = new StandardScalerModel(scaler2.std, scaler2.mean)
// data1 will be unit variance.
val data1 = data.map(x => (x.label, scaler1.transform(x.features)))
// data2 will be unit variance and zero mean.
val data2 = data.map(x => (x.label, scaler2.transform(Vectors.dense(x.features.toArray))))
// $example off$
println("data1: ")
data1.collect.foreach(x => println(x))
println("data2: ")
data2.collect.foreach(x => println(x))
sc.stop()
}
}
// scalastyle:on println
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