Github user yinxusen commented on a diff in the pull request:
https://github.com/apache/spark/pull/11142#discussion_r53560933
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/StandardScalerExample.scala
---
@@ -0,0 +1,56 @@
+/*
+ * 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
+// $example on$
+import org.apache.spark.SparkContext
+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]) {
+
+ 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)))
+
+ // Without converting the features into dense vectors, transformation
with zero mean will raise
+ // exception on sparse vector.
+ // data2 will be unit variance and zero mean.
+ val data2 = data.map(x => (x.label,
scaler2.transform(Vectors.dense(x.features.toArray))))
+ // $example off$
+
--- End diff --
Add outputs of data1 and data2
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