Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/1207#discussion_r15733244
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala ---
@@ -0,0 +1,94 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.mllib.feature
+
+import breeze.linalg.{DenseVector => BDV}
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.mllib.linalg.distributed.RowMatrix
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.rdd.RDD
+
+/**
+ * :: DeveloperApi ::
+ * Standardizes features by removing the mean and scaling to unit variance
using column summary
+ * statistics on the samples in the training set.
+ *
+ * @param withMean True by default. Centers the data with mean before
scaling. It will build a dense
+ * output, so this does not work on sparse input and will
raise an exception.
+ * @param withStd True by default. Scales the data to unit standard
deviation.
--- End diff --
sklearn.preprocessing.StandardScaler has this API. If we want to minimize
the set of parameters now, we can remove it for this release.
http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler
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