Github user mengxr commented on a diff in the pull request: https://github.com/apache/spark/pull/1207#discussion_r15733204 --- 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. + */ +@DeveloperApi +class StandardScaler(withMean: Boolean, withStd: Boolean) + extends VectorTransformer with Serializable { + + def this() = this(true, true) + + var mean: Vector = _ + var variance: Vector = _ + + /** + * Computes the mean and variance and stores as a model to be used for later scaling. + * + * @param data The data used to compute the mean and variance to build the transformation model. + * @return This StandardScalar object. + */ + def fit(data: RDD[Vector]): this.type = { + val summary = new RowMatrix(data).computeColumnSummaryStatistics --- End diff -- Using `OnlineSummarizer` here?
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