Github user mengxr commented on a diff in the pull request: https://github.com/apache/spark/pull/1207#discussion_r15739936 --- Diff: mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala --- @@ -0,0 +1,122 @@ +/* + * 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, SparseVector => BSV, Vector => BV} + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.mllib.linalg.{Vector, Vectors} +import org.apache.spark.mllib.rdd.RDDFunctions._ +import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer +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 False 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 { + + def this() = this(false, true) + + require(withMean || withStd, s"withMean and withStd both equal to false. Doing nothing.") + + private var mean: BV[Double] = _ + private var factor: BV[Double] = _ + + /** + * 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 = data.treeAggregate(new MultivariateOnlineSummarizer)( + (aggregator, data) => aggregator.add(data), + (aggregator1, aggregator2) => aggregator1.merge(aggregator2)) + + mean = summary.mean.toBreeze + factor = summary.variance.toBreeze + require(mean.length == factor.length) + + var i = 0 + while (i < factor.length) { + factor(i) = if (factor(i) != 0.0) 1.0 / Math.sqrt(factor(i)) else 0.0 + i += 1 + } + + this + } + + /** + * Applies standardization transformation on a vector. + * + * @param vector Vector to be standardized. + * @return Standardized vector. If the variance of a column is zero, it will return default `0.0` + * for the column with zero variance. + */ + override def transform(vector: Vector): Vector = { + if (mean == null || factor == null) { + throw new IllegalStateException( + "Haven't learned column summary statistics yet. Call fit first.") + } + + require(vector.toBreeze.length == mean.length) --- End diff -- `vector.size`
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