org.apache.spark.mllib.feature

Class StandardScaler

java.lang.Object

org.apache.spark.mllib.feature.StandardScaler

All Implemented Interfaces:

Logging

public class StandardScaler
extends java.lang.Object
implements Logging

Standardizes features by removing the mean and scaling to unit std 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.

Constructor Summary

Constructors

Constructor and Description
StandardScaler()
StandardScaler(boolean withMean, boolean withStd)

Method Summary

Methods

Modifier and Type	Method and Description
StandardScalerModel	fit(RDD<Vector> data) Computes the mean and variance and stores as a model to be used for later scaling.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

StandardScaler

public StandardScaler(boolean withMean,
              boolean withStd)

StandardScaler

public StandardScaler()

Method Detail

fit

public StandardScalerModel fit(RDD<Vector> data)

Computes the mean and variance and stores as a model to be used for later scaling.

Parameters:

data - The data used to compute the mean and variance to build the transformation model.

Returns:

a StandardScalarModel