org.apache.spark.mllib.feature

Class StandardScaler

Object

org.apache.spark.mllib.feature.StandardScaler

All Implemented Interfaces:

Logging

public class StandardScaler
extends Object
implements Logging

:: Experimental :: Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set.

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 Object

equals, 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