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 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

StandardScaler()

StandardScaler(boolean withMean, boolean withStd)

Method Summary

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