org.apache.spark.mllib.regression

Class GeneralizedLinearAlgorithm<M extends GeneralizedLinearModel>

Object

org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm<M>

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging

Direct Known Subclasses:

LassoWithSGD, LinearRegressionWithSGD, LogisticRegressionWithLBFGS, LogisticRegressionWithSGD, RidgeRegressionWithSGD, SVMWithSGD

public abstract class GeneralizedLinearAlgorithm<M extends GeneralizedLinearModel>
extends Object
implements org.apache.spark.internal.Logging, scala.Serializable

GeneralizedLinearAlgorithm implements methods to train a Generalized Linear Model (GLM). This class should be extended with an Optimizer to create a new GLM.

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

org.apache.spark.internal.Logging.SparkShellLoggingFilter

Constructor Summary

Constructors

Constructor and Description
GeneralizedLinearAlgorithm()

Method Summary

Modifier and Type	Method and Description
int	getNumFeatures() The dimension of training features.
boolean	isAddIntercept() Get if the algorithm uses addIntercept
abstract Optimizer	optimizer() The optimizer to solve the problem.
M	run(RDD<LabeledPoint> input) Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.
M	run(RDD<LabeledPoint> input, Vector initialWeights) Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.
GeneralizedLinearAlgorithm<M>	setIntercept(boolean addIntercept) Set if the algorithm should add an intercept.
GeneralizedLinearAlgorithm<M>	setValidateData(boolean validateData) Set if the algorithm should validate data before training.

Methods inherited from class Object

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

Methods inherited from interface org.apache.spark.internal.Logging

$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize

Constructor Detail

GeneralizedLinearAlgorithm

public GeneralizedLinearAlgorithm()

Method Detail

getNumFeatures

public int getNumFeatures()

The dimension of training features.

Returns:

(undocumented)

isAddIntercept

public boolean isAddIntercept()

Get if the algorithm uses addIntercept

Returns:

(undocumented)

optimizer

public abstract Optimizer optimizer()

The optimizer to solve the problem.

Returns:

(undocumented)

run

public M run(RDD<LabeledPoint> input)

Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.

Parameters:

input - (undocumented)

Returns:

(undocumented)

run

public M run(RDD<LabeledPoint> input,
             Vector initialWeights)

Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.

Parameters:

input - (undocumented)

initialWeights - (undocumented)

Returns:

(undocumented)

setIntercept

public GeneralizedLinearAlgorithm<M> setIntercept(boolean addIntercept)

Set if the algorithm should add an intercept. Default false. We set the default to false because adding the intercept will cause memory allocation.

Parameters:

addIntercept - (undocumented)

Returns:

(undocumented)

setValidateData

public GeneralizedLinearAlgorithm<M> setValidateData(boolean validateData)

Set if the algorithm should validate data before training. Default true.

Parameters:

validateData - (undocumented)

Returns:

(undocumented)