Package org.apache.spark.mllib.regression

Class GeneralizedLinearAlgorithm<M extends GeneralizedLinearModel>

Object
org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm<M>
All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging, scala.Serializable
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:

  • Nested Class Summary

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

    org.apache.spark.internal.Logging.SparkShellLoggingFilter

  • Constructor Summary

    Constructors
    Constructor
    Description
    GeneralizedLinearAlgorithm()
     

  • Method Summary

    Modifier and Type
    Method
    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 java.lang.Object

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

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

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

  • Constructor Details

    • GeneralizedLinearAlgorithm

      public GeneralizedLinearAlgorithm()

  • Method Details

    • 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)