Class

org.apache.spark.mllib.regression

GeneralizedLinearAlgorithm

Related Doc: package regression

Permalink

abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] extends Logging with Serializable

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

Annotations
@Since( "0.8.0" ) @DeveloperApi()
Source
GeneralizedLinearAlgorithm.scala
Linear Supertypes
Serializable, Serializable, Logging, AnyRef, Any
Known Subclasses
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. GeneralizedLinearAlgorithm
  2. Serializable
  3. Serializable
  4. Logging
  5. AnyRef
  6. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new GeneralizedLinearAlgorithm()

    Permalink

Abstract Value Members

  1. abstract def createModel(weights: Vector, intercept: Double): M

    Permalink

    Create a model given the weights and intercept

    Create a model given the weights and intercept

    Attributes
    protected
  2. abstract def optimizer: Optimizer

    Permalink

    The optimizer to solve the problem.

    The optimizer to solve the problem.

    Annotations
    @Since( "0.8.0" )

Concrete Value Members

  1. final def !=(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

    Permalink
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  4. var addIntercept: Boolean

    Permalink

    Whether to add intercept (default: false).

    Whether to add intercept (default: false).

    Attributes
    protected
  5. final def asInstanceOf[T0]: T0

    Permalink
    Definition Classes
    Any
  6. def clone(): AnyRef

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  7. final def eq(arg0: AnyRef): Boolean

    Permalink
    Definition Classes
    AnyRef
  8. def equals(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  9. def finalize(): Unit

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  10. def generateInitialWeights(input: RDD[LabeledPoint]): Vector

    Permalink

    Generate the initial weights when the user does not supply them

    Generate the initial weights when the user does not supply them

    Attributes
    protected
  11. final def getClass(): Class[_]

    Permalink
    Definition Classes
    AnyRef → Any
  12. def getNumFeatures: Int

    Permalink

    The dimension of training features.

    The dimension of training features.

    Annotations
    @Since( "1.4.0" )
  13. def hashCode(): Int

    Permalink
    Definition Classes
    AnyRef → Any
  14. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  15. def isAddIntercept: Boolean

    Permalink

    Get if the algorithm uses addIntercept

    Get if the algorithm uses addIntercept

    Annotations
    @Since( "1.4.0" )
  16. final def isInstanceOf[T0]: Boolean

    Permalink
    Definition Classes
    Any
  17. def isTraceEnabled(): Boolean

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  18. def log: Logger

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  19. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  20. def logDebug(msg: ⇒ String): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  21. def logError(msg: ⇒ String, throwable: Throwable): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  22. def logError(msg: ⇒ String): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  23. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  24. def logInfo(msg: ⇒ String): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  25. def logName: String

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  26. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  27. def logTrace(msg: ⇒ String): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  28. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  29. def logWarning(msg: ⇒ String): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  30. final def ne(arg0: AnyRef): Boolean

    Permalink
    Definition Classes
    AnyRef
  31. final def notify(): Unit

    Permalink
    Definition Classes
    AnyRef
  32. final def notifyAll(): Unit

    Permalink
    Definition Classes
    AnyRef
  33. var numFeatures: Int

    Permalink

    The dimension of training features.

    The dimension of training features.

    Attributes
    protected
  34. var numOfLinearPredictor: Int

    Permalink

    In GeneralizedLinearModel, only single linear predictor is allowed for both weights and intercept.

    In GeneralizedLinearModel, only single linear predictor is allowed for both weights and intercept. However, for multinomial logistic regression, with K possible outcomes, we are training K-1 independent binary logistic regression models which requires K-1 sets of linear predictor.

    As a result, the workaround here is if more than two sets of linear predictors are needed, we construct bigger weights vector which can hold both weights and intercepts. If the intercepts are added, the dimension of weights will be (numOfLinearPredictor) * (numFeatures + 1) . If the intercepts are not added, the dimension of weights will be (numOfLinearPredictor) * numFeatures.

    Thus, the intercepts will be encapsulated into weights, and we leave the value of intercept in GeneralizedLinearModel as zero.

    Attributes
    protected
  35. def run(input: RDD[LabeledPoint], initialWeights: Vector): M

    Permalink

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

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

    Annotations
    @Since( "1.0.0" )
  36. def run(input: RDD[LabeledPoint]): M

    Permalink

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

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

    Annotations
    @Since( "0.8.0" )
  37. def setIntercept(addIntercept: Boolean): GeneralizedLinearAlgorithm.this.type

    Permalink

    Set if the algorithm should add an intercept.

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

    Annotations
    @Since( "0.8.0" )
  38. def setValidateData(validateData: Boolean): GeneralizedLinearAlgorithm.this.type

    Permalink

    Set if the algorithm should validate data before training.

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

    Annotations
    @Since( "0.8.0" )
  39. final def synchronized[T0](arg0: ⇒ T0): T0

    Permalink
    Definition Classes
    AnyRef
  40. def toString(): String

    Permalink
    Definition Classes
    AnyRef → Any
  41. var validateData: Boolean

    Permalink
    Attributes
    protected
  42. val validators: Seq[(RDD[LabeledPoint]) ⇒ Boolean]

    Permalink
    Attributes
    protected
  43. final def wait(): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  44. final def wait(arg0: Long, arg1: Int): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  45. final def wait(arg0: Long): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

Inherited from Any

Ungrouped