org.apache.spark.mllib.regression

GeneralizedLinearAlgorithm

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
@DeveloperApi()
Linear Supertypes
Serializable, Serializable, Logging, AnyRef, Any
Known Subclasses
LassoWithSGD, LinearRegressionWithSGD, LogisticRegressionWithLBFGS, LogisticRegressionWithSGD, RidgeRegressionWithSGD, SVMWithSGD
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  1. GeneralizedLinearAlgorithm
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Instance Constructors

  1. new GeneralizedLinearAlgorithm()

Abstract Value Members

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

    Create a model given the weights and intercept

    Create a model given the weights and intercept

    Attributes
    protected

  2. abstract def optimizer: Optimizer

    The optimizer to solve the problem.

Concrete Value Members

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

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. var addIntercept: Boolean

    Whether to add intercept (default: false).

    Whether to add intercept (default: false).

    Attributes
    protected

  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )

  7. final def eq(arg0: AnyRef): Boolean

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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  10. final def getClass(): Class[_]

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  11. def hashCode(): Int

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  12. final def isInstanceOf[T0]: Boolean

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  13. def isTraceEnabled(): Boolean

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    protected
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    Logging

  14. def log: Logger

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    protected
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  15. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  16. def logDebug(msg: ⇒ String): Unit

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  17. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    protected
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  18. def logError(msg: ⇒ String): Unit

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  19. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  20. def logInfo(msg: ⇒ String): Unit

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  21. def logName: String

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  22. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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  23. def logTrace(msg: ⇒ String): Unit

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  24. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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  25. def logWarning(msg: ⇒ String): Unit

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  26. final def ne(arg0: AnyRef): Boolean

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  27. final def notify(): Unit

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  28. final def notifyAll(): Unit

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  29. def run(input: RDD[LabeledPoint], initialWeights: Vector): M

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

  30. def run(input: RDD[LabeledPoint]): M

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

  31. def setIntercept(addIntercept: Boolean): GeneralizedLinearAlgorithm.this.type

    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.

  32. def setValidateData(validateData: Boolean): GeneralizedLinearAlgorithm.this.type

    Set if the algorithm should validate data before training.

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

  33. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
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  34. def toString(): String

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  35. var validateData: Boolean

    Attributes
    protected

  36. val validators: Seq[(RDD[LabeledPoint]) ⇒ Boolean]

    Attributes
    protected

  37. final def wait(): Unit

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  38. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )

  39. final def wait(arg0: Long): Unit

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