org.apache.spark.mllib.classification

SVMWithSGD

class SVMWithSGD extends GeneralizedLinearAlgorithm[SVMModel] with Serializable

Train a Support Vector Machine (SVM) using Stochastic Gradient Descent. NOTE: Labels used in SVM should be {0, 1}.

Linear Supertypes
GeneralizedLinearAlgorithm[SVMModel], Serializable, Serializable, Logging, AnyRef, Any
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  1. SVMWithSGD
  2. GeneralizedLinearAlgorithm
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Instance Constructors

  1. new SVMWithSGD()

    Construct a SVM object with default parameters

Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  6. var addIntercept: Boolean

    Whether to add intercept (default: false).

    Whether to add intercept (default: false).

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm

  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  8. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  9. def createModel(weights: Vector, intercept: Double): SVMModel

    Create a model given the weights and intercept

    Create a model given the weights and intercept

    Attributes
    protected
    Definition Classes
    SVMWithSGD → GeneralizedLinearAlgorithm

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

    Definition Classes
    AnyRef

  11. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  12. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes
    AnyRef → Any

  14. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  15. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  16. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging

  17. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

  21. def logError(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

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

    Definition Classes
    AnyRef

  29. final def notify(): Unit

    Definition Classes
    AnyRef

  30. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  31. val optimizer: GradientDescent

    The optimizer to solve the problem.

    The optimizer to solve the problem.

    Definition Classes
    SVMWithSGD → GeneralizedLinearAlgorithm

  32. def run(input: RDD[LabeledPoint], initialWeights: Vector): SVMModel

    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.

    Definition Classes
    GeneralizedLinearAlgorithm

  33. def run(input: RDD[LabeledPoint]): SVMModel

    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.

    Definition Classes
    GeneralizedLinearAlgorithm

  34. def setIntercept(addIntercept: Boolean): SVMWithSGD.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.

    Definition Classes
    GeneralizedLinearAlgorithm

  35. def setValidateData(validateData: Boolean): SVMWithSGD.this.type

    Set if the algorithm should validate data before training.

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

    Definition Classes
    GeneralizedLinearAlgorithm

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

    Definition Classes
    AnyRef

  37. def toString(): String

    Definition Classes
    AnyRef → Any

  38. var validateData: Boolean

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm

  39. val validators: List[(RDD[LabeledPoint]) ⇒ Boolean]

    Attributes
    protected
    Definition Classes
    SVMWithSGD → GeneralizedLinearAlgorithm

  40. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from GeneralizedLinearAlgorithm[SVMModel]

Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

Inherited from Any

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