org.apache.spark.mllib.classification

SVMWithSGD

class SVMWithSGD extends GeneralizedLinearAlgorithm[SVMModel] with Serializable

Train a Support Vector Machine (SVM) using Stochastic Gradient Descent. By default L2 regularization is used, which can be changed via SVMWithSGD.optimizer. NOTE: Labels used in SVM should be {0, 1}.

Annotations
@Since( "0.8.0" )
Source
SVM.scala
Linear Supertypes
GeneralizedLinearAlgorithm[SVMModel], Serializable, Serializable, Logging, AnyRef, Any
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  1. SVMWithSGD
  2. GeneralizedLinearAlgorithm
  3. Serializable
  4. Serializable
  5. Logging
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Instance Constructors

  1. new SVMWithSGD()

    Construct a SVM object with default parameters: {stepSize: 1.

    Construct a SVM object with default parameters: {stepSize: 1.0, numIterations: 100, regParm: 0.01, miniBatchFraction: 1.0}.

    Annotations
    @Since( "0.8.0" )

Value Members

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

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

    Definition Classes
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  3. final def ##(): Int

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

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    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
    SVMWithSGDGeneralizedLinearAlgorithm
  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
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    Annotations
    @throws( classOf[java.lang.Throwable] )
  13. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  14. def getNumFeatures: Int

    The dimension of training features.

    The dimension of training features.

    Definition Classes
    GeneralizedLinearAlgorithm
    Annotations
    @Since( "1.4.0" )
  15. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  16. def isAddIntercept: Boolean

    Get if the algorithm uses addIntercept

    Get if the algorithm uses addIntercept

    Definition Classes
    GeneralizedLinearAlgorithm
    Annotations
    @Since( "1.4.0" )
  17. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  18. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging
  19. def log: Logger

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

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    protected
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    Logging
  21. def logDebug(msg: ⇒ String): Unit

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

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    protected
    Definition Classes
    Logging
  23. def logError(msg: ⇒ String): Unit

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

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

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  26. def logName: String

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

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    protected
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    Logging
  28. def logTrace(msg: ⇒ String): Unit

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    protected
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    Logging
  29. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    protected
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    Logging
  30. def logWarning(msg: ⇒ String): Unit

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    protected
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    Logging
  31. final def ne(arg0: AnyRef): Boolean

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  32. final def notify(): Unit

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  33. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  34. var numFeatures: Int

    The dimension of training features.

    The dimension of training features.

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  35. var numOfLinearPredictor: Int

    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
    Definition Classes
    GeneralizedLinearAlgorithm
  36. val optimizer: GradientDescent

    The optimizer to solve the problem.

    The optimizer to solve the problem.

    Definition Classes
    SVMWithSGDGeneralizedLinearAlgorithm
    Annotations
    @Since( "0.8.0" )
  37. 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
    Annotations
    @Since( "1.0.0" )
  38. 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
    Annotations
    @Since( "0.8.0" )
  39. 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
    Annotations
    @Since( "0.8.0" )
  40. 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
    Annotations
    @Since( "0.8.0" )
  41. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  42. def toString(): String

    Definition Classes
    AnyRef → Any
  43. var validateData: Boolean

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  44. val validators: List[(RDD[LabeledPoint]) ⇒ Boolean]

    Attributes
    protected
    Definition Classes
    SVMWithSGDGeneralizedLinearAlgorithm
  45. final def wait(): Unit

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    @throws( ... )
  46. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  47. final def wait(arg0: Long): Unit

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