Class/Object

org.apache.spark.mllib.regression

RidgeRegressionWithSGD

Related Docs: object RidgeRegressionWithSGD | package regression

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class RidgeRegressionWithSGD extends GeneralizedLinearAlgorithm[RidgeRegressionModel] with Serializable

Train a regression model with L2-regularization using Stochastic Gradient Descent. This solves the l2-regularized least squares regression formulation f(weights) = 1/2n ||A weights-y||2 + regParam/2 ||weights||2 Here the data matrix has n rows, and the input RDD holds the set of rows of A, each with its corresponding right hand side label y. See also the documentation for the precise formulation.

Annotations
@Since( "0.8.0" )
Source
RidgeRegression.scala
Linear Supertypes
GeneralizedLinearAlgorithm[RidgeRegressionModel], Serializable, Serializable, Logging, AnyRef, Any
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  1. RidgeRegressionWithSGD
  2. GeneralizedLinearAlgorithm
  3. Serializable
  4. Serializable
  5. Logging
  6. AnyRef
  7. Any
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Instance Constructors

  1. new RidgeRegressionWithSGD()

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

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

    Annotations
    @Since( "0.8.0" ) @deprecated
    Deprecated

    (Since version 2.0.0)

Value Members

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

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

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

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    AnyRef → Any
  4. var addIntercept: Boolean

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    Whether to add intercept (default: false).

    Whether to add intercept (default: false).

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  5. final def asInstanceOf[T0]: T0

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    Any
  6. def clone(): AnyRef

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    protected[java.lang]
    Definition Classes
    AnyRef
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    @throws( ... )
  7. def createModel(weights: Vector, intercept: Double): RidgeRegressionModel

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    Create a model given the weights and intercept

    Create a model given the weights and intercept

    Attributes
    protected
    Definition Classes
    RidgeRegressionWithSGDGeneralizedLinearAlgorithm
  8. final def eq(arg0: AnyRef): Boolean

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    AnyRef
  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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

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    Generate the initial weights when the user does not supply them

    Generate the initial weights when the user does not supply them

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  12. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  13. def getNumFeatures: Int

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    The dimension of training features.

    The dimension of training features.

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  16. def isAddIntercept: Boolean

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    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

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    Definition Classes
    Any
  18. def isTraceEnabled(): Boolean

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    protected
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    Logging
  19. def log: Logger

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

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

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

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

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

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

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    Logging
  26. def logName: String

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

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

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

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

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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

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    Definition Classes
    AnyRef
  34. var numFeatures: Int

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    The dimension of training features.

    The dimension of training features.

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  35. var numOfLinearPredictor: Int

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    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

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    The optimizer to solve the problem.

    The optimizer to solve the problem.

    Definition Classes
    RidgeRegressionWithSGDGeneralizedLinearAlgorithm
    Annotations
    @Since( "0.8.0" )
  37. def run(input: RDD[LabeledPoint], initialWeights: Vector): RidgeRegressionModel

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    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]): RidgeRegressionModel

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    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): RidgeRegressionWithSGD.this.type

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    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): RidgeRegressionWithSGD.this.type

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    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

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    AnyRef
  42. def toString(): String

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  43. var validateData: Boolean

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

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    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  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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Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

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