org.apache.spark.mllib.regression

LinearRegressionWithSGD

class LinearRegressionWithSGD extends GeneralizedLinearAlgorithm[LinearRegressionModel] with Serializable

Train a linear regression model with no regularization using Stochastic Gradient Descent.

Linear Supertypes
GeneralizedLinearAlgorithm[LinearRegressionModel], Serializable, Serializable, Logging, AnyRef, Any
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  1. LinearRegressionWithSGD
  2. GeneralizedLinearAlgorithm
  3. Serializable
  4. Serializable
  5. Logging
  6. AnyRef
  7. Any
Visibility
  1. Public
  2. All

Instance Constructors

  1. new LinearRegressionWithSGD()

    Construct a LinearRegression 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

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

    Definition Classes
    Any
  8. def clone(): AnyRef

    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws()
  9. def createModel(weights: Array[Double], intercept: Double): LinearRegressionModel

    Create a model given the weights and intercept

    Create a model given the weights and intercept

    Definition Classes
    LinearRegressionWithSGDGeneralizedLinearAlgorithm
  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[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws()
  13. final def getClass(): java.lang.Class[_]

    Definition Classes
    AnyRef → Any
  14. val gradient: SquaredGradient

  15. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  16. def initLogging(): Unit

    Attributes
    protected
    Definition Classes
    Logging
  17. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  18. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging
  19. def log: Logger

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

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

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

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

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

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

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

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

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

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

    Attributes
    protected
    Definition Classes
    Logging
  30. var miniBatchFraction: Double

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

    Definition Classes
    AnyRef
  32. final def notify(): Unit

    Definition Classes
    AnyRef
  33. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  34. var numIterations: Int

  35. val optimizer: GradientDescent

  36. def run(input: RDD[LabeledPoint], initialWeights: Array[Double]): LinearRegressionModel

    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
  37. def run(input: RDD[LabeledPoint]): LinearRegressionModel

    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
  38. def setIntercept(addIntercept: Boolean): LinearRegressionWithSGD.this.type

    Set if the algorithm should add an intercept.

    Set if the algorithm should add an intercept. Default true.

    Definition Classes
    GeneralizedLinearAlgorithm
  39. def setValidateData(validateData: Boolean): LinearRegressionWithSGD.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
  40. var stepSize: Double

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

    Definition Classes
    AnyRef
  42. def toString(): String

    Definition Classes
    AnyRef → Any
  43. val updater: SimpleUpdater

  44. var validateData: Boolean

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

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  46. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws()

Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

Inherited from Any