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. This solves the least squares regression formulation f(weights) = 1/n ||A weights-y||^2 (which is the mean squared error). 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.

Linear Supertypes
GeneralizedLinearAlgorithm[LinearRegressionModel], Serializable, Serializable, Logging, AnyRef, Any
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  1. LinearRegressionWithSGD
  2. GeneralizedLinearAlgorithm
  3. Serializable
  4. Serializable
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Instance Constructors

  1. new LinearRegressionWithSGD()

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

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

Value Members

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

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

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  3. final def ##(): Int

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

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  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]
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    @throws( ... )
  9. def createModel(weights: Vector, intercept: Double): LinearRegressionModel

    Create a model given the weights and intercept

    Create a model given the weights and intercept

    Attributes
    protected
    Definition Classes
    LinearRegressionWithSGDGeneralizedLinearAlgorithm
  10. final def eq(arg0: AnyRef): Boolean

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  11. def equals(arg0: Any): Boolean

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    AnyRef → Any
  12. def finalize(): Unit

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

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  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

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

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

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

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

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

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

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

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

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

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

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  29. final def notify(): Unit

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  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
    LinearRegressionWithSGDGeneralizedLinearAlgorithm
  32. def run(input: RDD[LabeledPoint], initialWeights: Vector): 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
  33. 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
  34. def setIntercept(addIntercept: Boolean): LinearRegressionWithSGD.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): 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
  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: Seq[(RDD[LabeledPoint]) ⇒ Boolean]

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

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

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

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    @throws( ... )

Inherited from Serializable

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