Class/Object

org.apache.spark.mllib.regression

LinearRegressionWithSGD

Related Docs: object LinearRegressionWithSGD | package regression

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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.

Annotations
@Since( "0.8.0" )
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
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Visibility
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Instance Constructors

  1. new LinearRegressionWithSGD()

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

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

    Annotations
    @Since( "0.8.0" ) @deprecated
    Deprecated

    (Since version 2.0.0) Use ml.regression.LinearRegression or LBFGS

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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    Definition Classes
    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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    Definition Classes
    Any
  6. def clone(): AnyRef

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

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    Create a model given the weights and intercept

    Create a model given the weights and intercept

    Attributes
    protected[org.apache.spark.mllib]
    Definition Classes
    LinearRegressionWithSGDGeneralizedLinearAlgorithm
  8. final def eq(arg0: AnyRef): Boolean

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

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    AnyRef → Any
  10. def finalize(): Unit

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    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @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, silent: Boolean = false): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  16. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    protected
    Definition Classes
    Logging
  17. 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" )
  18. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  19. def isTraceEnabled(): Boolean

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    protected
    Definition Classes
    Logging
  20. def log: Logger

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

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

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

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    protected
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    Logging
  24. def logError(msg: ⇒ String): Unit

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

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

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    protected
    Definition Classes
    Logging
  27. def logName: String

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

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

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

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

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

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

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

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    Definition Classes
    AnyRef
  35. var numFeatures: Int

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    The dimension of training features.

    The dimension of training features.

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  36. 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
  37. val optimizer: GradientDescent

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    The optimizer to solve the problem.

    The optimizer to solve the problem.

    Definition Classes
    LinearRegressionWithSGDGeneralizedLinearAlgorithm
    Annotations
    @Since( "0.8.0" )
  38. def run(input: RDD[LabeledPoint], initialWeights: Vector): LinearRegressionModel

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

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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" )
  40. def setIntercept(addIntercept: Boolean): LinearRegressionWithSGD.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" )
  41. def setValidateData(validateData: Boolean): LinearRegressionWithSGD.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" )
  42. final def synchronized[T0](arg0: ⇒ T0): T0

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    AnyRef
  43. def toString(): String

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  44. var validateData: Boolean

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

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

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

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

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Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

Inherited from Any

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