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
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  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

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    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

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  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

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  8. def clone(): AnyRef

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

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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  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

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    Logging
  17. def log: Logger

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

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  19. def logDebug(msg: ⇒ String): Unit

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

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

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  24. def logName: String

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

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  26. def logTrace(msg: ⇒ String): Unit

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

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

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

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  30. final def notify(): Unit

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  31. final def notifyAll(): Unit

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  32. var numFeatures: Int

    The dimension of training features.

    The dimension of training features.

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  33. 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
  34. val optimizer: GradientDescent

    The optimizer to solve the problem.

    The optimizer to solve the problem.

    Definition Classes
    LinearRegressionWithSGDGeneralizedLinearAlgorithm
  35. 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
  36. 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
  37. 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
  38. 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
  39. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  40. def toString(): String

    Definition Classes
    AnyRef → Any
  41. var validateData: Boolean

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

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

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  44. final def wait(arg0: Long, arg1: Int): Unit

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  45. final def wait(arg0: Long): Unit

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