Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

2.  final def ##(): Int

   Definition Classes

   AnyRef → Any

3.  final def ==(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

4.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

5.  def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @native() @throws( ... )

6.  final def eq(arg0: AnyRef): Boolean

   Definition Classes

   AnyRef

7.  def equals(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

8.  def finalize(): Unit

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( classOf[java.lang.Throwable] )

9.  final def getClass(): Class[_]

   Definition Classes

   AnyRef → Any

   Annotations

   @native()

10.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

11.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

12.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

13.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

14.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

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

    Definition Classes

    AnyRef

16.  def toString(): String

    Definition Classes

    AnyRef → Any

17.  def train(input: RDD[LabeledPoint], numIterations: Int): RidgeRegressionModel

    Train a RidgeRegression model given an RDD of (label, features) pairs.

    Train a RidgeRegression model given an RDD of (label, features) pairs. We run a fixed number of iterations of gradient descent using a step size of 1.0. We use the entire data set to compute the true gradient in each iteration.

    input

    RDD of (label, array of features) pairs.

    numIterations

    Number of iterations of gradient descent to run.

    returns

    a RidgeRegressionModel which has the weights and offset from training.

    Annotations

    @Since( "0.8.0" )

18.  def train(input: RDD[LabeledPoint], numIterations: Int, stepSize: Double, regParam: Double): RidgeRegressionModel

    Train a RidgeRegression model given an RDD of (label, features) pairs.

    Train a RidgeRegression model given an RDD of (label, features) pairs. We run a fixed number of iterations of gradient descent using the specified step size. We use the entire data set to compute the true gradient in each iteration.

    input

    RDD of (label, array of features) pairs.

    numIterations

    Number of iterations of gradient descent to run.

    stepSize

    Step size to be used for each iteration of Gradient Descent.

    regParam

    Regularization parameter.

    returns

    a RidgeRegressionModel which has the weights and offset from training.

    Annotations

    @Since( "0.8.0" )

19.  def train(input: RDD[LabeledPoint], numIterations: Int, stepSize: Double, regParam: Double, miniBatchFraction: Double): RidgeRegressionModel

    Train a RidgeRegression model given an RDD of (label, features) pairs.

    Train a RidgeRegression model given an RDD of (label, features) pairs. We run a fixed number of iterations of gradient descent using the specified step size. Each iteration uses miniBatchFraction fraction of the data to calculate a stochastic gradient.

    input

    RDD of (label, array of features) pairs.

    numIterations

    Number of iterations of gradient descent to run.

    stepSize

    Step size to be used for each iteration of gradient descent.

    regParam

    Regularization parameter.

    miniBatchFraction

    Fraction of data to be used per iteration.

    Annotations

    @Since( "0.8.0" )

20.  def train(input: RDD[LabeledPoint], numIterations: Int, stepSize: Double, regParam: Double, miniBatchFraction: Double, initialWeights: Vector): RidgeRegressionModel

    Train a RidgeRegression model given an RDD of (label, features) pairs.

    Train a RidgeRegression model given an RDD of (label, features) pairs. We run a fixed number of iterations of gradient descent using the specified step size. Each iteration uses miniBatchFraction fraction of the data to calculate a stochastic gradient. The weights used in gradient descent are initialized using the initial weights provided.

    input

    RDD of (label, array of features) pairs.

    numIterations

    Number of iterations of gradient descent to run.

    stepSize

    Step size to be used for each iteration of gradient descent.

    regParam

    Regularization parameter.

    miniBatchFraction

    Fraction of data to be used per iteration.

    initialWeights

    Initial set of weights to be used. Array should be equal in size to the number of features in the data.

    Annotations

    @Since( "1.0.0" )

21.  final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

23.  final def wait(arg0: Long): Unit

    Definition Classes

    AnyRef

    Annotations

    @native() @throws( ... )