Object/Class

org.apache.spark.mllib.regression

RidgeRegressionWithSGD

Related Docs: class RidgeRegressionWithSGD | package regression

Permalink

object RidgeRegressionWithSGD extends Serializable

Top-level methods for calling RidgeRegression.

Annotations
@Since( "0.8.0" ) @deprecated
Deprecated

(Since version 2.0.0)

Source
RidgeRegression.scala
Linear Supertypes
Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. RidgeRegressionWithSGD
  2. Serializable
  3. Serializable
  4. AnyRef
  5. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Value Members

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

    Permalink
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

    Permalink
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0

    Permalink
    Definition Classes
    Any
  5. def clone(): AnyRef

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean

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

    Permalink
    Definition Classes
    AnyRef → Any
  8. def finalize(): Unit

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]

    Permalink
    Definition Classes
    AnyRef → Any
  10. def hashCode(): Int

    Permalink
    Definition Classes
    AnyRef → Any
  11. final def isInstanceOf[T0]: Boolean

    Permalink
    Definition Classes
    Any
  12. final def ne(arg0: AnyRef): Boolean

    Permalink
    Definition Classes
    AnyRef
  13. final def notify(): Unit

    Permalink
    Definition Classes
    AnyRef
  14. final def notifyAll(): Unit

    Permalink
    Definition Classes
    AnyRef
  15. final def synchronized[T0](arg0: ⇒ T0): T0

    Permalink
    Definition Classes
    AnyRef
  16. def toString(): String

    Permalink
    Definition Classes
    AnyRef → Any
  17. def train(input: RDD[LabeledPoint], numIterations: Int): RidgeRegressionModel

    Permalink

    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

    Permalink

    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

    Permalink

    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

    Permalink

    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

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

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

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped