SVMWithSGD

Value Members

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

   Definition Classes

   AnyRef

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

   Definition Classes

   Any

3. final def ##(): Int

   Definition Classes

   AnyRef → Any

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

   Definition Classes

   AnyRef

5. final def ==(arg0: Any): Boolean

   Definition Classes

   Any

6. final def asInstanceOf[T0]: T0

   Definition Classes

   Any

7. def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

8. final def eq(arg0: AnyRef): Boolean

   Definition Classes

   AnyRef

9. def equals(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

10. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

11. final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

12. def hashCode(): Int

    Definition Classes

    AnyRef → Any

13. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

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

    Definition Classes

    AnyRef

15. final def notify(): Unit

    Definition Classes

    AnyRef

16. final def notifyAll(): Unit

    Definition Classes

    AnyRef

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

    Definition Classes

    AnyRef

18. def toString(): String

    Definition Classes

    AnyRef → Any

19. def train(input: RDD[LabeledPoint], numIterations: Int): SVMModel

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

    Train a SVM 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 update the gradient in each iteration. NOTE: Labels used in SVM should be {0, 1}

    input

    RDD of (label, array of features) pairs.

    numIterations

    Number of iterations of gradient descent to run.

    returns

    a SVMModel which has the weights and offset from training.

    Annotations

    @Since( "0.8.0" )

20. def train(input: RDD[LabeledPoint], numIterations: Int, stepSize: Double, regParam: Double): SVMModel

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

    Train a SVM 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 update the gradient in each iteration. NOTE: Labels used in SVM should be {0, 1}

    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 SVMModel which has the weights and offset from training.

    Annotations

    @Since( "0.8.0" )

21. def train(input: RDD[LabeledPoint], numIterations: Int, stepSize: Double, regParam: Double, miniBatchFraction: Double): SVMModel

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

    Train a SVM 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 the gradient. NOTE: Labels used in SVM should be {0, 1}

    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" )

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

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

    Train a SVM 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 the gradient. The weights used in gradient descent are initialized using the initial weights provided.

    NOTE: Labels used in SVM should be {0, 1}.

    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( "0.8.0" )

23. final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )