SVMWithSGD

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

   

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2. final def ##(): Int

   

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

   

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

   

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7. def equals(arg0: Any): Boolean

   

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8. def finalize(): Unit

   

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11. final def isInstanceOf[T0]: Boolean

    

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

    

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13. final def notify(): Unit

    

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

    

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16. def toString(): String

    

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17. 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" )

18. 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" )

19. 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" )

20. 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.

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    @Since( "0.8.0" )

21. final def wait(): Unit

    

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

    

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

    

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