LassoWithSGD

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

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

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

    Train a Lasso 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. Each pair describes a row of the data matrix A as well as the corresponding right hand side label y

    numIterations

    Number of iterations of gradient descent to run.

    returns

    a LassoModel which has the weights and offset from training.

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

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

    Train a Lasso 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 true gradient in each iteration.

    input

    RDD of (label, array of features) pairs. Each pair describes a row of the data matrix A as well as the corresponding right hand side label y

    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 LassoModel which has the weights and offset from training.

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

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

    Train a Lasso 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. Each pair describes a row of the data matrix A as well as the corresponding right hand side label y

    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.

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

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

    Train a Lasso 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. Each pair describes a row of the data matrix A as well as the corresponding right hand side label y

    numIterations

    Number of iterations of gradient descent to run.

    stepSize

    Step size scaling to be used for the iterations 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.

23. final def wait(): Unit

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

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

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