LinearRegressionWithSGD

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2. final def ##(): Int

   

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

    

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

    Train a LinearRegression 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 LinearRegressionModel which has the weights and offset from training.

    Annotations

    @Since( "0.8.0" )

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

    

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

    Train a LinearRegression 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. 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.

    returns

    a LinearRegressionModel which has the weights and offset from training.

    Annotations

    @Since( "0.8.0" )

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

    

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

    Train a LinearRegression 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.

    miniBatchFraction

    Fraction of data to be used per iteration.

    Annotations

    @Since( "0.8.0" )

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

    

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

    Train a Linear Regression 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 to be used for each iteration of gradient descent.

    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( "1.0.0" )

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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