LinearDataGenerator

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11. def generateLinearInput(intercept: Double, weights: Array[Double], nPoints: Int, seed: Int, eps: Double = 0.1): Seq[LabeledPoint]

    intercept

    Data intercept

    weights

    Weights to be applied.

    nPoints

    Number of points in sample.

    seed

    Random seed

    eps

    Epsilon scaling factor.

    returns

12. def generateLinearInputAsList(intercept: Double, weights: Array[Double], nPoints: Int, seed: Int, eps: Double): List[LabeledPoint]

    Return a Java List of synthetic data randomly generated according to a multi collinear model.

    Return a Java List of synthetic data randomly generated according to a multi collinear model.

    intercept

    Data intercept

    weights

    Weights to be applied.

    nPoints

    Number of points in sample.

    seed

    Random seed

    returns

    Java List of input.

13. def generateLinearRDD(sc: SparkContext, nexamples: Int, nfeatures: Int, eps: Double, nparts: Int = 2, intercept: Double = 0.0): RDD[LabeledPoint]

    Generate an RDD containing sample data for Linear Regression models - including Ridge, Lasso, and uregularized variants.

    Generate an RDD containing sample data for Linear Regression models - including Ridge, Lasso, and uregularized variants.

    sc

    SparkContext to be used for generating the RDD.

    nexamples

    Number of examples that will be contained in the RDD.

    nfeatures

    Number of features to generate for each example.

    eps

    Epsilon factor by which examples are scaled.

    nparts

    Number of partitions in the RDD. Default value is 2.

    returns

    RDD of LabeledPoint containing sample data.

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