org.apache.spark.mllib.util
Class LinearDataGenerator

Object
  org.apache.spark.mllib.util.LinearDataGenerator

public class LinearDataGenerator
extends Object

:: DeveloperApi :: Generate sample data used for Linear Data. This class generates uniformly random values for every feature and adds Gaussian noise with mean eps to the response variable Y.

Constructor Summary

LinearDataGenerator()

Method Summary

static scala.collection.Seq<LabeledPoint>	generateLinearInput(double intercept, double[] weights, double[] xMean, double[] xVariance, int nPoints, int seed, double eps)
static scala.collection.Seq<LabeledPoint>	generateLinearInput(double intercept, double[] weights, int nPoints, int seed, double eps) For compatibility, the generated data without specifying the mean and variance will have zero mean and variance of (1.0/3.0) since the original output range is [-1, 1] with uniform distribution, and the variance of uniform distribution is (b - a)^2^ / 12 which will be (1.0/3.0)
static java.util.List<LabeledPoint>	generateLinearInputAsList(double intercept, double[] weights, int nPoints, int seed, double eps) Return a Java List of synthetic data randomly generated according to a multi collinear model.
static RDD<LabeledPoint>	generateLinearRDD(SparkContext sc, int nexamples, int nfeatures, double eps, int nparts, double intercept) Generate an RDD containing sample data for Linear Regression models - including Ridge, Lasso, and uregularized variants.
static void	main(String[] args)

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

LinearDataGenerator

public LinearDataGenerator()

Method Detail

generateLinearInputAsList

public static java.util.List<LabeledPoint> generateLinearInputAsList(double intercept,
                                                                     double[] weights,
                                                                     int nPoints,
                                                                     int seed,
                                                                     double eps)

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

Parameters:

intercept - Data intercept

weights - Weights to be applied.

nPoints - Number of points in sample.

seed - Random seed

eps - (undocumented)

Returns:

Java List of input.

generateLinearInput

public static scala.collection.Seq<LabeledPoint> generateLinearInput(double intercept,
                                                                     double[] weights,
                                                                     int nPoints,
                                                                     int seed,
                                                                     double eps)

For compatibility, the generated data without specifying the mean and variance will have zero mean and variance of (1.0/3.0) since the original output range is [-1, 1] with uniform distribution, and the variance of uniform distribution is (b - a)^2^ / 12 which will be (1.0/3.0)

Parameters:

intercept - Data intercept

weights - Weights to be applied.

nPoints - Number of points in sample.

seed - Random seed

eps - Epsilon scaling factor.

Returns:

Seq of input.

generateLinearInput

public static scala.collection.Seq<LabeledPoint> generateLinearInput(double intercept,
                                                                     double[] weights,
                                                                     double[] xMean,
                                                                     double[] xVariance,
                                                                     int nPoints,
                                                                     int seed,
                                                                     double eps)

Parameters:

intercept - Data intercept

weights - Weights to be applied.

xMean - the mean of the generated features. Lots of time, if the features are not properly standardized, the algorithm with poor implementation will have difficulty to converge.

xVariance - the variance of the generated features.

nPoints - Number of points in sample.

seed - Random seed

eps - Epsilon scaling factor.

Returns:

Seq of input.

generateLinearRDD

public static RDD<LabeledPoint> generateLinearRDD(SparkContext sc,
                                                  int nexamples,
                                                  int nfeatures,
                                                  double eps,
                                                  int nparts,
                                                  double intercept)

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

Parameters:

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.

intercept - (undocumented)

Returns:

RDD of LabeledPoint containing sample data.

main

public static void main(String[] args)