Data intercept
Weights to be applied.
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.
the variance of the generated features.
Number of points in sample.
Random seed
Epsilon scaling factor.
Seq of input.
For compatibility, the generated data without specifying the mean and variance will have zero mean and variance of (1.
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)
Data intercept
Weights to be applied.
Number of points in sample.
Random seed
Epsilon scaling factor.
Seq of input.
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.
Data intercept
Weights to be applied.
Number of points in sample.
Random seed
Java List of input.
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.
SparkContext to be used for generating the RDD.
Number of examples that will be contained in the RDD.
Number of features to generate for each example.
Epsilon factor by which examples are scaled.
Number of partitions in the RDD. Default value is 2.
RDD of LabeledPoint containing sample data.
:: 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 variableY
.