object LinearDataGenerator
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.
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def
generateLinearInput(intercept: Double, weights: Array[Double], xMean: Array[Double], xVariance: Array[Double], nPoints: Int, seed: Int, eps: Double, sparsity: Double): Seq[LabeledPoint]
- 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.
- sparsity
The ratio of zero elements. If it is 0.0, LabeledPoints with DenseVector is returned.
- returns
Seq of input.
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def
generateLinearInput(intercept: Double, weights: Array[Double], xMean: Array[Double], xVariance: Array[Double], nPoints: Int, seed: Int, eps: Double): Seq[LabeledPoint]
- 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.
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def
generateLinearInput(intercept: Double, weights: Array[Double], nPoints: Int, seed: Int, eps: Double = 0.1): Seq[LabeledPoint]
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)
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)
- 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.
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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.
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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 unregularized variants.
Generate an RDD containing sample data for Linear Regression models - including Ridge, Lasso, and unregularized 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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