Random RBF Generator Events Data Stream Generator

Generates random data using MOA's RandomRBFGeneratorEvents stream.

Usage

DSD_RandomRBFGeneratorEvents(
  k = 3,
  d = 2,
  numClusterRange = 3L,
  kernelRadius = 0.07,
  kernelRadiusRange = 0,
  densityRange = 0,
  speed = 100L,
  speedRange = 0L,
  noiseLevel = 0.1,
  noiseInCluster = FALSE,
  eventFrequency = 30000L,
  eventMergeSplitOption = FALSE,
  eventDeleteCreate = FALSE,
  modelSeed = NULL,
  instanceSeed = NULL
)

Arguments

k: Average number of centroids in the model.
d: Number of dimensions in the generated stream.
numClusterRange: Range for the number of clusters.
kernelRadius: Average radius of the micro-clusters.
kernelRadiusRange: Range of variation in micro-cluster radii.
densityRange: Range of variation in cluster density.
speed: Number of points between kernel movements.
speedRange: Range of variation in kernel speed.
noiseLevel: Proportion of noise points.
noiseInCluster: If TRUE, allow noise points inside clusters.
eventFrequency: Number of points between concept-drift events.
eventMergeSplitOption: If TRUE, enable cluster merge and split events.
eventDeleteCreate: If TRUE, enable cluster deletion and creation events.
modelSeed: Random seed for the cluster model.
instanceSeed: Random seed for generated instances.

Value

An object of class DSD_RandomRBFGeneratorEvents (subclass of DSD_MOA, stream::DSD).

Details

Only a subset of the parameters supported by the underlying MOA generator is exposed. If modelSeed or instanceSeed is NULL, a seed is sampled from R's random-number generator. Set these arguments explicitly to reproduce a stream; call set.seed() to make the generated default seeds reproducible.

By default, the generator creates three clusters with concept drift. Cluster locations move over time, and clusters may merge.

References

Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl. MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering Journal of Machine Learning Research (JMLR), 2010.

Author

Michael Hahsler and John Forrest

Examples

stream <- DSD_RandomRBFGeneratorEvents()
get_points(stream, 10)
#>           X1        X2 .class
#> 1  0.4618077 0.1416636      2
#> 2  0.5278374 0.5637230      3
#> 3  0.5966436 0.1254461     NA
#> 4  0.4769715 0.5308331      3
#> 5  0.4736576 0.1626657      2
#> 6  0.4855850 0.2288092      2
#> 7  0.5218328 0.5648703      3
#> 8  0.3118304 0.9568799      1
#> 9  0.4443428 0.1496668      2
#> 10 0.1950602 0.9048127      1

if (interactive()) {
animate_data(stream, n = 5000, horizon = 100, xlim = c(0, 1), ylim = c(0, 1))
}