stream (version 1.2-3)

DSD_MG: DSD Moving Generator

Description

Creates an evolving DSD that consists of several MGCs.

Usage

DSD_MG(dimension = 2, ..., labels=NULL, description=NULL)
add_cluster(x, c, label=NULL)
get_clusters(x) 
remove_cluster(x, i)

Arguments

dimension

the dimension of the DSD object

...

initial set of MGCs

x

A DSD_MG object.

c

The cluster that should be added to the DSD_MG object.

i

The index of the cluster that should be removed from the DSD_MG object.

label, labels

integer representing the cluster label. NA represents noise. If labels are not specified, then each new cluster gets a new label.

description

An optional string used by print to describe the data generator.

Details

This DSD is able to generate complex datasets that are able to evolve over a period of time. Its behavior is determined by the MGCs it is composed of.

See Also

MGC_Function, MGC_Linear, MGC_Noise, MGC_Random for details on the different MGC objects.

Examples

### create an empty DSD_MG
stream <- DSD_MG(dim = 2)
stream  
  
### add two clusters
c1 <- MGC_Random(density=50, center=c(50,50), parameter=1, randomness = )
add_cluster(stream, c1)
stream
  
c2 <- MGC_Noise(density=1, range=rbind(c(-20,120), c(-20,120)))
add_cluster(stream, c2)
stream
  
get_clusters(stream)
plot(stream, xlim=c(-20,120), ylim=c(-20,120))
    
## Not run: 
# animate_data(stream, n=5000, xlim=c(-20,120), ylim=c(-20,120))
# ## End(Not run)
    
### remove cluster 1
remove_cluster(stream,1)
    
get_clusters(stream)
plot(stream, xlim=c(-20,120), ylim=c(-20,120))

### create a more complicated cluster structure (using 2 clusters with the same
### label to form an L shape)
stream <- DSD_MG(dim=2,
  MGC_Static(density=10, center=c(.5,.2), par=c(.4,.2), shape=MGC_Shape_Block),
  MGC_Static(density=10, center=c(.6,.5), par=c(.2,.4), shape=MGC_Shape_Block),
  MGC_Static(density=5, center=c(.39,.53), par=c(.16,.35), shape=MGC_Shape_Block),
  MGC_Noise(density=1, range=rbind(c(0,1), c(0,1))),
  labels= c(1, 1, 2, NA)
  )
   
plot(stream, xlim=c(0,1), ylim=c(0,1))
  
  
### simulate the clustering of a splitting cluster  
c1 <- MGC_Linear(dim = 2, keyframelist = list(
  keyframe(time = 1,  dens = 20, center = c(0,0),   param = 10),
  keyframe(time = 50, dens = 10, center = c(50,50), param = 10),
  keyframe(time = 100,dens = 10, center = c(50,100),param = 10)
))

### Note: Second cluster appearch at time=50
c2 <- MGC_Linear(dim = 2, keyframelist = list(
  keyframe(time = 50, dens = 10, center = c(50,50), param = 10),
  keyframe(time = 100,dens = 10, center = c(100,50),param = 10)
))

stream <- DSD_MG(dim = 2, c1, c2)
stream
  
dbstream <- DSC_DBSTREAM(r=10, lambda=0.1)
  
## Not run: 
# purity <- animate_cluster(dbstream, stream, n=2500, type="both", 
#   xlim=c(-10,120), ylim=c(-10,120), evaluationMeasure="purity", horizon=100)
# ## End(Not run)