MGC: Moving Generator Cluster

Description

Creates an evolving cluster for a DSD_MG.

Usage

MGC(...)
MGC_Function(density, center, parameter, shape = NULL)
MGC_Linear(dimension = 2, keyframelist = NULL, shape = NULL)
keyframe(time, density, center, parameter, reset = FALSE)
add_keyframe(x, time, density, center, parameter, reset = FALSE)
get_keyframes(x)
remove_keyframe(x, time)
MGC_Noise(density, range)
MGC_Random(density, center, parameter, randomness = 1, shape = NULL)
MGC_Shape_Gaussian(center, parameter)
MGC_Shape_Block(center, parameter)
MGC_Static(density = 1, center, parameter, shape = NULL)

Arguments

...: Further arguments.
density: The density of the cluster. For `MGC_Function, this attribute is a function and defines the density of a cluster at a given timestamp.
center: A list that defines the center of the cluster. The list should have a length equal to the dimensionality. For MGC_Function, this list consists of functions that define the movement of the cluster. For MGC_Random, this attribute defines the beginning location for the MGC before it begins moving.
parameter: Parameters for the shape. For the default shape MGC_Shape_Gaussian the parameter is the standard deviation, one per dimension. If a single value is specified then it is recycled for all dimensions.
shape: A function creating the shape of the cluster. It gets passed on the parameters argument from above. Available functions are MGC_Shape_Gaussian (the parameters are a vector containing standard deviations) and MGC_Shape_Block (parameters are the dimensions of the uniform block).
dimension: Dimensionality of the data stream.
keyframelist: a list of keyframes to initialize the MGC_Linear object with.
time: The time stamp the keyframe should be located or which keyframe should be removed.
reset: Should the cluster reset to the first keyframe (time 0) after this keyframe is finished?
x: An object of class MGC_Linear.
range: The area in which the noise should appear.
randomness: The maximum amount the cluster will move during one time step.

Author

Matthew Bolanos

Details

An MGC describes a single cluster for use within an DSD_MG. There are currently four different MGCs that allow a user to express many different behaviors within a single data stream.

An MGC_Linear creates an evolving Gaussian cluster for a DSD_MG who's behavior is determined by several keyframes. Several keyframe functions are provided to create, add and remove keyframes. See Examples section for details.

An MGC_Function allows for a creation of a DSD_MG that is defined by functions of time.

An MGC_Random allows for a creation of a DSD_MG that moves randomly.

An MGC_Noise allows for a creation of noise within a DSD_MG.

Examples

Run this code


MGC()

### Two static clusters
stream <- DSD_MG(dim=2,
  MGC_Static(den = 1, center=c(1, 0), par=.1),
  MGC_Static(den = 1, center=c(2, 0), par=.4, shape=MGC_Shape_Block)
)

plot(stream)

### Example of several MGC_Randoms
stream <- DSD_MG(dimension=2,
  MGC_Random(den = 100, center=c(1, 0), par=.1, rand=.1),
  MGC_Random(den = 100, center=c(2, 0), par=.4, shape=MGC_Shape_Block, rand=.1)
)

if (FALSE) {
  animate_data(stream, 2500, xlim=c(0,3), ylim=c(-2,2), horizon=100)
}


### Example of several MGC_Functions
stream <- DSD_MG(dim = 2)

### block-shaped cluster moving from bottom-left to top-right increasing size
c1 <- MGC_Function(
  density = function(t){100},
  parameter = function(t){1*t},
  center = function(t) c(t,t),
  shape = MGC_Shape_Block
  )
add_cluster(stream,c1)

### cluster moving in a circle (default shape is Gaussian)
c2 <- MGC_Function(
  density = function(t){25},
  parameter = function(t){5},
  center= function(t) c(sin(t/10)*50+50, cos(t/10)*50+50)
)
add_cluster(stream,c2)

if (FALSE) {
animate_data(stream,10000,xlim=c(-20,120),ylim=c(-20,120), horizon=100)
}

### Example of several MGC_Linears: A single cluster splits at time 50 into two.
### Note that c2 starts at time=50!
stream <- DSD_MG(dim = 2)
c1 <- MGC_Linear(dim = 2)
add_keyframe(c1, time=1,  dens=50, par=5, center=c(0,0))
add_keyframe(c1, time=50, dens=50, par=5, center=c(50,50))
add_keyframe(c1, time=100,dens=50, par=5, center=c(50,100))
add_cluster(stream,c1)

c2 <- MGC_Linear(dim = 2, shape=MGC_Shape_Block)
add_keyframe(c2, time=50, dens=25, par=c(10,10),  center=c(50,50))
add_keyframe(c2, time=100,dens=25, par=c(30,30), center=c(100,50))
add_cluster(stream,c2)

if (FALSE) {
animate_data(stream,5000,xlim=c(0,100),ylim=c(0,100), horiz=100)
}

### two fixed and a moving cluster
stream <- DSD_MG(dim = 2,
  MGC_Static(dens=1, par=.1, center=c(0,0)),
  MGC_Static(dens=1, par=.1, center=c(1,1)),
  MGC_Linear(dim=2,list(
    keyframe(time = 0, dens=1, par=.1, center=c(0,0)),
    keyframe(time = 1000, dens=1, par=.1, center=c(1,1)),
    keyframe(time = 2000, dens=1, par=.1, center=c(0,0), reset=TRUE)
  )))

noise <- MGC_Noise(dens=.1, range=rbind(c(-.2,1.2),c(-.2,1.2)))
add_cluster(stream, noise)

if (FALSE) {
animate_data(stream, n=2000*3.1, xlim=c(-.2,1.2), ylim=c(-.2,1.2), horiz=200)
}

#' @export MGC