CVT: Functional quantization via Centroidal Voronoi Tessellation

In elamrireda/FunctQuant: Data-Driven Functional Quantization

Description

Data driven functional quantization based on Centroidal Voronoi Tessellation.

Usage

CVT(data,size,iter=22)

Arguments

data	matrix that we want to quantize.
size	size of the quantization grids.
iter	the number of iterations.

Value

data	the input matrix.
quantizer	the quantizer grid.
weights	the associated weight of each curve (calculated using the input matrix).

See Also

StochGradient and GFQ

Examples

##### function to generate realizations of BM
BM <- function(N=1000,M=1,x0=0,t0=0,T=1,Dt=NULL)
{
  Dt <- (T - t0)/N
  t <- seq(t0, T, by=Dt)
  res <- data.frame(sapply(1:M,function(i) c(0,cumsum(rnorm(N,mean  =0,sd=sqrt(Dt))))))
  names(res) <- paste("X",1:M,sep="")
  X <- ts(res, start = t0, deltat = Dt)
  return(X)
}
data <- t(BM(N = 200 - 1, M = 200))
size <- 10
quant <- CVT(data,size,iter=22)

elamrireda/FunctQuant documentation built on Jan. 1, 2021, 2:50 p.m.