## Modele
###########
K = 2
- p = 20
+ p = 48
T = seq(0,1.5,length.out = p)
T2 = seq(0,3, length.out = 2*p)
- n = 30
+ n = 100
x1 = cos(2*base::pi*T) + 0.2*cos(4*2*base::pi*T) + 0.3*c(rep(0,round(length(T)/7)),rep(1,round(length(T)*(1-1/7))))+1
sigmaX = 0.12
sigmaY = 0.12
beta = list()
p1= 0.5
beta[[1]] =diag(c(rep(p1,5),rep(1,5), rep(p1,5), rep(1, p-15)))
- p2 = 1
+ p2 = 2
beta[[2]] = diag(c(rep(p2,5),rep(1,5), rep(p2,5), rep(1, p-15)))
ARI1 = ARI2 = ARI3 = 0
###########
require(wavelets)
XY = array(0, dim = c(2*p,n))
- Xproj = array(0, dim=c(48,n))
- Yproj = array(0, dim=c(48,n))
XYproj = array(0, dim=c(96,n))
x = x1 + matrix(rnorm(n*p, 0, sigmaX), ncol = n)
affec = sample(c(1,2), n, replace = TRUE)
Ay = dwt(y[,i], filter='haar')@V
Ay = rev(unlist(Ay))
Ay = Ay[2:(1+3)]
- Xproj[,i] = c(Ax,Dx)
- Yproj[,i] = c(Ay,Dy)
XYproj[,i] = c(Ax,Dx,Ay,Dy)
}
- res_valse = valse(x,y)
- res_valse_proj = valse(Xproj, Yproj)
+ res_valse = valse(t(x),t(y), kmax=2, verbose=TRUE, plot=FALSE, size_coll_mod = 1000)
+ res_valse_proj = valse(t(XYproj[1:p,]),t(XYproj[(p+1):(2*p),]), kmax=2, verbose=TRUE, plot=FALSE, size_coll_mod = 1000)
- save(res_valse,file=paste("./Out/Res_",ite, ".RData",sep=""))
- save(res_valse_proj,file=paste("./Out/ResProj_",ite, ".RData",sep=""))
+ save(res_valse,file=paste("Res_",ite, ".RData",sep=""))
+ save(res_valse_proj,file=paste("ResProj_",ite, ".RData",sep=""))
}