| 1 | simulData_17mars = function(ite){ |
| 2 | set.seed = 22021989+ite |
| 3 | |
| 4 | ########### |
| 5 | ## Modele |
| 6 | ########### |
| 7 | K = 2 |
| 8 | p = 48 |
| 9 | T = seq(0,1.5,length.out = p) |
| 10 | T2 = seq(0,3, length.out = 2*p) |
| 11 | n = 100 |
| 12 | 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 |
| 13 | sigmaX = 0.12 |
| 14 | sigmaY = 0.12 |
| 15 | beta = list() |
| 16 | p1= 0.5 |
| 17 | beta[[1]] =diag(c(rep(p1,5),rep(1,5), rep(p1,5), rep(1, p-15))) |
| 18 | p2 = 2 |
| 19 | beta[[2]] = diag(c(rep(p2,5),rep(1,5), rep(p2,5), rep(1, p-15))) |
| 20 | ARI1 = ARI2 = ARI3 = 0 |
| 21 | |
| 22 | ########### |
| 23 | ## Data + Projection |
| 24 | ########### |
| 25 | require(wavelets) |
| 26 | XY = array(0, dim = c(2*p,n)) |
| 27 | XYproj = array(0, dim=c(96,n)) |
| 28 | x = x1 + matrix(rnorm(n*p, 0, sigmaX), ncol = n) |
| 29 | affec = sample(c(1,2), n, replace = TRUE) |
| 30 | y = x |
| 31 | xy = matrix(0,ncol=n, nrow= 2*p) |
| 32 | for (i in c(1:n)){ |
| 33 | y[,i] = x[,i] %*% beta[[affec[i]]] + rnorm(p, 0, sigmaY) |
| 34 | xy[,i] = c(x[,i],y[,i]) |
| 35 | XY[,i] = xy[,i] - mean(xy[,i]) |
| 36 | Dx = dwt(x[,i], filter='haar')@W |
| 37 | Dx = rev(unlist(Dx)) |
| 38 | Dx = Dx[2:(1+3+6+12+24)] |
| 39 | Ax = dwt(x[,i], filter='haar')@V |
| 40 | Ax = rev(unlist(Ax)) |
| 41 | Ax = Ax[2:(1+3)] |
| 42 | Dy = dwt(y[,i], filter='haar')@W |
| 43 | Dy = rev(unlist(Dy)) |
| 44 | Dy = Dy[2:(1+3+6+12+24)] |
| 45 | Ay = dwt(y[,i], filter='haar')@V |
| 46 | Ay = rev(unlist(Ay)) |
| 47 | Ay = Ay[2:(1+3)] |
| 48 | XYproj[,i] = c(Ax,Dx,Ay,Dy) |
| 49 | } |
| 50 | |
| 51 | res_valse = valse(t(x),t(y), kmax=2, verbose=TRUE, plot=FALSE, size_coll_mod = 1000) |
| 52 | res_valse_proj = valse(t(XYproj[1:p,]),t(XYproj[(p+1):(2*p),]), kmax=2, verbose=TRUE, plot=FALSE, size_coll_mod = 1000) |
| 53 | |
| 54 | save(res_valse,file=paste("Res_",ite, ".RData",sep="")) |
| 55 | save(res_valse_proj,file=paste("ResProj_",ite, ".RData",sep="")) |
| 56 | } |