cosmetics

[valse.git] / reports / bazar Emilie / simulatedData.R

diff --git a/reports/bazar Emilie/simulatedData.R b/reports/bazar Emilie/simulatedData.R
deleted file mode 100644 (file)
index 6031822..0000000
--- a/reports/bazar Emilie/simulatedData.R
+++ /dev/null
@@ -1,120 +0,0 @@
-library("mclust")
-#library("R.matlab", lib.loc="~/R/x86_64-pc-linux-gnu-library/3.3")
-#redrawData = TRUE
-#if (redrawData==TRUE){
-  ###########
-  ## Model
-  ###########
-  K = 2
-  p = 48
-  T = seq(0,1.5,length.out = p)
-  T2 = seq(0,3, length.out = 2*p)
-  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
-  plot(T,x1)
-  lines(T,x1)
-  
-  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
-  beta[[2]] = diag(c(rep(p2,5),rep(1,5), rep(p2,5), rep(1, p-15)))
-  ITE = 100
-  ARI1 = ARI2 = ARI3 = rep(0,ITE)
-  XY = array(0, dim = c(ITE, 2*p,n))
-  XYproj = array(0, dim=c(ITE, 96,n))
-
-  affec = list()
-  ###########
-  ## Iterations
-  ###########
-  for (ite in c(1:ITE)){
-    ###########
-    ##Sample
-    ###########
-    x = x1 + matrix(rnorm(n*p, 0, sigmaX), ncol = n)
-    affec[[ite]] = sample(c(1,2), n, replace = TRUE)
-    y  = x
-    xy = matrix(0,ncol=n, nrow= 2*p)
-     for (i in c(1:n)){
-       y[,i] = x[,i] %*% beta[[affec[[ite]][i]]] + rnorm(p, 0, sigmaY)
-       xy[,i] = c(x[,i],y[,i])
-       XY[ite,,i] = xy[,i] - mean(xy[,i])
-    #   Dx = dwt(x[,i], filter='haar')@W
-    #   Dx = rev(unlist(Dx))
-    #   Dx = Dx[2:(1+3+6+12+24)]
-    #   Ax = dwt(x[,i], filter='haar')@V
-    #   Ax = rev(unlist(Ax))
-    #   Ax = Ax[2:(1+3)]
-    #   Dy = dwt(y[,i], filter='haar')@W
-    #   Dy = rev(unlist(Dy))
-    #   Dy = Dy[2:(1+3+6+12+24)]
-    #   Ay = dwt(y[,i], filter='haar')@V
-    #   Ay = rev(unlist(Ay))
-    #   Ay = Ay[2:(1+3)]
-    #   XYproj[ite,,i] = c(Ax,Dx,Ay,Dy)
-     }
-    print(ite)
-    #
-    #
-  }
-  xy[c(7,55),] = NA
- # write.table(XY,'data.csv', row.names=FALSE, col.names=FALSE)
-matplot(T2,xy[,affec[[ite]]==1],type='l', col='red', lty = 1)
-matplot(T2,xy[,affec[[ite]]==2],type='l', col='black', add=TRUE, lty= 1)
-abline(v = 1.5)
-text(0.75,0,'X', cex = 2 )
-text(0.75+1.5,0,'Y', cex = 2 )
-#proj =  read.table('dataProj.csv')
-#}
-
-
-#matplot(T,x,type='l', col='black', xlab = '', ylab='', lwd=1.5,lty=1)
-#matplot(T,y[,affec[[ite]]==1],type='l', col='red', xlab = '', ylab='', lwd=1.5,lty=1)
-#matplot(T,y[,affec[[ite]]==2],type='l', col='black', add=TRUE,lwd=2,lty=1)
-# proj2 = array(0,dim=c(ITE,2*p,n))
-# for (ite in c(1:ITE)){
-#   for (i in c(1:n)){
-#     A = proj[ite,(1+(i-1)*96):(i*96)]
-#     for (j in 1:96){
-#       proj2[ite,j,i] = A[1,j]
-#     }
-#   }
-#   print(ite)
-# }
-###########
-## Iterations
-###########
-Kmod2 = Kmod1 = rep(0,ITE)
-Kmod3 = rep(0,ITE)
-for (ite in c(1:ITE)){
-  print(ite)
-  ###########
-  ## k-means 1
-  ###########
-  mod1 = Mclust(t(XY[ite,,]),G = 1:2, mode='VII')
-  ARI1[ite] = adjustedRandIndex(mod1$classification, affec[[ite]])
-  Kmod1[ite] = mod1$G
-  # ###########
-  # ## k-means 2
-  # ###########
-  # #proj2 =
-  # mod2 = Mclust(t(XYproj[ite,,]),G = 1:8, mode='VII')
-  # ARI2[ite] = adjustedRandIndex(mod2$classification, affec[[ite]])
-  # Kmod2[ite] = mod2$G
-  # ###########
-  # ## k-means 1
-  # ###########
-  # #proj3 =
-  # mod3 = Mclust(t(XYproj[ite,c(4:12,52:60),]),G = 1:8, mode='VII')
-  # ARI3[ite] = adjustedRandIndex(mod3$classification, affec[[ite]])
-  # Kmod3[ite] = mod3$G
-}
-ARI0 = rep(1,ITE)
-par(cex.lab=1.5)
-par(cex.axis=1.5)
-boxplot(ARI0,ARI1, names = c('LassoMLE','K-means'), lwd=1.3)
-table(Kmod1)
-table(Kmod2)