T = seq(0,1.5,length.out = p)
T2 = seq(0,3, length.out = 2*p)
n = 100
- x1 = cos(2*pi*T) + 0.2*cos(4*2*pi*T) +2*c(rep(0,round(length(T)/7)),rep(1,round(length(T)*(1-1/7))))
+ 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 = 0.085
- sigmaY = 0.1
+
+ 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)))
###########
## k-means 1
###########
- mod1 = Mclust(t(XY[ite,,]),G = 2, mode='VII')
+ mod1 = Mclust(t(XY[ite,,]),G = 1:2, mode='VII')
ARI1[ite] = adjustedRandIndex(mod1$classification, affec[[ite]])
Kmod1[ite] = mod1$G
# ###########