norm.sim.ksc {akmeans} | R Documentation |
On the assumption that the two samples are already normalized to have L2 norm as 1, cosine distance is defined as 1 - inner product of the two samples.
norm.sim.ksc(A, k, init.cen = NULL, init.mem = NULL, iter.max = 100)
A |
n by p matrix, each row is a sample |
k |
the number of clusters |
init.cen |
initial cluster centers |
init.mem |
initial cluster member assignment |
iter.max |
the maximum number of iteration |
A list will be returned with components : cluster: A vector of integers indicating the cluster to which each point is allocated. centers: A matrix of cluster centres size: The number of points in each cluster
Jungsuk Kwac
############### ## test code ## 4 classes: a1,a2,a3,a4 ## for each class, 20 samples ############### n = 20; p = 32 a1 = 10*sin(0.1*(1:p)) a2 = 10*cos(0.1*(1:p))+10 a3 = c(1:(p/2),(p/2):1) a4 = c((p/2):1,1:(p/2)) A = c() for (i in 1:n){ A = rbind(A,a1+rnorm(p),a2+rnorm(p),a3+rnorm(p),a4+rnorm(p)) } res = norm.sim.ksc(quick.norm(A,1),4)