path.kruskal {GRelevance}

Basic description

Description

Calculates the shortest Hamiltonian path based on the sorted edge weights and the nodes

Usage

path.kruskal(nodes,edge_cost)

Arguments

nodes

sequence of nodes 1,...,n from the graph which is based on the high-dimensional data that is provided by the reader

edge_cost

sorted edge weights

Value

the shortest Hamiltonian path

See Also

Hpath

Examples

G=list()
set.seed(1)
n1=20;n2=40
N=n1+n2;
G[[1]]=c(1:n1);G[[2]]=c((n1+1):(n1+n2));
d=10
mu1=rep(0,d)
mu2=mu1+0.1
true.cov1=0.4^(abs(outer(1:d,1:d,"-")))
true.cov2=0.4^(abs(outer(1:d,1:d,"-")))
sam1=MASS::mvrnorm(n=n1,mu=mu1,Sigma=true.cov1)
sam2=MASS::mvrnorm(n=n2,mu=mu2,Sigma=true.cov2)
Data=rbind(sam1,sam2)
Dist=philentropy::distance(Data, method = "euclidean")
Dist[lower.tri(Dist)] <- NA
Dist[diag(Dist)] <- NA
mat=Dist
n1=1; n2=N; n0=n2-n1+1
edge.cost=matrix(NA,nrow=n0*(n0-1)/2,ncol=3)
temp=1;
for(i in n1:(n2-1))
 for(j in (i+1):(n2))
   {
    edge.cost[temp,3]=mat[i,j];edge.cost[temp,1]=i-n1+1;edge.cost[temp,2]=j-n1+1;temp=temp+1;}
edge.cost=edge.cost[sort.list(edge.cost[,3]), ]
path.kruskal(c(1:n0),edge.cost)