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#' Learns the CMI structure
#'
#' @param di.net.adj.matrix a directed network adjacency matrix
#' @param input.data data given as input data frame
#' @param num.nodes the number of nodes
#' @param beta value of parameter beta
#'
#' @return the directed network adjacency matrix after learning the CMI network
#'
#' @keywords internal
#' @noRd
learnCmiNetStruct <- function(di.net.adj.matrix, input.data, num.nodes, beta) {
if(!base::is.matrix(di.net.adj.matrix))
{
base::stop("Error in learnCmiNetStruct. di.net.adj.matrix is not a matrix")
}
if(!base::is.data.frame(input.data))
{
base::stop("Error in learnCmiNetStruct. input.data is not a data frame")
}
for(i in 1:num.nodes) # for each node i
{
adj_list_1 <- base::c()
adj_list_2 <- base::c()
for(j in 1:num.nodes) # for each node j
{
# if i is a parent of j in 'final_net'
if(di.net.adj.matrix[i,j] == 1)
{
for(k in 1:num.nodes) # for each node k
{
# if 'k' is a parent of 'i' and a grandparent of 'j'
# i.e. there exists a directed chain
# from 'k' to 'i' to 'j' in 'final_net'
# ToDo: Also check '(k != i)' because 'k' may be
# a parent as well as a grandparent of 'i'
if(di.net.adj.matrix[k,i] == 1 && k != j)
{
# Append 'k' to 'adj_list_1'
# 'adj_list_1' contains all grandparents of 'j' except 'j'
adj_list_1 <- base::c(adj_list_1, k)
}
}
#ToDo: Can not we merge this for loop with the prev for loop?
for(k in 1:num.nodes) # for each node k
{
# if 'k' (!= 'i') is a parent of 'j'
if(di.net.adj.matrix[k,j] == 1 && k != i)
{
# 'adj_list_2' contains all parents of 'j' except 'i'
adj_list_2 <- base::c(adj_list_2, k)
}
}
# 'commom_node' contains all nodes except 'i' that are grandparents
# as well as parents of 'j'
common_node <- base::intersect(adj_list_1,adj_list_2)
#print(length(common_node)) #---test
if(length(common_node) > 0)
{
max_I <- 0
# Store max(CMI('i', 'j' | 'node') for all 'node' in 'commom_node')
# in max_I
for (node in common_node)
{
# Calc CMI('i', 'j' | 'node')
# Another potential point of difference from the original LBN implementaiton.
# Here, "emp" estimator is used to calc CMI. The orig implementaion migh
# have used a diff estimator.
I <- computeCmi(input.data[,i],input.data[,j],input.data[,node])
if(I > max_I)
{
max_I <- I
}
}
#2nd order CMI
if(length(common_node) > 1)
{
# All combination of the nodes in 'commom_node' taken 2 at a time.
# In 'comb2', row = memeber index, col = combination index.
comb_2 <- utils::combn(common_node, 2)
# ToDo: 'colunm in 1:ncol(comb_2)'
# ToDo: Correct spelling 'colunm'
for (column in 1:ncol(comb_2))
{
# ToDo: I <- condinformation(dat[,i],dat[,j],dat[, comb_2[, colunm]],method="emp")
#I <- condinformation(dat[,i],dat[,j],dat[,column],method="emp")
I <- computeCmi(input.data[,i],input.data[,j],input.data[, comb_2[, column]])
# print(I) #---test
if(I > max_I)
{
max_I <- I
}
}
}
# ToDo: Differentiate alpha and beta
# ToDo: Instead of recording 'max_I', use 'break' construct to break out
# of a loop when 'I < alpha'
if(max_I < beta)
{
# Remove the directed edge from 'i' to 'j'
di.net.adj.matrix[i,j] <- 0
}
}
}
}
}
return (di.net.adj.matrix)
}
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