function ShortestPath(DAG, K2Score)

    for i = 1:length(K2Score)
        ind = findall(K2Score .== 0.0)
        #K2Score[1,ind] ~= root,
        #K2Score[1,ind] = 0.01 # ORIGINAL
        K2Score[ind] .= 0.01 # CHANGED BY HB
    end

    #  Finding the root.
    G = DAG
    M,N = size(DAG)
    k = 1
    for i in 1:M
        for j in 1:N
            #if G[i,j] ~= 0 # ORIGINAL
            if isapprox(G[i,j], 0; atol = 0.01) # CHANGED BY HB, ~= does not exit
                # Also, check how isapprox works, G[i,j] ~= 0 does not make sense
                # without an absolute tolerance.
                a[1,k] = i
                a[2,k] = j
                k = k +1
            end
        end
    end

    ##
    n = k-1
    k = 1;
    for j in 1:N
        flag = 0
        for i in 1:M
            # if G[i,j] ~= 0 # ORIGINAL
            if isapprox(G[i,j], 0; atol = 0.01) # CHANGED BY HB
                flag = 1
            end
        end
        if flag .== 0
            root[1,k] = j; # Must store all the values
            k = k+1
        end
    end

    ## Applying bellman algorithm
    for ii = 1:length(root)
        k =1
        for i = 1:n
            if a[1,i] .== root[1,ii]
                b[1,k] = a[1,i]
                b[2,k] = a[2,i]
                k = k+1
            end
        end

        for i = 1:n
            # if a[1,i] ~= root[1,ii] # ORIGINAL
            if isapprox(a[1,i], root[1, ii]; atol = 0.01) # CHANGED BY HB
                b[1,k] = a[1,i]
                b[2,k] = a[2,i]
                k = k+1
            end
        end

        for i = 1:M
            for ii = 1:length(root)
                dist[ii,i] = 20000; # infinity Value
            end
        end

        dist[ii,root[1,ii]] = 0
        for i = 1: M
            for j = 1:n
                k = dist[ii,b[1,j]] + 1
                if k .< dist[ii,b[2,j]]
                    dist[ii,b[2,j]] = k
                end
            end
        end
        ## Finding the minimum of distance
        final_dist = minimum(dist, dims = 1)
    end

    return final_dist

end

#using CSV, DataFrames
#dag = CSV.read("UCIV_WOMEN_DAG.csv", DataFrame)
#k2s = CSV.read("UCIV_WOMEN_K2Score.csv", DataFrame)
#ShortestPath(dag, k2s)

import DelimitedFiles
dag, header = DelimitedFiles.readdlm(
    "./UCIV_WOMEN_DAG.csv", ',', Float64, '\n'; header = true
)
k2s, header = DelimitedFiles.readdlm(
    "./UCIV_WOMEN_K2Score.csv", ',', Float64, '\n'; header = true
)
ShortestPath(dag, k2s)