Hello everyone We would like to know about a create Krukals a minimum spanning tree by matlab

Hello We are studying Silpakorn University,senior. Now we meet problem about write code matlab in create Krukals minimum spanning tree. We would like to write code matlab because our project create a minimum spanning tree 1000 loops. So we must use matlab. We use this code to run. We met the error ">> kruskal

Error using kruskal (line 103)
Not enough input arguments.".

We can not solve of error. We hope someone will help us. Thank you.

function [w_st, ST, X_st] = kruskal(X, w)
% function [w_st, ST, X_st] = kruskal(X, w)
% 
% This function finds the minimum spanning tree of the graph where each
% edge has a specified weight using the Kruskal's algorithm.
% 
% Assumptions
% -----------
%     N:  1x1  scalar      -  Number of nodes (vertices) of the graph
%    Ne:  1x1  scalar      -  Number of edges of the graph
%   Nst:  1x1  scalar      -  Number of edges of the minimum spanning tree
% 
% We further assume that the graph is labeled consecutively. That is, if
% there are N nodes, then nodes will be labeled from 1 to N.
%
% INPUT
% 
%     X:  NxN logical      -  Adjacency matrix
%             matrix          If X(i,j)=1, this means there is directed edge
%                             starting from node i and ending in node j.
%                             Each element takes values 0 or 1.
%                             If X symmetric, graph is undirected.
% 
%  or     Nex2 double      -  Neighbors' matrix
%              matrix         Each row represents an edge.
%                             Column 1 indicates the source node, while
%                             column 2 the target node.
% 
%     w:  NxN double       -  Weight matrix in adjacency form
%             matrix          If X symmetric (undirected graph), w has to
%                             be symmetric.
% 
%  or     Nex1 double      -  Weight matrix in neighbors' form
%              matrix         Each element represents the weight of that
%                             edge.
% 
% 
% OUTPUT
% 
%  w_st:    1x1 scalar     -  Total weight of minimum spanning tree
%    ST:  Nstx2 double     -  Neighbors' matrix of minimum spanning tree
%               matrix
%  X_st:  NstxNst logical  -  Adjacency matrix of minimum spanning tree
%                 matrix      If X_st symmetric, tree is undirected.
% 
% EXAMPLES
%
% Undirected graph
% ----------------
% Assume the undirected graph with adjacency matrix X and weights w:
%
%         1   
%       /   \
%      2     3
%     / \
%    4 - 5
% 
% X = [0 1 1 0 0;
%      1 0 0 1 1;
%      1 0 0 0 0;
%      0 1 0 0 1;
%      0 1 0 1 0];
%  
% w = [0 1 2 0 0;
%      1 0 0 2 1;
%      2 0 0 0 0;
%      0 2 0 0 3;
%      0 1 0 3 0];
% 
% [w_st, ST, X_st] = kruskal(X, w);
% The above function gives us the minimum spanning tree.
% 
% 
% Directed graph
% ----------------
% Assume the directed graph with adjacency matrix X and weights w:
%
%           1
%        / ^ \
%       / /   \
%      v       v
%       2 ---> 3
% 
% X = [0 1 1
%      1 0 1
%      0 0 0];
%  
% w = [0 1 4;
%      2 0 1;
%      0 0 0];
% 
% [w_st, ST, X_st] = kruskal(X, w);
% The above function gives us the minimum directed spanning tree.
% 
% 
% Author: Georgios Papachristoudis
% Copyright 2013 Georgios Papachristoudis
% Date: 2013/05/26 12:25:18

      isUndirGraph = 1;

      % Convert logical adjacent matrix to neighbors' matrix    
      if size(X,1)==size(X,2) && sum(X(:)==0)+sum(X(:)==1)==numel(X)        
          if any(any(X-X'))
              isUndirGraph = 0;
          end
          ne = cnvrtX2ne(X,isUndirGraph);
      else
          if size(unique(sort(X,2),'rows'),1)~=size(X,1)
              isUndirGraph = 0;
          end
          ne = X;
      end

      % Convert weight matrix from adjacent to neighbors' form
      if numel(w)~=length(w)
          if isUndirGraph && any(any(w-w'))
              error('If it is an undirected graph, weight matrix has to be symmetric.');
          end
          w = cnvrtw2ne(w,ne);
      end

      N    = max(ne(:));   % number of vertices
      Ne   = size(ne,1);   % number of edges    
      lidx = zeros(Ne,1);  % logical edge index; 1 for the edges that will be
                           % in the minimum spanning tree                         
      % Sort edges w.r.t. weight
      [w,idx] = sort(w);
      ne      = ne(idx,:);

      % Initialize: assign each node to itself
      [repr, rnk] = makeset(N);

      % Run Kruskal's algorithm
      for k = 1:Ne
          i = ne(k,1);
          j = ne(k,2);
          if fnd(i,repr) ~= fnd(j,repr)
              lidx(k) = 1;
              [repr, rnk] = union(i, j, repr, rnk);
          end
      end

      % Form the minimum spanning tree
      treeidx = find(lidx);
      ST      = ne(treeidx,:);

      % Generate adjacency matrix of the minimum spanning tree
      X_st = zeros(N);
      for k = 1:size(ST,1)
          X_st(ST(k,1),ST(k,2)) = 1;
          if isUndirGraph,  X_st(ST(k,2),ST(k,1)) = 1;  end
      end

      % Evaluate the total weight of the minimum spanning tree
      w_st = sum(w(treeidx));
  end

function ne = cnvrtX2ne(X, isUndirGraph)
    if isUndirGraph
        ne = zeros(sum(sum(X.*triu(ones(size(X))))),2);
    else
        ne = zeros(sum(X(:)),2);
    end
    cnt = 1;
    for i = 1:size(X,1)
        v       = find(X(i,:));        
        if isUndirGraph
            v(v<=i) = [];
        end
        u       = repmat(i, size(v));
        edges   = [u; v]';
        ne(cnt:cnt+size(edges,1)-1,:) = edges;
        cnt = cnt + size(edges,1);
    end
end

function w = cnvrtw2ne(w,ne)
    tmp = zeros(size(ne,1),1);
    cnt = 1;
    for k = 1:size(ne,1)
        tmp(cnt) = w(ne(k,1),ne(k,2));
        cnt = cnt + 1;
    end
    w = tmp;
end

function [repr, rnk] = makeset(N)
    repr = (1:N);
    rnk  = zeros(1,N);
end

function o = fnd(i,repr)
    while i ~= repr(i)
        i = repr(i);
    end
    o = i;
end

function [repr, rnk] = union(i, j, repr, rnk)
    r_i = fnd(i,repr);
    r_j = fnd(j,repr);
    if rnk(r_i) > rnk(r_j)
        repr(r_j) = r_i;
    else
        repr(r_i) = r_j;
        if rnk(r_i) == rnk(r_j)
            rnk(r_j) = rnk(r_j) + 1;
        end
    end
end

Copyright (c) 2014, Georgios Papachristoudis
All rights reserved.

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Answer 1

[Eng. Fredius Magige]

Hi Boonanan The loop is closed loop or pure tee/branch; The modified (MasterNeno) matbal funtion might solve the rear technical problem, provided that the weight known and arrange as its adjacency matrix; if possible post the data in my (<mailto:[e-mail] [e-mail]>) email Thanks

[Boonanan Lechayakittikorn]

thank you i sent mail to you.

[rasha abusamra]

Index exceeds matrix dimensions. Error in kruskal (line 129) ne = ne(idx,:);

hi, I have the same error how to solve that