ขอสอบถามเกี่ยวกับการใช้ matlab สร้าง minimum spanning tree ครับ คือ ตอนนี้ผมได้ทำโปรเจคแล้วต้องสร้างกราฟ minimum spanning tree 1000 ครั้ง อาจารย์ที่ปรึกษาเลยแนะนำให้ใช้ โปรแกรมmatlab ซึ่งผมไม่เคยเรียน ไม่มีความรู้ด้านโปรแกรม matlab ผมเลยลองค้นหาใน google ดูพบ code ในการสร้าง แต่พอนำ code  มา run กับพบ error ซึ่งผมก็ไม่รู้ว่า code  ผิดตรงไหน ใครที่พอรู้ช่วยหน่อยนะครับ ผมนั่งอ่านเท่าไรก็ไม่เข้าใจ ขอบคุณล่วงหน้าครับ
ถ้าใครพอมีความรู้ทางด้านนี้ ช่วยช่วยเหลือผมหน่อยนะครับ [open_code]id line :chanestea email: tb_ks@hotmail.com[/close_code] ขอบคุณล่วงหน้าครับ
นี่คือ code ที่ผมใช้ run คับ


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