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I want to train CHMM using Matlab and I am new to use this software. I am using BNET CHMM which is this:

function bnet = mk_chmm(N, Q, Y, discrete_obs, coupled, CPD)
% MK_CHMM Make a coupled Hidden Markov Model
%
% There are N hidden nodes, each connected to itself and its two nearest neighbors in the next
% slice (apart from the edges, where there is 1 nearest neighbor).
%
% Example: If N = 3, the hidden backbone is as follows, where all arrows point to the right
%
% X1--X2
%   \/ 
%   /\
% X2--X2
%   \/ 
%   /\
% X3--X3
%
% Each hidden node has a "private" observed child (not shown).
%
% BNET = MK_CHMM(N, Q, Y)
% Each hidden node is discrete and has Q values.
% Each observed node is a Gaussian vector of length Y.
%
% BNET = MK_CHMM(N, Q, Y, DISCRETE_OBS)
% If discrete_obs = 1, the observations are discrete (values in {1, .., Y}).
%
% BNET = MK_CHMM(N, Q, Y, DISCRETE_OBS, COUPLED)
% If coupled = 0, the chains are not coupled, i.e., we make N parallel HMMs.
%
% BNET = MK_CHMM(N, Q, Y, DISCRETE_OBS, COUPLED, CPDs)
% means use the specified CPD structures instead of creating random params.
%  CPD{i}.CPT, i=1:N specifies the prior
%  CPD{i}.CPT, i=2N+1:3N specifies the transition model
%  CPD{i}.mean, CPD{i}.cov, i=N+1:2N specifies the observation model if Gaussian
%  CPD{i}.CPT, i=N+1:2N if discrete


if nargin < 2, Q = 2; end
if nargin < 3, Y = 1; end
if nargin < 4, discrete_obs = 0; end
if nargin < 5, coupled = 1; end
if nargin < 6, rnd = 1; else rnd = 0; end

ss = N*2;
hnodes = 1:N;
onodes = (1:N)+N;

intra = zeros(ss);
for i=1:N
  intra(hnodes(i), onodes(i))=1;
end

inter = zeros(ss);
if coupled
  for i=1:N
    inter(i, max(i-1,1):min(i+1,N))=1;
  end
else
  inter(1:N, 1:N) = eye(N);
end  

ns = [Q*ones(1,N) Y*ones(1,N)]; 

eclass1 = [hnodes onodes];
eclass2 = [hnodes+ss onodes];
if discrete_obs
  dnodes = 1:ss;
else
  dnodes = hnodes;
end
bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2, ...
          'observed', onodes);

if rnd
  for i=hnodes(:)'
    bnet.CPD{i} = tabular_CPD(bnet, i);
  end
  for i=onodes(:)'
    if discrete_obs
      bnet.CPD{i} = tabular_CPD(bnet, i);
    else
      bnet.CPD{i} = gaussian_CPD(bnet, i);
    end
  end
  for i=hnodes(:)'+ss
    bnet.CPD{i} = tabular_CPD(bnet, i);
  end
else
  for i=hnodes(:)'
    bnet.CPD{i} = tabular_CPD(bnet, i, CPD{i}.CPT);
  end
  for i=onodes(:)'
    if discrete_obs
      bnet.CPD{i} = tabular_CPD(bnet, i, CPD{i}.CPT);
    else
      bnet.CPD{i} = gaussian_CPD(bnet, i, CPD{i}.mean, CPD{i}.cov);
    end
  end
  for i=hnodes(:)'+ss
    bnet.CPD{i} = tabular_CPD(bnet, i, CPD{i}.CPT);
  end
end

I put the parameters in mk_chmm(4, 4, 4, 1, 1, 0) in the editor window, but it gives me an error:

Undefined function 'mk_dbn' for input
arguments of type 'double'.

Error in mk_chmm (line 70)
bnet = mk_dbn(intra, inter, ns,
'discrete', dnodes, 'eclass1', eclass1,
'eclass2', eclass2, ..

Any ideas how can I solve it please?? Sorry I am a beginner.

share|improve this question

closed as off topic by whuber Jan 29 '13 at 21:23

Questions on Geographic Information Systems Stack Exchange are expected to relate to geographic information systems within the scope defined by the community. Consider editing the question or leaving comments for improvement if you believe the question can be reworded to fit within the scope. Read more about reopening questions here.If this question can be reworded to fit the rules in the help center, please edit the question.

    
Just a suggestion. Even if the use of you model is for GIS application you might try, stats.stackexchange.com. You question is a little broad in application and more people there will be more familar with MATLAB usage then here at GIS. –  RomaH Jan 29 '13 at 20:38
    
Stats is a good suggestion, @RomaH, but because this question is specifically about a MatLab problem, it will get better treatment on SO. Unfortunately, the OP is blocked from asking questions there, so all we can do is close it. –  whuber Jan 29 '13 at 21:22
    
You are right sorry about that! –  user1114676 Jan 30 '13 at 8:28
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