├── COPYING
├── README
├── adapt_gmm.m
├── convert_hmm_to_gaussian_emissions.m
├── decode_hmm.m
├── eval_gmm.m
├── eval_hmm.m
├── is_valid_gmm.m
├── is_valid_hmm.m
├── kmeans.m
├── lmvnpdf.m
├── logsum.m
├── merge_states.m
├── reorder_states.m
├── sample_gaussian.m
├── sample_gmm.m
├── sample_hmm.m
└── train_gmm.m
/COPYING:
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/README:
--------------------------------------------------------------------------------
1 | This package contains a set of functions for evaluating HMMs and GMMs.
2 |
3 | The portions written by me are distributed under the terms of the GNU
4 | General Public License. See the file COPYING for details.
5 |
6 | * Functions
7 |
8 | - The important ones:
9 | - train_gmm - train a GMM from data
10 | - eval_gmm - compute the posterior probability of a GMM given data
11 | - eval_hmm - compute the posterior probabilities of all possible HMM
12 | state sequences given data
13 | - decode_hmm - find the most likely state sequence through the HMM
14 | given data
15 |
16 | - Utility functions:
17 | - logsum - takes the sum of a matrix of log likelihoods
18 | - lmvnpdf - compute the log probability of data under a
19 | multivariate Gaussian distribution
20 |
21 | * Data Structures
22 |
23 | The functions in this toolbox pass around the following structures:
24 | Note: all probabilities are stored as log probabilities
25 |
26 | ** GMM
27 | - gmm.nmix - number of components in the mixture
28 | - gmm.priors - array of prior log probabilities over each state
29 | - gmm.means - matrix of means (column x is mean of component x)
30 | - gmm.covars - matrix of covariances (column x is the diagonal of the
31 | covariance matrix of component x)
32 |
33 | ** HMM with GMM observations (does not work with all functions)
34 | - hmm.name -
35 | - hmm.nstates - number of states in the HMM
36 | - hmm.emission_type - 'GMM'
37 | - hmm.start_prob - array of log probs P(first observation is state x)
38 | - hmm.end_prob - array of log probs P(last observation is state x)
39 | - hmm.transmat - matrix of transition log probs (transmat(x,y)
40 | = log(P(transition from state x to state y)))
41 | - hmm.labels - optional cell array of labels for each state in the HMM
42 | (for use in composing HMMs)
43 | - hmm.gmms - array of GMM structures
44 |
45 | ** HMM with Gaussian observations
46 | - hmm.nstates - number of states in the HMM
47 | - hmm.emission_type - 'gaussian'
48 | - hmm.start_prob - array of log probs P(first observation is state x)
49 | - hmm.end_prob - array of log probs P(last observation is state x)
50 | - hmm.transmat - matrix of transition log probs (transmat(x,y)
51 | = log(P(transition from state x to state y)))
52 | - hmm.labels - optional cell array of labels for each state in the HMM
53 | (for use in composing HMMs)
54 | - hmm.means - matrix of means (column x is mean of state x)
55 | - hmm.covars - matrix of means (column x is the diagonal of the
56 | covariance matrix of component x)
57 |
58 | Note that each row of exp(hmm.transmat) does not necessarily sum to 1
59 | because for each state x there is some probability
60 | (exp(hmm.end_prob(x))) that the next transition will be to a
61 | non-emitting exit state (i.e. the current observation is the last
62 | observation in the sequence). The correct invariant is:
63 | sum(exp(hmm.transmat), 2)' + exp(hmm.end_prob) == ones(hmm.nstates, 1)
64 |
65 | 2007-11-06 Ron Weiss
66 |
--------------------------------------------------------------------------------
/adapt_gmm.m:
--------------------------------------------------------------------------------
1 | function gmm = adapt_gmm(pgmm, trdata, niter, adapt_weight, verb, MINCV)
2 | % gmmparams = adapt_gmm(prior_gmm, trdata, nmix, niter,
3 | % adapt_weight, verb, min_cv)
4 | %
5 | % Adapt the given prior_gmm to the given training data using MAP
6 | % adaptation.
7 | %
8 | % Inputs:
9 | % prior_gmm - initial GMM parameters to adapt.
10 | % trdata - training data (cell array of training sequences, each
11 | % column of the sequences arrays contains an observation)
12 | % niter - number of EM iterations to perform. Defaults to 10.
13 | % adapt_weight - weight of initial GMM in update equations (see HTK
14 | % book section 9.3). If adapt_weight is a vector, the
15 | % first element corresponds to the adaptation weight
16 | % for the Gaussian means (tau), and the second
17 | % correponse to the weight for the covariances (alpha).
18 | % Defaults to 10 (i.e. do not update covar parameters).
19 | % verb - set to 1 to output loglik at each iteration
20 | % min_cv - minimum covariance to avoid overfitting. Defaults to 1.
21 | %
22 | % Outputs:
23 | % gmmparams - structure containing hmm parameters learned from training
24 | % data (gmm.priors, gmm.means(:,1:nmix), gmm.covars(:,1:nmix))
25 | %
26 | % 2009-06-15 ronw@ee.columbia.edu
27 |
28 | DEBUG = false;
29 |
30 | if nargin < 3
31 | niter = 10;
32 | end
33 | if nargin < 4
34 | adapt_weight = 10;
35 | end
36 | if nargin < 5
37 | verb = 0;
38 | end
39 | if nargin < 6
40 | MINCV = 1;
41 | end
42 |
43 | if ~iscell(trdata)
44 | trdata = {trdata};
45 | end
46 |
47 | ndata = length(trdata);
48 |
49 | T = adapt_weight(1);
50 | adapt_covars = false;
51 | if length(adapt_weight) == 2
52 | adapt_covars = true;
53 | A = adapt_weight(2);
54 | end
55 |
56 | % Initialization
57 | gmm = pgmm;
58 | ppriors = exp(pgmm.priors);
59 | nmix = pgmm.nmix;
60 |
61 | if size(trdata{1}, 1) ~= size(gmm.means, 1)
62 | error(['Dimensionality of initial GMM not compatible with given ' ...
63 | 'training data']);
64 | end
65 | ndim = size(trdata{1}, 1);
66 |
67 | % sufficient statistics
68 | norm = zeros(size(gmm.priors));
69 | means = zeros(size(gmm.means));
70 | covars = zeros(size(gmm.covars));
71 |
72 | last_loglik = -Inf;
73 | for iter = 1:niter
74 | % E-step
75 | loglik = 0;
76 | norm(:) = 0;
77 | means(:) = 0;
78 | covars(:) = 0;
79 | for n = 1:ndata
80 | curr_data = trdata{n};
81 | [ll, posteriors] = eval_gmm(gmm, curr_data);
82 |
83 | loglik = loglik + sum(ll);
84 |
85 | norm = norm + sum(posteriors, 2)';
86 | means = means + curr_data * posteriors';
87 | covars = covars + curr_data.^2 * posteriors';
88 | end
89 |
90 | if verb,
91 | fprintf('Iteration %d: log likelihood = %f\n', iter, loglik)
92 |
93 | if DEBUG
94 | figure(1)
95 | plot_on_same_axes(pgmm.priors, gmm.priors)
96 | figure(2)
97 | cax = [-80 -10];
98 | subplot(211); imagesc(pgmm.means); axis xy; colorbar; caxis(cax);
99 | subplot(212); imagesc(gmm.means); axis xy; colorbar; caxis(cax);
100 | figure(3)
101 | cax = [0 100];
102 | subplot(211); imagesc(pgmm.covars); axis xy; colorbar; caxis(cax);
103 | subplot(212); imagesc(gmm.covars); axis xy; colorbar; caxis(cax);
104 | drawnow
105 | end
106 | end
107 |
108 | % Check for convergence
109 | if abs(loglik - last_loglik) < 1e-5
110 | fprintf('Converged at iteration %d\n', iter)
111 | break
112 | end
113 | last_loglik = loglik;
114 |
115 | % M-step
116 | % based on Huang, Acero, Hon, "Spoken Language Processing", p. 443 - 445
117 | npriors = (ppriors - 1 + norm) ./ sum(ppriors - 1 + norm);
118 | npriors(npriors < 0) = 0;
119 | gmm.priors = log(npriors);
120 | nrm = repmat(norm, [ndim 1]);
121 | gmm.means = (T * pgmm.means + means) ./ (T + nrm);
122 | if adapt_covars
123 | gmm.covars = ((A - 1) * pgmm.covars ...
124 | + T * (gmm.means - pgmm.means).^2 ...
125 | + (covars - 2*gmm.means.*means) .* nrm + gmm.means.^2) ...
126 | ./ (A - 1 + nrm);
127 | gmm.covars(gmm.covars < MINCV) = MINCV;
128 | end
129 | end
130 |
--------------------------------------------------------------------------------
/convert_hmm_to_gaussian_emissions.m:
--------------------------------------------------------------------------------
1 | function new_hmms = convert_hmm_to_gaussian_emissions(hmms)
2 | % new_hmms = convert_hmm_to_gaussian_emissions(hmms)
3 | %
4 | % Convert HMMs with GMM emissions in hmms to new_hmms with gaussian
5 | % emissions. hmms can be an array of hmm structures.
6 | %
7 | % 2007-01-18 ronw@ee.columbia.edu
8 |
9 | % Copyright (C) 2007 Ron J. Weiss
10 | %
11 | % This program is free software: you can redistribute it and/or modify
12 | % it under the terms of the GNU General Public License as published by
13 | % the Free Software Foundation, either version 3 of the License, or
14 | % (at your option) any later version.
15 | %
16 | % This program is distributed in the hope that it will be useful,
17 | % but WITHOUT ANY WARRANTY; without even the implied warranty of
18 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
19 | % GNU General Public License for more details.
20 | %
21 | % You should have received a copy of the GNU General Public License
22 | % along with this program. If not, see .
23 |
24 | for n = 1:length(hmms)
25 | if strcmp(hmms(n).emission_type, 'gaussian')
26 | new_hmms(n) = hmms(n);
27 | continue;
28 | end
29 |
30 | new_hmms(n).name = hmms(n).name;
31 | new_hmms(n).emission_type = 'gaussian';
32 |
33 | nmix = [hmms(n).gmms(:).nmix];
34 | state_offset = cumsum([0, nmix(1:end-1)]);
35 |
36 | new_hmms(n).nstates = sum(nmix);
37 | new_hmms(n).transmat = repmat(-Inf, [new_hmms(n).nstates, new_hmms(n).nstates]);
38 |
39 | curr_state = 0;
40 | for s = 1:hmms(n).nstates
41 | ns = state_offset(s) + [1:nmix(s)];
42 |
43 | new_hmms(n).start_prob(ns) = hmms(n).start_prob(s);
44 | new_hmms(n).end_prob(ns) = hmms(n).end_prob(s);
45 |
46 | if(isfield(hmms(n), 'labels'))
47 | new_hmms(n).labels(ns) = hmms(n).labels(s);
48 | end
49 |
50 | new_hmms(n).means(:,ns) = hmms(n).gmms(s).means;
51 | new_hmms(n).covars(:,ns) = hmms(n).gmms(s).covars;
52 |
53 | for ss = 1:hmms(n).nstates
54 | nss = state_offset(ss) + [1:nmix(ss)];
55 | new_hmms(n).transmat(ns, nss) = hmms(n).transmat(s,ss);
56 | end
57 | end
58 |
59 | priors = [hmms(n).gmms(:).priors];
60 | % make sure priors is a column vector
61 | priors = priors(:);
62 | for r = 1:new_hmms(n).nstates
63 | new_hmms(n).transmat(r,:) = new_hmms(n).transmat(r,:) + priors';
64 | end
65 |
66 | new_hmms(n).start_prob = new_hmms(n).start_prob + priors';
67 | end
68 |
--------------------------------------------------------------------------------
/decode_hmm.m:
--------------------------------------------------------------------------------
1 | function [loglik, stateseq, recon, lattice, tb, mllattice] = decode_hmm(hmm, frameLogLike, maxRank, beamLogProb, normalize_lattice, verb)
2 | % [loglik, stateseq, recon, lattice, tb] = decode_hmm(hmm, seq, rank, beam)
3 | %
4 | % Performs Viterbi decode of seq. Does rank and beam pruning.
5 | % Assumes all hmm params are logprobs.
6 | %
7 | % 2007-02-26 ronw@ee.columbia.edu
8 |
9 | % Copyright (C) 2007 Ron J. Weiss
10 | %
11 | % This program is free software: you can redistribute it and/or modify
12 | % it under the terms of the GNU General Public License as published by
13 | % the Free Software Foundation, either version 3 of the License, or
14 | % (at your option) any later version.
15 | %
16 | % This program is distributed in the hope that it will be useful,
17 | % but WITHOUT ANY WARRANTY; without even the implied warranty of
18 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
19 | % GNU General Public License for more details.
20 | %
21 | % You should have received a copy of the GNU General Public License
22 | % along with this program. If not, see .
23 |
24 | zeroLogProb = -1e200;
25 |
26 | % no rank pruning by default
27 | if nargin < 3
28 | maxRank = 0;
29 | end
30 |
31 | % no beam pruning by default
32 | if nargin < 4
33 | beamLogProb = -Inf;
34 | end
35 |
36 | if nargin < 5
37 | normalize_lattice = 0;
38 | end
39 |
40 | if nargin < 6
41 | verb = 0;
42 | end
43 |
44 | [nstates, nobs] = size(frameLogLike);
45 | if nstates ~= hmm.nstates && nstates == size(hmm.means, 1)
46 | seq = frameLogLike;
47 | ndim = nstates;
48 | nstates = hmm.nstates;
49 | if strcmp(hmm.emission_type, 'gaussian')
50 | frameLogLike = lmvnpdf(seq, hmm.means, hmm.covars);
51 | elseif strcmp(hmm.emission_type, 'GMM')
52 | for s = 1:hmm.nstates
53 | frameLogLike(s,:) = eval_gmm(hmm.gmms(s), seq);
54 | end
55 | else
56 | error('Unknown HMM emission distribution.');
57 | end
58 | end
59 |
60 |
61 | % how big should our rank pruning histogram be?
62 | histSize = 1000;
63 |
64 | stateseq = zeros(1, nobs);
65 | tb = zeros(hmm.nstates, nobs);
66 |
67 | if nargout > 3
68 | lattice = repmat(zeroLogProb, [hmm.nstates, nobs]);
69 | end
70 | % FIXME - there is a bug here in how the first frame is handled -
71 | % an extra transition probability from the non-existant 0th frame
72 | % to the 1st frame is added into lattice...
73 | prevLatticeFrame = hmm.start_prob(:);
74 | %prevLatticeFrame = hmm.start_prob(:) + frameLogLike(:,1);
75 | tb = zeros(hmm.nstates, nobs);
76 |
77 | % fill in the lattice...
78 | avg_nactive = 0;
79 | prevFrameMaxLogProb = 0;
80 | for obs = 1:nobs
81 | if verb >= 2
82 | tic
83 | end
84 |
85 | % beam pruning
86 | threshLogProb = prevFrameMaxLogProb + beamLogProb;
87 |
88 | % rank pruning
89 | if maxRank > 0
90 | tmp = prevLatticeFrame(:);
91 | min_tmp = 2*min(tmp(tmp > zeroLogProb));
92 | tmp(tmp < zeroLogProb) = min_tmp;
93 |
94 | [hst cdf] = hist(tmp, histSize);
95 |
96 | % want to look at the high ranks of the last frame
97 | hst = hst(end:-1:1);
98 | cdf = cdf(end:-1:1);
99 |
100 | hst = cumsum(hst);
101 | idx = min(find(hst >= maxRank));
102 | rankThresh = cdf(idx);
103 |
104 | % only change the threshold if it is stricter than the beam
105 | % threshold
106 | threshLogProb = max(threshLogProb, rankThresh);
107 |
108 | if verb >= 3
109 | %imgsc(prevLatticeFrame), colorbar, title(num2str(obs)), drawnow
110 |
111 | disp(['beam thresh = ' num2str(prevFrameMaxLogProb+beamLogProb) ...
112 | ', rank thresh = ' num2str(rankThresh) ...
113 | ', final thresh = ' num2str(threshLogProb)]);
114 | end
115 | end
116 |
117 | % which states are active?
118 | s_idx = find(prevLatticeFrame >= threshLogProb);
119 | nactive = numel(s_idx);
120 | avg_nactive = avg_nactive + nactive/nobs;
121 |
122 | vitPr = hmm.transmat(s_idx, :)' + repmat(prevLatticeFrame(s_idx), [1, hmm.nstates])';
123 |
124 | currllik = frameLogLike(:,obs);
125 |
126 | % v_idx = find(max(vitPr,[], 2) > zeroLogProb);
127 | % nv = length(v_idx);
128 | % currllik = repmat(zeroLogProb, [hmm.nstates, 1]);
129 | % if strcmp(hmm.emission_type, 'gaussian')
130 | % currllik(v_idx) = lmvnpdf(seq(:,obs), hmm.means(:, v_idx), ...
131 | % hmm.covars(:, v_idx));
132 | % else
133 | % for s = 1:nv
134 | % currllik(v_idx(s)) = eval_gmm(hmm.gmms(v_idx(s)), seq(:,obs));
135 | % end
136 | % end
137 |
138 | [prevLatticeFrame tb_tmp] = max(vitPr + repmat(currllik, [1, nactive]), [], 2);
139 | % This is equivalent to the above?
140 | %[prevLatticeFrame tb_tmp] = max(vitPr, [], 2);
141 | %prevLatticeFrame = prevLatticeFrame + currllik;
142 | tb(:,obs) = s_idx(tb_tmp);
143 |
144 | if nargout > 3
145 | lattice(:,obs) = prevLatticeFrame;
146 | end
147 |
148 | prevFrameMaxLogProb = max(prevLatticeFrame);
149 |
150 | if verb >= 2
151 | T = toc;
152 | disp(['frame ' num2str(obs), ' (' num2str(T) ' sec)' ...
153 | ': total active states: ' num2str(nactive)]);
154 | end
155 | end
156 |
157 | % include end_prob in lattice:
158 | ptmp = prevLatticeFrame;
159 | prevLatticeFrame = prevLatticeFrame + hmm.end_prob(:);
160 |
161 | %%%
162 | % do the traceback:
163 | [loglik s] = max(prevLatticeFrame(:));
164 |
165 | % we might have pruned too much, don't want to give up if end_prob
166 | % restrictions are too strong - so just ignore them in this case
167 | if loglik <= zeroLogProb
168 | warning(['decode_hmm: overpruned during decode,' ...
169 | ' ignoring probabilities that the hmms end in a' ...
170 | ' particular state']);
171 | prevLatticeFrame = ptmp;
172 | [loglik s] = max(prevLatticeFrame);
173 | end
174 |
175 | if nargout > 3
176 | lattice(:,end) = prevLatticeFrame;
177 | end
178 |
179 | for obs = nobs:-1:1
180 | stateseq(obs) = s;
181 | s = tb(s, obs);
182 | end
183 |
184 |
185 | % need to keep track of which gmm component was used in the
186 | % traceback for reconstruction - since this code doesn't do that,
187 | % can only guess as to the right reconstruction for GMM emissions -
188 | % should use convert_hmm_to_gaussian_emissions before calling this
189 | % function to get the full traceback
190 | if strcmp(hmm.emission_type, 'gaussian')
191 | recon = hmm.means(:,stateseq);
192 | else
193 | for s = 1:hmm.nstates
194 | means(:,s) = hmm.gmms(s).means * exp(hmm.gmms(s).priors)';
195 | end
196 |
197 | recon = means(:,stateseq);
198 | end
199 |
200 | if nargout > 3 & normalize_lattice
201 | nrm = logsum(lattice, 1);
202 | lattice = exp(lattice - repmat(nrm, [hmm.nstates, 1]));
203 | lattice(lattice < 1e-5) = 0;
204 | end
205 |
206 | if nargout > 4
207 | mllattice = sparse(hmm.nstates, nobs);
208 | for o = 1:nobs
209 | mllattice(stateseq(o),o) = 1;
210 | end
211 | end
212 |
213 |
214 | if verb
215 | disp(['decode_hmm: log likelihood: ' num2str(loglik) ...
216 | ', average number of active states per frame: ' ...
217 | num2str(avg_nactive)]);
218 | end
219 |
--------------------------------------------------------------------------------
/eval_gmm.m:
--------------------------------------------------------------------------------
1 | function [ll, post, mlg, mmserecon] = eval_gmm(gmm, data, norm)
2 | % [loglik, posteriors, mlseq, recon] = eval_gmm(gmm, data)
3 | %
4 | % Evaluate the log probability of each column of data given GMM gmm.
5 | %
6 | % Outputs:
7 | % loglik - log likelihood of each colimn of data
8 | % posteriors - posterior probability of each GMM component for each
9 | % column of data
10 | % mlseq - index of the most likely GMM component for each
11 | % column of data
12 | % recon - MMSE reconstruction of data given the GMM
13 | %
14 | % 2005-11-20 ronw@ee.columbia.edu
15 |
16 | % Copyright (C) 2005-2007 Ron J. Weiss
17 | %
18 | % This program is free software: you can redistribute it and/or modify
19 | % it under the terms of the GNU General Public License as published by
20 | % the Free Software Foundation, either version 3 of the License, or
21 | % (at your option) any later version.
22 | %
23 | % This program is distributed in the hope that it will be useful,
24 | % but WITHOUT ANY WARRANTY; without even the implied warranty of
25 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
26 | % GNU General Public License for more details.
27 | %
28 | % You should have received a copy of the GNU General Public License
29 | % along with this program. If not, see .
30 |
31 | if nargin < 3, norm = 1; end
32 |
33 | [ndim, ndat] = size(data);
34 |
35 | post = lmvnpdf(data, gmm.means, gmm.covars) + repmat(gmm.priors(:), [1, ndat]);
36 | ll = logsum(post, 1);
37 |
38 | if nargout > 1 && norm
39 | post = exp(post - repmat(logsum(post,1), gmm.nmix, 1));
40 | end
41 |
42 | if nargout > 2
43 | [mlg tmp] = ind2sub(size(post), find(post == repmat(max(post),gmm.nmix,1)));
44 | end
45 |
46 | if nargout > 3
47 | if norm
48 | mmserecon = gmm.means*post;
49 | else
50 | postnorm = exp(post - repmat(logsum(post,1), gmm.nmix, 1));
51 | mmserecon = gmm.means*postnorm;
52 | end
53 | end
54 |
55 |
--------------------------------------------------------------------------------
/eval_hmm.m:
--------------------------------------------------------------------------------
1 | function [loglik, lattice, alpha, beta, gamma] = eval_hmm(hmm, frameLogLike, maxRank, beamLogProb, do_backward, verb)
2 | % [loglik, lattice] = eval_hmm(hmm, seq, rank, beam)
3 | %
4 | % Performs forward-backward inference on seq. Does rank and beam
5 | % pruning. Assumes all hmm params are logprobs.
6 | %
7 | % 2008-08-11 ronw@ee.columbia.edu
8 |
9 | % Copyright (C) 2006-2008 Ron J. Weiss
10 | %
11 | % This program is free software: you can redistribute it and/or modify
12 | % it under the terms of the GNU General Public License as published by
13 | % the Free Software Foundation, either version 3 of the License, or
14 | % (at your option) any later version.
15 | %
16 | % This program is distributed in the hope that it will be useful,
17 | % but WITHOUT ANY WARRANTY; without even the implied warranty of
18 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
19 | % GNU General Public License for more details.
20 | %
21 | % You should have received a copy of the GNU General Public License
22 | % along with this program. If not, see .
23 |
24 |
25 | % no rank pruning by default
26 | if nargin < 3
27 | maxRank = 0;
28 | end
29 |
30 | % No beam pruning by default.
31 | if nargin < 4
32 | beamLogProb = -Inf;
33 | end
34 |
35 | if nargin < 5
36 | do_backward = true;
37 | end
38 |
39 | if nargin < 6
40 | verb = 0;
41 | end
42 |
43 | % Don't bother doing backward calculation if all we want is log
44 | % likelihood.
45 | if nargout < 2
46 | do_backward = false;
47 | else
48 | do_backward = true;
49 | end
50 |
51 | zeroLogProb = -1e200;
52 | hmm.transmat(hmm.transmat < zeroLogProb) = zeroLogProb;
53 |
54 | % Verify type of observations. Can be observed sequence or
55 | % precomputed log likelihoods (i.e. for variational inference).
56 | [nstates, nobs] = size(frameLogLike);
57 | if nstates ~= hmm.nstates && nstates == size(hmm.means, 1)
58 | seq = frameLogLike;
59 | ndim = nstates;
60 | nstates = hmm.nstates;
61 | if strcmp(hmm.emission_type, 'gaussian')
62 | frameLogLike = lmvnpdf(seq, hmm.means, hmm.covars);
63 | elseif strcmp(hmm.emission_type, 'GMM')
64 | for s = 1:hmm.nstates
65 | frameLogLike(s,:) = eval_gmm(hmm.gmms(s), seq);
66 | end
67 | else
68 | error('Unknown HMM emission distribution.');
69 | end
70 | end
71 |
72 |
73 | %%%%%
74 | % Forward
75 | %%%%%
76 | alpha = zeros(nstates, nobs) - Inf;
77 | prevLatticeFrame = hmm.start_prob(:) + frameLogLike(:,1);
78 | alpha(:,1) = prevLatticeFrame;
79 | if verb >= 2
80 | fprintf('Starting forward pass...\n frame 1: ll = %f\n', ...
81 | logsum(prevLatticeFrame))
82 | end
83 |
84 | for obs = 2:nobs
85 | if verb >= 2; tic; end
86 |
87 | idx = prune_states(prevLatticeFrame, maxRank, beamLogProb, verb);
88 | pr = hmm.transmat(idx,:)' + repmat(prevLatticeFrame(idx), [1, hmm.nstates])';
89 | prevLatticeFrame = logsum(pr, 2) + frameLogLike(:, obs);
90 | alpha(:,obs) = prevLatticeFrame;
91 |
92 | if verb >= 2
93 | T = toc;
94 | fprintf(' frame %d: ll = %f (%f sec, %d active states)\n', obs, ...
95 | logsum(prevLatticeFrame), T, length(idx));
96 | end
97 | end
98 | alpha(alpha <= zeroLogProb) = -Inf;
99 |
100 | % Don't forget hmm.end_prob
101 | % This double counts frameLogLike(:,end)!!
102 | %nextLatticeFrame = hmm.end_prob(:) + frameLogLike(:,end);
103 | nextLatticeFrame = hmm.end_prob(:);
104 | loglik = logsum(prevLatticeFrame + nextLatticeFrame);
105 | if isinf(loglik) || isnan(loglik)
106 | nextLatticeFrame = frameLogLike(:,end);
107 | loglik = logsum(prevLatticeFrame + nextLatticeFrame);
108 | end
109 |
110 | if verb
111 | fprintf('eval_hmm: log likelihood = %f\n', loglik)
112 | end
113 |
114 |
115 | if ~do_backward
116 | return
117 | end
118 |
119 | %%%%%
120 | % Backward
121 | %%%%%
122 | beta = zeros(nstates, nobs) - Inf;
123 | beta(:,nobs) = nextLatticeFrame;
124 | if verb >= 2
125 | fprintf('Starting backward pass...\n frame %d: ll = %f\n', nobs, ...
126 | logsum(nextLatticeFrame));
127 | end
128 |
129 | for obs = nobs-1:-1:1
130 | if verb >= 2; tic; end
131 |
132 | % Do HTK style pruning (p. 137 of HTK Book version 3.4). Don't
133 | % bother computing backward probability if alpha*beta is more than a
134 | % certain distance from the total log likelihood.
135 | idx = prune_states(nextLatticeFrame + alpha(:,obs+1), 0, -20, verb);
136 | %idx = prune_states(nextLatticeFrame + alpha(:,obs+1), 10, -Inf, verb);
137 |
138 | pr = hmm.transmat(:,idx) + repmat(nextLatticeFrame(idx) ...
139 | + frameLogLike(idx,obs+1), [1, hmm.nstates])';
140 | nextLatticeFrame = logsum(pr, 2);
141 | beta(:,obs) = nextLatticeFrame;
142 |
143 | if verb >= 2
144 | T = toc;
145 | fprintf(' frame %d: ll = %f (%f sec, %d active states)\n', obs, ...
146 | logsum(nextLatticeFrame), T, length(idx));
147 | end
148 | end
149 | beta(beta <= zeroLogProb) = -Inf;
150 |
151 | gamma = alpha + beta;
152 | lattice = exp(gamma - repmat(logsum(gamma, 1), [hmm.nstates 1]));
153 |
154 |
155 |
156 | function [state_idx thresh] = prune_states(latticeFrame, ...
157 | maxRank, beamLogProb, verb)
158 | zeroLogProb = -1e200;
159 | frameLogProb = logsum(latticeFrame);
160 |
161 | % Beam pruning
162 | threshLogProb = frameLogProb + beamLogProb;
163 |
164 | % Rank pruning
165 | if maxRank > 0
166 | % How big should our rank pruning histogram be?
167 | histSize = 3*length(latticeFrame);
168 |
169 | tmp = latticeFrame(:);
170 | min_tmp = min(tmp(tmp > zeroLogProb)) - 1;
171 | tmp(tmp <= zeroLogProb) = min_tmp;
172 |
173 | [hst cdf] = hist(tmp, histSize);
174 |
175 | % Want to look at the high ranks of the last frame.
176 | hst = hst(end:-1:1);
177 | cdf = cdf(end:-1:1);
178 |
179 | hst = cumsum(hst);
180 | idx = min(find(hst >= maxRank));
181 | rankThresh = cdf(idx);
182 |
183 | % Only change the threshold if it is stricter than the beam
184 | % threshold.
185 | threshLogProb = max(threshLogProb, rankThresh);
186 |
187 | if verb >= 3
188 | fprintf('beam thresh = %f, rank thresh = %f, final thresh = %f\n', ...
189 | frameLogProb+beamLogProb, rankThresh, threshLogProb);
190 | end
191 | end
192 |
193 | % Which states are active?
194 | state_idx = find(latticeFrame >= threshLogProb);
195 |
196 |
--------------------------------------------------------------------------------
/is_valid_gmm.m:
--------------------------------------------------------------------------------
1 | function out = is_valid_gmm(gmm, verb)
2 | % y = is_valid_gmm(gmm)
3 | %
4 | % Returns 1 if and only if gmm is a valid GMM structure
5 | %
6 | % 2008-06-03 ronw@ee.columbia.edu
7 |
8 | % Copyright (C) 2008 Ron J. Weiss
9 | %
10 | % This program is free software: you can redistribute it and/or modify
11 | % it under the terms of the GNU General Public License as published by
12 | % the Free Software Foundation, either version 3 of the License, or
13 | % (at your option) any later version.
14 | %
15 | % This program is distributed in the hope that it will be useful,
16 | % but WITHOUT ANY WARRANTY; without even the implied warranty of
17 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
18 | % GNU General Public License for more details.
19 | %
20 | % You should have received a copy of the GNU General Public License
21 | % along with this program. If not, see .
22 |
23 | if nargin < 2; verb = 0; end
24 |
25 | out = true;
26 | if ~isstruct(gmm)
27 | out = false;
28 | if verb; fprintf('is_valid_gmm: not a structure\n'); end
29 | return
30 | end
31 |
32 | if ~isfield(gmm, 'nmix')
33 | out = false;
34 | if verb; fprintf('is_valid_gmm: missing nmix field\n'); end
35 | return
36 | end
37 |
38 | if ~isfield(gmm, 'priors')
39 | out = false;
40 | if verb; fprintf('is_valid_gmm: missing priors field\n'); end
41 | else
42 | if length(gmm.priors) ~= gmm.nmix
43 | out = false;
44 | if verb; fprintf('is_valid_gmm: priors field is wrong length\n'); end
45 | end
46 | if abs(logsum(gmm.priors)) > 1e-3
47 | out = false;
48 | if verb; fprintf('is_valid_gmm: priors don''t sum to 1\n'); end
49 | end
50 | end
51 |
52 | if ~isfield(gmm, 'means')
53 | out = false;
54 | if verb; fprintf('is_valid_gmm: missing means field\n'); end
55 | else
56 | if size(gmm.means, 2) ~= gmm.nmix
57 | out = false;
58 | if verb; fprintf('is_valid_gmm: means field is wrong length\n'); end
59 | end
60 | end
61 |
62 | if ~isfield(gmm, 'covars')
63 | out = false;
64 | if verb; fprintf('is_valid_gmm: missing covars field\n'); end
65 | else
66 | if size(gmm.covars, 2) ~= gmm.nmix
67 | out = false;
68 | if verb; fprintf('is_valid_gmm: covars field is wrong length\n'); end
69 | end
70 | if isfield(gmm, 'means') && size(gmm.means, 1) ~= size(gmm.covars,1)
71 | out = false;
72 | if verb
73 | fprintf('is_valid_gmm: means and covars have inconsistent dimensions\n');
74 | end
75 | end
76 | if ~all(all(gmm.covars > 0))
77 | out = false;
78 | if verb; fprintf('is_valid_gmm: 0 or negative covars\n'); end
79 | end
80 | end
81 |
82 |
83 |
--------------------------------------------------------------------------------
/is_valid_hmm.m:
--------------------------------------------------------------------------------
1 | function out = is_valid_hmm(hmm, verb)
2 | % y = is_valid_hmm(hmm)
3 | %
4 | % Returns 1 if and only if hmm is a valid HMM structure
5 | %
6 | % 2008-06-03 ronw@ee.columbia.edu
7 |
8 | % Copyright (C) 2008 Ron J. Weiss
9 | %
10 | % This program is free software: you can redistribute it and/or modify
11 | % it under the terms of the GNU General Public License as published by
12 | % the Free Software Foundation, either version 3 of the License, or
13 | % (at your option) any later version.
14 | %
15 | % This program is distributed in the hope that it will be useful,
16 | % but WITHOUT ANY WARRANTY; without even the implied warranty of
17 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
18 | % GNU General Public License for more details.
19 | %
20 | % You should have received a copy of the GNU General Public License
21 | % along with this program. If not, see .
22 |
23 | if nargin < 2; verb = 0; end
24 |
25 | out = true;
26 | if ~isstruct(hmm)
27 | out = false;
28 | if verb; fprintf('is_valid_hmm: not a structure\n'); end
29 | return
30 | end
31 |
32 | if ~isfield(hmm, 'nstates')
33 | out = false;
34 | if verb; fprintf('is_valid_hmm: missing nstates field\n'); end
35 | return
36 | end
37 | if ~isfield(hmm, 'emission_type')
38 | out = false;
39 | if verb; fprintf('is_valid_hmm: missing emissionn type field\n'); end
40 | end
41 |
42 |
43 | if ~isfield(hmm, 'start_prob')
44 | out = false;
45 | if verb; fprintf('is_valid_hmm: missing start_prob field\n'); end
46 | else
47 | if length(hmm.start_prob) ~= hmm.nstates
48 | out = false;
49 | if verb; fprintf('is_valid_hmm: start_prob field is wrong length\n'); end
50 | end
51 | if abs(logsum(hmm.start_prob)) > 1e-3
52 | out = false;
53 | if verb; fprintf('is_valid_hmm: start_prob doesn''t sum to 1\n'); end
54 | end
55 | end
56 |
57 | if ~isfield(hmm, 'end_prob')
58 | out = false;
59 | if verb; fprintf('is_valid_hmm: missing end_prob field\n'); end
60 | else
61 | if length(hmm.end_prob) ~= hmm.nstates
62 | out = false;
63 | if verb; fprintf('is_valid_hmm: end_prob field is wrong length\n'); end
64 | end
65 | if ~isfield(hmm, 'transmat')
66 | out = false;
67 | if verb; fprintf('is_valid_hmm: missing transmat field\n'); end
68 | else
69 | if ~all(size(hmm.transmat) == [hmm.nstates hmm.nstates])
70 | out = false;
71 | if verb; fprintf('is_valid_hmm: transmat is wrong size\n'); end
72 | end
73 | if ~all(abs(sum(exp(hmm.transmat), 2) + exp(hmm.end_prob(:)) - 1) < 1e-3)
74 | out = false;
75 | if verb;
76 | fprintf('is_valid_hmm: transmat is not normalized properly\n');
77 | end
78 | end
79 | end
80 | end
81 |
82 |
83 | if strcmp(hmm.emission_type, 'GMM')
84 | if ~isfield(hmm, 'gmms')
85 | out = false;
86 | if verb; fprintf('is_valid_hmm: missing gmms field\n'); end
87 | else
88 | for s = 1:length(hmm.gmms)
89 | tmp = is_valid_gmm(hmm.gmms(s));
90 | if ~tmp
91 | out = false;
92 | if verb
93 | fprintf('is_valid_hmm: Error in state %d:\n ', s);
94 | is_valid_gmm(hmm.gmms(s), verb);
95 | end
96 | break
97 | end
98 | end
99 | end
100 | end
101 | if strcmp(hmm.emission_type, 'gaussian')
102 | if ~isfield(hmm, 'means')
103 | out = false;
104 | if verb; fprintf('is_valid_hmm: missing means field\n'); end
105 | else
106 | if size(hmm.means, 2) ~= hmm.nstates
107 | out = false;
108 | if verb; fprintf('is_valid_hmm: means field is wrong length\n'); end
109 | end
110 | end
111 |
112 | if ~isfield(hmm, 'covars')
113 | out = false;
114 | if verb; fprintf('is_valid_hmm: missing covars field\n'); end
115 | else
116 | if size(hmm.covars, 2) ~= hmm.nstates
117 | out = false;
118 | if verb; fprintf('is_valid_hmm: covars field is wrong length\n'); end
119 | end
120 | if isfield(hmm, 'means') && size(hmm.means, 1) ~= size(hmm.covars,1)
121 | out = false;
122 | if verb
123 | fprintf(['is_valid_hmm: means and covars have inconsistent ' ...
124 | 'dimensions\n']);
125 | end
126 | end
127 | if ~all(all(hmm.covars > 0))
128 | out = false;
129 | if verb; fprintf('is_valid_hmm: 0 or negative covars\n'); end
130 | end
131 | end
132 | end
133 |
134 |
--------------------------------------------------------------------------------
/kmeans.m:
--------------------------------------------------------------------------------
1 | function codebook = kmeans(trdata, nclust, niter)
2 | % codebook = kmeans(data, nclust, niter)
3 | %
4 | % Learns a k-means codebook from data with nclust codewords.
5 | %
6 | % 2006-12-07 ronw@ee.columbia.edu
7 |
8 | % Copyright (C) 2006 Ron J. Weiss
9 | %
10 | % This program is free software: you can redistribute it and/or modify
11 | % it under the terms of the GNU General Public License as published by
12 | % the Free Software Foundation, either version 3 of the License, or
13 | % (at your option) any later version.
14 | %
15 | % This program is distributed in the hope that it will be useful,
16 | % but WITHOUT ANY WARRANTY; without even the implied warranty of
17 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
18 | % GNU General Public License for more details.
19 | %
20 | % You should have received a copy of the GNU General Public License
21 | % along with this program. If not, see .
22 |
23 | if nargin < 2
24 | nclust = 10;
25 | end
26 | if nargin < 3
27 | niter = 5;
28 | end
29 |
30 | [ndim, nobs] = size(trdata);
31 |
32 | % init using k-means:
33 | rp = randperm(nobs);
34 | % in case there aren't enough observations...
35 | rp = repmat(rp,1,ceil(nclust/nobs));
36 | codebook = trdata(:,rp(1:nclust));
37 | for i = 1:niter
38 | % ||x-y || = x^Tx -2x^Ty + y^Ty
39 | % x^Tx = repmat(sum(x.^2),xc,1);
40 | % y^Ty = repmat(sum(y.^2),yc,1);
41 | D = repmat(sum(trdata.^2,1)',1,nclust) - 2*trdata'*codebook ...
42 | + repmat(sum(codebook.^2,1),nobs,1);
43 |
44 | %assign each data point to one of the clusters
45 | [tmp idx] = min(D,[],2);
46 |
47 | for k = 1:nclust
48 | if sum(idx == k) > 0
49 | codebook(:,k) = mean(trdata(:,idx == k),2);
50 | end
51 | end
52 | end
53 |
--------------------------------------------------------------------------------
/lmvnpdf.m:
--------------------------------------------------------------------------------
1 | function lpr = lmvnpdf(obs, mu, cv);
2 | % lpr = lmvnpdf(obs, mu, cv)
3 | %
4 | % Return the log probability of obs under the Gaussian distribution
5 | % parameterized by mu and cv.
6 | %
7 | % obs is an array of column vectors (DxO). mu and cv are also arrays
8 | % of column vectors (this only supports diagonal covariance matrices,
9 | % so mu and cv must both be DxC where C is the number of Gaussians).
10 | % lpr will be a CxO matrix where each row contains the log probability
11 | % of each observation given one of the C Gaussians.
12 | %
13 | % 2006-06-19 ronw@ee.columbia.edu
14 |
15 | % Copyright (C) 2006-2007 Ron J. Weiss
16 | %
17 | % This program is free software: you can redistribute it and/or modify
18 | % it under the terms of the GNU General Public License as published by
19 | % the Free Software Foundation, either version 3 of the License, or
20 | % (at your option) any later version.
21 | %
22 | % This program is distributed in the hope that it will be useful,
23 | % but WITHOUT ANY WARRANTY; without even the implied warranty of
24 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
25 | % GNU General Public License for more details.
26 | %
27 | % You should have received a copy of the GNU General Public License
28 | % along with this program. If not, see .
29 |
30 | if nargin < 3
31 | cv = 1;
32 | end
33 |
34 | [ndim, nobs] = size(obs);
35 | [ndim_mu, nmu] = size(mu);
36 | [ndim_cv, ncv] = size(cv);
37 |
38 | % make sure all the arguments are consistent
39 | if ndim ~= ndim_mu
40 | error('lmvnpdf: obs and mu must have the same number of dimensions.');
41 | end
42 | if nmu ~= ncv
43 | if ncv == 1
44 | % use the same diagonal covariance for each distribution
45 | cv = repmat(cv, 1, nmu);
46 | else
47 | error('lmvnpdf: mu and cv must have the same number of components.');
48 | end
49 | end
50 | ngauss = nmu;
51 |
52 | % are covariances scalar?
53 | if ndim_cv == 1
54 | cv = repmat(cv, ndim, 1);
55 | end
56 |
57 |
58 | % vectorized like there is no tomorrow:
59 | % ||x-y|| = x'x - 2*x'y + y'y
60 | % x'x = repmat(sum(x.^2),xc,1);
61 | % y'y = repmat(sum(y.^2),yc,1);
62 | %
63 | % but here, its ||(x-y)/cv||:
64 | % where cv has the same size as x (mu), but not the same as y (obs)...
65 |
66 | lpr = -0.5*(repmat(sum((mu.^2)./cv, 1)' + sum(log(cv))', [1 nobs]) ...
67 | - 2*(mu./cv)'*obs + (1./cv)'*(obs.^2) + ndim*log(2*pi));
68 |
--------------------------------------------------------------------------------
/logsum.m:
--------------------------------------------------------------------------------
1 | function s = logsum(array, dim)
2 | % s = logsum(array, dim)
3 | %
4 | % returns log(sum(exp(array), dim)) minimizing possibility of over/underflow
5 |
6 |
7 | if nargin < 2
8 | amax = max(array(:));
9 | s = log(sum(exp(array - amax))) + amax;
10 | else
11 | amax = max(array,[],dim);
12 | rep = ones(1, length(size(array)));
13 | rep(dim) = size(array,dim);
14 | s = log(sum(exp(array - repmat(amax, rep)), dim)) + amax;
15 | end
16 |
--------------------------------------------------------------------------------
/merge_states.m:
--------------------------------------------------------------------------------
1 | function new_hmm = merge_states(hmm, idx)
2 | % new_hmm = merge_states(hmm, idx)
3 | %
4 | % Merge states of the given HMM. idx is a list of state indices to
5 | % merge. If idx is a matrix, the indices in each column will be
6 | % merged into a single state. Also works on GMM structures.
7 | %
8 | % Examples:
9 | % - Merge states 1, 3, and 5: merge_states(hmm, [1 3 5])
10 | % - Merge states 1:5 and 10:20: merge_states(hmm, [1:5; 10:20]')
11 | % - Merge succesive pairs of states:
12 | % merge_states(hmm, reshape(1:hmm.nstates, [2, hmm.nstates/2]))
13 | %
14 | % Note that the merging isn't at all correct - it just dumbly takes
15 | % the weighted average of the given states.
16 | %
17 | % 2008-06-03 ronw@ee.columbia.edu
18 |
19 | % Copyright (C) 2008 Ron J. Weiss
20 | %
21 | % This program is free software: you can redistribute it and/or modify
22 | % it under the terms of the GNU General Public License as published by
23 | % the Free Software Foundation, either version 3 of the License, or
24 | % (at your option) any later version.
25 | %
26 | % This program is distributed in the hope that it will be useful,
27 | % but WITHOUT ANY WARRANTY; without even the implied warranty of
28 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
29 | % GNU General Public License for more details.
30 | %
31 | % You should have received a copy of the GNU General Public License
32 | % along with this program. If not, see .
33 |
34 | [nr nc] = size(idx);
35 | if nr == 1 || nc == 1
36 | % Turn into a column vector
37 | idx = idx(:);
38 | end
39 |
40 | if is_valid_gmm(hmm)
41 | new_hmm = merge_states_gmm(hmm, idx);
42 | else
43 | new_hmm = merge_states_hmm(hmm, idx);
44 | end
45 |
46 |
47 |
48 | function new_gmm = merge_states_gmm(gmm, idx)
49 | [nr nc] = size(idx);
50 | new_gmm = gmm;
51 | states_to_delete = zeros(gmm.nmix, 1);
52 | for c = 1:nc
53 | i = idx(:,c);
54 | states_to_delete(i(2:end)) = 1;
55 |
56 | lp = gmm.priors(i);
57 | new_gmm.priors(i(1)) = logsum(lp);
58 |
59 | p = exp(lp(:) - logsum(lp));
60 | new_gmm.means(:,i(1)) = gmm.means(:,i) * p;
61 | new_gmm.covars(:,i(1)) = gmm.covars(:,i) * p;
62 | end
63 |
64 | i = find(~states_to_delete);
65 | new_gmm.nmix = length(i);
66 | new_gmm.priors = new_gmm.priors(i);
67 | new_gmm.means = new_gmm.means(:,i);
68 | new_gmm.covars = new_gmm.covars(:,i);
69 |
70 |
71 |
72 | function new_hmm = merge_states_hmm(hmm, idx)
73 | if strcmp(hmm.emission_type, 'GMM')
74 | error('HMMs with GMM emissions are not supported.');
75 | end
76 | [nr nc] = size(idx);
77 | new_hmm = hmm;
78 | states_to_delete = zeros(hmm.nstates, 1);
79 | for c = 1:nc
80 | i = idx(:,c);
81 | states_to_delete(i(2:end)) = 1;
82 |
83 | lp = logsum(hmm.transmat(:,i), 1);
84 | p = exp(lp(:) - logsum(lp));
85 |
86 | new_hmm.start_prob(i(1)) = logsum(hmm.start_prob(i));
87 | new_hmm.transmat(i(1),i(1)) = logsum(logsum(hmm.transmat(i,i), 2) + p);
88 | for s = 1:hmm.nstates
89 | new_hmm.transmat(s,i(1)) = logsum(hmm.transmat(s,i));
90 | end
91 | new_hmm.end_prob(i(1)) = logsum(hmm.end_prob(i));
92 |
93 | new_hmm.means(:,i(1)) = hmm.means(:,i) * p;
94 | new_hmm.covars(:,i(1)) = hmm.covars(:,i) * p;
95 | end
96 |
97 | i = find(~states_to_delete);
98 | new_hmm.nstates = length(i);
99 | new_hmm.start_prob = new_hmm.start_prob(i);
100 | new_hmm.transmat = new_hmm.transmat(i,i);
101 | new_hmm.end_prob = new_hmm.end_prob(i);
102 | new_hmm.means = new_hmm.means(:,i);
103 | new_hmm.covars = new_hmm.covars(:,i);
104 |
105 | % Get rid of NaNs introduced by logsum(-Inf)
106 | new_hmm.start_prob(isnan(new_hmm.start_prob)) = -Inf;
107 | new_hmm.end_prob(isnan(new_hmm.end_prob)) = -Inf;
108 | new_hmm.transmat(isnan(new_hmm.transmat)) = -Inf;
109 |
110 | % make sure transmat and end_prob are normalized properly
111 | norm = logsum(cat(2, logsum(new_hmm.transmat, 2), new_hmm.end_prob'), 2);
112 | new_hmm.transmat = new_hmm.transmat - repmat(norm, [1 new_hmm.nstates]);
113 | new_hmm.end_prob = new_hmm.end_prob - norm';
114 |
--------------------------------------------------------------------------------
/reorder_states.m:
--------------------------------------------------------------------------------
1 | function new_hmm = reorder_states(hmm, idx)
2 | % new_hmm = reorder_states(hmm, idx)
3 | %
4 | % Rearrange the states of hmm using the given indices.
5 | %
6 | % 2008-09-12 ronw@ee.columbia.edu
7 |
8 | % Copyright (C) 2008 Ron J. Weiss
9 | %
10 | % This program is free software: you can redistribute it and/or modify
11 | % it under the terms of the GNU General Public License as published by
12 | % the Free Software Foundation, either version 3 of the License, or
13 | % (at your option) any later version.
14 | %
15 | % This program is distributed in the hope that it will be useful,
16 | % but WITHOUT ANY WARRANTY; without even the implied warranty of
17 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
18 | % GNU General Public License for more details.
19 | %
20 | % You should have received a copy of the GNU General Public License
21 | % along with this program. If not, see .
22 |
23 | if is_valid_gmm(hmm)
24 | new_hmm = reorder_states_gmm(hmm, idx);
25 | else
26 | new_hmm = reorder_states_hmm(hmm, idx);
27 | end
28 |
29 | function new_gmm = reorder_states_gmm(gmm, idx)
30 | new_gmm = gmm;
31 | new_gmm.nmix = length(idx);
32 | new_gmm.means = gmm.means(:,idx);
33 | new_gmm.covars = gmm.covars(:,idx);
34 | % Ensure priors are normalized (if components are deleted and not
35 | % just rearranged).
36 | new_gmm.priors = gmm.priors(idx) - logsum(gmm.priors(idx));
37 |
38 |
39 | function new_hmm = reorder_states_hmm(hmm, idx)
40 | new_hmm = hmm;
41 | new_hmm.nstates = length(idx);
42 | if strcmp(hmm.emission_type, 'GMM')
43 | new_hmm.gmms = hmm.gmms(idx);
44 | else
45 | new_hmm.means = hmm.means(:,idx);
46 | new_hmm.covars = hmm.covars(:,idx);
47 | end
48 | new_hmm.start_prob = hmm.start_prob(idx);
49 | new_hmm.transmat = hmm.transmat(idx,idx);
50 | new_hmm.end_prob = hmm.end_prob(idx);
51 |
52 | % Make sure everything is normalized properly.
53 | new_hmm.start_prob = new_hmm.start_prob - logsum(new_hmm.start_prob);
54 | norm = logsum(cat(2, logsum(new_hmm.transmat, 2), new_hmm.end_prob'), 2);
55 | new_hmm.transmat = new_hmm.transmat - repmat(norm, [1 new_hmm.nstates]);
56 | new_hmm.end_prob = new_hmm.end_prob - norm';
57 |
--------------------------------------------------------------------------------
/sample_gaussian.m:
--------------------------------------------------------------------------------
1 | function y = sample_gaussian(mu, cv)
2 | % sample = sample_gaussian(mu, cv)
3 | %
4 | % Generate a random sample from a Gaussian distribution with the given
5 | % parameters.
6 | %
7 | % 2008-06-04 ronw@ee.columbia.edu
8 |
9 | % Copyright (C) 2008 Ron J. Weiss
10 | %
11 | % This program is free software: you can redistribute it and/or modify
12 | % it under the terms of the GNU General Public License as published by
13 | % the Free Software Foundation, either version 3 of the License, or
14 | % (at your option) any later version.
15 | %
16 | % This program is distributed in the hope that it will be useful,
17 | % but WITHOUT ANY WARRANTY; without even the implied warranty of
18 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
19 | % GNU General Public License for more details.
20 | %
21 | % You should have received a copy of the GNU General Public License
22 | % along with this program. If not, see .
23 |
24 | y = randn(size(mu)).*sqrt(cv) + mu;
25 |
--------------------------------------------------------------------------------
/sample_gmm.m:
--------------------------------------------------------------------------------
1 | function samples = sample_gmm(gmm, nsamp)
2 | % samples = sample_gmm(gmm, N)
3 | %
4 | % Generate N random samples from the given GMM.
5 | %
6 | % 2008-06-04 ronw@ee.columbia.edu
7 |
8 | % Copyright (C) 2008 Ron J. Weiss
9 | %
10 | % This program is free software: you can redistribute it and/or modify
11 | % it under the terms of the GNU General Public License as published by
12 | % the Free Software Foundation, either version 3 of the License, or
13 | % (at your option) any later version.
14 | %
15 | % This program is distributed in the hope that it will be useful,
16 | % but WITHOUT ANY WARRANTY; without even the implied warranty of
17 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
18 | % GNU General Public License for more details.
19 | %
20 | % You should have received a copy of the GNU General Public License
21 | % along with this program. If not, see .
22 |
23 | prior_pdf = exp(gmm.priors);
24 | prior_cdf = cumsum(prior_pdf);
25 |
26 | ndim = size(gmm.means, 1);
27 |
28 | samples = zeros(ndim, nsamp);
29 | for n = 1:nsamp
30 | p = rand(1);
31 | c = min(find(prior_cdf >= p));
32 | samples(:,n) = sample_gaussian(gmm.means(:,c), gmm.covars(:,c));
33 | end
34 |
35 |
--------------------------------------------------------------------------------
/sample_hmm.m:
--------------------------------------------------------------------------------
1 | function [samples state_seq] = sample_hmm(hmm)
2 | % [samples state_seq] = sample_hmm(hmm)
3 | %
4 | % Generate a random sample from the given HMM.
5 | %
6 | % 2008-06-04 ronw@ee.columbia.edu
7 |
8 | % Copyright (C) 2008 Ron J. Weiss
9 | %
10 | % This program is free software: you can redistribute it and/or modify
11 | % it under the terms of the GNU General Public License as published by
12 | % the Free Software Foundation, either version 3 of the License, or
13 | % (at your option) any later version.
14 | %
15 | % This program is distributed in the hope that it will be useful,
16 | % but WITHOUT ANY WARRANTY; without even the implied warranty of
17 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
18 | % GNU General Public License for more details.
19 | %
20 | % You should have received a copy of the GNU General Public License
21 | % along with this program. If not, see .
22 |
23 | sp_pdf = exp(hmm.start_prob);
24 | sp_cdf = cumsum(sp_pdf);
25 | trans_pdf = exp([hmm.transmat hmm.end_prob']);
26 | trans_cdf = cumsum(trans_pdf, 2);
27 |
28 | % Initial state
29 | p = rand(1);
30 | s = min(find(sp_cdf >= p));
31 | state_seq = s;
32 | samples = sample_from_state(hmm, s);
33 |
34 | i = 1;
35 | while true
36 | % Select next component or exit.
37 | p = rand(1);
38 | os = s;
39 | s = min(find(trans_cdf(os,:) >= p));
40 | if isempty(s)
41 | s = length(trans_cdf(os,:));
42 | end
43 |
44 | if s > hmm.nstates
45 | break
46 | else
47 | i = i + 1;
48 | samples(:,i) = sample_from_state(hmm, s);
49 | state_seq(i) = s;
50 | end
51 | end
52 |
53 |
54 | function y = sample_from_state(hmm, s)
55 | if strcmp(hmm.emission_type, 'GMM')
56 | y = sample_gmm(hmm.gmms(s), 1);
57 | elseif strcmp(hmm.emission_type, 'gaussian')
58 | y = sample_gaussian(hmm.means(:,s), hmm.covars(:,s));
59 | else
60 | error(['Invalid HMM emission type: ' hmm.emission_type]);
61 | end
62 |
--------------------------------------------------------------------------------
/train_gmm.m:
--------------------------------------------------------------------------------
1 | function gmm = train_gmm(trdata, nmix, niter, verb, CVPRIOR, mu0);
2 | % gmmparams = train_gmm(trdata, nmix, niter, verb, cvprior, mu0);
3 | %
4 | % Train a GMM with diagonal covariance.
5 | %
6 | % Inputs:
7 | % trdata - training data (cell array of training sequences, each
8 | % column of the sequences arrays contains an
9 | % observation)
10 | % nmix - number of mixture components. Defaults to 3.
11 | % niter - number of EM iterations to perform. Defaults to 10.
12 | % verb - set to 1 to output loglik at each iteration
13 | % cvprior -
14 | %
15 | % Outputs:
16 | % gmmparams - structure containing hmm parameters learned from training
17 | % data (gmm.priors, gmm.means(:,1:nmix), gmm.covars(:,1:nmix))
18 | %
19 | % 2007-11-06 ronw@ee.columbia.edu
20 |
21 | if nargin < 2
22 | nmix = 3;
23 | end
24 | if nargin < 3
25 | niter = 10;
26 | end
27 |
28 | if nargin < 4
29 | verb = 0;
30 | end
31 |
32 | % prior on observation covariances to avoid overfitting:
33 | if nargin < 5
34 | CVPRIOR = 1;
35 | end
36 |
37 | if ~iscell(trdata)
38 | trdata = {trdata};
39 | end
40 |
41 | ndata = length(trdata);
42 |
43 |
44 | % Initialization
45 | gmm.priors = log(ones(1, nmix)/nmix);
46 | gmm.nmix = nmix;
47 |
48 | if nargin < 6 | numel(mu0) == 1 & mu0 == 1
49 | gmm.means = kmeans(cat(2, trdata{:}), nmix, niter/2);
50 | else
51 | if size(mu0, 2) == nmix
52 | gmm.means = mu0;
53 | end
54 | end
55 |
56 | ndim = size(trdata{1}, 1);
57 | %gmm.covars = ones(ndim, nmix);
58 | gmm.covars(:,1:nmix) = repmat(var(trdata{1}')', [1 nmix]);
59 |
60 |
61 | % sufficient statistics
62 | norm = zeros(size(gmm.priors));
63 | means = zeros(size(gmm.means));
64 | covars = zeros(size(gmm.covars));
65 |
66 | last_loglik = 0;
67 | for iter = 1:niter
68 | % E-step
69 | loglik = 0;
70 | norm(:) = 0;
71 | means(:) = 0;
72 | covars(:) = 0;
73 | for n = 1:ndata
74 | curr_data = trdata{n};
75 | [ll, posteriors] = eval_gmm(gmm, curr_data);
76 |
77 | loglik = loglik + sum(ll);
78 |
79 | norm = norm + sum(posteriors, 2)';
80 | means = means + curr_data * posteriors';
81 | covars = covars + curr_data.^2 * posteriors';
82 | end
83 |
84 | if verb,
85 | fprintf('Iteration %d: log likelihood = %f\n', iter, loglik);
86 | end
87 |
88 | % Check for convergence
89 | if abs(loglik - last_loglik) < 1e-5
90 | break
91 | end
92 | last_loglik = loglik;
93 |
94 | % M-step
95 | gmm.priors = log(norm/sum(norm));
96 |
97 | nrm = repmat(1./norm, [ndim 1]);
98 | gmm.means = means .* nrm;
99 | gmm.covars = (covars - 2*gmm.means.*means) .* nrm + gmm.means.^2;
100 | gmm.covars(gmm.covars < CVPRIOR) = CVPRIOR;
101 | end
102 |
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