1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
|
function [mu, Sigma, weights] = mixgauss_init(M, data, cov_type, method)
% MIXGAUSS_INIT Initial parameter estimates for a mixture of Gaussians
% function [mu, Sigma, weights] = mixgauss_init(M, data, cov_type. method)
%
% INPUTS:
% data(:,t) is the t'th example
% M = num. mixture components
% cov_type = 'full', 'diag' or 'spherical'
% method = 'rnd' (choose centers randomly from data) or 'kmeans' (needs netlab)
%
% OUTPUTS:
% mu(:,k)
% Sigma(:,:,k)
% weights(k)
if nargin < 4, method = 'kmeans'; end
[d T] = size(data);
data = reshape(data, d, T); % in case it is data(:, t, sequence_num)
switch method
case 'rnd',
C = cov(data');
Sigma = repmat(diag(diag(C))*0.5, [1 1 M]);
% Initialize each mean to a random data point
indices = randperm(T);
mu = data(:,indices(1:M));
weights = normalise(ones(M,1));
case 'kmeans',
mix = gmm(d, M, cov_type);
options = foptions;
max_iter = 5;
options(1) = -1; % be quiet!
options(14) = max_iter;
mix = gmminit(mix, data', options);
mu = reshape(mix.centres', [d M]);
weights = mix.priors(:);
for m=1:M
switch cov_type
case 'diag',
Sigma(:,:,m) = diag(mix.covars(m,:));
case 'full',
Sigma(:,:,m) = mix.covars(:,:,m);
case 'spherical',
Sigma(:,:,m) = mix.covars(m) * eye(d);
end
end
end
|
