comparison toolboxes/FullBNT-1.0.7/netlabKPM/gmmem_multi_restart.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function [means, covs, weights, ll] = gmmem_multi_restart(K, data, varargin)
+% GMMEM_MULTI_RESTART Multiple restart wrapper for gmmem_kpm
+% function [means, covs, weights, ll] = gmmem_multi_restart(K, data, varargin)
+%
+% Input:
+% K = number of mixture components
+% data(i,:) is the i'th example (feature vector)
+%
+% Output:
+% The parameters for the k'th mixture component, k=1:K, are
+%   means(k,:), covs(:,:,k) and weights(k)
+%
+% [ ... ] = gmmem_multi_restart(..., 'param1',val1, 'param2',val2, ...)
+% allows you to specify optional parameter name/value pairs.
+% Parameters are below [default value in brackets]
+%
+% 'nrestarts' - number of EM restarts [2]
+% 'cov_type' - 'full', 'diag' or 'spherical' ['full']
+% 'init_cov' - the initial covariance matrix [0.1*cov(data) for each k]
+% 'init_means' - [] means sample from randn(); otherwise, use
+%               init_means(k,:,r) for the k'th comp. on the r'th restart [ [] ]
+% 'restartfn'  - this function, if non-empty,  will be called before/after every restart
+%               (e.g., to display the parameters as they evolve) [ [] ]
+%               The fn is called as fn(mix{r}, data, restart_num, niter, outerfnargs)
+%               where niter is the number of iterations performed (0 initially)
+% 'restartfnargs' - additional arguments to be passed to restartfn [ {} ]
+%
+% Optional arguments for gmmem_kpm are passed through.
+%
+% Written by Kevin P Murphy, 30 Dec 2002
+
+[ndata nfeatures] = size(data);
+
+%Cinit = repmat(0.1*diag(diag(cov(data))), [1 1 K]);
+Cinit = repmat(0.1*cov(data), [1 1 K]);
+
+[nrestarts, init_cov, init_means, cov_type, ...
+ restartfn, restartfnargs, unused_args] = ...
+    process_options(varargin, ...
+	'nrestarts', 2, 'init_cov',  Cinit,  'init_means', [], ...
+	'cov_type', 'full', 'restartfn', [], 'restartfnargs', {});
+
+mix = cell(1, nrestarts);
+cost = inf*ones(1,nrestarts);
+
+for r=1:nrestarts
+  mix{r} = gmm(nfeatures, K, cov_type); % random centers
+  if ~isempty(init_means), mix{r}.centres = init_means(:,:,r); end
+  mix{r}.covars = init_cov;
+  if ~isempty(restartfn)
+    feval(restartfn, mix{r}, data, r, 0, restartfnargs{:});
+  end
+  [mix{r}, niter, ll] = gmmem_kpm(mix{r}, data, unused_args{:}); 
+  cost(r) = -ll; %-sum(log(gmmprob(mix{r}, data)));
+  if ~isempty(restartfn)
+    feval(restartfn, mix{r}, data, r, niter, restartfnargs{:});
+  end
+end
+
+[nll, bestr] = min(cost);
+fprintf('best r = %d\n', bestr);
+ll = -nll;
+means = mix{bestr}.centres;
+covs = mix{bestr}.covars;
+weights = mix{bestr}.priors;