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