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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;
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