function [abest, mbest, sbest, ll] = gm(data, k, nr, sig)
%
% [alpha, mu, sigma, [ll] ] = gm(data, k, [nres, sig])
%
% INPUT:
%    data : n by d data points (n points of dimensionality d).
%    k    : number of desired clusters.
%    nres : number of random restarts <default is 1>
%    sig  : matrix of 0's and 1's used to cancel unwanted elements
%           in sigma matrices (for forced decoupling)
%
% OUTPUT:
%    alpha : weights of components (k by 1).
%    mu    : mean for each component (k by d).
%    sigma : covariance matrix for each comp. (d by d by k).
% optional:
%    ll(1:nres) : log-likelihood per restart
%
%
% NOTES:
%    This is a wrapper function that calls gmm.dll to do the actual job.
%    The purpose of this function is to supply defaults and to allow 
%    for variable number of parameters (by supplying defaults). It is 
%    only for convenience and doesn't do any fitting/calculations. 
%
%                by Igor Cadez 01/23/99

%get the number of restarts, default is just 1
if(~exist('nr'))
   nr = 1;
end

[n, d] = size(data);
%if sig is not specified, use full cov. matrices.
if(~exist('sig'))
   sig = ones(d);
end

%min values for preventing singularities...
minsigma = 1e-3;
sigmin = diag(cov(data)) * minsigma;

%precision and max interations
prec          = 1e-4;
maxsteps      = 100;

[abest, mbest, sbest, ll] = gmm(data, k, nr, sig,sigmin,maxsteps,prec);
sbest = reshape(sbest,d,d,k);

[i, j] = sort(-abest);
abest = abest(j);
mbest = mbest(j,:);
sbest = sbest(:,:,j);