You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: doHClustering.m
    path of file: ./repos/mtex/geometry/geometry_tools
    
    the code of the file until where you have to start completion: function [c,center] = doHClustering(obj,varargin)
% hirarchical clustering of rotations and vectors
%
% Syntax
%   [c,center] = doHCluster(ori,'numCluster',n)
%   [c,center] = doHCluster(ori,'maxAngle',omega)
%
% Input
%  ori - @orientation
%  n   - number of clusters

    
function [c,center] = doHClustering(obj,varargin)
% hirarchical clustering of rotations and vectors
%
% Syntax
%   [c,center] = doHCluster(ori,'numCluster',n)
%   [c,center] = doHCluster(ori,'maxAngle',omega)
%
% Input
%  ori - @orientation
%  n   - number of clusters
%  omega - maximum angle 
%
% Output
%  c - list of clusters
%  center - center of the clusters
%
% Example 
%
%   % generate orientation clustered around 5 centers
%   cs = crystalSymmetry('m-3m');
%   center = orientation.rand(5,cs); 
%   odf = unimodalODF(center,'halfwidth',5*degree)
%   ori = odf.discreteSample(3000);
%
%   % find the clusters and its centers
%   tic; [c,centerRec] = calcCluster(ori,'method','hierarchical','numCluster',5); toc
%
%   % visualize result
%   oR = fundamentalRegion(cs)
%   plot(oR)
% 
%   hold on
%   plot(ori,ind2color(c))
%   caxis([1,5])
%   plot(center,'MarkerSize',10,'MarkerFaceColor','k','MarkerEdgeColor','k')
%   plot(centerRec,'MarkerSize',10,'MarkerFaceColor','r','MarkerEdgeColor','k')
%   hold off 
%
%   %check the accuracy of the recomputed centers
%   min(angle_outer(center,centerRec)./degree)
%
%   odfRec = calcDensity(ori)
%   [~,centerRec2] = max(odfRec,5)
%   min(angle_outer(center,centerRec2)./degree)
%

% If the statistics toolbox is installed the following is also possible
% though it does not work so well
% d = angle_outer(ori,ori);
% tic;z = linkage(d);toc
% c = cluster(z,'maxclust',5);


% approximate clustering on a grid for very large data sets
if length(obj)>1000 && ~check_option(varargin,'exact')
  
  [c,center] = gridCluster(obj,varargin{:});
  return
end