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: trainDSH.m
    path of file: ./repos/hashing-baseline-for-image-retrieval/Method-DSH
    
    the code of the file until where you have to start completion: function DSHparam = trainDSH(X, DSHparam)
%function [model, B, elapse] = trainDSH(X, maxbits)
% DSH_learn: Training process in Density Sensitive Hashing
%
%     [model, B, elapse] = DSH_learn(X, maxbits)
%
%             Input:
%                 X - Data matrix. Each row vector of data is a sample vector. The data should be normalized and centralized to have zero mean before.
%           maxbits - Code length of hash codes
%
%            Output:
%             model - Struct value in Matlab. The field in model is:
%                        U - The matrix record the projection vectors
%                intercept - The vector record the intercept
%                        B - The binary codes for data points
%                   elapse - The training time        
%
%   Examples:
%
%          load('Flickr1M');
%          [model, TrainCode, TrainTime] = DSH_learn(fTrain, 32); 
%
%   Reference:
%          Yue Lin, Deng Cai and Cheng Li. "Density Sensitive Hashing"
%           
%           
%      version 1.0 -- Feb/2012
%     
%      Written by Yue Lin (linyue29@gmail.com)

%tmp_T = tic;

maxbits = DSHparam.nbits;


    
function DSHparam = trainDSH(X, DSHparam)
%function [model, B, elapse] = trainDSH(X, maxbits)
% DSH_learn: Training process in Density Sensitive Hashing
%
%     [model, B, elapse] = DSH_learn(X, maxbits)
%
%             Input:
%                 X - Data matrix. Each row vector of data is a sample vector. The data should be normalized and centralized to have zero mean before.
%           maxbits - Code length of hash codes
%
%            Output:
%             model - Struct value in Matlab. The field in model is:
%                        U - The matrix record the projection vectors
%                intercept - The vector record the intercept
%                        B - The binary codes for data points
%                   elapse - The training time        
%
%   Examples:
%
%          load('Flickr1M');
%          [model, TrainCode, TrainTime] = DSH_learn(fTrain, 32); 
%
%   Reference:
%          Yue Lin, Deng Cai and Cheng Li. "Density Sensitive Hashing"
%           
%           
%      version 1.0 -- Feb/2012
%     
%      Written by Yue Lin (linyue29@gmail.com)

%tmp_T = tic;

maxbits = DSHparam.nbits;

alpha = 1.5;
r = 3;
iter = 3;
cluster = round(maxbits * alpha);
[dump U] = litekmeans(X, cluster, 'MaxIter', iter);

[Nsamples, Nfeatures] = size(X);
DSHparam.U = zeros(maxbits, Nfeatures);
DSHparam.intercept = zeros(maxbits, 1);

clusize = zeros(size(U, 1), 1);
for i = 1 : size(U, 1)
    clusize(i) = size(find(dump == i), 1);
end