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: spm_kmeans1.m
    path of file: ./repos/bspm/thirdparty/spm12/toolbox/mixture
    
    the code of the file until where you have to start completion: function [mix1] = spm_kmeans1 (y,k)
% K-means clustering for 1-dimensional data
% FORMAT [mix1] = spm_kmeans1 (y,k)
% 
% y          [1 x N] data vector
% k          Number of components
%
% mix1       Returned model
%
% -------------------------------------------------------
% The fields in mix1 are:
% k                The number of components
% m                Vector of means, m=[m_1,m_2,...,m_k]
% v                Vector of variances, v=[v_1,v_2,..,v_k]
% pi               Vector of mixing proportions, pi=[pi_1,pi_2,..,pi_k]
% nloops           Number of iterations used
% assign           Which class data points are assigned to
%___________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging

% Will Penny 
% $Id: spm_kmeans1.m 1143 2008-02-07 19:33:33Z spm $

y=y(:)';
N=length(y);

% Spread seeds evenly according to CDF
[x,i]=sort(y);

    
function [mix1] = spm_kmeans1 (y,k)
% K-means clustering for 1-dimensional data
% FORMAT [mix1] = spm_kmeans1 (y,k)
% 
% y          [1 x N] data vector
% k          Number of components
%
% mix1       Returned model
%
% -------------------------------------------------------
% The fields in mix1 are:
% k                The number of components
% m                Vector of means, m=[m_1,m_2,...,m_k]
% v                Vector of variances, v=[v_1,v_2,..,v_k]
% pi               Vector of mixing proportions, pi=[pi_1,pi_2,..,pi_k]
% nloops           Number of iterations used
% assign           Which class data points are assigned to
%___________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging

% Will Penny 
% $Id: spm_kmeans1.m 1143 2008-02-07 19:33:33Z spm $

y=y(:)';
N=length(y);

% Spread seeds evenly according to CDF
[x,i]=sort(y);
seeds=[1,2*ones(1,k-1)]*N/(2*k);
seeds=ceil(cumsum(seeds));

last_i=ones(1,N);
m=x(seeds);
for loops=1:100,  
 for j=1:k,
   d(j,:)=(y-m(j)).^2;
 end