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: gproc.m
    path of file: ./repos/CanlabCore/CanlabCore/External/spider/reg/@gproc
    
    the code of the file until where you have to start completion: function a = gproc(hyper) 
   
%=============================================================================
% GPROC Gaussian-Process object
%=============================================================================  
%   

    
function a = gproc(hyper) 
   
%=============================================================================
% GPROC Gaussian-Process object
%=============================================================================  
%   
% A=GPROC(hyper) returns a GPROC object initialized with hyperparameters hyper. 
% minimize log-likelihood for length runs
%
% 
%  Hyperparameters, and their defaults
%
% 
%   child=kernel;        -- the kernel is stored as a member called "child"
%   length=50            -- default maximum iteration of line searches.
%  
%  Model
%  H			-- covariance hyperparameters
% 
%  Methods:
%  d=gen(toyreg({'o=1','n=2','l=200'}))
%  
%  [r,a]=train(gproc,get(d,1:100))
%  r=test(a,get(d,101:200))
%  % Note that r.X has 2 an extra output columns! 
%
%   train, test
%=============================================================================
% Reference : Introduction to gaussian processes
% Author    : David J.C. MacKay
% Link      : http://www.inference.phy.cam.ac.uk/mackay/gpB.ps.gz
%=============================================================================

  % hyperparams 
  a.input= [];
  a.target = [];
  a.length=50;
  a.H=[];
  a.pred_var=0;
  % model     
    
  p=algorithm('gproc');
  a= class(a,'gproc',p);
  a.algorithm.use_signed_output=0;
 
  if nargin==1
    eval_hyper;
  end