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: biclsfy_slrvar.m
    path of file: ./repos/malini/Classifiers/SLR/Classifier_Files/SLR
    
    the code of the file until where you have to start completion: function [ww, ix_eff_all, errTable_tr, errTable_te, parm, AXall,Ptr,Pte] =...
    biclsfy_slrvar(x_train, t_train, x_test, t_test, varargin)
% Binary classification by SLR with variational parameters approximation (SLR-VAR).
% 
% -- Usage
% [ww, ix_eff_all, errTable_tr, errTable_te, parm, AXall, Ptr, Pte] =
% biclsfy_slrvar(x_train, t_train, x_test, t_test, varargin)
%
% --- Input
% x_train :   [Nsamp_tr , Nfeat] 
% t_train :   [Nsamp_tr , 1]
% x_test  :   [Nsamp_te , Nfeat]
% t_test  :   [Nsamp_te , Nfeat]
%
% --- Optional Input
% parm = finputcheck(varargin, ...
% {'scale_mode' ,'string' , {'all','each','stdall','stdeach','none'}, 'each';...
%     'mean_mode'  ,'string' , {'all','each','none'}, 'each';...
%     'ax0'        ,'real'   ,  []     ,  [];...
%     'nlearn'     ,'integer',  [1 inf],  1000;...
%     'nstep'      ,'integer',  [1 inf],  100;...
%     'amax'       ,'real'   ,  [0 inf],  1e8;...
%     'usebias'    ,'boolean',  []     , 1;...
%     'norm_sep'   ,'boolean',  []     , 0;...
%     'displaytext','boolean',  []     , 1;...
%     'invhessian','boolean',  []     , 0;...
%     });
%
% --- Output
% ww          :   Estimated weight parameters. [Nfeat, 1]
% ix_eff_all  :   Index of features survived. cell array of 1*1
% errTable_tr :   Counting table of each label estimated. [2, 2]
% errTbale_te :   Counting table of each label estimated. [2, 2]
% parm        :   Parmaters used in this routine. [struct]
% AXall       :   History of hyperparameters updating. [Nfeat Nlearn]
% Ptr         :   Probaility of observing every label in training data. [Nsamp_tr 2]
%                 This value is used to put a label on each sample.
% Pte         :   Probaility of observing every label in training data. [Nsamp_te 2]
%                 This value is used to put a label on each sample.
%
% 2009/08/10 OY
% * Bug fix when 't_test' has only a single label of 't_train'.
% 2009/05/29 OY
% * The function is renamed from 'run_smlr_bi_var.m'.
% * Modified for solving "only" binary classification problem. 
% 2007/01/10 OY
% * A field 'nohesssian' is added to the optional inputs 
% 2006/10/23 OY
% * 'Nclass' is removed from inputs.
% 2006/09/11 OY
% * Add probe vectors 
% 2006/09/07 OY 
% * Output format is modified
% 2006/05/26 OY
% 2006/05/15 OY
%
% Copyright (c) 2009, Okito Yamashita, ATR CNS, oyamashi@atr.jp.


    
function [ww, ix_eff_all, errTable_tr, errTable_te, parm, AXall,Ptr,Pte] =...
    biclsfy_slrvar(x_train, t_train, x_test, t_test, varargin)
% Binary classification by SLR with variational parameters approximation (SLR-VAR).
% 
% -- Usage
% [ww, ix_eff_all, errTable_tr, errTable_te, parm, AXall, Ptr, Pte] =
% biclsfy_slrvar(x_train, t_train, x_test, t_test, varargin)
%
% --- Input
% x_train :   [Nsamp_tr , Nfeat] 
% t_train :   [Nsamp_tr , 1]
% x_test  :   [Nsamp_te , Nfeat]
% t_test  :   [Nsamp_te , Nfeat]
%
% --- Optional Input
% parm = finputcheck(varargin, ...
% {'scale_mode' ,'string' , {'all','each','stdall','stdeach','none'}, 'each';...
%     'mean_mode'  ,'string' , {'all','each','none'}, 'each';...
%     'ax0'        ,'real'   ,  []     ,  [];...
%     'nlearn'     ,'integer',  [1 inf],  1000;...
%     'nstep'      ,'integer',  [1 inf],  100;...
%     'amax'       ,'real'   ,  [0 inf],  1e8;...
%     'usebias'    ,'boolean',  []     , 1;...
%     'norm_sep'   ,'boolean',  []     , 0;...
%     'displaytext','boolean',  []     , 1;...
%     'invhessian','boolean',  []     , 0;...
%     });
%
% --- Output
% ww          :   Estimated weight parameters. [Nfeat, 1]
% ix_eff_all  :   Index of features survived. cell array of 1*1
% errTable_tr :   Counting table of each label estimated. [2, 2]
% errTbale_te :   Counting table of each label estimated. [2, 2]
% parm        :   Parmaters used in this routine. [struct]
% AXall       :   History of hyperparameters updating. [Nfeat Nlearn]
% Ptr         :   Probaility of observing every label in training data. [Nsamp_tr 2]
%                 This value is used to put a label on each sample.
% Pte         :   Probaility of observing every label in training data. [Nsamp_te 2]
%                 This value is used to put a label on each sample.
%
% 2009/08/10 OY
% * Bug fix when 't_test' has only a single label of 't_train'.
% 2009/05/29 OY
% * The function is renamed from 'run_smlr_bi_var.m'.
% * Modified for solving "only" binary classification problem. 
% 2007/01/10 OY
% * A field 'nohesssian' is added to the optional inputs 
% 2006/10/23 OY
% * 'Nclass' is removed from inputs.
% 2006/09/11 OY
% * Add probe vectors 
% 2006/09/07 OY 
% * Output format is modified
% 2006/05/26 OY
% 2006/05/15 OY
%
% Copyright (c) 2009, Okito Yamashita, ATR CNS, oyamashi@atr.jp.

if nargin < 4
    help biclsfy_slrvar;
    return
end