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    Metadata details:
    filename: biclsfy_rlrvar.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_rlrvar(x_train, t_train, x_test, t_test, varargin)
% Binary classification by the regularized logistic regression with variational 
% parameter approximation (RLR-VAR)
% 
% Normalization, parameter estimation, and performance evaluation are executed.
%
% -- Usage
% [ww, ix_eff_all, errTable_Tr, errTable_te, parm, AXall, Ptr, Pte] =...
%   biclsfy_rlrvar(x_train, t_train, x_test, t_test,  varargin)
%
% --- Input
% x_train :   [Nsamp_tr , Nfeat] 
% t_train :   [Nsamp_tr , 1]

    
function [ww, ix_eff_all, errTable_tr, errTable_te, parm, AXall, Ptr, Pte] =...
    biclsfy_rlrvar(x_train, t_train, x_test, t_test, varargin)
% Binary classification by the regularized logistic regression with variational 
% parameter approximation (RLR-VAR)
% 
% Normalization, parameter estimation, and performance evaluation are executed.
%
% -- Usage
% [ww, ix_eff_all, errTable_Tr, errTable_te, parm, AXall, Ptr, Pte] =...
%   biclsfy_rlrvar(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, Nclass]
% ix_eff_all  :   Index of features survived. cell array of {Nclass}
% errTable_tr :   Counting table of each label estimated. [Nclass, Nclass]
% errTbale_te :   Counting table of each label estimated. [Nclass, Nclass]
% parm        :   Parmaters used in this routine. [struct]
% AXall       :   History of hyperparameters updating. [Nfeat*Nclass Nlearn]
% Ptr         :   Probaility of observing every label in training data. [Nsamp_tr Nclass]
%                 This value is used to put a label on each sample.
% Pte         :   Probaility of observing every label in training data. [Nsamp_te Nclass]
%                 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_rlr_bi_var.m'.
% * Modified for solving "only" binary classification problem. 
% 2007/12/28 OY
% * RLR version.
% 2006/10/23 OY
% * 'Nclass' is removed from inputs.
% 2006/09/12 OY
%  * A field "nprobe" is introduced.
% 2006/09/06 OY  
%  * Output format modified (error table as output)
%  * Comment modified
% 2006/08/02 OY
% 2006/05/26 OY
%
% Copyright (c) 2009, Okito Yamashita, ATR CNS, oyamashi@atr.jp.

if nargin < 4
    help biclsfy_rlrvar;
    return
end