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    Metadata details:
    filename: rlr_learning_var2.m
    path of file: ./repos/malini/Classifiers/SLR/Classifier_Files/SLR
    
    the code of the file until where you have to start completion: function [w_eff, ix_eff, W, AX] = rlr_learning_var2(label, X, varargin)
% Learning parameters of ARD-regularized logistic regression model (RLR-var).
%
% The estimation algorthm is derived from lower bound method. 
% The likelihood function is approximated by Gaussian distribution using variational parameters.  (SLR-var)  
%
% Note that label vectors must be consisting of {0,1} values. 
%
% -- Usage 
% [w_eff, ix_eff, W, AX] = rlr_learning_var(label, X, varargin)
% 
% -- Input
% label : Teacher label vector consisting of {0,1}  [#sample*1]
% X     : Explanatory matrix                        [#sample*#feature]

    
function [w_eff, ix_eff, W, AX] = rlr_learning_var2(label, X, varargin)
% Learning parameters of ARD-regularized logistic regression model (RLR-var).
%
% The estimation algorthm is derived from lower bound method. 
% The likelihood function is approximated by Gaussian distribution using variational parameters.  (SLR-var)  
%
% Note that label vectors must be consisting of {0,1} values. 
%
% -- Usage 
% [w_eff, ix_eff, W, AX] = rlr_learning_var(label, X, varargin)
% 
% -- Input
% label : Teacher label vector consisting of {0,1}  [#sample*1]
% X     : Explanatory matrix                        [#sample*#feature]
%
% -- Field of Optional Input
% ax0   : Initial value of relevance parameters ax
% xi0   : Initial value of variational parameters xi
% nlearn : # of learning 
% nstep  : # of step at which parameters updataing is kept.
% amax   : Truncation criteria. Parameters whose relevance paramater ax is l
%          arger than this value are eliminated from the further iterations.
% invhesssian : If 1, the inverse of Hessian (#feature*#feature) is used.
%               If 0, the diagonal elements of posterior variance is calculated without   
%               doing Hessian matrix inversion (Much faster when #feature >> #sample).   
%
% -- Output
% w_e    : Estimated parameters
% ix_eff : Index of the effective feature
% W      : History of parameter learning
% AX     : History of hyper parameter learning
%
% -- Example
% > [w_e, ix_eff, W, AX] = rlr_learning_var2(t, X,...
%   'nlearn', 100, 'nstep', 10);
%
% 2009/05/29 OY
% * A field name of option is changed (nohessian --> invhessian).
% * Bug fix : inappropriate output arguments "w_eff" and "ix_eff"
% * Bug fix : ax_eff was not updated proprerly
%
% Copyright (c) 2009, Okito Yamashita, ATR CNS, oyamashi@atr.jp.


%% Error check
if nargin < 2
    help rlr_learning_var2
end