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    Metadata details:
    filename: slr_learning_l1.m
    path of file: ./repos/malini/Classifiers/RLR/Classifier_Files/RLR
    
    the code of the file until where you have to start completion: function [w, ix_eff, W, AX] = slr_learning_l1(label, X, hfun, gamma1, varargin)
% Learning parameters of l1-sparse logistic regression model.
%
% The model is based on the hierarchical prior formulation of Laplace prior
% described in Krishnapuram et.al 2004.
% The algorithm is derived using variational Bayesian method with 
% Laplace approximation in W-stap (SLR-Laplace).

    
function [w, ix_eff, W, AX] = slr_learning_l1(label, X, hfun, gamma1, varargin)
% Learning parameters of l1-sparse logistic regression model.
%
% The model is based on the hierarchical prior formulation of Laplace prior
% described in Krishnapuram et.al 2004.
% The algorithm is derived using variational Bayesian method with 
% Laplace approximation in W-stap (SLR-Laplace).
% Note that the label vectors must be consisting of {0,1} value.
%
% -- Usage
% [w, ix_eff, W, AX] = slr_learning_l1(label, X, hfun, gamma1, varargin)
% 
% -- Input
% label : Teacher label vector consisting of {0,1}  [N*1]
% X     : Explanatory matrix                        [N*#feature]
% hfun  : Function handle
% gamma1 : a regularization parameter
%
% -- Field of Optional Input
% w0    : Initial value of weight parameter w 
% ax0   : Initial value of relevance parameter ax
% nlearn : # of learning 
% amax   : Truncation criteria. Parameters whose relevance parameter is larger 
%          than this value are eliminated from further iterations.
% wmaxiter : # of learning in W-step
% wdisplay : 
% reweight : If this is 'ON', the sparsity is accelarated in unusual way (no mathematical gurantee). 
%            
% -- Output
% w      : Estimated parameters
% ix_eff : Index of the effective feature
% W      : History of parameter learning
% AX     : History of hyper parameter learning
%
% -- Example
% >  [w, ix_eff] = slr_learning(t, X, @linfun,...
% > 'wdisplay', 'off', 'nlearn', 100, 'nstep', 10);
% The result of W-step is not displayed. # of iterations (W-step & A-step)
% is 100 and the updating hisotry of AX and W is kept in each 10 iterations. 
%
% 2009/06/09 OY 1st version
%
% Copyright (c) 2009, Okito Yamashita, ATR CNS, oyamashi@atr.jp.

%% Error check
if nargin < 4
    help slr_learning_l1
end