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    filename: LogisticR_seq.m
    path of file: ./repos/CS_MoCo_LAB/reconstruction/matlab/CS_LAB_matlab/utils/utils_SLEP/SLEP/functions/L1/L1R
    
    the code of the file until where you have to start completion: function [x, c, funVal, ValueL]=LogisticR_seq(z, opts)
%
%%
% Function LogisticR_seq
%      Logistic Loss with the L1-norm Regularization
%
% Imporant Notice:
%
% 1) Compared to LogisticR, we read the data "A, y" from several sequential
% files in order to save memory requirement.
%
% 2) The current function only supports reading sparse data with the
% function "read_sparse" provided by C. J. Lin et al.
%
% We make use of the function
%
%          b=matrixVectorFile(opts, flag, x)
%
%  flag         - the flag for the kind of operation
%               1: A  * x
%               2: A' * x
%               3: mean(A,1)
%               4: sum(A.^2,1)
%               5: sum(A.^2,2)
%               6: y
% 
%% Problem
%
%  min  f(x,c) = - weight_i * log (p_i) + 1/2 rsL2 * ||x||_2^2
%                 + z * \|x\|_1
%
%  By default, rsL2=0.
%
%  a_i denotes a training sample,
%      and a_i' corresponds to the i-th row of the data matrix A
%
%  y_i (either 1 or -1) is the response
%
%  p_i= 1/ (1+ exp(-y_i (x' * a_i + c) ) ) denotes the probability
%
%  weight_i denotes the weight for the i-th sample
%

    
function [x, c, funVal, ValueL]=LogisticR_seq(z, opts)
%
%%
% Function LogisticR_seq
%      Logistic Loss with the L1-norm Regularization
%
% Imporant Notice:
%
% 1) Compared to LogisticR, we read the data "A, y" from several sequential
% files in order to save memory requirement.
%
% 2) The current function only supports reading sparse data with the
% function "read_sparse" provided by C. J. Lin et al.
%
% We make use of the function
%
%          b=matrixVectorFile(opts, flag, x)
%
%  flag         - the flag for the kind of operation
%               1: A  * x
%               2: A' * x
%               3: mean(A,1)
%               4: sum(A.^2,1)
%               5: sum(A.^2,2)
%               6: y
% 
%% Problem
%
%  min  f(x,c) = - weight_i * log (p_i) + 1/2 rsL2 * ||x||_2^2
%                 + z * \|x\|_1
%
%  By default, rsL2=0.
%
%  a_i denotes a training sample,
%      and a_i' corresponds to the i-th row of the data matrix A
%
%  y_i (either 1 or -1) is the response
%
%  p_i= 1/ (1+ exp(-y_i (x' * a_i + c) ) ) denotes the probability
%
%  weight_i denotes the weight for the i-th sample
%
%% Input parameters:
%
%  Note: A and y are read from the file (see opts)
%
%  A-         Matrix of size m x n
%                A can be a dense matrix
%                         a sparse matrix
%                         or a DCT matrix
%  y -        Response vector (of size mx1)
%
%
%  z -        Lq/L1 norm regularization parameter (z >=0)
%  opts-      Optional inputs (default value: opts=[])
%
%  The following fields are required for opts:
% 
%  opts.fileName- a string containing the complete path to the file and the
%                 name of the file (except for _1.txt, _2.txt, ...)
%  opts.fileNum - the number of files (all the files constitute the whole
%                 file that provides infor for A and y)
%  opts.m       - the value of m
%  opts.n       - the value of n
%
%% Output parameters:
%  x-         The obtained weight of size n x 1
%  c-         The obtained intercept (scalar)
%  funVal-    Function value during iterations
%
%% Copyright (C) 2009-2010 Jun Liu, and Jieping Ye
%
% You are suggested to first read the Manual.
%
% For any problem, please contact with Jun Liu via j.liu@asu.edu
%
% Last modified on March 27, 2010.
%
%% Related papers
%
% [1]  Jun Liu, Jianhui Chen, and Jieping Ye, Large-Scale Sparse Logistic
%      Regression, KDD09
%
% [2]  Jun Liu and Jieping Ye, Efficient Euclidean Projections
%      in Linear Time, ICML 2009.
%
%% Related functions
%
%  sll_opts, initFactor, pathSolutionLeast
%  LogisticC, nnLogisticR, nnLogisticC
%
%%

%% Verify and initialize the parameters
%%
if (nargin <2)
    error('\n Inputs: z and opts should be specified!\n');
end