function opts = sll_opts(opts)

% Options for Sparse Learning Library

%

% Notice:

% If one or several (even all) fields are empty, sll_opts shall assign the

% default settings.

%

% If some fields of opts have been defined, sll_opts shall check the fields

% for possible errors.

%

%

% Table of Options.  * * indicates default value.

%

%% FIELD            DESCRIPTION

%% Starting point

%

% .x0               Starting point of x. 

%                   Initialized according to .init.

%

% .c0               Starting point for the intercept c (for Logistic Loss)

%                   Initialized according to .init.

%

% .init             .init specifies how to initialize x.  

%                       * 0 => .x0 is set by the function initFactor *

%                         1 => .x0 and .c0 are defined

%                         2 => .x0= zeros(n,1), .c0=0

%

%% Termination

%

% .maxIter          Maximum number of iterations.

%                       *1e4*

%

% .tol              Tolerance parameter.

%                       *1e-4*

%

% .tFlag            Flag for termination.

%                       * 0 => abs( funVal(i)- funVal(i-1) ) <= .tol *

%                         1 => abs( funVal(i)- funVal(i-1) ) 

%                              <= .tol max( funVal(i-1), 1)

%                         2 => funVal(i) <= .tol

%                         3 => norm( x_i - x_{i-1}, 2) <= .tol

%                         4 => norm( x_i - x_{i-1}, 2) <= 

%                              <= .tol max( norm( x_{i-1}, 2), 1 )

%                         5 => Run the code for .maxIter iterations

%

%% Normalization

%

% .nFlag            Flag for implicit normalization of A.

%                       * 0 => Do not normalize A *

%                         1 => A=(A-repmat(mu, m, 1))*diag(nu)^{-1}

%                         2 => A=diag(nu)^{-1}*(A-repmat(mu,m,1)

%

% .mu               Row vector to be substracted from each sample.

%                           (.mu is used when .nFlag=1 or 2)

%                       If .mu is not specified, then

%                            * .mu=mean(A,1) *

%

% .nu               Weight (column) vector for normalization

%                           (.mu is used when .nFlag=1 or 2)

%                       If .nu is not specified, then

%                       * .nFlag=1 => .nu=(sum(A.^2, 1)'/m.^{0.5} *

%                       * .nFlag=2 => .nu=(sum(A.^2, 2)/n.^{0.5} *

%

%% Regularization

%

% .rFlag            Flag for regularization

%                           (.rFlag is used for the functions with "R")

%                        * 0 => lambda is the regularization parameter *

%                          1 => lambda = lambda * lambda_{max}

%                               where lambda_{max} is the maximum lambda

%                               that yields the zero solution

% .rsL2              Regularization parameter value of the squared L2 norm

%                           (.rsL2 is used only for l1 regularization)

%                        *.rsL2=0*

%                    If .rFlag=0, .rsL2 is used without scaling

%                       .rFlag=1, .rsL2=.rsL2 * lambda_{max}

%

%% Method & Line Search

% .lFlag

%

%% Grooup & Others

%

% .ind              Indices for k groups (a k+1 row vector)

%                   For group lasso only

%                   Indices for the i-th group are (ind(i)+1):ind(i+1)

%

% .q                Value of q in L1/Lq regularization

%                      *.q=2*

%

% .sWeight          The sample (positive and negative) weight

%                   For the Logistic Loss only

%                   Positive sample: .sWeight(1)

%                   Negative sample: sWeight(2)

%                   *1/m for both positive and negative samples*

%

% .gWeight          The weight for different groups

%                      *.gWeight=1*

%

% .fName            The name of the function

%

%% 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 7 August 2009.

%% Starting point

if isfield(opts,'init')

    if (opts.init~=0) && (opts.init~=1) && (opts.init~=2)

        opts.init=0; % if .init is not 0, 1, or 2, then use the default 0

    end

    if ~isfield(opts,'x0') && (opts.init==1)

        opts.init=0; % if .x0 is not defined and .init=1, set .init=0

    end

else

    opts.init = 0; 

                     % if .init is not specified, use "0"

end

%% Termination

if isfield(opts,'maxIter')

    if (opts.maxIter<1)

        opts.maxIter=10000;

    end

else

    opts.maxIter=10000;

end

if ~isfield(opts,'tol')

    opts.tol=1e-3;

end

if isfield(opts,'tFlag')

    if opts.tFlag<0

        opts.tFlag=0;

    elseif opts.tFlag>5

        opts.tFlag=5;

    else

        opts.tFlag=floor(opts.tFlag);

    end

else

    opts.tFlag=0;

end

%% Normalization

if isfield(opts,'nFlag')

    if (opts.nFlag~=1) && (opts.nFlag~=2)

        opts.nFlag=0;

    end

else

    opts.nFlag=0;

end

%% Regularization

if isfield(opts,'rFlag')

    if (opts.rFlag~=1)

        opts.rFlag=0;

    end

else

    opts.rFlag=0;

end

%% Method (Line Search)

if isfield(opts,'lFlag')

    if (opts.lFlag~=1)

        opts.lFlag=0;

    end

else

    opts.lFlag=0;

end

if isfield(opts,'mFlag')

    if (opts.mFlag~=1)

        opts.mFlag=0;

    end

else

    opts.mFlag=0;

end