% If you use this code, please cite 
% Sanjar Karaev, Pauli Miettinen and Jilles Vreeken, "Getting to Know the Unknown Unknowns:
% Destructive-Noise Resistant Boolean Matrix Factorization". Proceedings of SIAM
% International Conference on Data Mining (SDM15). 2015.

% --------------------------------------------------------------------------
% 

% To start please first run 'setpath' command.


% --------------------------------------------------------------------------
% For a given binary matrix A Nassau finds its Boolean decomposition into
% factors [B, C] by heuristically minimizing the description length.
%

% --------------------------------------------------------------------------
% Usage:
% [B, C] = Nassau(A)
% [B, C] = Nassau(A, param1, value1, ..., paramN, valueN)

% Optional parameters:
% verbose:                       1 - on, 0 off
% n_full_cycles                  # full round robin updates of the blocks.
% cyclic_interval                how often to run cyclic updates during block
%                                finding stage
% MAX_ITERS_WITHOUT_IMPROVEMENT  how many blocks can we add without
%                                improving the objective
% DLmeasure                      encoding to use, options: DtMtypedXor,
%                                DtMnaiveXor, typedXor
% init_temp                      Initial temperature for the simulated
%                                annealing (0 <= init_temp <= 1)
% mult_factor                    multiplicative update to th temperature
% cand_gen                       how to generate candidates, options:
%                                orginal - use column of the input matrix,
%                                asso - use Asso candidates
%                                random_walks - generate from random walks
% file_id                        save intermediate results to
%                                ./results/file_id  (no saving by default)
% tau                            thresholding parameter when using Asso
%                                candidates
% save_cand                      save candidates for this matrix, 1 - save,
%                                0 - not (default)
% cyclic_updates                 periodically run cyclic updates during
%                                the block finding stage, 1 - yes
%                                (default), 0 - not
% expand_factors                 before cyclic updates, pad the factors
% with
%                                some empty blocks to allow room for
%                                improvement, 1 - yes (default), 0 - not
% error_nnz                      use 0-1 error as the score, 1 - yes,
%                                0 - not (default)
% cand_ratio                     do not generate all candidates, but only
%                                fraction (0 < cand_ratio <= 1), 1 -default
% accept_all_cands               do not remove candidates even if they
%                                have to many or to few 1s, 1 - yes,
%                                0 - not (default)
% --------------------------------------------------------------------------

% The SYNTH command reruns all the synthetic experiments
%