// ====================================================================
// This file is part of the Endmember Induction Algorithms Toolbox for MATLAB 
// Copyright (C) Grupo de Inteligencia Computacional, Universidad del 
// País Vasco (UPV/EHU), Spain, released under the terms of the GNU 
// General Public License.
//
// Endmember Induction Algorithms Toolbox is free software: you can redistribute 
// it and/or modify it under the terms of the GNU General Public License 
// as published by the Free Software Foundation, either version 3 of the 
// License, or (at your option) any later version.
//
// Endmember Induction Algorithms Toolbox is distributed in the hope that it will
// be useful, but WITHOUT ANY WARRANTY; without even the implied warranty
// of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU 
// General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with Endmember Induction Algorithms Toolbox. 
// If not, see <http://www.gnu.org/licenses/>.
// ====================================================================

function [E,C] = EIA_2D(data,algorithm,varargin)
  //// [E,C] = EIA_2D(data,algorithm,varargin)
  // 
  // Manuel Grana <manuel.grana[AT]ehu.es>
  // Miguel Angel Veganzones <miguelangel.veganzones[AT]ehu.es>
  // Grupo de Inteligencia Computacional (GIC), Universidad del Pais Vasco /
  // Euskal Herriko Unibertsitatea (UPV/EHU)
  // http://www.ehu.es/computationalintelligence
  // 
  // Copyright (2011) Grupo de Inteligencia Computacional @ Universidad del Pais Vasco, Spain.
  //
  // Endmembers induction algorithms for 2-spatial dimensionality data.
  // 2-spatial data is defined as a cube where first dimension represents
  // the spectral information and, second and third dimensions are the spatial ones.
  // Examples of 2-spatial data are images where each pixel (spatial dimensionality) 
  // is an N-dimensional feature vector (spectral dimensionality). For
  // binaries or grey-scale images N==1. For RGB images N==3. For
  // hyperspectral images N is high.
  // ------------------------------------------------------------------------------
  // Input:   data      : column data cube [nvariables x nrows x ncolumns]
  //          algorithm : EIA to be used. It can be one of this:
  //                      {'ILSIA'(default)|'EIHA'|'WM'|'NFINDR'|'FIPPI'|'ATGP'}
  //          varargin  : options to be passed to the algorithm (see below).
  //
  // Output:  E         : set of induced endmembers [nvariables x p]
  //          C         : induced endmembers indexes vector [nrows x ncolumns] with {0,1} 
  //                      values, where '1' indicates that the corresponding sample
  //                      has been identified as an endmember. Some of the
  //                      algorithms do not select pixels as the endmembers
  //                      and, in that case C is empty.
  //                  
  // Now, a description of the algorithms is offered together to the options
  // that can be passed as parameters.
  //
  // * ILSIA: Incremental lattice Source Induction Algorithm (ILSIA) endmembers induction algorithm.
  //   - Bibliographical references:
  //       [1] M. Graña, D. Chyzhyk, M. García-Sebastián, y C. Hernández, “Lattice independent component analysis for functional magnetic resonance imaging”, Information Sciences, vol. 181, nº. 10, págs. 1910-1928, May. 2011.
  //   - Options: {'alpha'}
  //       'alpha': Chebyshev-best approximation tolerance threshold (>= 0). Default = 0.
  //
  // * EIHA: Endmember induction heuristic algorithm (EIHA) endmembers induction algorithm.
  //   - Bibliographical references:
  //       [1] M. Grana, I. Villaverde, J. O. Maldonado, y C. Hernandez, «Two lattice computing approaches for the unsupervised segmentation of hyperspectral images», Neurocomput., vol. 72, nº. 10-12, págs. 2111-2120, 2009.
  //   - Options: {'alpha'}
  //       'alpha': perturbation tolerance. Default = 2.
  //
  // * WM: Prof. Ritter's WM endmembers induction algorithm.
  //   - Bibliographical references:
  //       [1] G. X. Ritter y G. Urcid, “A lattice matrix method for hyperspectral image unmixing”, Information Sciences, vol. In Press, Corrected Proof, Oct. 2010.
  //   - Options: none.
  //
  // * NFINDR: N-FINDR endmembers induction algorithm.
  //   - Bibliographical references:
  //       [1] Winter, M. E., «N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data», presented at the Imaging Spectrometry V, Denver, CO, USA, 1999, vol. 3753, págs. 266-275.
  //   - Options: {'p'|'maxit'}
  //       'p': number of endmembers to be induced. If not provided it is calculated by HFC method with tol=10^(-5).
  //       'maxit': maximum number of iterations. Default = 3*p.
  //
  // * FIPPI: Fast Iterative Pixel Purity Index (FIPPI) endmembers induction algorithm.
  //   - Bibliographical references:
  //       [1] Chang, C.-I., “A fast iterative algorithm for implementation of pixel purity index”, Geoscience and Remote Sensing Letters, IEEE, vol. 3, nº. 1, págs. 63-67, 2006.
  //   - Options: {'alpha'}
  //       'p': number of endmembers to be induced. If not provided it is calculated by HFC method with tol=10^(-5).
  //       'maxit': maximum number of iterations. Default = 3*p.
  //
  // * ATGP: ATGP endmembers induction algorithm.
  //   - Bibliographical references:
  //       [1] A. Plaza y C.-I. Chang, “Impact of Initialization on Design of Endmember Extraction Algorithms”, Geoscience and Remote Sensing, IEEE Transactions on, vol. 44, nº. 11, págs. 3397-3407, 2006.
  //   - Options: {'p'}
  //       'p': number of endmembers to be induced. If not provided it is calculated by HFC method with tol=10^(-5).
  //
  
  //// Arguments
  [lhs,rhs]=argn(0);
  if lhs < 1
    error('Insuficient parameters');
  end 
  
  //// Reshape data
  [p m n] = size(data);
  data = reshape(data,p,m*n);
  
  //// Algorithms
  [lhs,rhs]=argn(0);
  select lower(algorithm)
  case 'eiha' then
    disp('EIHA algorithm');
    if rhs > 0
      alpha = [];
      for i=1:2:rhs
        if strcmpi(varargin(i),'alpha')
          alpha = varargin(i+1);
          break;
        end
      end
      [E,C] = EIA_EIHA(data,alpha);
    else
      [E,C] = EIA_EIHA(data);
    end
  case 'wm' then
    disp('WM algorithm');
    [E] = EIA_WM(data);
    C = [];
  case 'nfindr' then
    disp('N-FINDR algorithm');
    if rhs > 0
      p = []; maxit = [];
      for i=1:2:rhs
        if strcmpi(varargin(i),'p')
          p = varargin(i+1);
        elseif strcmpi(varargin(i),'maxit')
          maxit = varargin(i+1);
        end
      end
      [E,C] = EIA_NFINDR(data,p,maxit);
    else
      [E,C] = EIA_NFINDR(data);
    end
  case 'fippi' then
    disp('FIPPI algorithm');
    if rhs > 0
      p = []; maxit = [];
      for i=1:2:rhs
        if strcmpi(varargin(i),'p')
          p = varargin(i+1);
        elseif strcmpi(varargin(i),'maxit')
          maxit = varargin(i+1);
        end
      end
      [E,C] = EIA_FIPPI(data,p,maxit);
    else
      [E,C] = EIA_FIPPI(data);
    end
  case 'atgp' then
    disp('ATGP algorithm');
    if rhs > 0
      p = []; maxit = [];
      for i=1:2:rhs
        if strcmpi(varargin(i),'p')
          p = varargin(i+1);
        elseif strcmpi(varargin(i),'maxit')
          maxit = varargin(i+1);
        end
      end
      [E,C] = EIA_ATGP(data,p,maxit);
    else
      [E,C] = EIA_ATGP(data);
    end
  else // ILSIA
    disp('ILSIA algorithm');
    if rhs > 0
      alpha = [];
      for i=1:2:rhs
        if strcmpi(varargin(i),'alpha')
          alpha = varargin(i+1);
          break;
        end
      end
      [E,C] = EIA_ILSIA(data,alpha);
    else
      [E,C] = EIA_ILSIA(data);
    end
  end
  
  //// Reshape C
  if size(C) > 0
    C = reshape(c,p,m,n);
  end
  
endfunction