/*=========================================================================

  Program:   C3D: Command-line companion tool to ITK-SNAP
  Module:    VoxelwiseRegression.cxx
  Language:  C++
  Website:   itksnap.org/c3d
  Copyright (c) 2014 Paul A. Yushkevich
  
  This file is part of C3D, a command-line companion tool to ITK-SNAP

  C3D 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.

  This program 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 this program.  If not, see <http://www.gnu.org/licenses/>.

=========================================================================*/

#include "VoxelwiseRegression.h"
#include "vnl/vnl_file_matrix.h"
#include "vnl/vnl_rank.h"
#include "vnl/algo/vnl_matrix_inverse.h"

template <class TPixel, unsigned int VDim>
void
VoxelwiseRegression<TPixel, VDim>
::operator() (size_t order)
{
  // Two images are needed from the stack
  ImagePointer X = c->m_ImageStack[c->m_ImageStack.size() - 1];
  ImagePointer Y = c->m_ImageStack[c->m_ImageStack.size() - 2];

  // Build the design matrix and observation matrix
  size_t n = X->GetBufferedRegion().GetNumberOfPixels();
  vnl_matrix<double> design(n, order), observ(n, 1);
  TPixel *px = X->GetBufferPointer(); TPixel *py = Y->GetBufferPointer();
  for(size_t i = 0; i < n; i++, px++, py++)
    {
    TPixel vx = *px, vxj = 1.0;
    for(size_t j = 0; j < order; j++)
      {
      design(i,j) = vxj;
      vxj *= vx;
      }
    observ(i, 0) = *py;
    }

  // Execute GLM on the two matrices
  size_t rank = vnl_rank(design, vnl_rank_row);

  // Compute A
  vnl_matrix<double> A = 
    vnl_matrix_inverse<double>(design.transpose() * design).pinverse(rank);

  // Compute bhat = Ax * Y
  vnl_matrix<double> bhat = (A * design.transpose()) * observ;

  // We should report R^2, etc.
  for(size_t j = 0; j < order; j++)
    c->sout() << "REGCOEFF[" << j << "] = " << bhat(j,0) << endl;
}

// Invocations
template class VoxelwiseRegression<double, 2>;
template class VoxelwiseRegression<double, 3>;
template class VoxelwiseRegression<double, 4>;
