C++ matrix_type::col方法代码示例

        value_type iterate( matrix_type const& initial_matrix, matrix_type& result_matrix )
        {
            triple_homotopy_fitting<value_type> thf{ug_size};

            size_type const tilt_number = diag_matrix.row();

            matrix_type intensity{ intensity_matrix.col(), 1 };

            for ( size_type index = 0; index != tilt_number; ++index )
            {
                std::copy( intensity_matrix.row_begin(index), intensity_matrix.row_end(index), intensity.col_begin(0) );

                //TODO -- optimizaton here
                thf.register_entry( ar, 
                                    //C1 approximation
                                    alpha(progress_ratio), make_coefficient_matrix( thickness, diag_matrix.row_begin(index), diag_matrix.row_end(index), column_index ),
                                    //C/2 * C/2 approximation
                                    beta(progress_ratio), make_coefficient_matrix( thickness/2.0, diag_matrix.row_begin(index), diag_matrix.row_end(index) ), expm( make_structure_matrix(ar, initial_matrix, diag_matrix.row_begin(index), diag_matrix.row_end(index) ), thickness/2.0, column_index ),
                                    //standard expm
                                    gamma(progress_ratio), make_scattering_matrix( ar, initial_matrix, diag_matrix.row_begin(index), diag_matrix.row_end(index), thickness, column_index ),
                                    intensity, column_index );
            }

            result_matrix.resize( ug_size, 1 );
            value_type const residual = thf.output( result_matrix.begin() );
            /*
            std::cout << "\n current residual is " << residual << "\n"; 
            std::cout << "\n current ug is \n" << result_matrix.transpose() << "\n"; 
            */

            return residual;
        }

    const complex_matrix_type make_ug( const matrix_type& G, const matrix_type& A, const matrix_type& D ) const
    {
        assert( G.col() == 3 );
        assert( A.col() == 3 );
        assert( D.col() == 1 );
        assert( A.row() == D.row() );
        auto const M = make_matrix();
        auto const S = G * ( M.inverse() );
        matrix_type s( 1, S.row() );

        for ( size_type i = 0; i < S.row(); ++ i )
        {
            s[0][i] = value_type( 0.5 ) * std::sqrt( std::inner_product( S.row_begin( i ), S.row_end( i ), S.row_begin( i ), value_type( 0 ) ) );
        }

        auto const piomega =  3.141592553590 * feng::inner_product( array_type( M[0][0], M[1][0], M[2][0] ), 
                                                                    feng::cross_product( array_type( M[0][1], M[1][1], M[2][1] ), array_type( M[0][2], M[1][2], M[2][2] ) ) );
        auto const atomcellfacte = make_gaussian_electron( s, v0 );
        const complex_matrix_type dwss = D * feng::pow( s, value_type( 2 ) );
        const complex_matrix_type piag = A * G.transpose();
        auto fact = feng::exp( - dwss - piag * complex_type( 0, 6.2831853071796 ) );
        std::transform( fact.begin(), fact.end(), atomcellfacte.begin(), fact.begin(), [piomega]( const complex_type f,  const value_type a )
        {
            return f * a / piomega;
        } );
        complex_matrix_type Ug( fact.col(), 1 );

        for ( size_type i = 0; i < fact.col(); ++i )
        {
            Ug[i][0] = std::accumulate( fact.col_begin( i ), fact.col_end( i ), complex_type() );
            //if ( std::abs(Ug[i][0].real()) < 1.0e-8 ) Ug[i][0].real(0);
            //if ( std::abs(Ug[i][0].imag()) < 1.0e-8 ) Ug[i][0].imag(0);
        }

        return Ug;
    }

        void register_entry(    size_matrix_type const& ar, 
                                value_type alpha, complex_matrix_type const& lhs_matrix, complex_matrix_type const& rhs_matrix, 
                                value_type beta, complex_matrix_type const& expm_matrix, 
                                matrix_type const& intensity, size_type const column_index = 0 )
        {
            assert( ar.row() == ar.col() );
            assert( ar.row() == lhs_matrix.row() );
            assert( lhs_matrix.row() == lhs_matrix.col() );
            assert( ar.row() == rhs_matrix.row() );
            assert( ar.row() == intensity.row() );
            assert( 1 == intensity.col() );
            assert( (*(std::max_element(ar.begin(), ar.end()))) < ug_size );
            assert( alpha >= value_type{0} );
            assert( beta >= value_type{0} );
            assert( alpha <= value_type{1} );
            assert( beta <= value_type{1} );
            assert( std::abs(alpha+beta-value_type{1}) < value_type{ 1.0e-10} );
            //assert( c1_matrix.row() == ar.row() );
            //assert( c1_matrix.col() == 1 );
            assert( expm_matrix.row() == ar.row() );
            assert( expm_matrix.col() == 1 );
            assert( column_index < ar.row() );

            size_type const n = ar.row();
            size_type const m = ug_size;

            matrix_type real_part(m, 1);
            matrix_type imag_part(m, 1);

            value_type norm_factor{0};
            //norm only one column
            //std::for_each( expm_matrix.col_begin( column_index ), expm_matrix.col_end( column_index ), [&norm_factor]( complex_type const& c ){ norm_factor += std::norm(c); } );
            std::for_each( expm_matrix.begin(), expm_matrix.end(), [&norm_factor]( complex_type const& c ){ norm_factor += std::norm(c); } );
            norm_factor /= static_cast<value_type>( expm_matrix.row() );

            for ( size_type r = 0; r != ar.row(); ++r )
            {
                //for \beta C/2 C/2 part
                extract_inner_product_coefficients( m, n, ar.row_begin(r), lhs_matrix.row_begin(r), rhs_matrix.col_begin(column_index), real_part.begin(), imag_part.begin() );
                real_part *= alpha;
                imag_part *= alpha;

                //for \gamma E part
                real_part[0][0] += beta * std::real( expm_matrix[r][column_index] );
                imag_part[0][0] += beta * std::imag( expm_matrix[r][column_index] );
                //real_part[0][0] += beta * std::real( expm_matrix[r][column_index] ) / norm_factor;
                //imag_part[0][0] += beta * std::imag( expm_matrix[r][column_index] ) / norm_factor;

                //needs modifying here
                dsm.register_entry( intensity[r][0], real_part.begin(), imag_part.begin() );
            }

#if 0
            //register lambda, ensuring lambda to be 1
            std::fill( real_part.begin(), real_part.end(), value_type{} );
            value_type const factor = value_type{1.0};
            value_type const weigh = factor * std::sqrt( static_cast<value_type>( intensity.row() ) );
            real_part[0][0] = weigh;
            imag_part[0][0] = weigh;
            dsm.register_entry( value_type{2} * weigh * weigh, real_part.begin(), imag_part.begin() );
#endif
        }

void run()
{

  // Read filenames
  vector<std::string> filenames;
  {
    std::string folder = Path::dirname (argument[0]);
    std::ifstream ifs (argument[0].c_str());
    std::string temp;
    while (getline (ifs, temp)) {
      std::string filename (Path::join (folder, temp));
      size_t p = filename.find_last_not_of(" \t");
      if (std::string::npos != p)
        filename.erase(p+1);
      if (filename.size()) {
        if (!MR::Path::exists (filename))
          throw Exception ("Input connectome file not found: \"" + filename + "\"");
        filenames.push_back (filename);
      }
    }
  }

  const MR::Connectome::matrix_type example_connectome = load_matrix (filenames.front());
  if (example_connectome.rows() != example_connectome.cols())
    throw Exception ("Connectome of first subject is not square (" + str(example_connectome.rows()) + " x " + str(example_connectome.cols()) + ")");
  const MR::Connectome::node_t num_nodes = example_connectome.rows();

  // Initialise enhancement algorithm
  std::shared_ptr<Stats::EnhancerBase> enhancer;
  switch (int(argument[1])) {
    case 0: {
      auto opt = get_options ("threshold");
      if (!opt.size())
        throw Exception ("For NBS algorithm, -threshold option must be provided");
      enhancer.reset (new MR::Connectome::Enhance::NBS (num_nodes, opt[0][0]));
      }
      break;
    case 1: {
      std::shared_ptr<Stats::TFCE::EnhancerBase> base (new MR::Connectome::Enhance::NBS (num_nodes));
      enhancer.reset (new Stats::TFCE::Wrapper (base));
      load_tfce_parameters (*(dynamic_cast<Stats::TFCE::Wrapper*>(enhancer.get())));
      if (get_options ("threshold").size())
        WARN (std::string (argument[1]) + " is a threshold-free algorithm; -threshold option ignored");
      }
      break;
    case 2: {
      enhancer.reset (new MR::Connectome::Enhance::PassThrough());
      if (get_options ("threshold").size())
        WARN ("No enhancement algorithm being used; -threshold option ignored");
      }
      break;
    default:
      throw Exception ("Unknown enhancement algorithm");
  }

  size_t num_perms = get_option_value ("nperms", DEFAULT_NUMBER_PERMUTATIONS);
  const bool do_nonstationary_adjustment = get_options ("nonstationary").size();
  size_t nperms_nonstationary = get_option_value ("nperms_nonstationarity", DEFAULT_NUMBER_PERMUTATIONS_NONSTATIONARITY);

  // Load design matrix
  const matrix_type design = load_matrix (argument[2]);
  if (size_t(design.rows()) != filenames.size())
    throw Exception ("number of subjects does not match number of rows in design matrix");

  // Load permutations file if supplied
  auto opt = get_options("permutations");
  vector<vector<size_t> > permutations;
  if (opt.size()) {
    permutations = Math::Stats::Permutation::load_permutations_file (opt[0][0]);
    num_perms = permutations.size();
    if (permutations[0].size() != (size_t)design.rows())
      throw Exception ("number of rows in the permutations file (" + str(opt[0][0]) + ") does not match number of rows in design matrix");
  }

  // Load non-stationary correction permutations file if supplied
  opt = get_options("permutations_nonstationary");
  vector<vector<size_t> > permutations_nonstationary;
  if (opt.size()) {
    permutations_nonstationary = Math::Stats::Permutation::load_permutations_file (opt[0][0]);
    nperms_nonstationary = permutations.size();
    if (permutations_nonstationary[0].size() != (size_t)design.rows())
      throw Exception ("number of rows in the nonstationary permutations file (" + str(opt[0][0]) + ") does not match number of rows in design matrix");
  }


  // Load contrast matrix
  matrix_type contrast = load_matrix (argument[3]);
  if (contrast.cols() > design.cols())
    throw Exception ("too many contrasts for design matrix");
  contrast.conservativeResize (contrast.rows(), design.cols());

  const std::string output_prefix = argument[4];

  // Load input data
  // For compatibility with existing statistics code, symmetric matrix data is adjusted
  //   into vector form - one row per edge in the symmetric connectome. The Mat2Vec class
  //   deals with the re-ordering of matrix data into this form.
  MR::Connectome::Mat2Vec mat2vec (num_nodes);
  const size_t num_edges = mat2vec.vec_size();
  matrix_type data (num_edges, filenames.size());
//.........这里部分代码省略.........

        void fit()
        {

            std::cerr << "\nbefore the fit, thickness is set to be " << thickness << "\n";

            assert( ug_size );
            assert( ar_dim );
            assert( column_index < ar_dim );
            assert( std::abs(std::real(thickness)) < 1.0e-10 );
            assert( std::imag(thickness) > 1.0e-10 );
            assert( diag_matrix.col() == ar_dim );
            assert( diag_matrix.row() == intensity_matrix.row() );
            assert( intensity_matrix.col() == ar_dim );
            assert( initial_ug.row() == ug_size );
            assert( initial_ug.col() == 1 );
            assert( ar.row() == ar.col() );
            assert( ar_dim == ar.row() );
            assert( progress_ratio >= value_type{0} );
            assert( progress_ratio <= value_type{1} );
            assert( alpha );
            assert( beta );
            assert( gamma );

            new_residual = iterate( initial_ug, new_ug );

            matrix_type second_ug{ initial_ug };

            size_type current_iteration = 0;

            matrix_vector_type  vm;
            vector_type         vr;

            vm.push_back( new_ug );
            vr.push_back( new_residual );

            value_type best_residual_so_far = new_residual;

            while ( true )
            {
                value_type const second_residual = iterate( new_ug, second_ug );

                bool break_flag = false;

                //??
                if ( best_residual_so_far > max_iteration * second_residual ) break_flag = true;

                best_residual_so_far = std::min( second_residual, best_residual_so_far );

                if( ++current_iteration  > max_iteration ) break_flag = true;

                new_ug.swap( second_ug );
                new_residual = second_residual;

                vm.push_back( new_ug );
                vr.push_back( new_residual );

                if ( break_flag ) break;
            }

            size_type const elite_index = std::distance( vr.begin(), std::min_element( vr.begin(), vr.end() ) );
            std::copy( vm[elite_index].begin(), vm[elite_index].end(), new_ug.begin() );
            
            //std::cout << "\ncurrent elite residual is " << vr[elite_index] << ", at iteration " << current_iteration <<  std::endl;
        }