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// $Id$ |
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/* |
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Copyright (C) 2004 Jari Häkkinen |
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Copyright (C) 2005 Peter Johansson |
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Copyright (C) 2006 Jari Häkkinen |
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This file is part of the thep c++ tools library, |
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http://lev.thep.lu.se/trac/c++_tools |
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|
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The c++ tools library is free software; you can redistribute it |
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and/or modify it under the terms of the GNU General Public License |
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as published by the Free Software Foundation; either version 3 of |
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the License, or (at your option) any later version. |
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|
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The c++ tools library is distributed in the hope that it will be |
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useful, but WITHOUT ANY WARRANTY; without even the implied warranty |
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of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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General Public License for more details. |
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|
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You should have received a copy of the GNU General Public License |
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along with WeNNI. If not, see <http://www.gnu.org/licenses/>. |
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*/ |
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#include <c++_tools/utility/kNNI.h> |
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#include <algorithm> |
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#include <cmath> |
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#include <fstream> |
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#include <vector> |
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#include <c++_tools/utility/stl_utility.h> |
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namespace theplu { |
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namespace utility { |
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kNNI::kNNI(const gslapi::matrix& matrix,const gslapi::matrix& flag, |
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const unsigned int neighbours) |
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: NNI(matrix,flag,neighbours) |
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{ |
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for (unsigned int i=0; i<weight_.rows(); i++) |
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for (unsigned int j=0; j<weight_.columns(); j++) |
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if (!weight_(i,j)) { |
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mv_rows_.push_back(i); |
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break; |
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} |
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//estimate(); |
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} |
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// \hat{x_{ij}}=\frac{ \sum_{k=1,N} \frac{x_{kj}}{d_{ki}} } |
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// { \sum_{k=1,N} \frac{1 }{d_{ki}} }, |
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// where N is defined in the paper cited in the NNI class definition |
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// documentation. |
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unsigned int kNNI::estimate(void) |
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{ |
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using namespace std; |
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for (unsigned int i=0; i<mv_rows_.size(); i++) { |
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// Jari, avoid copying in next line |
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vector<pair<unsigned int,double> > distance=calculate_distances(mv_rows_[i]); |
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sort(distance.begin(),distance.end(), |
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pair_value_compare<unsigned int,double>()); |
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for (unsigned int j=0; j<data_.columns(); j++) |
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if (!weight_(mv_rows_[i],j)) { |
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vector<unsigned int> knn=nearest_neighbours(j,distance); |
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double new_value=0.0; |
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double norm=0.0; |
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for (vector<unsigned int>::const_iterator k=knn.begin(); k!=knn.end(); k++){ |
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// Jari, a small number needed here, use something standardized. |
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// Avoid division with zero (perfect match vectors) |
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double d=(distance[*k].second ? distance[*k].second : 1e-10); |
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new_value+=data_(distance[*k].first,j)/d; |
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norm+=1.0/d; |
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} |
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// No impute if no contributions from neighbours. |
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if (norm) |
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imputed_data_(mv_rows_[i],j)=new_value/norm; |
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else { |
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not_imputed_.push_back(i); |
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// if norm is zero for one column it is zero for all columns |
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// having zero weight |
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break; |
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} |
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} |
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} |
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return not_imputed_.size(); |
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} |
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}} // of namespace utility and namespace theplu |