SimpleNearestNeighbors.h

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//===========================================================================
/*!
 * 
 *
 * \brief       Efficient brute force implementation of nearest neighbors.
 * 
 * 
 *
 * \author      O.Krause
 * \date        2012
 *
 *
 * \par Copyright 1995-2017 Shark Development Team
 * 
 * <BR><HR>
 * This file is part of Shark.
 * <https://shark-ml.github.io/Shark/>
 * 
 * Shark is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as published 
 * by the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 * 
 * Shark 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 Lesser General Public License for more details.
 * 
 * You should have received a copy of the GNU Lesser General Public License
 * along with Shark.  If not, see <http://www.gnu.org/licenses/>.
 *
 */
//===========================================================================
 
#ifndef SHARK_ALGORITHMS_NEARESTNEIGHBORS_SIMPLENEARESTNEIGHBORS_H
#define SHARK_ALGORITHMS_NEARESTNEIGHBORS_SIMPLENEARESTNEIGHBORS_H
 
#include <shark/Algorithms/NearestNeighbors/AbstractNearestNeighbors.h>
#include <shark/Models/Kernels/AbstractMetric.h>
#include <shark/Core/OpenMP.h>
#include <algorithm>
 
 
namespace shark {
 
///\brief Brute force optimized nearest neighbor implementation
///
///Returns the labels and distances of the k nearest neighbors of a point 
/// The distance is measured using an arbitrary metric
template<class InputType, class LabelType>
class SimpleNearestNeighbors:public AbstractNearestNeighbors<InputType,LabelType>{
private:
    typedef AbstractNearestNeighbors<InputType,LabelType> base_type;
public:
    typedef LabeledData<InputType, LabelType> Dataset;
    typedef AbstractMetric<InputType> Metric;
    typedef typename base_type::DistancePair DistancePair;
    typedef typename Batch<InputType>::type BatchInputType;
 
    /// \brief Constructor.
    ///
    /// \par Construct a "brute force" nearest neighbors search algorithm
    /// from data and a metric. Refer to the AbstractMetric class for details.
    /// The "default" Euclidean metric is realized by providing a pointer to
    /// an object of type LinearKernel<InputType>.
    SimpleNearestNeighbors(Dataset const& dataset, Metric const* metric)
    :m_dataset(dataset), mep_metric(metric){
        this->m_inputShape=dataset.inputShape();
    }
 
    ///\brief Return the k nearest neighbors of the query point.
    std::vector<DistancePair> getNeighbors(BatchInputType const& patterns, std::size_t k)const{
        std::size_t numPatterns = batchSize(patterns);
        std::size_t maxThreads = std::min(SHARK_NUM_THREADS,m_dataset.numberOfBatches());
        //heaps of key value pairs (distance,classlabel). One heap for every pattern and thread.
        //For memory alignment reasons, all heaps are stored in one continuous array
        //the heaps are stored such, that for every pattern the heaps for every thread
        //are forming one memory area. so later we can just merge all 4 heaps using make_heap
        //be aware that the values created here allready form a heap since they are all
        //identical maximum distance.
        std::vector<DistancePair> heaps(k*numPatterns*maxThreads,DistancePair(std::numeric_limits<double>::max(),LabelType()));
        typedef typename std::vector<DistancePair>::iterator iterator;
        //iterate over all batches of the training set in parallel and let
        //every thread do a KNN-Search on it's subset of data
        SHARK_PARALLEL_FOR(int b = 0; b < (int)m_dataset.numberOfBatches(); ++b){
            //evaluate distances between the points of the patterns and the batch
            RealMatrix distances=mep_metric->featureDistanceSqr(patterns,m_dataset.batch(b).input);
            
            //now update the heaps with the distances
            for(std::size_t p = 0; p != numPatterns; ++p){
                std::size_t batchSize = distances.size2();
                
                //get current heap
                std::size_t heap = p*maxThreads+SHARK_THREAD_NUM;
                iterator heapStart=heaps.begin()+heap*k;
                iterator heapEnd=heapStart+k;
                iterator biggest=heapEnd-1;//position of biggest element
                
                //update heap values using the new distances
                for(std::size_t i = 0; i != batchSize; ++i){
                    if(biggest->key >= distances(p,i)){
                        //push the smaller neighbor in the heap and replace the biggest one
                        biggest->key=distances(p,i);
                        biggest->value=getBatchElement(m_dataset.batch(b).label,i);
                        std::push_heap(heapStart,heapEnd);
                        //pop biggest element, so that 
                        //biggest is again the biggest element
                        std::pop_heap(heapStart,heapEnd);
                    }
                }
            }
        }
        std::vector<DistancePair> results(k*numPatterns);
        //finally, we merge all threads in one heap which has the inverse ordering
        //and create a class histogram over the smallest k neighbors
        //std::cout<<"info "<<numPatterns<<" "<<maxThreads<<" "<<k<<std::endl;
        SHARK_PARALLEL_FOR(int p = 0; p < (int)numPatterns; ++p){
            //find range of the heaps for all threads
            iterator heapStart=heaps.begin()+p*maxThreads*k;
            iterator heapEnd=heapStart+maxThreads*k;
            iterator neighborEnd=heapEnd-k;
            iterator smallest=heapEnd-1;//position of biggest element
            //create one single heap of the range with inverse ordering
            //takes O(maxThreads*k)
            std::make_heap(heapStart,heapEnd,std::greater<DistancePair>());
            
            //create histogram from the neighbors
            for(std::size_t i = 0;heapEnd!=neighborEnd;--heapEnd,--smallest,++i){
                std::pop_heap(heapStart,heapEnd,std::greater<DistancePair>());
                results[i+p*k].key = smallest->key;
                results[i+p*k].value = smallest->value; 
            }
        }
        return results;
    }
 
    /// \brief Direct access to the underlying data set of nearest neighbor points.
    LabeledData<InputType,LabelType>const& dataset()const {
        return m_dataset;
    }
 
private:
    Dataset m_dataset;                        ///< data set of nearest neighbor points
    Metric const* mep_metric;                 ///< metric for measuring distances, usually given by a kernel function
};
 
 
}
#endif