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vigra/random_forest/rf_split.hxx |

    00001 /************************************************************************/
    00002 /*                                                                      */
    00003 /*        Copyright 2008-2009 by  Ullrich Koethe and Rahul Nair         */
    00004 /*                                                                      */
    00005 /*    This file is part of the VIGRA computer vision library.           */
    00006 /*    The VIGRA Website is                                              */
    00007 /*        http://hci.iwr.uni-heidelberg.de/vigra/                       */
    00008 /*    Please direct questions, bug reports, and contributions to        */
    00009 /*        ullrich.koethe@iwr.uni-heidelberg.de    or                    */
    00010 /*        vigra@informatik.uni-hamburg.de                               */
    00011 /*                                                                      */
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    00019 /*    conditions:                                                       */
    00020 /*                                                                      */
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    00023 /*    Software.                                                         */
    00024 /*                                                                      */
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    00033 /*                                                                      */
    00034 /************************************************************************/
    00035 #ifndef VIGRA_RANDOM_FOREST_SPLIT_HXX
    00036 #define VIGRA_RANDOM_FOREST_SPLIT_HXX
    00037 #include <algorithm>
    00038 #include <cstddef>
    00039 #include <map>
    00040 #include <numeric>
    00041 #include <math.h>
    00042 #include "../mathutil.hxx"
    00043 #include "../array_vector.hxx"
    00044 #include "../sized_int.hxx"
    00045 #include "../matrix.hxx"
    00046 #include "../random.hxx"
    00047 #include "../functorexpression.hxx"
    00048 #include "rf_nodeproxy.hxx"
    00049 //#include "rf_sampling.hxx"
    00050 #include "rf_region.hxx"
    00051 //#include "../hokashyap.hxx"
    00052 //#include "vigra/rf_helpers.hxx"
    00053
    00054 namespace vigra
    00055 {
    00056
    00057 // Incomplete Class to ensure that findBestSplit is always implemented in
    00058 // the derived classes of SplitBase
    00059 class CompileTimeError;
    00060
    00061
    00062 namespace detail
    00063 {
    00064     template<class Tag>
    00065     class Normalise
    00066     {
    00067       public:
    00068         template<class Iter>
    00069         static void exec(Iter begin, Iter  end)
    00070         {}
    00071     };
    00072
    00073     template<>
    00074     class Normalise<ClassificationTag>
    00075     {
    00076       public:
    00077         template<class Iter>
    00078         static void exec (Iter begin, Iter end)
    00079         {
    00080             double bla = std::accumulate(begin, end, 0.0);
    00081             for(int ii = 0; ii < end - begin; ++ii)
    00082                 begin[ii] = begin[ii]/bla ;
    00083         }
    00084     };
    00085 }
    00086
    00087
    00088 /** Base Class for all SplitFunctors used with the \ref RandomForest class
    00089     defines the interface used while learning a tree.
    00090 **/
    00091 template<class Tag>
    00092 class SplitBase
    00093 {
    00094   public:
    00095
    00096     typedef Tag           RF_Tag;
    00097     typedef DT_StackEntry<ArrayVectorView<Int32>::iterator>
    00098                                         StackEntry_t;
    00099
    00100     ProblemSpec<>           ext_param_;
    00101
    00102     NodeBase::T_Container_type          t_data;
    00103     NodeBase::P_Container_type          p_data;
    00104
    00105     NodeBase                            node_;
    00106
    00107     /** returns the DecisionTree Node created by
    00108         \ref SplitBase::findBestSplit() or \ref SplitBase::makeTerminalNode().
    00109     **/
    00110
    00111     template<class T>
    00112     void set_external_parameters(ProblemSpec<T> const & in)
    00113     {
    00114         ext_param_ = in;
    00115         t_data.push_back(in.column_count_);
    00116         t_data.push_back(in.class_count_);
    00117     }
    00118
    00119     NodeBase & createNode()
    00120     {
    00121         return node_;
    00122     }
    00123
    00124     int classCount() const
    00125     {
    00126         return int(t_data[1]);
    00127     }
    00128
    00129     int featureCount() const
    00130     {
    00131         return int(t_data[0]);
    00132     }
    00133
    00134     /** resets internal data. Should always be called before
    00135         calling findBestSplit or makeTerminalNode
    00136     **/
    00137     void reset()
    00138     {
    00139         t_data.resize(2);
    00140         p_data.resize(0);
    00141     }
    00142
    00143
    00144     /** findBestSplit has to be re-implemented in derived split functor.
    00145         The defaut implementation only insures that a CompileTime error is issued
    00146         if no such method was defined.
    00147     **/
    00148
    00149     template<class T, class C, class T2, class C2, class Region, class Random>
    00150     int findBestSplit(MultiArrayView<2, T, C> features,
    00151                       MultiArrayView<2, T2, C2> labels,
    00152                       Region region,
    00153                       ArrayVector<Region> childs,
    00154                       Random randint)
    00155     {
    00156 #ifndef __clang__
    00157         // FIXME: This compile-time checking trick does not work for clang.
    00158         CompileTimeError SplitFunctor__findBestSplit_member_was_not_defined;
    00159 #endif
    00160         return 0;
    00161     }
    00162
    00163     /** Default action for creating a terminal Node.
    00164         sets the Class probability of the remaining region according to
    00165         the class histogram
    00166     **/
    00167     template<class T, class C, class T2,class C2, class Region, class Random>
    00168     int makeTerminalNode(MultiArrayView<2, T, C> features,
    00169                       MultiArrayView<2, T2, C2>  labels,
    00170                       Region &                   region,
    00171                       Random                     randint)
    00172     {
    00173         Node<e_ConstProbNode> ret(t_data, p_data);
    00174         node_ = ret;
    00175         if(ext_param_.class_weights_.size() != region.classCounts().size())
    00176         {
    00177             std::copy(region.classCounts().begin(),
    00178                       region.classCounts().end(),
    00179                       ret.prob_begin());
    00180         }
    00181         else
    00182         {
    00183             std::transform(region.classCounts().begin(),
    00184                            region.classCounts().end(),
    00185                            ext_param_.class_weights_.begin(),
    00186                            ret.prob_begin(), std::multiplies<double>());
    00187         }
    00188         detail::Normalise<RF_Tag>::exec(ret.prob_begin(), ret.prob_end());
    00189 //        std::copy(ret.prob_begin(), ret.prob_end(), std::ostream_iterator<double>(std::cerr, ", " ));
    00190 //        std::cerr << std::endl;
    00191         ret.weights() = region.size();
    00192         return e_ConstProbNode;
    00193     }
    00194
    00195
    00196 };
    00197
    00198 /** Functor to sort the indices of a feature Matrix by a certain dimension
    00199 **/
    00200 template<class DataMatrix>
    00201 class SortSamplesByDimensions
    00202 {
    00203     DataMatrix const & data_;
    00204     MultiArrayIndex sortColumn_;
    00205     double thresVal_;
    00206   public:
    00207
    00208     SortSamplesByDimensions(DataMatrix const & data,
    00209                             MultiArrayIndex sortColumn,
    00210                             double thresVal = 0.0)
    00211     : data_(data),
    00212       sortColumn_(sortColumn),
    00213       thresVal_(thresVal)
    00214     {}
    00215
    00216     void setColumn(MultiArrayIndex sortColumn)
    00217     {
    00218         sortColumn_ = sortColumn;
    00219     }
    00220     void setThreshold(double value)
    00221     {
    00222         thresVal_ = value;
    00223     }
    00224
    00225     bool operator()(MultiArrayIndex l, MultiArrayIndex r) const
    00226     {
    00227         return data_(l, sortColumn_) < data_(r, sortColumn_);
    00228     }
    00229     bool operator()(MultiArrayIndex l) const
    00230     {
    00231         return data_(l, sortColumn_) < thresVal_;
    00232     }
    00233 };
    00234
    00235 template<class DataMatrix>
    00236 class DimensionNotEqual
    00237 {
    00238     DataMatrix const & data_;
    00239     MultiArrayIndex sortColumn_;
    00240
    00241   public:
    00242
    00243     DimensionNotEqual(DataMatrix const & data,
    00244                             MultiArrayIndex sortColumn)
    00245     : data_(data),
    00246       sortColumn_(sortColumn)
    00247     {}
    00248
    00249     void setColumn(MultiArrayIndex sortColumn)
    00250     {
    00251         sortColumn_ = sortColumn;
    00252     }
    00253
    00254     bool operator()(MultiArrayIndex l, MultiArrayIndex r) const
    00255     {
    00256         return data_(l, sortColumn_) != data_(r, sortColumn_);
    00257     }
    00258 };
    00259
    00260 template<class DataMatrix>
    00261 class SortSamplesByHyperplane
    00262 {
    00263     DataMatrix const & data_;
    00264     Node<i_HyperplaneNode> const & node_;
    00265
    00266   public:
    00267
    00268     SortSamplesByHyperplane(DataMatrix              const & data,
    00269                             Node<i_HyperplaneNode>  const & node)
    00270     :
    00271             data_(data),
    00272             node_(node)
    00273     {}
    00274
    00275     /** calculate the distance of a sample point to a hyperplane
    00276      */
    00277     double operator[](MultiArrayIndex l) const
    00278     {
    00279         double result_l = -1 * node_.intercept();
    00280         for(int ii = 0; ii < node_.columns_size(); ++ii)
    00281         {
    00282             result_l +=     rowVector(data_, l)[node_.columns_begin()[ii]]
    00283                         *   node_.weights()[ii];
    00284         }
    00285         return result_l;
    00286     }
    00287
    00288     bool operator()(MultiArrayIndex l, MultiArrayIndex r) const
    00289     {
    00290         return (*this)[l]  < (*this)[r];
    00291     }
    00292
    00293 };
    00294
    00295 /** makes a Class Histogram given indices in a labels_ array
    00296  *  usage:
    00297  *      MultiArrayView<2, T2, C2> labels = makeSomeLabels()
    00298  *      ArrayVector<int> hist(numberOfLabels(labels), 0);
    00299  *      RandomForestClassCounter<T2, C2, ArrayVector> counter(labels, hist);
    00300  *
    00301  *      Container<int> indices = getSomeIndices()
    00302  *      std::for_each(indices, counter);
    00303  */
    00304 template <class DataSource, class CountArray>
    00305 class RandomForestClassCounter
    00306 {
    00307     DataSource  const &     labels_;
    00308     CountArray        &     counts_;
    00309
    00310   public:
    00311
    00312     RandomForestClassCounter(DataSource  const & labels,
    00313                              CountArray & counts)
    00314     : labels_(labels),
    00315       counts_(counts)
    00316     {
    00317         reset();
    00318     }
    00319
    00320     void reset()
    00321     {
    00322         counts_.init(0);
    00323     }
    00324
    00325     void operator()(MultiArrayIndex l) const
    00326     {
    00327         counts_[labels_[l]] +=1;
    00328     }
    00329 };
    00330
    00331
    00332 /** Functor To Calculate the Best possible Split Based on the Gini Index
    00333     given Labels and Features along a given Axis
    00334 */
    00335
    00336 namespace detail
    00337 {
    00338     template<int N>
    00339     class ConstArr
    00340     {
    00341     public:
    00342         double operator[](size_t) const
    00343         {
    00344             return (double)N;
    00345         }
    00346     };
    00347
    00348
    00349 }
    00350
    00351
    00352
    00353
    00354 /** Functor to calculate the entropy based impurity
    00355  */
    00356 class EntropyCriterion
    00357 {
    00358 public:
    00359     /**calculate the weighted gini impurity based on class histogram
    00360      * and class weights
    00361      */
    00362     template<class Array, class Array2>
    00363     double operator()        (Array     const & hist,
    00364                               Array2    const & weights,
    00365                               double            total = 1.0) const
    00366     {
    00367         return impurity(hist, weights, total);
    00368     }
    00369
    00370     /** calculate the gini based impurity based on class histogram
    00371      */
    00372     template<class Array>
    00373     double operator()(Array const & hist, double total = 1.0) const
    00374     {
    00375         return impurity(hist, total);
    00376     }
    00377
    00378     /** static version of operator(hist total)
    00379      */
    00380     template<class Array>
    00381     static double impurity(Array const & hist, double total)
    00382     {
    00383         return impurity(hist, detail::ConstArr<1>(), total);
    00384     }
    00385
    00386     /** static version of operator(hist, weights, total)
    00387      */
    00388     template<class Array, class Array2>
    00389     static double impurity   (Array     const & hist,
    00390                               Array2    const & weights,
    00391                               double            total)
    00392     {
    00393
    00394         int     class_count     = hist.size();
    00395         double  entropy            = 0.0;
    00396         if(class_count == 2)
    00397         {
    00398             double p0           = (hist[0]/total);
    00399             double p1           = (hist[1]/total);
    00400             entropy             = 0 - weights[0]*p0*std::log(p0) - weights[1]*p1*std::log(p1);
    00401         }
    00402         else
    00403         {
    00404             for(int ii = 0; ii < class_count; ++ii)
    00405             {
    00406                 double w        = weights[ii];
    00407                 double pii      = hist[ii]/total;
    00408                 entropy         -= w*( pii*std::log(pii));
    00409             }
    00410         }
    00411         entropy             = total * entropy;
    00412         return entropy;
    00413     }
    00414 };
    00415
    00416 /** Functor to calculate the gini impurity
    00417  */
    00418 class GiniCriterion
    00419 {
    00420 public:
    00421     /**calculate the weighted gini impurity based on class histogram
    00422      * and class weights
    00423      */
    00424     template<class Array, class Array2>
    00425     double operator()        (Array     const & hist,
    00426                               Array2    const & weights,
    00427                               double            total = 1.0) const
    00428     {
    00429         return impurity(hist, weights, total);
    00430     }
    00431
    00432     /** calculate the gini based impurity based on class histogram
    00433      */
    00434     template<class Array>
    00435     double operator()(Array const & hist, double total = 1.0) const
    00436     {
    00437         return impurity(hist, total);
    00438     }
    00439
    00440     /** static version of operator(hist total)
    00441      */
    00442     template<class Array>
    00443     static double impurity(Array const & hist, double total)
    00444     {
    00445         return impurity(hist, detail::ConstArr<1>(), total);
    00446     }
    00447
    00448     /** static version of operator(hist, weights, total)
    00449      */
    00450     template<class Array, class Array2>
    00451     static double impurity   (Array     const & hist,
    00452                               Array2    const & weights,
    00453                               double            total)
    00454     {
    00455
    00456         int     class_count     = hist.size();
    00457         double  gini            = 0.0;
    00458         if(class_count == 2)
    00459         {
    00460             double w            = weights[0] * weights[1];
    00461             gini                = w * (hist[0] * hist[1] / total);
    00462         }
    00463         else
    00464         {
    00465             for(int ii = 0; ii < class_count; ++ii)
    00466             {
    00467                 double w        = weights[ii];
    00468                 gini           += w*( hist[ii]*( 1.0 - w * hist[ii]/total ) );
    00469             }
    00470         }
    00471         return gini;
    00472     }
    00473 };
    00474
    00475
    00476 template <class DataSource, class Impurity= GiniCriterion>
    00477 class ImpurityLoss
    00478 {
    00479
    00480     DataSource  const &         labels_;
    00481     ArrayVector<double>        counts_;
    00482     ArrayVector<double> const  class_weights_;
    00483     double                      total_counts_;
    00484     Impurity                    impurity_;
    00485
    00486   public:
    00487
    00488     template<class T>
    00489     ImpurityLoss(DataSource  const & labels,
    00490                                 ProblemSpec<T> const & ext_)
    00491     : labels_(labels),
    00492       counts_(ext_.class_count_, 0.0),
    00493       class_weights_(ext_.class_weights_),
    00494       total_counts_(0.0)
    00495     {}
    00496
    00497     void reset()
    00498     {
    00499         counts_.init(0);
    00500         total_counts_ = 0.0;
    00501     }
    00502
    00503     template<class Counts>
    00504     double increment_histogram(Counts const & counts)
    00505     {
    00506         std::transform(counts.begin(), counts.end(),
    00507                        counts_.begin(), counts_.begin(),
    00508                        std::plus<double>());
    00509         total_counts_ = std::accumulate( counts_.begin(),
    00510                                          counts_.end(),
    00511                                          0.0);
    00512         return impurity_(counts_, class_weights_, total_counts_);
    00513     }
    00514
    00515     template<class Counts>
    00516     double decrement_histogram(Counts const & counts)
    00517     {
    00518         std::transform(counts.begin(), counts.end(),
    00519                        counts_.begin(), counts_.begin(),
    00520                        std::minus<double>());
    00521         total_counts_ = std::accumulate( counts_.begin(),
    00522                                          counts_.end(),
    00523                                          0.0);
    00524         return impurity_(counts_, class_weights_, total_counts_);
    00525     }
    00526
    00527     template<class Iter>
    00528     double increment(Iter begin, Iter end)
    00529     {
    00530         for(Iter iter = begin; iter != end; ++iter)
    00531         {
    00532             counts_[labels_(*iter, 0)] +=1.0;
    00533             total_counts_ +=1.0;
    00534         }
    00535         return impurity_(counts_, class_weights_, total_counts_);
    00536     }
    00537
    00538     template<class Iter>
    00539     double decrement(Iter const &  begin, Iter const & end)
    00540     {
    00541         for(Iter iter = begin; iter != end; ++iter)
    00542         {
    00543             counts_[labels_(*iter,0)] -=1.0;
    00544             total_counts_ -=1.0;
    00545         }
    00546         return impurity_(counts_, class_weights_, total_counts_);
    00547     }
    00548
    00549     template<class Iter, class Resp_t>
    00550     double init (Iter begin, Iter end, Resp_t resp)
    00551     {
    00552         reset();
    00553         std::copy(resp.begin(), resp.end(), counts_.begin());
    00554         total_counts_ = std::accumulate(counts_.begin(), counts_.end(), 0.0);
    00555         return impurity_(counts_,class_weights_, total_counts_);
    00556     }
    00557
    00558     ArrayVector<double> const & response()
    00559     {
    00560         return counts_;
    00561     }
    00562 };
    00563
    00564
    00565
    00566     template <class DataSource>
    00567     class RegressionForestCounter
    00568     {
    00569     public:
    00570         typedef MultiArrayShape<2>::type Shp;
    00571         DataSource const &      labels_;
    00572         ArrayVector <double>    mean_;
    00573         ArrayVector <double>    variance_;
    00574         ArrayVector <double>    tmp_;
    00575         size_t                  count_;
    00576         int*                    end_;
    00577
    00578         template<class T>
    00579         RegressionForestCounter(DataSource const & labels,
    00580                                 ProblemSpec<T> const & ext_)
    00581         :
    00582         labels_(labels),
    00583         mean_(ext_.response_size_, 0.0),
    00584         variance_(ext_.response_size_, 0.0),
    00585         tmp_(ext_.response_size_),
    00586         count_(0)
    00587         {}
    00588
    00589         template<class Iter>
    00590         double increment (Iter begin, Iter end)
    00591         {
    00592             for(Iter iter = begin; iter != end; ++iter)
    00593             {
    00594                 ++count_;
    00595                 for(unsigned int ii = 0; ii < mean_.size(); ++ii)
    00596                     tmp_[ii] = labels_(*iter, ii) - mean_[ii];
    00597                 double f  = 1.0 / count_,
    00598                 f1 = 1.0 - f;
    00599                 for(unsigned int ii = 0; ii < mean_.size(); ++ii)
    00600                     mean_[ii] += f*tmp_[ii];
    00601                 for(unsigned int ii = 0; ii < mean_.size(); ++ii)
    00602                     variance_[ii] += f1*sq(tmp_[ii]);
    00603             }
    00604             double res = std::accumulate(variance_.begin(),
    00605                                          variance_.end(),
    00606                                          0.0,
    00607                                          std::plus<double>());
    00608             //std::cerr << res << "  ) = ";
    00609             return res;
    00610         }
    00611
    00612         template<class Iter>      //This is BROKEN
    00613         double decrement (Iter begin, Iter end)
    00614         {
    00615             for(Iter iter = begin; iter != end; ++iter)
    00616             {
    00617                 --count_;
    00618             }
    00619
    00620             begin = end;
    00621             end   = end + count_;
    00622
    00623
    00624             for(unsigned int ii = 0; ii < mean_.size(); ++ii)
    00625             {
    00626                 mean_[ii] = 0;
    00627                 for(Iter iter = begin; iter != end; ++iter)
    00628                 {
    00629                     mean_[ii] += labels_(*iter, ii);
    00630                 }
    00631                 mean_[ii] /= count_;
    00632                 variance_[ii] = 0;
    00633                 for(Iter iter = begin; iter != end; ++iter)
    00634                 {
    00635                     variance_[ii] += (labels_(*iter, ii) - mean_[ii])*(labels_(*iter, ii) - mean_[ii]);
    00636                 }
    00637             }
    00638             double res = std::accumulate(variance_.begin(),
    00639                                          variance_.end(),
    00640                                          0.0,
    00641                                          std::plus<double>());
    00642             //std::cerr << res << "  ) = ";
    00643             return res;
    00644         }
    00645
    00646
    00647         template<class Iter, class Resp_t>
    00648         double init (Iter begin, Iter end, Resp_t resp)
    00649         {
    00650             reset();
    00651             return this->increment(begin, end);
    00652
    00653         }
    00654
    00655
    00656         ArrayVector<double> const & response()
    00657         {
    00658             return mean_;
    00659         }
    00660
    00661         void reset()
    00662         {
    00663             mean_.init(0.0);
    00664             variance_.init(0.0);
    00665             count_ = 0;
    00666         }
    00667     };
    00668
    00669
    00670 template <class DataSource>
    00671 class RegressionForestCounter2
    00672 {
    00673 public:
    00674     typedef MultiArrayShape<2>::type Shp;
    00675     DataSource const &      labels_;
    00676     ArrayVector <double>    mean_;
    00677     ArrayVector <double>    variance_;
    00678     ArrayVector <double>    tmp_;
    00679     size_t                  count_;
    00680
    00681     template<class T>
    00682     RegressionForestCounter2(DataSource const & labels,
    00683                             ProblemSpec<T> const & ext_)
    00684     :
    00685         labels_(labels),
    00686         mean_(ext_.response_size_, 0.0),
    00687         variance_(ext_.response_size_, 0.0),
    00688         tmp_(ext_.response_size_),
    00689         count_(0)
    00690     {}
    00691
    00692     template<class Iter>
    00693     double increment (Iter begin, Iter end)
    00694     {
    00695         for(Iter iter = begin; iter != end; ++iter)
    00696         {
    00697             ++count_;
    00698             for(int ii = 0; ii < mean_.size(); ++ii)
    00699                 tmp_[ii] = labels_(*iter, ii) - mean_[ii];
    00700             double f  = 1.0 / count_,
    00701                    f1 = 1.0 - f;
    00702             for(int ii = 0; ii < mean_.size(); ++ii)
    00703                 mean_[ii] += f*tmp_[ii];
    00704             for(int ii = 0; ii < mean_.size(); ++ii)
    00705                 variance_[ii] += f1*sq(tmp_[ii]);
    00706         }
    00707         double res = std::accumulate(variance_.begin(),
    00708                                variance_.end(),
    00709                                0.0,
    00710                                std::plus<double>())
    00711                 /((count_ == 1)? 1:(count_ -1));
    00712         //std::cerr << res << "  ) = ";
    00713         return res;
    00714     }
    00715
    00716     template<class Iter>      //This is BROKEN
    00717     double decrement (Iter begin, Iter end)
    00718     {
    00719         for(Iter iter = begin; iter != end; ++iter)
    00720         {
    00721             double f  = 1.0 / count_,
    00722                    f1 = 1.0 - f;
    00723             for(int ii = 0; ii < mean_.size(); ++ii)
    00724                 mean_[ii] = (mean_[ii] - f*labels_(*iter,ii))/(1-f);
    00725             for(int ii = 0; ii < mean_.size(); ++ii)
    00726                 variance_[ii] -= f1*sq(labels_(*iter,ii) - mean_[ii]);
    00727             --count_;
    00728         }
    00729         double res =  std::accumulate(variance_.begin(),
    00730                                variance_.end(),
    00731                                0.0,
    00732                                std::plus<double>())
    00733                 /((count_ == 1)? 1:(count_ -1));
    00734         //std::cerr << "( " << res << " + ";
    00735         return res;
    00736     }
    00737     /* west's algorithm for incremental variance
    00738     // calculation
    00739     template<class Iter>
    00740     double increment (Iter begin, Iter end)
    00741     {
    00742         for(Iter iter = begin; iter != end; ++iter)
    00743         {
    00744             ++count_;
    00745             for(int ii = 0; ii < mean_.size(); ++ii)
    00746                 tmp_[ii] = labels_(*iter, ii) - mean_[ii];
    00747             double f  = 1.0 / count_,
    00748                    f1 = 1.0 - f;
    00749             for(int ii = 0; ii < mean_.size(); ++ii)
    00750                 mean_[ii] += f*tmp_[ii];
    00751             for(int ii = 0; ii < mean_.size(); ++ii)
    00752                 variance_[ii] += f1*sq(tmp_[ii]);
    00753         }
    00754         return std::accumulate(variance_.begin(),
    00755                                variance_.end(),
    00756                                0.0,
    00757                                std::plus<double>())
    00758                 /(count_ -1);
    00759     }
    00760
    00761     template<class Iter>
    00762     double decrement (Iter begin, Iter end)
    00763     {
    00764         for(Iter iter = begin; iter != end; ++iter)
    00765         {
    00766             --count_;
    00767             for(int ii = 0; ii < mean_.size(); ++ii)
    00768                 tmp_[ii] = labels_(*iter, ii) - mean_[ii];
    00769             double f  = 1.0 / count_,
    00770                    f1 = 1.0 + f;
    00771             for(int ii = 0; ii < mean_.size(); ++ii)
    00772                 mean_[ii] -= f*tmp_[ii];
    00773             for(int ii = 0; ii < mean_.size(); ++ii)
    00774                 variance_[ii] -= f1*sq(tmp_[ii]);
    00775         }
    00776         return std::accumulate(variance_.begin(),
    00777                                variance_.end(),
    00778                                0.0,
    00779                                std::plus<double>())
    00780                 /(count_ -1);
    00781     }*/
    00782
    00783     template<class Iter, class Resp_t>
    00784     double init (Iter begin, Iter end, Resp_t resp)
    00785     {
    00786         reset();
    00787         return this->increment(begin, end, resp);
    00788     }
    00789
    00790
    00791     ArrayVector<double> const & response()
    00792     {
    00793         return mean_;
    00794     }
    00795
    00796     void reset()
    00797     {
    00798         mean_.init(0.0);
    00799         variance_.init(0.0);
    00800         count_ = 0;
    00801     }
    00802 };
    00803
    00804 template<class Tag, class Datatyp>
    00805 struct LossTraits;
    00806
    00807 struct LSQLoss
    00808 {};
    00809
    00810 template<class Datatype>
    00811 struct LossTraits<GiniCriterion, Datatype>
    00812 {
    00813     typedef ImpurityLoss<Datatype, GiniCriterion> type;
    00814 };
    00815
    00816 template<class Datatype>
    00817 struct LossTraits<EntropyCriterion, Datatype>
    00818 {
    00819     typedef ImpurityLoss<Datatype, EntropyCriterion> type;
    00820 };
    00821
    00822 template<class Datatype>
    00823 struct LossTraits<LSQLoss, Datatype>
    00824 {
    00825     typedef RegressionForestCounter<Datatype> type;
    00826 };
    00827
    00828 /** Given a column, choose a split that minimizes some loss
    00829  */
    00830 template<class LineSearchLossTag>
    00831 class BestGiniOfColumn
    00832 {
    00833 public:
    00834     ArrayVector<double>     class_weights_;
    00835     ArrayVector<double>     bestCurrentCounts[2];
    00836     double                  min_gini_;
    00837     std::ptrdiff_t               min_index_;
    00838     double                  min_threshold_;
    00839     ProblemSpec<>           ext_param_;
    00840
    00841     BestGiniOfColumn()
    00842     {}
    00843
    00844     template<class T>
    00845     BestGiniOfColumn(ProblemSpec<T> const & ext)
    00846     :
    00847         class_weights_(ext.class_weights_),
    00848         ext_param_(ext)
    00849     {
    00850         bestCurrentCounts[0].resize(ext.class_count_);
    00851         bestCurrentCounts[1].resize(ext.class_count_);
    00852     }
    00853     template<class T>
    00854     void set_external_parameters(ProblemSpec<T> const & ext)
    00855     {
    00856         class_weights_ = ext.class_weights_;
    00857         ext_param_ = ext;
    00858         bestCurrentCounts[0].resize(ext.class_count_);
    00859         bestCurrentCounts[1].resize(ext.class_count_);
    00860     }
    00861     /** calculate the best gini split along a Feature Column
    00862      * \param column  the feature vector - has to support the [] operator
    00863      * \param labels  the label vector
    00864      * \param begin
    00865      * \param end     (in and out)
    00866      *                begin and end iterators to the indices of the
    00867      *                samples in the current region.
    00868      *                the range begin - end is sorted by the column supplied
    00869      *                during function execution.
    00870      * \param region_response
    00871      *                ???
    00872      *                class histogram of the range.
    00873      *
    00874      *  precondition: begin, end valid range,
    00875      *                class_counts positive integer valued array with the
    00876      *                class counts in the current range.
    00877      *                labels.size() >= max(begin, end);
    00878      *  postcondition:
    00879      *                begin, end sorted by column given.
    00880      *                min_gini_ contains the minimum gini found or
    00881      *                NumericTraits<double>::max if no split was found.
    00882      *                min_index_ contains the splitting index in the range
    00883      *                or invalid data if no split was found.
    00884      *                BestCirremtcounts[0] and [1] contain the
    00885      *                class histogram of the left and right region of
    00886      *                the left and right regions.
    00887      */
    00888     template<   class DataSourceF_t,
    00889                 class DataSource_t,
    00890                 class I_Iter,
    00891                 class Array>
    00892     void operator()(DataSourceF_t   const & column,
    00893                     DataSource_t    const & labels,
    00894                     I_Iter                & begin,
    00895                     I_Iter                & end,
    00896                     Array           const & region_response)
    00897     {
    00898         std::sort(begin, end,
    00899                   SortSamplesByDimensions<DataSourceF_t>(column, 0));
    00900         typedef typename
    00901             LossTraits<LineSearchLossTag, DataSource_t>::type LineSearchLoss;
    00902         LineSearchLoss left(labels, ext_param_); //initialize left and right region
    00903         LineSearchLoss right(labels, ext_param_);
    00904
    00905
    00906
    00907         min_gini_ = right.init(begin, end, region_response);
    00908         min_threshold_ = *begin;
    00909         min_index_     = 0;  //the starting point where to split
    00910         DimensionNotEqual<DataSourceF_t> comp(column, 0);
    00911
    00912         I_Iter iter = begin;
    00913         I_Iter next = std::adjacent_find(iter, end, comp);
    00914         //std::cerr << std::distance(begin, end) << std::endl;
    00915         while( next  != end)
    00916         {
    00917             double lr  =  right.decrement(iter, next + 1);
    00918             double ll  =  left.increment(iter , next + 1);
    00919             double loss = lr +ll;
    00920             //std::cerr <<lr << " + "<< ll << " " << loss << " ";
    00921 #ifdef CLASSIFIER_TEST
    00922             if(loss < min_gini_ && !closeAtTolerance(loss, min_gini_))
    00923 #else
    00924             if(loss < min_gini_ )
    00925 #endif
    00926             {
    00927                 bestCurrentCounts[0] = left.response();
    00928                 bestCurrentCounts[1] = right.response();
    00929 #ifdef CLASSIFIER_TEST
    00930                 min_gini_       = loss < min_gini_? loss : min_gini_;
    00931 #else
    00932                 min_gini_       = loss;
    00933 #endif
    00934                 min_index_      = next - begin +1 ;
    00935                 min_threshold_  = (double(column(*next,0)) + double(column(*(next +1), 0)))/2.0;
    00936             }
    00937             iter = next +1 ;
    00938             next = std::adjacent_find(iter, end, comp);
    00939         }
    00940         //std::cerr << std::endl << " 000 " << std::endl;
    00941         //int in;
    00942         //std::cin >> in;
    00943     }
    00944
    00945     template<class DataSource_t, class Iter, class Array>
    00946     double loss_of_region(DataSource_t const & labels,
    00947                           Iter & begin,
    00948                           Iter & end,
    00949                           Array const & region_response) const
    00950     {
    00951         typedef typename
    00952             LossTraits<LineSearchLossTag, DataSource_t>::type LineSearchLoss;
    00953         LineSearchLoss region_loss(labels, ext_param_);
    00954         return
    00955             region_loss.init(begin, end, region_response);
    00956     }
    00957
    00958 };
    00959
    00960 namespace detail
    00961 {
    00962     template<class T>
    00963     struct Correction
    00964     {
    00965         template<class Region, class LabelT>
    00966         static void exec(Region & in, LabelT & labels)
    00967         {}
    00968     };
    00969
    00970     template<>
    00971     struct Correction<ClassificationTag>
    00972     {
    00973         template<class Region, class LabelT>
    00974         static void exec(Region & region, LabelT & labels)
    00975         {
    00976             if(std::accumulate(region.classCounts().begin(),
    00977                                region.classCounts().end(), 0.0) != region.size())
    00978             {
    00979                 RandomForestClassCounter<   LabelT,
    00980                                             ArrayVector<double> >
    00981                     counter(labels, region.classCounts());
    00982                 std::for_each(  region.begin(), region.end(), counter);
    00983                 region.classCountsIsValid = true;
    00984             }
    00985         }
    00986     };
    00987 }
    00988
    00989 /** Chooses mtry columns and applies ColumnDecisionFunctor to each of the
    00990  * columns. Then Chooses the column that is best
    00991  */
    00992 template<class ColumnDecisionFunctor, class Tag = ClassificationTag>
    00993 class ThresholdSplit: public SplitBase<Tag>
    00994 {
    00995   public:
    00996
    00997
    00998     typedef SplitBase<Tag> SB;
    00999
    01000     ArrayVector<Int32>          splitColumns;
    01001     ColumnDecisionFunctor       bgfunc;
    01002
    01003     double                      region_gini_;
    01004     ArrayVector<double>         min_gini_;
    01005     ArrayVector<std::ptrdiff_t>      min_indices_;
    01006     ArrayVector<double>         min_thresholds_;
    01007
    01008     int                         bestSplitIndex;
    01009
    01010     double minGini() const
    01011     {
    01012         return min_gini_[bestSplitIndex];
    01013     }
    01014     int bestSplitColumn() const
    01015     {
    01016         return splitColumns[bestSplitIndex];
    01017     }
    01018     double bestSplitThreshold() const
    01019     {
    01020         return min_thresholds_[bestSplitIndex];
    01021     }
    01022
    01023     template<class T>
    01024     void set_external_parameters(ProblemSpec<T> const & in)
    01025     {
    01026         SB::set_external_parameters(in);
    01027         bgfunc.set_external_parameters( SB::ext_param_);
    01028         int featureCount_ = SB::ext_param_.column_count_;
    01029         splitColumns.resize(featureCount_);
    01030         for(int k=0; k<featureCount_; ++k)
    01031             splitColumns[k] = k;
    01032         min_gini_.resize(featureCount_);
    01033         min_indices_.resize(featureCount_);
    01034         min_thresholds_.resize(featureCount_);
    01035     }
    01036
    01037
    01038     template<class T, class C, class T2, class C2, class Region, class Random>
    01039     int findBestSplit(MultiArrayView<2, T, C> features,
    01040                       MultiArrayView<2, T2, C2>  labels,
    01041                       Region & region,
    01042                       ArrayVector<Region>& childRegions,
    01043                       Random & randint)
    01044     {
    01045
    01046         typedef typename Region::IndexIterator IndexIterator;
    01047         if(region.size() == 0)
    01048         {
    01049            std::cerr << "SplitFunctor::findBestSplit(): stackentry with 0 examples encountered\n"
    01050                         "continuing learning process....";
    01051         }
    01052         // calculate things that haven't been calculated yet.
    01053         detail::Correction<Tag>::exec(region, labels);
    01054
    01055
    01056         // Is the region pure already?
    01057         region_gini_ = bgfunc.loss_of_region(labels,
    01058                                              region.begin(),
    01059                                              region.end(),
    01060                                              region.classCounts());
    01061         if(region_gini_ <= SB::ext_param_.precision_)
    01062             return  this->makeTerminalNode(features, labels, region, randint);
    01063
    01064         // select columns  to be tried.
    01065         for(int ii = 0; ii < SB::ext_param_.actual_mtry_; ++ii)
    01066             std::swap(splitColumns[ii],
    01067                       splitColumns[ii+ randint(features.shape(1) - ii)]);
    01068
    01069         // find the best gini index
    01070         bestSplitIndex              = 0;
    01071         double  current_min_gini    = region_gini_;
    01072         int     num2try             = features.shape(1);
    01073         for(int k=0; k<num2try; ++k)
    01074         {
    01075             //this functor does all the work
    01076             bgfunc(columnVector(features, splitColumns[k]),
    01077                    labels,
    01078                    region.begin(), region.end(),
    01079                    region.classCounts());
    01080             min_gini_[k]            = bgfunc.min_gini_;
    01081             min_indices_[k]         = bgfunc.min_index_;
    01082             min_thresholds_[k]      = bgfunc.min_threshold_;
    01083 #ifdef CLASSIFIER_TEST
    01084             if(     bgfunc.min_gini_ < current_min_gini
    01085                &&  !closeAtTolerance(bgfunc.min_gini_, current_min_gini))
    01086 #else
    01087             if(bgfunc.min_gini_ < current_min_gini)
    01088 #endif
    01089             {
    01090                 current_min_gini = bgfunc.min_gini_;
    01091                 childRegions[0].classCounts() = bgfunc.bestCurrentCounts[0];
    01092                 childRegions[1].classCounts() = bgfunc.bestCurrentCounts[1];
    01093                 childRegions[0].classCountsIsValid = true;
    01094                 childRegions[1].classCountsIsValid = true;
    01095
    01096                 bestSplitIndex   = k;
    01097                 num2try = SB::ext_param_.actual_mtry_;
    01098             }
    01099         }
    01100         //std::cerr << current_min_gini << "curr " << region_gini_ << std::endl;
    01101         // did not find any suitable split
    01102         if(closeAtTolerance(current_min_gini, region_gini_))
    01103             return  this->makeTerminalNode(features, labels, region, randint);
    01104
    01105         //create a Node for output
    01106         Node<i_ThresholdNode>   node(SB::t_data, SB::p_data);
    01107         SB::node_ = node;
    01108         node.threshold()    = min_thresholds_[bestSplitIndex];
    01109         node.column()       = splitColumns[bestSplitIndex];
    01110
    01111         // partition the range according to the best dimension
    01112         SortSamplesByDimensions<MultiArrayView<2, T, C> >
    01113             sorter(features, node.column(), node.threshold());
    01114         IndexIterator bestSplit =
    01115             std::partition(region.begin(), region.end(), sorter);
    01116         // Save the ranges of the child stack entries.
    01117         childRegions[0].setRange(   region.begin()  , bestSplit       );
    01118         childRegions[0].rule = region.rule;
    01119         childRegions[0].rule.push_back(std::make_pair(1, 1.0));
    01120         childRegions[1].setRange(   bestSplit       , region.end()    );
    01121         childRegions[1].rule = region.rule;
    01122         childRegions[1].rule.push_back(std::make_pair(1, 1.0));
    01123
    01124         return i_ThresholdNode;
    01125     }
    01126 };
    01127
    01128 typedef  ThresholdSplit<BestGiniOfColumn<GiniCriterion> >                      GiniSplit;
    01129 typedef  ThresholdSplit<BestGiniOfColumn<EntropyCriterion> >                 EntropySplit;
    01130 typedef  ThresholdSplit<BestGiniOfColumn<LSQLoss>, RegressionTag>              RegressionSplit;
    01131
    01132 namespace rf
    01133 {
    01134
    01135 /** This namespace contains additional Splitfunctors.
    01136  *
    01137  * The Split functor classes are designed in a modular fashion because new split functors may
    01138  * share a lot of code with existing ones.
    01139  *
    01140  * ThresholdSplit implements the functionality needed for any split functor, that makes its
    01141  * decision via one dimensional axis-parallel cuts. The Template parameter defines how the split
    01142  * along one dimension is chosen.
    01143  *
    01144  * The BestGiniOfColumn class chooses a split that minimizes one of the Loss functions supplied
    01145  * (GiniCriterion for classification and LSQLoss for regression). Median chooses the Split in a
    01146  * kD tree fashion.
    01147  *
    01148  *
    01149  * Currently defined typedefs:
    01150  * \code
    01151  * typedef  ThresholdSplit<BestGiniOfColumn<GiniCriterion> >                 GiniSplit;
    01152  * typedef  ThresholdSplit<BestGiniOfColumn<LSQLoss>, RegressionTag>         RegressionSplit;
    01153  * typedef  ThresholdSplit<Median> MedianSplit;
    01154  * \endcode
    01155  */
    01156 namespace split
    01157 {
    01158
    01159 /** This Functor chooses the median value of a column
    01160  */
    01161 class Median
    01162 {
    01163 public:
    01164
    01165     typedef GiniCriterion   LineSearchLossTag;
    01166     ArrayVector<double>     class_weights_;
    01167     ArrayVector<double>     bestCurrentCounts[2];
    01168     double                  min_gini_;
    01169     std::ptrdiff_t          min_index_;
    01170     double                  min_threshold_;
    01171     ProblemSpec<>           ext_param_;
    01172
    01173     Median()
    01174     {}
    01175
    01176     template<class T>
    01177     Median(ProblemSpec<T> const & ext)
    01178     :
    01179         class_weights_(ext.class_weights_),
    01180         ext_param_(ext)
    01181     {
    01182         bestCurrentCounts[0].resize(ext.class_count_);
    01183         bestCurrentCounts[1].resize(ext.class_count_);
    01184     }
    01185
    01186     template<class T>
    01187     void set_external_parameters(ProblemSpec<T> const & ext)
    01188     {
    01189         class_weights_ = ext.class_weights_;
    01190         ext_param_ = ext;
    01191         bestCurrentCounts[0].resize(ext.class_count_);
    01192         bestCurrentCounts[1].resize(ext.class_count_);
    01193     }
    01194
    01195     template<   class DataSourceF_t,
    01196                 class DataSource_t,
    01197                 class I_Iter,
    01198                 class Array>
    01199     void operator()(DataSourceF_t   const & column,
    01200                     DataSource_t    const & labels,
    01201                     I_Iter                & begin,
    01202                     I_Iter                & end,
    01203                     Array           const & region_response)
    01204     {
    01205         std::sort(begin, end,
    01206                   SortSamplesByDimensions<DataSourceF_t>(column, 0));
    01207         typedef typename
    01208             LossTraits<LineSearchLossTag, DataSource_t>::type LineSearchLoss;
    01209         LineSearchLoss left(labels, ext_param_);
    01210         LineSearchLoss right(labels, ext_param_);
    01211         right.init(begin, end, region_response);
    01212
    01213         min_gini_ = NumericTraits<double>::max();
    01214         min_index_ = floor(double(end - begin)/2.0);
    01215         min_threshold_ =  column[*(begin + min_index_)];
    01216         SortSamplesByDimensions<DataSourceF_t>
    01217             sorter(column, 0, min_threshold_);
    01218         I_Iter part = std::partition(begin, end, sorter);
    01219         DimensionNotEqual<DataSourceF_t> comp(column, 0);
    01220         if(part == begin)
    01221         {
    01222             part= std::adjacent_find(part, end, comp)+1;
    01223
    01224         }
    01225         if(part >= end)
    01226         {
    01227             return;
    01228         }
    01229         else
    01230         {
    01231             min_threshold_ = column[*part];
    01232         }
    01233         min_gini_ = right.decrement(begin, part)
    01234               +     left.increment(begin , part);
    01235
    01236         bestCurrentCounts[0] = left.response();
    01237         bestCurrentCounts[1] = right.response();
    01238
    01239         min_index_      = part - begin;
    01240     }
    01241
    01242     template<class DataSource_t, class Iter, class Array>
    01243     double loss_of_region(DataSource_t const & labels,
    01244                           Iter & begin,
    01245                           Iter & end,
    01246                           Array const & region_response) const
    01247     {
    01248         typedef typename
    01249             LossTraits<LineSearchLossTag, DataSource_t>::type LineSearchLoss;
    01250         LineSearchLoss region_loss(labels, ext_param_);
    01251         return
    01252             region_loss.init(begin, end, region_response);
    01253     }
    01254
    01255 };
    01256
    01257 typedef  ThresholdSplit<Median> MedianSplit;
    01258
    01259
    01260 /** This Functor chooses a random value of a column
    01261  */
    01262 class RandomSplitOfColumn
    01263 {
    01264 public:
    01265
    01266     typedef GiniCriterion   LineSearchLossTag;
    01267     ArrayVector<double>     class_weights_;
    01268     ArrayVector<double>     bestCurrentCounts[2];
    01269     double                  min_gini_;
    01270     std::ptrdiff_t          min_index_;
    01271     double                  min_threshold_;
    01272     ProblemSpec<>           ext_param_;
    01273     typedef RandomMT19937   Random_t;
    01274     Random_t                random;
    01275
    01276     RandomSplitOfColumn()
    01277     {}
    01278
    01279     template<class T>
    01280     RandomSplitOfColumn(ProblemSpec<T> const & ext)
    01281     :
    01282         class_weights_(ext.class_weights_),
    01283         ext_param_(ext),
    01284         random(RandomSeed)
    01285     {
    01286         bestCurrentCounts[0].resize(ext.class_count_);
    01287         bestCurrentCounts[1].resize(ext.class_count_);
    01288     }
    01289
    01290     template<class T>
    01291     RandomSplitOfColumn(ProblemSpec<T> const & ext, Random_t & random_)
    01292     :
    01293         class_weights_(ext.class_weights_),
    01294         ext_param_(ext),
    01295         random(random_)
    01296     {
    01297         bestCurrentCounts[0].resize(ext.class_count_);
    01298         bestCurrentCounts[1].resize(ext.class_count_);
    01299     }
    01300
    01301     template<class T>
    01302     void set_external_parameters(ProblemSpec<T> const & ext)
    01303     {
    01304         class_weights_ = ext.class_weights_;
    01305         ext_param_ = ext;
    01306         bestCurrentCounts[0].resize(ext.class_count_);
    01307         bestCurrentCounts[1].resize(ext.class_count_);
    01308     }
    01309
    01310     template<   class DataSourceF_t,
    01311                 class DataSource_t,
    01312                 class I_Iter,
    01313                 class Array>
    01314     void operator()(DataSourceF_t   const & column,
    01315                     DataSource_t    const & labels,
    01316                     I_Iter                & begin,
    01317                     I_Iter                & end,
    01318                     Array           const & region_response)
    01319     {
    01320         std::sort(begin, end,
    01321                   SortSamplesByDimensions<DataSourceF_t>(column, 0));
    01322         typedef typename
    01323             LossTraits<LineSearchLossTag, DataSource_t>::type LineSearchLoss;
    01324         LineSearchLoss left(labels, ext_param_);
    01325         LineSearchLoss right(labels, ext_param_);
    01326         right.init(begin, end, region_response);
    01327
    01328
    01329         min_gini_ = NumericTraits<double>::max();
    01330         int tmp_pt = random.uniformInt(std::distance(begin, end));
    01331         min_index_ = tmp_pt;
    01332         min_threshold_ =  column[*(begin + min_index_)];
    01333         SortSamplesByDimensions<DataSourceF_t>
    01334             sorter(column, 0, min_threshold_);
    01335         I_Iter part = std::partition(begin, end, sorter);
    01336         DimensionNotEqual<DataSourceF_t> comp(column, 0);
    01337         if(part == begin)
    01338         {
    01339             part= std::adjacent_find(part, end, comp)+1;
    01340
    01341         }
    01342         if(part >= end)
    01343         {
    01344             return;
    01345         }
    01346         else
    01347         {
    01348             min_threshold_ = column[*part];
    01349         }
    01350         min_gini_ = right.decrement(begin, part)
    01351               +     left.increment(begin , part);
    01352
    01353         bestCurrentCounts[0] = left.response();
    01354         bestCurrentCounts[1] = right.response();
    01355
    01356         min_index_      = part - begin;
    01357     }
    01358
    01359     template<class DataSource_t, class Iter, class Array>
    01360     double loss_of_region(DataSource_t const & labels,
    01361                           Iter & begin,
    01362                           Iter & end,
    01363                           Array const & region_response) const
    01364     {
    01365         typedef typename
    01366             LossTraits<LineSearchLossTag, DataSource_t>::type LineSearchLoss;
    01367         LineSearchLoss region_loss(labels, ext_param_);
    01368         return
    01369             region_loss.init(begin, end, region_response);
    01370     }
    01371
    01372 };
    01373
    01374 typedef  ThresholdSplit<RandomSplitOfColumn> RandomSplit;
    01375 }
    01376 }
    01377
    01378
    01379 } //namespace vigra
    01380 #endif // VIGRA_RANDOM_FOREST_SPLIT_HXX

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany | html generated using doxygen and Python vigra 1.9.0 (Tue Nov 6 2012)