35#ifndef RF_VISITORS_HXX
36#define RF_VISITORS_HXX
39# include "vigra/hdf5impex.hxx"
41#include <vigra/windows.h>
46#include <vigra/metaprogramming.hxx>
47#include <vigra/multi_pointoperators.hxx>
142 template<
class Tree,
class Split,
class Region,
class Feature_t,
class Label_t>
148 Feature_t & features,
163 template<
class RF,
class PR,
class SM,
class ST>
166 ignore_argument(rf,
pr,
sm,
st,index);
175 template<
class RF,
class PR>
178 ignore_argument(rf,
pr);
187 template<
class RF,
class PR>
190 ignore_argument(rf,
pr);
205 template<
class TR,
class IntT,
class TopT,
class Feat>
208 ignore_argument(
tr,index,
node_t,features);
215 template<
class TR,
class IntT,
class TopT,
class Feat>
254template <
class Visitor,
class Next = StopVisiting>
269 next_(stop_), visitor_(
visitor)
272 template<
class Tree,
class Split,
class Region,
class Feature_t,
class Label_t>
273 void visit_after_split(
Tree & tree,
278 Feature_t & features,
281 if(visitor_.is_active())
282 visitor_.visit_after_split(tree, split,
289 template<
class RF,
class PR,
class SM,
class ST>
290 void visit_after_tree(
RF& rf,
PR &
pr,
SM &
sm,
ST &
st,
int index)
292 if(visitor_.is_active())
293 visitor_.visit_after_tree(rf,
pr,
sm,
st, index);
294 next_.visit_after_tree(rf,
pr,
sm,
st, index);
297 template<
class RF,
class PR>
298 void visit_at_beginning(
RF & rf,
PR &
pr)
300 if(visitor_.is_active())
301 visitor_.visit_at_beginning(rf,
pr);
302 next_.visit_at_beginning(rf,
pr);
304 template<
class RF,
class PR>
305 void visit_at_end(
RF & rf,
PR &
pr)
307 if(visitor_.is_active())
308 visitor_.visit_at_end(rf,
pr);
309 next_.visit_at_end(rf,
pr);
312 template<
class TR,
class IntT,
class TopT,
class Feat>
315 if(visitor_.is_active())
316 visitor_.visit_external_node(
tr, index,
node_t,features);
317 next_.visit_external_node(
tr, index,
node_t,features);
319 template<
class TR,
class IntT,
class TopT,
class Feat>
322 if(visitor_.is_active())
323 visitor_.visit_internal_node(
tr, index,
node_t,features);
324 next_.visit_internal_node(
tr, index,
node_t,features);
329 if(visitor_.is_active() && visitor_.has_value())
330 return visitor_.return_val();
331 return next_.return_val();
355template<
class A,
class B>
356detail::VisitorNode<A, detail::VisitorNode<B> >
369template<
class A,
class B,
class C>
370detail::VisitorNode<A, detail::VisitorNode<B, detail::VisitorNode<C> > >
385template<
class A,
class B,
class C,
class D>
386detail::VisitorNode<A, detail::VisitorNode<
B, detail::VisitorNode<
C,
387 detail::VisitorNode<D> > > >
404template<
class A,
class B,
class C,
class D,
class E>
405detail::VisitorNode<A, detail::VisitorNode<
B, detail::VisitorNode<
C,
406 detail::VisitorNode<D, detail::VisitorNode<E> > > > >
426template<
class A,
class B,
class C,
class D,
class E,
428detail::VisitorNode<A, detail::VisitorNode<
B, detail::VisitorNode<
C,
429 detail::VisitorNode<D, detail::VisitorNode<E, detail::VisitorNode<F> > > > > >
451template<
class A,
class B,
class C,
class D,
class E,
453detail::VisitorNode<A, detail::VisitorNode<
B, detail::VisitorNode<
C,
454 detail::VisitorNode<D, detail::VisitorNode<
E, detail::VisitorNode<
F,
455 detail::VisitorNode<G> > > > > > >
457 D & d,
E & e,
F & f,
G & g)
479template<
class A,
class B,
class C,
class D,
class E,
480 class F,
class G,
class H>
481detail::VisitorNode<A, detail::VisitorNode<
B, detail::VisitorNode<
C,
482 detail::VisitorNode<D, detail::VisitorNode<
E, detail::VisitorNode<
F,
483 detail::VisitorNode<G, detail::VisitorNode<H> > > > > > > >
510template<
class A,
class B,
class C,
class D,
class E,
511 class F,
class G,
class H,
class I>
512detail::VisitorNode<A, detail::VisitorNode<
B, detail::VisitorNode<
C,
513 detail::VisitorNode<D, detail::VisitorNode<
E, detail::VisitorNode<
F,
514 detail::VisitorNode<G, detail::VisitorNode<H, detail::VisitorNode<I> > > > > > > > >
542template<
class A,
class B,
class C,
class D,
class E,
543 class F,
class G,
class H,
class I,
class J>
544detail::VisitorNode<A, detail::VisitorNode<
B, detail::VisitorNode<
C,
545 detail::VisitorNode<D, detail::VisitorNode<
E, detail::VisitorNode<
F,
546 detail::VisitorNode<
G, detail::VisitorNode<
H, detail::VisitorNode<
I,
547 detail::VisitorNode<J> > > > > > > > > >
588 bool adjust_thresholds;
598 adjust_thresholds(
false), tree_id(0), last_node_id(0), current_label(0)
600 struct MarginalDistribution
603 Int32 leftTotalCounts;
605 Int32 rightTotalCounts;
612 struct TreeOnlineInformation
614 std::vector<MarginalDistribution> mag_distributions;
615 std::vector<IndexList> index_lists;
617 std::map<int,int> interior_to_index;
619 std::map<int,int> exterior_to_index;
623 std::vector<TreeOnlineInformation> trees_online_information;
627 template<
class RF,
class PR>
631 trees_online_information.resize(rf.options_.tree_count_);
638 trees_online_information[tree_id].mag_distributions.clear();
639 trees_online_information[tree_id].index_lists.clear();
640 trees_online_information[tree_id].interior_to_index.clear();
641 trees_online_information[tree_id].exterior_to_index.clear();
646 template<
class RF,
class PR,
class SM,
class ST>
652 template<
class Tree,
class Split,
class Region,
class Feature_t,
class Label_t>
653 void visit_after_split(
Tree & tree,
658 Feature_t & features,
662 int addr=tree.topology_.
size();
663 if(split.createNode().typeID() == i_ThresholdNode)
665 if(adjust_thresholds)
669 trees_online_information[tree_id].interior_to_index[addr]=
linear_index;
670 trees_online_information[tree_id].mag_distributions.push_back(MarginalDistribution());
672 trees_online_information[tree_id].mag_distributions.back().leftCounts=
leftChild.classCounts_;
673 trees_online_information[tree_id].mag_distributions.back().rightCounts=
rightChild.classCounts_;
675 trees_online_information[tree_id].mag_distributions.back().leftTotalCounts=
leftChild.size_;
676 trees_online_information[tree_id].mag_distributions.back().rightTotalCounts=
rightChild.size_;
678 double gap_left,gap_right;
680 gap_left=features(
leftChild[0],split.bestSplitColumn());
682 if(features(
leftChild[
i],split.bestSplitColumn())>gap_left)
683 gap_left=features(
leftChild[
i],split.bestSplitColumn());
684 gap_right=features(
rightChild[0],split.bestSplitColumn());
686 if(features(
rightChild[
i],split.bestSplitColumn())<gap_right)
687 gap_right=features(
rightChild[
i],split.bestSplitColumn());
688 trees_online_information[tree_id].mag_distributions.back().gap_left=gap_left;
689 trees_online_information[tree_id].mag_distributions.back().gap_right=gap_right;
696 trees_online_information[tree_id].exterior_to_index[addr]=
linear_index;
698 trees_online_information[tree_id].index_lists.push_back(IndexList());
700 trees_online_information[tree_id].index_lists.back().resize(parent.size_,0);
701 std::copy(parent.begin_,parent.end_,trees_online_information[tree_id].index_lists.back().
begin());
704 void add_to_index_list(
int tree,
int node,
int index)
708 TreeOnlineInformation &ti=trees_online_information[tree];
709 ti.index_lists[ti.exterior_to_index[node]].push_back(index);
711 void move_exterior_node(
int src_tree,
int src_index,
int dst_tree,
int dst_index)
715 trees_online_information[dst_tree].exterior_to_index[dst_index]=trees_online_information[src_tree].exterior_to_index[src_index];
716 trees_online_information[src_tree].exterior_to_index.erase(src_index);
723 template<
class TR,
class IntT,
class TopT,
class Feat>
727 if(adjust_thresholds)
729 vigra_assert(
node_t==i_ThresholdNode,
"We can only visit threshold nodes");
732 TreeOnlineInformation &
ti=trees_online_information[tree_id];
733 MarginalDistribution &
m=
ti.mag_distributions[
ti.interior_to_index[index]];
734 if(value>
m.gap_left && value<
m.gap_right)
737 if(
m.leftCounts[current_label]/
double(
m.leftTotalCounts)>
m.rightCounts[current_label]/
double(
m.rightTotalCounts))
752 ++
m.rightTotalCounts;
753 ++
m.rightCounts[current_label];
758 ++
m.rightCounts[current_label];
806 template<
class RF,
class PR,
class SM,
class ST>
810 if(
int(oobCount.
size()) != rf.ext_param_.row_count_)
812 oobCount.resize(rf.ext_param_.row_count_, 0);
813 oobErrorCount.resize(rf.ext_param_.row_count_, 0);
816 for(
int l = 0;
l < rf.ext_param_.row_count_; ++
l)
823 .predictLabel(rowVector(
pr.features(),
l))
824 !=
pr.response()(
l,0))
835 template<
class RF,
class PR>
881 void save(std::string
filen, std::string
pathn)
885 const char* filename =
filen.c_str();
894 template<
class RF,
class PR>
895 void visit_at_beginning(
RF & rf,
PR &)
897 class_count = rf.class_count();
898 tmp_prob.reshape(Shp(1, class_count), 0);
899 prob_oob.reshape(Shp(rf.ext_param().row_count_,class_count), 0);
900 is_weighted = rf.options().predict_weighted_;
901 indices.resize(rf.ext_param().row_count_);
902 if(
int(oobCount.
size()) != rf.ext_param_.row_count_)
904 oobCount.reshape(Shp(rf.ext_param_.row_count_, 1), 0);
906 for(
int ii = 0;
ii < rf.ext_param().row_count_; ++
ii)
912 template<
class RF,
class PR,
class SM,
class ST>
913 void visit_after_tree(RF& rf, PR & pr, SM & sm, ST &,
int index)
919 if(rf.ext_param_.actual_msample_ < pr.features().shape(0) - 10000)
921 ArrayVector<int> oob_indices;
922 ArrayVector<int> cts(class_count, 0);
923 std::random_device rd;
924 std::mt19937 g(rd());
925 std::shuffle(indices.
begin(), indices.
end(), g);
926 for(
int ii = 0; ii < rf.ext_param_.row_count_; ++ii)
928 if(!sm.is_used()[indices[ii]] && cts[pr.response()(indices[ii], 0)] < 40000)
930 oob_indices.push_back(indices[ii]);
931 ++cts[pr.response()(indices[ii], 0)];
934 for(
unsigned int ll = 0; ll < oob_indices.size(); ++ll)
937 ++oobCount[oob_indices[ll]];
940 int pos = rf.tree(index).getToLeaf(
rowVector(pr.features(),oob_indices[ll]));
941 Node<e_ConstProbNode> node ( rf.tree(index).topology_,
942 rf.tree(index).parameters_,
945 for(
int ii = 0; ii < class_count; ++ii)
947 tmp_prob[ii] = node.prob_begin()[ii];
951 for(
int ii = 0; ii < class_count; ++ii)
952 tmp_prob[ii] = tmp_prob[ii] * (*(node.prob_begin()-1));
954 rowVector(prob_oob, oob_indices[ll]) += tmp_prob;
959 for(
int ll = 0; ll < rf.ext_param_.row_count_; ++ll)
962 if(!sm.is_used()[ll])
968 int pos = rf.tree(index).getToLeaf(
rowVector(pr.features(),ll));
969 Node<e_ConstProbNode> node ( rf.tree(index).topology_,
970 rf.tree(index).parameters_,
973 for(
int ii = 0; ii < class_count; ++ii)
975 tmp_prob[ii] = node.prob_begin()[ii];
979 for(
int ii = 0; ii < class_count; ++ii)
980 tmp_prob[ii] = tmp_prob[ii] * (*(node.prob_begin()-1));
991 template<
class RF,
class PR>
995 int totalOobCount =0;
1001 if(
argMax(rowVector(prob_oob,
ll)) !=
pr.response()(
ll, 0))
1072 void save(std::string
filen, std::string
pathn)
1076 const char* filename =
filen.c_str();
1094 template<
class RF,
class PR>
1095 void visit_at_beginning(
RF & rf,
PR &)
1097 class_count = rf.class_count();
1098 if(class_count == 2)
1102 tmp_prob.reshape(Shp(1, class_count), 0);
1103 prob_oob.reshape(Shp(rf.ext_param().row_count_,class_count), 0);
1104 is_weighted = rf.options().predict_weighted_;
1108 if(
int(oobCount.
size()) != rf.ext_param_.row_count_)
1110 oobCount.reshape(Shp(rf.ext_param_.row_count_, 1), 0);
1111 oobErrorCount.reshape(Shp(rf.ext_param_.row_count_,1), 0);
1115 template<
class RF,
class PR,
class SM,
class ST>
1116 void visit_after_tree(RF& rf, PR & pr, SM & sm, ST &,
int index)
1121 for(
int ll = 0; ll < rf.ext_param_.row_count_; ++ll)
1124 if(!sm.is_used()[ll])
1132 int pos = rf.tree(index).getToLeaf(rowVector(pr.features(),ll));
1133 Node<e_ConstProbNode> node ( rf.tree(index).topology_,
1134 rf.tree(index).parameters_,
1137 for(
int ii = 0; ii < class_count; ++ii)
1139 tmp_prob[ii] = node.prob_begin()[ii];
1143 for(
int ii = 0; ii < class_count; ++ii)
1144 tmp_prob[ii] = tmp_prob[ii] * (*(node.prob_begin()-1));
1147 int label =
argMax(tmp_prob);
1149 if(label != pr.response()(ll, 0))
1154 ++oobErrorCount[ll];
1158 int breimanstyle = 0;
1159 int totalOobCount = 0;
1160 for(
int ll=0; ll < static_cast<int>(rf.ext_param_.row_count_); ++ll)
1177 MultiArrayView<3, double> current_roc
1179 for(
int gg = 0; gg < current_roc.shape(2); ++gg)
1181 for(
int ll=0; ll < static_cast<int>(rf.ext_param_.row_count_); ++ll)
1185 int pred = prob_oob(ll, 1) > (double(gg)/double(current_roc.shape(2)))?
1187 current_roc(pr.response()(ll, 0), pred, gg)+= 1;
1190 current_roc.bindOuter(gg)/= totalOobCount;
1194 oob_per_tree[index] = double(wrong_oob)/double(total_oob);
1200 template<
class RF,
class PR>
1205 int totalOobCount =0;
1211 if(
argMax(rowVector(prob_oob,
ll)) !=
pr.response()(
ll, 0))
1258 int repetition_count_;
1262 void save(std::string filename, std::string
prefix)
1284 template<
class Tree,
class Split,
class Region,
class Feature_t,
class Label_t>
1295 Int32 const class_count = tree.ext_param_.class_count_;
1296 Int32 const column_count = tree.ext_param_.column_count_;
1305 if(split.createNode().typeID() == i_ThresholdNode)
1309 += split.region_gini_ - split.minGini();
1319 template<
class RF,
class PR,
class SM,
class ST>
1323 Int32 column_count = rf.ext_param_.column_count_;
1324 Int32 class_count = rf.ext_param_.class_count_;
1335 typedef typename FeatureArray::value_type
FeatureValue;
1341 ArrayVector<Int32>::iterator
1343 for(
int ii = 0;
ii < rf.ext_param_.row_count_; ++
ii)
1344 if(!
sm.is_used()[
ii])
1351#ifdef CLASSIFIER_TEST
1373 .predictLabel(rowVector(features, *iter))
1374 ==
pr.response()(*iter, 0))
1383 for(
int ii = 0;
ii < column_count; ++
ii)
1396 for(
int rr = 0;
rr < repetition_count_; ++
rr)
1400 for(
int jj = n-1;
jj >= 1; --
jj)
1410 .predictLabel(rowVector(features, *iter))
1411 ==
pr.response()(*iter, 0))
1441 template<
class RF,
class PR,
class SM,
class ST>
1449 template<
class RF,
class PR>
1462 template<
class RF,
class PR,
class SM,
class ST>
1463 void visit_after_tree(
RF& rf,
PR &,
SM &,
ST &,
int index){
1464 if(index != rf.options().tree_count_-1) {
1465 std::cout <<
"\r[" << std::setw(10) << (index+1)/
static_cast<double>(rf.options().tree_count_)*100 <<
"%]"
1466 <<
" (" << index+1 <<
" of " << rf.options().tree_count_ <<
") done" << std::flush;
1469 std::cout <<
"\r[" << std::setw(10) << 100.0 <<
"%]" << std::endl;
1473 template<
class RF,
class PR>
1474 void visit_at_end(
RF const & rf,
PR const &) {
1475 std::string a =
TOCS;
1476 std::cout <<
"all " << rf.options().tree_count_ <<
" trees have been learned in " << a << std::endl;
1479 template<
class RF,
class PR>
1480 void visit_at_beginning(
RF const & rf,
PR const &) {
1482 std::cout <<
"growing random forest, which will have " << rf.options().tree_count_ <<
" trees" << std::endl;
1530 void save(std::string, std::string)
1548 template<
class RF,
class PR>
1549 void visit_at_beginning(
RF const & rf,
PR &
pr)
1552 int n = rf.ext_param_.column_count_;
1555 corr_l.reshape(Shp(n +1, 10));
1558 noise_l.reshape(Shp(
pr.features().shape(0), 10));
1562 noise[
ii] = random.uniform53();
1563 noise_l[
ii] = random.uniform53() > 0.5;
1565 bgfunc = ColumnDecisionFunctor( rf.ext_param_);
1566 tmp_labels.reshape(
pr.response().shape());
1571 template<
class RF,
class PR>
1572 void visit_at_end(
RF const &,
PR const &)
1586 for(
int jj = 0; jj < rC; ++jj)
1592 FindMinMax<double> minmax;
1595 for(
int jj = 0; jj < rC; ++jj)
1602 for(
int jj = 0; jj < rC; ++jj)
1605 FindMinMax<double> minmax2;
1607 for(
int jj = 0; jj < rC; ++jj)
1613 template<
class Tree,
class Split,
class Region,
class Feature_t,
class Label_t>
1614 void visit_after_split( Tree &,
1619 Feature_t & features,
1622 if(split.createNode().typeID() == i_ThresholdNode)
1626 for(
int ii = 0; ii < parent.size(); ++ii)
1628 tmp_labels[parent[ii]]
1629 = (features(parent[ii], split.bestSplitColumn()) < split.bestSplitThreshold());
1630 ++tmp_cc[tmp_labels[parent[ii]]];
1632 double region_gini = bgfunc.loss_of_region(tmp_labels,
1637 int n = split.bestSplitColumn();
1641 for(
int k = 0; k < features.shape(1); ++k)
1647 wgini = (region_gini - bgfunc.min_gini_);
1651 for(
int k = 0; k < 10; ++k)
1657 wgini = (region_gini - bgfunc.min_gini_);
1662 for(
int k = 0; k < 10; ++k)
1668 wgini = (region_gini - bgfunc.min_gini_);
1672 bgfunc(labels, tmp_labels, parent.begin(), parent.end(),tmp_cc);
1673 wgini = (region_gini - bgfunc.min_gini_);
1677 region_gini = split.region_gini_;
1679 Node<i_ThresholdNode> node(split.createNode());
1682 +=split.region_gini_ - split.minGini();
1684 for(
int k = 0; k < 10; ++k)
1689 parent.classCounts());
1695 for(
int k = 0; k < tree.ext_param_.actual_mtry_; ++k)
1697 wgini = region_gini - split.min_gini_[k];
1700 split.splitColumns[k])
1704 for(
int k=tree.ext_param_.actual_mtry_; k<features.shape(1); ++k)
1706 split.bgfunc(
columnVector(features, split.splitColumns[k]),
1709 parent.classCounts());
1710 wgini = region_gini - split.bgfunc.min_gini_;
1712 split.splitColumns[k]) += wgini;
1719 SortSamplesByDimensions<Feature_t>
1720 sorter(features, split.bestSplitColumn(), split.bestSplitThreshold());
1721 std::partition(parent.begin(), parent.end(), sorter);
const_pointer data() const
Definition array_vector.hxx:209
const_iterator end() const
Definition array_vector.hxx:237
MultiArrayView subarray(difference_type p, difference_type q) const
Definition multi_array.hxx:1530
const difference_type & shape() const
Definition multi_array.hxx:1650
MultiArrayView< N-M, T, StrideTag > bindOuter(const TinyVector< Index, M > &d) const
Definition multi_array.hxx:2186
difference_type_1 size() const
Definition multi_array.hxx:1643
MultiArrayView< N, T, StridedArrayTag > transpose() const
Definition multi_array.hxx:1569
void reshape(const difference_type &shape)
Definition multi_array.hxx:2863
Class for a single RGB value.
Definition rgbvalue.hxx:128
void init(Iterator i, Iterator end)
Definition tinyvector.hxx:708
size_type size() const
Definition tinyvector.hxx:913
iterator end()
Definition tinyvector.hxx:864
iterator begin()
Definition tinyvector.hxx:861
Class for fixed size vectors.
Definition tinyvector.hxx:1008
Definition rf_visitors.hxx:1014
double oob_per_tree2
Definition rf_visitors.hxx:1043
MultiArray< 2, double > breiman_per_tree
Definition rf_visitors.hxx:1048
double oob_mean
Definition rf_visitors.hxx:1026
double oob_breiman
Definition rf_visitors.hxx:1036
MultiArray< 2, double > oob_per_tree
Definition rf_visitors.hxx:1023
void visit_at_end(RF &rf, PR &pr)
Definition rf_visitors.hxx:1201
MultiArray< 4, double > oobroc_per_tree
Definition rf_visitors.hxx:1065
double oob_std
Definition rf_visitors.hxx:1029
Definition rf_visitors.hxx:1494
MultiArray< 2, double > distance
Definition rf_visitors.hxx:1522
MultiArray< 2, double > corr_noise
Definition rf_visitors.hxx:1507
MultiArray< 2, double > gini_missc
Definition rf_visitors.hxx:1499
MultiArray< 2, double > similarity
Definition rf_visitors.hxx:1519
ArrayVector< int > numChoices
Definition rf_visitors.hxx:1527
MultiArray< 2, double > noise
Definition rf_visitors.hxx:1503
Definition rf_visitors.hxx:865
double oob_breiman
Definition rf_visitors.hxx:875
void visit_at_end(RF &rf, PR &pr)
Definition rf_visitors.hxx:992
Definition rf_visitors.hxx:784
void visit_after_tree(RF &rf, PR &pr, SM &sm, ST &, int index)
Definition rf_visitors.hxx:807
double oobError
Definition rf_visitors.hxx:788
void visit_at_end(RF &rf, PR &)
Definition rf_visitors.hxx:836
Definition rf_visitors.hxx:585
void visit_internal_node(TR &tr, IntT index, TopT node_t, Feat &features)
Definition rf_visitors.hxx:724
void reset_tree(int tree_id)
Definition rf_visitors.hxx:636
void visit_after_tree(RF &, PR &, SM &, ST &, int)
Definition rf_visitors.hxx:647
void visit_at_beginning(RF &rf, const PR &)
Definition rf_visitors.hxx:628
Definition rf_visitors.hxx:1458
Definition rf_visitors.hxx:236
Definition rf_visitors.hxx:1229
void visit_after_split(Tree &tree, Split &split, Region &, Region &, Region &, Feature_t &, Label_t &)
Definition rf_visitors.hxx:1285
void visit_at_end(RF &rf, PR &)
Definition rf_visitors.hxx:1450
void visit_after_tree(RF &rf, PR &pr, SM &sm, ST &st, int index)
Definition rf_visitors.hxx:1442
void after_tree_ip_impl(RF &rf, PR &pr, SM &sm, ST &, int index)
Definition rf_visitors.hxx:1320
MultiArray< 2, double > variable_importance_
Definition rf_visitors.hxx:1257
Definition rf_visitors.hxx:103
void visit_at_beginning(RF const &rf, PR const &pr)
Definition rf_visitors.hxx:188
void visit_external_node(TR &tr, IntT index, TopT node_t, Feat &features)
Definition rf_visitors.hxx:206
void visit_after_split(Tree &tree, Split &split, Region &parent, Region &leftChild, Region &rightChild, Feature_t &features, Label_t &labels)
Definition rf_visitors.hxx:143
void visit_internal_node(TR &, IntT, TopT, Feat &)
Definition rf_visitors.hxx:216
void visit_after_tree(RF &rf, PR &pr, SM &sm, ST &st, int index)
Definition rf_visitors.hxx:164
void visit_at_end(RF const &rf, PR const &pr)
Definition rf_visitors.hxx:176
double return_val()
Definition rf_visitors.hxx:226
Definition rf_visitors.hxx:256
MultiArrayIndex columnCount(const MultiArrayView< 2, T, C > &x)
Definition matrix.hxx:684
MultiArrayView< 2, T, C > rowVector(MultiArrayView< 2, T, C > const &m, MultiArrayIndex d)
Definition matrix.hxx:697
MultiArrayIndex rowCount(const MultiArrayView< 2, T, C > &x)
Definition matrix.hxx:671
MultiArrayView< 2, T, C > columnVector(MultiArrayView< 2, T, C > const &m, MultiArrayIndex d)
Definition matrix.hxx:727
detail::VisitorNode< A > create_visitor(A &a)
Definition rf_visitors.hxx:345
void writeHDF5(...)
Store array data in an HDF5 file.
Iterator argMax(Iterator first, Iterator last)
Find the maximum element in a sequence.
Definition algorithm.hxx:96
void inspectMultiArray(...)
Call an analyzing functor at every element of a multi-dimensional array.
detail::SelectIntegerType< 32, detail::SignedIntTypes >::type Int32
32-bit signed int
Definition sized_int.hxx:175
#define TIC
Definition timing.hxx:321
#define TOCS
Definition timing.hxx:324