35#ifndef RF_VISITORS_HXX
36#define RF_VISITORS_HXX
39# include "vigra/hdf5impex.hxx"
41#include <vigra/windows.h>
45#include <vigra/metaprogramming.hxx>
46#include <vigra/multi_pointoperators.hxx>
141 template<
class Tree,
class Split,
class Region,
class Feature_t,
class Label_t>
147 Feature_t & features,
162 template<
class RF,
class PR,
class SM,
class ST>
165 ignore_argument(rf,
pr,
sm,
st,index);
174 template<
class RF,
class PR>
177 ignore_argument(rf,
pr);
186 template<
class RF,
class PR>
189 ignore_argument(rf,
pr);
204 template<
class TR,
class IntT,
class TopT,
class Feat>
207 ignore_argument(
tr,index,
node_t,features);
214 template<
class TR,
class IntT,
class TopT,
class Feat>
253template <
class Visitor,
class Next = StopVisiting>
268 next_(stop_), visitor_(
visitor)
271 template<
class Tree,
class Split,
class Region,
class Feature_t,
class Label_t>
272 void visit_after_split(
Tree & tree,
277 Feature_t & features,
280 if(visitor_.is_active())
281 visitor_.visit_after_split(tree, split,
288 template<
class RF,
class PR,
class SM,
class ST>
289 void visit_after_tree(
RF& rf,
PR &
pr,
SM &
sm,
ST &
st,
int index)
291 if(visitor_.is_active())
292 visitor_.visit_after_tree(rf,
pr,
sm,
st, index);
293 next_.visit_after_tree(rf,
pr,
sm,
st, index);
296 template<
class RF,
class PR>
297 void visit_at_beginning(
RF & rf,
PR &
pr)
299 if(visitor_.is_active())
300 visitor_.visit_at_beginning(rf,
pr);
301 next_.visit_at_beginning(rf,
pr);
303 template<
class RF,
class PR>
304 void visit_at_end(
RF & rf,
PR &
pr)
306 if(visitor_.is_active())
307 visitor_.visit_at_end(rf,
pr);
308 next_.visit_at_end(rf,
pr);
311 template<
class TR,
class IntT,
class TopT,
class Feat>
314 if(visitor_.is_active())
315 visitor_.visit_external_node(
tr, index,
node_t,features);
316 next_.visit_external_node(
tr, index,
node_t,features);
318 template<
class TR,
class IntT,
class TopT,
class Feat>
321 if(visitor_.is_active())
322 visitor_.visit_internal_node(
tr, index,
node_t,features);
323 next_.visit_internal_node(
tr, index,
node_t,features);
328 if(visitor_.is_active() && visitor_.has_value())
329 return visitor_.return_val();
330 return next_.return_val();
354template<
class A,
class B>
355detail::VisitorNode<A, detail::VisitorNode<B> >
368template<
class A,
class B,
class C>
369detail::VisitorNode<A, detail::VisitorNode<B, detail::VisitorNode<C> > >
384template<
class A,
class B,
class C,
class D>
385detail::VisitorNode<A, detail::VisitorNode<
B, detail::VisitorNode<
C,
386 detail::VisitorNode<D> > > >
403template<
class A,
class B,
class C,
class D,
class E>
404detail::VisitorNode<A, detail::VisitorNode<
B, detail::VisitorNode<
C,
405 detail::VisitorNode<D, detail::VisitorNode<E> > > > >
425template<
class A,
class B,
class C,
class D,
class E,
427detail::VisitorNode<A, detail::VisitorNode<
B, detail::VisitorNode<
C,
428 detail::VisitorNode<D, detail::VisitorNode<E, detail::VisitorNode<F> > > > > >
450template<
class A,
class B,
class C,
class D,
class E,
452detail::VisitorNode<A, detail::VisitorNode<
B, detail::VisitorNode<
C,
453 detail::VisitorNode<D, detail::VisitorNode<
E, detail::VisitorNode<
F,
454 detail::VisitorNode<G> > > > > > >
456 D & d,
E & e,
F & f,
G & g)
478template<
class A,
class B,
class C,
class D,
class E,
479 class F,
class G,
class H>
480detail::VisitorNode<A, detail::VisitorNode<
B, detail::VisitorNode<
C,
481 detail::VisitorNode<D, detail::VisitorNode<
E, detail::VisitorNode<
F,
482 detail::VisitorNode<G, detail::VisitorNode<H> > > > > > > >
509template<
class A,
class B,
class C,
class D,
class E,
510 class F,
class G,
class H,
class I>
511detail::VisitorNode<A, detail::VisitorNode<
B, detail::VisitorNode<
C,
512 detail::VisitorNode<D, detail::VisitorNode<
E, detail::VisitorNode<
F,
513 detail::VisitorNode<G, detail::VisitorNode<H, detail::VisitorNode<I> > > > > > > > >
541template<
class A,
class B,
class C,
class D,
class E,
542 class F,
class G,
class H,
class I,
class J>
543detail::VisitorNode<A, detail::VisitorNode<
B, detail::VisitorNode<
C,
544 detail::VisitorNode<D, detail::VisitorNode<
E, detail::VisitorNode<
F,
545 detail::VisitorNode<
G, detail::VisitorNode<
H, detail::VisitorNode<
I,
546 detail::VisitorNode<J> > > > > > > > > >
587 bool adjust_thresholds;
597 adjust_thresholds(
false), tree_id(0), last_node_id(0), current_label(0)
599 struct MarginalDistribution
602 Int32 leftTotalCounts;
604 Int32 rightTotalCounts;
611 struct TreeOnlineInformation
613 std::vector<MarginalDistribution> mag_distributions;
614 std::vector<IndexList> index_lists;
616 std::map<int,int> interior_to_index;
618 std::map<int,int> exterior_to_index;
622 std::vector<TreeOnlineInformation> trees_online_information;
626 template<
class RF,
class PR>
630 trees_online_information.resize(rf.options_.tree_count_);
637 trees_online_information[tree_id].mag_distributions.clear();
638 trees_online_information[tree_id].index_lists.clear();
639 trees_online_information[tree_id].interior_to_index.clear();
640 trees_online_information[tree_id].exterior_to_index.clear();
645 template<
class RF,
class PR,
class SM,
class ST>
651 template<
class Tree,
class Split,
class Region,
class Feature_t,
class Label_t>
652 void visit_after_split(
Tree & tree,
657 Feature_t & features,
661 int addr=tree.topology_.
size();
662 if(split.createNode().typeID() == i_ThresholdNode)
664 if(adjust_thresholds)
668 trees_online_information[tree_id].interior_to_index[addr]=
linear_index;
669 trees_online_information[tree_id].mag_distributions.push_back(MarginalDistribution());
671 trees_online_information[tree_id].mag_distributions.back().leftCounts=
leftChild.classCounts_;
672 trees_online_information[tree_id].mag_distributions.back().rightCounts=
rightChild.classCounts_;
674 trees_online_information[tree_id].mag_distributions.back().leftTotalCounts=
leftChild.size_;
675 trees_online_information[tree_id].mag_distributions.back().rightTotalCounts=
rightChild.size_;
677 double gap_left,gap_right;
679 gap_left=features(
leftChild[0],split.bestSplitColumn());
681 if(features(
leftChild[
i],split.bestSplitColumn())>gap_left)
682 gap_left=features(
leftChild[
i],split.bestSplitColumn());
683 gap_right=features(
rightChild[0],split.bestSplitColumn());
685 if(features(
rightChild[
i],split.bestSplitColumn())<gap_right)
686 gap_right=features(
rightChild[
i],split.bestSplitColumn());
687 trees_online_information[tree_id].mag_distributions.back().gap_left=gap_left;
688 trees_online_information[tree_id].mag_distributions.back().gap_right=gap_right;
695 trees_online_information[tree_id].exterior_to_index[addr]=
linear_index;
697 trees_online_information[tree_id].index_lists.push_back(IndexList());
699 trees_online_information[tree_id].index_lists.back().resize(parent.size_,0);
700 std::copy(parent.begin_,parent.end_,trees_online_information[tree_id].index_lists.back().
begin());
703 void add_to_index_list(
int tree,
int node,
int index)
707 TreeOnlineInformation &ti=trees_online_information[tree];
708 ti.index_lists[ti.exterior_to_index[node]].push_back(index);
710 void move_exterior_node(
int src_tree,
int src_index,
int dst_tree,
int dst_index)
714 trees_online_information[dst_tree].exterior_to_index[dst_index]=trees_online_information[src_tree].exterior_to_index[src_index];
715 trees_online_information[src_tree].exterior_to_index.erase(src_index);
722 template<
class TR,
class IntT,
class TopT,
class Feat>
726 if(adjust_thresholds)
728 vigra_assert(
node_t==i_ThresholdNode,
"We can only visit threshold nodes");
731 TreeOnlineInformation &
ti=trees_online_information[tree_id];
732 MarginalDistribution &
m=
ti.mag_distributions[
ti.interior_to_index[index]];
733 if(value>
m.gap_left && value<
m.gap_right)
736 if(
m.leftCounts[current_label]/
double(
m.leftTotalCounts)>
m.rightCounts[current_label]/
double(
m.rightTotalCounts))
751 ++
m.rightTotalCounts;
752 ++
m.rightCounts[current_label];
757 ++
m.rightCounts[current_label];
805 template<
class RF,
class PR,
class SM,
class ST>
809 if(
int(oobCount.
size()) != rf.ext_param_.row_count_)
811 oobCount.resize(rf.ext_param_.row_count_, 0);
812 oobErrorCount.resize(rf.ext_param_.row_count_, 0);
815 for(
int l = 0;
l < rf.ext_param_.row_count_; ++
l)
822 .predictLabel(rowVector(
pr.features(),
l))
823 !=
pr.response()(
l,0))
834 template<
class RF,
class PR>
880 void save(std::string
filen, std::string
pathn)
884 const char* filename =
filen.c_str();
893 template<
class RF,
class PR>
894 void visit_at_beginning(
RF & rf,
PR &)
896 class_count = rf.class_count();
897 tmp_prob.reshape(Shp(1, class_count), 0);
898 prob_oob.reshape(Shp(rf.ext_param().row_count_,class_count), 0);
899 is_weighted = rf.options().predict_weighted_;
900 indices.resize(rf.ext_param().row_count_);
901 if(
int(oobCount.
size()) != rf.ext_param_.row_count_)
903 oobCount.reshape(Shp(rf.ext_param_.row_count_, 1), 0);
905 for(
int ii = 0;
ii < rf.ext_param().row_count_; ++
ii)
911 template<
class RF,
class PR,
class SM,
class ST>
912 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_shuffle(indices.
begin(), indices.
end());
924 for(
int ii = 0; ii < rf.ext_param_.row_count_; ++ii)
926 if(!sm.is_used()[indices[ii]] && cts[pr.response()(indices[ii], 0)] < 40000)
928 oob_indices.push_back(indices[ii]);
929 ++cts[pr.response()(indices[ii], 0)];
932 for(
unsigned int ll = 0; ll < oob_indices.size(); ++ll)
935 ++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])
970 int pos = rf.tree(index).getToLeaf(
rowVector(pr.features(),ll));
971 Node<e_ConstProbNode> node ( rf.tree(index).topology_,
972 rf.tree(index).parameters_,
975 for(
int ii = 0; ii < class_count; ++ii)
977 tmp_prob[ii] = node.prob_begin()[ii];
981 for(
int ii = 0; ii < class_count; ++ii)
982 tmp_prob[ii] = tmp_prob[ii] * (*(node.prob_begin()-1));
993 template<
class RF,
class PR>
997 int totalOobCount =0;
1003 if(
argMax(rowVector(prob_oob,
ll)) !=
pr.response()(
ll, 0))
1074 void save(std::string
filen, std::string
pathn)
1078 const char* filename =
filen.c_str();
1096 template<
class RF,
class PR>
1097 void visit_at_beginning(
RF & rf,
PR &)
1099 class_count = rf.class_count();
1100 if(class_count == 2)
1104 tmp_prob.reshape(Shp(1, class_count), 0);
1105 prob_oob.reshape(Shp(rf.ext_param().row_count_,class_count), 0);
1106 is_weighted = rf.options().predict_weighted_;
1110 if(
int(oobCount.
size()) != rf.ext_param_.row_count_)
1112 oobCount.reshape(Shp(rf.ext_param_.row_count_, 1), 0);
1113 oobErrorCount.reshape(Shp(rf.ext_param_.row_count_,1), 0);
1117 template<
class RF,
class PR,
class SM,
class ST>
1118 void visit_after_tree(RF& rf, PR & pr, SM & sm, ST &,
int index)
1123 for(
int ll = 0; ll < rf.ext_param_.row_count_; ++ll)
1126 if(!sm.is_used()[ll])
1134 int pos = rf.tree(index).getToLeaf(rowVector(pr.features(),ll));
1135 Node<e_ConstProbNode> node ( rf.tree(index).topology_,
1136 rf.tree(index).parameters_,
1139 for(
int ii = 0; ii < class_count; ++ii)
1141 tmp_prob[ii] = node.prob_begin()[ii];
1145 for(
int ii = 0; ii < class_count; ++ii)
1146 tmp_prob[ii] = tmp_prob[ii] * (*(node.prob_begin()-1));
1149 int label =
argMax(tmp_prob);
1151 if(label != pr.response()(ll, 0))
1156 ++oobErrorCount[ll];
1160 int breimanstyle = 0;
1161 int totalOobCount = 0;
1162 for(
int ll=0; ll < static_cast<int>(rf.ext_param_.row_count_); ++ll)
1179 MultiArrayView<3, double> current_roc
1181 for(
int gg = 0; gg < current_roc.shape(2); ++gg)
1183 for(
int ll=0; ll < static_cast<int>(rf.ext_param_.row_count_); ++ll)
1187 int pred = prob_oob(ll, 1) > (double(gg)/double(current_roc.shape(2)))?
1189 current_roc(pr.response()(ll, 0), pred, gg)+= 1;
1192 current_roc.bindOuter(gg)/= totalOobCount;
1196 oob_per_tree[index] = double(wrong_oob)/double(total_oob);
1202 template<
class RF,
class PR>
1207 int totalOobCount =0;
1213 if(
argMax(rowVector(prob_oob,
ll)) !=
pr.response()(
ll, 0))
1260 int repetition_count_;
1264 void save(std::string filename, std::string
prefix)
1286 template<
class Tree,
class Split,
class Region,
class Feature_t,
class Label_t>
1297 Int32 const class_count = tree.ext_param_.class_count_;
1298 Int32 const column_count = tree.ext_param_.column_count_;
1307 if(split.createNode().typeID() == i_ThresholdNode)
1311 += split.region_gini_ - split.minGini();
1321 template<
class RF,
class PR,
class SM,
class ST>
1325 Int32 column_count = rf.ext_param_.column_count_;
1326 Int32 class_count = rf.ext_param_.class_count_;
1337 typedef typename FeatureArray::value_type
FeatureValue;
1343 ArrayVector<Int32>::iterator
1345 for(
int ii = 0;
ii < rf.ext_param_.row_count_; ++
ii)
1346 if(!
sm.is_used()[
ii])
1353#ifdef CLASSIFIER_TEST
1375 .predictLabel(rowVector(features, *iter))
1376 ==
pr.response()(*iter, 0))
1385 for(
int ii = 0;
ii < column_count; ++
ii)
1398 for(
int rr = 0;
rr < repetition_count_; ++
rr)
1402 for(
int jj = n-1;
jj >= 1; --
jj)
1412 .predictLabel(rowVector(features, *iter))
1413 ==
pr.response()(*iter, 0))
1443 template<
class RF,
class PR,
class SM,
class ST>
1451 template<
class RF,
class PR>
1464 template<
class RF,
class PR,
class SM,
class ST>
1465 void visit_after_tree(
RF& rf,
PR &,
SM &,
ST &,
int index){
1466 if(index != rf.options().tree_count_-1) {
1467 std::cout <<
"\r[" << std::setw(10) << (index+1)/
static_cast<double>(rf.options().tree_count_)*100 <<
"%]"
1468 <<
" (" << index+1 <<
" of " << rf.options().tree_count_ <<
") done" << std::flush;
1471 std::cout <<
"\r[" << std::setw(10) << 100.0 <<
"%]" << std::endl;
1475 template<
class RF,
class PR>
1476 void visit_at_end(
RF const & rf,
PR const &) {
1477 std::string a =
TOCS;
1478 std::cout <<
"all " << rf.options().tree_count_ <<
" trees have been learned in " << a << std::endl;
1481 template<
class RF,
class PR>
1482 void visit_at_beginning(
RF const & rf,
PR const &) {
1484 std::cout <<
"growing random forest, which will have " << rf.options().tree_count_ <<
" trees" << std::endl;
1532 void save(std::string, std::string)
1550 template<
class RF,
class PR>
1551 void visit_at_beginning(
RF const & rf,
PR &
pr)
1554 int n = rf.ext_param_.column_count_;
1557 corr_l.reshape(Shp(n +1, 10));
1560 noise_l.reshape(Shp(
pr.features().shape(0), 10));
1564 noise[
ii] = random.uniform53();
1565 noise_l[
ii] = random.uniform53() > 0.5;
1567 bgfunc = ColumnDecisionFunctor( rf.ext_param_);
1568 tmp_labels.reshape(
pr.response().shape());
1573 template<
class RF,
class PR>
1574 void visit_at_end(
RF const &,
PR const &)
1588 for(
int jj = 0; jj < rC; ++jj)
1594 FindMinMax<double> minmax;
1597 for(
int jj = 0; jj < rC; ++jj)
1604 for(
int jj = 0; jj < rC; ++jj)
1607 FindMinMax<double> minmax2;
1609 for(
int jj = 0; jj < rC; ++jj)
1615 template<
class Tree,
class Split,
class Region,
class Feature_t,
class Label_t>
1616 void visit_after_split( Tree &,
1621 Feature_t & features,
1624 if(split.createNode().typeID() == i_ThresholdNode)
1628 for(
int ii = 0; ii < parent.size(); ++ii)
1630 tmp_labels[parent[ii]]
1631 = (features(parent[ii], split.bestSplitColumn()) < split.bestSplitThreshold());
1632 ++tmp_cc[tmp_labels[parent[ii]]];
1634 double region_gini = bgfunc.loss_of_region(tmp_labels,
1639 int n = split.bestSplitColumn();
1643 for(
int k = 0; k < features.shape(1); ++k)
1649 wgini = (region_gini - bgfunc.min_gini_);
1653 for(
int k = 0; k < 10; ++k)
1659 wgini = (region_gini - bgfunc.min_gini_);
1664 for(
int k = 0; k < 10; ++k)
1670 wgini = (region_gini - bgfunc.min_gini_);
1674 bgfunc(labels, tmp_labels, parent.begin(), parent.end(),tmp_cc);
1675 wgini = (region_gini - bgfunc.min_gini_);
1679 region_gini = split.region_gini_;
1681 Node<i_ThresholdNode> node(split.createNode());
1684 +=split.region_gini_ - split.minGini();
1686 for(
int k = 0; k < 10; ++k)
1691 parent.classCounts());
1697 for(
int k = 0; k < tree.ext_param_.actual_mtry_; ++k)
1699 wgini = region_gini - split.min_gini_[k];
1702 split.splitColumns[k])
1706 for(
int k=tree.ext_param_.actual_mtry_; k<features.shape(1); ++k)
1708 split.bgfunc(
columnVector(features, split.splitColumns[k]),
1711 parent.classCounts());
1712 wgini = region_gini - split.bgfunc.min_gini_;
1714 split.splitColumns[k]) += wgini;
1721 SortSamplesByDimensions<Feature_t>
1722 sorter(features, split.bestSplitColumn(), split.bestSplitThreshold());
1723 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:1528
const difference_type & shape() const
Definition multi_array.hxx:1648
MultiArrayView< N-M, T, StrideTag > bindOuter(const TinyVector< Index, M > &d) const
Definition multi_array.hxx:2184
difference_type_1 size() const
Definition multi_array.hxx:1641
MultiArrayView< N, T, StridedArrayTag > transpose() const
Definition multi_array.hxx:1567
void reshape(const difference_type &shape)
Definition multi_array.hxx:2861
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:1016
double oob_per_tree2
Definition rf_visitors.hxx:1045
MultiArray< 2, double > breiman_per_tree
Definition rf_visitors.hxx:1050
double oob_mean
Definition rf_visitors.hxx:1028
double oob_breiman
Definition rf_visitors.hxx:1038
MultiArray< 2, double > oob_per_tree
Definition rf_visitors.hxx:1025
void visit_at_end(RF &rf, PR &pr)
Definition rf_visitors.hxx:1203
MultiArray< 4, double > oobroc_per_tree
Definition rf_visitors.hxx:1067
double oob_std
Definition rf_visitors.hxx:1031
Definition rf_visitors.hxx:1496
MultiArray< 2, double > distance
Definition rf_visitors.hxx:1524
MultiArray< 2, double > corr_noise
Definition rf_visitors.hxx:1509
MultiArray< 2, double > gini_missc
Definition rf_visitors.hxx:1501
MultiArray< 2, double > similarity
Definition rf_visitors.hxx:1521
ArrayVector< int > numChoices
Definition rf_visitors.hxx:1529
MultiArray< 2, double > noise
Definition rf_visitors.hxx:1505
Definition rf_visitors.hxx:864
double oob_breiman
Definition rf_visitors.hxx:874
void visit_at_end(RF &rf, PR &pr)
Definition rf_visitors.hxx:994
Definition rf_visitors.hxx:783
void visit_after_tree(RF &rf, PR &pr, SM &sm, ST &, int index)
Definition rf_visitors.hxx:806
double oobError
Definition rf_visitors.hxx:787
void visit_at_end(RF &rf, PR &)
Definition rf_visitors.hxx:835
Definition rf_visitors.hxx:584
void visit_internal_node(TR &tr, IntT index, TopT node_t, Feat &features)
Definition rf_visitors.hxx:723
void reset_tree(int tree_id)
Definition rf_visitors.hxx:635
void visit_after_tree(RF &, PR &, SM &, ST &, int)
Definition rf_visitors.hxx:646
void visit_at_beginning(RF &rf, const PR &)
Definition rf_visitors.hxx:627
Definition rf_visitors.hxx:1460
Definition rf_visitors.hxx:235
Definition rf_visitors.hxx:1231
void visit_after_split(Tree &tree, Split &split, Region &, Region &, Region &, Feature_t &, Label_t &)
Definition rf_visitors.hxx:1287
void visit_at_end(RF &rf, PR &)
Definition rf_visitors.hxx:1452
void visit_after_tree(RF &rf, PR &pr, SM &sm, ST &st, int index)
Definition rf_visitors.hxx:1444
void after_tree_ip_impl(RF &rf, PR &pr, SM &sm, ST &, int index)
Definition rf_visitors.hxx:1322
MultiArray< 2, double > variable_importance_
Definition rf_visitors.hxx:1259
Definition rf_visitors.hxx:102
void visit_at_beginning(RF const &rf, PR const &pr)
Definition rf_visitors.hxx:187
void visit_external_node(TR &tr, IntT index, TopT node_t, Feat &features)
Definition rf_visitors.hxx:205
void visit_after_split(Tree &tree, Split &split, Region &parent, Region &leftChild, Region &rightChild, Feature_t &features, Label_t &labels)
Definition rf_visitors.hxx:142
void visit_internal_node(TR &, IntT, TopT, Feat &)
Definition rf_visitors.hxx:215
void visit_after_tree(RF &rf, PR &pr, SM &sm, ST &st, int index)
Definition rf_visitors.hxx:163
void visit_at_end(RF const &rf, PR const &pr)
Definition rf_visitors.hxx:175
double return_val()
Definition rf_visitors.hxx:225
Definition rf_visitors.hxx:255
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:344
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:322
#define TOCS
Definition timing.hxx:325