vigra - vigra/random_forest/rf_common.hxx Source File

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rf_common.hxx |

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2 /* */
3 /* Copyright 2008-2009 by Ullrich Koethe and Rahul Nair */
4 /* */
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7 /* http://hci.iwr.uni-heidelberg.de/vigra/ */
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9 /* ullrich.koethe@iwr.uni-heidelberg.de or */
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35
36
37 #ifndef VIGRA_RF_COMMON_HXX
38 #define VIGRA_RF_COMMON_HXX
39
40 namespace vigra
41 {
42
43
44 struct ClassificationTag
45 {};
46
47 struct RegressionTag
48 {};
49
50 namespace detail
51 {
52 class RF_DEFAULT;
53 }
54 inline detail::RF_DEFAULT& rf_default();
55 namespace detail
56 {
57
58 /* \brief singleton default tag class -
59 *
60 * use the rf_default() factory function to use the tag.
61 * \sa RandomForest<>::learn();
62 */
63 class RF_DEFAULT
64 {
65 private:
66 RF_DEFAULT()
67 {}
68 public:
69 friend RF_DEFAULT& ::vigra::rf_default();
70
71 /** ok workaround for automatic choice of the decisiontree
72 * stackentry.
73 */
74 };
75
76 /* \brief chooses between default type and type supplied
77 *
78 * This is an internal class and you shouldn't really care about it.
79 * Just pass on used in RandomForest.learn()
80 * Usage:
81 *\code
82 * // example: use container type supplied by user or ArrayVector if
83 * // rf_default() was specified as argument;
84 * template<class Container_t>
85 * void do_some_foo(Container_t in)
86 * {
87 * typedef ArrayVector<int> Default_Container_t;
88 * Default_Container_t default_value;
89 * Value_Chooser<Container_t, Default_Container_t>
90 * choose(in, default_value);
91 *
92 * // if the user didn't care and the in was of type
93 * // RF_DEFAULT then default_value is used.
94 * do_some_more_foo(choose.value());
95 * }
96 * Value_Chooser choose_val<Type, Default_Type>
97 *\endcode
98 */
99 template<class T, class C>
100 class Value_Chooser
101 {
102 public:
103 typedef T type;
104 static T & choose(T & t, C &)
105 {
106 return t;
107 }
108 };
109
110 template<class C>
111 class Value_Chooser<detail::RF_DEFAULT, C>
112 {
113 public:
114 typedef C type;
115
116 static C & choose(detail::RF_DEFAULT &, C & c)
117 {
118 return c;
119 }
120 };
121
122
123
124
125 } //namespace detail
126
127
128 /**\brief factory function to return a RF_DEFAULT tag
129 * \sa RandomForest<>::learn()
130 */
131 detail::RF_DEFAULT& rf_default()
132 {
133 static detail::RF_DEFAULT result;
134 return result;
135 }
136
137 /** tags used with the RandomForestOptions class
138 * \sa RF_Traits::Option_t
139 */
140 enum RF_OptionTag { RF_EQUAL,
141 RF_PROPORTIONAL,
142 RF_EXTERNAL,
143 RF_NONE,
144 RF_FUNCTION,
145 RF_LOG,
146 RF_SQRT,
147 RF_CONST,
148 RF_ALL};
149
150
151 /** \addtogroup MachineLearning
152 **/
153 //@{
154
155 /**\brief Options object for the random forest
156 *
157 * usage:
158 * RandomForestOptions a = RandomForestOptions()
159 * .param1(value1)
160 * .param2(value2)
161 * ...
162 *
163 * This class only contains options/parameters that are not problem
164 * dependent. The ProblemSpec class contains methods to set class weights
165 * if necessary.
166 *
167 * Note that the return value of all methods is *this which makes
168 * concatenating of options as above possible.
169 */
170 class RandomForestOptions
171 {
172 public:
173 /**\name sampling options*/
174 /*\{*/
175 // look at the member access functions for documentation
176 double training_set_proportion_;
177 int training_set_size_;
178 int (*training_set_func_)(int);
179 RF_OptionTag
180 training_set_calc_switch_;
181
182 bool sample_with_replacement_;
183 RF_OptionTag
184 stratification_method_;
185
186
187 /**\name general random forest options
188 *
189 * these usually will be used by most split functors and
190 * stopping predicates
191 */
192 /*\{*/
193 RF_OptionTag mtry_switch_;
194 int mtry_;
195 int (*mtry_func_)(int) ;
196
197 bool predict_weighted_;
198 int tree_count_;
199 int min_split_node_size_;
200 bool prepare_online_learning_;
201 /*\}*/
202
203 typedef ArrayVector<double> double_array;
204 typedef std::map<std::string, double_array> map_type;
205
206 int serialized_size() const
207 {
208 return 12;
209 }
210
211
212 bool operator==(RandomForestOptions & rhs) const
213 {
214 bool result = true;
215 #define COMPARE(field) result = result && (this->field == rhs.field);
216 COMPARE(training_set_proportion_);
217 COMPARE(training_set_size_);
218 COMPARE(training_set_calc_switch_);
219 COMPARE(sample_with_replacement_);
220 COMPARE(stratification_method_);
221 COMPARE(mtry_switch_);
222 COMPARE(mtry_);
223 COMPARE(tree_count_);
224 COMPARE(min_split_node_size_);
225 COMPARE(predict_weighted_);
226 #undef COMPARE
227
228 return result;
229 }
230 bool operator!=(RandomForestOptions & rhs_) const
231 {
232 return !(*this == rhs_);
233 }
234 template<class Iter>
235 void unserialize(Iter const & begin, Iter const & end)
236 {
237 Iter iter = begin;
238 vigra_precondition(static_cast<int>(end - begin) == serialized_size(),
239 "RandomForestOptions::unserialize():"
240 "wrong number of parameters");
241 #define PULL(item_, type_) item_ = type_(*iter); ++iter;
242 PULL(training_set_proportion_, double);
243 PULL(training_set_size_, int);
244 ++iter; //PULL(training_set_func_, double);
245 PULL(training_set_calc_switch_, (RF_OptionTag)int);
246 PULL(sample_with_replacement_, 0 != );
247 PULL(stratification_method_, (RF_OptionTag)int);
248 PULL(mtry_switch_, (RF_OptionTag)int);
249 PULL(mtry_, int);
250 ++iter; //PULL(mtry_func_, double);
251 PULL(tree_count_, int);
252 PULL(min_split_node_size_, int);
253 PULL(predict_weighted_, 0 !=);
254 #undef PULL
255 }
256 template<class Iter>
257 void serialize(Iter const & begin, Iter const & end) const
258 {
259 Iter iter = begin;
260 vigra_precondition(static_cast<int>(end - begin) == serialized_size(),
261 "RandomForestOptions::serialize():"
262 "wrong number of parameters");
263 #define PUSH(item_) *iter = double(item_); ++iter;
264 PUSH(training_set_proportion_);
265 PUSH(training_set_size_);
266 if(training_set_func_ != 0)
267 {
268 PUSH(1);
269 }
270 else
271 {
272 PUSH(0);
273 }
274 PUSH(training_set_calc_switch_);
275 PUSH(sample_with_replacement_);
276 PUSH(stratification_method_);
277 PUSH(mtry_switch_);
278 PUSH(mtry_);
279 if(mtry_func_ != 0)
280 {
281 PUSH(1);
282 }
283 else
284 {
285 PUSH(0);
286 }
287 PUSH(tree_count_);
288 PUSH(min_split_node_size_);
289 PUSH(predict_weighted_);
290 #undef PUSH
291 }
292
293 void make_from_map(map_type & in) // -> const: .operator[] -> .find
294 {
295 #define PULL(item_, type_) item_ = type_(in[#item_][0]);
296 #define PULLBOOL(item_, type_) item_ = type_(in[#item_][0] > 0);
297 PULL(training_set_proportion_,double);
298 PULL(training_set_size_, int);
299 PULL(mtry_, int);
300 PULL(tree_count_, int);
301 PULL(min_split_node_size_, int);
302 PULLBOOL(sample_with_replacement_, bool);
303 PULLBOOL(prepare_online_learning_, bool);
304 PULLBOOL(predict_weighted_, bool);
305
306 PULL(training_set_calc_switch_, (RF_OptionTag)(int));
307
308 PULL(stratification_method_, (RF_OptionTag)(int));
309 PULL(mtry_switch_, (RF_OptionTag)(int));
310
311 /*don't pull*/
312 //PULL(mtry_func_!=0, int);
313 //PULL(training_set_func,int);
314 #undef PULL
315 #undef PULLBOOL
316 }
317 void make_map(map_type & in) const
318 {
319 #define PUSH(item_, type_) in[#item_] = double_array(1, double(item_));
320 #define PUSHFUNC(item_, type_) in[#item_] = double_array(1, double(item_!=0));
321 PUSH(training_set_proportion_,double);
322 PUSH(training_set_size_, int);
323 PUSH(mtry_, int);
324 PUSH(tree_count_, int);
325 PUSH(min_split_node_size_, int);
326 PUSH(sample_with_replacement_, bool);
327 PUSH(prepare_online_learning_, bool);
328 PUSH(predict_weighted_, bool);
329
330 PUSH(training_set_calc_switch_, RF_OptionTag);
331 PUSH(stratification_method_, RF_OptionTag);
332 PUSH(mtry_switch_, RF_OptionTag);
333
334 PUSHFUNC(mtry_func_, int);
335 PUSHFUNC(training_set_func_,int);
336 #undef PUSH
337 #undef PUSHFUNC
338 }
339
340
341 /**\brief create a RandomForestOptions object with default initialisation.
342 *
343 * look at the other member functions for more information on default
344 * values
345 */
346 RandomForestOptions()
347 :
348 training_set_proportion_(1.0),
349 training_set_size_(0),
350 training_set_func_(0),
351 training_set_calc_switch_(RF_PROPORTIONAL),
352 sample_with_replacement_(true),
353 stratification_method_(RF_NONE),
354 mtry_switch_(RF_SQRT),
355 mtry_(0),
356 mtry_func_(0),
357 predict_weighted_(false),
358 tree_count_(256),
359 min_split_node_size_(1),
360 prepare_online_learning_(false)
361 {}
362
363 /**\brief specify stratification strategy
364 *
365 * default: RF_NONE
366 * possible values: RF_EQUAL, RF_PROPORTIONAL,
367 * RF_EXTERNAL, RF_NONE
368 * RF_EQUAL: get equal amount of samples per class.
369 * RF_PROPORTIONAL: sample proportional to fraction of class samples
370 * in population
371 * RF_EXTERNAL: strata_weights_ field of the ProblemSpec_t object
372 * has been set externally. (defunct)
373 */
374 RandomForestOptions & use_stratification(RF_OptionTag in)
375 {
376 vigra_precondition(in == RF_EQUAL ||
377 in == RF_PROPORTIONAL ||
378 in == RF_EXTERNAL ||
379 in == RF_NONE,
380 "RandomForestOptions::use_stratification()"
381 "input must be RF_EQUAL, RF_PROPORTIONAL,"
382 "RF_EXTERNAL or RF_NONE");
383 stratification_method_ = in;
384 return *this;
385 }
386
387 RandomForestOptions & prepare_online_learning(bool in)
388 {
389 prepare_online_learning_=in;
390 return *this;
391 }
392
393 /**\brief sample from training population with or without replacement?
394 *
395 * <br> Default: true
396 */
397 RandomForestOptions & sample_with_replacement(bool in)
398 {
399 sample_with_replacement_ = in;
400 return *this;
401 }
402
403 /**\brief specify the fraction of the total number of samples
404 * used per tree for learning.
405 *
406 * This value should be in [0.0 1.0] if sampling without
407 * replacement has been specified.
408 *
409 * <br> default : 1.0
410 */
411 RandomForestOptions & samples_per_tree(double in)
412 {
413 training_set_proportion_ = in;
414 training_set_calc_switch_ = RF_PROPORTIONAL;
415 return *this;
416 }
417
418 /**\brief directly specify the number of samples per tree
419 */
420 RandomForestOptions & samples_per_tree(int in)
421 {
422 training_set_size_ = in;
423 training_set_calc_switch_ = RF_CONST;
424 return *this;
425 }
426
427 /**\brief use external function to calculate the number of samples each
428 * tree should be learnt with.
429 *
430 * \param in function pointer that takes the number of rows in the
431 * learning data and outputs the number samples per tree.
432 */
433 RandomForestOptions & samples_per_tree(int (*in)(int))
434 {
435 training_set_func_ = in;
436 training_set_calc_switch_ = RF_FUNCTION;
437 return *this;
438 }
439
440 /**\brief weight each tree with number of samples in that node
441 */
442 RandomForestOptions & predict_weighted()
443 {
444 predict_weighted_ = true;
445 return *this;
446 }
447
448 /**\brief use built in mapping to calculate mtry
449 *
450 * Use one of the built in mappings to calculate mtry from the number
451 * of columns in the input feature data.
452 * \param in possible values: RF_LOG, RF_SQRT or RF_ALL
453 * <br> default: RF_SQRT.
454 */
455 RandomForestOptions & features_per_node(RF_OptionTag in)
456 {
457 vigra_precondition(in == RF_LOG ||
458 in == RF_SQRT||
459 in == RF_ALL,
460 "RandomForestOptions()::features_per_node():"
461 "input must be of type RF_LOG or RF_SQRT");
462 mtry_switch_ = in;
463 return *this;
464 }
465
466 /**\brief Set mtry to a constant value
467 *
468 * mtry is the number of columns/variates/variables randomly chosen
469 * to select the best split from.
470 *
471 */
472 RandomForestOptions & features_per_node(int in)
473 {
474 mtry_ = in;
475 mtry_switch_ = RF_CONST;
476 return *this;
477 }
478
479 /**\brief use a external function to calculate mtry
480 *
481 * \param in function pointer that takes int (number of columns
482 * of the and outputs int (mtry)
483 */
484 RandomForestOptions & features_per_node(int(*in)(int))
485 {
486 mtry_func_ = in;
487 mtry_switch_ = RF_FUNCTION;
488 return *this;
489 }
490
491 /** How many trees to create?
492 *
493 * <br> Default: 255.
494 */
495 RandomForestOptions & tree_count(int in)
496 {
497 tree_count_ = in;
498 return *this;
499 }
500
501 /**\brief Number of examples required for a node to be split.
502 *
503 * When the number of examples in a node is below this number,
504 * the node is not split even if class separation is not yet perfect.
505 * Instead, the node returns the proportion of each class
506 * (among the remaining examples) during the prediction phase.
507 * <br> Default: 1 (complete growing)
508 */
509 RandomForestOptions & min_split_node_size(int in)
510 {
511 min_split_node_size_ = in;
512 return *this;
513 }
514 };
515
516
517 /** \brief problem types
518 */
519 enum Problem_t{REGRESSION, CLASSIFICATION, CHECKLATER};
520
521
522 /** \brief problem specification class for the random forest.
523 *
524 * This class contains all the problem specific parameters the random
525 * forest needs for learning. Specification of an instance of this class
526 * is optional as all necessary fields will be computed prior to learning
527 * if not specified.
528 *
529 * if needed usage is similar to that of RandomForestOptions
530 */
531
532 template<class LabelType = double>
533 class ProblemSpec
534 {
535
536
537 public:
538
539 /** \brief problem class
540 */
541
542 typedef LabelType Label_t;
543 ArrayVector<Label_t> classes;
544 typedef ArrayVector<double> double_array;
545 typedef std::map<std::string, double_array> map_type;
546
547 int column_count_; // number of features
548 int class_count_; // number of classes
549 int row_count_; // number of samples
550
551 int actual_mtry_; // mtry used in training
552 int actual_msample_; // number if in-bag samples per tree
553
554 Problem_t problem_type_; // classification or regression
555
556 int used_; // this ProblemSpec is valid
557 ArrayVector<double> class_weights_; // if classes have different importance
558 int is_weighted_; // class_weights_ are used
559 double precision_; // termination criterion for regression loss
560 int response_size_;
561
562 template<class T>
563 void to_classlabel(int index, T & out) const
564 {
565 out = T(classes[index]);
566 }
567 template<class T>
568 int to_classIndex(T index) const
569 {
570 return std::find(classes.begin(), classes.end(), index) - classes.begin();
571 }
572
573 #define EQUALS(field) field(rhs.field)
574 ProblemSpec(ProblemSpec const & rhs)
575 :
576 EQUALS(column_count_),
577 EQUALS(class_count_),
578 EQUALS(row_count_),
579 EQUALS(actual_mtry_),
580 EQUALS(actual_msample_),
581 EQUALS(problem_type_),
582 EQUALS(used_),
583 EQUALS(class_weights_),
584 EQUALS(is_weighted_),
585 EQUALS(precision_),
586 EQUALS(response_size_)
587 {
588 std::back_insert_iterator<ArrayVector<Label_t> >
589 iter(classes);
590 std::copy(rhs.classes.begin(), rhs.classes.end(), iter);
591 }
592 #undef EQUALS
593 #define EQUALS(field) field(rhs.field)
594 template<class T>
595 ProblemSpec(ProblemSpec<T> const & rhs)
596 :
597 EQUALS(column_count_),
598 EQUALS(class_count_),
599 EQUALS(row_count_),
600 EQUALS(actual_mtry_),
601 EQUALS(actual_msample_),
602 EQUALS(problem_type_),
603 EQUALS(used_),
604 EQUALS(class_weights_),
605 EQUALS(is_weighted_),
606 EQUALS(precision_),
607 EQUALS(response_size_)
608 {
609 std::back_insert_iterator<ArrayVector<Label_t> >
610 iter(classes);
611 std::copy(rhs.classes.begin(), rhs.classes.end(), iter);
612 }
613 #undef EQUALS
614
615 #define EQUALS(field) (this->field = rhs.field);
616 ProblemSpec & operator=(ProblemSpec const & rhs)
617 {
618 EQUALS(column_count_);
619 EQUALS(class_count_);
620 EQUALS(row_count_);
621 EQUALS(actual_mtry_);
622 EQUALS(actual_msample_);
623 EQUALS(problem_type_);
624 EQUALS(used_);
625 EQUALS(is_weighted_);
626 EQUALS(precision_);
627 EQUALS(response_size_)
628 class_weights_.clear();
629 std::back_insert_iterator<ArrayVector<double> >
630 iter2(class_weights_);
631 std::copy(rhs.class_weights_.begin(), rhs.class_weights_.end(), iter2);
632 classes.clear();
633 std::back_insert_iterator<ArrayVector<Label_t> >
634 iter(classes);
635 std::copy(rhs.classes.begin(), rhs.classes.end(), iter);
636 return *this;
637 }
638
639 template<class T>
640 ProblemSpec<Label_t> & operator=(ProblemSpec<T> const & rhs)
641 {
642 EQUALS(column_count_);
643 EQUALS(class_count_);
644 EQUALS(row_count_);
645 EQUALS(actual_mtry_);
646 EQUALS(actual_msample_);
647 EQUALS(problem_type_);
648 EQUALS(used_);
649 EQUALS(is_weighted_);
650 EQUALS(precision_);
651 EQUALS(response_size_)
652 class_weights_.clear();
653 std::back_insert_iterator<ArrayVector<double> >
654 iter2(class_weights_);
655 std::copy(rhs.class_weights_.begin(), rhs.class_weights_.end(), iter2);
656 classes.clear();
657 std::back_insert_iterator<ArrayVector<Label_t> >
658 iter(classes);
659 std::copy(rhs.classes.begin(), rhs.classes.end(), iter);
660 return *this;
661 }
662 #undef EQUALS
663
664 template<class T>
665 bool operator==(ProblemSpec<T> const & rhs)
666 {
667 bool result = true;
668 #define COMPARE(field) result = result && (this->field == rhs.field);
669 COMPARE(column_count_);
670 COMPARE(class_count_);
671 COMPARE(row_count_);
672 COMPARE(actual_mtry_);
673 COMPARE(actual_msample_);
674 COMPARE(problem_type_);
675 COMPARE(is_weighted_);
676 COMPARE(precision_);
677 COMPARE(used_);
678 COMPARE(class_weights_);
679 COMPARE(classes);
680 COMPARE(response_size_)
681 #undef COMPARE
682 return result;
683 }
684
685 bool operator!=(ProblemSpec & rhs)
686 {
687 return !(*this == rhs);
688 }
689
690
691 size_t serialized_size() const
692 {
693 return 10 + class_count_ *int(is_weighted_+1);
694 }
695
696
697 template<class Iter>
698 void unserialize(Iter const & begin, Iter const & end)
699 {
700 Iter iter = begin;
701 vigra_precondition(end - begin >= 10,
702 "ProblemSpec::unserialize():"
703 "wrong number of parameters");
704 #define PULL(item_, type_) item_ = type_(*iter); ++iter;
705 PULL(column_count_,int);
706 PULL(class_count_, int);
707
708 vigra_precondition(end - begin >= 10 + class_count_,
709 "ProblemSpec::unserialize(): 1");
710 PULL(row_count_, int);
711 PULL(actual_mtry_,int);
712 PULL(actual_msample_, int);
713 PULL(problem_type_, Problem_t);
714 PULL(is_weighted_, int);
715 PULL(used_, int);
716 PULL(precision_, double);
717 PULL(response_size_, int);
718 if(is_weighted_)
719 {
720 vigra_precondition(end - begin == 10 + 2*class_count_,
721 "ProblemSpec::unserialize(): 2");
722 class_weights_.insert(class_weights_.end(),
723 iter,
724 iter + class_count_);
725 iter += class_count_;
726 }
727 classes.insert(classes.end(), iter, end);
728 #undef PULL
729 }
730
731
732 template<class Iter>
733 void serialize(Iter const & begin, Iter const & end) const
734 {
735 Iter iter = begin;
736 vigra_precondition(end - begin == serialized_size(),
737 "RandomForestOptions::serialize():"
738 "wrong number of parameters");
739 #define PUSH(item_) *iter = double(item_); ++iter;
740 PUSH(column_count_);
741 PUSH(class_count_)
742 PUSH(row_count_);
743 PUSH(actual_mtry_);
744 PUSH(actual_msample_);
745 PUSH(problem_type_);
746 PUSH(is_weighted_);
747 PUSH(used_);
748 PUSH(precision_);
749 PUSH(response_size_);
750 if(is_weighted_)
751 {
752 std::copy(class_weights_.begin(),
753 class_weights_.end(),
754 iter);
755 iter += class_count_;
756 }
757 std::copy(classes.begin(),
758 classes.end(),
759 iter);
760 #undef PUSH
761 }
762
763 void make_from_map(map_type & in) // -> const: .operator[] -> .find
764 {
765 #define PULL(item_, type_) item_ = type_(in[#item_][0]);
766 PULL(column_count_,int);
767 PULL(class_count_, int);
768 PULL(row_count_, int);
769 PULL(actual_mtry_,int);
770 PULL(actual_msample_, int);
771 PULL(problem_type_, (Problem_t)int);
772 PULL(is_weighted_, int);
773 PULL(used_, int);
774 PULL(precision_, double);
775 PULL(response_size_, int);
776 class_weights_ = in["class_weights_"];
777 #undef PUSH
778 }
779 void make_map(map_type & in) const
780 {
781 #define PUSH(item_) in[#item_] = double_array(1, double(item_));
782 PUSH(column_count_);
783 PUSH(class_count_)
784 PUSH(row_count_);
785 PUSH(actual_mtry_);
786 PUSH(actual_msample_);
787 PUSH(problem_type_);
788 PUSH(is_weighted_);
789 PUSH(used_);
790 PUSH(precision_);
791 PUSH(response_size_);
792 in["class_weights_"] = class_weights_;
793 #undef PUSH
794 }
795
796 /**\brief set default values (-> values not set)
797 */
798 ProblemSpec()
799 : column_count_(0),
800 class_count_(0),
801 row_count_(0),
802 actual_mtry_(0),
803 actual_msample_(0),
804 problem_type_(CHECKLATER),
805 used_(false),
806 is_weighted_(false),
807 precision_(0.0),
808 response_size_(1)
809 {}
810
811
812 ProblemSpec & column_count(int in)
813 {
814 column_count_ = in;
815 return *this;
816 }
817
818 /**\brief supply with class labels -
819 *
820 * the preprocessor will not calculate the labels needed in this case.
821 */
822 template<class C_Iter>
823 ProblemSpec & classes_(C_Iter begin, C_Iter end)
824 {
825 int size = end-begin;
826 for(int k=0; k<size; ++k, ++begin)
827 classes.push_back(detail::RequiresExplicitCast<LabelType>::cast(*begin));
828 class_count_ = size;
829 return *this;
830 }
831
832 /** \brief supply with class weights -
833 *
834 * this is the only case where you would really have to
835 * create a ProblemSpec object.
836 */
837 template<class W_Iter>
838 ProblemSpec & class_weights(W_Iter begin, W_Iter end)
839 {
840 class_weights_.insert(class_weights_.end(), begin, end);
841 is_weighted_ = true;
842 return *this;
843 }
844
845
846
847 void clear()
848 {
849 used_ = false;
850 classes.clear();
851 class_weights_.clear();
852 column_count_ = 0 ;
853 class_count_ = 0;
854 actual_mtry_ = 0;
855 actual_msample_ = 0;
856 problem_type_ = CHECKLATER;
857 is_weighted_ = false;
858 precision_ = 0.0;
859 response_size_ = 0;
860
861 }
862
863 bool used() const
864 {
865 return used_ != 0;
866 }
867 };
868
869
870 //@}
871
872
873
874 /**\brief Standard early stopping criterion
875 *
876 * Stop if region.size() < min_split_node_size_;
877 */
878 class EarlyStoppStd
879 {
880 public:
881 int min_split_node_size_;
882
883 template<class Opt>
884 EarlyStoppStd(Opt opt)
885 : min_split_node_size_(opt.min_split_node_size_)
886 {}
887
888 template<class T>
889 void set_external_parameters(ProblemSpec<T>const &, int /* tree_count */ = 0, bool /* is_weighted_ */ = false)
890 {}
891
892 template<class Region>
893 bool operator()(Region& region)
894 {
895 return region.size() < min_split_node_size_;
896 }
897
898 template<class WeightIter, class T, class C>
899 bool after_prediction(WeightIter, int /* k */, MultiArrayView<2, T, C> /* prob */, double /* totalCt */)
900 {
901 return false;
902 }
903 };
904
905
906 } // namespace vigra
907
908 #endif //VIGRA_RF_COMMON_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 1.10.0 (Mon Nov 18 2013)