/usr/include/vigra/random_forest.hxx is in libvigraimpex-dev 1.10.0+dfsg-3ubuntu2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 | /************************************************************************/
/* */
/* Copyright 2008-2009 by Ullrich Koethe and Rahul Nair */
/* */
/* This file is part of the VIGRA computer vision library. */
/* The VIGRA Website is */
/* http://hci.iwr.uni-heidelberg.de/vigra/ */
/* Please direct questions, bug reports, and contributions to */
/* ullrich.koethe@iwr.uni-heidelberg.de or */
/* vigra@informatik.uni-hamburg.de */
/* */
/* Permission is hereby granted, free of charge, to any person */
/* obtaining a copy of this software and associated documentation */
/* files (the "Software"), to deal in the Software without */
/* restriction, including without limitation the rights to use, */
/* copy, modify, merge, publish, distribute, sublicense, and/or */
/* sell copies of the Software, and to permit persons to whom the */
/* Software is furnished to do so, subject to the following */
/* conditions: */
/* */
/* The above copyright notice and this permission notice shall be */
/* included in all copies or substantial portions of the */
/* Software. */
/* */
/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND */
/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES */
/* OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND */
/* NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT */
/* HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, */
/* WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING */
/* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR */
/* OTHER DEALINGS IN THE SOFTWARE. */
/* */
/************************************************************************/
#ifndef VIGRA_RANDOM_FOREST_HXX
#define VIGRA_RANDOM_FOREST_HXX
#include <iostream>
#include <algorithm>
#include <map>
#include <set>
#include <list>
#include <numeric>
#include "mathutil.hxx"
#include "array_vector.hxx"
#include "sized_int.hxx"
#include "matrix.hxx"
#include "random.hxx"
#include "functorexpression.hxx"
#include "random_forest/rf_common.hxx"
#include "random_forest/rf_nodeproxy.hxx"
#include "random_forest/rf_split.hxx"
#include "random_forest/rf_decisionTree.hxx"
#include "random_forest/rf_visitors.hxx"
#include "random_forest/rf_region.hxx"
#include "sampling.hxx"
#include "random_forest/rf_preprocessing.hxx"
#include "random_forest/rf_online_prediction_set.hxx"
#include "random_forest/rf_earlystopping.hxx"
#include "random_forest/rf_ridge_split.hxx"
namespace vigra
{
/** \addtogroup MachineLearning Machine Learning
This module provides classification algorithms that map
features to labels or label probabilities.
Look at the RandomForest class first for a overview of most of the
functionality provided as well as use cases.
**/
//@{
namespace detail
{
/* \brief sampling option factory function
*/
inline SamplerOptions make_sampler_opt ( RandomForestOptions & RF_opt)
{
SamplerOptions return_opt;
return_opt.withReplacement(RF_opt.sample_with_replacement_);
return_opt.stratified(RF_opt.stratification_method_ == RF_EQUAL);
return return_opt;
}
}//namespace detail
/** Random Forest class
*
* \tparam <LabelType = double> Type used for predicted labels.
* \tparam <PreprocessorTag = ClassificationTag> Class used to preprocess
* the input while learning and predicting. Currently Available:
* ClassificationTag and RegressionTag. It is recommended to use
* Splitfunctor::Preprocessor_t while using custom splitfunctors
* as they may need the data to be in a different format.
* \sa Preprocessor
*
* Simple usage for classification (regression is not yet supported):
* look at RandomForest::learn() as well as RandomForestOptions() for additional
* options.
*
* \code
* using namespace vigra;
* using namespace rf;
* typedef xxx feature_t; \\ replace xxx with whichever type
* typedef yyy label_t; \\ likewise
*
* // allocate the training data
* MultiArrayView<2, feature_t> f = get_training_features();
* MultiArrayView<2, label_t> l = get_training_labels();
*
* RandomForest<label_t> rf;
*
* // construct visitor to calculate out-of-bag error
* visitors::OOB_Error oob_v;
*
* // perform training
* rf.learn(f, l, visitors::create_visitor(oob_v));
*
* std::cout << "the out-of-bag error is: " << oob_v.oob_breiman << "\n";
*
* // get features for new data to be used for prediction
* MultiArrayView<2, feature_t> pf = get_features();
*
* // allocate space for the response (pf.shape(0) is the number of samples)
* MultiArrayView<2, label_t> prediction(pf.shape(0), 1);
* MultiArrayView<2, double> prob(pf.shape(0), rf.class_count());
*
* // perform prediction on new data
* rf.predictLabels(pf, prediction);
* rf.predictProbabilities(pf, prob);
*
* \endcode
*
* Additional information such as Variable Importance measures are accessed
* via Visitors defined in rf::visitors.
* Have a look at rf::split for other splitting methods.
*
*/
template <class LabelType = double , class PreprocessorTag = ClassificationTag >
class RandomForest
{
public:
//public typedefs
typedef RandomForestOptions Options_t;
typedef detail::DecisionTree DecisionTree_t;
typedef ProblemSpec<LabelType> ProblemSpec_t;
typedef GiniSplit Default_Split_t;
typedef EarlyStoppStd Default_Stop_t;
typedef rf::visitors::StopVisiting Default_Visitor_t;
typedef DT_StackEntry<ArrayVectorView<Int32>::iterator>
StackEntry_t;
typedef LabelType LabelT;
//problem independent data.
Options_t options_;
//problem dependent data members - is only set if
//a copy constructor, some sort of import
//function or the learn function is called
ArrayVector<DecisionTree_t> trees_;
ProblemSpec_t ext_param_;
/*mutable ArrayVector<int> tree_indices_;*/
rf::visitors::OnlineLearnVisitor online_visitor_;
void reset()
{
ext_param_.clear();
trees_.clear();
}
public:
/** \name Constructors
* Note: No copy Constructor specified as no pointers are manipulated
* in this class
*/
/*\{*/
/**\brief default constructor
*
* \param options general options to the Random Forest. Must be of Type
* Options_t
* \param ext_param problem specific values that can be supplied
* additionally. (class weights , labels etc)
* \sa RandomForestOptions, ProblemSpec
*
*/
RandomForest(Options_t const & options = Options_t(),
ProblemSpec_t const & ext_param = ProblemSpec_t())
:
options_(options),
ext_param_(ext_param)/*,
tree_indices_(options.tree_count_,0)*/
{
/*for(int ii = 0 ; ii < int(tree_indices_.size()); ++ii)
tree_indices_[ii] = ii;*/
}
/**\brief Create RF from external source
* \param treeCount Number of trees to add.
* \param topology_begin
* Iterator to a Container where the topology_ data
* of the trees are stored.
* Iterator should support at least treeCount forward
* iterations. (i.e. topology_end - topology_begin >= treeCount
* \param parameter_begin
* iterator to a Container where the parameters_ data
* of the trees are stored. Iterator should support at
* least treeCount forward iterations.
* \param problem_spec
* Extrinsic parameters that specify the problem e.g.
* ClassCount, featureCount etc.
* \param options (optional) specify options used to train the original
* Random forest. This parameter is not used anywhere
* during prediction and thus is optional.
*
*/
/* TODO: This constructor may be replaced by a Constructor using
* NodeProxy iterators to encapsulate the underlying data type.
*/
template<class TopologyIterator, class ParameterIterator>
RandomForest(int treeCount,
TopologyIterator topology_begin,
ParameterIterator parameter_begin,
ProblemSpec_t const & problem_spec,
Options_t const & options = Options_t())
:
trees_(treeCount, DecisionTree_t(problem_spec)),
ext_param_(problem_spec),
options_(options)
{
for(unsigned int k=0; k<treeCount; ++k, ++topology_begin, ++parameter_begin)
{
trees_[k].topology_ = *topology_begin;
trees_[k].parameters_ = *parameter_begin;
}
}
/*\}*/
/** \name Data Access
* data access interface - usage of member variables is deprecated
*/
/*\{*/
/**\brief return external parameters for viewing
* \return ProblemSpec_t
*/
ProblemSpec_t const & ext_param() const
{
vigra_precondition(ext_param_.used() == true,
"RandomForest::ext_param(): "
"Random forest has not been trained yet.");
return ext_param_;
}
/**\brief set external parameters
*
* \param in external parameters to be set
*
* set external parameters explicitly.
* If Random Forest has not been trained the preprocessor will
* either ignore filling values set this way or will throw an exception
* if values specified manually do not match the value calculated
& during the preparation step.
*/
void set_ext_param(ProblemSpec_t const & in)
{
vigra_precondition(ext_param_.used() == false,
"RandomForest::set_ext_param():"
"Random forest has been trained! Call reset()"
"before specifying new extrinsic parameters.");
}
/**\brief access random forest options
*
* \return random forest options
*/
Options_t & set_options()
{
return options_;
}
/**\brief access const random forest options
*
* \return const Option_t
*/
Options_t const & options() const
{
return options_;
}
/**\brief access const trees
*/
DecisionTree_t const & tree(int index) const
{
return trees_[index];
}
/**\brief access trees
*/
DecisionTree_t & tree(int index)
{
return trees_[index];
}
/*\}*/
/**\brief return number of features used while
* training.
*/
int feature_count() const
{
return ext_param_.column_count_;
}
/**\brief return number of features used while
* training.
*
* deprecated. Use feature_count() instead.
*/
int column_count() const
{
return ext_param_.column_count_;
}
/**\brief return number of classes used while
* training.
*/
int class_count() const
{
return ext_param_.class_count_;
}
/**\brief return number of trees
*/
int tree_count() const
{
return options_.tree_count_;
}
template<class U,class C1,
class U2, class C2,
class Split_t,
class Stop_t,
class Visitor_t,
class Random_t>
void onlineLearn( MultiArrayView<2,U,C1> const & features,
MultiArrayView<2,U2,C2> const & response,
int new_start_index,
Visitor_t visitor_,
Split_t split_,
Stop_t stop_,
Random_t & random,
bool adjust_thresholds=false);
template <class U, class C1, class U2,class C2>
void onlineLearn( MultiArrayView<2, U, C1> const & features,
MultiArrayView<2, U2,C2> const & labels,int new_start_index,bool adjust_thresholds=false)
{
RandomNumberGenerator<> rnd = RandomNumberGenerator<>(RandomSeed);
onlineLearn(features,
labels,
new_start_index,
rf_default(),
rf_default(),
rf_default(),
rnd,
adjust_thresholds);
}
template<class U,class C1,
class U2, class C2,
class Split_t,
class Stop_t,
class Visitor_t,
class Random_t>
void reLearnTree(MultiArrayView<2,U,C1> const & features,
MultiArrayView<2,U2,C2> const & response,
int treeId,
Visitor_t visitor_,
Split_t split_,
Stop_t stop_,
Random_t & random);
template<class U, class C1, class U2, class C2>
void reLearnTree(MultiArrayView<2, U, C1> const & features,
MultiArrayView<2, U2, C2> const & labels,
int treeId)
{
RandomNumberGenerator<> rnd = RandomNumberGenerator<>(RandomSeed);
reLearnTree(features,
labels,
treeId,
rf_default(),
rf_default(),
rf_default(),
rnd);
}
/**\name Learning
* Following functions differ in the degree of customization
* allowed
*/
/*\{*/
/**\brief learn on data with custom config and random number generator
*
* \param features a N x M matrix containing N samples with M
* features
* \param response a N x D matrix containing the corresponding
* response. Current split functors assume D to
* be 1 and ignore any additional columns.
* This is not enforced to allow future support
* for uncertain labels, label independent strata etc.
* The Preprocessor specified during construction
* should be able to handle features and labels
* features and the labels.
* see also: SplitFunctor, Preprocessing
*
* \param visitor visitor which is to be applied after each split,
* tree and at the end. Use rf_default() for using
* default value. (No Visitors)
* see also: rf::visitors
* \param split split functor to be used to calculate each split
* use rf_default() for using default value. (GiniSplit)
* see also: rf::split
* \param stop
* predicate to be used to calculate each split
* use rf_default() for using default value. (EarlyStoppStd)
* \param random RandomNumberGenerator to be used. Use
* rf_default() to use default value.(RandomMT19337)
*
*
*/
template <class U, class C1,
class U2,class C2,
class Split_t,
class Stop_t,
class Visitor_t,
class Random_t>
void learn( MultiArrayView<2, U, C1> const & features,
MultiArrayView<2, U2,C2> const & response,
Visitor_t visitor,
Split_t split,
Stop_t stop,
Random_t const & random);
template <class U, class C1,
class U2,class C2,
class Split_t,
class Stop_t,
class Visitor_t>
void learn( MultiArrayView<2, U, C1> const & features,
MultiArrayView<2, U2,C2> const & response,
Visitor_t visitor,
Split_t split,
Stop_t stop)
{
RandomNumberGenerator<> rnd = RandomNumberGenerator<>(RandomSeed);
learn( features,
response,
visitor,
split,
stop,
rnd);
}
template <class U, class C1, class U2,class C2, class Visitor_t>
void learn( MultiArrayView<2, U, C1> const & features,
MultiArrayView<2, U2,C2> const & labels,
Visitor_t visitor)
{
learn( features,
labels,
visitor,
rf_default(),
rf_default());
}
template <class U, class C1, class U2,class C2,
class Visitor_t, class Split_t>
void learn( MultiArrayView<2, U, C1> const & features,
MultiArrayView<2, U2,C2> const & labels,
Visitor_t visitor,
Split_t split)
{
learn( features,
labels,
visitor,
split,
rf_default());
}
/**\brief learn on data with default configuration
*
* \param features a N x M matrix containing N samples with M
* features
* \param labels a N x D matrix containing the corresponding
* N labels. Current split functors assume D to
* be 1 and ignore any additional columns.
* this is not enforced to allow future support
* for uncertain labels.
*
* learning is done with:
*
* \sa rf::split, EarlyStoppStd
*
* - Randomly seeded random number generator
* - default gini split functor as described by Breiman
* - default The standard early stopping criterion
*/
template <class U, class C1, class U2,class C2>
void learn( MultiArrayView<2, U, C1> const & features,
MultiArrayView<2, U2,C2> const & labels)
{
learn( features,
labels,
rf_default(),
rf_default(),
rf_default());
}
/*\}*/
/**\name prediction
*/
/*\{*/
/** \brief predict a label given a feature.
*
* \param features: a 1 by featureCount matrix containing
* data point to be predicted (this only works in
* classification setting)
* \param stop: early stopping criterion
* \return double value representing class. You can use the
* predictLabels() function together with the
* rf.external_parameter().class_type_ attribute
* to get back the same type used during learning.
*/
template <class U, class C, class Stop>
LabelType predictLabel(MultiArrayView<2, U, C>const & features, Stop & stop) const;
template <class U, class C>
LabelType predictLabel(MultiArrayView<2, U, C>const & features)
{
return predictLabel(features, rf_default());
}
/** \brief predict a label with features and class priors
*
* \param features: same as above.
* \param prior: iterator to prior weighting of classes
* \return sam as above.
*/
template <class U, class C>
LabelType predictLabel(MultiArrayView<2, U, C> const & features,
ArrayVectorView<double> prior) const;
/** \brief predict multiple labels with given features
*
* \param features: a n by featureCount matrix containing
* data point to be predicted (this only works in
* classification setting)
* \param labels: a n by 1 matrix passed by reference to store
* output.
*
* If the input contains an NaN value, an precondition exception is thrown.
*/
template <class U, class C1, class T, class C2>
void predictLabels(MultiArrayView<2, U, C1>const & features,
MultiArrayView<2, T, C2> & labels) const
{
vigra_precondition(features.shape(0) == labels.shape(0),
"RandomForest::predictLabels(): Label array has wrong size.");
for(int k=0; k<features.shape(0); ++k)
{
vigra_precondition(!detail::contains_nan(rowVector(features, k)),
"RandomForest::predictLabels(): NaN in feature matrix.");
labels(k,0) = detail::RequiresExplicitCast<T>::cast(predictLabel(rowVector(features, k), rf_default()));
}
}
/** \brief predict multiple labels with given features
*
* \param features: a n by featureCount matrix containing
* data point to be predicted (this only works in
* classification setting)
* \param labels: a n by 1 matrix passed by reference to store
* output.
* \param nanLabel: label to be returned for the row of the input that
* contain an NaN value.
*/
template <class U, class C1, class T, class C2>
void predictLabels(MultiArrayView<2, U, C1>const & features,
MultiArrayView<2, T, C2> & labels,
LabelType nanLabel) const
{
vigra_precondition(features.shape(0) == labels.shape(0),
"RandomForest::predictLabels(): Label array has wrong size.");
for(int k=0; k<features.shape(0); ++k)
{
if(detail::contains_nan(rowVector(features, k)))
labels(k,0) = nanLabel;
else
labels(k,0) = detail::RequiresExplicitCast<T>::cast(predictLabel(rowVector(features, k), rf_default()));
}
}
/** \brief predict multiple labels with given features
*
* \param features: a n by featureCount matrix containing
* data point to be predicted (this only works in
* classification setting)
* \param labels: a n by 1 matrix passed by reference to store
* output.
* \param stop: an early stopping criterion.
*/
template <class U, class C1, class T, class C2, class Stop>
void predictLabels(MultiArrayView<2, U, C1>const & features,
MultiArrayView<2, T, C2> & labels,
Stop & stop) const
{
vigra_precondition(features.shape(0) == labels.shape(0),
"RandomForest::predictLabels(): Label array has wrong size.");
for(int k=0; k<features.shape(0); ++k)
labels(k,0) = detail::RequiresExplicitCast<T>::cast(predictLabel(rowVector(features, k), stop));
}
/** \brief predict the class probabilities for multiple labels
*
* \param features same as above
* \param prob a n x class_count_ matrix. passed by reference to
* save class probabilities
* \param stop earlystopping criterion
* \sa EarlyStopping
When a row of the feature array contains an NaN, the corresponding instance
cannot belong to any of the classes. The corresponding row in the probability
array will therefore contain all zeros.
*/
template <class U, class C1, class T, class C2, class Stop>
void predictProbabilities(MultiArrayView<2, U, C1>const & features,
MultiArrayView<2, T, C2> & prob,
Stop & stop) const;
template <class T1,class T2, class C>
void predictProbabilities(OnlinePredictionSet<T1> & predictionSet,
MultiArrayView<2, T2, C> & prob);
/** \brief predict the class probabilities for multiple labels
*
* \param features same as above
* \param prob a n x class_count_ matrix. passed by reference to
* save class probabilities
*/
template <class U, class C1, class T, class C2>
void predictProbabilities(MultiArrayView<2, U, C1>const & features,
MultiArrayView<2, T, C2> & prob) const
{
predictProbabilities(features, prob, rf_default());
}
template <class U, class C1, class T, class C2>
void predictRaw(MultiArrayView<2, U, C1>const & features,
MultiArrayView<2, T, C2> & prob) const;
/*\}*/
};
template <class LabelType, class PreprocessorTag>
template<class U,class C1,
class U2, class C2,
class Split_t,
class Stop_t,
class Visitor_t,
class Random_t>
void RandomForest<LabelType, PreprocessorTag>::onlineLearn(MultiArrayView<2,U,C1> const & features,
MultiArrayView<2,U2,C2> const & response,
int new_start_index,
Visitor_t visitor_,
Split_t split_,
Stop_t stop_,
Random_t & random,
bool adjust_thresholds)
{
online_visitor_.activate();
online_visitor_.adjust_thresholds=adjust_thresholds;
using namespace rf;
//typedefs
typedef Processor<PreprocessorTag,LabelType,U,C1,U2,C2> Preprocessor_t;
typedef UniformIntRandomFunctor<Random_t>
RandFunctor_t;
// default values and initialization
// Value Chooser chooses second argument as value if first argument
// is of type RF_DEFAULT. (thanks to template magic - don't care about
// it - just smile and wave.
#define RF_CHOOSER(type_) detail::Value_Chooser<type_, Default_##type_>
Default_Stop_t default_stop(options_);
typename RF_CHOOSER(Stop_t)::type stop
= RF_CHOOSER(Stop_t)::choose(stop_, default_stop);
Default_Split_t default_split;
typename RF_CHOOSER(Split_t)::type split
= RF_CHOOSER(Split_t)::choose(split_, default_split);
rf::visitors::StopVisiting stopvisiting;
typedef rf::visitors::detail::VisitorNode
<rf::visitors::OnlineLearnVisitor,
typename RF_CHOOSER(Visitor_t)::type>
IntermedVis;
IntermedVis
visitor(online_visitor_, RF_CHOOSER(Visitor_t)::choose(visitor_, stopvisiting));
#undef RF_CHOOSER
vigra_precondition(options_.prepare_online_learning_,"onlineLearn: online learning must be enabled on RandomForest construction");
// Preprocess the data to get something the split functor can work
// with. Also fill the ext_param structure by preprocessing
// option parameters that could only be completely evaluated
// when the training data is known.
ext_param_.class_count_=0;
Preprocessor_t preprocessor( features, response,
options_, ext_param_);
// Make stl compatible random functor.
RandFunctor_t randint ( random);
// Give the Split functor information about the data.
split.set_external_parameters(ext_param_);
stop.set_external_parameters(ext_param_);
//Create poisson samples
PoissonSampler<RandomTT800> poisson_sampler(1.0,vigra::Int32(new_start_index),vigra::Int32(ext_param().row_count_));
//TODO: visitors for online learning
//visitor.visit_at_beginning(*this, preprocessor);
// THE MAIN EFFING RF LOOP - YEAY DUDE!
for(int ii = 0; ii < (int)trees_.size(); ++ii)
{
online_visitor_.tree_id=ii;
poisson_sampler.sample();
std::map<int,int> leaf_parents;
leaf_parents.clear();
//Get all the leaf nodes for that sample
for(int s=0;s<poisson_sampler.numOfSamples();++s)
{
int sample=poisson_sampler[s];
online_visitor_.current_label=preprocessor.response()(sample,0);
online_visitor_.last_node_id=StackEntry_t::DecisionTreeNoParent;
int leaf=trees_[ii].getToLeaf(rowVector(features,sample),online_visitor_);
//Add to the list for that leaf
online_visitor_.add_to_index_list(ii,leaf,sample);
//TODO: Class count?
//Store parent
if(Node<e_ConstProbNode>(trees_[ii].topology_,trees_[ii].parameters_,leaf).prob_begin()[preprocessor.response()(sample,0)]!=1.0)
{
leaf_parents[leaf]=online_visitor_.last_node_id;
}
}
std::map<int,int>::iterator leaf_iterator;
for(leaf_iterator=leaf_parents.begin();leaf_iterator!=leaf_parents.end();++leaf_iterator)
{
int leaf=leaf_iterator->first;
int parent=leaf_iterator->second;
int lin_index=online_visitor_.trees_online_information[ii].exterior_to_index[leaf];
ArrayVector<Int32> indeces;
indeces.clear();
indeces.swap(online_visitor_.trees_online_information[ii].index_lists[lin_index]);
StackEntry_t stack_entry(indeces.begin(),
indeces.end(),
ext_param_.class_count_);
if(parent!=-1)
{
if(NodeBase(trees_[ii].topology_,trees_[ii].parameters_,parent).child(0)==leaf)
{
stack_entry.leftParent=parent;
}
else
{
vigra_assert(NodeBase(trees_[ii].topology_,trees_[ii].parameters_,parent).child(1)==leaf,"last_node_id seems to be wrong");
stack_entry.rightParent=parent;
}
}
//trees_[ii].continueLearn(preprocessor.features(),preprocessor.response(),stack_entry,split,stop,visitor,randint,leaf);
trees_[ii].continueLearn(preprocessor.features(),preprocessor.response(),stack_entry,split,stop,visitor,randint,-1);
//Now, the last one moved onto leaf
online_visitor_.move_exterior_node(ii,trees_[ii].topology_.size(),ii,leaf);
//Now it should be classified correctly!
}
/*visitor
.visit_after_tree( *this,
preprocessor,
poisson_sampler,
stack_entry,
ii);*/
}
//visitor.visit_at_end(*this, preprocessor);
online_visitor_.deactivate();
}
template<class LabelType, class PreprocessorTag>
template<class U,class C1,
class U2, class C2,
class Split_t,
class Stop_t,
class Visitor_t,
class Random_t>
void RandomForest<LabelType, PreprocessorTag>::reLearnTree(MultiArrayView<2,U,C1> const & features,
MultiArrayView<2,U2,C2> const & response,
int treeId,
Visitor_t visitor_,
Split_t split_,
Stop_t stop_,
Random_t & random)
{
using namespace rf;
typedef UniformIntRandomFunctor<Random_t>
RandFunctor_t;
// See rf_preprocessing.hxx for more info on this
ext_param_.class_count_=0;
typedef Processor<PreprocessorTag,LabelType, U, C1, U2, C2> Preprocessor_t;
// default values and initialization
// Value Chooser chooses second argument as value if first argument
// is of type RF_DEFAULT. (thanks to template magic - don't care about
// it - just smile and wave.
#define RF_CHOOSER(type_) detail::Value_Chooser<type_, Default_##type_>
Default_Stop_t default_stop(options_);
typename RF_CHOOSER(Stop_t)::type stop
= RF_CHOOSER(Stop_t)::choose(stop_, default_stop);
Default_Split_t default_split;
typename RF_CHOOSER(Split_t)::type split
= RF_CHOOSER(Split_t)::choose(split_, default_split);
rf::visitors::StopVisiting stopvisiting;
typedef rf::visitors::detail::VisitorNode
<rf::visitors::OnlineLearnVisitor,
typename RF_CHOOSER(Visitor_t)::type> IntermedVis;
IntermedVis
visitor(online_visitor_, RF_CHOOSER(Visitor_t)::choose(visitor_, stopvisiting));
#undef RF_CHOOSER
vigra_precondition(options_.prepare_online_learning_,"reLearnTree: Re learning trees only makes sense, if online learning is enabled");
online_visitor_.activate();
// Make stl compatible random functor.
RandFunctor_t randint ( random);
// Preprocess the data to get something the split functor can work
// with. Also fill the ext_param structure by preprocessing
// option parameters that could only be completely evaluated
// when the training data is known.
Preprocessor_t preprocessor( features, response,
options_, ext_param_);
// Give the Split functor information about the data.
split.set_external_parameters(ext_param_);
stop.set_external_parameters(ext_param_);
/**\todo replace this crappy class out. It uses function pointers.
* and is making code slower according to me.
* Comment from Nathan: This is copied from Rahul, so me=Rahul
*/
Sampler<Random_t > sampler(preprocessor.strata().begin(),
preprocessor.strata().end(),
detail::make_sampler_opt(options_)
.sampleSize(ext_param().actual_msample_),
&random);
//initialize First region/node/stack entry
sampler
.sample();
StackEntry_t
first_stack_entry( sampler.sampledIndices().begin(),
sampler.sampledIndices().end(),
ext_param_.class_count_);
first_stack_entry
.set_oob_range( sampler.oobIndices().begin(),
sampler.oobIndices().end());
online_visitor_.reset_tree(treeId);
online_visitor_.tree_id=treeId;
trees_[treeId].reset();
trees_[treeId]
.learn( preprocessor.features(),
preprocessor.response(),
first_stack_entry,
split,
stop,
visitor,
randint);
visitor
.visit_after_tree( *this,
preprocessor,
sampler,
first_stack_entry,
treeId);
online_visitor_.deactivate();
}
template <class LabelType, class PreprocessorTag>
template <class U, class C1,
class U2,class C2,
class Split_t,
class Stop_t,
class Visitor_t,
class Random_t>
void RandomForest<LabelType, PreprocessorTag>::
learn( MultiArrayView<2, U, C1> const & features,
MultiArrayView<2, U2,C2> const & response,
Visitor_t visitor_,
Split_t split_,
Stop_t stop_,
Random_t const & random)
{
using namespace rf;
//this->reset();
//typedefs
typedef UniformIntRandomFunctor<Random_t>
RandFunctor_t;
// See rf_preprocessing.hxx for more info on this
typedef Processor<PreprocessorTag,LabelType, U, C1, U2, C2> Preprocessor_t;
vigra_precondition(features.shape(0) == response.shape(0),
"RandomForest::learn(): shape mismatch between features and response.");
// default values and initialization
// Value Chooser chooses second argument as value if first argument
// is of type RF_DEFAULT. (thanks to template magic - don't care about
// it - just smile and wave).
#define RF_CHOOSER(type_) detail::Value_Chooser<type_, Default_##type_>
Default_Stop_t default_stop(options_);
typename RF_CHOOSER(Stop_t)::type stop
= RF_CHOOSER(Stop_t)::choose(stop_, default_stop);
Default_Split_t default_split;
typename RF_CHOOSER(Split_t)::type split
= RF_CHOOSER(Split_t)::choose(split_, default_split);
rf::visitors::StopVisiting stopvisiting;
typedef rf::visitors::detail::VisitorNode<
rf::visitors::OnlineLearnVisitor,
typename RF_CHOOSER(Visitor_t)::type> IntermedVis;
IntermedVis
visitor(online_visitor_, RF_CHOOSER(Visitor_t)::choose(visitor_, stopvisiting));
#undef RF_CHOOSER
if(options_.prepare_online_learning_)
online_visitor_.activate();
else
online_visitor_.deactivate();
// Make stl compatible random functor.
RandFunctor_t randint ( random);
// Preprocess the data to get something the split functor can work
// with. Also fill the ext_param structure by preprocessing
// option parameters that could only be completely evaluated
// when the training data is known.
Preprocessor_t preprocessor( features, response,
options_, ext_param_);
// Give the Split functor information about the data.
split.set_external_parameters(ext_param_);
stop.set_external_parameters(ext_param_);
//initialize trees.
trees_.resize(options_.tree_count_ , DecisionTree_t(ext_param_));
Sampler<Random_t > sampler(preprocessor.strata().begin(),
preprocessor.strata().end(),
detail::make_sampler_opt(options_)
.sampleSize(ext_param().actual_msample_),
&random);
visitor.visit_at_beginning(*this, preprocessor);
// THE MAIN EFFING RF LOOP - YEAY DUDE!
for(int ii = 0; ii < (int)trees_.size(); ++ii)
{
//initialize First region/node/stack entry
sampler
.sample();
StackEntry_t
first_stack_entry( sampler.sampledIndices().begin(),
sampler.sampledIndices().end(),
ext_param_.class_count_);
first_stack_entry
.set_oob_range( sampler.oobIndices().begin(),
sampler.oobIndices().end());
trees_[ii]
.learn( preprocessor.features(),
preprocessor.response(),
first_stack_entry,
split,
stop,
visitor,
randint);
visitor
.visit_after_tree( *this,
preprocessor,
sampler,
first_stack_entry,
ii);
}
visitor.visit_at_end(*this, preprocessor);
// Only for online learning?
online_visitor_.deactivate();
}
template <class LabelType, class Tag>
template <class U, class C, class Stop>
LabelType RandomForest<LabelType, Tag>
::predictLabel(MultiArrayView<2, U, C> const & features, Stop & stop) const
{
vigra_precondition(columnCount(features) >= ext_param_.column_count_,
"RandomForestn::predictLabel():"
" Too few columns in feature matrix.");
vigra_precondition(rowCount(features) == 1,
"RandomForestn::predictLabel():"
" Feature matrix must have a singlerow.");
MultiArray<2, double> probabilities(Shape2(1, ext_param_.class_count_), 0.0);
LabelType d;
predictProbabilities(features, probabilities, stop);
ext_param_.to_classlabel(argMax(probabilities), d);
return d;
}
//Same thing as above with priors for each label !!!
template <class LabelType, class PreprocessorTag>
template <class U, class C>
LabelType RandomForest<LabelType, PreprocessorTag>
::predictLabel( MultiArrayView<2, U, C> const & features,
ArrayVectorView<double> priors) const
{
using namespace functor;
vigra_precondition(columnCount(features) >= ext_param_.column_count_,
"RandomForestn::predictLabel(): Too few columns in feature matrix.");
vigra_precondition(rowCount(features) == 1,
"RandomForestn::predictLabel():"
" Feature matrix must have a single row.");
Matrix<double> prob(1,ext_param_.class_count_);
predictProbabilities(features, prob);
std::transform( prob.begin(), prob.end(),
priors.begin(), prob.begin(),
Arg1()*Arg2());
LabelType d;
ext_param_.to_classlabel(argMax(prob), d);
return d;
}
template<class LabelType,class PreprocessorTag>
template <class T1,class T2, class C>
void RandomForest<LabelType,PreprocessorTag>
::predictProbabilities(OnlinePredictionSet<T1> & predictionSet,
MultiArrayView<2, T2, C> & prob)
{
//Features are n xp
//prob is n x NumOfLabel probability for each feature in each class
vigra_precondition(rowCount(predictionSet.features) == rowCount(prob),
"RandomFroest::predictProbabilities():"
" Feature matrix and probability matrix size mismatch.");
// num of features must be bigger than num of features in Random forest training
// but why bigger?
vigra_precondition( columnCount(predictionSet.features) >= ext_param_.column_count_,
"RandomForestn::predictProbabilities():"
" Too few columns in feature matrix.");
vigra_precondition( columnCount(prob)
== (MultiArrayIndex)ext_param_.class_count_,
"RandomForestn::predictProbabilities():"
" Probability matrix must have as many columns as there are classes.");
prob.init(0.0);
//store total weights
std::vector<T1> totalWeights(predictionSet.indices[0].size(),0.0);
//Go through all trees
int set_id=-1;
for(int k=0; k<options_.tree_count_; ++k)
{
set_id=(set_id+1) % predictionSet.indices[0].size();
typedef std::set<SampleRange<T1> > my_set;
typedef typename my_set::iterator set_it;
//typedef std::set<std::pair<int,SampleRange<T1> > >::iterator set_it;
//Build a stack with all the ranges we have
std::vector<std::pair<int,set_it> > stack;
stack.clear();
for(set_it i=predictionSet.ranges[set_id].begin();
i!=predictionSet.ranges[set_id].end();++i)
stack.push_back(std::pair<int,set_it>(2,i));
//get weights predicted by single tree
int num_decisions=0;
while(!stack.empty())
{
set_it range=stack.back().second;
int index=stack.back().first;
stack.pop_back();
++num_decisions;
if(trees_[k].isLeafNode(trees_[k].topology_[index]))
{
ArrayVector<double>::iterator weights=Node<e_ConstProbNode>(trees_[k].topology_,
trees_[k].parameters_,
index).prob_begin();
for(int i=range->start;i!=range->end;++i)
{
//update votecount.
for(int l=0; l<ext_param_.class_count_; ++l)
{
prob(predictionSet.indices[set_id][i], l) += (T2)weights[l];
//every weight in totalWeight.
totalWeights[predictionSet.indices[set_id][i]] += (T1)weights[l];
}
}
}
else
{
if(trees_[k].topology_[index]!=i_ThresholdNode)
{
throw std::runtime_error("predicting with online prediction sets is only supported for RFs with threshold nodes");
}
Node<i_ThresholdNode> node(trees_[k].topology_,trees_[k].parameters_,index);
if(range->min_boundaries[node.column()]>=node.threshold())
{
//Everything goes to right child
stack.push_back(std::pair<int,set_it>(node.child(1),range));
continue;
}
if(range->max_boundaries[node.column()]<node.threshold())
{
//Everything goes to the left child
stack.push_back(std::pair<int,set_it>(node.child(0),range));
continue;
}
//We have to split at this node
SampleRange<T1> new_range=*range;
new_range.min_boundaries[node.column()]=FLT_MAX;
range->max_boundaries[node.column()]=-FLT_MAX;
new_range.start=new_range.end=range->end;
int i=range->start;
while(i!=range->end)
{
//Decide for range->indices[i]
if(predictionSet.features(predictionSet.indices[set_id][i],node.column())>=node.threshold())
{
new_range.min_boundaries[node.column()]=std::min(new_range.min_boundaries[node.column()],
predictionSet.features(predictionSet.indices[set_id][i],node.column()));
--range->end;
--new_range.start;
std::swap(predictionSet.indices[set_id][i],predictionSet.indices[set_id][range->end]);
}
else
{
range->max_boundaries[node.column()]=std::max(range->max_boundaries[node.column()],
predictionSet.features(predictionSet.indices[set_id][i],node.column()));
++i;
}
}
//The old one ...
if(range->start==range->end)
{
predictionSet.ranges[set_id].erase(range);
}
else
{
stack.push_back(std::pair<int,set_it>(node.child(0),range));
}
//And the new one ...
if(new_range.start!=new_range.end)
{
std::pair<set_it,bool> new_it=predictionSet.ranges[set_id].insert(new_range);
stack.push_back(std::pair<int,set_it>(node.child(1),new_it.first));
}
}
}
predictionSet.cumulativePredTime[k]=num_decisions;
}
for(unsigned int i=0;i<totalWeights.size();++i)
{
double test=0.0;
//Normalise votes in each row by total VoteCount (totalWeight
for(int l=0; l<ext_param_.class_count_; ++l)
{
test+=prob(i,l);
prob(i, l) /= totalWeights[i];
}
assert(test==totalWeights[i]);
assert(totalWeights[i]>0.0);
}
}
template <class LabelType, class PreprocessorTag>
template <class U, class C1, class T, class C2, class Stop_t>
void RandomForest<LabelType, PreprocessorTag>
::predictProbabilities(MultiArrayView<2, U, C1>const & features,
MultiArrayView<2, T, C2> & prob,
Stop_t & stop_) const
{
//Features are n xp
//prob is n x NumOfLabel probability for each feature in each class
vigra_precondition(rowCount(features) == rowCount(prob),
"RandomForestn::predictProbabilities():"
" Feature matrix and probability matrix size mismatch.");
// num of features must be bigger than num of features in Random forest training
// but why bigger?
vigra_precondition( columnCount(features) >= ext_param_.column_count_,
"RandomForestn::predictProbabilities():"
" Too few columns in feature matrix.");
vigra_precondition( columnCount(prob)
== (MultiArrayIndex)ext_param_.class_count_,
"RandomForestn::predictProbabilities():"
" Probability matrix must have as many columns as there are classes.");
#define RF_CHOOSER(type_) detail::Value_Chooser<type_, Default_##type_>
Default_Stop_t default_stop(options_);
typename RF_CHOOSER(Stop_t)::type & stop
= RF_CHOOSER(Stop_t)::choose(stop_, default_stop);
#undef RF_CHOOSER
stop.set_external_parameters(ext_param_, tree_count());
prob.init(NumericTraits<T>::zero());
/* This code was originally there for testing early stopping
* - we wanted the order of the trees to be randomized
if(tree_indices_.size() != 0)
{
std::random_shuffle(tree_indices_.begin(),
tree_indices_.end());
}
*/
//Classify for each row.
for(int row=0; row < rowCount(features); ++row)
{
MultiArrayView<2, U, StridedArrayTag> currentRow(rowVector(features, row));
// when the features contain an NaN, the instance doesn't belong to any class
// => indicate this by returning a zero probability array.
if(detail::contains_nan(currentRow))
{
rowVector(prob, row).init(0.0);
continue;
}
ArrayVector<double>::const_iterator weights;
//totalWeight == totalVoteCount!
double totalWeight = 0.0;
//Let each tree classify...
for(int k=0; k<options_.tree_count_; ++k)
{
//get weights predicted by single tree
weights = trees_[k /*tree_indices_[k]*/].predict(currentRow);
//update votecount.
int weighted = options_.predict_weighted_;
for(int l=0; l<ext_param_.class_count_; ++l)
{
double cur_w = weights[l] * (weighted * (*(weights-1))
+ (1-weighted));
prob(row, l) += (T)cur_w;
//every weight in totalWeight.
totalWeight += cur_w;
}
if(stop.after_prediction(weights,
k,
rowVector(prob, row),
totalWeight))
{
break;
}
}
//Normalise votes in each row by total VoteCount (totalWeight
for(int l=0; l< ext_param_.class_count_; ++l)
{
prob(row, l) /= detail::RequiresExplicitCast<T>::cast(totalWeight);
}
}
}
template <class LabelType, class PreprocessorTag>
template <class U, class C1, class T, class C2>
void RandomForest<LabelType, PreprocessorTag>
::predictRaw(MultiArrayView<2, U, C1>const & features,
MultiArrayView<2, T, C2> & prob) const
{
//Features are n xp
//prob is n x NumOfLabel probability for each feature in each class
vigra_precondition(rowCount(features) == rowCount(prob),
"RandomForestn::predictProbabilities():"
" Feature matrix and probability matrix size mismatch.");
// num of features must be bigger than num of features in Random forest training
// but why bigger?
vigra_precondition( columnCount(features) >= ext_param_.column_count_,
"RandomForestn::predictProbabilities():"
" Too few columns in feature matrix.");
vigra_precondition( columnCount(prob)
== (MultiArrayIndex)ext_param_.class_count_,
"RandomForestn::predictProbabilities():"
" Probability matrix must have as many columns as there are classes.");
#define RF_CHOOSER(type_) detail::Value_Chooser<type_, Default_##type_>
prob.init(NumericTraits<T>::zero());
/* This code was originally there for testing early stopping
* - we wanted the order of the trees to be randomized
if(tree_indices_.size() != 0)
{
std::random_shuffle(tree_indices_.begin(),
tree_indices_.end());
}
*/
//Classify for each row.
for(int row=0; row < rowCount(features); ++row)
{
ArrayVector<double>::const_iterator weights;
//totalWeight == totalVoteCount!
double totalWeight = 0.0;
//Let each tree classify...
for(int k=0; k<options_.tree_count_; ++k)
{
//get weights predicted by single tree
weights = trees_[k /*tree_indices_[k]*/].predict(rowVector(features, row));
//update votecount.
int weighted = options_.predict_weighted_;
for(int l=0; l<ext_param_.class_count_; ++l)
{
double cur_w = weights[l] * (weighted * (*(weights-1))
+ (1-weighted));
prob(row, l) += (T)cur_w;
//every weight in totalWeight.
totalWeight += cur_w;
}
}
}
prob/= options_.tree_count_;
}
//@}
} // namespace vigra
#include "random_forest/rf_algorithm.hxx"
#endif // VIGRA_RANDOM_FOREST_HXX
|