1 | #ifndef _theplu_yat_classifier_knn_ |
---|
2 | #define _theplu_yat_classifier_knn_ |
---|
3 | |
---|
4 | // $Id: KNN.h 3562 2017-01-04 01:16:07Z peter $ |
---|
5 | |
---|
6 | /* |
---|
7 | Copyright (C) 2007, 2008 Jari Häkkinen, Peter Johansson, Markus Ringnér |
---|
8 | Copyright (C) 2009, 2010, 2017 Peter Johansson |
---|
9 | |
---|
10 | This file is part of the yat library, http://dev.thep.lu.se/yat |
---|
11 | |
---|
12 | The yat library is free software; you can redistribute it and/or |
---|
13 | modify it under the terms of the GNU General Public License as |
---|
14 | published by the Free Software Foundation; either version 3 of the |
---|
15 | License, or (at your option) any later version. |
---|
16 | |
---|
17 | The yat library is distributed in the hope that it will be useful, |
---|
18 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
---|
19 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
---|
20 | General Public License for more details. |
---|
21 | |
---|
22 | You should have received a copy of the GNU General Public License |
---|
23 | along with yat. If not, see <http://www.gnu.org/licenses/>. |
---|
24 | */ |
---|
25 | |
---|
26 | #include "DataLookup1D.h" |
---|
27 | #include "DataLookupWeighted1D.h" |
---|
28 | #include "KNN_Uniform.h" |
---|
29 | #include "MatrixLookup.h" |
---|
30 | #include "MatrixLookupWeighted.h" |
---|
31 | #include "SupervisedClassifier.h" |
---|
32 | #include "Target.h" |
---|
33 | #include "yat/utility/concept_check.h" |
---|
34 | #include "yat/utility/Exception.h" |
---|
35 | #include "yat/utility/Matrix.h" |
---|
36 | #include "yat/utility/Vector.h" |
---|
37 | #include "yat/utility/VectorConstView.h" |
---|
38 | #include "yat/utility/VectorView.h" |
---|
39 | #include "yat/utility/yat_assert.h" |
---|
40 | |
---|
41 | #include <boost/concept_check.hpp> |
---|
42 | |
---|
43 | #include <cmath> |
---|
44 | #include <limits> |
---|
45 | #include <map> |
---|
46 | #include <stdexcept> |
---|
47 | #include <vector> |
---|
48 | |
---|
49 | namespace theplu { |
---|
50 | namespace yat { |
---|
51 | namespace classifier { |
---|
52 | |
---|
53 | /** |
---|
54 | \brief Nearest Neighbor Classifier |
---|
55 | |
---|
56 | A sample is predicted based on the classes of its k nearest |
---|
57 | neighbors among the training data samples. KNN supports using |
---|
58 | different measures, for example, Euclidean distance, to define |
---|
59 | distance between samples. KNN also supports using different ways to |
---|
60 | weight the votes of the k nearest neighbors. For example, using a |
---|
61 | uniform vote a test sample gets a vote for each class which is the |
---|
62 | number of nearest neighbors belonging to the class. |
---|
63 | |
---|
64 | The template argument Distance should be a class modelling the |
---|
65 | concept \ref concept_distance. The template argument |
---|
66 | NeighborWeighting should be a class modelling the concept \ref |
---|
67 | concept_neighbor_weighting. |
---|
68 | */ |
---|
69 | template <typename Distance, typename NeighborWeighting=KNN_Uniform> |
---|
70 | class KNN : public SupervisedClassifier |
---|
71 | { |
---|
72 | |
---|
73 | public: |
---|
74 | /** |
---|
75 | \brief Default constructor. |
---|
76 | |
---|
77 | The number of nearest neighbors (k) is set to 3. Distance and |
---|
78 | NeighborWeighting are initialized using their default |
---|
79 | constructuors. |
---|
80 | */ |
---|
81 | KNN(void); |
---|
82 | |
---|
83 | |
---|
84 | /** |
---|
85 | \brief Constructor using an intialized distance measure. |
---|
86 | |
---|
87 | The number of nearest neighbors (k) is set to |
---|
88 | 3. NeighborWeighting is initialized using its default |
---|
89 | constructor. This constructor should be used if Distance has |
---|
90 | parameters and the user wants to specify the parameters by |
---|
91 | initializing Distance prior to constructing the KNN. |
---|
92 | */ |
---|
93 | KNN(const Distance&); |
---|
94 | |
---|
95 | |
---|
96 | /** |
---|
97 | Destructor |
---|
98 | */ |
---|
99 | virtual ~KNN(); |
---|
100 | |
---|
101 | |
---|
102 | /** |
---|
103 | \brief Get the number of nearest neighbors. |
---|
104 | \return The number of neighbors. |
---|
105 | */ |
---|
106 | unsigned int k() const; |
---|
107 | |
---|
108 | /** |
---|
109 | \brief Set the number of nearest neighbors. |
---|
110 | |
---|
111 | Sets the number of neighbors to \a k_in. |
---|
112 | */ |
---|
113 | void k(unsigned int k_in); |
---|
114 | |
---|
115 | |
---|
116 | KNN<Distance,NeighborWeighting>* make_classifier(void) const; |
---|
117 | |
---|
118 | /** |
---|
119 | \brief Make predictions for unweighted test data. |
---|
120 | |
---|
121 | Predictions are calculated and returned in \a results. For |
---|
122 | each sample in \a data, \a results contains the weighted number |
---|
123 | of nearest neighbors which belong to each class. Numbers of |
---|
124 | nearest neighbors are weighted according to |
---|
125 | NeighborWeighting. If a class has no training samples NaN's are |
---|
126 | returned for this class in \a results. |
---|
127 | */ |
---|
128 | void predict(const MatrixLookup& data , utility::Matrix& results) const; |
---|
129 | |
---|
130 | /** |
---|
131 | \brief Make predictions for weighted test data. |
---|
132 | |
---|
133 | Predictions are calculated and returned in \a results. For |
---|
134 | each sample in \a data, \a results contains the weighted |
---|
135 | number of nearest neighbors which belong to each class as in |
---|
136 | predict(const MatrixLookup& data, utility::Matrix& results). |
---|
137 | If a test and training sample pair has no variables with |
---|
138 | non-zero weights in common, there are no variables which can |
---|
139 | be used to calculate the distance between the two samples. In |
---|
140 | this case the distance between the two is set to infinity. |
---|
141 | */ |
---|
142 | void predict(const MatrixLookupWeighted& data, |
---|
143 | utility::Matrix& results) const; |
---|
144 | |
---|
145 | |
---|
146 | /** |
---|
147 | \brief Train the KNN using unweighted training data with known |
---|
148 | targets. |
---|
149 | |
---|
150 | For KNN there is no actual training; the entire training data |
---|
151 | set is stored with targets. KNN only stores references to \a data |
---|
152 | and \a targets as copying these would make the %classifier |
---|
153 | slow. If the number of training samples set is smaller than k, |
---|
154 | k is set to the number of training samples. |
---|
155 | |
---|
156 | \note If \a data or \a targets go out of scope ore are |
---|
157 | deleted, the KNN becomes invalid and further use is undefined |
---|
158 | unless it is trained again. |
---|
159 | */ |
---|
160 | void train(const MatrixLookup& data, const Target& targets); |
---|
161 | |
---|
162 | /** |
---|
163 | \brief Train the KNN using weighted training data with known targets. |
---|
164 | |
---|
165 | See train(const MatrixLookup& data, const Target& targets) for |
---|
166 | additional information. |
---|
167 | */ |
---|
168 | void train(const MatrixLookupWeighted& data, const Target& targets); |
---|
169 | |
---|
170 | private: |
---|
171 | |
---|
172 | const MatrixLookup* data_ml_; |
---|
173 | const MatrixLookupWeighted* data_mlw_; |
---|
174 | const Target* target_; |
---|
175 | |
---|
176 | // The number of neighbors |
---|
177 | unsigned int k_; |
---|
178 | |
---|
179 | Distance distance_; |
---|
180 | NeighborWeighting weighting_; |
---|
181 | |
---|
182 | void calculate_unweighted(const MatrixLookup&, |
---|
183 | const MatrixLookup&, |
---|
184 | utility::Matrix*) const; |
---|
185 | void calculate_weighted(const MatrixLookupWeighted&, |
---|
186 | const MatrixLookupWeighted&, |
---|
187 | utility::Matrix*) const; |
---|
188 | |
---|
189 | void predict_common(const utility::Matrix& distances, |
---|
190 | utility::Matrix& prediction) const; |
---|
191 | |
---|
192 | }; |
---|
193 | |
---|
194 | |
---|
195 | /** |
---|
196 | \brief Concept check for a \ref concept_neighbor_weighting |
---|
197 | |
---|
198 | This class is intended to be used in a <a |
---|
199 | href="\boost_url/concept_check/using_concept_check.htm"> |
---|
200 | BOOST_CONCEPT_ASSERT </a> |
---|
201 | |
---|
202 | \code |
---|
203 | template<class Distance> |
---|
204 | void some_function(double x) |
---|
205 | { |
---|
206 | BOOST_CONCEPT_ASSERT((DistanceConcept<Distance>)); |
---|
207 | ... |
---|
208 | } |
---|
209 | \endcode |
---|
210 | |
---|
211 | \since New in yat 0.7 |
---|
212 | */ |
---|
213 | template <class T> |
---|
214 | class NeighborWeightingConcept |
---|
215 | : public boost::DefaultConstructible<T>, public boost::Assignable<T> |
---|
216 | { |
---|
217 | public: |
---|
218 | /** |
---|
219 | \brief function doing the concept test |
---|
220 | */ |
---|
221 | BOOST_CONCEPT_USAGE(NeighborWeightingConcept) |
---|
222 | { |
---|
223 | T neighbor_weighting; |
---|
224 | utility::Vector vec; |
---|
225 | const utility::VectorBase& distance(vec); |
---|
226 | utility::VectorMutable& prediction(vec); |
---|
227 | std::vector<size_t> k_sorted; |
---|
228 | Target target; |
---|
229 | neighbor_weighting(distance, k_sorted, target, prediction); |
---|
230 | } |
---|
231 | private: |
---|
232 | }; |
---|
233 | |
---|
234 | // template implementation |
---|
235 | |
---|
236 | template <typename Distance, typename NeighborWeighting> |
---|
237 | KNN<Distance, NeighborWeighting>::KNN() |
---|
238 | : SupervisedClassifier(),data_ml_(0),data_mlw_(0),target_(0),k_(3) |
---|
239 | { |
---|
240 | BOOST_CONCEPT_ASSERT((utility::DistanceConcept<Distance>)); |
---|
241 | BOOST_CONCEPT_ASSERT((NeighborWeightingConcept<NeighborWeighting>)); |
---|
242 | } |
---|
243 | |
---|
244 | template <typename Distance, typename NeighborWeighting> |
---|
245 | KNN<Distance, NeighborWeighting>::KNN(const Distance& dist) |
---|
246 | : SupervisedClassifier(), data_ml_(0), data_mlw_(0), target_(0), k_(3), |
---|
247 | distance_(dist) |
---|
248 | { |
---|
249 | BOOST_CONCEPT_ASSERT((utility::DistanceConcept<Distance>)); |
---|
250 | BOOST_CONCEPT_ASSERT((NeighborWeightingConcept<NeighborWeighting>)); |
---|
251 | } |
---|
252 | |
---|
253 | |
---|
254 | template <typename Distance, typename NeighborWeighting> |
---|
255 | KNN<Distance, NeighborWeighting>::~KNN() |
---|
256 | { |
---|
257 | } |
---|
258 | |
---|
259 | |
---|
260 | template <typename Distance, typename NeighborWeighting> |
---|
261 | void KNN<Distance, NeighborWeighting>::calculate_unweighted |
---|
262 | (const MatrixLookup& training, const MatrixLookup& test, |
---|
263 | utility::Matrix* distances) const |
---|
264 | { |
---|
265 | for(size_t i=0; i<training.columns(); i++) { |
---|
266 | for(size_t j=0; j<test.columns(); j++) { |
---|
267 | (*distances)(i,j) = distance_(training.begin_column(i), |
---|
268 | training.end_column(i), |
---|
269 | test.begin_column(j)); |
---|
270 | YAT_ASSERT(!std::isnan((*distances)(i,j))); |
---|
271 | } |
---|
272 | } |
---|
273 | } |
---|
274 | |
---|
275 | |
---|
276 | template <typename Distance, typename NeighborWeighting> |
---|
277 | void |
---|
278 | KNN<Distance, NeighborWeighting>::calculate_weighted |
---|
279 | (const MatrixLookupWeighted& training, const MatrixLookupWeighted& test, |
---|
280 | utility::Matrix* distances) const |
---|
281 | { |
---|
282 | for(size_t i=0; i<training.columns(); i++) { |
---|
283 | for(size_t j=0; j<test.columns(); j++) { |
---|
284 | (*distances)(i,j) = distance_(training.begin_column(i), |
---|
285 | training.end_column(i), |
---|
286 | test.begin_column(j)); |
---|
287 | // If the distance is NaN (no common variables with non-zero weights), |
---|
288 | // the distance is set to infinity to be sorted as a neighbor at the end |
---|
289 | if(std::isnan((*distances)(i,j))) |
---|
290 | (*distances)(i,j)=std::numeric_limits<double>::infinity(); |
---|
291 | } |
---|
292 | } |
---|
293 | } |
---|
294 | |
---|
295 | |
---|
296 | template <typename Distance, typename NeighborWeighting> |
---|
297 | unsigned int KNN<Distance, NeighborWeighting>::k() const |
---|
298 | { |
---|
299 | return k_; |
---|
300 | } |
---|
301 | |
---|
302 | template <typename Distance, typename NeighborWeighting> |
---|
303 | void KNN<Distance, NeighborWeighting>::k(unsigned int k) |
---|
304 | { |
---|
305 | k_=k; |
---|
306 | } |
---|
307 | |
---|
308 | |
---|
309 | template <typename Distance, typename NeighborWeighting> |
---|
310 | KNN<Distance, NeighborWeighting>* |
---|
311 | KNN<Distance, NeighborWeighting>::make_classifier() const |
---|
312 | { |
---|
313 | // All private members should be copied here to generate an |
---|
314 | // identical but untrained classifier |
---|
315 | KNN* knn=new KNN<Distance, NeighborWeighting>(distance_); |
---|
316 | knn->weighting_=this->weighting_; |
---|
317 | knn->k(this->k()); |
---|
318 | return knn; |
---|
319 | } |
---|
320 | |
---|
321 | |
---|
322 | template <typename Distance, typename NeighborWeighting> |
---|
323 | void KNN<Distance, NeighborWeighting>::train(const MatrixLookup& data, |
---|
324 | const Target& target) |
---|
325 | { |
---|
326 | utility::yat_assert<utility::runtime_error> |
---|
327 | (data.columns()==target.size(), |
---|
328 | "KNN::train called with different sizes of target and data"); |
---|
329 | // k has to be at most the number of training samples. |
---|
330 | if(data.columns()<k_) |
---|
331 | k_=data.columns(); |
---|
332 | data_ml_=&data; |
---|
333 | data_mlw_=0; |
---|
334 | target_=⌖ |
---|
335 | } |
---|
336 | |
---|
337 | template <typename Distance, typename NeighborWeighting> |
---|
338 | void KNN<Distance, NeighborWeighting>::train(const MatrixLookupWeighted& data, |
---|
339 | const Target& target) |
---|
340 | { |
---|
341 | utility::yat_assert<utility::runtime_error> |
---|
342 | (data.columns()==target.size(), |
---|
343 | "KNN::train called with different sizes of target and data"); |
---|
344 | // k has to be at most the number of training samples. |
---|
345 | if(data.columns()<k_) |
---|
346 | k_=data.columns(); |
---|
347 | data_ml_=0; |
---|
348 | data_mlw_=&data; |
---|
349 | target_=⌖ |
---|
350 | } |
---|
351 | |
---|
352 | |
---|
353 | template <typename Distance, typename NeighborWeighting> |
---|
354 | void |
---|
355 | KNN<Distance, NeighborWeighting>::predict(const MatrixLookup& test, |
---|
356 | utility::Matrix& prediction) const |
---|
357 | { |
---|
358 | // matrix with training samples as rows and test samples as columns |
---|
359 | utility::Matrix* distances = 0; |
---|
360 | // unweighted training data |
---|
361 | if(data_ml_ && !data_mlw_) { |
---|
362 | utility::yat_assert<utility::runtime_error> |
---|
363 | (data_ml_->rows()==test.rows(), |
---|
364 | "KNN::predict different number of rows in training and test data"); |
---|
365 | distances=new utility::Matrix(data_ml_->columns(),test.columns()); |
---|
366 | calculate_unweighted(*data_ml_,test,distances); |
---|
367 | } |
---|
368 | else if (data_mlw_ && !data_ml_) { |
---|
369 | // weighted training data |
---|
370 | utility::yat_assert<utility::runtime_error> |
---|
371 | (data_mlw_->rows()==test.rows(), |
---|
372 | "KNN::predict different number of rows in training and test data"); |
---|
373 | distances=new utility::Matrix(data_mlw_->columns(),test.columns()); |
---|
374 | calculate_weighted(*data_mlw_,MatrixLookupWeighted(test), |
---|
375 | distances); |
---|
376 | } |
---|
377 | else { |
---|
378 | throw utility::runtime_error("KNN::predict no training data"); |
---|
379 | } |
---|
380 | |
---|
381 | prediction.resize(target_->nof_classes(),test.columns(),0.0); |
---|
382 | predict_common(*distances,prediction); |
---|
383 | if(distances) |
---|
384 | delete distances; |
---|
385 | } |
---|
386 | |
---|
387 | template <typename Distance, typename NeighborWeighting> |
---|
388 | void |
---|
389 | KNN<Distance, NeighborWeighting>::predict(const MatrixLookupWeighted& test, |
---|
390 | utility::Matrix& prediction) const |
---|
391 | { |
---|
392 | // matrix with training samples as rows and test samples as columns |
---|
393 | utility::Matrix* distances=0; |
---|
394 | // unweighted training data |
---|
395 | if(data_ml_ && !data_mlw_) { |
---|
396 | utility::yat_assert<utility::runtime_error> |
---|
397 | (data_ml_->rows()==test.rows(), |
---|
398 | "KNN::predict different number of rows in training and test data"); |
---|
399 | distances=new utility::Matrix(data_ml_->columns(),test.columns()); |
---|
400 | calculate_weighted(MatrixLookupWeighted(*data_ml_),test,distances); |
---|
401 | } |
---|
402 | // weighted training data |
---|
403 | else if (data_mlw_ && !data_ml_) { |
---|
404 | utility::yat_assert<utility::runtime_error> |
---|
405 | (data_mlw_->rows()==test.rows(), |
---|
406 | "KNN::predict different number of rows in training and test data"); |
---|
407 | distances=new utility::Matrix(data_mlw_->columns(),test.columns()); |
---|
408 | calculate_weighted(*data_mlw_,test,distances); |
---|
409 | } |
---|
410 | else { |
---|
411 | throw utility::runtime_error("KNN::predict no training data"); |
---|
412 | } |
---|
413 | |
---|
414 | prediction.resize(target_->nof_classes(),test.columns(),0.0); |
---|
415 | predict_common(*distances,prediction); |
---|
416 | |
---|
417 | if(distances) |
---|
418 | delete distances; |
---|
419 | } |
---|
420 | |
---|
421 | template <typename Distance, typename NeighborWeighting> |
---|
422 | void KNN<Distance, NeighborWeighting>::predict_common |
---|
423 | (const utility::Matrix& distances, utility::Matrix& prediction) const |
---|
424 | { |
---|
425 | for(size_t sample=0;sample<distances.columns();sample++) { |
---|
426 | std::vector<size_t> k_index; |
---|
427 | utility::VectorConstView dist=distances.column_const_view(sample); |
---|
428 | utility::sort_smallest_index(k_index,k_,dist); |
---|
429 | utility::VectorView pred=prediction.column_view(sample); |
---|
430 | weighting_(dist,k_index,*target_,pred); |
---|
431 | } |
---|
432 | |
---|
433 | // classes for which there are no training samples should be set |
---|
434 | // to nan in the predictions |
---|
435 | for(size_t c=0;c<target_->nof_classes(); c++) |
---|
436 | if(!target_->size(c)) |
---|
437 | for(size_t j=0;j<prediction.columns();j++) |
---|
438 | prediction(c,j)=std::numeric_limits<double>::quiet_NaN(); |
---|
439 | } |
---|
440 | }}} // of namespace classifier, yat, and theplu |
---|
441 | |
---|
442 | #endif |
---|