KNNTutorial.cpp
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1 //===========================================================================
2 /*!
3  *
4  *
5  * \brief Nearest Neighbor Tutorial Sample Code
6  *
7  *
8  *
9  *
10  * \author C. Igel
11  * \date 2011
12  *
13  *
14  * \par Copyright 1995-2017 Shark Development Team
15  *
16  * <BR><HR>
17  * This file is part of Shark.
18  * <http://shark-ml.org/>
19  *
20  * Shark is free software: you can redistribute it and/or modify
21  * it under the terms of the GNU Lesser General Public License as published
22  * by the Free Software Foundation, either version 3 of the License, or
23  * (at your option) any later version.
24  *
25  * Shark is distributed in the hope that it will be useful,
26  * but WITHOUT ANY WARRANTY; without even the implied warranty of
27  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
28  * GNU Lesser General Public License for more details.
29  *
30  * You should have received a copy of the GNU Lesser General Public License
31  * along with Shark. If not, see <http://www.gnu.org/licenses/>.
32  *
33  */
34 //===========================================================================
35 
36 #include <shark/Data/Csv.h>
37 #include <shark/Models/NearestNeighborModel.h>
38 #include <shark/Algorithms/NearestNeighbors/TreeNearestNeighbors.h>
39 #include <shark/Models/Trees/KDTree.h>
40 #include <shark/ObjectiveFunctions/Loss/ZeroOneLoss.h>
41 #include <shark/Data/DataView.h>
42 #include <iostream>
43 
44 using namespace shark;
45 using namespace std;
46 
47 int main(int argc, char **argv) {
48  if(argc < 2) {
49  cerr << "usage: " << argv[0] << " (filename)" << endl;
50  exit(EXIT_FAILURE);
51  }
52  // read data
53  ClassificationDataset data;
54  try {
55  importCSV(data, argv[1], LAST_COLUMN, ' ');
56  }
57  catch (...) {
58  cerr << "unable to read data from file " << argv[1] << endl;
59  exit(EXIT_FAILURE);
60  }
61 
62  cout << "number of data points: " << data.numberOfElements()
63  << " number of classes: " << numberOfClasses(data)
64  << " input dimension: " << inputDimension(data) << endl;
65 
66  // split data into training and test set
67  ClassificationDataset dataTest = splitAtElement(data, static_cast<std::size_t>(.5 * data.numberOfElements()));
68  cout << "training data points: " << data.numberOfElements() << endl;
69  cout << "test data points: " << dataTest.numberOfElements() << endl;
70 
71  //create a binary search tree and initialize the search algorithm - a fast tree search
72  KDTree<RealVector> tree(data.inputs());
73  TreeNearestNeighbors<RealVector,unsigned int> algorithm(data,&tree);
74  //instantiate the classifier
75  const unsigned int K = 1; // number of neighbors for kNN
76  NearestNeighborModel<RealVector, unsigned int> KNN(&algorithm,K);
77 
78  // evaluate classifier
79  ZeroOneLoss<unsigned int> loss;
80  Data<unsigned int> prediction = KNN(data.inputs());
81  cout << K << "-KNN on training set accuracy: " << 1. - loss.eval(data.labels(), prediction) << endl;
82  prediction = KNN(dataTest.inputs());
83  cout << K << "-KNN on test set accuracy: " << 1. - loss.eval(dataTest.labels(), prediction) << endl;
84 }