RFTutorial.cpp
Go to the documentation of this file.
1//===========================================================================
2/*!
3 *
4 *
5 * \brief Linear Regression Tutorial Sample Code
6 *
7 * This file is part of the "Random Forest" tutorial.
8 * It requires some toy sample data that comes with the library.
9 *
10 *
11 *
12 * \author K. N. Hansen
13 * \date 2012
14 *
15 *
16 * \par Copyright 1995-2017 Shark Development Team
17 *
18 * <BR><HR>
19 * This file is part of Shark.
20 * <https://shark-ml.github.io/Shark/>
21 *
22 * Shark is free software: you can redistribute it and/or modify
23 * it under the terms of the GNU Lesser General Public License as published
24 * by the Free Software Foundation, either version 3 of the License, or
25 * (at your option) any later version.
26 *
27 * Shark is distributed in the hope that it will be useful,
28 * but WITHOUT ANY WARRANTY; without even the implied warranty of
29 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
30 * GNU Lesser General Public License for more details.
31 *
32 * You should have received a copy of the GNU Lesser General Public License
33 * along with Shark. If not, see <http://www.gnu.org/licenses/>.
34 *
35 */
36//===========================================================================
37
38#include <shark/Data/Csv.h> //importing the file
39#include <shark/Algorithms/Trainers/RFTrainer.h> //the random forest trainer
40#include <shark/ObjectiveFunctions/Loss/ZeroOneLoss.h> //zero one loss for evaluation
41
42#include <iostream>
43
44using namespace std;
45using namespace shark;
46
47
48int main() {
49
50 //*****************LOAD AND PREPARE DATA***********************//
51 //Read Sample data set C.csv
52
53 ClassificationDataset data;
54 importCSV(data, "data/C.csv", LAST_COLUMN, ' ');
55
56 //Split the dataset into a training and a test dataset
57 ClassificationDataset dataTest = splitAtElement(data,311);
58
59 cout << "Training set - number of data points: " << data.numberOfElements()
60 << " number of classes: " << numberOfClasses(data)
61 << " input dimension: " << inputDimension(data) << endl;
62
63 cout << "Test set - number of data points: " << dataTest.numberOfElements()
64 << " number of classes: " << numberOfClasses(dataTest)
65 << " input dimension: " << inputDimension(dataTest) << endl;
66
67 //Generate a random forest
68 RFTrainer<unsigned int> trainer;
69 RFClassifier<unsigned int> model;
70 trainer.train(model, data);
71
72 // evaluate Random Forest classifier
73 ZeroOneLoss<> loss;
74 auto prediction = model(data.inputs());
75 cout << "Random Forest on training set accuracy: " << 1. - loss.eval(data.labels(), prediction) << endl;
76
77 prediction = model(dataTest.inputs());
78 cout << "Random Forest on test set accuracy: " << 1. - loss.eval(dataTest.labels(), prediction) << endl;
79}