Java Machine Learning Library (Java-ML)

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Java Machine Learning Library 0.1.7 released

• Thomas Abeel
• It's been a long time, but there is a new release. 0.1.6 has been release quietly a while ago, so this will be number 0.1.7.
  

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• API documentation
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Things that are new or changed since the last announcement:

- Spectral clustering algorithms are available in SVN, will be included in the main release further down the line.
- A whole lot more unit tests
- Bayes classifiers
- More tutorials
- Improved sampling strategies
- fixed getWeights for sparse data sets
- improved Dynamic Time Warping implementation
- upgraded most dependencies to more recent versions (libsvm 2.9.1, ajt 2.9)
- fixed bug in Kmeans that entered an infinite loop in a particular case
- fixed bug in normalization for attributes that have no range
- Fixed a whole bunch of bugs contributed through the Sourceforge ticketing system
- Removed OPTICS implementation until further notice because it is incomplete

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关键词：Learning machine earning Library BRARY available included further release number

1. /**
   
2. * This file is part of the Java Machine Learning Library
   
3. *
   
4. * The Java Machine Learning Library is free software; you can redistribute it and/or modify
   
5. * it under the terms of the GNU General Public License as published by
   
6. * the Free Software Foundation; either version 2 of the License, or
   
7. * (at your option) any later version.
   
8. *
   
9. * The Java Machine Learning Library is distributed in the hope that it will be useful,
   
10. * but WITHOUT ANY WARRANTY; without even the implied warranty of
    
11. * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    
12. * GNU General Public License for more details.
    
13. *
    
14. * You should have received a copy of the GNU General Public License
    
15. * along with the Java Machine Learning Library; if not, write to the Free Software
    
16. * Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
    
17. *
    
18. * Copyright (c) 2006-2009, Thomas Abeel
    
19. *
    
20. * Project: http://java-ml.sourceforge.net/
    
21. *
    
22. */
    
23. package tutorials.classification;
    
24. 
    
25. import java.io.File;
    
26. 
    
27. import net.sf.javaml.classification.Classifier;
    
28. import net.sf.javaml.classification.KNearestNeighbors;
    
29. import net.sf.javaml.core.Dataset;
    
30. import net.sf.javaml.core.Instance;
    
31. import net.sf.javaml.tools.data.FileHandler;
    
32. 
    
33. /**
    
34. * This tutorial show how to use a the k-nearest neighbors classifier.
    
35. *
    
36. * @author Thomas Abeel
    
37. *
    
38. */
    
39. public class TutorialKNN {
    
40.     /**
    
41.      * Shows the default usage of the KNN algorithm.
    
42.      */
    
43.     public static void main(String[] args)throws Exception {
    
44. 
    
45.         /* Load a data set */
    
46.         Dataset data = FileHandler.loadDataset(new File("devtools/data/iris.data"), 4, ",");
    
47.         /*
    
48.          * Contruct a KNN classifier that uses 5 neighbors to make a decision.
    
49.          */
    
50.         Classifier knn = new KNearestNeighbors(5);
    
51.         knn.buildClassifier(data);
    
52. 
    
53.         /*
    
54.          * Load a data set for evaluation, this can be a different one, but we
    
55.          * will use the same one.
    
56.          */
    
57.         Dataset dataForClassification = FileHandler.loadDataset(new File("devtools/data/iris.data"), 4, ",");
    
58.         /* Counters for correct and wrong predictions. */
    
59.         int correct = 0, wrong = 0;
    
60.         /* Classify all instances and check with the correct class values */
    
61.         for (Instance inst : dataForClassification) {
    
62.             Object predictedClassValue = knn.classify(inst);
    
63.             Object realClassValue = inst.classValue();
    
64.             if (predictedClassValue.equals(realClassValue))
    
65.                 correct++;
    
66.             else
    
67.                 wrong++;
    
68.         }
    
69.         System.out.println("Correct predictions  " + correct);
    
70.         System.out.println("Wrong predictions " + wrong);
    
71. 
    
72.     }
    
73. 
    
74. }

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