/**
* Parses a given list of options.
*
* <p>
* <!-- options-start -->
* Valid options are:
*
* <p>
*
* <pre> -folds <folds>
* The number of folds for splitting the training set into
* train and test set. The first fold is always the training
* set. With '-V' you can invert this, i.e., instead of 20/80
* for 5 folds you'll get 80/20.
* (default 5)</pre>
*
* <pre> -V
* Inverts the fold selection, i.e., instead of using the first
* fold for the training set it is used for test set and the
* remaining folds for training.</pre>
*
* <pre> -verbose
* Whether to print some more information during building the
* classifier.
* (default is off)</pre>
*
* <pre> -insight
* Whether to use the labels of the original test set for more
* statistics (not used for learning!).
* (default is off)</pre>
*
* <pre> -S <num>
* Random number seed.
* (default 1)</pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
* <pre> -naive
* Uses a sorted list (ordered according to distance) instead of the
* KDTree for finding the neighbors.
* (default is KDTree)</pre>
*
* <pre> -I
* Weight neighbours by the inverse of their distance
* (use when k > 1)</pre>
*
* <pre> -F
* Weight neighbours by 1 - their distance
* (use when k > 1)</pre>
*
* <pre> -K <number of neighbors>
* Number of nearest neighbours (k) used in classification.
* (Default = 1)</pre>
*
* <pre> -A
* The nearest neighbour search algorithm to use (default: LinearNN).
* </pre>
*
* <!-- options-end -->
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
super.setOptions(options);
setUseNaiveSearch(Utils.getFlag("naive", options));
m_Classifier.setOptions(options);
m_KNN = m_Classifier.getKNN(); // backup KNN
m_Classifier.setCrossValidate(true); // always on!
m_Classifier.setWindowSize(0); // always off!
m_Classifier.setMeanSquared(false); // always off!
}
/** performs initialization of members */
protected void initializeMembers() {
super.initializeMembers();
m_KNNdetermined = -1;
m_NeighborsTestset = null;
m_TrainsetNew = null;
m_TestsetNew = null;
m_UseNaiveSearch = false;
m_LabeledTestset = null;
m_Missing = new ReplaceMissingValues();
m_Classifier = new IBk();
m_Classifier.setKNN(10);
m_Classifier.setCrossValidate(true);
m_Classifier.setWindowSize(0);
m_Classifier.setMeanSquared(false);
m_KNN = m_Classifier.getKNN();
m_AdditionalMeasures.add("measureDeterminedKNN");
}
/**
* generates copies of the original datasets and also builds a relation (hashtable) between each
* original instance and new instance. This is necessary to retrieve the determined class value in
* the <code>getDistribution(Instance)</code> method.
*
* @see #getDistribution(Instance)
* @throws Exception if anything goes wrong
*/
protected void generateSets() throws Exception {
super.generateSets();
m_TrainsetNew = new Instances(m_Trainset);
m_TestsetNew = new Instances(m_Testset);
}
