weka.clusterers.AbstractClusterer Java Examples

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Example #1
Source File: ClassificationViaClustering.java    From tsml with GNU General Public License v3.0 4 votes vote down vote up 
/**
 * Parses the options for this object. <p/>
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console</pre>
 * 
 * <pre> -W
 *  Full name of clusterer.
 *  (default: weka.clusterers.SimpleKMeans)</pre>
 * 
 * <pre> 
 * Options specific to clusterer weka.clusterers.SimpleKMeans:
 * </pre>
 * 
 * <pre> -N &lt;num&gt;
 *  number of clusters.
 *  (default 2).</pre>
 * 
 * <pre> -V
 *  Display std. deviations for centroids.
 * </pre>
 * 
 * <pre> -M
 *  Replace missing values with mean/mode.
 * </pre>
 * 
 * <pre> -S &lt;num&gt;
 *  Random number seed.
 *  (default 10)</pre>
 * 
 <!-- options-end -->
 *
 * @param options	the options to use
 * @throws Exception	if setting of options fails
 */
public void setOptions(String[] options) throws Exception {
  String	tmpStr;

  super.setOptions(options);

  tmpStr = Utils.getOption('W', options);
  if (tmpStr.length() > 0) { 
    // This is just to set the classifier in case the option 
    // parsing fails.
    setClusterer(AbstractClusterer.forName(tmpStr, null));
    setClusterer(AbstractClusterer.forName(tmpStr, Utils.partitionOptions(options)));
  }
  else {
    // This is just to set the classifier in case the option 
    // parsing fails.
    setClusterer(AbstractClusterer.forName(defaultClustererString(), null));
    setClusterer(AbstractClusterer.forName(defaultClustererString(), Utils.partitionOptions(options)));
  }
}