Tabnine Logo
weka.clusterers
Code IndexAdd Tabnine to your IDE (free)

How to use weka.clusterers

Best Java code snippets using weka.clusterers (Showing top 20 results out of 315)

origin: nz.ac.waikato.cms.weka/weka-stable

 /**
  * Test method for this class
  * 
  * @param args the commandline options
  */
 public static void main(String[] args) {
  runCheck(new CheckClusterer(), args);
 }
}
origin: nz.ac.waikato.cms.weka/weka-stable

 /**
  * Main method.
  * 
  * @param argv the commandline options
  */
 public static void main(String[] argv) {
  runClusterer(new Cobweb(), argv);
 }
}
origin: nz.ac.waikato.cms.weka/distributedWekaBase

public void updateFinished() {
 if (m_canopy != null && !m_finalized) {
  m_canopy.updateFinished();
  m_finalized = true;
 }
}
origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * Initialize with the fartherst first centers
 * 
 * @param data the training data
 * @throws Exception if a problem occurs
 */
protected void farthestFirstInit(Instances data) throws Exception {
 FarthestFirst ff = new FarthestFirst();
 ff.setNumClusters(m_NumClusters);
 ff.buildClusterer(data);
 m_ClusterCentroids = ff.getClusterCentroids();
}
origin: nz.ac.waikato.cms.weka/weka-stable

 /**
  * Main method for testing this class.
  * 
  * @param argv should contain the following arguments:
  *          <p>
  *          -t training file [-T test file] [-N number of clusters] [-S random
  *          seed]
  */
 public static void main(String[] argv) {
  runClusterer(new EM(), argv);
 }
}
origin: nz.ac.waikato.cms.weka/weka-stable

/** Creates a default Cobweb */
public Clusterer getClusterer() {
 Cobweb cb = new Cobweb();
 cb.setSeed(-1); // Make sure data is not randomized in buildClassifier() (incremental == batch)
 return cb;
}
origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * tests whether the scheme declares a serialVersionUID.
 */
public void testSerialVersionUID() {
 boolean[]     result;
 result = m_Tester.declaresSerialVersionUID();
 if (!result[0])
  fail("Doesn't declare serialVersionUID!");
}
origin: nz.ac.waikato.cms.weka/weka-stable

 /**
  * Main method for testing this class.
  * 
  * @param argv should contain the following arguments:
  *          <p>
  *          -t training file [-N number of clusters]
  */
 public static void main(String[] argv) {
  runClusterer(new FarthestFirst(), argv);
 }
}
origin: Waikato/weka-trunk

 /**
  * Main method for testing this class.
  * 
  * @param args the commandline options, use "-h" for help
  */
 public static void main(String[] args) {
  runClusterer(new FilteredClusterer(), args);
 }
}
origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * default constructor
 */
public Cobweb() {
 super();
 m_SeedDefault = 42;
 setSeed(m_SeedDefault);
}
origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * the default constructor.
 */
public SimpleKMeans() {
 super();
 m_SeedDefault = 10;
 setSeed(m_SeedDefault);
}
origin: nz.ac.waikato.cms.weka/distributedWekaBase

 /**
  * Get the number of clusters from the base clusterer
  *
  * @return the number of clusters
  * @throws Exception if a problem occurs
  */
 public int numberOfClusters() throws Exception {
  return m_model.numberOfClusters();
 }
}
origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * Returns the tip text for this property
 * 
 * @return tip text for this property suitable for displaying in the
 *         explorer/experimenter gui
 */
@Override
public String seedTipText() {
 String result = super.seedTipText() + " Use -1 for no randomization.";
 return result;
}
origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * Constructor.
 * 
 **/
public EM() {
 super();
 m_SeedDefault = 100;
 resetOptions();
}
origin: nz.ac.waikato.cms.weka/weka-stable

 @Override
 public double[] call() {
  return moveCentroid(m_centroidIndex, m_cluster, true, false);
 }
}
origin: Waikato/weka-trunk

/**
 * Contructs a MakeDensityBasedClusterer wrapping a given Clusterer.
 * 
 * @param toWrap the clusterer to wrap around
 */
public MakeDensityBasedClusterer(Clusterer toWrap) {
 setClusterer(toWrap);
}
origin: Waikato/weka-trunk

/**
 * Initialize with the fartherst first centers
 * 
 * @param data the training data
 * @throws Exception if a problem occurs
 */
protected void farthestFirstInit(Instances data) throws Exception {
 FarthestFirst ff = new FarthestFirst();
 ff.setNumClusters(m_NumClusters);
 ff.buildClusterer(data);
 m_ClusterCentroids = ff.getClusterCentroids();
}
origin: Waikato/weka-trunk

 /**
  * Main method for testing this class.
  * 
  * @param argv should contain the following arguments:
  *          <p>
  *          -t training file [-T test file] [-N number of clusters] [-S random
  *          seed]
  */
 public static void main(String[] argv) {
  runClusterer(new EM(), argv);
 }
}
origin: Waikato/weka-trunk

/**
 * tests whether the scheme declares a serialVersionUID.
 */
public void testSerialVersionUID() {
 boolean[]     result;
 result = m_Tester.declaresSerialVersionUID();
 if (!result[0])
  fail("Doesn't declare serialVersionUID!");
}
origin: Waikato/weka-trunk

/**
 * Returns the tip text for this property
 * 
 * @return tip text for this property suitable for displaying in the
 *         explorer/experimenter gui
 */
@Override
public String seedTipText() {
 String result = super.seedTipText() + " Use -1 for no randomization.";
 return result;
}
weka.clusterers

Most used classes

  • SimpleKMeans
    Cluster data using the k means algorithm. Can use either the Euclidean distance (default) or the Ma
  • Clusterer
    Interface for clusterers. Clients will typically extend either AbstractClusterer or AbstractDensityB
  • AbstractClusterer
    Abstract clusterer.
  • EM
    Simple EM (expectation maximisation) class. EM assigns a probability distribution to each instance
  • FilteredClusterer
    Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter.
  • Canopy,
  • CheckClusterer,
  • ClusterEvaluation,
  • Cobweb,
  • FarthestFirst,
  • MakeDensityBasedClusterer,
  • NumberOfClustersRequestable,
  • RandomizableClusterer,
  • AbstractClustererTest,
  • AbstractDensityBasedClusterer,
  • AllTests,
  • CanopyTest,
  • Cobweb$CNode,
  • CobwebTest
Tabnine Logo
  • Products

    Search for Java codeSearch for JavaScript code
  • IDE Plugins

    IntelliJ IDEAWebStormVisual StudioAndroid StudioEclipseVisual Studio CodePyCharmSublime TextPhpStormVimGoLandRubyMineEmacsJupyter NotebookJupyter LabRiderDataGripAppCode
  • Company

    About UsContact UsCareers
  • Resources

    FAQBlogTabnine AcademyTerms of usePrivacy policyJava Code IndexJavascript Code Index
Get Tabnine for your IDE now