/** * Main method for testing this class. * * @param argv the options */ public static void main(String[] argv) { runClusterer(new MakeDensityBasedClusterer(), argv); } }
/** * Contructs a MakeDensityBasedClusterer wrapping a given Clusterer. * * @param toWrap the clusterer to wrap around */ public MakeDensityBasedClusterer(Clusterer toWrap) { setClusterer(toWrap); }
setMinStdDev((new Double(optionString)).doubleValue()); } else { setMinStdDev(1e-6); wString = defaultClustererString(); setClusterer(AbstractClusterer.forName(wString, Utils.partitionOptions(options)));
/** Creates a default MakeDensityBasedClusterer */ public Clusterer getClusterer() { return new MakeDensityBasedClusterer(); }
/** * Gets the current settings of the clusterer. * * @return an array of strings suitable for passing to setOptions() */ @Override public String[] getOptions() { Vector<String> options = new Vector<String>(); options.add("-M"); options.add("" + getMinStdDev()); if (getClusterer() != null) { options.add("-W"); options.add(getClusterer().getClass().getName()); if (m_wrappedClusterer instanceof OptionHandler) { String[] clustererOptions = ((OptionHandler) m_wrappedClusterer) .getOptions(); if (clustererOptions.length > 0) { options.add("--"); Collections.addAll(options, clustererOptions); } } } Collections.addAll(options, super.getOptions()); return options.toArray(new String[0]); }
/** * Returns an enumeration describing the available options.. * * @return an enumeration of all the available options. */ @Override public Enumeration<Option> listOptions() { Vector<Option> result = new Vector<Option>(); result.addElement(new Option( "\tminimum allowable standard deviation for normal density computation " + "\n\t(default 1e-6)", "M", 1, "-M <num>")); result.addElement(new Option("\tClusterer to wrap.\n" + "\t(default " + defaultClustererString() + ")", "W", 1, "-W <clusterer name>")); result.addAll(Collections.list(super.listOptions())); if ((m_wrappedClusterer != null) && (m_wrappedClusterer instanceof OptionHandler)) { result.addElement(new Option("", "", 0, "\nOptions specific to clusterer " + m_wrappedClusterer.getClass().getName() + ":")); result.addAll(Collections.list(((OptionHandler) m_wrappedClusterer) .listOptions())); } return result.elements(); }
logprob += Math.log(m_model[i][j].getProbability(inst.value(j))); } else { // numeric attribute logprob += logNormalDens(inst.value(j), m_modelNormal[i][j][0], m_modelNormal[i][j][1]);
public void buildClusterer(Instances data) throws Exception { getCapabilities().testWithFail(data);
setMinStdDev((new Double(optionString)).doubleValue()); } else { setMinStdDev(1e-6); wString = defaultClustererString(); setClusterer(AbstractClusterer.forName(wString, Utils.partitionOptions(options)));
/** Creates a default MakeDensityBasedClusterer */ public Clusterer getClusterer() { return new MakeDensityBasedClusterer(); }
/** * Gets the current settings of the clusterer. * * @return an array of strings suitable for passing to setOptions() */ @Override public String[] getOptions() { Vector<String> options = new Vector<String>(); options.add("-M"); options.add("" + getMinStdDev()); if (getClusterer() != null) { options.add("-W"); options.add(getClusterer().getClass().getName()); if (m_wrappedClusterer instanceof OptionHandler) { String[] clustererOptions = ((OptionHandler) m_wrappedClusterer) .getOptions(); if (clustererOptions.length > 0) { options.add("--"); Collections.addAll(options, clustererOptions); } } } Collections.addAll(options, super.getOptions()); return options.toArray(new String[0]); }
/** * Returns an enumeration describing the available options.. * * @return an enumeration of all the available options. */ @Override public Enumeration<Option> listOptions() { Vector<Option> result = new Vector<Option>(); result.addElement(new Option( "\tminimum allowable standard deviation for normal density computation " + "\n\t(default 1e-6)", "M", 1, "-M <num>")); result.addElement(new Option("\tClusterer to wrap.\n" + "\t(default " + defaultClustererString() + ")", "W", 1, "-W <clusterer name>")); result.addAll(Collections.list(super.listOptions())); if ((m_wrappedClusterer != null) && (m_wrappedClusterer instanceof OptionHandler)) { result.addElement(new Option("", "", 0, "\nOptions specific to clusterer " + m_wrappedClusterer.getClass().getName() + ":")); result.addAll(Collections.list(((OptionHandler) m_wrappedClusterer) .listOptions())); } return result.elements(); }
logprob += Math.log(m_model[i][j].getProbability(inst.value(j))); } else { // numeric attribute logprob += logNormalDens(inst.value(j), m_modelNormal[i][j][0], m_modelNormal[i][j][1]);
public void buildClusterer(Instances data) throws Exception { getCapabilities().testWithFail(data);
/** * Main method for testing this class. * * @param argv the options */ public static void main(String[] argv) { runClusterer(new MakeDensityBasedClusterer(), argv); } }
/** * Contructs a MakeDensityBasedClusterer wrapping a given Clusterer. * * @param toWrap the clusterer to wrap around */ public MakeDensityBasedClusterer(Clusterer toWrap) { setClusterer(toWrap); }