/** * Generates a linear regression function predictor. * * @param argv the options */ public static void main(String argv[]) { runClassifier(new LinearRegression(), argv); }
/** * Main method for running this classifier. * * @param args the commandline options */ public static void main(String[] args) { runClassifier(new SMOreg(), args); } }
/** * Main method for testing this class * * @param argv options */ public static void main(String[] argv) { runClassifier(new SimpleLinearRegression(), argv); } }
/** Creates a default SGD */ public Classifier getClassifier() { SGD p = new SGD(); p.setDontNormalize(true); p.setDontReplaceMissing(true); p.setEpochs(1); p.setLearningRate(0.001); return p; }
/** * Main method for testing this class. * * @param argv should contain the command line arguments to the scheme (see * Evaluation) */ public static void main(String[] argv) { runClassifier(new Logistic(), argv); }
/** * Main method for testing this class. */ public static void main(String[] argv) { runClassifier(new SMO(), argv); } }
/** * Constructor. */ public MultiClassClassifier() { m_Classifier = new weka.classifiers.functions.Logistic(); }
/** * Main method for testing this class. * * @param argv should contain command line options (see setOptions) */ public static void main(String[] argv) { runClassifier(new MultilayerPerceptron(), argv); }
/** Creates a default SPegasos */ public Classifier getClassifier() { SPegasos p = new SPegasos(); p.setDontNormalize(true); p.setDontReplaceMissing(true); p.setEpochs(1); return p; }
/** * Constructor */ public MultiClassClassifierUpdateable() { m_Classifier = new weka.classifiers.functions.SGD(); }
/** * Main method. * * @param argv the commandline options */ public static void main(String[] argv) { runClassifier(new VotedPerceptron(), argv); } }
/** * Main method for testing this class * * @param argv commandline options */ public static void main(String[] argv) { runClassifier(new SimpleLogistic(), argv); }
/** * Disables or enables the checks (which could be time-consuming). Use with * caution! * * @param value if true turns off all checks */ public void setChecksTurnedOff(boolean value) { if (value) turnChecksOff(); else turnChecksOn(); }
/** * Generates an FLDA classifier. * * @param argv the options */ public static void main(String [] argv){ runClassifier(new FLDA(), argv); } }
/** * Updates the classifier with the given instance. * * @param instance the new training instance to include in the model * @exception Exception if the instance could not be incorporated in the * model. */ @Override public void updateClassifier(Instance instance) throws Exception { updateClassifier(instance, true); }
/** * this will reset all the nodes in the network. */ private void resetNetwork() { for (int noc = 0; noc < m_numClasses; noc++) { m_outputs[noc].reset(); } }
@Override public void actionPerformed(ActionEvent e) { m_accepted = true; blocker(false); } });
/** * Constructor that sets the default number of decimal places to 4. */ public Logistic() { setNumDecimalPlaces(4); }
/** * Default (argument-less) constructor. Will initialize the instance with * a default Weka-classifier (here, that default is, NaiveBayes with Kernel density estimation) * * @throws ClassifierException */ public EDABinaryClassifierFromWeka() throws ClassifierException { this(new Logistic(), null); // logistic regression is generally go good in most situations. //this(new NaiveBayes(), new String[] {"-K"}); }
/** * Disables or enables the checks (which could be time-consuming). Use with * caution! * * @param value if true turns off all checks */ public void setChecksTurnedOff(boolean value) { if (value) turnChecksOff(); else turnChecksOn(); }