List of buildClassifier() Examples

Examples of buildClassifier()

org.goai.classification.impl.WekaClassifier.buildClassifier()
Trains classifier with passed date set @param itemClassMap Map
weka.classifiers.Classifier.buildClassifier()
Generates a classifier. Must initialize all fields of the classifier that are not being set via options (ie. multiple calls of buildClassifier must always lead to the same result). Must not change the dataset in any way. @param data set of instances serving as training data @exception Exception if the classifier has not been generated successfully
weka.classifiers.bayes.BayesNet.buildClassifier()
Generates the classifier. @param instances set of instances serving as training data @throws Exception if the classifier has not been generatedsuccessfully
weka.classifiers.bayes.NaiveBayesUpdateable.buildClassifier()
weka.classifiers.functions.LibSVM.buildClassifier()
builds the classifier @param insts the training instances @throws Exception if libsvm classes not in classpath or libsvmencountered a problem
weka.classifiers.functions.LinearRegression.buildClassifier()
Builds a regression model for the given data. @param data the training data to be used for generating thelinear regression function @throws Exception if the classifier could not be built successfully
weka.classifiers.functions.MultilayerPerceptron.buildClassifier()
Call this function to build and train a neural network for the training data provided. @param i The training data. @throws Exception if can't build classification properly.
weka.classifiers.functions.SMO.buildClassifier()
Method for building the classifier. Implements a one-against-one wrapper for multi-class problems. @param insts the set of training instances @throws Exception if the classifier can't be built successfully
weka.classifiers.functions.SPegasos.buildClassifier()
Method for building the classifier. @param data the set of training instances. @throws Exception if the classifier can't be built successfully.
weka.classifiers.meta.FilteredClassifier.buildClassifier()
Build the classifier on the filtered data. @param data the training data @throws Exception if the classifier could not be built successfully
weka.classifiers.misc.InputMappedClassifier.buildClassifier()
Build the classifier @param data the training data to be used for generating thebagged classifier. @throws Exception if the classifier could not be built successfully
weka.classifiers.trees.J48.buildClassifier()
Generates the classifier. @param instances the data to train the classifier with @throws Exception if classifier can't be built successfully

Examples of org.goai.classification.impl.WekaClassifier.buildClassifier()

        try {
            // Inicialize GOAI Classifier with Weka classifier
            Classifier<double[], String> goaiClassifier = new WekaClassifier(wekaClassifier);
            
            //Process data from Map
            goaiClassifier.buildClassifier(makeMapOutOfInstances(super.getProblemSrc()));
            
            return goaiClassifier;
        } catch (Exception ex) {
            Logger.getLogger(WekaClassifierEvaluation.class.getName()).log(Level.SEVERE, null, ex);
        }

View Full Code Here

Examples of weka.classifiers.Classifier.buildClassifier()

    try {
      current = AbstractClassifier.makeCopy(template);
    } catch (Exception ex) {
      m_Log.logMessage("Problem copying classifier: " + ex.getMessage());
    }
    current.buildClassifier(train);
    Instances test = inst.testCV(numFolds, fold);
    m_Log.statusMessage("Evaluating model for fold "
            + (fold + 1) + "...");
    for (int jj=0;jj<test.numInstances();jj++) {
      plotInstances.process(test.instance(jj), current, eval);

View Full Code Here

Examples of weka.classifiers.Classifier.buildClassifier()

        try {
    current = AbstractClassifier.makeCopy(template);
        } catch (Exception ex) {
    m_Log.logMessage("Problem copying classifier: " + ex.getMessage());
        }
        current.buildClassifier(train);
        eval = new Evaluation(train, costMatrix);
        
        // make adjustments if the classifier is an InputMappedClassifier
              eval = setupEval(eval, classifier, train, costMatrix, 
                  plotInstances, classificationOutput, false);

View Full Code Here

Examples of weka.classifiers.Classifier.buildClassifier()

      DenseInstance d = new DenseInstance(1.0, vals);
      simple.add(d);
    }


    simple.setClassIndex(1);
    lagFiller.buildClassifier(simple);
    System.err.println(lagFiller);
    simple = new Instances(simple, 0);


    List<Object> results = new ArrayList<Object>();
    results.add(lagFiller);

View Full Code Here

Examples of weka.classifiers.Classifier.buildClassifier()

          // As part of learning, we also prune some of the nodes where the ratio of correctly-classified pairs to those incorrectly classified is comparable.
          // The significant advantage of not pruning is that the result is no longer sensitive to the order of elements in the tree and hence does not depend on the order in which elements have been obtained by concurrent threads.
          //final weka.classifiers.lazy.IB1 ib1 = new weka.classifiers.lazy.IB1();
          //final weka.classifiers.trees.J48 classifier = new weka.classifiers.trees.J48();
          final Classifier classifier = repTree;
          classifier.buildClassifier(dataCollector.trainingData);
          System.out.println("Entries in the classifier: "+dataCollector.trainingData.numInstances());
          System.out.println(classifier);
          dataCollector=null;// throw all the training data away.
          
          {// serialise the classifier, this is the only way to store it.

View Full Code Here

Examples of weka.classifiers.Classifier.buildClassifier()

          // As part of learning, we also prune some of the nodes where the ratio of correctly-classified pairs to those incorrectly classified is comparable.
          // The significant advantage of not pruning is that the result is no longer sensitive to the order of elements in the tree and hence does not depend on the order in which elements have been obtained by concurrent threads.
          //final weka.classifiers.lazy.IB1 ib1 = new weka.classifiers.lazy.IB1();
          //final weka.classifiers.trees.J48 classifier = new weka.classifiers.trees.J48();
          final Classifier classifier = repTree;
          classifier.buildClassifier(dataCollector.trainingData);
          System.out.println("Entries in the classifier: "+dataCollector.trainingData.numInstances());
          System.out.println(classifier);
          dataCollector=null;// throw all the training data away.
          
          {// serialise the classifier, this is the only way to store it.

View Full Code Here

Examples of weka.classifiers.Classifier.buildClassifier()

          // As part of learning, we also prune some of the nodes where the ratio of correctly-classified pairs to those incorrectly classified is comparable.
          // The significant advantage of not pruning is that the result is no longer sensitive to the order of elements in the tree and hence does not depend on the order in which elements have been obtained by concurrent threads.
          //final weka.classifiers.lazy.IB1 ib1 = new weka.classifiers.lazy.IB1();
          //final weka.classifiers.trees.J48 classifier = new weka.classifiers.trees.J48();
          final Classifier classifier = repTree;
          classifier.buildClassifier(dataCollector.trainingData);
          System.out.println("Entries in the classifier: "+dataCollector.trainingData.numInstances());
          System.out.println(classifier);
          dataCollector=null;// throw all the training data away.
          
          {// serialise the classifier, this is the only way to store it.

View Full Code Here

Examples of weka.classifiers.Classifier.buildClassifier()

          // As part of learning, we also prune some of the nodes where the ratio of correctly-classified pairs to those incorrectly classified is comparable.
          // The significant advantage of not pruning is that the result is no longer sensitive to the order of elements in the tree and hence does not depend on the order in which elements have been obtained by concurrent threads.
          //final weka.classifiers.lazy.IB1 ib1 = new weka.classifiers.lazy.IB1();
          //final weka.classifiers.trees.J48 classifier = new weka.classifiers.trees.J48();
          final Classifier classifier = repTree;
          classifier.buildClassifier(dataCollector.trainingData);
          System.out.println("Entries in the classifier: "+dataCollector.trainingData.numInstances());
          System.out.println(classifier);
          dataCollector=null;// throw all the training data away.
          
          {// serialise the classifier, this is the only way to store it.

View Full Code Here

Examples of weka.classifiers.Classifier.buildClassifier()

        cModel=(Classifier)Util.loadObject(f);
      }
      else
      {
        cModel = (Classifier)Class.forName(this.classifier).newInstance();        
             cModel.buildClassifier(ins);
             Util.writeObject(f, cModel);
      }
      //Instantiate the classifier
        Instance base=ins.firstInstance();
      Instance sample=new Instance(base.numAttributes());

View Full Code Here

Examples of weka.classifiers.Classifier.buildClassifier()

          // As part of learning, we also prune some of the nodes where the ratio of correctly-classified pairs to those incorrectly classified is comparable.
          // The significant advantage of not pruning is that the result is no longer sensitive to the order of elements in the tree and hence does not depend on the order in which elements have been obtained by concurrent threads.
          //final weka.classifiers.lazy.IB1 ib1 = new weka.classifiers.lazy.IB1();
          //final weka.classifiers.trees.J48 classifier = new weka.classifiers.trees.J48();
          final Classifier classifier = repTree;
          classifier.buildClassifier(dataCollector.trainingData);
          System.out.println("Entries in the classifier: "+dataCollector.trainingData.numInstances());
          System.out.println(classifier);
          dataCollector=null;// throw all the training data away.
          
          {// serialise the classifier, this is the only way to store it.

View Full Code Here

0 1 2 3 4 5

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.