Example usage for weka.classifiers.functions VotedPerceptron VotedPerceptron

Introduction

In this page you can find the example usage for weka.classifiers.functions VotedPerceptron VotedPerceptron.

Prototype

VotedPerceptron

Source Link

Usage

From source file:machinelearningcw.MachineLearningCw.java

public static void main(String[] args) throws Exception {

    Instances data[] = getAllFiles();/*from  ww  w.  j  a  va  2 s . co  m*/
    //writes the data to excel
    writer = new FileWriter(
            "\\\\ueahome4\\stusci5\\ypf12pxu\\data\\Documents\\Machine Learning\\adamt94-machinelearning-da75565f2abe\\adamt94-machinelearning-da75565f2abe\\data.csv");
    writer.append("DataName");
    writer.append(",");//next column
    writer.append("Offline");
    writer.append(",");
    writer.append("Online");
    writer.append(",");
    writer.append("Offlinestd");
    writer.append(",");
    writer.append("Onlinestd");
    writer.append(",");
    writer.append("CrossValidation");
    writer.append(",");
    writer.append("Ensemble");
    writer.append(",");
    writer.append("WEKA1");
    writer.append(",");
    writer.append("WEKA2");
    writer.append("\n");//new row
    for (int i = 0; i < data.length; i++) {

        System.out.println("===============" + fileNames.get(i) + "=============");
        writer.append(fileNames.get(i));
        writer.append(",");
        data[i].setClassIndex(data[i].numAttributes() - 1);
        //1. Is one learning algorithm better than the other?
        //   compareAlgorithms(data[i]);

        /*2. Does standardising the data produce a 
         more accurate classifier? 
         You can test this on both learningalgorithms.*/
        //  standardiseData(data[i]);

        /*3. Does choosing the learning algorithm through 
         cross validation produce a more accurate classifier?*/
        //   crossValidation(data[i]);

        // 4. Does using an ensemble produce a more accurate classifier?
        //     ensemble(data[i]);

        /*5. Weka contains several related classifiers in the 
         package weka.classifiers.functions. 
         Comparetwo of your classifiers (including the ensemble) 
         to at least two of the following*/

        /*=======================================
              Weka Classifiers
        =========================================*/

        //            VotedPerceptron mp = new VotedPerceptron();
        // Logistic l = new Logistic();
        //  SimpleLogistic sl = new SimpleLogistic();
        //  MultilayerPerceptron mp = new MultilayerPerceptron();
        //  VotedPerceptron vp = new VotedPerceptron();
        //            
        //            int numFolds = 10;
        //            EvaluationUtils eval = new EvaluationUtils();
        //            ArrayList<Prediction> preds
        //                    = eval.getCVPredictions(mp, data[i], numFolds);
        //            int correct = 0;
        //            int total = 0;
        //            for (Prediction pred : preds) {
        //                if (pred.predicted() == pred.actual()) {
        //                    correct++;
        //                }
        //                total++;
        //            }
        //            double acc = ((double) correct / total);
        //
        //            System.out.println("Logistic Accuracy: " + acc);
        //            writer.append(acc + ",");
        int j = data[i].numClasses();
        writer.append(j + ",");
        writer.append("\n");

    }

    /*=======================================================
     TIMING EXPIREMENT
     =========================================================
     */
    //create all the classifiers
    perceptronClassifier online = new perceptronClassifier();
    EnhancedLinearPerceptron offline = new EnhancedLinearPerceptron();
    EnhancedLinearPerceptron onlinestd = new EnhancedLinearPerceptron();
    onlinestd.setStandardiseAttributes = true;
    EnhancedLinearPerceptron offlinestd = new EnhancedLinearPerceptron();
    offlinestd.setStandardiseAttributes = true;
    EnhancedLinearPerceptron crossvalidate = new EnhancedLinearPerceptron();
    crossvalidate.setStandardiseAttributes = true;
    RandomLinearPerceptron random = new RandomLinearPerceptron();
    Logistic l = new Logistic();
    SimpleLogistic sl = new SimpleLogistic();
    MultilayerPerceptron mp = new MultilayerPerceptron();
    VotedPerceptron vp = new VotedPerceptron();
    //    timingExperiment(online, data);
    //  timingExperiment(offline, data);
    //timingExperiment(onlinestd, data);
    //timingExperiment(offlinestd, data);
    //timingExperiment(crossvalidate, data);
    timingExperiment(random, data);
    //timingExperiment(l, data);
    //timingExperiment(sl, data);
    //  timingExperiment(mp, data);
    // timingExperiment(vp, data);
    writer.flush();
    writer.close();

}

---

From source file:machinelearningcw.MachineLearningCw.java

public static void wekaClassifiers() {
    Logistic l = new Logistic();
    SimpleLogistic sl = new SimpleLogistic();
    MultilayerPerceptron mp = new MultilayerPerceptron();
    VotedPerceptron vp = new VotedPerceptron();
}

---