Example usage for weka.classifiers.bayes NaiveBayes toString

Introduction

In this page you can find the example usage for weka.classifiers.bayes NaiveBayes toString.

Prototype

@Override
public String toString() 

Source Link

Document

Returns a description of the classifier.

Usage

From source file:controller.BothClassificationsServlet.java

@Override
protected void doPost(HttpServletRequest request, HttpServletResponse response)
        throws ServletException, IOException {
    request.setCharacterEncoding("UTF-8");
    String dir = "/data/";
    String path = getServletContext().getRealPath(dir);

    String action = request.getParameter("action");

    switch (action) {
    case "create": {
        String fileName = request.getParameter("file");

        String aux = fileName.substring(0, fileName.indexOf("."));
        String pathInput = path + "/" + request.getParameter("file");
        String pathTrainingOutput = path + "/" + aux + "-training-arff.txt";
        String pathTestOutput = path + "/" + aux + "-test-arff.txt";
        String pathBothClassifications = path + "/" + aux + "-bothClassifications.txt";

        String name = request.getParameter("name");
        int range = Integer.parseInt(request.getParameter("range"));

        int size = Integer.parseInt(request.getParameter("counter"));
        String[] columns = new String[size];
        String[] types = new String[size];
        int[] positions = new int[size];
        int counter = 0;
        for (int i = 0; i < size; i++) {
            if (request.getParameter("column-" + (i + 1)) != null) {
                columns[counter] = request.getParameter("column-" + (i + 1));
                types[counter] = request.getParameter("type-" + (i + 1));
                positions[counter] = Integer.parseInt(request.getParameter("position-" + (i + 1)));
                counter++;//from   w w w  .  ja  v  a 2  s  .co  m
            }
        }

        FormatFiles.convertTxtToArff(pathInput, pathTrainingOutput, pathTestOutput, name, columns, types,
                positions, counter, range);
        try {
            J48 j48 = new J48();

            BufferedReader readerTraining = new BufferedReader(new FileReader(pathTrainingOutput));
            Instances instancesTraining = new Instances(readerTraining);
            instancesTraining.setClassIndex(instancesTraining.numAttributes() - 1);

            j48.buildClassifier(instancesTraining);

            BufferedReader readerTest = new BufferedReader(new FileReader(pathTestOutput));
            //BufferedReader readerTest = new BufferedReader(new FileReader(pathTrainingOutput));
            Instances instancesTest = new Instances(readerTest);
            instancesTest.setClassIndex(instancesTest.numAttributes() - 1);

            int correctsDecisionTree = 0;

            for (int i = 0; i < instancesTest.size(); i++) {
                Instance instance = instancesTest.get(i);
                double correctValue = instance.value(instance.attribute(instancesTest.numAttributes() - 1));
                double classification = j48.classifyInstance(instance);

                if (correctValue == classification) {
                    correctsDecisionTree++;
                }
            }

            Evaluation eval = new Evaluation(instancesTraining);
            eval.evaluateModel(j48, instancesTest);

            PrintWriter writer = new PrintWriter(
                    new BufferedWriter(new FileWriter(pathBothClassifications, false)));

            writer.println("?rvore de Deciso\n\n");

            writer.println(j48.toString());

            writer.println("");
            writer.println("");
            writer.println("Results");
            writer.println(eval.toSummaryString());

            NaiveBayes naiveBayes = new NaiveBayes();

            naiveBayes.buildClassifier(instancesTraining);

            eval = new Evaluation(instancesTraining);
            eval.evaluateModel(naiveBayes, instancesTest);

            int correctsNaiveBayes = 0;

            for (int i = 0; i < instancesTest.size(); i++) {
                Instance instance = instancesTest.get(i);
                double correctValue = instance.value(instance.attribute(instancesTest.numAttributes() - 1));
                double classification = naiveBayes.classifyInstance(instance);

                if (correctValue == classification) {
                    correctsNaiveBayes++;
                }
            }

            writer.println("Naive Bayes\n\n");

            writer.println(naiveBayes.toString());

            writer.println("");
            writer.println("");
            writer.println("Results");
            writer.println(eval.toSummaryString());

            writer.close();

            response.sendRedirect("BothClassifications?action=view&correctsDecisionTree=" + correctsDecisionTree
                    + "&correctsNaiveBayes=" + correctsNaiveBayes + "&totalTest=" + instancesTest.size()
                    + "&totalTrainig=" + instancesTraining.size() + "&range=" + range + "&fileName=" + aux
                    + "-bothClassifications.txt");
        } catch (Exception e) {
            System.out.println(e.getMessage());
            response.sendRedirect("Navigation?action=decisionTree");
        }

        break;
    }
    default:
        response.sendError(404);
    }
}

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From source file:controller.NaiveBayesServlet.java

@Override
protected void doPost(HttpServletRequest request, HttpServletResponse response)
        throws ServletException, IOException {
    request.setCharacterEncoding("UTF-8");
    String dir = "/data/";
    String path = getServletContext().getRealPath(dir);

    String action = request.getParameter("action");

    switch (action) {
    case "create": {
        String fileName = request.getParameter("file");

        String aux = fileName.substring(0, fileName.indexOf("."));
        String pathInput = path + "/" + request.getParameter("file");
        String pathTrainingOutput = path + "/" + aux + "-training-arff.txt";
        String pathTestOutput = path + "/" + aux + "-test-arff.txt";
        String pathNaivebayes = path + "/" + aux + "-naiveBayes.txt";

        String name = request.getParameter("name");
        int range = Integer.parseInt(request.getParameter("range"));

        int size = Integer.parseInt(request.getParameter("counter"));
        String[] columns = new String[size];
        String[] types = new String[size];
        int[] positions = new int[size];
        int counter = 0;

        for (int i = 0; i < size; i++) {
            if (request.getParameter("column-" + (i + 1)) != null) {
                columns[counter] = request.getParameter("column-" + (i + 1));
                types[counter] = request.getParameter("type-" + (i + 1));
                positions[counter] = Integer.parseInt(request.getParameter("position-" + (i + 1)));
                counter++;//from  w ww .  ja va  2 s.  c om
            }
        }

        FormatFiles.convertTxtToArff(pathInput, pathTrainingOutput, pathTestOutput, name, columns, types,
                positions, counter, range);

        try {
            NaiveBayes naiveBayes = new NaiveBayes();

            BufferedReader readerTraining = new BufferedReader(new FileReader(pathTrainingOutput));
            Instances instancesTraining = new Instances(readerTraining);
            instancesTraining.setClassIndex(instancesTraining.numAttributes() - 1);

            naiveBayes.buildClassifier(instancesTraining);

            BufferedReader readerTest = new BufferedReader(new FileReader(pathTestOutput));
            //BufferedReader readerTest = new BufferedReader(new FileReader(pathTrainingOutput));
            Instances instancesTest = new Instances(readerTest);
            instancesTest.setClassIndex(instancesTest.numAttributes() - 1);

            Evaluation eval = new Evaluation(instancesTraining);
            eval.evaluateModel(naiveBayes, instancesTest);

            int corrects = 0;
            int truePositive = 0;
            int trueNegative = 0;
            int falsePositive = 0;
            int falseNegative = 0;

            for (int i = 0; i < instancesTest.size(); i++) {
                Instance instance = instancesTest.get(i);
                double correctValue = instance.value(instance.attribute(instancesTest.numAttributes() - 1));
                double classification = naiveBayes.classifyInstance(instance);

                if (correctValue == classification) {
                    corrects++;
                }
                if (correctValue == 1 && classification == 1) {
                    truePositive++;
                }
                if (correctValue == 1 && classification == 0) {
                    falseNegative++;
                }
                if (correctValue == 0 && classification == 1) {
                    falsePositive++;
                }
                if (correctValue == 0 && classification == 0) {
                    trueNegative++;
                }
            }

            PrintWriter writer = new PrintWriter(new BufferedWriter(new FileWriter(pathNaivebayes, false)));

            writer.println(naiveBayes.toString());

            writer.println("");
            writer.println("");
            writer.println("Results");
            writer.println(eval.toSummaryString());

            writer.close();

            response.sendRedirect(
                    "NaiveBayes?action=view&corrects=" + corrects + "&totalTest=" + instancesTest.size()
                            + "&totalTrainig=" + instancesTraining.size() + "&range=" + range + "&truePositive="
                            + truePositive + "&trueNegative=" + trueNegative + "&falsePositive=" + falsePositive
                            + "&falseNegative=" + falseNegative + "&fileName=" + aux + "-naiveBayes.txt");

        } catch (Exception e) {
            System.out.println(e.getMessage());
            response.sendRedirect("Navigation?action=naiveBayes");
        }

        break;
    }
    default:
        response.sendError(404);
    }

}

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