Example usage for weka.classifiers.lazy IBk setKNN

Introduction

In this page you can find the example usage for weka.classifiers.lazy IBk setKNN.

Prototype

public void setKNN(int k) 

Source Link

Document

Set the number of neighbours the learner is to use.

Usage

From source file:jjj.asap.sas.models1.job.BuildCosineModels.java

License:Open Source License

@Override
protected void run() throws Exception {

    // validate args
    if (!Bucket.isBucket("datasets", inputBucket)) {
        throw new FileNotFoundException(inputBucket);
    }//from w ww .  j av  a 2  s  .co m
    if (!Bucket.isBucket("models", outputBucket)) {
        throw new FileNotFoundException(outputBucket);
    }

    // init multi-threading
    Job.startService();
    final Queue<Future<Object>> queue = new LinkedList<Future<Object>>();

    // get the input from the bucket
    List<String> names = Bucket.getBucketItems("datasets", this.inputBucket);
    for (String dsn : names) {

        int essaySet = Contest.getEssaySet(dsn);

        int k = -1;
        switch (essaySet) {

        case 3:
            k = 13;
            break;
        case 5:
        case 7:
            k = 55;
            break;
        case 2:
        case 6:
        case 10:
            k = 21;
            break;
        case 1:
        case 4:
        case 8:
        case 9:
            k = 34;
            break;
        }

        if (k == -1) {
            throw new IllegalArgumentException("not k defined for " + essaySet);
        }

        LinearNNSearch search = new LinearNNSearch();
        search.setDistanceFunction(new CosineDistance());
        search.setSkipIdentical(false);

        IBk knn = new IBk();
        knn.setKNN(k);
        knn.setDistanceWeighting(INVERSE);
        knn.setNearestNeighbourSearchAlgorithm(search);

        queue.add(Job.submit(new ModelBuilder(dsn, "KNN-" + k, knn, this.outputBucket)));
    }

    // wait on complete
    Progress progress = new Progress(queue.size(), this.getClass().getSimpleName());
    while (!queue.isEmpty()) {
        try {
            queue.remove().get();
        } catch (Exception e) {
            Job.log("ERROR", e.toString());
            e.printStackTrace(System.err);
        }
        progress.tick();
    }
    progress.done();
    Job.stopService();

}

---

From source file:tcc.FeatureExtraction.java

public void knn() throws IOException {
    //parsing CSV to Arff
    CSVLoader loader = new CSVLoader();
    loader.setSource(new File("/root/TCC/Resultados/Parte 4 - Novos Casos/TamuraHaralickMomentos.csv"));
    Instances inst = loader.getDataSet();

    ArffSaver saver = new ArffSaver();
    saver.setInstances(inst);// ww w.  java2  s. c  om
    saver.setFile(new File("/root/TCC/Resultados/Parte 4 - Novos Casos/TamuraHaralickMomentos.arff"));
    saver.setDestination(new File("/root/TCC/Resultados/Parte 4 - Novos Casos/TamuraHaralickMomentos.arff"));
    saver.writeBatch();

    BufferedReader reader = new BufferedReader(
            new FileReader("/root/TCC/Resultados/Parte 4 - Novos Casos/TamuraHaralickMomentos.arff"));
    Instances data = new Instances(reader);
    reader.close();
    data.setClassIndex(data.numAttributes() - 1);

    //Normalizando
    try {
        Normalize norm = new Normalize();
        norm.setInputFormat(data);
        data = Filter.useFilter(data, norm);

    } catch (Exception ex) {
        Logger.getLogger(FeatureExtraction.class.getName()).log(Level.SEVERE, null, ex);
    }

    File csv = new File("/root/TCC/Resultados/knn.csv");
    FileWriter fw = new FileWriter(csv);
    BufferedWriter bw = new BufferedWriter(fw);

    for (int i = 1; i < 51; i++) {
        //instanciando o classificador
        IBk knn = new IBk();
        knn.setKNN(i);

        try {

            knn.buildClassifier(data);
            Evaluation eval = new Evaluation(data);
            //System.out.println(eval.toSummaryString("\nResults\n======\n", false));
            eval.crossValidateModel(knn, data, 10, new Random(1), new Object[] {});
            double auc = eval.areaUnderROC(1);
            System.out.println(auc);
            bw.write(Double.toString(auc));
            bw.newLine();

        } catch (Exception ex) {
            Logger.getLogger(FeatureExtraction.class.getName()).log(Level.SEVERE, null, ex);
        }
    }
    bw.close();

}

---