Example usage for weka.core Instances numAttributes

Introduction

In this page you can find the example usage for weka.core Instances numAttributes.

Prototype

publicint numAttributes() 

Source Link

Document

Returns the number of attributes.

Usage

From source file:experimentalclassifier.ExperimentalClassifier.java

/**
 * @param args the command line arguments
 *//*from  w ww. j a v a2s.c om*/
public static void main(String[] args) throws Exception {

    DataSource source = new DataSource("data/iris.csv");

    Instances data = source.getDataSet();

    if (data.classIndex() == -1) {
        data.setClassIndex(data.numAttributes() - 1);
    }

    data.randomize(new Random());

    String[] options = weka.core.Utils.splitOptions("-P 30");
    RemovePercentage remove = new RemovePercentage();
    remove.setOptions(options);
    remove.setInputFormat(data);
    Instances train = Filter.useFilter(data, remove);

    remove.setInvertSelection(true);
    remove.setInputFormat(data);
    Instances test = Filter.useFilter(data, remove);

    Classifier classifier = new HardCodedClassifier();
    classifier.buildClassifier(train);//Currently, this does nothing
    Evaluation eval = new Evaluation(train);
    eval.evaluateModel(classifier, test);
    System.out.println(eval.toSummaryString("\nResults\n======\n", false));
}

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From source file:explorer.ChordalysisModelling.java

License:Open Source License

public void buildModelNoExplore(Instances dataset) {
    this.nbInstances = dataset.numInstances();
    this.dataset = dataset;
    int[] variables = new int[dataset.numAttributes()];
    int[] nbValuesForAttribute = new int[variables.length];
    for (int i = 0; i < variables.length; i++) {
        variables[i] = i;//w ww . j  a va  2s  .  c o  m
        nbValuesForAttribute[i] = dataset.numDistinctValues(i);
    }
    this.lattice = new Lattice(dataset);
    this.entropyComputer = new EntropyComputer(dataset.numInstances(), this.lattice);
    this.scorer = new GraphActionScorerPValue(nbInstances, entropyComputer);
    this.bestModel = new DecomposableModel(variables, nbValuesForAttribute);
    this.pq = new MyPriorityQueue(variables.length, bestModel, scorer);
    for (int i = 0; i < variables.length; i++) {
        for (int j = i + 1; j < variables.length; j++) {
            pq.enableEdge(i, j);
        }
    }

}

---

From source file:explorer.ChordalysisModelling.java

License:Open Source License

public void buildModelNoExplore(Instances dataset, ArffReader loader) throws IOException {
    this.dataset = dataset;
    int[] variables = new int[dataset.numAttributes()];
    int[] nbValuesForAttribute = new int[variables.length];
    for (int i = 0; i < variables.length; i++) {
        variables[i] = i;//from   w  w w  . ja  v a 2 s  . co  m
        nbValuesForAttribute[i] = dataset.numDistinctValues(i);
    }
    this.lattice = new Lattice(dataset, loader);
    this.nbInstances = this.lattice.getNbInstances();

    this.entropyComputer = new EntropyComputer(nbInstances, this.lattice);
    this.scorer = new GraphActionScorerPValue(nbInstances, entropyComputer);
    this.bestModel = new DecomposableModel(variables, nbValuesForAttribute);
    this.pq = new MyPriorityQueue(variables.length, bestModel, scorer);
    for (int i = 0; i < variables.length; i++) {
        for (int j = i + 1; j < variables.length; j++) {
            pq.enableEdge(i, j);
        }
    }

}

---

From source file:explorer.ChordalysisModellingMML.java

License:Open Source License

public void buildModelNoExplore(Instances dataset) {
    this.nbInstances = dataset.numInstances();
    this.dataset = dataset;
    int[] variables = new int[dataset.numAttributes()];
    int[] nbValuesForAttribute = new int[variables.length];
    for (int i = 0; i < variables.length; i++) {
        variables[i] = i;/*ww  w. ja v a  2  s .  c  o  m*/
        nbValuesForAttribute[i] = dataset.numDistinctValues(i);
    }
    this.lattice = new Lattice(dataset);
    this.computer = new MessageLengthFactorialComputer(dataset.numInstances(), this.lattice);
    this.scorer = new GraphActionScorerMML(nbInstances, computer);
    this.bestModel = new DecomposableModel(variables, nbValuesForAttribute);
    this.pq = new MyPriorityQueue(variables.length, bestModel, scorer);
    for (int i = 0; i < variables.length; i++) {
        for (int j = i + 1; j < variables.length; j++) {
            pq.enableEdge(i, j);
        }
    }

}

---

From source file:explorer.ChordalysisModellingMML.java

License:Open Source License

public void buildModelNoExplore(Instances dataset, ArffReader loader) throws IOException {
    this.dataset = dataset;
    int[] variables = new int[dataset.numAttributes()];
    int[] nbValuesForAttribute = new int[variables.length];
    for (int i = 0; i < variables.length; i++) {
        variables[i] = i;//from  w w  w  .  j a  va  2 s  . c o  m
        nbValuesForAttribute[i] = dataset.numDistinctValues(i);
    }
    this.lattice = new Lattice(dataset, loader);
    this.nbInstances = this.lattice.getNbInstances();

    this.computer = new MessageLengthFactorialComputer(nbInstances, this.lattice);
    this.scorer = new GraphActionScorerMML(nbInstances, computer);
    this.bestModel = new DecomposableModel(variables, nbValuesForAttribute);
    this.pq = new MyPriorityQueue(variables.length, bestModel, scorer);
    for (int i = 0; i < variables.length; i++) {
        for (int j = i + 1; j < variables.length; j++) {
            pq.enableEdge(i, j);
        }
    }

}

---

From source file:expshell.ExpShell.java

/**
 * @param args the command line arguments
 * @throws java.lang.Exception/*from   w w w  .  j a v  a 2s . c  o m*/
 */
public static void main(String[] args) throws Exception {
    String file = "C:\\Users\\YH Jonathan Kwok\\Documents\\NetBeansProjects\\ExpShell\\src\\expshell\\iris.csv";

    DataSource source = new DataSource(file);
    Instances data = source.getDataSet();

    if (data.classIndex() == -1)
        data.setClassIndex(data.numAttributes() - 1);

    //Randomize it
    data.randomize(new Random(1));

    RemovePercentage rp = new RemovePercentage();
    rp.setPercentage(70);

    rp.setInputFormat(data);
    Instances training = Filter.useFilter(data, rp);

    rp.setInvertSelection(true);
    rp.setInputFormat(data);
    Instances test = Filter.useFilter(data, rp);

    //standardize the data
    Standardize filter = new Standardize();
    filter.setInputFormat(training);

    Instances newTest = Filter.useFilter(test, filter);
    Instances newTraining = Filter.useFilter(training, filter);

    //Part 5 - Now it's a knn
    Classifier knn = new NeuralClassifier();
    knn.buildClassifier(newTraining);
    Evaluation eval = new Evaluation(newTraining);
    eval.evaluateModel(knn, newTest);

    System.out.println(eval.toSummaryString("***** Overall results: *****", false));

}

---

From source file:expshell.NeuralClassifier.java

@Override
public void buildClassifier(Instances i) throws Exception {
    List<Integer> numNodes = new ArrayList<Integer>();
    //numNodes.add(5);
    //numNodes.add(6);
    numNodes.add(i.numClasses());//from   w  w  w.  j  a  va 2s.  co  m

    nn = new NeuralNetwork(numNodes.size(), i.numAttributes() - 1, numNodes);
    for (int j = 0; j < i.numInstances(); j++) {
        System.out.println(nn.run(i.instance(j)));
    }

}

---

From source file:eyetracker.MLPProcessor.java

public MLPProcessor() {
    try {//  ww w . j a v  a 2s . c  om
        FileReader fr = new FileReader("trainingData.arff");
        Instances training = new Instances(fr);
        training.setClassIndex(training.numAttributes() - 1);
        mlp = new MultilayerPerceptron();
        mlp.setOptions(Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H 5"));
        mlp.buildClassifier(training);

        FileReader tr = new FileReader("trainingData.arff");
        Instances testdata = new Instances(tr);
        inst = testdata;
        testdata.setClassIndex(testdata.numAttributes() - 1);
        Evaluation eval = new Evaluation(training);
        eval.evaluateModel(mlp, testdata);
        System.out.println(eval.toSummaryString("\nResults\n*******\n", false));
        tr.close();
        fr.close();
    } catch (FileNotFoundException e) {
        e.printStackTrace();
    } catch (IOException e) {
        e.printStackTrace();
    } catch (Exception e) {
        e.printStackTrace();
    }

}

---

From source file:facebookpostpuller.PostModel.java

public static void convertToArff(File file) throws Exception {

    FastVector atts;/*from www  .  j  a v  a2 s . c  om*/
    FastVector attVals;
    Instances data;
    double[] vals;

    file = new File(file + ".arff");

    atts = new FastVector();
    atts.addElement(new Attribute(("name"), (FastVector) null)); // 5/27/2014
    atts.addElement(new Attribute(("message"), (FastVector) null));

    attVals = new FastVector();
    attVals.addElement("13-17");
    attVals.addElement("18-24");
    attVals.addElement("25-34");
    attVals.addElement("35-44");
    attVals.addElement("45-54");
    atts.addElement(new Attribute("age-group", attVals));

    data = new Instances("predict_age", atts, 0);

    Iterator it = posts.entrySet().iterator();

    while (it.hasNext()) {
        Map.Entry pairs = (Map.Entry) it.next();

        vals = new double[data.numAttributes()];
        User user = (User) pairs.getValue();
        String name = user.getName(); // 5/27/2014
        String message = ((Post) (pairs.getKey())).getMessage();

        Preprocess pre = new Preprocess();
        message = pre.emoticons(message);
        message = pre.emoji(message);
        message = pre.url(message);

        //StringFilter filter = new StringFilter(message);
        vals[0] = data.attribute(0).addStringValue(name); // 5/27/2014
        vals[1] = data.attribute(1).addStringValue(message);

        int age = calculateAge(user.getBirthdayAsDate());
        if (age >= 13 && age <= 17) {
            vals[2] = attVals.indexOf("13-17");
        } else if (age >= 18 && age <= 24) {
            vals[2] = attVals.indexOf("18-24");
        } else if (age >= 25 && age <= 34) {
            vals[2] = attVals.indexOf("25-34");
        } else if (age >= 35 && age <= 44) {
            vals[2] = attVals.indexOf("35-44");
        } else if (age >= 45) { // Modified 6/11/2014 
            vals[2] = attVals.indexOf("45-54");
        }

        data.add(new Instance(1.0, vals));

        it.remove();
    }

    ArffSaver saver = new ArffSaver();
    saver.setInstances(data);
    saver.setFile(file);
    saver.writeBatch();
}

---

From source file:facebookpostpuller.PostModelBACKUP.java

public static void convertToArff(File file) throws Exception {

    FastVector atts;/*from   www.ja  va 2s. c  o m*/
    FastVector attVals;
    Instances data;
    double[] vals;

    file = new File(file + ".arff");

    atts = new FastVector();
    atts.addElement(new Attribute(("name"), (FastVector) null)); // 5/27/2014
    atts.addElement(new Attribute(("message"), (FastVector) null));

    attVals = new FastVector();
    attVals.addElement("13-17");
    attVals.addElement("18-24");
    attVals.addElement("25-34");
    attVals.addElement("35-44");
    attVals.addElement("45-54");
    atts.addElement(new Attribute("age-group", attVals));

    data = new Instances("predict_age", atts, 0);

    Iterator it = posts.entrySet().iterator();

    while (it.hasNext()) {
        Map.Entry pairs = (Map.Entry) it.next();

        vals = new double[data.numAttributes()];
        User user = (User) pairs.getValue();
        String name = user.getName(); // 5/27/2014
        String message = ((Post) (pairs.getKey())).getMessage();
        //StringFilter filter = new StringFilter(message);
        vals[0] = data.attribute(0).addStringValue(name); // 5/27/2014
        vals[1] = data.attribute(1).addStringValue(message);

        int age = calculateAge(user.getBirthdayAsDate());
        if (age >= 13 && age <= 17) {
            vals[2] = attVals.indexOf("13-17");
        } else if (age >= 18 && age <= 24) {
            vals[2] = attVals.indexOf("18-24");
        } else if (age >= 25 && age <= 34) {
            vals[2] = attVals.indexOf("25-34");
        } else if (age >= 35 && age <= 44) {
            vals[2] = attVals.indexOf("35-44");
        } else if (age >= 45 && age <= 54) {
            vals[2] = attVals.indexOf("45-54");
        }

        data.add(new Instance(1.0, vals));

        it.remove();
    }

    ArffSaver saver = new ArffSaver();
    saver.setInstances(data);
    saver.setFile(file);
    saver.writeBatch();
}

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