Example usage for weka.experiment InstanceQuery setCustomPropsFile

Introduction

In this page you can find the example usage for weka.experiment InstanceQuery setCustomPropsFile.

Prototype

public void setCustomPropsFile(File value) 

Source Link

Document

Sets the custom properties file to use.

Usage

From source file:hurtowniedanych.FXMLController.java

public void trainAndTestKNN() throws FileNotFoundException, IOException, Exception {

    InstanceQuery instanceQuery = new InstanceQuery();
    instanceQuery.setUsername("postgres");
    instanceQuery.setPassword("szupek");
    instanceQuery.setCustomPropsFile(new File("./src/data/DatabaseUtils.props")); // Wskazanie pliku z ustawieniami dla PostgreSQL

    String query = "select ks.wydawnictwo,ks.gatunek, kl.mia-sto\n" + "from zakupy z,ksiazki ks,klienci kl\n"
            + "where ks.id_ksiazka=z.id_ksiazka and kl.id_klient=z.id_klient";

    instanceQuery.setQuery(query);/*from  w w w.  j  a  va2s  .  c o  m*/
    Instances data = instanceQuery.retrieveInstances();
    data.setClassIndex(data.numAttributes() - 1);

    data.randomize(new Random());
    double percent = 70.0;
    int trainSize = (int) Math.round(data.numInstances() * percent / 100);
    int testSize = data.numInstances() - trainSize;
    Instances trainData = new Instances(data, 0, trainSize);
    Instances testData = new Instances(data, trainSize, testSize);

    int lSasiadow = Integer.parseInt(textFieldKnn.getText());
    System.out.println(lSasiadow);

    IBk ibk = new IBk(lSasiadow);

    // Ustawienie odleglosci
    EuclideanDistance euclidean = new EuclideanDistance(); // euklidesowej
    ManhattanDistance manhatan = new ManhattanDistance(); // miejska  

    LinearNNSearch linearNN = new LinearNNSearch();

    if (comboboxOdleglosc.getSelectionModel().getSelectedItem().equals("Manhatan")) {
        linearNN.setDistanceFunction(manhatan);
    } else {
        linearNN.setDistanceFunction(euclidean);
    }

    ibk.setNearestNeighbourSearchAlgorithm(linearNN); // ustawienie sposobu szukania sasiadow

    // Tworzenie klasyfikatora
    ibk.buildClassifier(trainData);

    Evaluation eval = new Evaluation(trainData);
    eval.evaluateModel(ibk, testData);
    spr.setVisible(true);
    labelKnn.setVisible(true);
    labelOdleglosc.setVisible(true);
    labelKnn.setText(textFieldKnn.getText());
    labelOdleglosc.setText(comboboxOdleglosc.getSelectionModel().getSelectedItem().toString());
    spr.setText(eval.toSummaryString("Wynik:", true));
}

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