Examples with Instance weka.core.Instance used on opensource projects

Example 6 with Instance

use of weka.core.Instance in project cia by Hack23.

the class WordCounterImpl method calculateWordCount.

@Override
public Map<String, Integer> calculateWordCount(final DocumentContentData documentContentData, final int maxResult) {
    final String html = documentContentData.getContent();
    final Attribute input = new Attribute(HTML, (ArrayList<String>) null);
    final ArrayList<Attribute> inputVec = new ArrayList<>();
    inputVec.add(input);
    final Instances htmlInst = new Instances(HTML, inputVec, 1);
    htmlInst.add(new DenseInstance(1));
    htmlInst.instance(0).setValue(0, html);
    final StopwordsHandler stopwordsHandler = new StopwordsHandler() {

        @Override
        public boolean isStopword(final String word) {
            return word.length() < 5;
        }
    };
    final NGramTokenizer tokenizer = new NGramTokenizer();
    tokenizer.setNGramMinSize(1);
    tokenizer.setNGramMaxSize(1);
    tokenizer.setDelimiters(TOKEN_DELIMITERS);
    final StringToWordVector filter = new StringToWordVector();
    filter.setTokenizer(tokenizer);
    filter.setStopwordsHandler(stopwordsHandler);
    filter.setLowerCaseTokens(true);
    filter.setOutputWordCounts(true);
    filter.setWordsToKeep(maxResult);
    final Map<String, Integer> result = new HashMap<>();
    try {
        filter.setInputFormat(htmlInst);
        final Instances dataFiltered = Filter.useFilter(htmlInst, filter);
        final Instance last = dataFiltered.lastInstance();
        final int numAttributes = last.numAttributes();
        for (int i = 0; i < numAttributes; i++) {
            result.put(last.attribute(i).name(), Integer.valueOf(last.toString(i)));
        }
    } catch (final Exception e) {
        LOGGER.warn("Problem calculating wordcount for : {} , exception:{}", documentContentData.getId(), e);
    }
    return result;
}

Example 7 with Instance

use of weka.core.Instance in project dkpro-tc by dkpro.

the class ReplaceMissingValuesWithZeroFilter method convertInstance.

/**
 * Convert a single instance over. The converted instance is added to the end of the output
 * queue.
 *
 * @param instance
 *            the instance to convert
 */
private void convertInstance(Instance instance) {
    Instance inst = null;
    if (instance instanceof SparseInstance) {
        double[] vals = new double[instance.numValues()];
        int[] indices = new int[instance.numValues()];
        int num = 0;
        for (int j = 0; j < instance.numValues(); j++) {
            if (instance.isMissingSparse(j) && (getInputFormat().classIndex() != instance.index(j)) && (instance.attributeSparse(j).isNominal() || instance.attributeSparse(j).isNumeric())) {
            } else {
                vals[num] = instance.valueSparse(j);
                indices[num] = instance.index(j);
                num++;
            }
        }
        if (num == instance.numValues()) {
            inst = new SparseInstance(instance.weight(), vals, indices, instance.numAttributes());
        } else {
            double[] tempVals = new double[num];
            int[] tempInd = new int[num];
            System.arraycopy(vals, 0, tempVals, 0, num);
            System.arraycopy(indices, 0, tempInd, 0, num);
            inst = new SparseInstance(instance.weight(), tempVals, tempInd, instance.numAttributes());
        }
    } else {
        double[] vals = new double[getInputFormat().numAttributes()];
        for (int j = 0; j < instance.numAttributes(); j++) {
            if (instance.isMissing(j) && (getInputFormat().classIndex() != j) && (getInputFormat().attribute(j).isNominal() || getInputFormat().attribute(j).isNumeric())) {
                vals[j] = 0.0d;
            } else {
                vals[j] = instance.value(j);
            }
        }
        inst = new DenseInstance(instance.weight(), vals);
    }
    inst.setDataset(instance.dataset());
    push(inst);
}

Example 8 with Instance

use of weka.core.Instance in project TrakEM2 by trakem2.

the class LineageClassifier method classify.

public static final boolean classify(final double[] vector) throws Exception {
    // Obtain or generate a Thread-local instance
    Operator op;
    synchronized (table) {
        // avoid clashes within weka
        final Thread t = Thread.currentThread();
        op = table.get(t);
        if (null == op) {
            op = new Operator();
            table.put(t, op);
        }
    }
    // Future weka versions will use new DenseInstance(1, vector) instead
    final Instance ins = new DenseInstance(1, vector);
    ins.setDataset(op.data);
    // Was trained to return true or false, represented in weka as 0 or 1
    return 1 == ((int) Math.round(op.c.classifyInstance(ins)));
}

Example 9 with Instance

use of weka.core.Instance in project lobcder by skoulouzis.

the class LDClustering method getNextState.

@Override
public Vertex getNextState(Vertex currentState) {
    ArrayList<Vertex> states = new ArrayList<>();
    String rName = currentState.getResourceName();
    if (!rName.endsWith("/")) {
        rName += "/";
    }
    rName = rName.replaceFirst("/lobcder/dav/", "");
    try (Connection connection = getConnection()) {
        LogicalData data = getLogicalDataByPath(Path.path(rName), connection);
        Instance instance = getInstances(data, currentState.getMethod()).get(0);
        double[] features = instance.toDoubleArray();
        switch(type) {
            case state:
                return getNextLobState(connection, features);
            case resource:
                return getNextResourceState(connection, features);
            case method:
                return getNextMethodState(connection, features);
            default:
                return getNextLobState(connection, features);
        }
    } catch (SQLException ex) {
        Logger.getLogger(LDClustering.class.getName()).log(Level.SEVERE, null, ex);
    }
    return null;
}

Example 10 with Instance

use of weka.core.Instance in project 490 by pauleibye.

the class MainFrame method initialize.

/**
 * Initialize the contents of the frame.
 */
private void initialize() {
    frame = new JFrame();
    frame.getContentPane().setFont(new Font("Tahoma", Font.PLAIN, 18));
    frame.setBounds(100, 100, 615, 377);
    frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
    GridBagLayout gridBagLayout = new GridBagLayout();
    gridBagLayout.columnWidths = new int[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 };
    gridBagLayout.rowHeights = new int[] { 0, 0, 0, 0, 0 };
    gridBagLayout.columnWeights = new double[] { 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, Double.MIN_VALUE };
    gridBagLayout.rowWeights = new double[] { 0.0, 0.0, 1.0, 1.0, Double.MIN_VALUE };
    frame.getContentPane().setLayout(gridBagLayout);
    JButton btnParse = new JButton("Parse");
    GridBagConstraints gbc_btnParse = new GridBagConstraints();
    gbc_btnParse.insets = new Insets(0, 0, 5, 5);
    gbc_btnParse.gridx = 2;
    gbc_btnParse.gridy = 1;
    frame.getContentPane().add(btnParse, gbc_btnParse);
    for (int i = 0; i < numFiles; i++) {
        listModel.addElement("Audio File: " + i);
    }
    lblFeatures = new JLabel("Features");
    lblFeatures.setFont(new Font("Tahoma", Font.PLAIN, 36));
    GridBagConstraints gbc_lblFeatures = new GridBagConstraints();
    gbc_lblFeatures.insets = new Insets(0, 0, 5, 5);
    gbc_lblFeatures.gridx = 4;
    gbc_lblFeatures.gridy = 1;
    frame.getContentPane().add(lblFeatures, gbc_lblFeatures);
    btnPlayAudio = new JButton("Play Audio");
    GridBagConstraints gbc_btnPlayAudio = new GridBagConstraints();
    gbc_btnPlayAudio.insets = new Insets(0, 0, 5, 5);
    gbc_btnPlayAudio.gridx = 5;
    gbc_btnPlayAudio.gridy = 1;
    frame.getContentPane().add(btnPlayAudio, gbc_btnPlayAudio);
    final JList<JCheckBox> list = new JList(listModel);
    GridBagConstraints gbc_list = new GridBagConstraints();
    gbc_list.gridheight = 2;
    gbc_list.insets = new Insets(0, 0, 0, 5);
    gbc_list.fill = GridBagConstraints.BOTH;
    gbc_list.gridx = 2;
    gbc_list.gridy = 2;
    frame.getContentPane().add(list, gbc_list);
    resultLabel = new JLabel("New label");
    resultLabel.setFont(new Font("Tahoma", Font.PLAIN, 18));
    GridBagConstraints gbc_resultLabel = new GridBagConstraints();
    gbc_resultLabel.insets = new Insets(0, 0, 5, 5);
    gbc_resultLabel.gridx = 4;
    gbc_resultLabel.gridy = 2;
    frame.getContentPane().add(resultLabel, gbc_resultLabel);
    featuresDisplay = new JLabel("No Image Proccessed");
    featuresDisplay.setFont(new Font("Tahoma", Font.PLAIN, 18));
    GridBagConstraints gbc_featuresDisplay = new GridBagConstraints();
    gbc_featuresDisplay.insets = new Insets(0, 0, 0, 5);
    gbc_featuresDisplay.gridx = 4;
    gbc_featuresDisplay.gridy = 3;
    frame.getContentPane().add(featuresDisplay, gbc_featuresDisplay);
    this.resultLabel.setText("No Image Processed");
    labelCorrect = new JLabel("");
    GridBagConstraints gbc_labelCorrect = new GridBagConstraints();
    gbc_labelCorrect.insets = new Insets(0, 0, 0, 5);
    gbc_labelCorrect.gridx = 5;
    gbc_labelCorrect.gridy = 3;
    frame.getContentPane().add(labelCorrect, gbc_labelCorrect);
    createCSVandArff(featureExtractor);
    buildModel();
    /*
         * Parse button handler
         * Takes selected file and runs it against the machine learning model
         * updates label to display if algorithm was correct or not
         */
    btnParse.addActionListener(new ActionListener() {

        public void actionPerformed(ActionEvent e) {
            try {
                if (list.isSelectionEmpty()) {
                    return;
                }
                selected = list.getSelectedIndex();
                String path = convertToImagePath();
                FeatureExtractor fe = new FeatureExtractor();
                Double[] features = fe.extractFeaturesForTest("resources/testing/" + path);
                Instance inst = new DenseInstance(features.length);
                inst.setDataset(trainDataSet);
                for (int i = 0; i < features.length; i++) {
                    inst.setValue(i, features[i]);
                }
                double result = nb.classifyInstance(inst);
                String predStr = trainDataSet.classAttribute().value((int) result);
                String actual = convertToImagePath();
                resultLabel.setText("Selected File: " + actual);
                featuresDisplay.setText("Algorithm Prediction: " + predStr);
                if (predStr.substring(0, 1).toLowerCase().equals(actual.substring(0, 1).toLowerCase())) {
                    labelCorrect.setText("Yes, Prediction is correct");
                } else {
                    labelCorrect.setText("No, Prediction is wrong");
                }
            } catch (IOException e1) {
                e1.printStackTrace();
            } catch (UnsupportedAudioFileException e1) {
                e1.printStackTrace();
            } catch (Exception e1) {
                e1.printStackTrace();
            }
        }
    });
    /*
         * Plays selected audio file on click, NOTE, file has to parsed first
         */
    btnPlayAudio.addActionListener(new ActionListener() {

        public void actionPerformed(ActionEvent e) {
            File file = new File("resources/testing/" + convertToImagePath());
            AudioInputStream audioIn;
            try {
                audioIn = AudioSystem.getAudioInputStream(file);
                Clip clip = AudioSystem.getClip();
                clip.open(audioIn);
                clip.start();
            } catch (UnsupportedAudioFileException e1) {
                e1.printStackTrace();
            } catch (IOException e1) {
                e1.printStackTrace();
            } catch (LineUnavailableException e1) {
                e1.printStackTrace();
            }
        }
    });
}