Example usage for weka.core Instance value

List of usage examples for weka.core Instance value

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Introduction

In this page you can find the example usage for weka.core Instance value.

Prototype

public double value(Attribute att);

Source Link

Document

Returns an instance's attribute value in internal format.

Usage

From source file:lu.lippmann.cdb.lab.mds.ClassicMDS.java

License:Open Source License

public static void main(String[] args) throws Exception {
    final Instances ds = WekaDataAccessUtil.loadInstancesFromARFFOrCSVFile(
            //new File("./samples/csv/uci/zoo.csv"));
            new File("./samples/csv/test50bis.csv"));
    //new File("./samples/arff/UCI/cmc.arff"));
    //new File("./samples/csv/direct-marketing-bank-reduced.csv"));
    //new File("./samples/csv/bank.csv"));
    //new File("./samples/csv/preswissroll.csv"));
    //new File("./samples/csv/preswissroll-mod4.csv"));

    ds.setClassIndex(-1);// ww w  . j  a  va2s  . c  o  m

    final int N = ds.size();
    final int M = ds.numAttributes();
    SimpleMatrix dist = new SimpleMatrix(N, N);

    for (int i = 0; i < N; i++) {
        for (int j = i + 1; j < N; j++) {
            Instance xi = ds.instance(i);
            Instance xj = ds.instance(j);
            double d = 0, s = 0, a = 0, b = 0;
            for (int k = 1; k < M; k++) {
                s += xi.value(k) * xj.value(k);
                a += xi.value(k) * xi.value(k);
                b += xi.value(k) * xi.value(k);
            }
            d = 1 - s / (Math.sqrt(a) * Math.sqrt(b));
            dist.set(i, j, d);
            dist.set(j, i, d);
        }
    }

    final MDSResult res = ClassicMDS.doMDSV1(ds, dist);

    JXPanel p2 = MDSViewBuilder.buildMDSViewFromDataSet(ds, res, 5000, null);
    p2.setPreferredSize(new Dimension(800, 600));

    final JXFrame f = new JXFrame();
    f.setPreferredSize(new Dimension(1024, 768));
    final Container c = f.getContentPane();
    c.add(p2);
    f.pack();
    f.setVisible(true);
    f.setDefaultCloseOperation(JXFrame.EXIT_ON_CLOSE);

    System.out.println("Kruskal stress : =" + getKruskalStressFromMDSResult(res));

}

From source file:lu.lippmann.cdb.lab.mds.MDSViewBuilder.java

License:Open Source License

/**
 * //from  www .j a v a  2 s .  co  m
 */
private static void buildFilteredSeries(final MDSResult mdsResult, final XYPlot xyPlot,
        final String... attrNameToUseAsPointTitle) throws Exception {

    final CollapsedInstances distMdsRes = mdsResult.getCInstances();
    final Instances instances = distMdsRes.getInstances();

    final SimpleMatrix coordinates = mdsResult.getCoordinates();

    final Instances collapsedInstances = mdsResult.getCollapsedInstances();
    int maxSize = 0;
    if (distMdsRes.isCollapsed()) {
        final List<Instances> clusters = distMdsRes.getCentroidMap().getClusters();
        final int nbCentroids = clusters.size();
        maxSize = clusters.get(0).size();
        for (int i = 1; i < nbCentroids; i++) {
            final int currentSize = clusters.get(i).size();
            if (currentSize > maxSize) {
                maxSize = currentSize;
            }
        }
    }

    Attribute clsAttribute = null;
    int nbClass = 1;
    if (instances.classIndex() != -1) {
        clsAttribute = instances.classAttribute();
        nbClass = clsAttribute.numValues();
    }
    final XYSeriesCollection dataset = (XYSeriesCollection) xyPlot.getDataset();
    final int fMaxSize = maxSize;

    final List<XYSeries> lseries = new ArrayList<XYSeries>();

    //No class : add one dummy serie
    if (nbClass <= 1) {
        lseries.add(new XYSeries("Serie #1", false));
    } else {
        //Some class : add one serie per class
        for (int i = 0; i < nbClass; i++) {
            lseries.add(new XYSeries(clsAttribute.value(i), false));
        }
    }
    dataset.removeAllSeries();

    /**
     * Initialize filtered series
     */
    final List<Instances> filteredInstances = new ArrayList<Instances>();
    for (int i = 0; i < lseries.size(); i++) {
        filteredInstances.add(new Instances(collapsedInstances, 0));
    }

    final Map<Tuple<Integer, Integer>, Integer> correspondanceMap = new HashMap<Tuple<Integer, Integer>, Integer>();
    for (int i = 0; i < collapsedInstances.numInstances(); i++) {
        final Instance oInst = collapsedInstances.instance(i);
        int indexOfSerie = 0;
        if (oInst.classIndex() != -1) {
            if (distMdsRes.isCollapsed()) {
                indexOfSerie = getStrongestClass(i, distMdsRes);
            } else {
                indexOfSerie = (int) oInst.value(oInst.classAttribute());
            }
        }
        lseries.get(indexOfSerie).add(coordinates.get(i, 0), coordinates.get(i, 1));

        filteredInstances.get(indexOfSerie).add(oInst);
        if (distMdsRes.isCollapsed()) {
            correspondanceMap.put(new Tuple<Integer, Integer>(indexOfSerie,
                    filteredInstances.get(indexOfSerie).numInstances() - 1), i);
        }
    }

    final List<Paint> colors = new ArrayList<Paint>();

    for (final XYSeries series : lseries) {
        dataset.addSeries(series);
    }

    if (distMdsRes.isCollapsed()) {
        final XYLineAndShapeRenderer xyRenderer = new XYLineAndShapeRenderer(false, true) {
            private static final long serialVersionUID = -6019883886470934528L;

            @Override
            public void drawItem(Graphics2D g2, XYItemRendererState state, java.awt.geom.Rectangle2D dataArea,
                    PlotRenderingInfo info, XYPlot plot, ValueAxis domainAxis, ValueAxis rangeAxis,
                    XYDataset dataset, int series, int item, CrosshairState crosshairState, int pass) {

                if (distMdsRes.isCollapsed()) {

                    final Integer centroidIndex = correspondanceMap
                            .get(new Tuple<Integer, Integer>(series, item));
                    final Instances cluster = distMdsRes.getCentroidMap().getClusters().get(centroidIndex);
                    int size = cluster.size();

                    final int shapeSize = (int) (MAX_POINT_SIZE * size / fMaxSize + 1);

                    final double x1 = plot.getDataset().getX(series, item).doubleValue();
                    final double y1 = plot.getDataset().getY(series, item).doubleValue();

                    Map<Object, Integer> mapRepartition = new HashMap<Object, Integer>();
                    mapRepartition.put("No class", size);
                    if (cluster.classIndex() != -1) {
                        mapRepartition = WekaDataStatsUtil.getClassRepartition(cluster);
                    }

                    final RectangleEdge xAxisLocation = plot.getDomainAxisEdge();
                    final RectangleEdge yAxisLocation = plot.getRangeAxisEdge();
                    final double fx = domainAxis.valueToJava2D(x1, dataArea, xAxisLocation);
                    final double fy = rangeAxis.valueToJava2D(y1, dataArea, yAxisLocation);

                    setSeriesShape(series,
                            new Ellipse2D.Double(-shapeSize / 2, -shapeSize / 2, shapeSize, shapeSize));

                    super.drawItem(g2, state, dataArea, info, plot, domainAxis, rangeAxis, dataset, series,
                            item, crosshairState, pass);

                    //Draw pie
                    if (ENABLE_PIE_SHART) {
                        createPieChart(g2, (int) (fx - shapeSize / 2), (int) (fy - shapeSize / 2), shapeSize,
                                mapRepartition, size, colors);
                    }

                } else {

                    super.drawItem(g2, state, dataArea, info, plot, domainAxis, rangeAxis, dataset, series,
                            item, crosshairState, pass);

                }

            }

        };

        xyPlot.setRenderer(xyRenderer);
    }

    final XYToolTipGenerator gen = new XYToolTipGenerator() {
        @Override
        public String generateToolTip(XYDataset dataset, int series, int item) {
            if (distMdsRes.isCollapsed()) {
                final StringBuilder res = new StringBuilder("<html>");
                final Integer centroidIndex = correspondanceMap.get(new Tuple<Integer, Integer>(series, item));
                final Instance centroid = distMdsRes.getCentroidMap().getCentroids().get(centroidIndex);
                final Instances cluster = distMdsRes.getCentroidMap().getClusters().get(centroidIndex);

                //Set same class index for cluster than for original instances
                //System.out.println("Cluster index = "  + cluster.classIndex() + "/" + instances.classIndex());
                cluster.setClassIndex(instances.classIndex());

                Map<Object, Integer> mapRepartition = new HashMap<Object, Integer>();
                mapRepartition.put("No class", cluster.size());
                if (cluster.classIndex() != -1) {
                    mapRepartition = WekaDataStatsUtil.getClassRepartition(cluster);
                }
                res.append(InstanceFormatter.htmlFormat(centroid, false)).append("<br/>");
                for (final Map.Entry<Object, Integer> entry : mapRepartition.entrySet()) {
                    if (entry.getValue() != 0) {
                        res.append("Class :<b>'" + StringEscapeUtils.escapeHtml(entry.getKey().toString())
                                + "</b>' -> " + entry.getValue()).append("<br/>");
                    }
                }
                res.append("</html>");
                return res.toString();
            } else {
                //return InstanceFormatter.htmlFormat(filteredInstances.get(series).instance(item),true);
                return InstanceFormatter.shortHtmlFormat(filteredInstances.get(series).instance(item));
            }
        }
    };

    final Shape shape = new Ellipse2D.Float(0f, 0f, MAX_POINT_SIZE, MAX_POINT_SIZE);

    ((XYLineAndShapeRenderer) xyPlot.getRenderer()).setUseOutlinePaint(true);

    for (int p = 0; p < nbClass; p++) {
        xyPlot.getRenderer().setSeriesToolTipGenerator(p, gen);
        ((XYLineAndShapeRenderer) xyPlot.getRenderer()).setLegendShape(p, shape);
        xyPlot.getRenderer().setSeriesOutlinePaint(p, Color.BLACK);
    }

    for (int ii = 0; ii < nbClass; ii++) {
        colors.add(xyPlot.getRenderer().getItemPaint(ii, 0));
    }

    if (attrNameToUseAsPointTitle.length > 0) {
        final Attribute attrToUseAsPointTitle = instances.attribute(attrNameToUseAsPointTitle[0]);
        if (attrToUseAsPointTitle != null) {
            final XYItemLabelGenerator lg = new XYItemLabelGenerator() {
                @Override
                public String generateLabel(final XYDataset dataset, final int series, final int item) {
                    return filteredInstances.get(series).instance(item).stringValue(attrToUseAsPointTitle);
                }
            };
            xyPlot.getRenderer().setBaseItemLabelGenerator(lg);
            xyPlot.getRenderer().setBaseItemLabelsVisible(true);
        }
    }
}

From source file:lu.lippmann.cdb.lab.mds.MDSViewBuilder.java

License:Open Source License

/**
 * /*from   w w w .java2 s .c o  m*/
 * @param clusters
 */
public static void buildKMeansChart(final List<Instances> clusters) {
    final XYSeriesCollection dataset = new XYSeriesCollection();

    final JFreeChart chart = ChartFactory.createScatterPlot("", // title 
            "X", "Y", // axis labels 
            dataset, // dataset 
            PlotOrientation.VERTICAL, true, // legend? yes 
            true, // tooltips? yes 
            false // URLs? no 
    );

    final XYPlot xyPlot = (XYPlot) chart.getPlot();

    ((NumberAxis) xyPlot.getDomainAxis()).setTickUnit(new NumberTickUnit(2.0));
    ((NumberAxis) xyPlot.getRangeAxis()).setTickUnit(new NumberTickUnit(2.0));

    Attribute clsAttribute = null;
    int nbClass = 1;
    Instances cluster0 = clusters.get(0);
    if (cluster0.classIndex() != -1) {
        clsAttribute = cluster0.classAttribute();
        nbClass = clsAttribute.numValues();
    }
    if (nbClass <= 1) {
        dataset.addSeries(new XYSeries("Serie #1", false));
    } else {
        for (int i = 0; i < nbClass; i++) {
            dataset.addSeries(new XYSeries(clsAttribute.value(i), false));
        }
    }

    final XYToolTipGenerator gen = new XYToolTipGenerator() {
        @Override
        public String generateToolTip(XYDataset dataset, int series, int item) {
            return "TODO";
        }
    };

    for (int i = 0; i < nbClass; i++) {
        dataset.getSeries(i).clear();
        xyPlot.getRenderer().setSeriesToolTipGenerator(i, gen);
    }

    final int nbClusters = clusters.size();
    for (int i = 0; i < nbClusters; i++) {
        Instances instances = clusters.get(i);
        final int nbInstances = instances.numInstances();
        for (int j = 0; j < nbInstances; j++) {
            final Instance oInst = instances.instance(j);
            dataset.getSeries(i).add(oInst.value(0), oInst.value(1));
        }
    }

    final TitledBorder titleBorder = new TitledBorder("Kmeans of projection");
    ChartPanel chartPanel = new ChartPanel(chart);
    chartPanel.setMouseWheelEnabled(true);
    chartPanel.setPreferredSize(new Dimension(1200, 900));
    chartPanel.setBorder(titleBorder);
    chartPanel.setBackground(Color.WHITE);

    JXFrame frame = new JXFrame();
    frame.getContentPane().add(chartPanel);
    frame.setVisible(true);
    frame.pack();

}

From source file:lu.lippmann.cdb.lab.mds.UniversalMDS.java

License:Open Source License

public JXPanel buildMDSViewFromDataSet(Instances ds, MDSTypeEnum type) throws Exception {

    final XYSeriesCollection dataset = new XYSeriesCollection();

    final JFreeChart chart = ChartFactory.createScatterPlot("", // title 
            "X", "Y", // axis labels 
            dataset, // dataset 
            PlotOrientation.VERTICAL, true, // legend? yes 
            true, // tooltips? yes 
            false // URLs? no 
    );//from w  w  w  .  j  a v a 2  s .  c om

    final XYPlot xyPlot = (XYPlot) chart.getPlot();

    chart.setTitle(type.name() + " MDS");

    Attribute clsAttribute = null;
    int nbClass = 1;
    if (ds.classIndex() != -1) {
        clsAttribute = ds.classAttribute();
        nbClass = clsAttribute.numValues();
    }

    final List<XYSeries> lseries = new ArrayList<XYSeries>();
    if (nbClass <= 1) {
        lseries.add(new XYSeries("Serie #1", false));
    } else {
        for (int i = 0; i < nbClass; i++) {
            lseries.add(new XYSeries(clsAttribute.value(i), false));
        }
    }
    dataset.removeAllSeries();

    /**
     * Initialize filtered series
     */
    final List<Instances> filteredInstances = new ArrayList<Instances>();
    for (int i = 0; i < lseries.size(); i++) {
        filteredInstances.add(new Instances(ds, 0));
    }

    for (int i = 0; i < ds.numInstances(); i++) {
        final Instance oInst = ds.instance(i);
        int indexOfSerie = 0;
        if (oInst.classIndex() != -1) {
            indexOfSerie = (int) oInst.value(oInst.classAttribute());
        }
        lseries.get(indexOfSerie).add(coordinates[i][0], coordinates[i][1]);
        filteredInstances.get(indexOfSerie).add(oInst);
    }

    final List<Paint> colors = new ArrayList<Paint>();

    for (final XYSeries series : lseries) {
        dataset.addSeries(series);
    }

    final XYToolTipGenerator gen = new XYToolTipGenerator() {
        @Override
        public String generateToolTip(XYDataset dataset, int series, int item) {
            return InstanceFormatter.htmlFormat(filteredInstances.get(series).instance(item), true);
        }
    };

    final Shape shape = new Ellipse2D.Float(0f, 0f, 5f, 5f);

    ((XYLineAndShapeRenderer) xyPlot.getRenderer()).setUseOutlinePaint(true);

    for (int p = 0; p < nbClass; p++) {
        xyPlot.getRenderer().setSeriesToolTipGenerator(p, gen);
        ((XYLineAndShapeRenderer) xyPlot.getRenderer()).setLegendShape(p, shape);
        xyPlot.getRenderer().setSeriesOutlinePaint(p, Color.BLACK);
    }

    for (int ii = 0; ii < nbClass; ii++) {
        colors.add(xyPlot.getRenderer().getItemPaint(ii, 0));
    }

    final ChartPanel chartPanel = new ChartPanel(chart);
    chartPanel.setMouseWheelEnabled(true);
    chartPanel.setPreferredSize(new Dimension(1200, 900));
    chartPanel.setBorder(new TitledBorder("MDS Projection"));
    chartPanel.setBackground(Color.WHITE);

    final JXPanel allPanel = new JXPanel();
    allPanel.setLayout(new BorderLayout());
    allPanel.add(chartPanel, BorderLayout.CENTER);

    return allPanel;
}

From source file:machinelearning.KnnClassifier.java

public double classifyInstance(Instance instance, int k) {
    int size = trainingData.numInstances();
    int attributes = trainingData.numAttributes() - 1;
    float dist;/*w w  w  . j a v a2  s .  c o  m*/
    Map<Float, Instance> neighbors = new TreeMap<>();

    Instance test;

    for (int i = 0; i < size; i++) {
        dist = 0;
        test = trainingData.instance(i);

        for (int j = 0; j < attributes; j++) {
            if (test.attribute(j).isNominal()) {
                if (instance.attribute(j).equals(test.attribute(j)))
                    dist++;
            } else
                dist += Math.abs(test.value(test.attribute(j)) - instance.value(test.attribute(j)));
        }

        neighbors.put(dist, test);
    }

    return findMostCommon(neighbors, k);
}

From source file:machinelearningcw.EnhancedLinearPerceptron.java

@Override
public double classifyInstance(Instance instnc) throws Exception {
    double y = 0;
    //create a new instance so it doesnt change the orginal dataset
    Instance newInstance = new DenseInstance(instnc);
    if (setStandardiseAttributes) {
        standardizeAtrrbutes(newInstance);
    }//  www.java2 s .  co m
    for (int i = 0; i < newInstance.numAttributes() - 1; i++) {
        y += w[i] * (newInstance.value(i));
    }

    return (y >= 0) ? 1 : 0;
}

From source file:machinelearningcw.EnhancedLinearPerceptron.java

public double offlinePerceptron(Instances ins) {
    double error_count = 0;//count the number of errors
    double changeinWeights[];

    for (int h = 0; h < numberofiterations; h++) {
        changeinWeights = new double[ins.numAttributes() - 1];
        //error_count = 0;
        for (Instance instance : ins) {
            double y = 0;
            for (int i = 0; i < ins.numAttributes() - 1; i++) {

                y += w[i] * (instance.value(i));
            }/*from  w  w  w.  j a  va  2s.  c  om*/
            double match;
            if (y >= 0) {
                match = 1;
            } else {
                match = 0;
            }
            double difference = instance.classValue() - match;

            for (int j = 0; j < ins.numAttributes() - 1; j++) {

                changeinWeights[j] = changeinWeights[j]
                        + (0.5 * learning_rate) * ((difference) * instance.value(j));

            }
            error_count += (difference * difference);

        }
        /*  for (int j = 0; j < ins.numAttributes() - 1; j++) {
                
        System.out.print("w[" + j + "]: " + changeinWeights[j] + "|");
          }
          System.out.println("");*/

        //update all the weights at the end 
        for (int j = 0; j < w.length; j++) {

            w[j] += changeinWeights[j];

        }

    }
    error_count = error_count / numberofiterations;// average error count

    return error_count;
}

From source file:machinelearningcw.EnhancedLinearPerceptron.java

public double perceptron(Instances ins) {
    double error_count = 0;//count the number of errors
    for (int h = 0; h < numberofiterations; h++)//stopping condition
    {/*from   w  ww .j a v a  2 s.  c  o  m*/
        error_count = 0;
        for (Instance instance : ins) {
            double y = 0;
            for (int i = 0; i < ins.numAttributes() - 1; i++) {
                y += w[i] * (instance.value(i));
            }
            // System.out.println(y);

            double match;
            if (y >= 0) {
                match = 1;
            } else {
                match = 0;
            }
            double difference = instance.classValue() - match;

            //  System.out.println(match);
            for (int j = 0; j < ins.numAttributes() - 1; j++) {

                w[j] = w[j] + 0.5 * learning_rate * ((difference) * instance.value(j));
                // System.out.print(w[j] + ", ");

            }

            error_count += difference * difference;
        }
    }
    return error_count;

}

From source file:machinelearningcw.EnhancedLinearPerceptron.java

public Instance standardizeAtrrbutes(Instance ins) {

    for (int n = 0; n < ins.numAttributes() - 1; n++) {
        double x = ((ins.value(n) - means[n]) / std[n]);

        ins.setValue(n, x);//from ww w  .j a v a2s  .  c  om

    }

    return ins;
}

From source file:machinelearningcw.EnhancedLinearPerceptron.java

public Instances standardizeAtrrbutes(Instances ins) {

    for (Instance i : ins) {
        for (int n = 0; n < i.numAttributes() - 1; n++) {
            double x = ((i.value(n) - means[n]) / std[n]);

            i.setValue(n, x);//ww w . j  ava 2s .  c  o m

        }
    }

    return ins;
}