Example usage for weka.core Instances Instances

Introduction

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

Prototype

public Instances(String name, ArrayList<Attribute> attInfo, int capacity) 

Source Link

Document

Creates an empty set of instances.

Usage

From source file:edu.oregonstate.eecs.mcplan.ml.GameTreeStateSimilarityDataset.java

License:Open Source License

@Override
public void run() {
    System.out.println("*** Extracting state nodes");
    final Visitor visitor = new Visitor();
    tree_.root().accept(visitor);//w ww  .  j a va2s.c o m

    // This extracts only the level-1 nodes.
    // TODO: Do this somewhere better.
    final HashMap<List<ActionNode<S, A>>, List<StateNode<S, A>>> tx = new HashMap<List<ActionNode<S, A>>, List<StateNode<S, A>>>();
    final ArrayList<StateNode<S, A>> depth_1 = new ArrayList<StateNode<S, A>>();
    for (final Map.Entry<List<ActionNode<S, A>>, List<StateNode<S, A>>> e : visitor.xs.entrySet()) {
        if (e.getKey() == null || e.getKey().size() != 1) {
            continue;
        } else {
            depth_1.addAll(e.getValue());
        }
    }
    tx.put(null, depth_1);

    final Comparator<Instance> weight_comp = new Comparator<Instance>() {
        @Override
        public int compare(final Instance a, final Instance b) {
            return (int) Math.signum(a.weight() - b.weight());
        }
    };
    final int max_cap = max_instances_ + 1;
    final PriorityQueue<Instance> positive = new PriorityQueue<Instance>(max_cap, weight_comp);
    final PriorityQueue<Instance> negative = new PriorityQueue<Instance>(max_cap, weight_comp);
    System.out.println("*** Building Instances");
    for (final Map.Entry<List<ActionNode<S, A>>, List<StateNode<S, A>>> e : tx.entrySet()) {
        System.out.println("***** key = " + e.getKey() + ", value.size() = " + e.getValue().size());

        final String name = (e.getKey() != null ? e.getKey().toString() : "null");
        final List<StateNode<S, A>> values = e.getValue();
        final int[] num_instances = { 0, 0 };
        int count = 0;
        for (int i = 0; i < values.size(); ++i) {
            for (int j = i + 1; j < values.size(); ++j) {
                if (count++ % 100 == 0) {
                    System.out.println("***** instance " + (count - 1));
                }

                final StateNode<S, A> s_i = values.get(i);
                final StateNode<S, A> s_j = values.get(j);
                if (s_i.n() < min_samples_ || s_j.n() < min_samples_) {
                    System.out.println("! skipping under-sampled state pair");
                    continue;
                }
                final double[] phi_i = s_i.token.phi();
                final double[] phi_j = s_j.token.phi();
                assert (phi_i.length == phi_j.length);
                if (phi_i.length != attributes_.size() - 1) {
                    System.out.println("! phi_i.length = " + phi_i.length);
                    System.out.println("! attributes_.size() = " + attributes_.size());
                }
                assert (phi_i.length == attributes_.size() - 1);
                // Feature vector is absolute difference of the two state
                // feature vectors.
                final double[] phi_labeled = new double[phi_i.length + 1];
                for (int k = 0; k < phi_i.length; ++k) {
                    phi_labeled[k] = Math.abs(phi_i[k] - phi_j[k]);
                }
                final Tuple2<Integer, Double> labeled = label(e.getKey(), player_, s_i, s_j);
                final int label = labeled._1;
                final double weight = labeled._2;
                final String label_string = Integer.toString(label);
                phi_labeled[label_index] = label; //attributes.get( label_index ).indexOfValue( label_string );

                num_instances[label] += 1;

                final Instance instance = new DenseInstance(weight, phi_labeled);
                if (label == 0) {
                    negative.add(instance);
                    if (negative.size() >= max_cap) {
                        negative.poll();
                    }
                } else {
                    positive.add(instance);
                    if (positive.size() >= max_cap) {
                        positive.poll();
                    }
                }
            } // for j
        } // for i
        System.out.println("num_instances = " + Arrays.toString(num_instances));
        final Instances x = new Instances(name, attributes_, negative.size() + positive.size());
        x.setClassIndex(label_index);
        x.addAll(negative);
        x.addAll(positive);
        xs_.put(e.getKey(), x);
    }
}

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From source file:edu.stanford.rsl.conrad.segmentation.GLCMFeatureExtractor.java

License:Open Source License

@Override
public void configure() throws Exception {

    patchSize = UserUtil.queryInt("Set GLCM patch size", patchSize);
    imagelevels = UserUtil.queryInt("Set number of different grey levels", imagelevels);
    configured = true;/*from   w w w .j  a v  a  2s  .  c  om*/
    String[] nameString = { "angular second moment", "contrast", "correlation", "variance",
            "linear difference moment", "entropy", "energy", "homogenity", "inertia" };

    if (this.dataGrid instanceof MultiChannelGrid2D) {
        MultiChannelGrid2D multiChannelGrid = (MultiChannelGrid2D) this.dataGrid;
        attribs = new ArrayList<Attribute>(GLCMnumFeatures * multiChannelGrid.getNumberOfChannels() + 1);

        for (int i = 0; i < multiChannelGrid.getNumberOfChannels(); i++) {
            for (int j = 0; j < GLCMnumFeatures; j++) {
                String name = "Slice " + i + ": GLCM " + nameString[j];
                attribs.add(new weka.core.Attribute(name));
            }
        }
    } else {
        attribs = new ArrayList<Attribute>(GLCMnumFeatures + 1);
        for (int j = 0; j < GLCMnumFeatures; j++) {
            String name = "Slice " + j + ": GLCM " + nameString[j];
            attribs.add(new weka.core.Attribute(name));
        }
    }

    Attribute classAttribute = generateClassAttribute();
    attribs.add(classAttribute);
    // leeres set von feature vectoren
    instances = new Instances(className, attribs, 0);
    instances.setClass(classAttribute);

}

---

From source file:edu.stanford.rsl.conrad.segmentation.GridFeatureExtractor.java

License:Open Source License

public void clearInstances(Attribute classAttribute) {
    // leeres set von feature vectoren
    instances = new Instances(className, attribs, 0);
    instances.setClass(classAttribute);/*from  www  .  j a  va2 s.  c o  m*/
}

---

From source file:edu.stanford.rsl.conrad.segmentation.HistogramFeatureExtractor.java

License:Open Source License

@Override
public void configure() throws Exception {

    patchSize = UserUtil.queryInt("Set histogram patch size", patchSize);
    numberOfBins = UserUtil.queryInt("Select number of histogram bins", numberOfBins);
    min = (float) UserUtil.queryDouble("Minimal histogram value", min);
    max = (float) UserUtil.queryDouble("Maximal histogram value ", max);
    configured = true;/*from  ww  w  .  java 2s .  c  o  m*/

    String[] nameString = { "mean grey value", "standard deviation", "entropy", "coefficient of variance",
            "skewness", "kurtosis" };

    if (this.dataGrid instanceof MultiChannelGrid2D) {
        MultiChannelGrid2D multiChannelGrid = (MultiChannelGrid2D) this.dataGrid;

        attribs = new ArrayList<Attribute>(HistoFeatures * multiChannelGrid.getNumberOfChannels() + 1);

        for (int i = 0; i < multiChannelGrid.getNumberOfChannels(); i++) {
            for (int j = 0; j < HistoFeatures; j++) {
                String name = "Slice " + i + ": Histogram " + nameString[j];
                attribs.add(new weka.core.Attribute(name));
            }
        }

    } else {

        attribs = new ArrayList<Attribute>(HistoFeatures + 1);
        for (int j = 0; j < HistoFeatures; j++) {
            String name = ": Histogram " + nameString[j];
            attribs.add(new weka.core.Attribute(name));
        }

    }
    Attribute classAttribute = generateClassAttribute();
    attribs.add(classAttribute);
    // leeres set von feature vectoren
    instances = new Instances(className, attribs, 0);
    instances.setClass(classAttribute);

}

---

From source file:edu.stanford.rsl.conrad.segmentation.MetaFeatureExtractor.java

License:Open Source License

public void configure() throws Exception {

    knownExtractors = CONRAD.getInstancesFromConrad(GridFeatureExtractor.class);
    selection = new boolean[knownExtractors.size()];

    GenericDialog gDialog = new GenericDialog("Feature Configuration");
    gDialog.addMessage("Select features:");

    for (int i = 0; i < knownExtractors.size(); i++) {
        GridFeatureExtractor gridFex = (GridFeatureExtractor) knownExtractors.get(i);
        if (!(gridFex instanceof MetaFeatureExtractor)) {
            gDialog.addCheckbox(gridFex.toString(), true);
        }/*w  w w.  jav a2  s .  c o m*/
    }

    gDialog.showDialog();

    for (int i = 0; i < knownExtractors.size(); i++) {
        GridFeatureExtractor gridFex = (GridFeatureExtractor) knownExtractors.get(i);
        if (!(gridFex instanceof MetaFeatureExtractor)) {
            selection[i] = gDialog.getNextBoolean();
            if (selection[i]) {
                gridFex.labelGrid = this.labelGrid;
                gridFex.dataGrid = this.dataGrid;
                gridFex.configure();
                this.attribs = combineAttribs(this.attribs, gridFex.attribs);
            }
        } else {
            selection[i] = false;
        }
    }

    Attribute classAttribute = generateClassAttribute();
    attribs.add(classAttribute);

    // leeres set von feature vectoren
    instances = new Instances(className, attribs, 0);
    instances.setClass(classAttribute);
    configured = true;
}

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From source file:edu.stanford.rsl.conrad.segmentation.MultiChannelGridFeatureExtractor.java

License:Open Source License

@Override
public void configure() throws Exception {
    multiChannelGrid = (MultiChannelGrid2D) dataGrid;
    int numFeatures = multiChannelGrid.getNumberOfChannels();
    // attribs entspricht features
    attribs = new ArrayList<Attribute>(numFeatures + 1);
    for (int i = 0; i < numFeatures; i++) {
        String nameString = multiChannelGrid.getChannelNames()[i];
        attribs.add(new weka.core.Attribute(nameString));
    }/*from   w w w .  ja  va2s.  c o  m*/
    Attribute classAttribute = generateClassAttribute();
    attribs.add(classAttribute);
    //leeres set von feature vectoren
    instances = new Instances(className, attribs, 0);
    instances.setClass(classAttribute);
    configured = true;
}

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From source file:edu.stanford.rsl.conrad.segmentation.PolynomialMultiChannelGridFeatureExtractor.java

License:Open Source License

@Override
public void configure() throws Exception {
    polydegree = UserUtil.queryInt("Enter degree of polynomial transformation:", polydegree);
    multiChannelGrid = (MultiChannelGrid2D) dataGrid;
    int numFeatures = multiChannelGrid.getNumberOfChannels();
    attribs = new ArrayList<Attribute>(numFeatures + 1);
    int combinations = (int) Math.pow(polydegree + 1, numFeatures);
    int[] index = new int[numFeatures];
    for (int i = 1; i < combinations; i++) {
        int current = i;
        String indexString = "";
        for (int j = 0; j < numFeatures; j++) {
            index[j] = current % (polydegree + 1);
            current /= (polydegree + 1);
            indexString += " " + index[j];
        }/*  w w  w .j av a 2s.c o  m*/
        String nameString = "Feature " + i + " degree " + indexString;
        attribs.add(new weka.core.Attribute(nameString));
    }
    Attribute classAttribute = generateClassAttribute();
    attribs.add(classAttribute);
    instances = new Instances(className, attribs, 0);
    instances.setClass(classAttribute);
    configured = true;
}

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From source file:edu.stanford.rsl.conrad.segmentation.VesselnessFeatureExtractor.java

License:Open Source License

@Override
public void configure() throws Exception {

    ImagePlus trainingImage = ImageUtil.wrapGrid(new Grid2D(2, 2), "Data Grid");
    if (dataGrid instanceof MultiChannelGrid2D) {
        int channels = ((MultiChannelGrid2D) dataGrid).getNumberOfChannels();
        trainingImage = ImageUtil.wrapGrid(new MultiChannelGrid2D(2, 2, channels), "Data Grid");
    }/*from w  ww  .  j  av  a2  s  . co  m*/

    FeatureStackArray featureStackArray = new FeatureStackArray(trainingImage.getImageStackSize(), minimumSigma,
            maximumSigma, useNeighbors, membraneThickness, membranePatchSize, enabledFeatures);
    for (int i = 0; i < trainingImage.getImageStackSize(); i++) {
        FeatureStack stack = new FeatureStack(trainingImage.getImageStack().getProcessor(i + 1));
        stack.setEnabledFeatures(featureStackArray.getEnabledFeatures());
        stack.setMembranePatchSize(membranePatchSize);
        stack.setMembraneSize(membraneThickness);
        stack.setMaximumSigma(maximumSigma);
        stack.setMinimumSigma(minimumSigma);
        stack.updateFeaturesMT();
        featureStackArray.set(stack, i);
    }

    ImageJnumFeatures = featureStackArray.getNumOfFeatures();

    String[] GaussianKernel = { "0,0", "1,0", "2,0", "4,0", "8,0", "16,0" };
    attribs = new ArrayList<Attribute>(19);

    for (int i = 0; i < trainingImage.getImageStackSize(); i++) {
        for (int j = 0; j < GaussianKernel.length; j++) {
            String name = "Slice " + i + ": Veselness for Gaussian Kernel " + GaussianKernel[j];
            attribs.add(new weka.core.Attribute(name));
        }

    }

    Attribute classAttribute = generateClassAttribute();
    attribs.add(classAttribute);
    // leeres set von feature vectoren
    instances = new Instances(className, attribs, 0);
    instances.setClass(classAttribute);
    configured = true;

}

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From source file:edu.tamu.recognition.paleo.PaleoFeatureExtractor.java

License:BSD License

/**
 * Builds and returns a new (and empty) dataset that contains the attributes
 * of this extractor to be used by WEKA/*from  w  w  w. j  av a 2  s  . com*/
 * 
 * @return new empty dataset
 * @throws Exception
 */
public Instances getNewDataset() throws Exception {
    Instances dataset;
    FVector fv = getFeatureVector();
    m_classLabels = new FastVector();
    for (int j = 0; j < m_config.getShapesTurnedOn().size(); j++)
        m_classLabels.addElement(m_config.getShapesTurnedOn().get(j));
    FastVector fast = new FastVector(fv.getNumFeatures());
    for (int j = 0; j < fv.getNumFeatures(); j++)
        fast.addElement(new Attribute("a" + j));
    fast.addElement(new Attribute("label", m_classLabels));
    dataset = new Instances("PaleoTrain", fast, 0);
    return dataset;
}

---

From source file:edu.tamu.recognition.paleo.PaleoFeatureExtractor.java

License:BSD License

/**
 * Builds and returns a new (and empty) dataset that contains the attributes
 * of this extractor to be used by WEKA//from w w w .j a v a 2 s  . com
 * 
 * @return new empty dataset
 * @throws Exception
 */
public Instances getDataset() throws Exception {
    Instances dataset;
    FVector fv = getFeatureVector();
    m_classLabels = new FastVector();
    m_classLabels.addElement("Toddler");
    m_classLabels.addElement("SchoolAge");

    FastVector fast = new FastVector(fv.getNumFeatures());
    for (int j = 0; j < fv.getNumFeatures(); j++)
        fast.addElement(new Attribute("a" + j));
    fast.addElement(new Attribute("label", m_classLabels));
    dataset = new Instances("AgeTrain", fast, 0);
    return dataset;
}

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