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:edu.oregonstate.eecs.mcplan.ml.WekaGlue.java

License:Open Source License

public static double[] toDoubleArray(final Instance inst) {
    final double[] v = new double[inst.numAttributes()];
    for (int i = 0; i < inst.numAttributes(); ++i) {
        v[i] = inst.value(i);
    }//from   ww  w.ja  va  2  s .  c o m
    return v;
}

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

License:Open Source License

public static SequentialProjectionHashLearner createSequentialProjectionHashLearner(final RandomGenerator rng,
        final Instances labeled, final Instances unlabeled, final int K, final double eta, final double alpha) {
    assert (labeled.classIndex() >= 0);
    final int Nfeatures = labeled.numAttributes() - 1;

    final RealMatrix X = new Array2DRowRealMatrix(Nfeatures, labeled.size() + unlabeled.size());
    final RealMatrix XL = new Array2DRowRealMatrix(Nfeatures, labeled.size() * 2);
    final RealMatrix S = new Array2DRowRealMatrix(XL.getColumnDimension(), XL.getColumnDimension());

    for (int j = 0; j < labeled.size(); ++j) {
        final Instance inst = labeled.get(j);
        for (int i = 0; i < XL.getRowDimension(); ++i) {
            X.setEntry(i, j, inst.value(i));
            XL.setEntry(i, j, inst.value(i));
        }//from w w  w.  j a va 2 s .c  o  m

        int sj = -1;
        Instance s = null;
        do {
            sj = rng.nextInt(labeled.size());
            s = labeled.get(sj);
        } while (s == inst || s.classValue() != inst.classValue());
        S.setEntry(j, sj, 1);

        int dj = -1;
        Instance d = null;
        do {
            dj = rng.nextInt(labeled.size());
            d = labeled.get(dj);
        } while (d == inst || d.classValue() == inst.classValue());
        S.setEntry(j, dj, -1);
    }

    for (int j = 0; j < unlabeled.size(); ++j) {
        final Instance inst = unlabeled.get(j);
        for (int i = 0; i < X.getRowDimension(); ++i) {
            X.setEntry(i, labeled.size() + j, inst.value(i));
        }
    }

    return new SequentialProjectionHashLearner(X, XL, S, K, eta, alpha);
}

From source file:edu.umbc.cs.maple.utils.WekaUtils.java

License:Open Source License

/** Converts a set of instances to svm-light format
 * @param data the weka instances//  w w  w.  j  av  a  2  s. co m
 * @return the weka instances in svm-light format
 */
public static String arffToSVMLight(Instances data, SVMLightLabelFormat labelFormat) {

    if (labelFormat == SVMLightLabelFormat.CLASSIFICATION && data.numClasses() != 2) {
        throw new IllegalArgumentException(
                "SVM-light classification label format requires that the data contain only two classes.");
    }

    String str = "";
    String endline = System.getProperty("line.separator");

    int numInstances = data.numInstances();
    int numAttributes = data.numAttributes();
    int classAttIdx = data.classIndex();

    for (int instIdx = 0; instIdx < numInstances; instIdx++) {

        Instance inst = data.instance(instIdx);

        // convert the instance label
        if (labelFormat == SVMLightLabelFormat.CLASSIFICATION) {
            str += (inst.classValue() == 0) ? "-1" : "1";
        } else {
            str += inst.classValue();
        }

        str += " ";

        // convert each feature
        for (int attIdx = 0; attIdx < numAttributes; attIdx++) {
            // skip the class attribute
            if (attIdx == classAttIdx)
                continue;
            str += (attIdx + 1) + ":" + inst.value(attIdx) + " ";
        }

        // append the instance info string
        str += "# " + instIdx;

        str += endline;
    }

    return str;
}

From source file:edu.umbc.cs.maple.utils.WekaUtils.java

License:Open Source License

/** Converts a set of instances to svm-light format
 * @param data the weka instances// w  w w . j  a va  2s.co m
 * @return the weka instances in svm-light format
 */
public static String arffToSVMLight(Instance data, SVMLightLabelFormat labelFormat) {

    if (labelFormat == SVMLightLabelFormat.CLASSIFICATION && data.numClasses() != 2) {
        throw new IllegalArgumentException(
                "SVM-light classification label format requires that the data contain only two classes.");
    }

    String str = "";
    String endline = System.getProperty("line.separator");

    int numAttributes = data.numAttributes();
    int classAttIdx = data.classIndex();

    // convert the instance label
    if (labelFormat == SVMLightLabelFormat.CLASSIFICATION) {
        str += (data.classValue() == 0) ? "-1" : "1";
    } else {
        str += data.classValue();
    }

    str += " ";

    // convert each feature
    for (int attIdx = 0; attIdx < numAttributes; attIdx++) {
        // skip the class attribute
        if (attIdx == classAttIdx)
            continue;
        str += (attIdx + 1) + ":" + data.value(attIdx) + " ";
    }

    // append the instance info string
    str += "#";

    str += endline;

    return str;
}

From source file:elh.eus.absa.WekaWrapper.java

License:Open Source License

/**
 *   Simple function to print the results of a multilabel prediction.
 * // w ww . j  av a  2s.c  o  m
 * @param HashMap<Instance, double[]> pred hashmap containing a set of instances and their corresponding
 *         multilabel prediction, as computed by the multiLabelPrediction() function in this class.
 */
public void printMultilabelPredictions(HashMap<Instance, double[]> pred) {
    for (Instance i : pred.keySet()) {
        double[] kk = pred.get(i);
        int c = 0;
        System.out.print("instance " + Integer.parseInt(Double.toString(i.value(0))) + " (" + i.classValue()
                + "|" + i.stringValue(i.classIndex()) + ") --> ");
        for (double d : kk) {
            System.out.print("cl_" + c + "=" + d + "; ");
            c++;
        }
        System.out.print("\n");
    }

}

From source file:elh.eus.absa.WekaWrapper.java

License:Open Source License

/**
 *      Train one vs all models over the given training data.
 *  //ww  w . j  a va2  s.com
 * @param modelpath directory to store each model for the one vs. all method
 * @param prefix prefix the models should have (each model will have the name of its class appended
 * @throws Exception
 */
public HashMap<Integer, HashMap<String, Double>> predictOneVsAll(String modelpath, String prefix)
        throws Exception {
    HashMap<Integer, HashMap<String, Double>> rslt = new HashMap<Integer, HashMap<String, Double>>();
    if ((testdata == null) || testdata.isEmpty()) {
        System.err.println("WekaWrapper: testModel() - no test data available, model won't be evaluated");
        System.exit(9);
    }

    Enumeration<Object> classValues = traindata.classAttribute().enumerateValues();
    HashMap<String, Classifier> cls = new HashMap<String, Classifier>();
    while (classValues.hasMoreElements()) {
        String v = (String) classValues.nextElement();
        //needed because of weka's sparse data format problems THIS IS TROUBLE! ...

        if (v.equalsIgnoreCase("dummy")) {
            continue;
        }

        try {
            Classifier cl = loadModel(modelpath + File.separator + prefix + "_" + v + ".model");
            cls.put(v, cl);
        } catch (Exception e) {
            System.err.println("classifier for class " + v + " could not be loaded, prediction aborted");
            System.exit(9);
        }
    }

    for (int i = 0; i < testdata.numInstances(); i++) {
        HashMap<String, Double> clResults = new HashMap<String, Double>();
        Instance inst = testdata.instance(i);
        int instId = (int) inst.value(testdata.attribute("instanceId").index());
        inst.setClassMissing();
        for (String currentClass : cls.keySet()) {
            double[] dist = cls.get(currentClass).distributionForInstance(inst);
            String[] classes = { "dummy", currentClass, "UNKNOWN" };
            System.out.print("instance " + instId + " (" + currentClass + ") --> ");
            for (int c = 0; c < dist.length; c++) {
                System.out.print("\t cl_" + c + " (" + classes[c] + ") = " + dist[c] + "; ");
            }
            System.out.print("\n");

            //first class is always the class to identify, if unknown class has better score store -1 for the class
            clResults.put(currentClass, dist[1]);
        }
        rslt.put(instId, clResults);
    }

    return rslt;
}

From source file:elh.eus.absa.WekaWrapper.java

License:Open Source License

/**
 *      Train one vs all models over the given training data.
 *  //from   ww w .  j a  v a  2s.c  om
 * @param modelpath directory to store each model for the one vs. all method
 * @param prefix prefix the models should have (each model will have the name of its class appended
 * @throws Exception
 */
public HashMap<Integer, HashMap<String, Double>> addOneVsAllPredictions(String modelpath, String prefix,
        double thres) throws Exception {
    HashMap<Integer, HashMap<String, Double>> rslt = new HashMap<Integer, HashMap<String, Double>>();
    if ((testdata == null) || testdata.isEmpty()) {
        System.err.println("WekaWrapper: testModel() - no test data available, model won't be evaluated");
        System.exit(9);
    }

    Enumeration<Object> classValues = traindata.classAttribute().enumerateValues();
    HashMap<String, Classifier> cls = new HashMap<String, Classifier>();
    while (classValues.hasMoreElements()) {
        String v = (String) classValues.nextElement();
        //needed because of weka's sparse data format problems THIS IS TROUBLE! ...

        if (v.equalsIgnoreCase("dummy")) {
            continue;
        }

        try {
            Classifier cl = loadModel(modelpath + File.separator + prefix + "_" + v + ".model");
            cls.put(v, cl);
        } catch (Exception e) {
            System.err.println("classifier for class " + v + " could not be loaded, prediction aborted");
            System.exit(9);
        }
    }

    for (int i = 0; i < testdata.numInstances(); i++) {
        HashMap<String, Double> clResults = new HashMap<String, Double>();
        Instance inst = testdata.instance(i);
        int instId = (int) inst.value(testdata.attribute("instanceId").index());
        inst.setClassMissing();
        for (String currentClass : cls.keySet()) {
            double[] dist = cls.get(currentClass).distributionForInstance(inst);

            System.out.print("instance " + instId + " (" + currentClass + ") --> \n");
            /*   for (int c=0; c<dist.length; c++)
               {               
                  System.out.print("\t cl_"+c+" ("+") = "+dist[c]+"; ");               
               }
               System.out.print("\n");
            */
            //first class is always the class to identify, if unknown class has better score store -1 for the class
            clResults.put(currentClass, dist[1]);
        }
        rslt.put(instId, clResults);
    }

    return rslt;
}

From source file:en_deep.mlprocess.manipulation.featmodif.FeatureModifierFilter.java

License:Open Source License

/**
 * Convert a single instance over if the class is nominal. The converted
 * instance is added to the end of the output queue.
 *
 * @param instance the instance to convert
 *//*from  www. j av a2s .c  om*/
private void convertInstance(Instance instance) {

    double[] vals = new double[outputFormatPeek().numAttributes()];
    String[] stringVals = new String[vals.length];
    int attSoFar = 0;

    for (int j = 0; j < getInputFormat().numAttributes(); j++) {
        Attribute att = instance.attribute(j);
        if (!m_Columns.isInRange(j)) {
            vals[attSoFar] = instance.value(j);
            attSoFar++;
        } else {
            // store new string values, make double values "missing" for now (if some string
            // values are missing, the double values will remain missing)
            if (instance.value(0) == 12 && instance.value(1) == 9 && att.name().equals("sempos")) {
                attSoFar = attSoFar;
            }
            attSoFar += getAttributeOutputValue(att, instance.value(j), vals, stringVals, attSoFar);
        }
    }
    Instance inst = null;
    if (instance instanceof SparseInstance) {
        inst = new SparseInstance(instance.weight(), vals);
    } else {
        inst = new DenseInstance(instance.weight(), vals);
    }

    inst.setDataset(getOutputFormat());
    copyValues(inst, false, instance.dataset(), getOutputFormat());

    // add new string values to the output data set and to the instance
    for (int i = 0; i < stringVals.length; ++i) {
        if (stringVals[i] != null) {
            vals[i] = inst.dataset().attribute(i).addStringValue(stringVals[i]);
        }
    }
    inst.replaceMissingValues(vals);

    inst.setDataset(getOutputFormat());
    push(inst);
}

From source file:en_deep.mlprocess.manipulation.SetAwareNominalToBinary.java

License:Open Source License

/**
 * Convert a single instance over if the class is nominal. The converted
 * instance is added to the end of the output queue.
 *
 * @param instance the instance to convert
 *//*from  w  w  w . j a  v  a  2 s  .  c o  m*/
private void convertInstance(Instance instance) {

    double[] vals = new double[outputFormatPeek().numAttributes()];
    int attSoFar = 0;

    for (int j = 0; j < getInputFormat().numAttributes(); j++) {
        Attribute att = getInputFormat().attribute(j);
        if (!att.isNominal() || (j == getInputFormat().classIndex()) || !m_Columns.isInRange(j)) {
            vals[attSoFar] = instance.value(j);
            attSoFar++;
        } else {
            if ((att.numValues() <= 2) && (!m_TransformAll)) {
                vals[attSoFar] = instance.value(j);
                attSoFar++;
            } else {
                attSoFar += setConvertedAttribute(att, instance.value(j), vals, attSoFar);
            }
        }
    }
    Instance inst = null;
    if (instance instanceof SparseInstance) {
        inst = new SparseInstance(instance.weight(), vals);
    } else {
        inst = new DenseInstance(instance.weight(), vals);
    }
    inst.setDataset(getOutputFormat());
    copyValues(inst, false, instance.dataset(), getOutputFormat());
    inst.setDataset(getOutputFormat());
    push(inst);
}

From source file:ergasia2pkg.LP_ROS.java

/**
 * Groups instances by their labels/*from   w  w w  .  jav a  2  s.  c o  m*/
 *
 * @param MultilabelInstances labeled instances
 * @return HashMap<String,List<Instance>> returned Hashmap with grouping
 */
public HashMap groupByLabelSet(MultiLabelInstances mlData) {

    Instances inst = mlData.getDataSet();

    Set<Attribute> atts = mlData.getLabelAttributes();
    HashMap LabelSetGroups = new HashMap<String, List<Instance>>();

    for (int i = 0; i < inst.numInstances(); i++) {
        Instance in = inst.get(i);
        String labelsetName = "";
        for (Attribute att : atts) {
            if (in.value(att) != 0) {
                labelsetName = labelsetName + att.name();
            }

        }
        if (LabelSetGroups.containsKey(labelsetName)) {
            List myList = (List) LabelSetGroups.get(labelsetName);
            myList.add(in);
            LabelSetGroups.put(labelsetName, myList);
        } else {
            List<Instance> myList = new ArrayList<Instance>();
            myList.add(in);
            LabelSetGroups.put(labelsetName, myList);
        }

    }

    return LabelSetGroups;
}