Example usage for weka.core Instance value

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:main.NaiveBayes.java

License:Open Source License

/**
 * Calculates the class membership probabilities for the given test instance.
 * //from  w  ww.j  a  va2s. c o  m
 * @param instance the instance to be classified
 * @return predicted class probability distribution
 * @exception Exception if there is a problem generating the prediction
 */
@Override
public double[] distributionForInstance(Instance instance) throws Exception {

    if (m_UseDiscretization) {
        m_Disc.input(instance);
        instance = m_Disc.output();
    }
    double[] probs = new double[m_NumClasses];
    for (int j = 0; j < m_NumClasses; j++) {
        probs[j] = m_ClassDistribution.getProbability(j);
    }
    Enumeration<Attribute> enumAtts = instance.enumerateAttributes();
    int attIndex = 0;
    while (enumAtts.hasMoreElements()) {
        Attribute attribute = enumAtts.nextElement();
        if (!instance.isMissing(attribute)) {
            double temp, max = 0;
            for (int j = 0; j < m_NumClasses; j++) {
                temp = Math.max(1e-75,
                        Math.pow(m_Distributions[attIndex][j].getProbability(instance.value(attribute)),
                                m_Instances.attribute(attIndex).weight()));
                probs[j] *= temp;
                if (probs[j] > max) {
                    max = probs[j];
                }
                if (Double.isNaN(probs[j])) {
                    throw new Exception("NaN returned from estimator for attribute " + attribute.name() + ":\n"
                            + m_Distributions[attIndex][j].toString());
                }
            }
            if ((max > 0) && (max < 1e-75)) { // Danger of probability underflow
                for (int j = 0; j < m_NumClasses; j++) {
                    probs[j] *= 1e75;
                }
            }
        }
        attIndex++;
    }

    // Display probabilities
    Utils.normalize(probs);
    return probs;
}

---

From source file:maui.main.MauiTopicExtractor.java

License:Open Source License

/**
 * Builds the model from the files//from w w w  .  ja v  a 2 s  .c o m
 */
public void extractKeyphrases(HashSet<String> fileNames, VocabularyStore store) throws Exception {

    // Check whether there is actually any data
    if (fileNames.size() == 0) {
        throw new Exception("Couldn't find any data in " + inputDirectoryName);
    }

    mauiFilter.setVocabularyName(getVocabularyName());
    mauiFilter.setVocabularyFormat(getVocabularyFormat());
    mauiFilter.setDocumentLanguage(getDocumentLanguage());
    mauiFilter.setStemmer(getStemmer());
    mauiFilter.setStopwords(getStopwords());
    if (wikipedia != null) {
        mauiFilter.setWikipedia(wikipedia);
    } else if (wikipediaServer.equals("localhost") && wikipediaDatabase.equals("database")) {
        mauiFilter.setWikipedia(wikipedia);
    } else {
        mauiFilter.setWikipedia(wikipediaServer, wikipediaDatabase, cacheWikipediaData, wikipediaDataDirectory);
    }
    if (!vocabularyName.equals("none") && !vocabularyName.equals("wikipedia")) {
        mauiFilter.loadThesaurus(getStemmer(), getStopwords(), store);
    }

    FastVector atts = new FastVector(3);
    atts.addElement(new Attribute("filename", (FastVector) null));
    atts.addElement(new Attribute("doc", (FastVector) null));
    atts.addElement(new Attribute("keyphrases", (FastVector) null));
    Instances data = new Instances("keyphrase_training_data", atts, 0);

    System.err.println("-- Extracting keyphrases... ");

    Vector<Double> correctStatistics = new Vector<Double>();
    Vector<Double> precisionStatistics = new Vector<Double>();
    Vector<Double> recallStatistics = new Vector<Double>();

    for (String fileName : fileNames) {

        double[] newInst = new double[3];

        newInst[0] = (double) data.attribute(0).addStringValue(fileName);
        ;

        File documentTextFile = new File(inputDirectoryName + "/" + fileName + ".txt");
        File documentTopicsFile = new File(inputDirectoryName + "/" + fileName + ".key");

        try {

            InputStreamReader is;
            if (!documentEncoding.equals("default")) {
                is = new InputStreamReader(new FileInputStream(documentTextFile), documentEncoding);
            } else {
                is = new InputStreamReader(new FileInputStream(documentTextFile));
            }

            // Reading the file content
            StringBuffer txtStr = new StringBuffer();
            int c;
            while ((c = is.read()) != -1) {
                txtStr.append((char) c);
            }
            is.close();

            // Adding the text of the document to the instance
            newInst[1] = (double) data.attribute(1).addStringValue(txtStr.toString());

        } catch (Exception e) {
            System.err.println("Problem with reading " + documentTextFile);
            e.printStackTrace();
            newInst[1] = Instance.missingValue();
        }

        try {

            InputStreamReader is;
            if (!documentEncoding.equals("default")) {
                is = new InputStreamReader(new FileInputStream(documentTopicsFile), documentEncoding);
            } else {
                is = new InputStreamReader(new FileInputStream(documentTopicsFile));
            }

            // Reading the content of the keyphrase file
            StringBuffer keyStr = new StringBuffer();
            int c;
            while ((c = is.read()) != -1) {
                keyStr.append((char) c);
            }

            // Adding the topics to the file
            newInst[2] = (double) data.attribute(2).addStringValue(keyStr.toString());

        } catch (Exception e) {
            if (debugMode) {
                System.err.println("No existing topics for " + documentTextFile);
            }
            newInst[2] = Instance.missingValue();
        }

        data.add(new Instance(1.0, newInst));

        mauiFilter.input(data.instance(0));

        data = data.stringFreeStructure();
        if (debugMode) {
            System.err.println("-- Processing document: " + fileName);
        }
        Instance[] topRankedInstances = new Instance[topicsPerDocument];
        Instance inst;

        // Iterating over all extracted keyphrases (inst)
        while ((inst = mauiFilter.output()) != null) {

            int index = (int) inst.value(mauiFilter.getRankIndex()) - 1;

            if (index < topicsPerDocument) {
                topRankedInstances[index] = inst;
            }
        }

        if (debugMode) {
            System.err.println("-- Keyphrases and feature values:");
        }
        FileOutputStream out = null;
        PrintWriter printer = null;

        if (!documentTopicsFile.exists()) {
            out = new FileOutputStream(documentTopicsFile);
            if (!documentEncoding.equals("default")) {
                printer = new PrintWriter(new OutputStreamWriter(out, documentEncoding));
            } else {
                printer = new PrintWriter(out);
            }
        }

        double numExtracted = 0, numCorrect = 0;
        wikipedia = mauiFilter.getWikipedia();

        HashMap<Article, Integer> topics = null;

        if (printGraph) {
            topics = new HashMap<Article, Integer>();
        }

        int p = 0;
        String root = "";
        for (int i = 0; i < topicsPerDocument; i++) {
            if (topRankedInstances[i] != null) {
                if (!topRankedInstances[i].isMissing(topRankedInstances[i].numAttributes() - 1)) {
                    numExtracted += 1.0;
                }
                if ((int) topRankedInstances[i].value(topRankedInstances[i].numAttributes() - 1) == 1) {
                    numCorrect += 1.0;
                }
                if (printer != null) {
                    String topic = topRankedInstances[i].stringValue(mauiFilter.getOutputFormIndex());
                    printer.print(topic);

                    if (printGraph) {

                        Article article = wikipedia.getArticleByTitle(topic);
                        if (article == null) {
                            article = wikipedia.getMostLikelyArticle(topic, new CaseFolder());
                        }
                        if (article != null) {
                            if (root == "") {
                                root = article.getTitle();
                            }
                            topics.put(article, new Integer(p));
                        } else {
                            if (debugMode) {
                                System.err.println(
                                        "Couldn't find article for " + topic + " in " + documentTopicsFile);
                            }
                        }
                        p++;
                    }
                    if (additionalInfo) {
                        printer.print("\t");
                        printer.print(topRankedInstances[i].stringValue(mauiFilter.getNormalizedFormIndex()));
                        printer.print("\t");
                        printer.print(Utils.doubleToString(
                                topRankedInstances[i].value(mauiFilter.getProbabilityIndex()), 4));
                    }
                    printer.println();
                }
                if (debugMode) {
                    System.err.println(topRankedInstances[i]);
                }
            }
        }

        if (printGraph) {
            String graphFile = documentTopicsFile.getAbsolutePath().replace(".key", ".gv");
            computeGraph(topics, root, graphFile);
        }
        if (numExtracted > 0) {
            if (debugMode) {
                System.err.println("-- " + numCorrect + " correct");
            }
            double totalCorrect = mauiFilter.getTotalCorrect();
            correctStatistics.addElement(new Double(numCorrect));
            precisionStatistics.addElement(new Double(numCorrect / numExtracted));
            recallStatistics.addElement(new Double(numCorrect / totalCorrect));

        }
        if (printer != null) {
            printer.flush();
            printer.close();
            out.close();
        }
    }

    if (correctStatistics.size() != 0) {

        double[] st = new double[correctStatistics.size()];
        for (int i = 0; i < correctStatistics.size(); i++) {
            st[i] = correctStatistics.elementAt(i).doubleValue();
        }
        double avg = Utils.mean(st);
        double stdDev = Math.sqrt(Utils.variance(st));

        if (correctStatistics.size() == 1) {
            System.err.println("\n-- Evaluation results based on 1 document:");

        } else {
            System.err.println("\n-- Evaluation results based on " + correctStatistics.size() + " documents:");
        }
        System.err.println("Avg. number of correct keyphrases per document: " + Utils.doubleToString(avg, 2)
                + " +/- " + Utils.doubleToString(stdDev, 2));

        st = new double[precisionStatistics.size()];
        for (int i = 0; i < precisionStatistics.size(); i++) {
            st[i] = precisionStatistics.elementAt(i).doubleValue();
        }
        double avgPrecision = Utils.mean(st);
        double stdDevPrecision = Math.sqrt(Utils.variance(st));

        System.err.println("Precision: " + Utils.doubleToString(avgPrecision * 100, 2) + " +/- "
                + Utils.doubleToString(stdDevPrecision * 100, 2));

        st = new double[recallStatistics.size()];
        for (int i = 0; i < recallStatistics.size(); i++) {
            st[i] = recallStatistics.elementAt(i).doubleValue();
        }
        double avgRecall = Utils.mean(st);
        double stdDevRecall = Math.sqrt(Utils.variance(st));

        System.err.println("Recall: " + Utils.doubleToString(avgRecall * 100, 2) + " +/- "
                + Utils.doubleToString(stdDevRecall * 100, 2));

        double fMeasure = 2 * avgRecall * avgPrecision / (avgRecall + avgPrecision);
        System.err.println("F-Measure: " + Utils.doubleToString(fMeasure * 100, 2));

        System.err.println("");
    }
    mauiFilter.batchFinished();
}

---

From source file:meka.classifiers.multilabel.CDN.java

License:Open Source License

@Override
public double[] distributionForInstance(Instance x) throws Exception {

    int L = x.classIndex();
    //ArrayList<double[]> collection = new ArrayList<double[]>(100);

    double y[] = new double[L]; // for collectiing marginal
    int sequence[] = A.make_sequence(L);

    double likelihood[] = new double[L];

    for (int i = 0; i < I; i++) {
        Collections.shuffle(Arrays.asList(sequence));
        for (int j : sequence) {
            // x = [x,y[1],...,y[j-1],y[j+1],...,y[L]]
            x.setDataset(D_templates[j]);
            // q = h_j(x)    i.e. p(y_j | x)

            double dist[] = h[j].distributionForInstance(x);
            int k = A.samplePMF(dist, m_R);
            x.setValue(j, k);// w w w .  ja v a  2 s .c  o  m
            likelihood[j] = dist[k];
            // likelihood
            double s = Utils.sum(likelihood);
            // collect  // and where is is good 
            if (i > (I - I_c)) {
                y[j] += x.value(j);
            }
            // else still burning in
        }
    }
    // finish, calculate marginals
    for (int j = 0; j < L; j++) {
        y[j] /= I_c;
    }

    return y;
}

---

From source file:meka.classifiers.multilabel.incremental.RTUpdateable.java

License:Open Source License

@Override
public void updateClassifier(Instance x) throws Exception {

    int L = x.classIndex();

    for (int j = 0; j < L; j++) {
        if (x.value(j) > 0.0) {
            Instance x_j = convertInstance(x);
            x_j.setClassValue(j);//from w ww .  j a  va  2 s .com
            ((UpdateableClassifier) m_Classifier).updateClassifier(x_j);
        }
    }
}

---

From source file:meka.classifiers.multitarget.CCp.java

License:Open Source License

@Override
public double[] distributionForInstance(Instance x) throws Exception {
    int L = x.classIndex();
    confidences = new double[L];
    root.classify(x);//from w w w  .j a  va2  s .  c om
    double y[] = new double[L * 2];
    for (int j = 0; j < L; j++) {
        y[j] = x.value(j);
        y[j + L] = confidences[j]; // <--- this is the extra line
    }
    return y;
}

---

From source file:meka.core.Metrics.java

License:Open Source License

/** Get Data for Plotting PR and ROC curves. */
public static Instances curveDataMacroAveraged(int Y[][], double P[][]) {

    // Note: 'Threshold' contains the probability threshold that gives rise to the previous performance values.

    Instances curveData[] = curveData(Y, P);

    int L = curveData.length;

    int noNullIndex = -1;

    for (int i = 0; i < curveData.length; i++) {
        if (curveData[i] == null) {
            L--;//from  w  ww  .  j a v  a 2 s  .  c om
        } else {
            if (noNullIndex == -1) {
                // checking for the first curveData that is not null (=does not consist of
                // only missing values or 0s)
                noNullIndex = i;
            }

        }

    }

    Instances avgCurve = new Instances(curveData[noNullIndex], 0);
    int D = avgCurve.numAttributes();

    for (double t = 0.0; t < 1.; t += 0.01) {
        Instance x = (Instance) curveData[noNullIndex].instance(0).copy();
        //System.out.println("x1\n"+x);
        boolean firstloop = true;
        for (int j = 0; j < L; j++) {

            // if there are only missing values in a column, curveData[j] is null

            if (curveData[j] == null) {
                continue;
            }

            int i = ThresholdCurve.getThresholdInstance(curveData[j], t);
            if (firstloop) {
                // reset
                for (int a = 0; a < D; a++) {
                    x.setValue(a, curveData[j].instance(i).value(a) * 1. / L);
                }
                firstloop = false;
            } else {
                // add
                for (int a = 0; a < D; a++) {
                    double v = x.value(a);
                    x.setValue(a, v + curveData[j].instance(i).value(a) * 1. / L);
                }
            }
        }
        //System.out.println("x2\n"+x);
        avgCurve.add(x);
    }

    /*
      System.out.println(avgCurve);
      System.exit(1);
            
      // Average everything
      for (int i = 0; i < avgCurve.numInstances(); i++) {
      for(int j = 0; j < L; j++) {
      for (int a = 0; a < D; a++) {
      double o = avgCurve.instance(i).value(a);
      avgCurve.instance(i).setValue(a, o / L);
      }
      }
      }
    */
    return avgCurve;
}

---

From source file:meka.core.MLUtils.java

License:Open Source License

/**
 * Instance with L labels to double[] of length L.
 * Rounds to the nearest whole number./*from  w w  w  . j  av  a2  s .  co  m*/
 */
public static final double[] toDoubleArray(Instance x, int L) {
    double a[] = new double[L];
    for (int i = 0; i < L; i++) {
        a[i] = Math.round(x.value(i));
    }
    return a;
}

---

From source file:meka.core.MLUtils.java

License:Open Source License

/**
 * ToBitString - returns a String representation of x = [0,0,1,0,1,0,0,0], e.g., "000101000".
 * NOTE: It may be better to use a sparse representation for some applications.
 *//*from w w  w  .jav  a  2s  .c  o m*/
public static final String toBitString(Instance x, int L) {
    StringBuilder sb = new StringBuilder(L);
    for (int i = 0; i < L; i++) {
        sb.append((int) Math.round(x.value(i)));
    }
    return sb.toString();
}

---

From source file:meka.core.MLUtils.java

License:Open Source License

/**
 * To Sub Indices Set - return the indices out of 'sub_indices', in x, whose values are greater than 1.
 *///from  ww  w. ja  v  a 2  s  . c  o m
public static final List toSubIndicesSet(Instance x, int sub_indices[]) {
    List<Integer> y_list = new ArrayList<Integer>();
    for (int j : sub_indices) {
        if (x.value(j) > 0.) {
            y_list.add(j);
        }
    }
    return y_list;
}

---

From source file:meka.core.MLUtils.java

License:Open Source License

/**
 * To Indices Set - return the indices in x, whose values are greater than 1.
 *///from w  w  w . jav a 2s  . c  om
public static final List<Integer> toIndicesSet(Instance x, int L) {
    List<Integer> y_list = new ArrayList<Integer>();
    for (int j = 0; j < L; j++) {
        if (x.value(j) > 0.) {
            y_list.add(j);
        }
    }
    return y_list;
}

---

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