Example usage for weka.core Instance attribute

Introduction

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

Prototype

public Attribute attribute(int index);

Source Link

Document

Returns the attribute with the given index.

Usage

From source file:cn.ict.zyq.bestConf.bestConf.BestConf.java

License:Open Source License

public static void getBestPerfFrom(String path) {
    try {/*from  w  w  w  .  j  ava2 s .c  o m*/
        BestConf bestconf = new BestConf();
        Instances trainingSet = DataIOFile.loadDataFromArffFile(path);
        Instance best = trainingSet.firstInstance();
        //set the best configuration to the cluster
        Map<Attribute, Double> attsmap = new HashMap<Attribute, Double>();
        for (int i = 0; i < best.numAttributes() - 1; i++) {
            attsmap.put(best.attribute(i), best.value(i));
        }

        double bestPerf = bestconf.setOptimal(attsmap, "getBestPerfFrom");
        System.out.println("=========================================");
        System.err.println("The actual performance for the best point is : " + bestPerf);
        System.out.println("=========================================");
    } catch (IOException e) {
        e.printStackTrace();
    }
}

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From source file:cn.ict.zyq.bestConf.cluster.Main.AutoTestAdjust.java

License:Open Source License

public static String getMD5(Instance ins) {
    StringBuffer name = new StringBuffer("");
    for (int i = 0; i < ins.numAttributes() - 2; i++) {
        name.append(Math.round(ins.value(ins.attribute(i))) + ",");
    }//  w  w  w.java2 s . c  o m
    return getMD5(name.toString());
}

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From source file:cn.ict.zyq.bestConf.cluster.Main.AutoTestAdjust.java

License:Open Source License

private void writePerfstoFile(Instance ins) {
    File perfFolder = new File(perfsfilepath);
    if (!perfFolder.exists())
        perfFolder.mkdirs();/*from   ww w  . ja v a  2s  .c  o  m*/

    File file = new File(perfsfilepath + "/" + getMD5(ins));
    BufferedWriter writer;
    try {
        writer = new BufferedWriter(new FileWriter(file));
        writer.write(ins.value(ins.attribute(ins.numAttributes() - 1)) + "\n");
        writer.close();
    } catch (IOException e) {
        e.printStackTrace();
    }
}

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From source file:cn.ict.zyq.bestConf.cluster.Main.AutoTestAdjust.java

License:Open Source License

public Instances runExp(Instances samplePoints, String perfAttName) {
    Instances retVal = null;/*from   ww  w  .j ava 2s .c o m*/
    if (samplePoints.attribute(perfAttName) == null) {
        Attribute performance = new Attribute(perfAttName);
        samplePoints.insertAttributeAt(performance, samplePoints.numAttributes());
    }
    int pos = samplePoints.numInstances();
    int count = 0;
    for (int i = 0; i < pos; i++) {
        Instance ins = samplePoints.get(i);
        HashMap hm = new HashMap();
        int tot = 0;
        for (int j = 0; j < ins.numAttributes(); j++) {
            hm.put(ins.attribute(j).name(), ins.value(ins.attribute(j)));
        }

        boolean testRet;
        if (Double.isNaN(ins.value(ins.attribute(ins.numAttributes() - 1)))) {
            testRet = this.startTest(hm, i, isInterrupt);
            double y = 0;
            if (!testRet) {// the setting does not work, we skip it
                y = -1;
                count++;
                if (count >= targetTestErrorNum) {
                    System.out.println(
                            "There must be somthing wrong with the system. Please check and restart.....");
                    System.exit(1);
                }
            } else {
                y = getPerformanceByType(performanceType);
                count = 0;
            }

            ins.setValue(samplePoints.numAttributes() - 1, y);
            writePerfstoFile(ins);
        } else {
            continue;
        }
    }
    retVal = samplePoints;
    retVal.setClassIndex(retVal.numAttributes() - 1);

    return retVal;
}

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From source file:com.hack23.cia.service.impl.action.user.wordcount.WordCounterImpl.java

License:Apache License

@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);/*  ww w  . j av a 2 s.  c  o  m*/

    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(" \r\n\t.,;:'\"()?!'");

    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;
}

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From source file:com.mycompany.id3classifier.kNNClassifier.java

private static double findDistance(Instance instance1, Instance instance2) {
    double total = 0;
    int totalAttributes = instance1.numAttributes();
    for (int i = 0; i < totalAttributes; i++) {
        if (instance1.classIndex() == i)
            continue;

        double difference = 0;

        if (instance1.attribute(i).isNumeric()) {
            difference = Math.abs(instance1.value(i) - instance2.value(i));
        }/* ww  w.ja  va  2 s  .c  o m*/

        else {
            if (!instance1.stringValue(i).equals(instance2.stringValue(i))) {
                difference = 1;
            }
        }

        total += Math.pow(difference, totalAttributes);
    }

    return Math.pow(total, 1.0 / totalAttributes);
}

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From source file:com.openkm.kea.filter.KEAPhraseFilter.java

License:Open Source License

/** 
 * Converts an instance by removing all non-alphanumeric characters
 * from its string attribute values.//from   w w w . j av  a 2 s  .  co m
 */
private void convertInstance(Instance instance) throws Exception {

    double[] instVals = new double[instance.numAttributes()];

    for (int i = 0; i < instance.numAttributes(); i++) {
        if (!instance.attribute(i).isString() || instance.isMissing(i)) {
            instVals[i] = instance.value(i);
        } else {
            if (!m_SelectCols.isInRange(i)) {
                int index = getOutputFormat().attribute(i).addStringValue(instance.stringValue(i));
                instVals[i] = (double) index;
                continue;
            }
            // aly: str = text of the document
            String str = instance.stringValue(i);

            String tokenized = tokenize(str);

            // aly: resultStr is the clean version of str
            // log.info(resultStr.toString());
            int index = getOutputFormat().attribute(i).addStringValue(tokenized);
            instVals[i] = (double) index;
        }
    }
    Instance inst = new Instance(instance.weight(), instVals);
    inst.setDataset(getOutputFormat());
    push(inst);
}

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From source file:com.openkm.kea.filter.NumbersFilter.java

License:Open Source License

/** 
 * Converts an instance. A phrase boundary is inserted where
 * a number is found./*www .  j  a  va2s . co m*/
 */
private void convertInstance(Instance instance) throws Exception {

    double[] instVals = new double[instance.numAttributes()];

    for (int i = 0; i < instance.numAttributes(); i++) {
        if ((!instance.attribute(i).isString()) || instance.isMissing(i)) {
            instVals[i] = instance.value(i);
        } else {
            String str = instance.stringValue(i);

            StringBuffer resultStr = new StringBuffer();
            StringTokenizer tok = new StringTokenizer(str, " \t\n", true);
            while (tok.hasMoreTokens()) {
                String token = tok.nextToken();

                // Everything that doesn't contain at least
                // one letter is considered to be a number
                boolean isNumber = true;
                for (int j = 0; j < token.length(); j++) {
                    if (Character.isLetter(token.charAt(j))) {
                        isNumber = false;
                        break;
                    }
                }
                if (!isNumber) {
                    resultStr.append(token);
                } else {
                    if (token.equals(" ") || token.equals("\t") || token.equals("\n")) {
                        resultStr.append(token);
                    } else {
                        resultStr.append(" \n ");
                    }
                }
            }
            int index = getOutputFormat().attribute(i).addStringValue(resultStr.toString());
            instVals[i] = (double) index;
        }
    }
    Instance inst = new Instance(instance.weight(), instVals);
    inst.setDataset(getOutputFormat());
    push(inst);
}

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From source file:com.spread.experiment.tempuntilofficialrelease.ClassificationViaClustering108.java

License:Open Source License

/**
 * Returns class probability distribution for the given instance.
 * /*from   w  w w. ja v a2s  .  co m*/
 * @param instance the instance to be classified
 * @return the class probabilities
 * @throws Exception if an error occurred during the prediction
 */
@Override
public double[] distributionForInstance(Instance instance) throws Exception {

    if (m_ZeroR != null) {
        return m_ZeroR.distributionForInstance(instance);
    } else {
        double[] result = new double[instance.numClasses()];

        if (m_ActualClusterer != null) {
            // build new instance
            Instances tempData = m_ClusteringHeader.stringFreeStructure();
            double[] values = new double[tempData.numAttributes()];
            int n = 0;
            for (int i = 0; i < instance.numAttributes(); i++) {
                if (i == instance.classIndex()) {
                    continue;
                }
                if (instance.attribute(i).isString()) {
                    values[n] = tempData.attribute(n).addStringValue(instance.stringValue(i));
                } else if (instance.attribute(i).isRelationValued()) {
                    values[n] = tempData.attribute(n).addRelation(instance.relationalValue(i));
                } else {
                    values[n] = instance.value(i);
                }
                n++;
            }
            Instance newInst = new DenseInstance(instance.weight(), values);
            newInst.setDataset(tempData);

            if (!getLabelAllClusters()) {

                // determine cluster/class
                double r = m_ClustersToClasses[m_ActualClusterer.clusterInstance(newInst)];
                if (r == -1) {
                    return result; // Unclassified
                } else {
                    result[(int) r] = 1.0;
                    return result;
                }
            } else {
                double[] classProbs = new double[instance.numClasses()];
                double[] dist = m_ActualClusterer.distributionForInstance(newInst);
                for (int i = 0; i < dist.length; i++) {
                    for (int j = 0; j < instance.numClasses(); j++) {
                        classProbs[j] += dist[i] * m_ClusterClassProbs[i][j];
                    }
                }
                Utils.normalize(classProbs);
                return classProbs;
            }
        } else {
            return result; // Unclassified
        }
    }
}

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From source file:control.CosineDistance.java

License:Open Source License

/**
 * Calculates the distance between two instances.
 * /*from  www. jav a 2 s .  c o m*/
 * @param first    the first instance
 * @param second    the second instance
 * @return       the distance between the two given instances
 */
public double distance(Instance first, Instance second) {

    HashMap<String, Double> fInstance = new HashMap<String, Double>();
    HashMap<String, Double> sInstance = new HashMap<String, Double>();

    for (int i = 0; i < first.numAttributes(); i++) {
        fInstance.put(first.attribute(i).name(), first.value(i));
        sInstance.put(second.attribute(i).name(), second.value(i));
    }

    return 1 - CosineSimilarity.calculateCosineSimilarity(fInstance, sInstance);
}

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