1. 包列表
  2. 类列表
  3. weka.core.Attribute

Java 类weka.core.Attribute 实例源码

项目：G-eRules    文件：GeRules.java    
private boolean isClassTheMajortiy(ArrayList<Instance> instances, double classification){

    List<Instance> instancesList = new ArrayList<>(instances);
    TreeMap<Double, Double> classificationProbability = new TreeMap<>();
    Attribute classAttribute = instances.get(0).classAttribute();

    for (double i = 0; i < classAttribute.numValues(); i++) {
        int matchedClassCount = 0;

        for (Instance instance : instancesList) {
            if(instance.classValue() == i){
                matchedClassCount++;
            }
        }

        classificationProbability.put(((double) matchedClassCount / (double) instancesList.size()), i);
    }

    return (classificationProbability.lastEntry().getValue() == classification);
}
项目：bestconf    文件：LHSInitializer.java    
public static void main(String[] args){
    ArrayList<Attribute> atts = new ArrayList<Attribute>();

    /*Properties p1 = new Properties();
    p1.setProperty("range", "[0,1]");
    ProtectedProperties prop1 = new ProtectedProperties(p1);*/

    Properties p2 = new Properties();
    p2.setProperty("range", "[321,1E9]");
    ProtectedProperties prop2 = new ProtectedProperties(p2);

    ArrayList<String> attVals = new ArrayList<String>();
    for (int i = 0; i < 5; i++)
          attVals.add("val" + (i+1));

    //atts.add(new Attribute("att1", prop1));
    atts.add(new Attribute("att2", prop2));
    //atts.add(new Attribute("att3", attVals));
    //Instances data = LHSInitializer.getMultiDimContinuous(atts, 10, false);
    //Instances data = LHSInitializer.getMultiDim(atts, 10, false);
    Instances data = LHSInitializer.getMultiDimContinuous(atts, 1, false);

    System.out.println(data);
}
项目：ijcnlp2017-cmaps    文件：ConceptGrouperSimLog.java    
private Instances createDataSet(List<ConceptSimilarityMeasure> sims) {

        ArrayList<Attribute> atts = new ArrayList<Attribute>();
        for (ConceptSimilarityMeasure sim : sims)
            atts.add(new Attribute(sim.getName()));

        List<String> classes = new ArrayList<String>();
        classes.add("no merge");
        classes.add("merge");
        atts.add(new Attribute("class", classes));

        Instances data = new Instances("data", atts, 0);
        data.setClassIndex(data.numAttributes() - 1);

        return data;
    }
项目：hungrydragon    文件：ModelClassifier.java    
public ModelClassifier() {
    name = new Attribute("name");
    type = new Attribute("type");
    attributes = new ArrayList();
    classVal = new ArrayList();
    classVal.add("Monday");
    classVal.add("Tuesday");
    classVal.add("Wednesday");
    classVal.add("Thursday");
    classVal.add("Friday");
    classVal.add("Saturday");
    classVal.add("Sunday");

    attributes.add(name);
    attributes.add(type);

    attributes.add(new Attribute("class", classVal));
    dataRaw = new Instances("TestInstances", attributes, 0);
    dataRaw.setClassIndex(dataRaw.numAttributes() - 1);
}
项目：weka-xgboost    文件：DMatrixLoader.java    
public static DMatrix instanceToDenseDMatrix(Instance instance) throws XGBoostError {
    Attribute classAttribute = instance.classAttribute();
    int classAttrIndex = classAttribute.index();

    int colNum = instance.numAttributes()-1;
    int rowNum = 1;

    float[] data = new float[colNum*rowNum];

    Enumeration<Attribute> attributeEnumeration = instance.enumerateAttributes();
    int dataIndex = 0;
    while (attributeEnumeration.hasMoreElements()) {
        Attribute attribute = attributeEnumeration.nextElement();
        int attrIndex = attribute.index();
        if(attrIndex == classAttrIndex){
            continue;
        }
        data[dataIndex]= (float) instance.value(attribute);
        dataIndex++;
    }

    return new DMatrix(data, rowNum, colNum);
}
项目：bestconf    文件：LHSSampler.java    
public static void main(String[] args){
    ArrayList<Attribute> atts = new ArrayList<Attribute>();

    /*Properties p1 = new Properties();
    p1.setProperty("range", "[0,1]");
    ProtectedProperties prop1 = new ProtectedProperties(p1);*/

    Properties p2 = new Properties();
    p2.setProperty("range", "[321,1E9]");
    ProtectedProperties prop2 = new ProtectedProperties(p2);

    ArrayList<String> attVals = new ArrayList<String>();
    for (int i = 0; i < 5; i++)
          attVals.add("val" + (i+1));

    //atts.add(new Attribute("att1", prop1));
    atts.add(new Attribute("att2", prop2));
    //atts.add(new Attribute("att3", attVals));
    //Instances data = LHSInitializer.getMultiDimContinuous(atts, 10, false);
    //Instances data = LHSInitializer.getMultiDim(atts, 10, false);
    LHSSampler sampler = new LHSSampler();
    Instances data = sampler.sampleMultiDimContinuous(atts, 1, false);

    System.out.println(data);
}
项目：imputedb    文件：WekaUtil.java    
/**
 * Create an Instances object from the tuples provided. The Instances has
 * name `name` and every value from every tuple. The TupleDesc is provided
 * separately just to validate that all of the provided Tuples share this
 * TupleDesc.
 * @param name the name of the resulting Instances object
 * @param ts list of Tuples
 * @param td TupleDesc
 * @param fields indices identifying which fields should be included in the new Instances object.
 * @return new Instances object containing the values from all the tuples.
 */
public static Instances relationToInstances(String name, List<Tuple> ts, TupleDesc td,
        List<Integer> fields){
    ArrayList<Attribute> attrs = tupleDescToAttributeList(td, fields);
    int relationSize = ts.size();
    Instances instances = new Instances(name, attrs, relationSize);

    for (int i=0; i<ts.size(); i++){
        Tuple t = ts.get(i);
        if (!t.getTupleDesc().equals(td)){
            throw new RuntimeException("All TupleDescs must match.");
        }
        instances.add(i, tupleToInstance(t, attrs, fields));
    }

    return instances;
}
项目：BestConfig    文件：AutoTestAdjust.java    
/**
 * set the bestConf to cluster and get the running performance
 * @param attributeToVal
 * @return
 */
public double setOptimal(Map<Attribute,Double> attributeToVal){
    HashMap hm = new HashMap();
    for(Attribute key : attributeToVal.keySet()){
        Double value = attributeToVal.get(key);
        hm.put(key.name(), value);
    }
    this.startTest(hm, 0, false);
    double y = 0;
    y = performance;
    return y;
}
项目：sstore-soft    文件：FeatureClusterer.java    
/**
 * Helper method to convet Feature keys to Attributes
 * @param data
 * @param prefixes
 * @return
 */
public static Set<Attribute> prefix2attributes(Instances data, String...prefixes) {
    Set<Attribute> attributes = new ListOrderedSet<Attribute>();
    for (String key : prefixes) {
        Attribute attribute = data.attribute(key);
        assert(attribute != null) : "Invalid Attribute key '" + key + "'";
        attributes.add(attribute);
    } // FOR
    return (attributes);
}
项目：CS-436_580L_Introduction-to-Machine-Learning    文件：Utils.java    
public static Instances convertToArff(List<Document> dataSet, List<String> vocabulary, String fileName) {
    int dataSetSize = dataSet.size();
    /* Create features */
    ArrayList<Attribute> attributes = new ArrayList<>();
    for (int i = 0; i < vocabulary.size(); i++) {
        attributes.add(new Attribute("word_" + i));
    }
    Attribute classAttribute = new Attribute("Class");
    attributes.add(classAttribute);

    /* Add examples */
    System.out.println("Building instances...");
    Instances trainingDataSet = new Instances(fileName, attributes, 0);
    for (int k = 0; k < dataSetSize; k++) {
        Document document = dataSet.get(k);
        Instance example = new DenseInstance(attributes.size());
        for (int i = 0; i < vocabulary.size(); i++) {
            String word = vocabulary.get(i);
            example.setValue(i, Collections.frequency(document.getTerms(), word));
        }
        example.setValue(classAttribute, document.getDocumentClass());
        trainingDataSet.add(example);
        int progress = (int) ((k * 100.0) / dataSetSize);
        System.out.printf("\rPercent completed: %3d%%", progress);
    }
    trainingDataSet.setClass(classAttribute);
    System.out.println();

    System.out.println("Writing to file ...");
    try {
        ArffSaver saver = new ArffSaver();
        saver.setInstances(trainingDataSet);
        saver.setFile(new File(fileName));
        saver.writeBatch();
    } catch (IOException e) {
        e.printStackTrace();
    }

    return trainingDataSet;
}
项目：BestConfig    文件：LHSInitializer.java    
private static void uniBoundsGeneration(double[] bounds, Attribute crntAttr, int sampleSetSize){
    bounds[0] = crntAttr.getLowerNumericBound();
    bounds[sampleSetSize] = crntAttr.getUpperNumericBound();
    double pace = (bounds[sampleSetSize] - bounds[0])/sampleSetSize;
    for(int j=1;j<sampleSetSize;j++){
        bounds[j] = bounds[j-1] + pace;
    }
}
项目：GeneralisedRulesAlgorithm    文件：GRules.java    
/**
 * Constructor for numeric attribute, each numeric attribute is
 * represented by a term in form of x <= value < y
 * 
 * @param attributeIn
 * @param attributeIndexIn
 */
public Term(Attribute attributeIn, double attributeIndexIn){

    attribute = attributeIn;
    setAttributeIndex(attributeIndexIn);

    // set type of the attribute
    if(attributeIn.isNumeric()){
        attributeType = TypeNumeric;
    }else{
        attributeType = 0;
    }
}
项目：bestconf    文件：LHSInitializer.java    
private static void uniBoundsGeneration(double[] bounds, Attribute crntAttr, int sampleSetSize){
    bounds[0] = crntAttr.getLowerNumericBound();
    bounds[sampleSetSize] = crntAttr.getUpperNumericBound();
    double pace = (bounds[sampleSetSize] - bounds[0])/sampleSetSize;
    for(int j=1;j<sampleSetSize;j++){
        bounds[j] = bounds[j-1] + pace;
    }
}
项目：imputedb    文件：WekaUtil.java    
/**
 * Create a list of Weka Attributes from a TupleDesc. The resulting list is
 * suitable to pass to an Instances object.
 * @param td the TupleDesc
 * @return the list of Attributes
 */
public static ArrayList<Attribute> tupleDescToAttributeList(TupleDesc td){
    List<Integer> fields = new ArrayList<>();
    for (int i=0; i<td.numFields(); i++){
        fields.add(i);
    }
    return tupleDescToAttributeList(td, fields);
}
项目：BestConfig    文件：DDSSampler.java    
public static void main(String[] args){
    ArrayList<Attribute> atts = new ArrayList<Attribute>();

    Properties p1 = new Properties();
    p1.setProperty("range", "[0,1]");
    ProtectedProperties prop1 = new ProtectedProperties(p1);

    Properties p2 = new Properties();
    p2.setProperty("range", "[321,1E9]");
    ProtectedProperties prop2 = new ProtectedProperties(p2);

    Properties p3 = new Properties();
    p3.setProperty("range", "[1,30]");
    ProtectedProperties prop3 = new ProtectedProperties(p3);

    ArrayList<String> attVals = new ArrayList<String>();
    for (int i = 0; i < 5; i++)
          attVals.add("val" + (i+1));

    atts.add(new Attribute("att1", prop1));
    atts.add(new Attribute("att2", prop2));
    atts.add(new Attribute("att3", prop3));
    //atts.add(new Attribute("att4", attVals));
    //Instances data = LHSInitializer.getMultiDimContinuous(atts, 10, false);
    //Instances data = LHSInitializer.getMultiDim(atts, 10, false);
    DDSSampler sampler = new DDSSampler(3);

    sampler.setCurrentRound(0);
    Instances data = sampler.sampleMultiDimContinuous(atts, 2, false);
    System.out.println(data);

    sampler.setCurrentRound(01);
    data = sampler.sampleMultiDimContinuous(atts, 2, false);
    System.out.println(data);

    sampler.setCurrentRound(2);
    data = sampler.sampleMultiDimContinuous(atts, 2, false);
    System.out.println(data);
}
项目：imputedb    文件：WekaUtil.java    
/**
 * Create a list of Weka Attributes from a TupleDesc. The resulting list is
 * suitable to pass to an Instances object. This does no validation on `fields`.
 * @param td the TupleDesc
 * @param fields indices identifying which fields should be included
 * @return the list of Attributes
 */
public static ArrayList<Attribute> tupleDescToAttributeList(TupleDesc td, List<Integer> fields){
    ArrayList<Attribute> attrs = new ArrayList<>(fields.size());
    for (int i : fields){
        Attribute attr = fieldToAttribute(td, i);
        attrs.add(attr);
    }

    return attrs;
}
项目：twitter-user-gender-classification    文件：BaseFeaturesFactory.java    
private Attribute getNominalAttribute(Fields field) {
    Set<String> values = new HashSet<>();
    for(Object obj: dataDomain.get(field.name()))
        values.add(Conversion.getValueAsStr(obj));

    return new Attribute(field.name(), new ArrayList<>(values));
}
项目：twitter-user-gender-classification    文件：BaseFeaturesFactory.java    
private Attribute getNominalColorAttribute(Fields field) {
    Set<String> values = new HashSet<>();
    for(Object obj: dataDomain.get(field.name()))
        values.add(Conversion.getHexColorAsNominal(obj.toString()));

    return new Attribute(field.name(), new ArrayList<>(values));
}
项目：twitter-user-gender-classification    文件：FeatureSetFactory.java    
public static List<Pair<String, Attribute>> getStandardFeatureSet(Map<String, Set<Object>> dataDomain) {
        BaseFeaturesFactory factory = new BaseFeaturesFactory(dataDomain);
        List<Pair<String, Attribute>> featureVector = new ArrayList<>();

        featureVector.add(new Pair(Fields.GOLDEN.name(), factory.getGolden()));
        featureVector.add(new Pair(Fields.UNIT_STATE.name(), factory.getUnitState()));
        featureVector.add(new Pair(Fields.TRUSTED_JUDGEMENTS.name(), factory.getTrustedJudgements()));
//        featureVector.add(new Pair(Fields.LAST_JUDGEMENT_TIME.name(), factory.getLastJudgementTime()));
        featureVector.add(new Pair(Fields.GENDER_CONFIDENCE.name(), factory.getGenderConfidence()));
        featureVector.add(new Pair(Fields.PROFILE_EXISTS.name(), factory.getProfileExists()));
        featureVector.add(new Pair(Fields.PROFILE_EXISTS_CONFIDENCE.name(), factory.getProfileExistsConfidence()));
//        featureVector.add(new Pair(Fields.PROFILE_CREATION_DATE.name(), factory.getProfileCreationDate()));
//        featureVector.add(new Pair(Fields.PROFILE_DESCRIPTION.name(), factory.getDescription()));
        featureVector.add(new Pair(Fields.FAVORITES_NUMBER.name(), factory.getFavoritesNumber()));
        featureVector.add(new Pair(Fields.GENDER_GOLDEN.name(), factory.getGenderGold()));
        featureVector.add(new Pair(Fields.LINK_COLOR.name(), factory.getLinkColor()));
//        featureVector.add(new Pair(Fields.USERNAME.name(), factory.getUserName()));
//        featureVector.add(new Pair(Fields.RETWEET_COUNT.name(), factory.getRetweetsCount()));
        featureVector.add(new Pair(Fields.SIDEBAR_COLOR.name(), factory.getSidebarColor()));

        featureVector.add(new Pair(Fields.TWEETS_COUNT.name(), factory.getTweetsCount()));
//        featureVector.add(new Pair(Fields.TWEET_LOCATION.name(), factory.getTweetLocation()));
//        featureVector.add(new Pair(Fields.USER_TIMEZONE.name(), factory.getUserTimezone()));

        // Class attribute should be last
        featureVector.add(new Pair(Fields.GENDER.name(), factory.getGender()));

        return featureVector;
    }
项目：twitter-user-gender-classification    文件：FeatureSetFactory.java    
public static List<Pair<String, Attribute>> getExtendedFeatureSet(Map<String, Set<Object>> dataDomain) {
        List<Pair<String, Attribute>> featureVector = getStandardFeatureSet(dataDomain);
        ExtraFeaturesFactory extraFactory = new ExtraFeaturesFactory(dataDomain);

        // Location
//        featureVector.add(new Pair(ExtraFields.TWEET_COORDINATES_LATITUDE.name(), extraFactory.getTweetLatitude()));
//        featureVector.add(new Pair(ExtraFields.TWEET_COORDINATES_LONGITUDE.name(), extraFactory.getTweetLongitude()));
        // Text score
        featureVector.add(new Pair(ExtraFields.TWEET_MALE_FEMALE_WORDS_SCORE.name(), extraFactory.getTweetMaleFemaleWordsScore()));
        featureVector.add(new Pair(ExtraFields.DESCRIPTION_MALE_FEMALE_WORDS_SCORE.name(), extraFactory.getDescriptionMaleFemaleWordsScore()));
        // Tweet PMI
        featureVector.add(new Pair(ExtraFields.TWEET_TEXT_PMI_MALE.name(), extraFactory.getTweetTextPMIMale()));
        featureVector.add(new Pair(ExtraFields.TWEET_TEXT_PMI_FEMALE.name(), extraFactory.getTweetTextPMIFemale()));
        featureVector.add(new Pair(ExtraFields.TWEET_TEXT_PMI_BRAND.name(), extraFactory.getTweetTextPMIBrand()));
        featureVector.add(new Pair(ExtraFields.TWEET_TEXT_PMI_UNKNOWN.name(), extraFactory.getTweetTextPMIUnknown()));
        featureVector.add(new Pair(ExtraFields.TWEET_TEXT_GENDER_PREDICT.name(), extraFactory.getTweetTextGenderPrediction()));
        // User Description PMI
        featureVector.add(new Pair(ExtraFields.USER_DESC_PMI_MALE.name(), extraFactory.getUserDescriptionPMIMale()));
        featureVector.add(new Pair(ExtraFields.USER_DESC_PMI_FEMALE.name(), extraFactory.getUserDescriptionPMIFemale()));
        featureVector.add(new Pair(ExtraFields.USER_DESC_PMI_BRAND.name(), extraFactory.getUserDescriptionPMIBrand()));
        featureVector.add(new Pair(ExtraFields.USER_DESC_PMI_UNKNOWN.name(), extraFactory.getUserDescriptionPMIUnknown()));
        featureVector.add(new Pair(ExtraFields.USER_DESC_GENDER_PREDICT.name(), extraFactory.getUserDescriptionGenderPrediction()));
        // Prediction of gender based on first name extracted from username
        featureVector.add(new Pair(ExtraFields.FIRST_NAME_FROM_USERNAME_BASED_GENDER_PREDICT.name(), extraFactory.getFirstNameFromUserNameBasedGenderPrediction()));

        return featureVector;
    }
项目：bestconf    文件：BestConf.java    
public static Map<Attribute, Double> instanceToMap(Instance ins){
    HashMap<Attribute, Double> retval = new HashMap<Attribute, Double>();
    Enumeration<Attribute> enu = ins.enumerateAttributes();
    while(enu.hasMoreElements()){
        Attribute temp = enu.nextElement();
        retval.put(temp, ins.value(temp));
    }
    return retval;
}
项目：BestConfig    文件：RBSoDDSOptimization.java    
private void saveProps(ArrayList<Attribute> props, int round, int subround){
    try {
        HashMap propMap = new HashMap();
        for(Attribute att : props){
            propMap.put(att.name(),att.getMetadata().getProperty(propKey));
        }
        writeToYaml(resumeFolder+"/props_"+round+"_"+subround, propMap);

        File file = new File(resumeFolder+"/props_"+round+"_"+subround+"_OK");
        file.createNewFile();
    } catch (IOException e) {
        e.printStackTrace();
    }
}
项目：imputedb    文件：WekaUtil.java    
/**
 * Create a Weka Attribute with name `name`.
 * @param name name of attribute
 * @param type type of attribute. This is currently only required to enforce
 * the *lack* of String support.
 * @return the new Attribute
 */
public static Attribute fieldToAttribute(String name, Type type){
    if (!(type == Type.INT_TYPE || type == Type.DOUBLE_TYPE)){
        throw new UnsupportedOperationException();
    }

    return new Attribute(name);
}
项目：weka-xgboost    文件：DMatrixLoader.java    
public static DMatrix instancesToDenseDMatrix(Instances instances) throws XGBoostError {
    int colNum = instances.numAttributes()-1;
    int rowNum = instances.size();

    float[] data = new float[colNum*rowNum];
    float[] labels = new float[instances.size()];
    Attribute classAttribute = instances.classAttribute();
    int classAttrIndex = classAttribute.index();

    for(int i=0, dataIndex = 0; i<instances.size(); i++) {
        Instance instance = instances.get(i);

        labels[i] = (float) instance.classValue();
        Enumeration<Attribute> attributeEnumeration = instance.enumerateAttributes();
        while (attributeEnumeration.hasMoreElements()) {
            Attribute attribute = attributeEnumeration.nextElement();
            int attrIndex = attribute.index();
            if(attrIndex == classAttrIndex){
                continue;
            }
            data[dataIndex]= (float) instance.value(attribute);
            dataIndex++;
        }
    }


    DMatrix dMatrix = new DMatrix(data, rowNum, colNum);

    dMatrix.setLabel(labels);
    return dMatrix;

}
项目：java-ml-projects    文件：Clustering.java    
private List<Series<Number, Number>> buildLabeledSeries() {
    List<XYChart.Series<Number, Number>> realSeries = new ArrayList<>();
    Attribute irisClasses = data.attribute(2);
    data.stream().collect(Collectors.groupingBy(d -> {
        int i = (int) d.value(2);
        return irisClasses.value(i);
    })).forEach((e, instances) -> {
        XYChart.Series<Number, Number> series = new XYChart.Series<>();
        series.setName(e);
        instances.stream().map(this::instancetoChartData).forEach(series.getData()::add);
        realSeries.add(series);
    });
    return realSeries;
}
项目：BestConfig    文件：BestConf.java    
public Instances generateMore(int number, int existedNum,
        Instances header) {
    ArrayList<Attribute> localAtts = new ArrayList<Attribute>();
    Enumeration<Attribute> enu = header.enumerateAttributes();
    while (enu.hasMoreElements()) {
        localAtts.add(enu.nextElement());
    }
    Instances samplePoints = LHSInitializer.getMultiDimContinuous(
            localAtts, number + existedNum, false);
    samplePoints.insertAttributeAt(header.classAttribute(),
            samplePoints.numAttributes());
    samplePoints.setClassIndex(samplePoints.numAttributes() - 1);
    return samplePoints;
}
项目：bestconf    文件：RBSoDDSOptimization.java    
private void saveProps(ArrayList<Attribute> props, int round, int subround){
    try {
        HashMap propMap = new HashMap();
        for(Attribute att : props){
            propMap.put(att.name(),att.getMetadata().getProperty(propKey));
        }
        writeToYaml(resumeFolder+"/props_"+round+"_"+subround, propMap);

        File file = new File(resumeFolder+"/props_"+round+"_"+subround+"_OK");
        file.createNewFile();
    } catch (IOException e) {
        e.printStackTrace();
    }
}
项目：alvisnlp    文件：PredictionElementClassifier.java    
protected static String[] getClasses(Instances instances) {
    Attribute classAttribute = instances.classAttribute();
    String[] result = new String[classAttribute.numValues()];
    for (int i = 0; i < result.length; ++i)
        result[i] = classAttribute.value(i);
    return result;
}
项目：BestConfig    文件：ConfigSampler.java    
public Instances getMultiDimContinuous(ArrayList<Attribute> atts, int sampleSetSize, boolean useMid, BestConf bestconf){
    Instances retval = sampleMultiDimContinuous(atts, sampleSetSize, useMid), temp;
    while(retval.size()<sampleSetSize){
        temp = sampleMultiDimContinuous(atts, sampleSetSize, useMid);
        retval.addAll(temp);
    }

    //make sure the set size is equal to the setting
    while(retval.size()>sampleSetSize)
        retval.remove(retval.size()-1);

    return retval;
}
项目：ijcnlp2017-cmaps    文件：Feature.java    
public static Attribute createWekaAttribute(String name, Class<Object> c) {
    if (c.equals(Double.class) || c.equals(Float.class) || c.equals(Integer.class))
        return new Attribute(name);
    if (c.equals(Boolean.class) || c.equals(String.class))
        return new Attribute(name, createValueListBoolean());
    return null;
}
项目：bestconf    文件：COMT2.java    
private ArrayList<Branch2> getLeavesInfoForM5P(M5P model){
    ArrayList<Branch2> retval = new ArrayList<Branch2>();
    ArrayList<RuleNode> leafNodes = new ArrayList<RuleNode>();
    model.getM5RootNode().returnLeaves(new ArrayList[]{leafNodes});

    for(RuleNode leaf : leafNodes){
        Branch2 branch = new Branch2();
        ArrayList<PreConstructedLinearModel> lmodel = new ArrayList<PreConstructedLinearModel>();
        lmodel.add(leaf.getModel());
        branch.setLinearModels(lmodel);

        Map<Attribute,Range<Double>> rangeMap = branch.getRangeMap();
        RuleNode parent = leaf, child;
        while(parent.parentNode()!=null){
            child = parent;
            parent = parent.parentNode();

            Attribute att = this.labeledInstances.attribute(parent.splitAtt());
            Range<Double> previous = null;
            if(parent.leftNode()==child)
                previous = rangeMap.put(att,Range.atMost(parent.splitVal()));
            else
                previous = rangeMap.put(att, Range.greaterThan(parent.splitVal()));
            //the attribute is visited previously
            if(previous!=null){
                 previous = rangeMap.get(att).intersection(previous);
                 rangeMap.put(att, previous);
            }
        }

        retval.add(branch);
    }

    return retval;
}
项目：bestconf    文件：AutoTestAdjust.java    
@Override
public Instances collectPerfs(Instances samplePoints, String perfAttName) {
    Instances retVal = null;

    if(samplePoints.attribute(perfAttName) == null){
        Attribute performance = new Attribute(perfAttName);
        samplePoints.insertAttributeAt(performance, samplePoints.numAttributes());
    }

    File perfFolder = new File(perfsfilepath);
    int tot=0;
    if(perfFolder.exists()){
        //let's get all the name set for the sample points
        Iterator<Instance> itr = samplePoints.iterator();
        TreeSet<String> insNameSet = new TreeSet<String>();
        HashMap<String, Integer> mapping = new HashMap<String, Integer>();
        int pos=0;
        while(itr.hasNext()){
            String mdstr = getMD5(itr.next());
            insNameSet.add(mdstr);
            mapping.put(mdstr, new Integer(pos++));
        }

        //now we collect
        File[] perfFiles = perfFolder.listFiles(new PerfsFileFilter(insNameSet));
        tot = perfFiles.length;
        if(tot > 0) isInterrupt = true;
        for(int i=0;i<tot;i++){
            Instance ins = samplePoints.get(mapping.get(perfFiles[i].getName()));
            double[] results = getPerf(perfFiles[i].getAbsolutePath());
            if(results!=null){
                ins.setValue(samplePoints.numAttributes()-1, results[0]);
            }
        }
    }
    retVal = samplePoints;
    retVal.setClassIndex(retVal.numAttributes()-1);
    System.out.println("Total number of collected performances is : "+tot);
    return retVal;
}
项目：BestConfig    文件：COMT2.java    
private ArrayList<Branch2> getLeavesInfoForM5P(M5P model){
    ArrayList<Branch2> retval = new ArrayList<Branch2>();
    ArrayList<RuleNode> leafNodes = new ArrayList<RuleNode>();
    model.getM5RootNode().returnLeaves(new ArrayList[]{leafNodes});

    for(RuleNode leaf : leafNodes){
        Branch2 branch = new Branch2();
        ArrayList<PreConstructedLinearModel> lmodel = new ArrayList<PreConstructedLinearModel>();
        lmodel.add(leaf.getModel());
        branch.setLinearModels(lmodel);

        Map<Attribute,Range<Double>> rangeMap = branch.getRangeMap();
        RuleNode parent = leaf, child;
        while(parent.parentNode()!=null){
            child = parent;
            parent = parent.parentNode();

            Attribute att = this.labeledInstances.attribute(parent.splitAtt());
            Range<Double> previous = null;
            if(parent.leftNode()==child)
                previous = rangeMap.put(att,Range.atMost(parent.splitVal()));
            else
                previous = rangeMap.put(att, Range.greaterThan(parent.splitVal()));
            //the attribute is visited previously
            if(previous!=null){
                 previous = rangeMap.get(att).intersection(previous);
                 rangeMap.put(att, previous);
            }
        }

        retval.add(branch);
    }

    return retval;
}
项目：bestconf    文件：LHSSampler.java    
private static void uniBoundsGeneration(double[] bounds, Attribute crntAttr, int sampleSetSize){
    bounds[0] = crntAttr.getLowerNumericBound();
    bounds[sampleSetSize] = crntAttr.getUpperNumericBound();
    double pace = (bounds[sampleSetSize] - bounds[0])/sampleSetSize;
    for(int j=1;j<sampleSetSize;j++){
        bounds[j] = bounds[j-1] + pace;
    }
}
项目：bestconf    文件：DDSSampler.java    
public static void main(String[] args){
    ArrayList<Attribute> atts = new ArrayList<Attribute>();

    Properties p1 = new Properties();
    p1.setProperty("range", "[0,1]");
    ProtectedProperties prop1 = new ProtectedProperties(p1);

    Properties p2 = new Properties();
    p2.setProperty("range", "[321,1E9]");
    ProtectedProperties prop2 = new ProtectedProperties(p2);

    Properties p3 = new Properties();
    p3.setProperty("range", "[1,30]");
    ProtectedProperties prop3 = new ProtectedProperties(p3);

    ArrayList<String> attVals = new ArrayList<String>();
    for (int i = 0; i < 5; i++)
          attVals.add("val" + (i+1));

    atts.add(new Attribute("att1", prop1));
    atts.add(new Attribute("att2", prop2));
    atts.add(new Attribute("att3", prop3));
    //atts.add(new Attribute("att4", attVals));
    //Instances data = LHSInitializer.getMultiDimContinuous(atts, 10, false);
    //Instances data = LHSInitializer.getMultiDim(atts, 10, false);
    DDSSampler sampler = new DDSSampler(3);

    sampler.setCurrentRound(0);
    Instances data = sampler.sampleMultiDimContinuous(atts, 2, false);
    System.out.println(data);

    sampler.setCurrentRound(01);
    data = sampler.sampleMultiDimContinuous(atts, 2, false);
    System.out.println(data);

    sampler.setCurrentRound(2);
    data = sampler.sampleMultiDimContinuous(atts, 2, false);
    System.out.println(data);
}
项目：BestConfig    文件：ConfigSampler.java    
private static ArrayList<Attribute> scaleDownMindists(Instances previousSet, Instance center){
    ArrayList<Attribute> localAtts = new ArrayList<Attribute>();
    int attNum = center.numAttributes();

    int pos = previousSet.attribute(PerformanceAttName).index();

    //traverse each dimension
    Enumeration<Instance> enu;
    double minDis;
    for(int i=0;i<attNum;i++){
        if(i==pos)
            continue;

        enu = previousSet.enumerateInstances();
        minDis = Double.MAX_VALUE;

        while(enu.hasMoreElements()){
            Instance ins = enu.nextElement();
            if(!ins.equals(center))
                minDis = Math.min((double)((int)(Math.abs(ins.value(i)-center.value(i))*1000))/1000.0, minDis);
        }

        //now we set the range
        Properties p1 = new Properties();
        double upper = center.value(i)+minDis, lower=center.value(i)-minDis;

        TreeSet<Double> detourSet = new TreeSet<Double>();
        detourSet.add(upper);
        detourSet.add(lower);
        detourSet.add(previousSet.attribute(i).getUpperNumericBound());
        detourSet.add(previousSet.attribute(i).getLowerNumericBound());
        switch(detourSet.size()){
        case 1:
            upper=lower=detourSet.first();
            break;
        case 2:
            upper = detourSet.last();
            lower = detourSet.first();
            break;
        case 3:
            upper=lower=detourSet.higher(detourSet.first());
            break;
        default://case 4:
            upper=detourSet.lower(detourSet.last());
            lower=detourSet.higher(detourSet.first());
            break;
        }

        p1.setProperty("range", "["+String.valueOf(lower)+","+String.valueOf(upper)+"]");
        ProtectedProperties prop1 = new ProtectedProperties(p1);

        localAtts.add(new Attribute(previousSet.attribute(i).name(), prop1));
    }

    return localAtts;
}
项目：bestconf    文件：AutoTestAdjust.java    
public Instances runExp(Instances samplePoints, String perfAttName){
    Instances retVal = null;
    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;
}
项目：sstore-soft    文件：MarkovAttributeSet.java    
public MarkovAttributeSet(Set<Attribute> items) {
    super(items);
}
项目：sstore-soft    文件：MarkovAttributeSet.java    
public MarkovAttributeSet(Attribute...items) {
    super((Set<Attribute>)CollectionUtil.addAll(new HashSet<Attribute>(), items));
}
项目：BestConfig    文件：BestConf.java    
public static ArrayList<Attribute> scaleDownDetour(Instances previousSet, Instance center){
    ArrayList<Attribute> localAtts = new ArrayList<Attribute>();
    int attNum = center.numAttributes();

    int pos = previousSet.attribute(PerformanceAttName).index();

    //traverse each dimension
    Enumeration<Instance> enu;
    double minDis;
    for(int i=0;i<attNum;i++){
        if(i==pos)
            continue;

        enu = previousSet.enumerateInstances();
        minDis = Double.MAX_VALUE;

        while(enu.hasMoreElements()){
            Instance ins = enu.nextElement();
            if(!ins.equals(center))
                minDis = Math.min((double)((int)(Math.abs(ins.value(i)-center.value(i))*100))/100.0, minDis);
        }

        //now we set the range
        Properties p1 = new Properties();
        double upper = center.value(i)+minDis, lower=center.value(i)-minDis;

        TreeSet<Double> detourSet = new TreeSet<Double>();
        detourSet.add(upper);
        detourSet.add(lower);
        detourSet.add(previousSet.attribute(i).getUpperNumericBound());
        detourSet.add(previousSet.attribute(i).getLowerNumericBound());
        switch(detourSet.size()){
        case 1:
            upper=lower=detourSet.first();
            break;
        case 2:
            upper = detourSet.last();
            lower = detourSet.first();
            break;
        case 3:
            upper=lower=detourSet.higher(detourSet.first());
            break;
        default://case 4:
            upper=detourSet.lower(detourSet.last());
            lower=detourSet.higher(detourSet.first());
            break;
        }

        p1.setProperty("range", "["+String.valueOf(lower)+","+String.valueOf(upper)+"]");
        ProtectedProperties prop1 = new ProtectedProperties(p1);

        localAtts.add(new Attribute(previousSet.attribute(i).name(), prop1));
    }

    return localAtts;
}