Example usage for weka.core Instance setValue

List of usage examples for weka.core Instance setValue

  1. HOME
  2. Java
  3. weka
  4. weka.core.*
  5. Instance
  6. setValue

Introduction

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

Prototype

public void setValue(Attribute att, String value);

Source Link

Document

Sets a value of an nominal or string attribute to the given value.

Usage

From source file:cezeri.utils.FactoryInstance.java

public static Instances getSubsetData(Instances data, String[] attList) {
    Instances temp = new Instances(data);
    for (int i = 0; i < data.numAttributes(); i++) {
        if (!temp.attribute(0).equals(temp.classAttribute())) {
            temp.deleteAttributeAt(0);/*w w w . ja  va 2s.c  o  m*/
        }
    }
    double[][] m = new double[attList.length + 1][data.numInstances()];
    for (int i = 0; i < attList.length; i++) {
        int n = attList.length - 1 - i;
        String str = attList[n];
        Attribute t = data.attribute(str);
        double[] d = data.attributeToDoubleArray(t.index());
        m[n] = d;
        temp.insertAttributeAt(t, 0);
    }
    m[attList.length] = data.attributeToDoubleArray(data.classIndex());
    m = CMatrix.getInstance(m).transpose().get2DArrayDouble();

    FastVector att = new FastVector();
    for (int i = 0; i < temp.numAttributes(); i++) {
        att.addElement(temp.attribute(i));
    }
    Instances ret = new Instances(temp.relationName(), att, m.length);
    for (int i = 0; i < m.length; i++) {
        Instance ins = new Instance(m[0].length);
        for (int j = 0; j < m[0].length; j++) {
            ins.setValue(j, m[i][j]);
        }
        ret.add(ins);
    }
    ret.setClassIndex(temp.classIndex());

    return ret;
}

From source file:clasificacion.Clasificacion.java

public String clasificar(String[] testCases) throws Exception {
    String ruta = "nursery_model.model";

    InputStream classModelStream;
    classModelStream = getClass().getResourceAsStream(ruta);
    //classModel = (Classifier)SerializationHelper.read(classModelStream);
    Classifier clasify = (Classifier) SerializationHelper.read(classModelStream);

    FastVector parents = new FastVector();
    parents.addElement("usual");
    parents.addElement("pretentious");
    parents.addElement("great_pret");
    Attribute _parent = new Attribute("parents", parents);

    FastVector nurs = new FastVector();
    nurs.addElement("proper");
    nurs.addElement("less_proper");
    nurs.addElement("improper");
    nurs.addElement("critical");
    nurs.addElement("very_crit");
    Attribute _has_nurs = new Attribute("has_nurs", nurs);

    FastVector form = new FastVector();
    form.addElement("complete");
    form.addElement("completed");
    form.addElement("incomplete");
    form.addElement("foster");
    Attribute _form = new Attribute("form", form);

    FastVector children = new FastVector();
    children.addElement("1");
    children.addElement("2");
    children.addElement("3");
    children.addElement("more");
    Attribute _children = new Attribute("children", children);

    FastVector housing = new FastVector();
    housing.addElement("convenient");
    housing.addElement("less_conv");
    housing.addElement("critical");
    Attribute _housing = new Attribute("housing", housing);

    FastVector finance = new FastVector();
    finance.addElement("convenient");
    finance.addElement("inconv");
    Attribute _finance = new Attribute("finance", finance);

    FastVector social = new FastVector();
    social.addElement("nonprob");
    social.addElement("slightly_prob");
    social.addElement("problematic");
    Attribute _social = new Attribute("social", social);

    FastVector health = new FastVector();
    health.addElement("recommended");
    health.addElement("priority");
    health.addElement("not_recom");
    Attribute _health = new Attribute("health", health);

    FastVector Class = new FastVector();
    Class.addElement("not_recom");
    Class.addElement("recommend");
    Class.addElement("very_recom");
    Class.addElement("priority");
    Class.addElement("spec_prior");
    Attribute _Class = new Attribute("class", Class);

    FastVector atributos = new FastVector(9);
    atributos.addElement(_parent);//  www  .j  av a  2 s . com
    atributos.addElement(_has_nurs);
    atributos.addElement(_form);
    atributos.addElement(_children);
    atributos.addElement(_housing);
    atributos.addElement(_finance);
    atributos.addElement(_social);
    atributos.addElement(_health);
    atributos.addElement(_Class);

    ArrayList<Attribute> atributs = new ArrayList<>();
    atributs.add(_parent);
    atributs.add(_has_nurs);
    atributs.add(_form);
    atributs.add(_children);
    atributs.add(_housing);
    atributs.add(_finance);
    atributs.add(_social);
    atributs.add(_health);
    atributs.add(_Class);

    //Aqu se crea la instacia, que tiene todos los atributos del modelo
    Instances dataTest = new Instances("TestCases", atributos, 1);
    dataTest.setClassIndex(8);

    Instance setPrueba = new Instance(9);

    int index = -1;
    for (int i = 0; i < 8; i++) {
        index = atributs.get(i).indexOfValue(testCases[i]);
        //System.out.println(i + " " + atributs.get(i)  + " " + index + " " + testCases[i]);
        setPrueba.setValue(atributs.get(i), index);
    }

    //Agregando el set que se desea evaluar.
    dataTest.add(setPrueba);

    //Realizando la Prediccin
    //La instancia es la 0 debido a que es la unica que se encuentra.
    double valorP = clasify.classifyInstance(dataTest.instance(0));
    //get the name of the class value
    String prediccion = dataTest.classAttribute().value((int) valorP);

    return prediccion;
}

From source file:classes.AbdoAgglomerativeClusterer.java

/**
 * calculates the with-in-cluster variance as the sum squares of differences
 * between instances contained in the cluster and its mean divided by
 * cluster size/*from  www .  j  av  a  2s  . c om*/
 *
 * @param cluster
 * @return
 */
double calcWithinVariance(Vector<Integer> cluster) {

    double variance = 0.0;
    double[] fValues1 = new double[m_instances.numAttributes()];
    for (int i = 0; i < cluster.size(); i++) {
        Instance instance = m_instances.instance(cluster.elementAt(i));
        for (int j = 0; j < m_instances.numAttributes(); j++) {
            fValues1[j] += instance.value(j);
        }
    }
    for (int j = 0; j < m_instances.numAttributes(); j++) {
        fValues1[j] /= cluster.size();
    }
    // set up two instances for distance function
    Instance centroid = (Instance) m_instances.instance(cluster.elementAt(0)).copy();
    for (int j = 0; j < m_instances.numAttributes(); j++) {
        centroid.setValue(j, fValues1[j]);
    }

    for (int i = 0; i < cluster.size(); i++) {
        double temp = 0;
        Instance instance = m_instances.instance(cluster.elementAt(i));
        for (int j = 0; j < m_instances.numAttributes(); j++) {
            temp += (instance.value(j) - centroid.value(j));
        }

        variance += temp * temp;
    }
    return variance / cluster.size();
}

From source file:cluster.ABC.ClusterUtils.java

License:Open Source License

/** Normalizes the values of a normal Instance in L2 norm
 *
 * @author Sugato Basu/*www .  java  2s.com*/
 * @param inst Instance to be normalized
 */

public static void normalizeInstance(Instance inst) throws Exception {
    double norm = 0;
    double values[] = inst.toDoubleArray();

    if (inst instanceof SparseInstance) {
        System.err.println("Is SparseInstance, using normalizeSparseInstance function instead");
        normalizeSparseInstance(inst);
    }

    for (int i = 0; i < values.length; i++) {
        if (i != inst.classIndex()) { // don't normalize the class index 
            norm += values[i] * values[i];
        }
    }
    norm = Math.sqrt(norm);
    for (int i = 0; i < values.length; i++) {
        if (i != inst.classIndex()) { // don't normalize the class index 
            values[i] /= norm;
        }
    }
    for (int i = 0; i < inst.numAttributes(); i++) {
        inst.setValue(i, values[i]);
    }
    //inst.setValueArray(values);
}

From source file:cluster.ABC.ClusterUtils.java

License:Open Source License

/** This function divides every attribute value in an instance by
 *  the instance weight -- useful to find the mean of a cluster in
 *  Euclidean space //  w ww. j a  v a  2  s.  c o m
 *  @param inst Instance passed in for normalization (destructive update)
 */
public static void normalizeByWeight(Instance inst) {
    double weight = inst.weight();
    if (inst instanceof SparseInstance) {
        for (int i = 0; i < inst.numValues(); i++) {
            inst.setValueSparse(i, inst.valueSparse(i) / weight);
        }
    } else if (!(inst instanceof SparseInstance)) {
        for (int i = 0; i < inst.numAttributes(); i++) {
            inst.setValue(i, inst.value(i) / weight);
        }
    }
}

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

License:Open Source License

public Instances loadPropertiesAsInstancesPre(String Path) {
    HashMap<String, String> pmap = null;
    try {/*from  ww  w  . j a va 2 s. co m*/
        pmap = Yaml.loadType(new FileInputStream(yamlPath), HashMap.class);
    } catch (FileNotFoundException e) {
        e.printStackTrace();
    }

    atts = new ArrayList<Attribute>();
    Instance dfIns = new DenseInstance(pmap.size());
    int pos = 0;
    double[] vals = new double[pmap.size()];
    for (Map.Entry<String, String> ent : pmap.entrySet()) {
        try {
            double val = Double.valueOf(String.valueOf(ent.getValue()));
            vals[pos] = val;

            Properties p1 = new Properties();
            double upper, lower;
            if (val != 0) {
                upper = val * (1. + 0.5);
                lower = val * (1. - 0.5);
            } else {
                lower = val;
                upper = 1;
            }

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

            atts.add(new Attribute(String.valueOf(ent.getKey()), prop1));
            pos++;
        } catch (Exception e) {
        }
    }

    Instances dfProp = new Instances("DefaultConfig", atts, 1);
    dfProp.add(dfIns);
    dfIns.setDataset(dfProp);
    for (int i = 0; i < pos; i++) {
        dfIns.setValue(atts.get(i), vals[i]);
        //System.err.println(atts.get(i)+":"+vals[i]);
    }

    return dfProp;
}

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

License:Open Source License

public Instances loadPropertiesAsInstances(String Path) {
    HashMap<String, String> pmap = null;
    HashMap rangeMap = null;/*w  ww. j a va 2 s  . c  om*/
    try {
        pmap = Yaml.loadType(new FileInputStream(yamlPath), HashMap.class);
        rangeMap = Yaml.loadType(new FileInputStream(yamlPath + "_range"), HashMap.class);
    } catch (FileNotFoundException e) {
        e.printStackTrace();
    }

    atts = new ArrayList<Attribute>();
    int pos = 0;
    double[] vals = new double[pmap.size()];
    Object range = null;
    for (Map.Entry<String, String> ent : pmap.entrySet()) {
        try {
            double val = Double.valueOf(String.valueOf(ent.getValue()));
            vals[pos] = val;

            Properties p1 = new Properties();

            range = rangeMap.get(ent.getKey());
            if (range != null) {
                String list = (String) range;
                if (list.indexOf('[') == -1 && list.indexOf('(') == -1)
                    throw new Exception("No Range for You" + ent.getKey());
                p1.setProperty("range", list.trim());
            } else {
                double upper, lower;
                if (val != 0) {
                    upper = val * (1. + 0.5);
                    lower = val * (1. - 0.5);
                } else {
                    lower = val;
                    upper = 1;
                }
                p1.setProperty("range", "[" + String.valueOf(lower) + "," + String.valueOf(upper) + "]");
            }

            ProtectedProperties prop1 = new ProtectedProperties(p1);

            atts.add(new Attribute(String.valueOf(ent.getKey()), prop1));
            pos++;
        } catch (Exception e) {
        }
    }

    Instances dfProp = new Instances("DefaultConfig", atts, 1);
    Instance dfIns = new DenseInstance(atts.size());
    for (int i = 0; i < pos; i++) {
        dfIns.setValue(atts.get(i), vals[i]);
        //System.err.println(atts.get(i)+":"+vals[i]);
    }
    dfProp.add(dfIns);
    dfIns.setDataset(dfProp);

    return dfProp;
}

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  .jav  a  2 s .com
    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;
}

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

License:Open Source License

@Override
public Instances collectPerfs(Instances samplePoints, String perfAttName) {
    Instances retVal = null;//from  w ww  .  ja  v a  2 s  .  c  om

    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;
}

From source file:cn.ict.zyq.bestConf.COMT2.Branch2.java

License:Open Source License

public Instance maxPoint(Instances dataset) throws Exception {
    Instance max = new DenseInstance(dataset.numAttributes());
    max.setDataset(dataset);/*www . jav  a  2s. c o m*/

    double[] combinedCoefs = null;
    int len = 0;
    for (PreConstructedLinearModel model : linearModelList) {
        //initialization
        if (combinedCoefs == null) {
            len = model.coefficients().length;
            combinedCoefs = new double[len];
            for (int i = 0; i < len; i++)
                combinedCoefs[i] = 0;
        }

        for (int i = 0; i < len; i++)
            combinedCoefs[i] += model.coefficients()[i];
    }

    //the max value is obtained at ends of a range
    for (Map.Entry<Attribute, Range<Double>> ent : rangeMap.entrySet()) {
        int attIdx = ent.getKey().index();
        if (combinedCoefs[attIdx] > 0) {
            //use the upper bound
            if (ent.getValue().hasUpperBound())
                max.setValue(attIdx, ent.getValue().upperEndpoint());
        } else if (combinedCoefs[attIdx] < 0) {
            //use the lower bound
            if (ent.getValue().hasLowerBound())
                max.setValue(attIdx, ent.getValue().lowerEndpoint());
        }
    }

    //now we set the predicted values
    double y = 0;
    for (PreConstructedLinearModel model : linearModelList) {
        y += model.classifyInstance(max);
    }
    y /= linearModelList.size();
    max.setClassValue(y);

    return max;
}