Example usage for weka.core Instance setDataset

Introduction

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

Prototype

public void setDataset(Instances instances);

Source Link

Document

Sets the reference to the dataset.

Usage

From source file:com.yahoo.research.scoring.classifier.NutchOnlineClassifier.java

License:Apache License

/**
 * Converts an {@link AnthURL} into an {@link Instance} which can be handled
 * by the {@link Classifier}.//  w  ww. j ava2 s .co  m
 * 
 * @param url
 *            the {@link AnthURL} which should be transformed/converted.
 * @return the resulting {@link Instance}.
 */
private static Instance convert(AnthURL url) {
    if (url != null) {

        Instance inst = new SparseInstance(dimension);
        inst.replaceMissingValues(replaceMissingValues);

        inst.setDataset(instances);
        inst.setValue(attributesIndex.get("class"), (url.sem ? "sem" : "nonsem"));
        inst.setValue(attributesIndex.get("sempar"), (url.semFather ? 1 : 0));
        inst.setValue(attributesIndex.get("nonsempar"), (url.nonSemFather ? 1 : 0));
        inst.setValue(attributesIndex.get("semsib"), (url.semSibling ? 1 : 0));
        inst.setValue(attributesIndex.get("nonsempar"), (url.nonSemFather ? 1 : 0));
        inst.setValue(attributesIndex.get("domain"), url.uri.getHost());
        Set<String> tokens = new HashSet<String>();

        tokens.addAll(tokenizer(url.uri.getPath()));
        tokens.addAll(tokenizer(url.uri.getQuery()));
        tokens.addAll(tokenizer(url.uri.getFragment()));
        for (String tok : tokens) {
            inst.setValue(attributesIndex.get(getAttributeNameOfHash(getHash(tok, hashTrickSize))), 1);
        }
        return inst;

    } else {
        System.out.println("Input AnthURL for convertion into instance was null.");
        return null;
    }
}

---

From source file:com.zazhu.BlueHub.BlueHub.java

License:Apache License

/**
 * receives the last reads from the sensors and creates the features we use
 * only the acc x,y,z (either from internal or external sensor)
 * /* w ww  .j  a  v  a2s  . c  o m*/
 * @param sensorQueue
 * @throws Exception 
 */
private Instance processingSenseData(Queue<String> sensorQueue, char whatSensor) throws Exception {

    BufferedReader reader;
    Instances format;
    Instance newInstance = null;

    Log.d(TAG, "Queue size = " + mQueueSize);

    if (sensorQueue.size() <= 0)
        throw new Exception("Queue empty");

    // create the arrays that will contain the accelerometer data
    // s.x s.y s.z
    double[] sx = new double[sensorQueue.size()];
    double[] sy = new double[sensorQueue.size()];
    double[] sz = new double[sensorQueue.size()];

    String rawReading;
    StringTokenizer st;

    int index;

    if (D)
        Log.e(TAG, "+++ COMPUTING FEATURES +++");

    // 1. collect raw data. what kind of sensing data? external vs. internal
    switch (whatSensor) {
    case EXTERNAL:
        index = 0;
        while ((rawReading = sensorQueue.poll()) != null) {
            // FORMAT:
            // "Time_SensorName_SensorNumber_Counter_Xacc_Yacc_Zacc_Xgyro_Ygyro_checksum"
            // position of the values needed: s.x = 4, s.y = 5, s.z = 6
            st = new StringTokenizer(rawReading, FIELD_SEP);
            // not needed data
            for (int i = 0; i < 4; i++)
                st.nextToken();
            // s.x, s.y, s.z
            sx[index] = Double.valueOf(st.nextToken());
            sy[index] = Double.valueOf(st.nextToken());
            sz[index] = Double.valueOf(st.nextToken());

            index += 1;
        }

        // 2. process raw data
        // 2.1 read the input format for the instance (TODO must be changed to
        // use weka classes)
        reader = new BufferedReader(new InputStreamReader(getResources().openRawResource(R.raw.format_extern)));

        try {
            format = new Instances(reader);

            if (format.classIndex() == -1)
                format.setClassIndex(format.numAttributes() - 1);

            // 2.2 create a new instance
            newInstance = new DenseInstance(7);
            newInstance.setDataset(format);
            // set attributes
            newInstance.setValue(format.attribute(0), Feature.getStd(sx));
            newInstance.setValue(format.attribute(1), Feature.getStd(sy));
            newInstance.setValue(format.attribute(2), Feature.getStd(sz));
            newInstance.setValue(format.attribute(3), Feature.getMean(sx));
            newInstance.setValue(format.attribute(4), Feature.getMean(sy));
            newInstance.setValue(format.attribute(5), Feature.getMean(sz));
            // set unknown class
            newInstance.setMissing(format.attribute(6));

        } catch (IOException e) {
            e.printStackTrace();
        }

        break;
    case INTERNAL:

        index = 0;
        while ((rawReading = sensorQueue.poll()) != null) {

            // FORMAT "Xacc_Yacc_Zacc"
            // position of the values needed: s.x = 0, s.y = 1, s.z = 2
            st = new StringTokenizer(rawReading, FIELD_SEP);

            // s.x, s.y, s.z
            sx[index] = Double.valueOf(st.nextToken());
            sy[index] = Double.valueOf(st.nextToken());
            sz[index] = Double.valueOf(st.nextToken());

            index += 1;
        }

        // 2. process raw data
        // 2.1 read the input format for the instance (TODO must be changed to
        // use weka classes)
        reader = new BufferedReader(new InputStreamReader(getResources().openRawResource(R.raw.format_intern)));

        try {
            format = new Instances(reader);

            if (format.classIndex() == -1)
                format.setClassIndex(format.numAttributes() - 1);

            // 2.2 create a new instance
            newInstance = new DenseInstance(7);
            newInstance.setDataset(format);
            // set attributes
            newInstance.setValue(format.attribute(0), Feature.getStd(sx));
            newInstance.setValue(format.attribute(1), Feature.getStd(sy));
            newInstance.setValue(format.attribute(2), Feature.getStd(sz));
            newInstance.setValue(format.attribute(3), Feature.getMean(sx));
            newInstance.setValue(format.attribute(4), Feature.getMean(sy));
            newInstance.setValue(format.attribute(5), Feature.getMean(sz));
            // set unknown class
            newInstance.setMissing(format.attribute(6));

        } catch (IOException e) {
            e.printStackTrace();
        }

        break;
    default:
        if (D)
            Log.e(TAG, "+++ COMPUTING FEATURES: NO VALUE FOR THE SENSOR READING +++");
        break;
    }

    return newInstance;

}

---

From source file:core.classification.Classifiers.java

License:Open Source License

private Instance buildYNCInstance(Context p, Context c) {
    double RAREA = 0, H = 0, D = 0, V = 0.;
    int parentClass = 1, childClass = 1;

    RAREA = ((double) c.area()) / p.area();
    H = c.getH(p);// w  w w .  j a  v  a  2s .c  om
    D = c.getD(p);
    V = c.getDX(p);
    parentClass = (p.getSymbolClass() > 0) ? p.getSymbolClass() : 1;
    childClass = (c.getSymbolClass() > 0) ? c.getSymbolClass() : 1;

    double[] values = new double[7];
    values[0] = dataStructYC.attribute(0).indexOfValue("" + parentClass);
    values[1] = dataStructYC.attribute(1).indexOfValue("" + childClass);
    values[2] = RAREA;
    values[3] = H;
    values[4] = D;
    values[5] = V;
    values[6] = dataStructYC.attribute(6).indexOfValue("N");

    Instance inst = new Instance(1.0, values);
    inst.setDataset(dataStructYC);
    inst.setClassMissing();

    return inst;
}

---

From source file:core.Core.java

public String run() throws Exception {
    ConverterUtils.DataSource source = new ConverterUtils.DataSource("src/files/powerpuffgirls.arff");

    HashMap<String, Classifier> hash = new HashMap<>();

    hash.put("J48", new J48());
    hash.put("NaiveBayes", new NaiveBayes());
    hash.put("IBk=1", new IBk(1));
    hash.put("IBk=3", new IBk(3));
    hash.put("MultilayerPerceptron", new MultilayerPerceptron());

    LibSVM svm = new LibSVM();
    hash.put("LibSVM", svm);
    Instances ins = source.getDataSet();

    ins.setClassIndex(4);//from w  ww  .ja v a 2  s . co m

    StringBuilder sb = new StringBuilder();

    int blossom = 0;
    int bubbles = 0;

    Instance test = null;

    for (Map.Entry<String, Classifier> entry : hash.entrySet()) {
        Classifier c = entry.getValue();
        c.buildClassifier(ins);

        test = new Instance(5);

        float[] array = classifyImage();

        test.setDataset(ins);
        test.setValue(0, array[0]);
        test.setValue(1, array[1]);
        test.setValue(2, array[2]);
        test.setValue(3, array[3]);

        double prob[] = c.distributionForInstance(test);

        sb.append("<em>");
        sb.append(entry.getKey());
        sb.append(":</em>");
        sb.append("<br/>");

        for (int i = 0; i < prob.length; i++) {
            String value = test.classAttribute().value(i);

            if (getRoundedValue(prob[i]) >= CUT_NOTE) {
                if (getClassValue(value))
                    blossom++;
                else
                    bubbles++;
            }

            sb.append(getClassName(value));
            sb.append(": ");
            sb.append("<strong>");
            sb.append(getRoundedValue(prob[i]) < CUT_NOTE ? "Rejeitado!" : getValueFormatted(prob[i]));
            sb.append("</strong>");
            sb.append("&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
        }

        sb.append("<br/>");

        System.out.println("blossom: " + blossom);
        System.out.println("bubbles: " + bubbles);
        System.out.println("=================\n");
    }

    sb.append(blossom > bubbles ? "<h3> a Florzinha!</h3>" : "<h3> a Lindinha!</h3>");

    blossom = 0;
    bubbles = 0;

    return sb.toString();
}

---

From source file:core.me.Context.java

License:Open Source License

public Instance buildSCAInstance() {
    Instances dataStruc = Classifiers.getInst(false).getDataStructSCA();

    double[] values = new double[2];
    values[0] = this.theSymbol.getRatio();
    values[1] = dataStruc.attribute(1).indexOfValue("0");

    Instance inst = new Instance(1.0, values);
    inst.setDataset(dataStruc);
    inst.setClassMissing();/*from   ww w .j av  a  2 s.com*/
    return inst;
}

---

From source file:core.me.Context.java

License:Open Source License

public Instance buildSCBInstance() {
    Instances dataStruc = Classifiers.getInst(false).getDataStructSCB();

    double H = 0, D = 0, DX = 0.;
    int parentClass = 1;

    if (this.getParent() != null) {
        H = this.getH();
        D = this.getD();
        DX = this.getDX();
        parentClass = this.getParentSymbol().getSymbolClass();
    }/* ww  w. jav a  2  s .c o  m*/

    double[] values = new double[5];
    values[0] = H;
    values[1] = D;
    values[2] = DX;
    values[3] = dataStruc.attribute(3).indexOfValue(Integer.toString(parentClass));
    values[4] = dataStruc.attribute(4).indexOfValue("0");

    Instance inst = new Instance(1.0, values);
    inst.setDataset(dataStruc);
    inst.setClassMissing();

    return inst;
}

---

From source file:core.me.Context.java

License:Open Source License

public Instance buildSCC1Instance() {
    Instances dataStruc = Classifiers.getInst(false).getDataStructSCC(Relationship.INLINE);

    double LH, LD, LDX;
    int LCLASS;//from   w  w  w . j a v a  2  s .  co  m
    double height = (double) this.theSymbol.getHeight();

    LH = height / ((double) this.horSymbol.getHeight());
    LD = (this.theSymbol.getCenter() - this.horSymbol.getCenter()) / height;
    LDX = this.getHor().getDX();
    LCLASS = this.horSymbol.getSymbolClass();

    double[] values = new double[5];
    values[0] = LH;
    values[1] = LD;
    values[2] = LDX;
    values[3] = dataStruc.attribute(3).indexOfValue(Integer.toString(LCLASS));
    values[4] = dataStruc.attribute(4).indexOfValue("0");

    Instance inst = new Instance(1.0, values);
    inst.setDataset(dataStruc);
    inst.setClassMissing();
    return inst;

}

---

From source file:core.me.Context.java

License:Open Source License

public Instance buildSCC2Instance() {
    Instances dataStruc = Classifiers.getInst(false).getDataStructSCC(Relationship.SUPERSCRIPT);

    double EH, ED, EDX;
    int ECLASS;//from w  w w  . ja  va  2 s.com
    double height = (double) this.theSymbol.getHeight();

    EH = height / ((double) this.supSymbol.getHeight());
    ED = (this.theSymbol.getCenter() - this.supSymbol.getCenter()) / height;
    EDX = this.getSup().getDX();
    ECLASS = this.supSymbol.getSymbolClass();

    double[] values = new double[5];
    values[0] = EH;
    values[1] = ED;
    values[2] = EDX;
    values[3] = dataStruc.attribute(3).indexOfValue(Integer.toString(ECLASS));
    values[4] = dataStruc.attribute(4).indexOfValue("0");

    Instance inst = new Instance(1.0, values);
    inst.setDataset(dataStruc);
    inst.setClassMissing();
    return inst;

}

---

From source file:core.me.Context.java

License:Open Source License

public Instance buildSCC3Instance() {
    Instances dataStruc = Classifiers.getInst(false).getDataStructSCC(Relationship.SUBSCRIPT);

    double SH, SD, SDX;
    int SCLASS;//from  w  w  w.  j a v a2s .  c o m
    double height = (double) this.theSymbol.getHeight();

    SH = height / ((double) this.subSymbol.getHeight());
    SD = (this.theSymbol.getCenter() - this.subSymbol.getCenter()) / height;
    SDX = this.getSub().getDX();
    SCLASS = this.subSymbol.getSymbolClass();

    double[] values = new double[5];
    values[0] = SH;
    values[1] = SD;
    values[2] = SDX;
    values[3] = dataStruc.attribute(3).indexOfValue(Integer.toString(SCLASS));
    values[4] = dataStruc.attribute(4).indexOfValue("0");

    Instance inst = new Instance(1.0, values);
    inst.setDataset(dataStruc);
    inst.setClassMissing();
    return inst;

}

---

From source file:core.me.Context.java

License:Open Source License

public Instance buildRCInstance() {

    Instances dataStruc = Classifiers.getInst(false).getDataStructRC();

    double H = 0, D = 0, DX = 0.;
    int parentClass = 1;

    if (this.getParent() != null) {
        H = ((double) this.getParentSymbol().getHeight()) / this.theSymbol.getHeight();
        D = (this.getParentSymbol().getCenter() - this.theSymbol.getCenter())
                / this.getParentSymbol().getHeight();
        DX = this.getDX();
        parentClass = this.getParentSymbol().getSymbolClass();
    }//from  w  w  w  .  j  av a2  s  .c o  m

    double[] values = new double[6];
    values[0] = H;
    values[1] = D;
    values[2] = DX;
    values[3] = dataStruc.attribute(3).indexOfValue("" + this.theClass.get());
    values[4] = dataStruc.attribute(4).indexOfValue("" + parentClass);
    values[5] = dataStruc.attribute(5).indexOfValue("0");

    Instance inst = new Instance(1.0, values);
    inst.setDataset(dataStruc);
    inst.setClassMissing();

    return inst;

}

---

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