Example usage for weka.core Instance 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:dataHandlers.DataClusterHandler.java

private Instance toInstance(User user, Instances dataSet) {
    Instance tempInstance = new Instance(userPoints.numAttributes());
    tempInstance.setDataset(userPoints);
    String userDataString = user.getUserID() + user.getTasteString(LastFMDataHandler.getTagCount());
    String[] dataArray = userDataString.split(",");
    for (int index = 0; index < dataArray.length; index++) {
        tempInstance.setValue(index, Integer.parseInt(dataArray[index]));
    }//from w  w w  .  java 2s  . c  om
    return tempInstance;
}

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From source file:dataMining.KMeans.java

/**
 * Metoda do wyznaczania nowych rodkw grup.
 *
 * @return lista zawierajca nowe rodki.//from   w  ww .jav  a2  s  .  co m
 */
private ArrayList<Instance> makeNewMeans() {
    ArrayList<Instance> listOfMeans = new ArrayList<>();
    for (Instance i : groups.keySet()) {
        ArrayList<Instance> list = groups.get(i);
        double[] tab = new double[i.numAttributes()];
        for (Instance in : list) {
            for (int j = 0; j < tab.length; j++) {
                double d = 0;
                try {
                    d = Double.parseDouble(in.toString(j));
                } catch (NumberFormatException ex) {
                    d = 0;
                }
                tab[j] = tab[j] + d;
            }
        }
        for (int j = 0; j < tab.length; j++) {
            tab[j] = tab[j] / list.size();
        }
        Instance ins = new Instance(tab.length);
        for (int j = 0; j < tab.length; j++) {
            ins.setValue(j, tab[j]);
        }
        listOfMeans.add(ins);
    }
    return listOfMeans;
}

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From source file:dataMining.kNN.java

/**
 * Metoda, w ktrej wyszukiwane jest k najbliszych ssiadw ze zbioru
 * danych treningowych dla danych testowych, nastpnie proponowana jest
 * decyzja ktra wystpia na najwikszej liczbie spord k ssiadw.
 *
 * @return Zestaw zmienionych danych typu String.
 *//*from w  ww.ja  va  2 s .c o m*/
public String reviewData() {
    String st = "";
    int type = 0;
    numAtt = 0;
    for (int i = 0; i < testData.numAttributes(); i++) {
        String s = testData.attribute(i).name();
        if (s.equals(attributName)) {
            numAtt = i;
            type = testData.attribute(i).type();
            break;
        }
    }
    for (int i = 0; i < testData.numInstances(); i++) {
        try {
            Instance ins = testData.instance(i);
            if (type == 0) {
                ins.setValue(numAtt, selectValue(findNeighbors(ins)));
            } else {
                ins.setValue(numAtt, selectValue(findNeighbors(ins)));
            }
            st = st + ins.toString() + "\n";
        } catch (IndexOutOfBoundsException e) {

        }

    }

    return st;
}

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From source file:DataMining_FP.interfaz.java

private void Evaluacion_Actividad(String SimonDijo) {
    String resultado_prediccion = null;
    try {//w w  w.ja v  a2s  .  com
        int num = 1150;//Un numero cualquiera
        Instance nueva_entrada = data.instance(num);//Crear una copia

        //Modificando los valores
        //Agregar los valores de los bins
        float[] bins_result_x = bins(maxmin(accelerometer_x_array), accelerometer_x_array);
        float[] bins_result_y = bins(maxmin(accelerometer_y_array), accelerometer_y_array);
        float[] bins_result_z = bins(maxmin(accelerometer_z_array), accelerometer_z_array);

        for (int i = 0; i < 10; i++) {
            nueva_entrada.setValue(i, bins_result_x[i]);//bins_x
            nueva_entrada.setValue(i + 10, bins_result_y[i]);//bins_y
            nueva_entrada.setValue(i + 20, bins_result_z[i]);//bins_z
        }

        //AVG
        nueva_entrada.setValue(30, get_avg(accelerometer_x_array));
        nueva_entrada.setValue(31, get_avg(accelerometer_y_array));
        nueva_entrada.setValue(32, get_avg(accelerometer_z_array));

        //PEAK
        nueva_entrada.setValue(33, tiempo_entre_picos(accelerometer_x_array, tiempo_de_lecturas));
        nueva_entrada.setValue(34, tiempo_entre_picos(accelerometer_y_array, tiempo_de_lecturas));
        nueva_entrada.setValue(35, tiempo_entre_picos(accelerometer_z_array, tiempo_de_lecturas));

        //ABSOLDEV
        nueva_entrada.setValue(36, get_avg_absolute_difference(accelerometer_x_array));
        nueva_entrada.setValue(37, get_avg_absolute_difference(accelerometer_y_array));
        nueva_entrada.setValue(38, get_avg_absolute_difference(accelerometer_z_array));

        //STANDDEV
        nueva_entrada.setValue(39, get_std_deviation(accelerometer_x_array));
        nueva_entrada.setValue(40, get_std_deviation(accelerometer_y_array));
        nueva_entrada.setValue(41, get_std_deviation(accelerometer_z_array));

        //RESULTANT
        nueva_entrada.setValue(42, get_avg_resultant_acceleration(accelerometer_x_array, accelerometer_y_array,
                accelerometer_z_array));
        double clsLabel = tree.classifyInstance(nueva_entrada); // Clasificando una nueva instancia
        resultado_prediccion = data.classAttribute().value((int) clsLabel);

        //Se actualiza lo que predijo en el textfield
        prediccion.setText(resultado_prediccion);
        //System.out.println("\n\n\n[resultado = "+resultado_prediccion+"]");
    } catch (Exception e) {
        e.printStackTrace();
        System.out.println("Error al predecir la instancia capturada");
    }

    //verificamos si el algoritmo logro predecir la actividad correctamente
    if (SimonDijo.equals(resultado_prediccion)) {//Predijo correctamente!
        if (!ult_prediccion)//Si en la ultima vez que jugo no se predijo correctamente solo sumamos un punto.
            puntos_acumulados += 15;
        else//se ha predecido bien >= 2 veces
            puntos_acumulados += 30;
    } else {//No se predijo bien
        if ((puntos_acumulados - 4) > 0)//si no tiene puntos acumulados, no se le resta nada, de lo contrario se le quita un punto
            puntos_acumulados -= 4;
        else
            puntos_acumulados = 0;
    }
    tf_puntaje.setText(String.valueOf(puntos_acumulados));

}

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From source file:de.fub.maps.project.detector.model.inference.processhandler.EvaluationProcessHandler.java

License:Open Source License

protected Instance getInstance(String className, TrackSegment dataset) {
    Instance instance = new DenseInstance(getInferenceModel().getAttributes().size());

    for (FeatureProcess feature : getInferenceModel().getFeatureList()) {
        feature.setInput(dataset);// w w w  .ja va 2 s  . c  o m
        feature.run();
        String featureName = feature.getName();
        Attribute attribute = getInferenceModel().getAttributeMap().get(featureName);
        Double result = feature.getResult();
        instance.setValue(attribute, result);
    }

    instance.setValue(getInferenceModel().getAttributeMap().get(AbstractInferenceModel.CLASSES_ATTRIBUTE_NAME),
            className);
    return instance;
}

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From source file:de.fub.maps.project.detector.model.inference.processhandler.InferenceDataProcessHandler.java

License:Open Source License

private Instance getInstance(TrackSegment segment) {
    Instance instance = new DenseInstance(getInferenceModel().getAttributes().size());

    for (FeatureProcess feature : getInferenceModel().getFeatureList()) {
        feature.setInput(segment);/*from  w  w w. ja  v  a  2  s . c o  m*/
        feature.run();
        String featureName = feature.getName();
        Attribute attribute = getInferenceModel().getAttributeMap().get(featureName);
        Double result = feature.getResult();
        instance.setValue(attribute, result);
    }
    return instance;
}

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From source file:de.ugoe.cs.cpdp.dataprocessing.MORPH.java

License:Apache License

/**
 * <p>//www.j  av  a 2  s.  c  om
 * Applies MORPH to a single instance
 * </p>
 *
 * @param instance
 *            instance that is morphed
 * @param data
 *            data based on which the instance is morphed
 */
public void morphInstance(Instance instance, Instances data) {
    Instance nearestUnlikeNeighbor = getNearestUnlikeNeighbor(instance, data);
    if (nearestUnlikeNeighbor == null) {
        throw new RuntimeException(
                "could not find nearest unlike neighbor within the data: " + data.relationName());
    }
    for (int j = 0; j < data.numAttributes(); j++) {
        if (data.attribute(j) != data.classAttribute() && data.attribute(j).isNumeric()) {
            double randVal = rand.nextDouble() * (beta - alpha) + alpha;
            instance.setValue(j,
                    instance.value(j) + randVal * (instance.value(j) - nearestUnlikeNeighbor.value(j)));
        }
    }
}

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From source file:de.ugoe.cs.cpdp.dataprocessing.NormalizationUtil.java

License:Apache License

/**
 * <p>/*from  w ww. j  a  v  a2  s .  c o  m*/
 * Min-Max normalization to scale all data to the interval [0,1] (N1 in Transfer Defect Learning
 * by Nam et al.).
 * </p>
 *
 * @param data
 *            data that is normalized
 */
public static void minMax(Instances data) {
    for (int j = 0; j < data.numAttributes(); j++) {
        if (data.classIndex() != j) {
            double min = data.attributeStats(j).numericStats.min;
            double max = data.attributeStats(j).numericStats.max;

            for (int i = 0; i < data.numInstances(); i++) {
                Instance inst = data.instance(i);
                double newValue = (inst.value(j) - min) / (max - min);
                inst.setValue(j, newValue);
            }
        }
    }
}

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From source file:de.ugoe.cs.cpdp.dataprocessing.NormalizationUtil.java

License:Apache License

/**
 * <p>/*from  w w  w .  ja  v  a2  s  .c  om*/
 * Internal helper function
 * </p>
 */
private static void applyZScore(Instances data, double[] mean, double[] std) {
    for (int i = 0; i < data.numInstances(); i++) {
        Instance instance = data.instance(i);
        for (int j = 0; j < data.numAttributes(); j++) {
            if (data.classIndex() != j) {
                instance.setValue(j, instance.value(j) - mean[j] / std[j]);
            }
        }
    }
}

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From source file:decisiontree.MyC45.java

/**
* Splits a dataset according to the values of a numeric attribute.
*
* @param data the data which is to be split
* @param att the attribute to be used for splitting
* @return the sets of instances produced by the split
*//* w ww  .  j  av a  2s .  co m*/
private Instances[] splitData(Instances data, Attribute att, double threshold) {
    Instances[] splitData = new Instances[2];
    for (int i = 0; i < 2; i++) {
        splitData[i] = new Instances(data, data.numInstances());
    }

    Enumeration instEnum = data.enumerateInstances();
    while (instEnum.hasMoreElements()) {
        Instance inst = (Instance) instEnum.nextElement();
        if (inst.value(att) >= threshold) {
            inst.setValue(att, threshold);
            splitData[1].add(inst);
        } else {
            inst.setValue(att, 0);
            splitData[0].add(inst);
        }
    }
    for (int i = 0; i < splitData.length; i++) {
        splitData[i].compactify();
    }
    return splitData;
}

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