Example usage for weka.core Instance value

Introduction

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

Prototype

public double value(Attribute att);

Source Link

Document

Returns an instance's attribute value in internal format.

Usage

From source file:meka.core.MLUtils.java

License:Open Source License

/** 
 * ToIntArray - raw instance to int[] representation
 *//* w ww  . j  a v a 2  s. c o  m*/
public static final int[] toIntArray(Instance x, int L) {
    int y[] = new int[L];
    for (int j = 0; j < L; j++) {
        // added the following if-statement to change missing values to -1 
        if (x.isMissing(j)) {
            y[j] = -1;
        } else {
            y[j] = (int) Math.round(x.value(j));
        }
    }
    return y;
}

From source file:meka.core.MLUtils.java

License:Open Source License

/**
 * CopyValues - Set x_dest[j+offset] = x_src[i+from].
 *//*from w ww  .j a  v a  2s  . c o m*/
public static final Instance copyValues(Instance x_dest, Instance x_src, int from, int offset) {
    int d = x_src.numAttributes();
    for (int i = from, j = 0; i < d; i++, j++) {
        x_dest.setValue(j + offset, x_src.value(i));
    }
    return x_dest;
}

From source file:meka.core.MLUtils.java

License:Open Source License

/**
 * CopyValues - Set x_dest[i++] = x_src[j] for all j in indices[].
 *//*from   w  w  w  .j av a2 s.c  om*/
public static final Instance copyValues(Instance x_dest, Instance x_src, int indices[]) {
    int i = 0;
    for (int j : indices) {
        x_dest.setValue(i++, x_src.value(j));
    }
    return x_dest;
}

From source file:meka.core.MLUtils.java

License:Open Source License

public static final String toDebugString(Instance x) {
    int L = x.classIndex();
    StringBuilder sb = new StringBuilder();
    sb.append("y = [");
    for (int j = 0; j < L; j++) {
        sb.append(x.value(j) + " ");
    }//from w  ww.jav  a 2 s .co m
    sb.append("], x = [");
    for (int j = L; j < L + 10; j++) {
        sb.append(x.value(j) + " ");
    }
    sb.append(" ... ]");
    return sb.toString();
}

From source file:meka.core.MLUtils.java

License:Open Source License

/**
 * Get K - get the number of values associated with each label L.
 * @param   D    a dataset//  w w w  .  java2s .  c  o  m
 * @return   a vector of size L: K_1,...,K_L
 */
public int[] getK(Instances D) {
    int L = D.classIndex();
    HashSet counts[] = new HashSet[L];
    int K[] = new int[L];
    for (int j = 0; j < L; j++) {
        counts[j] = new HashSet<Integer>();
        for (Instance x : D) {
            int k = (int) x.value(j);
            counts[j].add(k);
        }
        K[j] = counts[j].size();
        /*
           System.out.println(""+j+" = "+counts[j]);
           if (counts[j].size() < 2) {
           System.out.println("OK, this is a problem ...");
        //System.exit(1);
           }
           */
    }
    return K;
}

From source file:meka.core.StatUtils.java

License:Open Source License

/**
 * Delta(x_1,x_2,x_3 = v_1,v_2,v_3) for j = 1,2,3, k = 1,2,3.
 *//*from w  w  w . j a  v a  2 s.co  m*/
private static boolean match(Instance x, int indices[], int values[]) {
    for (int j = 0; j < indices.length; j++) {
        int v = (int) Math.round(x.value(indices[j]));
        if (v != values[j]) {
            return false;
        }
    }
    return true;
}

From source file:meka.core.SuperLabelUtils.java

License:Open Source License

/** Encode a vector of integer values to a string */
public static String encodeValue(Instance x, int indices[]) {
    int values[] = new int[indices.length];

    for (int j = 0; j < indices.length; j++) {
        values[j] = (int) x.value(indices[j]);
    }//www .  j  a  va 2s.  c  om
    return new LabelVector(values).toString();
}

From source file:meka.experiment.statisticsexporters.WekaFilter.java

License:Open Source License

/**
 * Converts the Instances back into statistics.
 *
 * @param data          the data to convert
 * @return              the generated statistics
 *//*  w w  w . ja  va 2 s  .c o  m*/
protected List<EvaluationStatistics> fromInstances(Instances data) {
    List<EvaluationStatistics> result;
    EvaluationStatistics stat;
    MultiLabelClassifier cls;
    String rel;
    int i;
    int n;
    Instance inst;

    result = new ArrayList<>();

    if (data.attribute(EvaluationStatistics.KEY_CLASSIFIER) == null) {
        log("Failed to locate attribute: " + EvaluationStatistics.KEY_CLASSIFIER);
        return result;
    }
    if (data.attribute(EvaluationStatistics.KEY_RELATION) == null) {
        log("Failed to locate attribute: " + EvaluationStatistics.KEY_RELATION);
        return result;
    }

    for (i = 0; i < data.numInstances(); i++) {
        inst = data.instance(i);
        try {
            cls = OptionUtils.fromCommandLine(MultiLabelClassifier.class,
                    inst.stringValue(data.attribute(EvaluationStatistics.KEY_CLASSIFIER)));
            rel = inst.stringValue(data.attribute(EvaluationStatistics.KEY_RELATION));
            stat = new EvaluationStatistics(cls, rel, null);
            for (n = 0; n < inst.numAttributes(); n++) {
                if (inst.attribute(n).isNumeric() && !inst.isMissing(n)) {
                    stat.put(inst.attribute(n).name(), inst.value(n));
                }
            }
            result.add(stat);
        } catch (Exception e) {
            handleException("Failed to process instance: " + inst, e);
        }
    }

    return result;
}

From source file:milk.classifiers.MINND.java

License:Open Source License

/**
 * Calculates the distance between two instances
 *
 * @param first the first instance/*from ww w.j  a  v  a2s  .c o m*/
 * @param second the second instance
 * @return the distance between the two given instances
 */
private double distance(Instance first, double[] mean, double[] var, int pos) {

    double diff, distance = 0;
    int j = 0;
    for (int i = 0; i < first.numAttributes(); i++) {
        // Skipp nominal attributes (incl. class & ID)
        if ((i == m_ClassIndex) || (i == m_IdIndex))
            continue;

        // If attribute is numeric
        if (first.attribute(i).isNumeric()) {
            if (!first.isMissing(i)) {
                diff = first.value(i) - mean[j];
                if (Utils.gr(var[j], m_ZERO))
                    distance += m_Change[pos][j] * var[j] * diff * diff;
                else
                    distance += m_Change[pos][j] * diff * diff;
            } else {
                if (Utils.gr(var[j], m_ZERO))
                    distance += m_Change[pos][j] * var[j];
                else
                    distance += m_Change[pos][j] * 1.0;
            }
        }
        j++;
    }

    return distance;
}

From source file:milk.classifiers.MINND.java

License:Open Source License

/**
 * Updates the minimum and maximum values for all the attributes
 * based on a new exemplar./*from  w  w w . j a  va2 s. c  o m*/
 *
 * @param ex the new exemplar
 */
private void updateMinMax(Exemplar ex) {
    Instances insts = ex.getInstances();
    int m = 0;
    for (int j = 0; j < insts.numAttributes(); j++) {
        if ((j != ex.idIndex()) && (j != ex.classIndex())) {
            if (insts.attribute(j).isNumeric()) {
                for (int k = 0; k < insts.numInstances(); k++) {
                    Instance ins = insts.instance(k);
                    if (!ins.isMissing(j)) {
                        if (Double.isNaN(m_MinArray[m])) {
                            m_MinArray[m] = ins.value(j);
                            m_MaxArray[m] = ins.value(j);
                        } else {
                            if (ins.value(j) < m_MinArray[m])
                                m_MinArray[m] = ins.value(j);
                            else if (ins.value(j) > m_MaxArray[m])
                                m_MaxArray[m] = ins.value(j);
                        }
                    }
                }
            }
            m++;
        }
    }
}