Example usage for weka.core Instance classAttribute

Introduction

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

Prototype

public Attribute classAttribute();

Source Link

Document

Returns class attribute.

Usage

From source file:smo2.SMO.java

License:Open Source License

public void mygenstat(Instances m_tinst) {
    System.out.println("remove text till here...");
    String[] m_cnames = this.classAttributeNames();
    try {/*from   w ww.  j av  a 2 s  .  c o m*/
        for (int m_k = 0; m_k < m_tinst.numInstances(); m_k++) {
            Instance mc_inst = m_tinst.instance(m_k);
            String m_classname = mc_inst.stringValue(mc_inst.classAttribute());
            // Instance mc_wmiss = (Instance)mc_inst.copy();
            // mc_wmiss.setData(m_tinst);
            // double mpval =
            // ((Classifier)classifier).classifyInstance(mc_wmiss);

            /*
             * int m_rid=0; if(m_classname.equals(m_cnames[0])) { m_rid =0;
             * }else if(m_classname.equals(m_cnames[1])){ m_rid=1; }else{
             * System.out.println("unknown class: m_classname " +
             * m_classname + ", m_cname " + m_cnames[0] + ", " +
             * m_cnames[1]); System.exit(1); }
             */

            // System.out.println("classvalue " + mc_inst.classValue() +
            // " , classname " + m_classname);
            double m_output = obtainVotes(mc_inst);
            double[][] m_bias = bias();
            double m_myscore = myScore(m_output, m_bias[0][1]);

            // if(mc_inst.classAttribute().isNumeric())
            // {
            // System.out.println(m_k + "]class=" + m_classname + ",output="
            // + m_output + ",bias=" + m_bias[0][1] + ",score=" +
            // m_myscore+",pred="+mpval);
            // }else{
            // String m_pclass = mc_inst.classAttribute().value((int)mpval);
            // double
            // mprob=classifier.distributionForInstance(withMissing)[(int)mpval];
            // System.out.println(m_k + "]class=" + m_classname + ",output="
            // + m_output + ",bias=" + m_bias[0][1] + ",score=" +
            // m_myscore+",pred="+m_pclass+",prob="+m_prob);
            // }

            System.out.println(m_k + "]class=" + m_classname + ",output=" + m_output + ",bias=" + m_bias[0][1]
                    + ",score=" + m_myscore);
        }
    } catch (Exception e) {
        e.printStackTrace();
        System.err.println(e.getMessage());
    }

}

From source file:tubes1.myClassifiers.myC45.java

public int[] calculateCount(Instances instances) {

    int numClasses = instances.classAttribute().numValues();
    int numInstances = instances.numInstances();
    int[] num = new int[numClasses];
    for (int i = 0; i < numClasses; i++) {
        num[i] = 0;//from   w  w  w .  j  a v a2  s  .  c om
    }
    for (int i = 0; i < numInstances; i++) {
        Instance instance = instances.instance(i);
        double classValue = instance.value(instance.classAttribute());
        int classIndex = (int) classValue;
        num[classIndex]++;
    }
    return num;
}

From source file:xlong.urlclassify.others.LibLINEAR.java

License:Open Source License

/**
 * Computes the distribution for a given instance.
 *
 * @param instance       the instance for which distribution is computed
 * @return          the distribution//from   w  w  w  .  j  a va2 s.co m
 * @throws Exception       if the distribution can't be computed successfully
 */
public double[] distributionForInstance(Instance instance) throws Exception {

    if (!getDoNotReplaceMissingValues()) {
        m_ReplaceMissingValues.input(instance);
        m_ReplaceMissingValues.batchFinished();
        instance = m_ReplaceMissingValues.output();
    }

    if (getConvertNominalToBinary() && m_NominalToBinary != null) {
        m_NominalToBinary.input(instance);
        m_NominalToBinary.batchFinished();
        instance = m_NominalToBinary.output();
    }

    if (m_Filter != null) {
        m_Filter.input(instance);
        m_Filter.batchFinished();
        instance = m_Filter.output();
    }

    FeatureNode[] x = instanceToArray(instance);
    double[] result = new double[instance.numClasses()];
    if (m_ProbabilityEstimates) {
        if (m_SolverType != SolverType.L2R_LR && m_SolverType != SolverType.L2R_LR_DUAL
                && m_SolverType != SolverType.L1R_LR) {
            throw new WekaException("probability estimation is currently only "
                    + "supported for L2-regularized logistic regression");
        }

        int[] labels = m_Model.getLabels();
        double[] prob_estimates = new double[instance.numClasses()];

        Linear.predictProbability(m_Model, x, prob_estimates);

        // Return order of probabilities to canonical weka attribute order
        for (int k = 0; k < labels.length; k++) {
            result[labels[k]] = prob_estimates[k];
        }
    } else {
        int prediction = Linear.predict(m_Model, x);
        assert (instance.classAttribute().isNominal());
        result[prediction] = 1;
    }

    return result;
}