Example usage for weka.core Instance dataset

Introduction

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

Prototype

public Instances dataset();

Source Link

Document

Returns the dataset this instance has access to.

Usage

From source file:test.org.moa.opencl.IBk.java

License:Open Source License

/**
 * Adds the supplied instance to the training set.
 *
 * @param instance the instance to add/*from www  .j av  a 2  s  . c  om*/
 * @throws Exception if instance could not be incorporated
 * successfully
 */
public void updateClassifier(Instance instance) throws Exception {

    if (m_Train.equalHeaders(instance.dataset()) == false) {
        throw new Exception("Incompatible instance types\n" + m_Train.equalHeadersMsg(instance.dataset()));
    }
    if (instance.classIsMissing()) {
        return;
    }

    m_Train.add(instance);
    m_NNSearch.update(instance);
    m_kNNValid = false;
    if ((m_WindowSize > 0) && (m_Train.numInstances() > m_WindowSize)) {
        boolean deletedInstance = false;
        while (m_Train.numInstances() > m_WindowSize) {
            m_Train.delete(0);
            deletedInstance = true;
        }
        //rebuild datastructure KDTree currently can't delete
        if (deletedInstance == true)
            m_NNSearch.setInstances(m_Train);
    }
}

From source file:tr.gov.ulakbim.jDenetX.classifiers.WEKAClassifier.java

License:Open Source License

public void trainOnInstanceImpl(Instance inst) {
    try {//w w  w . j a  va2s  .co  m
        if (numberInstances == 0) {
            this.instancesBuffer = new Instances(inst.dataset());
            if (classifier instanceof UpdateableClassifier) {
                classifier.buildClassifier(instancesBuffer);
                this.isClassificationEnabled = true;
            } else {
                this.isBufferStoring = true;
            }
        }
        numberInstances++;

        if (classifier instanceof UpdateableClassifier) {
            if (numberInstances > 0) {
                ((UpdateableClassifier) classifier).updateClassifier(inst);
            }
        } else {
            if (numberInstances == widthInitOption.getValue()) {
                //Build first time Classifier
                buildClassifier();
                isClassificationEnabled = true;
                //Continue to store instances
                if (sampleFrequencyOption.getValue() != 0) {
                    isBufferStoring = true;
                }
            }
            if (widthOption.getValue() == 0) {
                //Used from SingleClassifierDrift
                if (isBufferStoring == true) {
                    instancesBuffer.add(inst);
                }
            } else {
                //Used form WekaClassifier without using SingleClassifierDrift
                int numInstances = numberInstances % sampleFrequencyOption.getValue();
                if (sampleFrequencyOption.getValue() == 0) {
                    numInstances = numberInstances;
                }
                if (numInstances == 0) {
                    //Begin to store instances
                    isBufferStoring = true;
                }
                if (isBufferStoring == true && numInstances <= widthOption.getValue()) {
                    //Store instances
                    instancesBuffer.add(inst);
                }
                if (numInstances == widthOption.getValue()) {
                    //Build Classifier
                    buildClassifier();
                    isClassificationEnabled = true;
                    this.instancesBuffer = new Instances(inst.dataset());
                }
            }
        }
    } catch (Exception e) {
        System.err.println("Training: " + e.getMessage());
    }
}

From source file:uzholdem.classifier.UpdateableMultilayerPerceptron.java

License:Open Source License

/**
 * Online backpropagation/*  ww w  .  j  a  v  a2  s.  c  o m*/
 * 
 * @return      the revision
 */
public void updateClassifier(Instance i) throws Exception {

    // setup m_instances
    if (this.m_instances == null) {

        this.m_instances = new Instances(i.dataset(), 0, 0);
    }
    ///////////

    if (m_useNomToBin) {
        if (this.m_nominalToBinaryFilter == null) {
            m_nominalToBinaryFilter = new NominalToBinary();
            m_nominalToBinaryFilter.setInputFormat(m_instances);
        }
        m_nominalToBinaryFilter.input(i);
        m_currentInstance = m_nominalToBinaryFilter.output();
        //    this.m_instances.add(m_currentInstance);
    } else {
        m_currentInstance = i;
        m_currentInstance.setDataset(m_instances);
        //      m_instances.add(m_currentInstance);
    }

    if (!m_currentInstance.classIsMissing()) {

        //this is where the network updating (and training occurs, for the
        //training set
        resetNetwork();
        calculateOutputs();

        this.calculateErrors();
        updateNetworkWeights(this.m_learningRate, m_momentum);

    }

}