Example usage for weka.core Instance classAttribute

List of usage examples for weka.core Instance classAttribute

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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:moa.classifiers.featureselection.OFSP.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    double y_t, f_t, denom, m_bias;
    int[] indices = new int[this.numSelectOption.getValue()];
    double[] m_weights;

    if (this.weights == null) {

        this.weights = new double[inst.numValues()];
        for (int i = 0; i < this.weights.length; i++)
            this.weights[i] = this.rand.nextGaussian();
        this.bias = 0.0;
        this.weights = truncate(this.weights, this.numSelectOption.getValue());
    }//from  w  w w  . jav  a  2 s  .  co  m

    if (inst.classAttribute().isNominal()) {
        y_t = (inst.classValue() == 0) ? -1 : 1;
    } else {
        y_t = inst.classValue();
    }
    double[] x_t = inst.toDoubleArray();
    double[] x_hat = inst.toDoubleArray();

    if (this.rand.nextDouble() < this.searchOption.getValue()) {
        int[] indices_perm = perm(inst.numAttributes());
        for (int i = 0; i < this.numSelectOption.getValue(); i++)
            indices[i] = indices_perm[i];

    } else {
        int[] sorted_indices = bubblesort_index(abs_vector(this.weights));

        for (int i = 0; i < inst.numAttributes() - this.numSelectOption.getValue(); i++)
            x_hat[sorted_indices[i]] = 0.0;

        for (int i = 0; i < this.numSelectOption.getValue(); i++)
            indices[i] = sorted_indices[sorted_indices.length - i - 1];
    }

    f_t = 0;
    for (int i = 0; i < this.numSelectOption.getValue(); i++)
        f_t += this.weights[indices[i]] * x_t[indices[i]];
    f_t += this.bias;

    if (f_t * y_t < 0) {

        for (int i = 0; i < x_hat.length; i++) {
            denom = this.numSelectOption.getValue() / x_hat.length * this.searchOption.getValue();
            if (this.weights[i] != 0)
                denom += (1 - this.searchOption.getValue()) * this.weights[i];
            x_hat[i] /= denom;
        }

        m_weights = scalar_vector(y_t * this.stepSizeOption.getValue(), x_hat);
        m_bias = y_t * this.stepSizeOption.getValue() * this.bias;
        m_weights = vector_add(m_weights, this.weights);
        m_bias += m_bias + this.bias;

        m_weights = l2_projection(m_weights, m_bias, this.boundOption.getValue());
        m_weights = truncate(m_weights, this.numSelectOption.getValue());

        for (int i = 0; i < m_weights.length - 1; i++)
            this.weights[i] = m_weights[i];
        this.bias = m_weights[m_weights.length - 1];
    }

}

From source file:moa.classifiers.functions.SGD.java

License:Open Source License

/**
 * Trains the classifier with the given instance.
 *
 * @param instance    the new training instance to include in the model
 *//*from   w  w  w . j  a v a 2s .  com*/
@Override
public void trainOnInstanceImpl(Instance instance) {

    if (m_weights == null) {
        m_weights = new DoubleVector();
        m_bias = 0.0;
    }

    if (!instance.classIsMissing()) {

        double wx = dotProd(instance, m_weights, instance.classIndex());

        double y;
        double z;
        if (instance.classAttribute().isNominal()) {
            y = (instance.classValue() == 0) ? -1 : 1;
            z = y * (wx + m_bias);
        } else {
            y = instance.classValue();
            z = y - (wx + m_bias);
            y = 1;
        }

        // Compute multiplier for weight decay
        double multiplier = 1.0;
        if (m_numInstances == 0) {
            multiplier = 1.0 - (m_learningRate * m_lambda) / m_t;
        } else {
            multiplier = 1.0 - (m_learningRate * m_lambda) / m_numInstances;
        }
        for (int i = 0; i < m_weights.numValues(); i++) {
            m_weights.setValue(i, m_weights.getValue(i) * multiplier);
        }

        // Only need to do the following if the loss is non-zero
        if (m_loss != HINGE || (z < 1)) {

            // Compute Factor for updates
            double factor = m_learningRate * y * dloss(z);

            // Update coefficients for attributes
            int n1 = instance.numValues();
            for (int p1 = 0; p1 < n1; p1++) {
                int indS = instance.index(p1);
                if (indS != instance.classIndex() && !instance.isMissingSparse(p1)) {
                    m_weights.addToValue(indS, factor * instance.valueSparse(p1));
                }
            }

            // update the bias
            m_bias += factor;
        }
        m_t++;
    }
}

From source file:moa.classifiers.functions.SGD.java

License:Open Source License

/**
 * Calculates the class membership probabilities for the given test
 * instance./*  w  ww . j  a  va 2  s .c  om*/
 *
 * @param instance    the instance to be classified
 * @return       predicted class probability distribution
 */
@Override
public double[] getVotesForInstance(Instance inst) {

    if (m_weights == null) {
        return new double[inst.numClasses()];
    }
    double[] result = (inst.classAttribute().isNominal()) ? new double[2] : new double[1];

    double wx = dotProd(inst, m_weights, inst.classIndex());// * m_wScale;
    double z = (wx + m_bias);

    if (inst.classAttribute().isNumeric()) {
        result[0] = z;
        return result;
    }

    if (z <= 0) {
        //  z = 0;
        if (m_loss == LOGLOSS) {
            result[0] = 1.0 / (1.0 + Math.exp(z));
            result[1] = 1.0 - result[0];
        } else {
            result[0] = 1;
        }
    } else {
        if (m_loss == LOGLOSS) {
            result[1] = 1.0 / (1.0 + Math.exp(-z));
            result[0] = 1.0 - result[1];
        } else {
            result[1] = 1;
        }
    }
    return result;
}

From source file:moa.classifiers.functions.SGDMultiClass.java

License:Open Source License

/**
 * Trains the classifier with the given instance.
 *
 * @param instance    the new training instance to include in the model
 *//*from w  w w .  j a  va  2 s  .  c o m*/
@Override
public void trainOnInstanceImpl(Instance instance) {

    if (m_weights == null) {
        int length;
        if (instance.classAttribute().isNominal()) {
            length = instance.numClasses();
        } else {
            length = 1;
        }
        m_weights = new DoubleVector[length];
        m_bias = new double[length];
        for (int i = 0; i < m_weights.length; i++) {
            m_weights[i] = new DoubleVector();
            m_bias[i] = 0.0;
        }
    }
    for (int i = 0; i < m_weights.length; i++) {
        this.trainOnInstanceImpl(instance, i);
    }
    m_t++;
}

From source file:moa.classifiers.functions.SGDMultiClass.java

License:Open Source License

public void trainOnInstanceImpl(Instance instance, int classLabel) {
    if (!instance.classIsMissing()) {

        double wx = dotProd(instance, m_weights[classLabel], instance.classIndex());

        double y;
        double z;
        if (instance.classAttribute().isNominal()) {
            y = (instance.classValue() != classLabel) ? -1 : 1;
            z = y * (wx + m_bias[classLabel]);
        } else {//w w  w .j ava 2 s .c o m
            y = instance.classValue();
            z = y - (wx + m_bias[classLabel]);
            y = 1;
        }

        // Compute multiplier for weight decay
        double multiplier = 1.0;
        if (m_numInstances == 0) {
            multiplier = 1.0 - (m_learningRate * m_lambda) / m_t;
        } else {
            multiplier = 1.0 - (m_learningRate * m_lambda) / m_numInstances;
        }
        for (int i = 0; i < m_weights[classLabel].numValues(); i++) {
            m_weights[classLabel].setValue(i, m_weights[classLabel].getValue(i) * multiplier);
        }

        // Only need to do the following if the loss is non-zero
        if (m_loss != HINGE || (z < 1)) {

            // Compute Factor for updates
            double factor = m_learningRate * y * dloss(z);

            // Update coefficients for attributes
            int n1 = instance.numValues();
            for (int p1 = 0; p1 < n1; p1++) {
                int indS = instance.index(p1);
                if (indS != instance.classIndex() && !instance.isMissingSparse(p1)) {
                    m_weights[classLabel].addToValue(indS, factor * instance.valueSparse(p1));
                }
            }

            // update the bias
            m_bias[classLabel] += factor;
        }

    }
}

From source file:moa.classifiers.functions.SGDMultiClass.java

License:Open Source License

/**
 * Calculates the class membership probabilities for the given test
 * instance./*from  w  ww. j a v a  2s .  com*/
 *
 * @param instance    the instance to be classified
 * @return       predicted class probability distribution
 */
@Override
public double[] getVotesForInstance(Instance inst) {

    if (m_weights == null) {
        return new double[inst.numClasses()];
    }
    double[] result = (inst.classAttribute().isNominal()) ? new double[inst.numClasses()] : new double[1];

    if (inst.classAttribute().isNumeric()) {
        double wx = dotProd(inst, m_weights[0], inst.classIndex());// * m_wScale;
        double z = (wx + m_bias[0]);
        result[0] = z;
        return result;
    }

    for (int i = 0; i < m_weights.length; i++) {
        double wx = dotProd(inst, m_weights[i], inst.classIndex());// * m_wScale;
        double z = (wx + m_bias[i]);
        if (z <= 0) {
            //  z = 0;
            if (m_loss == LOGLOSS) {
                //result[0] = 1.0 / (1.0 + Math.exp(z));
                //result[1] = 1.0 - result[0];
                result[i] = 1.0 - 1.0 / (1.0 + Math.exp(z));
            } else {
                //result[0] = 1;
                result[i] = 0;
            }
        } else {
            if (m_loss == LOGLOSS) {
                //result[1] = 1.0 / (1.0 + Math.exp(-z));
                //result[0] = 1.0 - result[1];
                result[i] = 1.0 / (1.0 + Math.exp(-z));
            } else {
                //result[1] = 1;
                result[i] = 1;
            }
        }
    }
    return result;
}

From source file:moa.classifiers.functions.SGDOld.java

License:Open Source License

/**
 * Trains the classifier with the given instance.
 *
 * @param instance    the new training instance to include in the model
 *///from www .  j av  a  2s.  co m
@Override
public void trainOnInstanceImpl(Instance instance) {

    if (m_weights == null) {
        m_weights = new double[instance.numAttributes() + 1];
    }

    if (!instance.classIsMissing()) {

        double wx = dotProd(instance, m_weights, instance.classIndex());

        double y;
        double z;
        if (instance.classAttribute().isNominal()) {
            y = (instance.classValue() == 0) ? -1 : 1;
            z = y * (wx + m_weights[m_weights.length - 1]);
        } else {
            y = instance.classValue();
            z = y - (wx + m_weights[m_weights.length - 1]);
            y = 1;
        }

        // Compute multiplier for weight decay
        double multiplier = 1.0;
        if (m_numInstances == 0) {
            multiplier = 1.0 - (m_learningRate * m_lambda) / m_t;
        } else {
            multiplier = 1.0 - (m_learningRate * m_lambda) / m_numInstances;
        }
        for (int i = 0; i < m_weights.length - 1; i++) {
            m_weights[i] *= multiplier;
        }

        // Only need to do the following if the loss is non-zero
        if (m_loss != HINGE || (z < 1)) {

            // Compute Factor for updates
            double factor = m_learningRate * y * dloss(z);

            // Update coefficients for attributes
            int n1 = instance.numValues();
            for (int p1 = 0; p1 < n1; p1++) {
                int indS = instance.index(p1);
                if (indS != instance.classIndex() && !instance.isMissingSparse(p1)) {
                    m_weights[indS] += factor * instance.valueSparse(p1);
                }
            }

            // update the bias
            m_weights[m_weights.length - 1] += factor;
        }
        m_t++;
    }
}

From source file:moa.classifiers.functions.SGDOld.java

License:Open Source License

/**
 * Calculates the class membership probabilities for the given test
 * instance./*from  w  w w. j  a v  a2  s  . c  om*/
 *
 * @param instance    the instance to be classified
 * @return       predicted class probability distribution
 */
@Override
public double[] getVotesForInstance(Instance inst) {

    if (m_weights == null) {
        return new double[inst.numAttributes() + 1];
    }
    double[] result = (inst.classAttribute().isNominal()) ? new double[2] : new double[1];

    double wx = dotProd(inst, m_weights, inst.classIndex());// * m_wScale;
    double z = (wx + m_weights[m_weights.length - 1]);

    if (inst.classAttribute().isNumeric()) {
        result[0] = z;
        return result;
    }

    if (z <= 0) {
        //  z = 0;
        if (m_loss == LOGLOSS) {
            result[0] = 1.0 / (1.0 + Math.exp(z));
            result[1] = 1.0 - result[0];
        } else {
            result[0] = 1;
        }
    } else {
        if (m_loss == LOGLOSS) {
            result[1] = 1.0 / (1.0 + Math.exp(-z));
            result[0] = 1.0 - result[1];
        } else {
            result[1] = 1;
        }
    }
    return result;
}

From source file:moa.classifiers.meta.RandomRules.java

License:Open Source License

private Instance transformInstance(Instance inst, int classifierIndex) {
    if (this.listAttributes == null) {
        this.numAttributes = (int) (this.numAttributesPercentageOption.getValue() * inst.numAttributes()
                / 100.0);/*from w  w w  .  ja  va  2 s .  c o  m*/
        this.listAttributes = new int[this.numAttributes][this.ensemble.length];
        this.dataset = new InstancesHeader[this.ensemble.length];
        for (int ensembleIndex = 0; ensembleIndex < this.ensemble.length; ensembleIndex++) {
            for (int attributeIndex = 0; attributeIndex < this.numAttributes; attributeIndex++) {
                boolean isUnique = false;
                while (isUnique == false) {
                    this.listAttributes[attributeIndex][ensembleIndex] = this.classifierRandom
                            .nextInt(inst.numAttributes() - 1);
                    isUnique = true;
                    for (int k = 0; k < attributeIndex; k++) {
                        if (this.listAttributes[attributeIndex][ensembleIndex] == this.listAttributes[k][ensembleIndex]) {
                            isUnique = false;
                            break;
                        }
                    }
                }
                //this.listAttributes[attributeIndex][ensembleIndex] = attributeIndex;
            }
            //Create Header
            FastVector attributes = new FastVector();
            for (int attributeIndex = 0; attributeIndex < this.numAttributes; attributeIndex++) {
                attributes.addElement(inst.attribute(this.listAttributes[attributeIndex][ensembleIndex]));
                System.out.print(this.listAttributes[attributeIndex][ensembleIndex]);
            }
            System.out.println("Number of attributes: " + this.numAttributes + "," + inst.numAttributes());
            attributes.addElement(inst.classAttribute());
            this.dataset[ensembleIndex] = new InstancesHeader(
                    new Instances(getCLICreationString(InstanceStream.class), attributes, 0));
            this.dataset[ensembleIndex].setClassIndex(this.numAttributes);
            this.ensemble[ensembleIndex].setModelContext(this.dataset[ensembleIndex]);
        }
    }
    //Instance instance = new DenseInstance(this.numAttributes+1);
    //instance.setDataset(dataset[classifierIndex]);
    double[] attVals = new double[this.numAttributes + 1];
    for (int attributeIndex = 0; attributeIndex < this.numAttributes; attributeIndex++) {
        //instance.setValue(attributeIndex, inst.value(this.listAttributes[attributeIndex][classifierIndex]));
        attVals[attributeIndex] = inst.value(this.listAttributes[attributeIndex][classifierIndex]);
    }
    Instance instance = new DenseInstance(1.0, attVals);
    instance.setDataset(dataset[classifierIndex]);
    instance.setClassValue(inst.classValue());
    // System.out.println(inst.toString());
    // System.out.println(instance.toString());
    // System.out.println("============");
    return instance;
}

From source file:moa.clusterers.outliers.AnyOut.AnyOut.java

License:Apache License

@Override
protected void ProcessNewStreamObj(Instance i) {
    if (trainingSetSize >= trainingCount) {
        if (trainingSet == null) {
            trainingSet = new DataSet(i.numAttributes() - 1);
        }/*from   w  w  w . j ava  2  s.  co  m*/
        //fill training set
        DataObject o = new DataObject(idCounter++, i);
        trainingSet.addObject(o);
        trainingCount++;
    } else {
        // Train once.
        if (trainingSetSize != -1) {
            anyout.train(trainingSet);
            trainingSet.clear();
            trainingSetSize = -1;
            outlierClass = i.classAttribute().numValues() - 1;
        }

        // Create DataObject from instance.
        DataObject o = new DataObject(idCounter++, i);
        objects.add(o);

        // Count ground truth.
        if (o.getClassLabel() == outlierClass) {
            totalOutliers += 1;
        }

        // Update window objects.
        if (objects.size() > windowSize) {
            DataObject obj = objects.get(0);
            objects.remove(0);
            anyout.removeObject(obj.getId());
            RemoveExpiredOutlier(new Outlier(obj.getInstance(), obj.getId(), obj));
        }

        // Calculate scores for the object.
        anyout.initObject(o.getId(), o.getFeatures());

        // Simulate anyout characteristics.
        double depth = Math.random();
        if (depth < minDepth) {
            depth = minDepth;
        } else if (depth > maxDepth) {
            depth = maxDepth;
        }

        while (anyout.moreImprovementsPossible(o.getId(), depth)) {
            anyout.improveObjectOnce(o.getId());
        }

        // Learn object into ClusTree.
        anyout.learnObject(o.getFeatures());

        // Evaluation of the window objects.
        for (DataObject obj : objects) {
            int id = obj.getId();
            if (anyout.isOutlier(id)) {
                if (obj.isOutiler() == false) { // not already outlier.
                    // Statistics gathering.
                    if (obj.getClassLabel() == outlierClass) {
                        truePositive += 1;
                    } else {
                        falsePositive += 1;
                    }
                    AddOutlier(new Outlier(obj.getInstance(), id, obj));
                    obj.setOutiler(true);
                }
            } else {
                RemoveOutlier(new Outlier(obj.getInstance(), id, obj));
                obj.setOutiler(false);
            }
        }
    }
}