Example usage for weka.core Instance numAttributes

Introduction

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

Prototype

public int numAttributes();

Source Link

Document

Returns the number of attributes.

Usage

From source file:moa.classifiers.bayes.NaiveBayes.java

License:Open Source License

public static double[] doNaiveBayesPrediction(Instance inst, DoubleVector observedClassDistribution,
        AutoExpandVector<AttributeClassObserver> attributeObservers) {
    double[] votes = new double[observedClassDistribution.numValues()];
    double observedClassSum = observedClassDistribution.sumOfValues();
    for (int classIndex = 0; classIndex < votes.length; classIndex++) {
        votes[classIndex] = observedClassDistribution.getValue(classIndex) / observedClassSum;
        for (int attIndex = 0; attIndex < inst.numAttributes() - 1; attIndex++) {
            int instAttIndex = modelAttIndexToInstanceAttIndex(attIndex, inst);
            AttributeClassObserver obs = attributeObservers.get(attIndex);
            if ((obs != null) && !inst.isMissing(instAttIndex)) {
                votes[classIndex] *= obs.probabilityOfAttributeValueGivenClass(inst.value(instAttIndex),
                        classIndex);//  w ww.  jav a 2s .co  m
            }
        }
    }
    // TODO: need logic to prevent underflow?
    return votes;
}

---

From source file:moa.classifiers.bayes.NaiveBayes.java

License:Open Source License

public static double[] doNaiveBayesPredictionLog(Instance inst, DoubleVector observedClassDistribution,
        AutoExpandVector<AttributeClassObserver> observers,
        AutoExpandVector<AttributeClassObserver> observers2) {
    AttributeClassObserver obs;/*from   ww w. j  a  v a 2  s.c  om*/
    double[] votes = new double[observedClassDistribution.numValues()];
    double observedClassSum = observedClassDistribution.sumOfValues();
    for (int classIndex = 0; classIndex < votes.length; classIndex++) {
        votes[classIndex] = Math.log10(observedClassDistribution.getValue(classIndex) / observedClassSum);
        for (int attIndex = 0; attIndex < inst.numAttributes() - 1; attIndex++) {
            int instAttIndex = modelAttIndexToInstanceAttIndex(attIndex, inst);
            if (inst.attribute(instAttIndex).isNominal()) {
                obs = observers.get(attIndex);
            } else {
                obs = observers2.get(attIndex);
            }

            if ((obs != null) && !inst.isMissing(instAttIndex)) {
                votes[classIndex] += Math
                        .log10(obs.probabilityOfAttributeValueGivenClass(inst.value(instAttIndex), classIndex));

            }
        }
    }
    return votes;

}

---

From source file:moa.classifiers.bayes.NaiveBayesMultinomial.java

License:Open Source License

/**
 * Trains the classifier with the given instance.
 *
 * @param instance the new training instance to include in the model
 *///from  ww  w  .  ja  v a  2  s. c o m
@Override
public void trainOnInstanceImpl(Instance inst) {
    if (this.reset == true) {
        this.m_numClasses = inst.numClasses();
        double laplace = this.laplaceCorrectionOption.getValue();
        int numAttributes = inst.numAttributes();

        m_probOfClass = new double[m_numClasses];
        Arrays.fill(m_probOfClass, laplace);

        m_classTotals = new double[m_numClasses];
        Arrays.fill(m_classTotals, laplace * numAttributes);

        m_wordTotalForClass = new DoubleVector[m_numClasses];
        for (int i = 0; i < m_numClasses; i++) {
            //Arrays.fill(wordTotal, laplace);
            m_wordTotalForClass[i] = new DoubleVector();
        }
        this.reset = false;
    }
    // Update classifier
    int classIndex = inst.classIndex();
    int classValue = (int) inst.value(classIndex);

    double w = inst.weight();
    m_probOfClass[classValue] += w;

    m_classTotals[classValue] += w * totalSize(inst);
    double total = m_classTotals[classValue];

    for (int i = 0; i < inst.numValues(); i++) {
        int index = inst.index(i);
        if (index != classIndex && !inst.isMissing(i)) {
            //m_wordTotalForClass[index][classValue] += w * inst.valueSparse(i);
            double laplaceCorrection = 0.0;
            if (m_wordTotalForClass[classValue].getValue(index) == 0) {
                laplaceCorrection = this.laplaceCorrectionOption.getValue();
            }
            m_wordTotalForClass[classValue].addToValue(index, w * inst.valueSparse(i) + laplaceCorrection);
        }
    }
}

---

From source file:moa.classifiers.DecisionStump.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    this.observedClassDistribution.addToValue((int) inst.classValue(), inst.weight());
    for (int i = 0; i < inst.numAttributes() - 1; i++) {
        int instAttIndex = modelAttIndexToInstanceAttIndex(i, inst);
        AttributeClassObserver obs = this.attributeObservers.get(i);
        if (obs == null) {
            obs = inst.attribute(instAttIndex).isNominal() ? newNominalClassObserver()
                    : newNumericClassObserver();
            this.attributeObservers.set(i, obs);
        }//from  ww w  .  jav a2  s  .  c o m
        obs.observeAttributeClass(inst.value(instAttIndex), (int) inst.classValue(), inst.weight());
    }
    if (this.trainingWeightSeenByModel - this.weightSeenAtLastSplit >= this.gracePeriodOption.getValue()) {
        this.bestSplit = findBestSplit((SplitCriterion) getPreparedClassOption(this.splitCriterionOption));
        this.weightSeenAtLastSplit = this.trainingWeightSeenByModel;
    }
}

---

From source file:moa.classifiers.featureselection.OFSP.java

License:Open Source License

@Override
public double[] getVotesForInstance(Instance inst) {
    if (this.weights == null)
        return (inst.classAttribute().isNominal()) ? new double[2] : new double[1];

    double[] result = (inst.classAttribute().isNominal()) ? new double[2] : new double[1];
    double f_t = 0;
    int[] indices = new int[this.numSelectOption.getValue()];

    if (this.evalOption.getChosenIndex() == 0) {
        f_t = dot(inst.toDoubleArray(), this.weights);
        f_t += this.bias;
    } else {// www  .ja  va 2 s.  c  o m
        for (int i = 0; i < this.numSelectOption.getValue(); i++)
            indices[i] = this.rand.nextInt(inst.numAttributes());
    }

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

    if (f_t <= 0) {
        result[0] = 1;
    } else {
        result[1] = 1;
    }

    return result;
}

---

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());
    }//  ww  w.  ja  va 2s  .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.Perceptron.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {

    //Init Perceptron
    if (this.reset == true) {
        this.reset = false;
        this.numberAttributes = inst.numAttributes();
        this.numberClasses = inst.numClasses();
        this.weightAttribute = new double[inst.numClasses()][inst.numAttributes()];
        for (int i = 0; i < inst.numClasses(); i++) {
            for (int j = 0; j < inst.numAttributes(); j++) {
                weightAttribute[i][j] = 0.2 * this.classifierRandom.nextDouble() - 0.1;
            }/*from  w w  w.ja v a  2 s. com*/
        }
    }

    double[] preds = new double[inst.numClasses()];
    for (int i = 0; i < inst.numClasses(); i++) {
        preds[i] = prediction(inst, i);
    }
    double learningRatio = learningRatioOption.getValue();

    int actualClass = (int) inst.classValue();
    for (int i = 0; i < inst.numClasses(); i++) {
        double actual = (i == actualClass) ? 1.0 : 0.0;
        double delta = (actual - preds[i]) * preds[i] * (1 - preds[i]);
        for (int j = 0; j < inst.numAttributes() - 1; j++) {
            this.weightAttribute[i][j] += learningRatio * delta * inst.value(j);
        }
        this.weightAttribute[i][inst.numAttributes() - 1] += learningRatio * delta;
    }
}

---

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

License:Open Source License

public double prediction(Instance inst, int classVal) {
    double sum = 0.0;
    for (int i = 0; i < inst.numAttributes() - 1; i++) {
        sum += weightAttribute[classVal][i] * inst.value(i);
    }/*from ww  w . j av a 2  s  .c  o m*/
    sum += weightAttribute[classVal][inst.numAttributes() - 1];
    return 1.0 / (1.0 + Math.exp(-sum));
}

---

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
 */// w w  w .  ja v a  2 s  .  c om
@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 .  java  2  s .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.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;
}

---

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