Example usage for weka.core Instance setValue

Introduction

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

Prototype

public void setValue(Attribute att, String value);

Source Link

Document

Sets a value of an nominal or string attribute to the given value.

Usage

From source file:moa.clusterer.FeS2.java

License:Apache License

/**
 * /*  ww  w  .j av  a  2s  .c om*/
 * @param c cluster that is being compared against
 * @param x real data instance
 * @return DenseInstance made to work with the outlier-detecting perceptron
 */
private Instance makePerceptronInstance(Riffle c, Instance x) {
    Instance pseudoPoint = new DenseInstance(this.outlierPerceptronTrainingSet.numAttributes());
    pseudoPoint.setDataset(outlierPerceptronTrainingSet);
    double p = c.getInclusionProbability(x);
    double r = (c.getRadius() != 0) ? c.getRadius() : 1;
    //double w = c.getWeight();
    double N = Math.min(c.size(), 1.0 / (this.learningRateAlphaOption.getValue() + 1e-9));
    double d = c.getCenterDistance(x);
    double logP = (p == 0) ? 0 : Math.log(p);
    double logDR = (r == 0 || (d / r) == 0) ? 0 : Math.log(d / r);
    pseudoPoint.setValue(0, logP);
    pseudoPoint.setValue(1, logDR);
    pseudoPoint.setValue(2, logDR * logP);
    pseudoPoint.setValue(3,
            logP - Math.log(1.0 / Math.pow(2.0 * N, this.universalCluster.getHeader().numAttributes())));
    pseudoPoint.setClassValue(0);
    pseudoPoint.setWeight(0.0);
    return pseudoPoint;
}

---

From source file:moa.clusterer.outliers.Sieve.java

License:Apache License

/**
 *
 * @param c cluster that is being compared against
 * @param x real data instance//  ww  w .j a v  a 2s  . c  o m
 * @return DenseInstance made to work with the outlier-detecting perceptron
 */
private Instance makePerceptronInstance(Riffle c, Instance x) {
    Instance pseudoPoint = new DenseInstance(this.outlierPerceptronTrainingSet.numAttributes());
    pseudoPoint.setDataset(outlierPerceptronTrainingSet);
    double p = c.getInclusionProbability(x);
    double r = (c.getRadius() != 0) ? c.getRadius() : 1;
    //double w = c.getWeight();
    double N = Math.min(c.size(), this.cacheSizeOption.getValue());
    double d = c.getCenterDistance(x);
    double logP = (p == 0) ? 0 : Math.log(p);
    double logDR = (r == 0 || (d / r) == 0) ? 0 : Math.log(d / r);
    pseudoPoint.setValue(0, logP);
    pseudoPoint.setValue(1, logDR);
    pseudoPoint.setValue(2, logDR * logP);
    pseudoPoint.setValue(3,
            logP - Math.log(1.0 / Math.pow(2.0 * N, this.universalCluster.getHeader().numAttributes())));
    pseudoPoint.setClassValue(0);
    pseudoPoint.setWeight(0.0);
    return pseudoPoint;
}

---

From source file:moa.streams.clustering.FileStream.java

License:Apache License

protected boolean readNextInstanceFromFile() {
    try {/*from  w w w.  j  a  v a2s . co  m*/

        if (this.instances.readInstance(this.fileReader)) {
            Instance rawInstance = this.instances.instance(0);

            //remove dataset from instance so we can delete attributes
            rawInstance.setDataset(null);
            for (int i = removeAttributes.length - 1; i >= 0; i--) {
                rawInstance.deleteAttributeAt(removeAttributes[i]);
            }
            //set adjusted dataset for instance
            rawInstance.setDataset(filteredDataset);

            if (normalizeOption.isSet() && valuesMinMaxDiff != null) {
                for (int i = 0; i < rawInstance.numAttributes(); i++) {
                    if (valuesMinMaxDiff.get(i)[2] != 1 && // Already normalized
                            valuesMinMaxDiff.get(i)[2] != 0 && // Max. value is 0 (unable to be normalized)
                            i != rawInstance.classIndex()) { // Class label is not subject to be normalized
                        double v = rawInstance.value(i);
                        v = (v - valuesMinMaxDiff.get(i)[0]) / valuesMinMaxDiff.get(i)[2];
                        rawInstance.setValue(i, v);
                    }
                }
            }

            this.lastInstanceRead = rawInstance;
            this.instances.delete(); // keep instances clean
            this.numInstancesRead++;
            return true;
        }
        if (this.fileReader != null) {
            this.fileReader.close();
            this.fileReader = null;
        }
        return false;
    } catch (IOException ioe) {
        throw new RuntimeException("ArffFileStream failed to read instance from stream.", ioe);
    }
}

---

From source file:moa.streams.filters.AddNoiseFilter.java

License:Open Source License

@Override
public Instance nextInstance() {
    Instance inst = (Instance) this.inputStream.nextInstance().copy();
    for (int i = 0; i < inst.numAttributes(); i++) {
        double noiseFrac = i == inst.classIndex() ? this.classNoiseFractionOption.getValue()
                : this.attNoiseFractionOption.getValue();
        if (inst.attribute(i).isNominal()) {
            DoubleVector obs = (DoubleVector) this.attValObservers.get(i);
            if (obs == null) {
                obs = new DoubleVector();
                this.attValObservers.set(i, obs);
            }/*from   w  w w  .ja  v  a  2  s  .  co m*/
            int originalVal = (int) inst.value(i);
            if (!inst.isMissing(i)) {
                obs.addToValue(originalVal, inst.weight());
            }
            if ((this.random.nextDouble() < noiseFrac) && (obs.numNonZeroEntries() > 1)) {
                do {
                    inst.setValue(i, this.random.nextInt(obs.numValues()));
                } while (((int) inst.value(i) == originalVal) || (obs.getValue((int) inst.value(i)) == 0.0));
            }
        } else {
            GaussianEstimator obs = (GaussianEstimator) this.attValObservers.get(i);
            if (obs == null) {
                obs = new GaussianEstimator();
                this.attValObservers.set(i, obs);
            }
            obs.addObservation(inst.value(i), inst.weight());
            inst.setValue(i, inst.value(i) + this.random.nextGaussian() * obs.getStdDev() * noiseFrac);
        }
    }
    return inst;
}

---

From source file:moa.streams.filters.ReplacingMissingValuesFilter.java

License:Open Source License

@Override
public Instance nextInstance() {
    Instance inst = (Instance) this.inputStream.nextInstance().copy();

    // Initialization
    if (numAttributes < 0) {
        numAttributes = inst.numAttributes();
        columnsStatistics = new double[numAttributes];
        numberOfSamples = new long[numAttributes];
        lastNominalValues = new String[numAttributes];
        frequencies = new HashMap[numAttributes];
        for (int i = 0; i < inst.numAttributes(); i++) {
            if (inst.attribute(i).isNominal())
                frequencies[i] = new HashMap<String, Integer>();
        }/*  w w w .j  a  v  a 2 s  . c om*/

        numericalSelectedStrategy = this.numericReplacementStrategyOption.getChosenIndex();
        nominalSelectedStrategy = this.nominalReplacementStrategyOption.getChosenIndex();
    }

    for (int i = 0; i < numAttributes; i++) {

        // ---- Numerical values ----
        if (inst.attribute(i).isNumeric()) {
            // Handle missing value
            if (inst.isMissing(i)) {
                switch (numericalSelectedStrategy) {
                case 0: // NOTHING
                    break;
                case 1: // LAST KNOWN VALUE
                case 2: // MEAN
                case 3: // MAX
                case 4: // MIN
                    inst.setValue(i, columnsStatistics[i]);
                    break;
                case 5: // CONSTANT
                    inst.setValue(i, numericalConstantValueOption.getValue());
                    break;
                default:
                    continue;
                }
            }
            // Update statistics with non-missing values
            else {
                switch (numericalSelectedStrategy) {
                case 1: // LAST KNOWN VALUE
                    columnsStatistics[i] = inst.value(i);
                    break;
                case 2: // MEAN
                    numberOfSamples[i]++;
                    columnsStatistics[i] = columnsStatistics[i]
                            + (inst.value(i) - columnsStatistics[i]) / numberOfSamples[i];
                    break;
                case 3: // MAX
                    columnsStatistics[i] = columnsStatistics[i] < inst.value(i) ? inst.value(i)
                            : columnsStatistics[i];
                    break;
                case 4: // MIN
                    columnsStatistics[i] = columnsStatistics[i] > inst.value(i) ? inst.value(i)
                            : columnsStatistics[i];
                    break;
                default:
                    continue;
                }
            }
        }
        // ---- Nominal values ----
        else if (inst.attribute(i).isNominal()) {
            // Handle missing value
            if (inst.isMissing(i)) {
                switch (nominalSelectedStrategy) {
                case 0: // NOTHING
                    break;
                case 1: // LAST KNOWN VALUE
                    if (lastNominalValues[i] != null) {
                        inst.setValue(i, lastNominalValues[i]);
                    }
                    break;
                case 2: // MODE
                    if (!frequencies[i].isEmpty()) {
                        // Sort the map to get the most frequent value
                        Map<String, Integer> sortedMap = MapUtil.sortByValue(frequencies[i]);
                        inst.setValue(i, sortedMap.entrySet().iterator().next().getKey());
                    }
                    break;
                default:
                    continue;
                }
            }
            // Update statistics with non-missing values
            else {
                switch (nominalSelectedStrategy) {
                case 1: // LAST KNOWN VALUE
                    lastNominalValues[i] = inst.stringValue(i);
                    break;
                case 2: // MODE
                    Integer previousCounter = frequencies[i].get(inst.stringValue(i));
                    if (previousCounter == null)
                        previousCounter = 0;
                    frequencies[i].put(inst.stringValue(i), ++previousCounter);
                    break;
                default:
                    continue;
                }
            }
        }
    }

    return inst;
}

---

From source file:moa.streams.generators.AgrawalGenerator.java

License:Open Source License

@Override
public Instance nextInstance() {
    double salary = 0, commission = 0, hvalue = 0, loan = 0;
    int age = 0, elevel = 0, car = 0, zipcode = 0, hyears = 0, group = 0;
    boolean desiredClassFound = false;
    while (!desiredClassFound) {
        // generate attributes
        salary = 20000.0 + 130000.0 * this.instanceRandom.nextDouble();
        commission = (salary >= 75000.0) ? 0 : (10000.0 + 65000.0 * this.instanceRandom.nextDouble());
        // true to c implementation:
        // if (instanceRandom.nextDouble() < 0.5 && salary < 75000.0)
        // commission = 10000.0 + 65000.0 * instanceRandom.nextDouble();
        age = 20 + this.instanceRandom.nextInt(61);
        elevel = this.instanceRandom.nextInt(5);
        car = this.instanceRandom.nextInt(20);
        zipcode = this.instanceRandom.nextInt(9);
        hvalue = (9.0 - zipcode) * 100000.0 * (0.5 + this.instanceRandom.nextDouble());
        hyears = 1 + this.instanceRandom.nextInt(30);
        loan = this.instanceRandom.nextDouble() * 500000.0;
        // determine class
        group = classificationFunctions[this.functionOption.getValue() - 1].determineClass(salary, commission,
                age, elevel, car, zipcode, hvalue, hyears, loan);
        if (!this.balanceClassesOption.isSet()) {
            desiredClassFound = true;//from  w  ww.j  ava  2 s  . c o m
        } else {
            // balance the classes
            if ((this.nextClassShouldBeZero && (group == 0)) || (!this.nextClassShouldBeZero && (group == 1))) {
                desiredClassFound = true;
                this.nextClassShouldBeZero = !this.nextClassShouldBeZero;
            } // else keep searching
        }
    }
    // perturb values
    if (this.peturbFractionOption.getValue() > 0.0) {
        salary = perturbValue(salary, 20000, 150000);
        if (commission > 0) {
            commission = perturbValue(commission, 10000, 75000);
        }
        age = (int) Math.round(perturbValue(age, 20, 80));
        hvalue = perturbValue(hvalue, (9.0 - zipcode) * 100000.0, 0, 135000);
        hyears = (int) Math.round(perturbValue(hyears, 1, 30));
        loan = perturbValue(loan, 0, 500000);
    }
    // construct instance
    InstancesHeader header = getHeader();
    Instance inst = new DenseInstance(header.numAttributes());
    inst.setValue(0, salary);
    inst.setValue(1, commission);
    inst.setValue(2, age);
    inst.setValue(3, elevel);
    inst.setValue(4, car);
    inst.setValue(5, zipcode);
    inst.setValue(6, hvalue);
    inst.setValue(7, hyears);
    inst.setValue(8, loan);
    inst.setDataset(header);
    inst.setClassValue(group);
    return inst;
}

---

From source file:moa.streams.generators.cd.AbstractConceptDriftGenerator.java

License:Open Source License

public Instance nextInstance() {
    this.numInstances++;
    InstancesHeader header = getHeader();
    Instance inst = new DenseInstance(header.numAttributes());
    inst.setDataset(header);/*w w w . j  a  v  a2  s .  c om*/
    double nextValue = this.nextValue();
    if (this.notBinaryStreamOption.isSet()) {
        inst.setValue(0, nextValue);
    } else {
        inst.setValue(0, this.nextbinaryValue(nextValue));
    }
    //Ground truth
    inst.setValue(1, this.getChange() ? 1 : 0);
    if (this.getChange() == true) {
        //this.clusterEvents.add(new ClusterEvent(this, this.numInstances, "Change", "Drift"));
    }
    inst.setValue(2, nextValue);
    return inst;
}

---

From source file:moa.streams.generators.LEDGenerator.java

License:Open Source License

@Override
public Instance nextInstance() {
    InstancesHeader header = getHeader();
    Instance inst = new DenseInstance(header.numAttributes());
    inst.setDataset(header);/*from w w w  . ja va2s . c  om*/
    int selected = this.instanceRandom.nextInt(10);
    for (int i = 0; i < 7; i++) {
        if ((1 + (this.instanceRandom.nextInt(100))) <= this.noisePercentageOption.getValue()) {
            inst.setValue(i, originalInstances[selected][i] == 0 ? 1 : 0);
        } else {
            inst.setValue(i, originalInstances[selected][i]);
        }
    }
    if (!this.suppressIrrelevantAttributesOption.isSet()) {
        for (int i = 0; i < NUM_IRRELEVANT_ATTRIBUTES; i++) {
            inst.setValue(i + 7, this.instanceRandom.nextInt(2));
        }
    }
    inst.setClassValue(selected);
    return inst;
}

---

From source file:moa.streams.generators.LEDGeneratorDrift.java

License:Open Source License

@Override
public Instance nextInstance() {
    InstancesHeader header = getHeader();
    Instance inst = new DenseInstance(header.numAttributes());
    inst.setDataset(header);/*ww w  . j  av a  2 s  . c o m*/
    int selected = this.instanceRandom.nextInt(10);
    for (int i = 0; i < 7; i++) {
        if ((1 + (this.instanceRandom.nextInt(100))) <= this.noisePercentageOption.getValue()) {
            inst.setValue(this.numberAttribute[i], originalInstances[selected][i] == 0 ? 1 : 0);
        } else {
            inst.setValue(this.numberAttribute[i], originalInstances[selected][i]);
        }
    }
    if (!this.suppressIrrelevantAttributesOption.isSet()) {
        for (int i = 0; i < NUM_IRRELEVANT_ATTRIBUTES; i++) {
            inst.setValue(this.numberAttribute[i + 7], this.instanceRandom.nextInt(2));
        }
    }
    inst.setClassValue(selected);
    return inst;
}

---

From source file:moa.streams.generators.multilabel.MetaMultilabelGenerator.java

License:Open Source License

/**
 * GenerateMLInstance./* w  ww  . j av a 2s. c o m*/
 *
 * @param   Y   a set of label [indices]
 * @return a multit-labelled example
 */
private Instance generateMLInstance(HashSet<Integer> Y) {

    // create a multi-label instance:
    Instance x_ml = new SparseInstance(this.multilabelStreamTemplate.numAttributes());
    x_ml.setDataset(this.multilabelStreamTemplate);

    // set classes
    for (int j = 0; j < m_L; j++) {
        x_ml.setValue(j, 0.0);
    }
    for (int l : Y) {
        x_ml.setValue(l, 1.0);
    }

    // generate binary instances
    Instance x_0 = getNextWithBinary(0);
    Instance x_1 = getNextWithBinary(1);

    // Loop through each feature attribute @warning: assumes class is last index
    for (int a = 0; a < m_A; a++) {

        // The combination is present: use a positive value
        if (Y.containsAll(m_TopCombinations[a])) {
            x_ml.setValue(m_L + a, x_1.value(a));
            //x_ml.setValue(m_L+a,1.0);
        } // The combination is absent: use a negative value
        else {
            x_ml.setValue(m_L + a, x_0.value(a));
            //x_ml.setValue(m_L+a,0.0);
        }
    }

    return x_ml;
}

---

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