Examples with VectorInstance org.dmg.pmml.VectorInstanceDocument.VectorInstance used on opensource projects

Example 1 with VectorInstance

use of org.dmg.pmml.VectorInstanceDocument.VectorInstance in project knime-core by knime.

the class PMMLSVMTranslator method addVectorDictionary.

/**
 * Adds the vector dictionary to the SVM model.
 *
 * @param svmModel the SVM model to add the dictionary to
 * @param learningCol a list with the names of the learning columns
 */
private void addVectorDictionary(final SupportVectorMachineModel svmModel, final List<String> learningCol) {
    VectorDictionary dict = svmModel.addNewVectorDictionary();
    Set<DoubleVector> supportVectors = new LinkedHashSet<DoubleVector>();
    for (Svm svm : m_svms) {
        supportVectors.addAll(Arrays.asList(svm.getSupportVectors()));
    }
    dict.setNumberOfVectors(BigInteger.valueOf(supportVectors.size()));
    VectorFields vectorFields = dict.addNewVectorFields();
    vectorFields.setNumberOfFields(BigInteger.valueOf(learningCol.size()));
    for (String field : learningCol) {
        vectorFields.addNewFieldRef().setField(m_nameMapper.getDerivedFieldName(field));
    }
    for (DoubleVector vector : supportVectors) {
        VectorInstance vectorInstance = dict.addNewVectorInstance();
        vectorInstance.setId(vector.getClassValue() + CLASS_KEY_SEPARATOR + vector.getKey().getString());
        REALSparseArray pmmlRealSparseArray = vectorInstance.addNewREALSparseArray1();
        int nrValues = vector.getNumberValues();
        pmmlRealSparseArray.setN(BigInteger.valueOf(nrValues));
        // set Indices and Entries
        List<String> indicesList = new ArrayList<String>();
        List<String> entriesList = new ArrayList<String>();
        for (int i = 1; i <= nrValues; i++) {
            indicesList.add(String.valueOf(i));
            entriesList.add(String.valueOf(vector.getValue(i - 1)));
        }
        pmmlRealSparseArray.setIndices(indicesList);
        pmmlRealSparseArray.setREALEntries(entriesList);
    }
}

Example 2 with VectorInstance

use of org.dmg.pmml.VectorInstanceDocument.VectorInstance in project knime-core by knime.

the class PMMLSVMTranslator method initSVMs.

/**
 * @param svmModel
 */
private void initSVMs(final SupportVectorMachineModel svmModel) {
    final Map<String, ArrayList<Double>> vectors = new LinkedHashMap<String, ArrayList<Double>>();
    for (VectorInstance vectorInstance : svmModel.getVectorDictionary().getVectorInstanceArray()) {
        REALSparseArray sparseArray = vectorInstance.getREALSparseArray1();
        ArrayList<Double> values = new ArrayList<Double>();
        for (Object realEntry : sparseArray.getREALEntries()) {
            values.add((Double) realEntry);
        }
        vectors.put(vectorInstance.getId(), values);
    }
    for (SupportVectorMachine supportVectorMachine : svmModel.getSupportVectorMachineArray()) {
        // collect support vectors
        SupportVectors svs = supportVectorMachine.getSupportVectors();
        DoubleVector[] supportVectors = new DoubleVector[svs.getNumberOfSupportVectors().intValue()];
        SupportVector[] supportVectorArray = svs.getSupportVectorArray();
        for (int i = 0; i < supportVectorArray.length; i++) {
            SupportVector supportVector = supportVectorArray[i];
            String vectorId = supportVector.getVectorId();
            String classValue = getClassValue(vectorId);
            supportVectors[i] = new DoubleVector(new RowKey(vectorId), vectors.get(vectorId), classValue);
        }
        Coefficients coef = supportVectorMachine.getCoefficients();
        double threshold = coef.getAbsoluteValue();
        // collect coefficients
        Coefficient[] coefficientArray = coef.getCoefficientArray();
        double[] alpha = new double[coefficientArray.length];
        for (int i = 0; i < coefficientArray.length; i++) {
            /**
             * The alpha in KNIME is always positive. When calculating the
             * distance it is multiplied with a target factor that is 1 or
             * -1 depending on whether we have a positive example or not.
             * (see {@link SVM#getTargetAlphas()} for details)
             */
            alpha[i] = Math.abs(coefficientArray[i].getValue());
        }
        m_svms.add(new Svm(supportVectors, alpha, supportVectorMachine.getTargetCategory(), threshold, m_kernel));
        /* The KNIME internal representation requires two SVMs for the
             * binary classification case. Therefore add a second SVM with the
             * same configuration as the first one except for the negative
             * threshold. */
        if (svmModel.getSupportVectorMachineArray().length == 1) {
            m_svms.add(new Svm(supportVectors.clone(), alpha, supportVectorMachine.getAlternateTargetCategory(), threshold * -1, m_kernel));
        }
    }
}