Examples with GaussianDistribution org.dmg.pmml.GaussianDistribution used on opensource projects

Example 1 with GaussianDistribution

use of org.dmg.pmml.GaussianDistribution in project jpmml-r by jpmml.

the class NaiveBayesConverter method encodeModel.

@Override
public Model encodeModel(Schema schema) {
    RGenericVector naiveBayes = getObject();
    RIntegerVector apriori = naiveBayes.getIntegerElement("apriori");
    RGenericVector tables = naiveBayes.getGenericElement("tables");
    CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
    List<? extends Feature> features = schema.getFeatures();
    BayesInputs bayesInputs = new BayesInputs();
    for (int i = 0; i < features.size(); i++) {
        Feature feature = features.get(i);
        String name = feature.getName();
        RDoubleVector table = tables.getDoubleElement(name);
        RStringVector tableRows = table.dimnames(0);
        RStringVector tableColumns = table.dimnames(1);
        BayesInput bayesInput = new BayesInput(name, null, null);
        if (feature instanceof CategoricalFeature) {
            CategoricalFeature categoricalFeature = (CategoricalFeature) feature;
            for (int column = 0; column < tableColumns.size(); column++) {
                TargetValueCounts targetValueCounts = new TargetValueCounts();
                List<Double> probabilities = FortranMatrixUtil.getColumn(table.getValues(), tableRows.size(), tableColumns.size(), column);
                for (int row = 0; row < tableRows.size(); row++) {
                    double count = apriori.getValue(row) * probabilities.get(row);
                    TargetValueCount targetValueCount = new TargetValueCount(tableRows.getValue(row), count);
                    targetValueCounts.addTargetValueCounts(targetValueCount);
                }
                PairCounts pairCounts = new PairCounts(tableColumns.getValue(column), targetValueCounts);
                bayesInput.addPairCounts(pairCounts);
            }
        } else if (feature instanceof ContinuousFeature) {
            ContinuousFeature continuousFeature = (ContinuousFeature) feature;
            TargetValueStats targetValueStats = new TargetValueStats();
            for (int row = 0; row < tableRows.size(); row++) {
                List<Double> stats = FortranMatrixUtil.getRow(table.getValues(), tableRows.size(), 2, row);
                double mean = stats.get(0);
                double variance = Math.pow(stats.get(1), 2);
                TargetValueStat targetValueStat = new TargetValueStat(tableRows.getValue(row), new GaussianDistribution(mean, variance));
                targetValueStats.addTargetValueStats(targetValueStat);
            }
            bayesInput.setTargetValueStats(targetValueStats);
        } else {
            throw new IllegalArgumentException();
        }
        bayesInputs.addBayesInputs(bayesInput);
    }
    BayesOutput bayesOutput = new BayesOutput().setField(categoricalLabel.getName());
    {
        TargetValueCounts targetValueCounts = new TargetValueCounts();
        RStringVector aprioriRows = apriori.dimnames(0);
        for (int row = 0; row < aprioriRows.size(); row++) {
            int count = apriori.getValue(row);
            TargetValueCount targetValueCount = new TargetValueCount(aprioriRows.getValue(row), count);
            targetValueCounts.addTargetValueCounts(targetValueCount);
        }
        bayesOutput.setTargetValueCounts(targetValueCounts);
    }
    NaiveBayesModel naiveBayesModel = new NaiveBayesModel(0d, MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), bayesInputs, bayesOutput).setOutput(ModelUtil.createProbabilityOutput(DataType.DOUBLE, categoricalLabel));
    return naiveBayesModel;
}