Examples with ExpressionBasedModel com.sri.ai.praise.core.representation.classbased.expressionbased.api.ExpressionBasedModel used on opensource projects

Example 1 with ExpressionBasedModel

use of com.sri.ai.praise.core.representation.classbased.expressionbased.api.ExpressionBasedModel in project aic-praise by aic-sri-international.

the class AbstractHOGMv1_to_Target_Translator method translate.

// END-Translator
// 
@Override
protected void translate(String inputIdentifier, Reader[] inputModelReaders, PrintWriter[] translatedOutputs) throws Exception {
    // 
    // 1. Get the HOGM FactorNetwork Definition and Parse It
    String hogmv1Model = Util.readAll(inputModelReaders[0]);
    HOGMParserWrapper parser = new HOGMParserWrapper();
    HOGModel parsedModel = parser.parseModel(hogmv1Model);
    ExpressionBasedModel factorsAndTypes = new HOGMExpressionBasedModel(parsedModel);
    // Each additional input is treated as an evidence expression
    List<Expression> evidence = new ArrayList<>();
    if (inputModelReaders.length > 1) {
        for (int i = 1; i < inputModelReaders.length; i++) {
            evidence.add(Expressions.parse(Util.readAll(inputModelReaders[i])));
        }
    }
    translate(inputIdentifier, factorsAndTypes, evidence, translatedOutputs);
}

Example 2 with ExpressionBasedModel

use of com.sri.ai.praise.core.representation.classbased.expressionbased.api.ExpressionBasedModel in project aic-praise by aic-sri-international.

the class Evaluation method getDomainSizes.

private String getDomainSizes(String model) {
    ExpressionBasedModel factorsAndTypes = new HOGMExpressionBasedModel(model);
    Collection<Type> types = factorsAndTypes.getAdditionalTypes();
    List<Integer> domainSizes = mapIntoList(types, t -> t.cardinality().intValueExact());
    String result = Util.join(domainSizes);
    return result;
}

Example 3 with ExpressionBasedModel

use of com.sri.ai.praise.core.representation.classbased.expressionbased.api.ExpressionBasedModel in project aic-praise by aic-sri-international.

the class UsefulOperationsParameterEstimation method buildOptimizedExpressionBasedModel.

/**
 * Return an ExpressionBasedModel with the parameters replaced with the
 * optimized values.
 */
public static ExpressionBasedModel buildOptimizedExpressionBasedModel(Map<Expression, Double> optimizedParameters, ExpressionBasedModel model) {
    List<Expression> factors = buildNewFactors(model);
    Map<String, String> mapFromRandomVariableNameToTypeName = buildNewMapFromRandomVariableNameToTypeName(model);
    Map<String, String> mapFromNonUniquelyNamedConstantNameToTypeName = buildNewMapFromNonUniquelyNamedConstantNameToTypeName(model);
    Map<String, String> mapFromUniquelyNamedConstantNameToTypeName = model.getMapFromUniquelyNamedConstantNameToTypeName();
    Map<String, String> mapFromCategoricalTypeNameToSizeString = model.getMapFromCategoricalTypeNameToSizeString();
    Collection<Type> additionalTypes = model.getAdditionalTypes();
    boolean isKnownToBeBayesianNetwork = model.isKnownToBeBayesianNetwork();
    for (Expression parameter : optimizedParameters.keySet()) {
        Double value = optimizedParameters.get(parameter);
        int k = 0;
        for (Expression factor : factors) {
            String factorString = factor.toString();
            if (factorString.contains(parameter.toString())) {
                Double oneMinusValue = 1.0 - value;
                factorString = factorString.replaceAll("\\b1 - " + parameter.toString() + "\\b", oneMinusValue.toString());
                factorString = factorString.replaceAll("\\b" + parameter.toString() + "\\b", value.toString());
                Expression newFactor = parse(factorString);
                factors.set(k, newFactor);
            }
            k++;
        }
        mapFromNonUniquelyNamedConstantNameToTypeName.remove(parameter.toString());
    }
    ExpressionBasedModel result = new DefaultExpressionBasedModel(factors, mapFromRandomVariableNameToTypeName, mapFromNonUniquelyNamedConstantNameToTypeName, mapFromUniquelyNamedConstantNameToTypeName, mapFromCategoricalTypeNameToSizeString, additionalTypes, isKnownToBeBayesianNetwork);
    return result;
}

Example 4 with ExpressionBasedModel

use of com.sri.ai.praise.core.representation.classbased.expressionbased.api.ExpressionBasedModel in project aic-praise by aic-sri-international.

the class PRAiSEController method importUAIModel.

private void importUAIModel(ActionEvent ae) {
    File uaiModelFile = uaiFileChooser.showOpenDialog(mainStage);
    if (uaiModelFile != null) {
        File uaiEvidenceFile = new File(uaiModelFile.getParent(), uaiModelFile.getName() + ".evid");
        StringWriter hogmWriter = new StringWriter();
        try (BufferedReader uaiModelReader = new BufferedReader(new FileReader(uaiModelFile));
            BufferedReader uaiEvidenceReader = new BufferedReader(new FileReader(uaiEvidenceFile));
            PrintWriter hogmPrintWriter = new PrintWriter(hogmWriter)) {
            UAI_to_HOGMv1_Using_Equalities_Translator translator = new UAI_to_HOGMv1_Using_Equalities_Translator();
            translator.translate(uaiModelFile.getName(), new Reader[] { uaiModelReader, uaiEvidenceReader }, new PrintWriter[] { hogmPrintWriter }, new TranslatorOptions());
            String hogmModel = hogmWriter.toString();
            // For convenience, pull out all possible queries
            HOGMParserWrapper parser = new HOGMParserWrapper();
            HOGModel parsedModel = parser.parseModel(hogmModel);
            ExpressionBasedModel factorsAndTypes = new HOGMExpressionBasedModel(parsedModel);
            List<String> queries = new ArrayList<>(factorsAndTypes.getMapFromRandomVariableNameToTypeName().keySet());
            newModel(hogmModel, queries);
        } catch (Throwable th) {
            FXUtil.exception(th);
        }
    }
}

Example 5 with ExpressionBasedModel

use of com.sri.ai.praise.core.representation.classbased.expressionbased.api.ExpressionBasedModel in project aic-praise by aic-sri-international.

the class ExpressionBayesianModel method learnModelParametersFromCompleteDataAndConvertToAnExpressionBasedModel.

/**
 * Learn the parameters of the model and convert the learned ExpressionBayesianModel into an ExpressionBasedModel, ready for inferences.
 *
 * @param dataset
 * @param mapFromCategoricalTypeNameToSizeString (the user specifies the categorical types used, usually an empty map)
 * @param additionalTypes (the user specifies the additional types used, usually an empty list)
 *
 * @return the final learned and already converted model
 */
public ExpressionBasedModel learnModelParametersFromCompleteDataAndConvertToAnExpressionBasedModel(Dataset dataset, Map<String, String> mapFromCategoricalTypeNameToSizeString, Collection<Type> additionalTypes) {
    ExpressionBayesianModel learnedModel = (ExpressionBayesianModel) this.learnModelParametersFromCompleteData(dataset);
    ExpressionBasedModel convertedModel = learnedModel.convertToAnExpressionBasedModelAfterLearning(mapFromCategoricalTypeNameToSizeString, additionalTypes);
    return convertedModel;
}