Examples with Parameter dr.inference.model.Parameter used on opensource projects

Example 96 with Parameter

use of dr.inference.model.Parameter in project beast-mcmc by beast-dev.

the class ComplexSubstitutionModelParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    Parameter ratesParameter;
    XMLObject cxo;
    if (xo.hasChildNamed(FREQUENCIES)) {
        cxo = xo.getChild(FREQUENCIES);
    } else {
        cxo = xo.getChild(ROOT_FREQUENCIES);
    }
    FrequencyModel freqModel = (FrequencyModel) cxo.getChild(FrequencyModel.class);
    DataType dataType = freqModel.getDataType();
    cxo = xo.getChild(RATES);
    int states = dataType.getStateCount();
    Logger.getLogger("dr.app.beagle.evomodel").info("  Complex Substitution Model (stateCount=" + states + ")");
    ratesParameter = (Parameter) cxo.getChild(Parameter.class);
    int rateCount = (dataType.getStateCount() - 1) * dataType.getStateCount();
    if (ratesParameter == null) {
        if (rateCount == 1) {
        // simplest model for binary traits...
        } else {
            throw new XMLParseException("No rates parameter found in " + getParserName());
        }
    } else if (ratesParameter.getDimension() != rateCount) {
        throw new XMLParseException("Rates parameter in " + getParserName() + " element should have " + rateCount + " dimensions.");
    }
    boolean checkConditioning = xo.getAttribute(CHECK_CONDITIONING, true);
    if (!xo.hasChildNamed(INDICATOR)) {
        if (!checkConditioning) {
            return new ComplexSubstitutionModel(COMPLEX_SUBSTITUTION_MODEL, dataType, freqModel, ratesParameter) {

                protected EigenSystem getDefaultEigenSystem(int stateCount) {
                    return new ComplexColtEigenSystem(stateCount, false, ColtEigenSystem.defaultMaxConditionNumber, ColtEigenSystem.defaultMaxIterations);
                }
            };
        } else {
            return new ComplexSubstitutionModel(COMPLEX_SUBSTITUTION_MODEL, dataType, freqModel, ratesParameter);
        }
    }
    cxo = xo.getChild(INDICATOR);
    Parameter indicatorParameter = (Parameter) cxo.getChild(Parameter.class);
    if (indicatorParameter == null || ratesParameter == null || indicatorParameter.getDimension() != ratesParameter.getDimension())
        throw new XMLParseException("Rates and indicator parameters in " + getParserName() + " element must be the same dimension.");
    if (xo.hasAttribute(BSSVS_TOLERANCE)) {
        double tolerance = xo.getAttribute(BSSVS_TOLERANCE, BayesianStochasticSearchVariableSelection.Utils.getTolerance());
        if (tolerance > BayesianStochasticSearchVariableSelection.Utils.getTolerance()) {
            // Only increase smallest allowed tolerance
            BayesianStochasticSearchVariableSelection.Utils.setTolerance(tolerance);
            Logger.getLogger("dr.app.beagle.evomodel").info("\tIncreasing BSSVS tolerance to " + tolerance);
        }
    }
    if (xo.hasAttribute(BSSVS_SCALAR)) {
        double scalar = xo.getAttribute(BSSVS_SCALAR, BayesianStochasticSearchVariableSelection.Utils.getScalar());
        if (scalar < BayesianStochasticSearchVariableSelection.Utils.getScalar()) {
            BayesianStochasticSearchVariableSelection.Utils.setScalar(scalar);
            Logger.getLogger("dr.app.beagle.evomodel").info("\tDecreasing BSSVS scalar to " + scalar);
        }
    }
    SVSComplexSubstitutionModel model;
    if (!checkConditioning) {
        model = new SVSComplexSubstitutionModel(SVS_COMPLEX_SUBSTITUTION_MODEL, dataType, freqModel, ratesParameter, indicatorParameter) {

            protected EigenSystem getDefaultEigenSystem(int stateCount) {
                return new ComplexColtEigenSystem(stateCount, false, ColtEigenSystem.defaultMaxConditionNumber, ColtEigenSystem.defaultMaxIterations);
            }
        };
    } else {
        model = new SVSComplexSubstitutionModel(SVS_COMPLEX_SUBSTITUTION_MODEL, dataType, freqModel, ratesParameter, indicatorParameter);
    }
    boolean randomize = xo.getAttribute(RANDOMIZE, false);
    if (randomize) {
        // Randomization may need multiple tries
        int tries = 0;
        boolean valid = false;
        while (!valid && tries < maxRandomizationTries) {
            BayesianStochasticSearchVariableSelection.Utils.randomize(indicatorParameter, dataType.getStateCount(), false);
            valid = !Double.isInfinite(model.getLogLikelihood());
            tries++;
        }
        Logger.getLogger("dr.app.beagle.evomodel").info("\tRandomization attempts: " + tries);
    }
    if (!xo.getAttribute(NORMALIZED, true)) {
        model.setNormalization(false);
        Logger.getLogger("dr.app.beagle.evomodel").info("\tNormalization: false");
    }
    Logger.getLogger("dr.app.beagle.evomodel").info("\t\tPlease cite: Edwards, Suchard et al. (2011)\n");
    return model;
}

Example 97 with Parameter

use of dr.inference.model.Parameter in project beast-mcmc by beast-dev.

the class GY94CodonModelParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    Codons codons = Codons.UNIVERSAL;
    if (xo.hasAttribute(GeneticCode.GENETIC_CODE)) {
        String codeStr = xo.getStringAttribute(GeneticCode.GENETIC_CODE);
        codons = Codons.findByName(codeStr);
    }
    Parameter omegaParameter = (Parameter) xo.getElementFirstChild(OMEGA);
    int dim = omegaParameter.getDimension();
    double value = omegaParameter.getParameterValue(dim - 1);
    if (value < 0) {
        throw new RuntimeException("Negative Omega parameter value " + value);
    }
    //END: negative check
    Parameter kappaParameter = (Parameter) xo.getElementFirstChild(KAPPA);
    dim = kappaParameter.getDimension();
    value = kappaParameter.getParameterValue(dim - 1);
    if (value < 0) {
        throw new RuntimeException("Negative kappa parameter value value " + value);
    }
    //END: negative check
    FrequencyModel freqModel = (FrequencyModel) xo.getChild(FrequencyModel.class);
    GY94CodonModel codonModel = new GY94CodonModel(codons, omegaParameter, kappaParameter, freqModel);
    return codonModel;
}

Example 98 with Parameter

use of dr.inference.model.Parameter in project beast-mcmc by beast-dev.

the class GeneralSubstitutionModelParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    Parameter ratesParameter = null;
    FrequencyModel freqModel = null;
    if (xo.hasChildNamed(FREQUENCIES)) {
        XMLObject cxo = xo.getChild(FREQUENCIES);
        freqModel = (FrequencyModel) cxo.getChild(FrequencyModel.class);
    }
    DataType dataType = DataTypeUtils.getDataType(xo);
    if (dataType == null)
        dataType = (DataType) xo.getChild(DataType.class);
    if (dataType == null)
        dataType = freqModel.getDataType();
    if (dataType != freqModel.getDataType()) {
        throw new XMLParseException("Data type of " + getParserName() + " element does not match that of its frequencyModel.");
    }
    XMLObject cxo = xo.getChild(RATES);
    ratesParameter = (Parameter) cxo.getChild(Parameter.class);
    int states = dataType.getStateCount();
    Logger.getLogger("dr.evomodel").info("  General Substitution Model (stateCount=" + states + ")");
    boolean hasRelativeRates = cxo.hasChildNamed(RELATIVE_TO) || (cxo.hasAttribute(RELATIVE_TO) && cxo.getIntegerAttribute(RELATIVE_TO) > 0);
    int nonReversibleRateCount = ((dataType.getStateCount() - 1) * dataType.getStateCount());
    int reversibleRateCount = (nonReversibleRateCount / 2);
    boolean isNonReversible = ratesParameter.getDimension() == nonReversibleRateCount;
    boolean hasIndicator = xo.hasChildNamed(INDICATOR);
    if (!hasRelativeRates) {
        Parameter indicatorParameter = null;
        if (ratesParameter.getDimension() != reversibleRateCount && ratesParameter.getDimension() != nonReversibleRateCount) {
            throw new XMLParseException("Rates parameter in " + getParserName() + " element should have " + (reversibleRateCount) + " dimensions for reversible model or " + nonReversibleRateCount + " dimensions for non-reversible. " + "However parameter dimension is " + ratesParameter.getDimension());
        }
        if (hasIndicator) {
            // this is using BSSVS
            cxo = xo.getChild(INDICATOR);
            indicatorParameter = (Parameter) cxo.getChild(Parameter.class);
            if (indicatorParameter.getDimension() != ratesParameter.getDimension()) {
                throw new XMLParseException("Rates and indicator parameters in " + getParserName() + " element must be the same dimension.");
            }
            boolean randomize = xo.getAttribute(ComplexSubstitutionModelParser.RANDOMIZE, false);
            if (randomize) {
                BayesianStochasticSearchVariableSelection.Utils.randomize(indicatorParameter, dataType.getStateCount(), !isNonReversible);
            }
        }
        if (isNonReversible) {
            //                if (xo.hasChildNamed(ROOT_FREQ)) {
            //                    cxo = xo.getChild(ROOT_FREQ);
            //                    FrequencyModel rootFreq = (FrequencyModel) cxo.getChild(FrequencyModel.class);
            //
            //                    if (dataType != rootFreq.getDataType()) {
            //                        throw new XMLParseException("Data type of " + getParserName() + " element does not match that of its rootFrequencyModel.");
            //                    }
            //
            //                    Logger.getLogger("dr.evomodel").info("  Using BSSVS Complex Substitution Model");
            //                    return new SVSComplexSubstitutionModel(getParserName(), dataType, freqModel, ratesParameter, indicatorParameter);
            //
            //                } else {
            //                    throw new XMLParseException("Non-reversible model missing " + ROOT_FREQ + " element");
            //                }
            Logger.getLogger("dr.evomodel").info("  Using BSSVS Complex Substitution Model");
            return new SVSComplexSubstitutionModel(getParserName(), dataType, freqModel, ratesParameter, indicatorParameter);
        } else {
            Logger.getLogger("dr.evomodel").info("  Using BSSVS General Substitution Model");
            return new SVSGeneralSubstitutionModel(getParserName(), dataType, freqModel, ratesParameter, indicatorParameter);
        }
    } else {
        if (ratesParameter.getDimension() != reversibleRateCount - 1) {
            throw new XMLParseException("Rates parameter in " + getParserName() + " element should have " + (reversibleRateCount - 1) + " dimensions. However parameter dimension is " + ratesParameter.getDimension());
        }
        int relativeTo = 0;
        if (hasRelativeRates) {
            relativeTo = cxo.getIntegerAttribute(RELATIVE_TO) - 1;
        }
        if (relativeTo < 0 || relativeTo >= reversibleRateCount) {
            throw new XMLParseException(RELATIVE_TO + " must be 1 or greater");
        } else {
            int t = relativeTo;
            int s = states - 1;
            int row = 0;
            while (t >= s) {
                t -= s;
                s -= 1;
                row += 1;
            }
            int col = t + row + 1;
            Logger.getLogger("dr.evomodel").info("  Rates relative to " + dataType.getCode(row) + "<->" + dataType.getCode(col));
        }
        if (ratesParameter == null) {
            if (reversibleRateCount == 1) {
            // simplest model for binary traits...
            } else {
                throw new XMLParseException("No rates parameter found in " + getParserName());
            }
        }
        return new GeneralSubstitutionModel(getParserName(), dataType, freqModel, ratesParameter, relativeTo);
    }
}

Example 99 with Parameter

use of dr.inference.model.Parameter in project beast-mcmc by beast-dev.

the class MarkovModulatedHiddenClassRewardParser method parseXMLObject.

@Override
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    MarkovModulatedSubstitutionModel substitutionModel = (MarkovModulatedSubstitutionModel) xo.getChild(MarkovModulatedSubstitutionModel.class);
    HiddenDataType hiddenDataType = (HiddenDataType) substitutionModel.getDataType();
    int classNumber = xo.getIntegerAttribute(CLASS_NUMBER);
    int hiddenClassCount = hiddenDataType.getHiddenClassCount();
    if (classNumber < 1 || classNumber > hiddenClassCount) {
        throw new XMLParseException("Invalid class number in " + xo.getId());
    }
    // Use zero-indexed number
    classNumber--;
    int stateCount = hiddenDataType.getStateCount() / hiddenClassCount;
    // Construct reward parameter
    Parameter parameter = new Parameter.Default(stateCount * hiddenClassCount, 0.0);
    for (int i = 0; i < stateCount; ++i) {
        parameter.setParameterValue(i + classNumber * stateCount, 1.0);
    }
    if (xo.hasAttribute(NAME)) {
        parameter.setId((String) xo.getAttribute(NAME));
    } else {
        parameter.setId(substitutionModel.getId() + "_" + Integer.toString(classNumber + 1));
    }
    return parameter;
}

Example 100 with Parameter

use of dr.inference.model.Parameter in project beast-mcmc by beast-dev.

the class MarkovModulatedSubstitutionModelParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    DataType dataType = DataTypeUtils.getDataType(xo);
    if (!(dataType instanceof HiddenDataType)) {
        throw new XMLParseException("Must construct " + MARKOV_MODULATED_MODEL + " with hidden data types");
    }
    Parameter switchingRates = (Parameter) xo.getElementFirstChild(SWITCHING_RATES);
    List<SubstitutionModel> substModels = new ArrayList<SubstitutionModel>();
    for (int i = 0; i < xo.getChildCount(); i++) {
        Object cxo = xo.getChild(i);
        if (cxo instanceof SubstitutionModel) {
            substModels.add((SubstitutionModel) cxo);
        }
    }
    boolean geometricRates = xo.getAttribute(GEOMETRIC_RATES, false);
    Parameter rateScalar = xo.hasChildNamed(RATE_SCALAR) ? (Parameter) xo.getChild(RATE_SCALAR).getChild(Parameter.class) : null;
    SiteRateModel siteRateModel = (SiteRateModel) xo.getChild(SiteRateModel.class);
    if (siteRateModel != null) {
        if (siteRateModel.getCategoryCount() != substModels.size() && substModels.size() % siteRateModel.getCategoryCount() != 0) {
            throw new XMLParseException("Number of gamma categories must equal number of substitution models in " + xo.getId());
        }
    }
    MarkovModulatedSubstitutionModel mmsm = new MarkovModulatedSubstitutionModel(xo.getId(), substModels, switchingRates, dataType, null, rateScalar, geometricRates, siteRateModel);
    if (xo.getAttribute(RENORMALIZE, false)) {
        mmsm.setNormalization(true);
    }
    return mmsm;
}