{
rules =
new XMLSyntaxRule[] {
AttributeRule.newBooleanRule(NON_INFORMATIVE, true),
AttributeRule.newDoubleRule(DF, true),
new ElementRule(
SCALE_MATRIX,
new XMLSyntaxRule[] {new ElementRule(MatrixParameter.class)},
true),
new ElementRule(
DATA,
new XMLSyntaxRule[] {
new ElementRule(MatrixParameter.class, 1, Integer.MAX_VALUE)
})
};
}
/** @author Marc Suchard */
public class MultivariateDistributionLikelihood extends AbstractDistributionLikelihood {
public static final String MVN_PRIOR = "multivariateNormalPrior";
public static final String MVN_MEAN = "meanParameter";
public static final String MVN_PRECISION = "precisionParameter";
public static final String MVN_CV = "coefficientOfVariation";
public static final String WISHART_PRIOR = "multivariateWishartPrior";
public static final String INV_WISHART_PRIOR = "multivariateInverseWishartPrior";
public static final String DIRICHLET_PRIOR = "dirichletPrior";
public static final String DF = "df";
public static final String SCALE_MATRIX = "scaleMatrix";
public static final String MVGAMMA_PRIOR = "multivariateGammaPrior";
public static final String MVGAMMA_SHAPE = "shapeParameter";
public static final String MVGAMMA_SCALE = "scaleParameter";
public static final String COUNTS = "countsParameter";
public static final String NON_INFORMATIVE = "nonInformative";
public static final String MULTIVARIATE_LIKELIHOOD = "multivariateDistributionLikelihood";
public static final String DATA_AS_MATRIX = "dataAsMatrix";
// public static final String TREE_TRAIT = "treeTraitNormalDistribution";
public static final String TREE_TRAIT = "treeTraitNormalDistributionLikelihood";
public static final String TREE_TRAIT_NORMAL = "treeTraitNormalDistribution";
public static final String ROOT_VALUE = "rootValue";
public static final String CONDITION = "conditionOnRoot";
public static final String DATA = "data";
private final MultivariateDistribution distribution;
private final Transform[] transforms;
public MultivariateDistributionLikelihood(
String name, ParametricMultivariateDistributionModel model) {
this(name, model, null);
}
public MultivariateDistributionLikelihood(
String name, ParametricMultivariateDistributionModel model, Transform[] transforms) {
super(model);
this.distribution = model;
this.transforms = transforms;
}
public MultivariateDistributionLikelihood(String name, MultivariateDistribution distribution) {
this(name, distribution, null);
}
public MultivariateDistributionLikelihood(
String name, MultivariateDistribution distribution, Transform[] transforms) {
super(new DefaultModel(name));
this.distribution = distribution;
this.transforms = transforms;
}
public MultivariateDistributionLikelihood(MultivariateDistribution distribution) {
this(distribution, null);
}
public MultivariateDistributionLikelihood(
MultivariateDistribution distribution, Transform[] transforms) {
this(distribution.getType(), distribution, transforms);
}
public String toString() {
return getClass().getName() + "(" + getLogLikelihood() + ")";
}
public double calculateLogLikelihood() {
double logL = 0.0;
for (Attribute<double[]> data : dataList) {
double[] x = data.getAttributeValue();
if (transforms != null) {
double[] y = new double[x.length];
for (int i = 0; i < x.length; ++i) {
logL += transforms[i].getLogJacobian(x[i]);
y[i] = transforms[i].transform(x[i]);
}
logL += distribution.logPdf(y);
} else {
logL += distribution.logPdf(x);
}
}
return logL;
}
@Override
public void addData(Attribute<double[]> data) {
super.addData(data);
if (data instanceof Variable && getModel() instanceof DefaultModel) {
((DefaultModel) getModel()).addVariable((Variable) data);
}
}
public MultivariateDistribution getDistribution() {
return distribution;
}
public static Transform[] parseListOfTransforms(XMLObject xo, int maxDim)
throws XMLParseException {
Transform[] transforms = null;
boolean anyTransforms = false;
for (int i = 0; i < xo.getChildCount(); ++i) {
if (xo.getChild(i) instanceof Transform.ParsedTransform) {
Transform.ParsedTransform t = (Transform.ParsedTransform) xo.getChild(i);
if (transforms == null) {
transforms = Transform.Util.getListOfNoTransforms(maxDim);
}
t.end = Math.max(t.end, maxDim);
if (t.start < 0 || t.end < 0 || t.start > t.end) {
throw new XMLParseException("Invalid bounds for transform in " + xo.getId());
}
for (int j = t.start; j < t.end; j += t.every) {
transforms[j] = t.transform;
anyTransforms = true;
}
}
}
if (anyTransforms) {
StringBuilder sb =
new StringBuilder("Using distributional transforms in " + xo.getId() + "\n");
for (int i = 0; i < transforms.length; ++i) {
if (transforms[i] != Transform.NONE) {
sb.append("\t")
.append(transforms[i].getTransformName())
.append(" on index ")
.append(i + 1)
.append("\n");
}
}
sb.append("Please cite:\n").append(Citable.Utils.getCitationString(Transform.LOG));
Logger.getLogger("dr.utils.Transform").info(sb.toString());
}
return transforms;
}
public static XMLObjectParser DIRICHLET_PRIOR_PARSER =
new AbstractXMLObjectParser() {
public String getParserName() {
return DIRICHLET_PRIOR;
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
XMLObject cxo = xo.getChild(COUNTS);
Parameter counts = (Parameter) cxo.getChild(Parameter.class);
DirichletDistribution dirichlet = new DirichletDistribution(counts.getParameterValues());
MultivariateDistributionLikelihood likelihood =
new MultivariateDistributionLikelihood(dirichlet);
cxo = xo.getChild(DATA);
for (int j = 0; j < cxo.getChildCount(); j++) {
if (cxo.getChild(j) instanceof Parameter) {
likelihood.addData((Parameter) cxo.getChild(j));
} else {
throw new XMLParseException(
"illegal element in " + xo.getName() + " element " + cxo.getName());
}
}
return likelihood;
}
public XMLSyntaxRule[] getSyntaxRules() {
return rules;
}
private final XMLSyntaxRule[] rules = {
new ElementRule(COUNTS, new XMLSyntaxRule[] {new ElementRule(Parameter.class)}),
new ElementRule(
DATA, new XMLSyntaxRule[] {new ElementRule(Parameter.class)}, 1, Integer.MAX_VALUE),
};
public String getParserDescription() {
return "Calculates the likelihood of some data under a Dirichlet distribution.";
}
public Class getReturnType() {
return Likelihood.class;
}
};
public static XMLObjectParser INV_WISHART_PRIOR_PARSER =
new AbstractXMLObjectParser() {
public String getParserName() {
return INV_WISHART_PRIOR;
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
int df = xo.getIntegerAttribute(DF);
XMLObject cxo = xo.getChild(SCALE_MATRIX);
MatrixParameter scaleMatrix = (MatrixParameter) cxo.getChild(MatrixParameter.class);
InverseWishartDistribution invWishart =
new InverseWishartDistribution(df, scaleMatrix.getParameterAsMatrix());
MultivariateDistributionLikelihood likelihood =
new MultivariateDistributionLikelihood(invWishart);
cxo = xo.getChild(DATA);
for (int j = 0; j < cxo.getChildCount(); j++) {
if (cxo.getChild(j) instanceof MatrixParameter) {
likelihood.addData((MatrixParameter) cxo.getChild(j));
} else {
throw new XMLParseException(
"illegal element in " + xo.getName() + " element " + cxo.getName());
}
}
return likelihood;
}
public XMLSyntaxRule[] getSyntaxRules() {
return rules;
}
private final XMLSyntaxRule[] rules = {
AttributeRule.newDoubleRule(DF),
new ElementRule(
SCALE_MATRIX, new XMLSyntaxRule[] {new ElementRule(MatrixParameter.class)}),
};
public String getParserDescription() {
return "Calculates the likelihood of some data under an Inverse-Wishart distribution.";
}
public Class getReturnType() {
return Likelihood.class;
}
};
public static XMLObjectParser WISHART_PRIOR_PARSER =
new AbstractXMLObjectParser() {
public String getParserName() {
return WISHART_PRIOR;
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
MultivariateDistributionLikelihood likelihood;
if (xo.hasAttribute(NON_INFORMATIVE) && xo.getBooleanAttribute(NON_INFORMATIVE)) {
// Make non-informative settings
XMLObject cxo = xo.getChild(DATA);
int dim = ((MatrixParameter) cxo.getChild(0)).getColumnDimension();
likelihood = new MultivariateDistributionLikelihood(new WishartDistribution(dim));
} else {
if (!xo.hasAttribute(DF) || !xo.hasChildNamed(SCALE_MATRIX)) {
throw new XMLParseException("Must specify both a df and scaleMatrix");
}
double df = xo.getDoubleAttribute(DF);
XMLObject cxo = xo.getChild(SCALE_MATRIX);
MatrixParameter scaleMatrix = (MatrixParameter) cxo.getChild(MatrixParameter.class);
likelihood =
new MultivariateDistributionLikelihood(
new WishartDistribution(df, scaleMatrix.getParameterAsMatrix()));
}
XMLObject cxo = xo.getChild(DATA);
for (int j = 0; j < cxo.getChildCount(); j++) {
if (cxo.getChild(j) instanceof MatrixParameter) {
likelihood.addData((MatrixParameter) cxo.getChild(j));
} else {
throw new XMLParseException(
"illegal element in " + xo.getName() + " element " + cxo.getName());
}
}
return likelihood;
}
public XMLSyntaxRule[] getSyntaxRules() {
return rules;
}
private final XMLSyntaxRule[] rules;
{
rules =
new XMLSyntaxRule[] {
AttributeRule.newBooleanRule(NON_INFORMATIVE, true),
AttributeRule.newDoubleRule(DF, true),
new ElementRule(
SCALE_MATRIX,
new XMLSyntaxRule[] {new ElementRule(MatrixParameter.class)},
true),
new ElementRule(
DATA,
new XMLSyntaxRule[] {
new ElementRule(MatrixParameter.class, 1, Integer.MAX_VALUE)
})
};
}
public String getParserDescription() {
return "Calculates the likelihood of some data under a Wishart distribution.";
}
public Class getReturnType() {
return Likelihood.class;
}
};
public static XMLObjectParser MULTIVARIATE_LIKELIHOOD_PARSER =
new AbstractXMLObjectParser() {
public String getParserName() {
return MULTIVARIATE_LIKELIHOOD;
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
XMLObject cxo = xo.getChild(DistributionLikelihoodParser.DISTRIBUTION);
ParametricMultivariateDistributionModel distribution =
(ParametricMultivariateDistributionModel)
cxo.getChild(ParametricMultivariateDistributionModel.class);
// Parse transforms here
int maxDim = distribution.getMean().length;
Transform[] transforms = parseListOfTransforms(xo, maxDim);
MultivariateDistributionLikelihood likelihood =
new MultivariateDistributionLikelihood(xo.getId(), distribution, transforms);
boolean dataAsMatrix = xo.getAttribute(DATA_AS_MATRIX, false);
cxo = xo.getChild(DATA);
if (cxo != null) {
for (int j = 0; j < cxo.getChildCount(); j++) {
if (cxo.getChild(j) instanceof Parameter) {
Parameter data = (Parameter) cxo.getChild(j);
if (data instanceof MatrixParameter) {
MatrixParameter matrix = (MatrixParameter) data;
if (dataAsMatrix) {
likelihood.addData(matrix);
} else {
if (matrix.getParameter(0).getDimension() != distribution.getMean().length)
throw new XMLParseException(
"dim("
+ data.getStatisticName()
+ ") = "
+ matrix.getParameter(0).getDimension()
+ " is not equal to dim("
+ distribution.getType()
+ ") = "
+ distribution.getMean().length
+ " in "
+ xo.getName()
+ "element");
for (int i = 0; i < matrix.getParameterCount(); i++) {
likelihood.addData(matrix.getParameter(i));
}
}
} else {
if (data.getDimension() != distribution.getMean().length)
throw new XMLParseException(
"dim("
+ data.getStatisticName()
+ ") = "
+ data.getDimension()
+ " is not equal to dim("
+ distribution.getType()
+ ") = "
+ distribution.getMean().length
+ " in "
+ xo.getName()
+ "element");
likelihood.addData(data);
}
} else {
throw new XMLParseException("illegal element in " + xo.getName() + " element");
}
}
}
return likelihood;
}
public XMLSyntaxRule[] getSyntaxRules() {
return rules;
}
private final XMLSyntaxRule[] rules = {
new ElementRule(
DistributionLikelihoodParser.DISTRIBUTION,
new XMLSyntaxRule[] {new ElementRule(ParametricMultivariateDistributionModel.class)}),
AttributeRule.newBooleanRule(DATA_AS_MATRIX, true),
new ElementRule(Transform.ParsedTransform.class, 0, Integer.MAX_VALUE),
new ElementRule(
DATA,
new XMLSyntaxRule[] {new ElementRule(Parameter.class, 1, Integer.MAX_VALUE)},
true)
};
public String getParserDescription() {
return "Calculates the likelihood of some data under a given multivariate distribution.";
}
public Class getReturnType() {
return MultivariateDistributionLikelihood.class;
}
};
public static XMLObjectParser MVN_PRIOR_PARSER =
new AbstractXMLObjectParser() {
public String getParserName() {
return MVN_PRIOR;
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
XMLObject cxo = xo.getChild(MVN_MEAN);
Parameter mean = (Parameter) cxo.getChild(Parameter.class);
cxo = xo.getChild(MVN_PRECISION);
MatrixParameter precision = (MatrixParameter) cxo.getChild(MatrixParameter.class);
if (mean.getDimension() != precision.getRowDimension()
|| mean.getDimension() != precision.getColumnDimension())
throw new XMLParseException(
"Mean and precision have wrong dimensions in " + xo.getName() + " element");
Transform[] transforms = parseListOfTransforms(xo, mean.getDimension());
MultivariateDistributionLikelihood likelihood =
new MultivariateDistributionLikelihood(
new MultivariateNormalDistribution(
mean.getParameterValues(), precision.getParameterAsMatrix()),
transforms);
cxo = xo.getChild(DATA);
if (cxo != null) {
for (int j = 0; j < cxo.getChildCount(); j++) {
if (cxo.getChild(j) instanceof Parameter) {
Parameter data = (Parameter) cxo.getChild(j);
if (data instanceof MatrixParameter) {
MatrixParameter matrix = (MatrixParameter) data;
if (matrix.getParameter(0).getDimension() != mean.getDimension())
throw new XMLParseException(
"dim("
+ data.getStatisticName()
+ ") = "
+ matrix.getParameter(0).getDimension()
+ " is not equal to dim("
+ mean.getStatisticName()
+ ") = "
+ mean.getDimension()
+ " in "
+ xo.getName()
+ "element");
for (int i = 0; i < matrix.getParameterCount(); i++) {
likelihood.addData(matrix.getParameter(i));
}
} else {
if (data.getDimension() != mean.getDimension())
throw new XMLParseException(
"dim("
+ data.getStatisticName()
+ ") = "
+ data.getDimension()
+ " is not equal to dim("
+ mean.getStatisticName()
+ ") = "
+ mean.getDimension()
+ " in "
+ xo.getName()
+ "element");
likelihood.addData(data);
}
} else {
throw new XMLParseException("illegal element in " + xo.getName() + " element");
}
}
}
return likelihood;
}
public XMLSyntaxRule[] getSyntaxRules() {
return rules;
}
private final XMLSyntaxRule[] rules = {
new ElementRule(MVN_MEAN, new XMLSyntaxRule[] {new ElementRule(Parameter.class)}),
new ElementRule(
MVN_PRECISION, new XMLSyntaxRule[] {new ElementRule(MatrixParameter.class)}),
new ElementRule(Transform.ParsedTransform.class, 0, Integer.MAX_VALUE),
new ElementRule(
DATA,
new XMLSyntaxRule[] {new ElementRule(Parameter.class, 1, Integer.MAX_VALUE)},
true)
};
public String getParserDescription() {
return "Calculates the likelihood of some data under a given multivariate-normal distribution.";
}
public Class getReturnType() {
return MultivariateDistributionLikelihood.class;
}
};
public static XMLObjectParser MVGAMMA_PRIOR_PARSER =
new AbstractXMLObjectParser() {
public String getParserName() {
return MVGAMMA_PRIOR;
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
double[] shape;
double[] scale;
if (xo.hasChildNamed(MVGAMMA_SHAPE)) {
XMLObject cxo = xo.getChild(MVGAMMA_SHAPE);
shape = ((Parameter) cxo.getChild(Parameter.class)).getParameterValues();
cxo = xo.getChild(MVGAMMA_SCALE);
scale = ((Parameter) cxo.getChild(Parameter.class)).getParameterValues();
if (shape.length != scale.length)
throw new XMLParseException(
"Shape and scale have wrong dimensions in " + xo.getName() + " element");
} else {
XMLObject cxo = xo.getChild(MVN_MEAN);
double[] mean = ((Parameter) cxo.getChild(Parameter.class)).getParameterValues();
cxo = xo.getChild(MVN_CV);
double[] cv = ((Parameter) cxo.getChild(Parameter.class)).getParameterValues();
if (mean.length != cv.length)
throw new XMLParseException(
"Mean and CV have wrong dimensions in " + xo.getName() + " element");
final int dim = mean.length;
shape = new double[dim];
scale = new double[dim];
for (int i = 0; i < dim; i++) {
double c2 = cv[i] * cv[i];
shape[i] = 1.0 / c2;
scale[i] = c2 * mean[i];
}
}
MultivariateDistributionLikelihood likelihood =
new MultivariateDistributionLikelihood(
new MultivariateGammaDistribution(shape, scale));
XMLObject cxo = xo.getChild(DATA);
for (int j = 0; j < cxo.getChildCount(); j++) {
if (cxo.getChild(j) instanceof Parameter) {
Parameter data = (Parameter) cxo.getChild(j);
likelihood.addData(data);
if (data.getDimension() != shape.length)
throw new XMLParseException(
"dim("
+ data.getStatisticName()
+ ") != "
+ shape.length
+ " in "
+ xo.getName()
+ "element");
} else {
throw new XMLParseException("illegal element in " + xo.getName() + " element");
}
}
return likelihood;
}
public XMLSyntaxRule[] getSyntaxRules() {
return rules;
}
private final XMLSyntaxRule[] rules = {
new XORRule(
new ElementRule(
MVGAMMA_SHAPE, new XMLSyntaxRule[] {new ElementRule(Parameter.class)}),
new ElementRule(MVN_MEAN, new XMLSyntaxRule[] {new ElementRule(Parameter.class)})),
new XORRule(
new ElementRule(
MVGAMMA_SCALE, new XMLSyntaxRule[] {new ElementRule(Parameter.class)}),
new ElementRule(MVN_CV, new XMLSyntaxRule[] {new ElementRule(Parameter.class)})),
new ElementRule(
DATA, new XMLSyntaxRule[] {new ElementRule(Parameter.class, 1, Integer.MAX_VALUE)})
};
public String getParserDescription() {
return "Calculates the likelihood of some data under a given multivariate-gamma distribution.";
}
public Class getReturnType() {
return MultivariateDistributionLikelihood.class;
}
};
public static XMLObjectParser TREE_TRAIT_MODEL =
new AbstractXMLObjectParser() {
public String getParserName() {
return TREE_TRAIT_NORMAL;
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
boolean conditionOnRoot = xo.getAttribute(CONDITION, false);
FullyConjugateMultivariateTraitLikelihood traitModel =
(FullyConjugateMultivariateTraitLikelihood)
xo.getChild(FullyConjugateMultivariateTraitLikelihood.class);
TreeTraitNormalDistributionModel treeTraitModel;
if (xo.getChild(ROOT_VALUE) != null) {
XMLObject cxo = xo.getChild(ROOT_VALUE);
Parameter rootValue = (Parameter) cxo.getChild(Parameter.class);
treeTraitModel =
new TreeTraitNormalDistributionModel(traitModel, rootValue, conditionOnRoot);
} else {
treeTraitModel = new TreeTraitNormalDistributionModel(traitModel, conditionOnRoot);
}
return treeTraitModel;
}
public XMLSyntaxRule[] getSyntaxRules() {
return rules;
}
private final XMLSyntaxRule[] rules = {
AttributeRule.newBooleanRule(CONDITION, true),
new ElementRule(FullyConjugateMultivariateTraitLikelihood.class)
};
public String getParserDescription() {
return "Parses TreeTraitNormalDistributionModel";
}
public Class getReturnType() {
return TreeTraitNormalDistributionModel.class;
}
};
public static XMLObjectParser TREE_TRAIT_DISTRIBUTION =
new AbstractXMLObjectParser() {
public String getParserName() {
return TREE_TRAIT;
}
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
/*
boolean conditionOnRoot = xo.getAttribute(CONDITION, false);
FullyConjugateMultivariateTraitLikelihood traitModel = (FullyConjugateMultivariateTraitLikelihood)
xo.getChild(FullyConjugateMultivariateTraitLikelihood.class);
*/
TreeTraitNormalDistributionModel treeTraitModel =
(TreeTraitNormalDistributionModel)
xo.getChild(TreeTraitNormalDistributionModel.class);
MultivariateDistributionLikelihood likelihood =
new MultivariateDistributionLikelihood(
// new TreeTraitNormalDistributionModel(traitModel, conditionOnRoot)
treeTraitModel);
XMLObject cxo = xo.getChild(DATA);
for (int j = 0; j < cxo.getChildCount(); j++) {
if (cxo.getChild(j) instanceof Parameter) {
likelihood.addData((Parameter) cxo.getChild(j));
} else {
throw new XMLParseException(
"illegal element in " + xo.getName() + " element " + cxo.getName());
}
}
return likelihood;
}
public XMLSyntaxRule[] getSyntaxRules() {
return rules;
}
private final XMLSyntaxRule[] rules = {
// AttributeRule.newBooleanRule(CONDITION, true),
// new ElementRule(FullyConjugateMultivariateTraitLikelihood.class),
new ElementRule(TreeTraitNormalDistributionModel.class),
new ElementRule(
DATA, new XMLSyntaxRule[] {new ElementRule(Parameter.class, 1, Integer.MAX_VALUE)})
};
public String getParserDescription() {
return "Calculates the likelihood of some data under a given multivariate-gamma distribution.";
}
public Class getReturnType() {
return MultivariateDistributionLikelihood.class;
}
};
}
