/** Constructor */
public SampleStateAndCategoryModel(
Parameter muParameter, Parameter categoriesParameter, Vector substitutionModels) {
super(SampleStateAndCategoryModelParser.SAMPLE_STATE_AND_CATEGORY_MODEL);
this.substitutionModels = substitutionModels;
for (int i = 0; i < substitutionModels.size(); i++) {
addModel((SubstitutionModel) substitutionModels.elementAt(i));
}
this.categoryCount = substitutionModels.size();
sitesInCategory = new int[categoryCount];
// stateCount =
// ((SubstitutionModel)substitutionModels.elementAt(0)).getDataType().getStateCount();
this.muParameter = muParameter;
addVariable(muParameter);
muParameter.addBounds(new Parameter.DefaultBounds(1000.0, 0.0, 1));
this.categoriesParameter = categoriesParameter;
addVariable(categoriesParameter);
if (categoryCount > 1) {
for (int i = 0; i < categoryCount; i++) {
Parameter p = (Parameter) ((YangCodonModel) substitutionModels.elementAt(i)).getVariable(0);
Parameter lower = null;
Parameter upper = null;
if (i == 0) {
upper = (Parameter) ((YangCodonModel) substitutionModels.elementAt(i + 1)).getVariable(0);
p.addBounds(new omegaBounds(lower, upper));
} else {
if (i == (categoryCount - 1)) {
lower =
(Parameter) ((YangCodonModel) substitutionModels.elementAt(i - 1)).getVariable(0);
p.addBounds(new omegaBounds(lower, upper));
} else {
upper =
(Parameter) ((YangCodonModel) substitutionModels.elementAt(i + 1)).getVariable(0);
lower =
(Parameter) ((YangCodonModel) substitutionModels.elementAt(i - 1)).getVariable(0);
p.addBounds(new omegaBounds(lower, upper));
}
}
}
}
}
/** Construct demographic model with default settings */
public ExponentialLogisticModel(
String name,
Parameter N0Parameter,
Parameter logisticGrowthParameter,
Parameter logisticShapeParameter,
Parameter exponentialGrowthParameter,
Parameter transistionTimeParameter,
double alpha,
Type units) {
super(name);
exponentialLogistic = new ExponentialLogistic(units);
this.N0Parameter = N0Parameter;
addVariable(N0Parameter);
N0Parameter.addBounds(new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, 0.0, 1));
this.logisticGrowthParameter = logisticGrowthParameter;
addVariable(logisticGrowthParameter);
logisticGrowthParameter.addBounds(
new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, 0.0, 1));
this.logisticShapeParameter = logisticShapeParameter;
addVariable(logisticShapeParameter);
logisticShapeParameter.addBounds(new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, 0.0, 1));
this.exponentialGrowthParameter = exponentialGrowthParameter;
addVariable(exponentialGrowthParameter);
exponentialGrowthParameter.addBounds(
new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, 0.0, 1));
this.transistionTimeParameter = transistionTimeParameter;
addVariable(transistionTimeParameter);
transistionTimeParameter.addBounds(
new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, 0.0, 1));
this.alpha = alpha;
setUnits(units);
}
public PopsIOSpeciesTreeModel(
PopsIOSpeciesBindings piosb, Parameter popPriorScale, PriorComponent[] priorComponents) {
super(PopsIOSpeciesTreeModelParser.PIO_SPECIES_TREE);
this.piosb = piosb;
this.popPriorScale = popPriorScale;
addVariable(popPriorScale);
popPriorScale.addBounds(new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, 0.0, 1));
this.priorComponents = priorComponents;
PopsIOSpeciesBindings.SpInfo[] species = piosb.getSpecies();
int nTaxa = species.length;
int nNodes = 2 * nTaxa - 1;
pionodes = new PopsIONode[nNodes];
for (int n = 0; n < nNodes; n++) {
pionodes[n] = new PopsIONode(n);
}
ArrayList<Integer> tojoin = new ArrayList<Integer>(nTaxa);
for (int n = 0; n < nTaxa; n++) {
pionodes[n].setTaxon(species[n].name);
pionodes[n].setHeight(0.0);
pionodes[n].setUnion(piosb.tipUnionFromTaxon(pionodes[n].getTaxon()));
tojoin.add(n);
}
double rate = 1.0;
double treeheight = 0.0;
for (int i = 0; i < nTaxa - 1; i++) {
int numtojoin = tojoin.size();
int j = MathUtils.nextInt(numtojoin);
Integer child0 = tojoin.get(j);
tojoin.remove(j);
int k = MathUtils.nextInt(numtojoin - 1);
Integer child1 = tojoin.get(k);
tojoin.remove(k);
pionodes[nTaxa + i].addChildren(pionodes[child0], pionodes[child1]);
pionodes[nTaxa + i].setHeight(treeheight + randomnodeheight(numtojoin * rate));
treeheight = pionodes[nTaxa + i].getHeight();
tojoin.add(nTaxa + i);
}
rootn = pionodes.length - 1;
double scale = 0.99 * piosb.initialMinGeneNodeHeight() / pionodes[rootn].height;
scaleAllHeights(scale);
pionodes[rootn].fillinUnionsInSubtree(piosb.getSpecies().length);
stree = makeSimpleTree();
Logger.getLogger("dr.evomodel.speciation.popsio")
.info(
"\tConstructing a PopsIO Species Tree Model, please cite:\n"
+ Citable.Utils.getCitationString(this));
}
/** Construct demographic model with default settings */
public EmergingEpidemicModel(
String name,
Parameter growthRateParameter,
Parameter generationTimeParameter,
Parameter generationShapeParameter,
Parameter offspringDispersionParameter,
TreeModel treeModel,
Type units) {
super(name);
exponentialGrowth = new ExponentialGrowth(units);
this.growthRateParameter = growthRateParameter;
addVariable(growthRateParameter);
growthRateParameter.addBounds(new Parameter.DefaultBounds(Double.MAX_VALUE, 0.0, 1));
this.generationTimeParameter = generationTimeParameter;
addVariable(generationTimeParameter);
generationTimeParameter.addBounds(new Parameter.DefaultBounds(Double.MAX_VALUE, 0.0, 1));
this.generationShapeParameter = generationShapeParameter;
addVariable(generationShapeParameter);
generationShapeParameter.addBounds(new Parameter.DefaultBounds(Double.MAX_VALUE, 0.0, 1));
this.offspringDispersionParameter = offspringDispersionParameter;
addVariable(offspringDispersionParameter);
offspringDispersionParameter.addBounds(new Parameter.DefaultBounds(Double.MAX_VALUE, 0.0, 1));
this.treeModel = treeModel;
addModel(treeModel);
addStatistic(new N0Statistic("N0"));
addStatistic(new RStatistic("R"));
setUnits(units);
}
public MixtureModelBranchRates(
TreeModel tree,
Parameter rateCategoryQuantilesParameter,
ParametricDistributionModel[] models,
Parameter distributionIndexParameter,
boolean useQuantilesForRates,
boolean normalize,
double normalizeBranchRateTo) {
super(MixtureModelBranchRatesParser.MIXTURE_MODEL_BRANCH_RATES);
this.useQuantilesForRates = useQuantilesForRates;
this.rateCategoryQuantiles =
new TreeParameterModel(tree, rateCategoryQuantilesParameter, false);
rates = new double[tree.getNodeCount()];
this.normalize = normalize;
this.treeModel = tree;
this.distributionModels = models;
this.normalizeBranchRateTo = normalizeBranchRateTo;
this.tree = new SimpleTree(tree);
this.distributionIndexParameter = distributionIndexParameter;
addVariable(this.distributionIndexParameter);
// Force the boundaries of rateCategoryParameter to match the category count
// d Parameter.DefaultBounds bound = new Parameter.DefaultBounds(categoryCount - 1, 0,
// rateCategoryParameter.getDimension());
// d rateCategoryParameter.addBounds(bound);
// rateCategoryQuantilesParameter.;
Parameter.DefaultBounds bound =
new Parameter.DefaultBounds(1.0, 0.0, rateCategoryQuantilesParameter.getDimension());
rateCategoryQuantilesParameter.addBounds(bound);
Parameter.DefaultBounds bound2 = new Parameter.DefaultBounds(models.length, 0.0, 1);
distributionIndexParameter.addBounds(bound2);
distributionIndexParameter.setParameterValue(0, 0);
// Parameter distributionIndexParameter;
for (ParametricDistributionModel distributionModel : distributionModels) {
addModel(distributionModel);
}
// AR - commented out: changes to the tree are handled by model changed events fired by
// rateCategories
// addModel(tree);
// d addModel(rateCategories);
addModel(rateCategoryQuantiles);
// addModel(treeModel); // Maybe
// AR - commented out: changes to rateCategoryParameter are handled by model changed events
// fired by rateCategories
// addVariable(rateCategoryParameter);
if (normalize) {
tree.addModelListener(
new ModelListener() {
public void modelChangedEvent(Model model, Object object, int index) {
computeFactor();
}
public void modelRestored(Model model) {
computeFactor();
}
});
}
setupRates();
}
/** Constructor. */
public CenancestorTreeLikelihood(
PatternList patternList,
TreeModel treeModel,
SiteModel siteModel,
CenancestorBranchRateModel branchRateModel,
TipStatesModel tipStatesModel,
Parameter cenancestorHeight,
Parameter cenancestorBranch,
// Parameter asStatistic,
boolean useAmbiguities,
boolean allowMissingTaxa,
boolean storePartials,
boolean forceJavaCore,
boolean forceRescaling) {
super(CenancestorTreeLikelihoodParser.TREE_LIKELIHOOD, patternList, treeModel);
this.storePartials = storePartials;
nodeCount = treeModel.getNodeCount() + 1;
updateNode = new boolean[nodeCount];
for (int i = 0; i < nodeCount; i++) {
updateNode[i] = true;
}
try {
this.siteModel = siteModel;
addModel(siteModel);
this.frequencyModel = siteModel.getFrequencyModel();
addModel(frequencyModel);
this.tipStatesModel = tipStatesModel;
integrateAcrossCategories = siteModel.integrateAcrossCategories();
this.categoryCount = siteModel.getCategoryCount();
this.cenancestorHeight = cenancestorHeight;
addVariable(cenancestorHeight);
cenancestorHeight.addBounds(
new Parameter.DefaultBounds(
Double.POSITIVE_INFINITY,
0.0,
1)); // TODO The lower bound should be the maximum leaf height
this.cenancestorBranch = cenancestorBranch;
cenancestorBranch.addBounds(
new Parameter.DefaultBounds(
Double.POSITIVE_INFINITY,
0.0,
1)); // TODO The upper bound should be the maximum leaf height
addVariable(cenancestorBranch);
// if (asStatistic == cenancestorHeight){
// this.branchRules=true;
// }
// if (branchRules==true){
updateCenancestorHeight(); // Trying to avoid improper initial values
// }
// else{
// updateCenancestorBranch();
// }
final Logger logger = Logger.getLogger("dr.evomodel");
String coreName = "Java general";
/** TODO: Check if is worthy to implement other datatypes. */
final DataType dataType = patternList.getDataType();
if (dataType instanceof dr.evolution.datatype.TwoStates) {
coreName = "Java cenancestor binary";
cenancestorlikelihoodCore =
new GeneralCenancestorLikelihoodCore(patternList.getStateCount());
} else if (dataType instanceof dr.evolution.datatype.GeneralDataType) {
coreName = "Java cenancestor CNV";
cenancestorlikelihoodCore =
new GeneralCenancestorLikelihoodCore(patternList.getStateCount());
}
/* if (integrateAcrossCategories) {
final DataType dataType = patternList.getDataType();
if (dataType instanceof dr.evolution.datatype.Nucleotides) {
if (!forceJavaCore && NativeNucleotideLikelihoodCore.isAvailable()) {
coreName = "native nucleotide";
likelihoodCore = new NativeNucleotideLikelihoodCore();
} else {
coreName = "Java nucleotide";
likelihoodCore = new NucleotideLikelihoodCore();
}
} else if (dataType instanceof dr.evolution.datatype.AminoAcids) {
if (!forceJavaCore && NativeAminoAcidLikelihoodCore.isAvailable()) {
coreName = "native amino acid";
likelihoodCore = new NativeAminoAcidLikelihoodCore();
} else {
coreName = "Java amino acid";
likelihoodCore = new AminoAcidLikelihoodCore();
}
// The codon core was out of date and did nothing more than the general core...
} else if (dataType instanceof dr.evolution.datatype.Codons) {
if (!forceJavaCore && NativeGeneralLikelihoodCore.isAvailable()) {
coreName = "native general";
likelihoodCore = new NativeGeneralLikelihoodCore(patternList.getStateCount());
} else {
coreName = "Java general";
likelihoodCore = new GeneralLikelihoodCore(patternList.getStateCount());
}
useAmbiguities = true;
} else {
if (!forceJavaCore && NativeGeneralLikelihoodCore.isAvailable()) {
coreName = "native general";
likelihoodCore = new NativeGeneralLikelihoodCore(patternList.getStateCount());
} else {
coreName = "Java general";
likelihoodCore = new GeneralLikelihoodCore(patternList.getStateCount());
}
}
} else {
likelihoodCore = new GeneralLikelihoodCore(patternList.getStateCount());
}*/
{
final String id = getId();
logger.info(
"TreeLikelihood("
+ ((id != null) ? id : treeModel.getId())
+ ") using "
+ coreName
+ " likelihood core");
logger.info(
" " + (useAmbiguities ? "Using" : "Ignoring") + " ambiguities in tree likelihood.");
logger.info(" With " + patternList.getPatternCount() + " unique site patterns.");
}
if (branchRateModel != null) {
this.branchRateModel = branchRateModel;
logger.info("Branch rate model used: " + branchRateModel.getModelName());
} else {
this.branchRateModel = new DefaultCenancestorBranchRateModel();
}
addModel(this.branchRateModel);
probabilities = new double[stateCount * stateCount];
cenancestorlikelihoodCore.initialize(
nodeCount, patternCount, categoryCount, integrateAcrossCategories);
int extNodeCount = treeModel.getExternalNodeCount();
int intNodeCount = treeModel.getInternalNodeCount();
if (tipStatesModel != null) {
tipStatesModel.setTree(treeModel);
tipPartials = new double[patternCount * stateCount];
for (int i = 0; i < extNodeCount; i++) {
// Find the id of tip i in the patternList
String id = treeModel.getTaxonId(i);
int index = patternList.getTaxonIndex(id);
if (index == -1) {
throw new TaxonList.MissingTaxonException(
"Taxon, "
+ id
+ ", in tree, "
+ treeModel.getId()
+ ", is not found in patternList, "
+ patternList.getId());
}
tipStatesModel.setStates(patternList, index, i, id);
cenancestorlikelihoodCore.createNodePartials(i);
}
addModel(tipStatesModel);
} else {
for (int i = 0; i < extNodeCount; i++) {
// Find the id of tip i in the patternList
String id = treeModel.getTaxonId(i);
int index = patternList.getTaxonIndex(id);
if (index == -1) {
if (!allowMissingTaxa) {
throw new TaxonList.MissingTaxonException(
"Taxon, "
+ id
+ ", in tree, "
+ treeModel.getId()
+ ", is not found in patternList, "
+ patternList.getId());
}
if (useAmbiguities) {
setMissingPartials((LikelihoodCore) cenancestorlikelihoodCore, i);
} else {
setMissingStates((LikelihoodCore) cenancestorlikelihoodCore, i);
}
} else {
if (useAmbiguities) {
setPartials(
(LikelihoodCore) cenancestorlikelihoodCore, patternList, categoryCount, index, i);
} else {
setStates((LikelihoodCore) cenancestorlikelihoodCore, patternList, index, i);
}
}
}
}
for (int i = 0; i <= intNodeCount; i++) { // Added one step for the cenancestor
cenancestorlikelihoodCore.createNodePartials(extNodeCount + i);
}
if (forceRescaling) {
cenancestorlikelihoodCore.setUseScaling(true);
logger.info(" Forcing use of partials rescaling.");
}
} catch (TaxonList.MissingTaxonException mte) {
throw new RuntimeException(mte.toString());
}
addStatistic(new SiteLikelihoodsStatistic());
}