/**
   * @param name the name of the covarion substitution model
   * @param dataType the data type
   * @param freqModel the equlibrium frequencies
   * @param hiddenClassRates the relative rates of the hidden categories (first hidden category has
   *     rate 1.0 so this parameter has dimension one less than number of hidden categories. each
   *     hidden category.
   * @param switchingRates rate of switching between hidden categories
   */
  public AbstractCovarionDNAModel(
      String name,
      HiddenNucleotides dataType,
      Parameter hiddenClassRates,
      Parameter switchingRates,
      FrequencyModel freqModel) {

    super(name, dataType, freqModel);

    hiddenClassCount = dataType.getHiddenClassCount();

    this.hiddenClassRates = hiddenClassRates;
    this.switchingRates = switchingRates;

    assert hiddenClassRates.getDimension() == hiddenClassCount - 1;

    int hiddenClassCount = getHiddenClassCount();

    int switchingClassCount = hiddenClassCount * (hiddenClassCount - 1) / 2;

    if (switchingRates.getDimension() != switchingClassCount) {
      throw new IllegalArgumentException(
          "switching rate parameter must have "
              + switchingClassCount
              + " rates for "
              + hiddenClassCount
              + " classes");
    }
    addVariable(switchingRates);
    addVariable(hiddenClassRates);
    constructRateMatrixMap();
  }
Esempio n. 2
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  protected double calculateLogFieldLikelihood() {

    if (!intervalsKnown) {
      // intervalsKnown -> false when handleModelChanged event occurs in super.
      wrapSetupIntervals();
      setupGMRFWeights();
      intervalsKnown = true;
    }

    double currentLike = 0;
    DenseVector diagonal1 = new DenseVector(fieldLength);
    DenseVector currentGamma = new DenseVector(popSizeParameter.getParameterValues());

    SymmTridiagMatrix currentQ =
        getScaledWeightMatrix(
            precisionParameter.getParameterValue(0), lambdaParameter.getParameterValue(0));
    currentQ.mult(currentGamma, diagonal1);

    //        currentLike += 0.5 * logGeneralizedDeterminant(currentQ) - 0.5 *
    // currentGamma.dot(diagonal1);

    currentLike +=
        0.5 * (fieldLength - 1) * Math.log(precisionParameter.getParameterValue(0))
            - 0.5 * currentGamma.dot(diagonal1);
    if (lambdaParameter.getParameterValue(0) == 1) {
      currentLike -= (fieldLength - 1) / 2.0 * LOG_TWO_TIMES_PI;
    } else {
      currentLike -= fieldLength / 2.0 * LOG_TWO_TIMES_PI;
    }

    return currentLike;
  }
  private void printInformtion(Parameter par) {
    StringBuffer sb = new StringBuffer("parameter \n");
    for (int j = 0; j < par.getDimension(); j++) {
      sb.append(par.getParameterValue(j));
    }

    Logger.getLogger("dr.evomodel").info(sb.toString());
  };
 public DriftedLocationsStatistic(
     MatrixParameter locationsParameter,
     Parameter offsetsParameter,
     Parameter locationDriftParameter) {
   this.locationsParameter = locationsParameter;
   locationsParameter.addParameterListener(this);
   this.offsetsParameter = offsetsParameter;
   offsetsParameter.addParameterListener(this);
   this.locationDriftParameter = locationDriftParameter;
   locationDriftParameter.addParameterListener(this);
 }
Esempio n. 5
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 public double calculateWeightedSSE() {
   double weightedSSE = 0;
   double currentPopSize = popSizeParameter.getParameterValue(0);
   double currentInterval = coalescentIntervals[0];
   for (int j = 1; j < fieldLength; j++) {
     double nextPopSize = popSizeParameter.getParameterValue(j);
     double nextInterval = coalescentIntervals[j];
     double delta = nextPopSize - currentPopSize;
     double weight = (currentInterval + nextInterval) / 2.0;
     weightedSSE += delta * delta / weight;
     currentPopSize = nextPopSize;
     currentInterval = nextInterval;
   }
   return weightedSSE;
 }
  /** 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));
          }
        }
      }
    }
  }
  /** Calculates the actual rates corresponding to the category indices. */
  protected void setupRates() {

    // System.out.println("BRRRTTZZZ " + distributionIndexParameter.getValue(0));
    for (int i = 0; i < tree.getNodeCount(); i++) {
      // rates[i] = distributionModel.quantile(rateCategoryQuantiles.getNodeValue(
      // rateCategoryQuantiles.getTreeModel(), rateCategoryQuantiles.getTreeModel().getNode(i) ));
      if (!tree.isRoot(tree.getNode(i))) {

        if (useQuantilesForRates) {
            /* Using quantiles to represent rates */
          rates[tree.getNode(i).getNumber()] =
              distributionModels[(int) Math.round(distributionIndexParameter.getValue(0))].quantile(
                  rateCategoryQuantiles.getNodeValue(tree, tree.getNode(i)));
        } else {
            /* Not using quantiles to represent rates. This is practically useless for anything else other than simulation */
          rates[tree.getNode(i).getNumber()] =
              rateCategoryQuantiles.getNodeValue(tree, tree.getNode(i));
        }
      }
    }
    /*System.out.print(distributionModels[(int) Math.round(distributionIndexParameter.getValue(0))].getClass().getName() + "\t" + (int) Math.round(distributionIndexParameter.getValue(0)) + "\t" + rates[1] + "\t" + rateCategoryQuantiles.getNodeValue(tree, tree.getNode(1)));// + "\t" + distributionModels[(int) Math.round(distributionIndexParameter.getValue(0))].);
    if(distributionModels[(int) Math.round(distributionIndexParameter.getValue(0))].getClass().getName().equals("dr.inference.distribution.LogNormalDistributionModel")) {
        LogNormalDistributionModel lndm = (LogNormalDistributionModel) distributionModels[(int) Math.round(distributionIndexParameter.getValue(0))];
        System.out.println("\t" + lndm.getS());
    }
    else if (distributionModels[(int) Math.round(distributionIndexParameter.getValue(0))].getClass().getName().equals("dr.inference.distribution.InverseGaussianDistributionModel")) {
        InverseGaussianDistributionModel lndm = (InverseGaussianDistributionModel) distributionModels[(int) Math.round(distributionIndexParameter.getValue(0))];
        System.out.println("\t" + lndm.getS());
    }*/
    if (normalize) computeFactor();
  }
    /** @return the lower limit of this hypervolume in the given dimension. */
    public Double getLowerLimit(int dimension) {

      if (dimension != 0)
        throw new RuntimeException("omega parameters have wrong dimension " + dimension);

      if (lowerOmega == null) return OMEGA_MIN_VALUE;
      else return lowerOmega.getParameterValue(dimension);
    }
  public Object parseXMLObject(XMLObject xo) throws XMLParseException {

    // final int overSampling = xo.getAttribute(OVERSAMPLING, 1);
    final boolean normalize = xo.getAttribute(NORMALIZE, false);
    final double normalizeBranchRateTo = xo.getAttribute(NORMALIZE_BRANCH_RATE_TO, Double.NaN);

    TreeModel tree = (TreeModel) xo.getChild(TreeModel.class);
    ParametricDistributionModel distributionModel =
        (ParametricDistributionModel) xo.getElementFirstChild(DISTRIBUTION);

    // Parameter rateCategoryParameter = (Parameter) xo.getElementFirstChild(RATE_CATEGORIES);

    Parameter rateCategoryQuantilesParameter =
        (Parameter) xo.getElementFirstChild(RATE_CATEGORY_QUANTILES);

    Logger.getLogger("dr.evomodel").info("Using continuous relaxed clock model.");
    // Logger.getLogger("dr.evomodel").info("  over sampling = " + overSampling);
    Logger.getLogger("dr.evomodel")
        .info("  parametric model = " + distributionModel.getModelName());
    // Logger.getLogger("dr.evomodel").info("   rate categories = " +
    // rateCategoryParameter.getDimension());
    Logger.getLogger("dr.evomodel")
        .info("   rate categories = " + rateCategoryQuantilesParameter.getDimension());
    if (normalize) {
      Logger.getLogger("dr.evomodel")
          .info("   mean rate is normalized to " + normalizeBranchRateTo);
    }

    if (xo.hasAttribute(SINGLE_ROOT_RATE)) {
      // singleRootRate = xo.getBooleanAttribute(SINGLE_ROOT_RATE);
      Logger.getLogger("dr.evomodel").warning("   WARNING: single root rate is not implemented!");
    }

    /* if (xo.hasAttribute(NORMALIZED_MEAN)) {
        dbr.setNormalizedMean(xo.getDoubleAttribute(NORMALIZED_MEAN));
    }*/

    return new ContinuousBranchRates(
        tree, /*rateCategoryParameter, */
        rateCategoryQuantilesParameter,
        distributionModel, /*overSampling,*/
        normalize,
        normalizeBranchRateTo);
  }
  public int[] connected(int i, Parameter clusteringParameter) {
    int n = clusteringParameter.getDimension();
    int[] visited = new int[n + 1];
    visited[0] = i + 1;
    int tv = 1;

    for (int j = 0; j < n; j++) {
      if (visited[j] != 0) {
        int curr = visited[j] - 1;

        /*look forward
         */

        int forward = (int) clusteringParameter.getParameterValue(curr);
        visited[tv] = forward + 1;
        tv++;
        // Check to see if is isn't already on the list

        for (int ii = 0; ii < tv - 1; ii++) {
          if (visited[ii] == forward + 1) {
            tv--;
            visited[tv] = 0;
          }
        }

        /*look back
         */
        for (int jj = 0; jj < n; jj++) {
          if ((int) clusteringParameter.getParameterValue(jj) == curr) {
            visited[tv] = jj + 1;
            tv++;

            for (int ii = 0; ii < tv - 1; ii++) {
              if (visited[ii] == jj + 1) {
                tv--;
                visited[tv] = 0;
              }
            }
          }
        }
      }
    }
    return visited;
  }
  public double getStatisticValue(int dim) {

    double val;

    // x is location count, y is location dimension
    int x = getColumnIndex(dim);
    int y = getRowIndex(dim);
    Parameter loc = locationsParameter.getParameter(x);

    if (y == 0) {
      val =
          loc.getParameterValue(y)
              + locationDriftParameter.getParameterValue(0) * offsetsParameter.getParameterValue(x);
    } else {
      val = loc.getParameterValue(y);
    }

    return val;
  }
  /** 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));
  }
  protected double getRawBranchRate(final Tree tree, final NodeRef node) {

    double rate = 0.0;
    double[] processValues = getProcessValues(tree, node);

    double totalTime = 0;
    if (indicatorParameter != null) {
      double absRate = rateParameter.getParameterValue(0);

      for (int i = 0; i < indicatorParameter.getDimension(); i++) {
        int index = (int) indicatorParameter.getParameterValue(i);
        if (index == 0) {
          rate += absRate * processValues[i];
        } else {
          rate +=
              (absRate * (1.0 + relativeRatesParameter.getParameterValue(index - 1)))
                  * processValues[i];
        }
        totalTime += processValues[i];
      }
    } else {
      for (int i = 0; i < rateParameter.getDimension(); i++) {
        rate += rateParameter.getParameterValue(i) * processValues[i];
        totalTime += processValues[i];
      }
    }
    rate /= totalTime;

    return rate;
  }
  public DistanceDependentCRPGibbsOperator(
      Parameter links,
      Parameter assignments,
      Parameter chiParameter,
      NPAntigenicLikelihood Likelihood,
      double weight) {

    this.links = links;
    this.assignments = assignments;
    this.modelLikelihood = Likelihood;
    this.chiParameter = chiParameter;
    this.depMatrix = Likelihood.getLogDepMatrix();

    for (int i = 0; i < links.getDimension(); i++) {
      links.setParameterValue(i, i);
    }

    setWeight(weight);

    // double[][] x=modelLikelihood.getData();
    // modelLikelihood.printInformtion(x[0][0]);

    this.m = new double[2];
    m[0] = modelLikelihood.priorMean.getParameterValue(0);
    m[1] = modelLikelihood.priorMean.getParameterValue(1);

    this.v0 = 2;

    this.k0 =
        modelLikelihood.priorPrec.getParameterValue(0)
            / modelLikelihood.clusterPrec.getParameterValue(0);

    this.T0Inv = new double[2][2];
    T0Inv[0][0] = v0 / modelLikelihood.clusterPrec.getParameterValue(0);
    T0Inv[1][1] = v0 / modelLikelihood.clusterPrec.getParameterValue(0);
    T0Inv[1][0] = 0.0;
    T0Inv[0][1] = 0.0;

    this.logDetT0 = -Math.log(T0Inv[0][0] * T0Inv[1][1]);
  }
  private void restrictNodePartials(int nodeIndex) {

    Parameter restrictionParameter = partialsMap[nodeIndex];
    if (restrictionParameter == null) {
      return;
    }

    getPartials(nodeIndex, partials);

    double[] restriction = restrictionParameter.getParameterValues();
    final int partialsLengthPerCategory = stateCount * patternCount;
    if (restriction.length == partialsLengthPerCategory) {
      for (int i = 0; i < categoryCount; i++) {
        componentwiseMultiply(
            partials, partialsLengthPerCategory * i, restriction, 0, partialsLengthPerCategory);
      }
    } else {
      componentwiseMultiply(partials, 0, restriction, 0, partialsLengthPerCategory * categoryCount);
    }

    setPartials(nodeIndex, partials);
  }
        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;
        }
Esempio n. 18
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  protected void getDiagonalRates(double[] diagonalRates) {
    double kappa = substitutionModel.getKappa();
    double[] freq = substitutionModel.getFrequencyModel().getFrequencies();
    double mutationRate = mutationParameter.getParameterValue(0);
    double beta =
        0.5
            / ((freq[0] + freq[2]) * (freq[1] + freq[3])
                + kappa * (freq[0] * freq[2] + freq[1] * freq[3]));

    diagonalRates[0] = ((freq[1] + freq[3]) + freq[2] * kappa) * mutationRate * beta;
    diagonalRates[1] = ((freq[0] + freq[2]) + freq[3] * kappa) * mutationRate * beta;
    diagonalRates[2] = ((freq[1] + freq[3]) + freq[0] * kappa) * mutationRate * beta;
    diagonalRates[3] = ((freq[0] + freq[2]) + freq[1] * kappa) * mutationRate * beta;
  }
Esempio n. 19
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  public Object parseXMLObject(XMLObject xo) throws XMLParseException {

    Parameter kappaParam;
    Parameter switchingRates;
    Parameter hiddenClassRates;
    FrequencyModel freqModel;

    kappaParam = (Parameter) xo.getElementFirstChild(KAPPA);
    switchingRates = (Parameter) xo.getElementFirstChild(AbstractCovarionDNAModel.SWITCHING_RATES);
    hiddenClassRates =
        (Parameter) xo.getElementFirstChild(AbstractCovarionDNAModel.HIDDEN_CLASS_RATES);
    freqModel = (FrequencyModel) xo.getElementFirstChild(AbstractCovarionDNAModel.FREQUENCIES);

    if (!(freqModel.getDataType() instanceof HiddenNucleotides)) {
      throw new IllegalArgumentException("Datatype must be hidden nucleotides!!");
    }

    HiddenNucleotides dataType = (HiddenNucleotides) freqModel.getDataType();

    int hiddenStateCount = dataType.getHiddenClassCount();

    int switchingRatesCount = hiddenStateCount * (hiddenStateCount - 1) / 2;

    if (switchingRates.getDimension() != switchingRatesCount) {
      throw new IllegalArgumentException(
          "switching rates parameter must have "
              + switchingRatesCount
              + " dimensions, for "
              + hiddenStateCount
              + " hidden categories");
    }

    CovarionHKY model =
        new CovarionHKY(dataType, kappaParam, hiddenClassRates, switchingRates, freqModel);
    System.out.println(model);
    return model;
  }
  /** 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 MultivariateNormalIndependenceSampler(
     Parameter parameter,
     SelfControlledCaseSeries sccs,
     double setSizeMean,
     double weight,
     double scaleFactor,
     CoercionMode mode) {
   super(mode);
   this.scaleFactor = scaleFactor;
   this.parameter = parameter;
   setWeight(weight);
   dim = parameter.getDimension();
   setWeight(weight);
   this.sccs = sccs;
   this.setSizeMean = setSizeMean;
 }
  public double logLhoodAllGeneTreesInSpeciesTree() {
    double[] llhoodcpts = new double[priorComponents.length];

    double totalweight = 0.0;
    for (int i = 0; i < priorComponents.length; i++) {
      totalweight += priorComponents[i].weight;
    }
    for (int i = 0; i < priorComponents.length; i++) {
      llhoodcpts[i] = Math.log(priorComponents[i].weight / totalweight);
      double sigma = popPriorScale.getParameterValue(0);
      double alpha = priorComponents[i].alpha;
      double beta = sigma * priorComponents[i].beta;
      llhoodcpts[i] += logLhoodAllGeneTreesInSpeciesSubtree(pionodes[rootn], alpha, beta);
    }
    return logsumexp(llhoodcpts);
  }
Esempio n. 23
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  /**
   * Calculates the log likelihood of this set of coalescent intervals, given a demographic model.
   *
   * @return coalescent part of density
   */
  protected double calculateLogCoalescentLikelihood() {

    if (!intervalsKnown) {
      // intervalsKnown -> false when handleModelChanged event occurs in super.
      wrapSetupIntervals();
      setupGMRFWeights();
      intervalsKnown = true;
    }

    // Matrix operations taken from block update sampler to calculate data likelihood and field
    // prior

    double currentLike = 0;
    double[] currentGamma = popSizeParameter.getParameterValues();

    for (int i = 0; i < fieldLength; i++) {
      currentLike += -currentGamma[i] - sufficientStatistics[i] * Math.exp(-currentGamma[i]);
    }

    return currentLike; // +
    // LogNormalDistribution.logPdf(Math.exp(popSizeParameter.getParameterValue(coalescentIntervals.length - 1)), mu, sigma);
  }
  /** change the parameter and return the hastings ratio. */
  public double doOperation() throws OperatorFailedException {

    double[] mean = sccs.getMode();
    double[] currentValue = parameter.getParameterValues();
    double[] newValue = new double[dim];

    Set<Integer> updateSet = new HashSet<Integer>();

    if (setSizeMean != -1.0) {
      final int listLength = Poisson.nextPoisson(setSizeMean);
      while (updateSet.size() < listLength) {
        int newInt = MathUtils.nextInt(parameter.getDimension());
        if (!updateSet.contains(newInt)) {
          updateSet.add(newInt);
        }
      }
    } else {
      for (int i = 0; i < dim; ++i) {
        updateSet.add(i);
      }
    }

    double logq = 0;
    for (Integer i : updateSet) {
      newValue[i] = mean[i] + scaleFactor * MathUtils.nextGaussian();
      if (UPDATE_ALL) {
        parameter.setParameterValueQuietly(i, newValue[i]);
      } else {
        parameter.setParameterValue(i, newValue[i]);
      }

      logq +=
          (NormalDistribution.logPdf(currentValue[i], mean[i], scaleFactor)
              - NormalDistribution.logPdf(newValue[i], mean[i], scaleFactor));
    }

    //        for (Integer i : updateSet) {
    //            parameter.setParameterValueQuietly(i, newValue[i]);
    //        }

    if (UPDATE_ALL) {
      parameter.setParameterValueNotifyChangedAll(0, parameter.getParameterValue(0));
    }

    return logq;
  }
  public Object parseXMLObject(XMLObject xo) throws XMLParseException {

    final boolean onLogitScale = xo.getAttribute(ON_LOGIT_SCALE, false);

    XMLObject cxo = xo.getChild(COUNTS);
    Parameter countsParam = (Parameter) cxo.getChild(Parameter.class);

    cxo = xo.getChild(PROPORTION);
    Parameter proportionParam = (Parameter) cxo.getChild(Parameter.class);

    if (proportionParam.getDimension() != 1
        && proportionParam.getDimension() != countsParam.getDimension()) {
      throw new XMLParseException(
          "Proportion dimension ("
              + proportionParam.getDimension()
              + ") "
              + "must equal 1 or counts dimension ("
              + countsParam.getDimension()
              + ")");
    }

    cxo = xo.getChild(TRIALS);
    Parameter trialsParam;
    if (cxo.hasAttribute(VALUES)) {
      int[] tmp = cxo.getIntegerArrayAttribute(VALUES);
      double[] v = new double[tmp.length];
      for (int i = 0; i < tmp.length; ++i) {
        v[i] = tmp[i];
      }
      trialsParam = new Parameter.Default(v);
    } else {
      trialsParam = (Parameter) cxo.getChild(Parameter.class);
    }

    if (trialsParam.getDimension() != countsParam.getDimension()) {
      throw new XMLParseException(
          "Trials dimension ("
              + trialsParam.getDimension()
              + ") must equal counts dimension ("
              + countsParam.getDimension()
              + ")");
    }

    return new BinomialLikelihood(trialsParam, proportionParam, countsParam, onLogitScale);
  }
Esempio n. 26
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 public void initializationReport() {
   System.out.println("Creating a GMRF smoothed skyride model:");
   System.out.println("\tPopulation sizes: " + popSizeParameter.getDimension());
   System.out.println(
       "\tIf you publish results using this model, please reference: Minin, Bloomquist and Suchard (2008) Molecular Biology and Evolution, 25, 1459-1471.");
 }
Esempio n. 27
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  public GMRFSkyrideLikelihood(
      List<Tree> treeList,
      Parameter popParameter,
      Parameter groupParameter,
      Parameter precParameter,
      Parameter lambda,
      Parameter beta,
      MatrixParameter dMatrix,
      boolean timeAwareSmoothing,
      boolean rescaleByRootHeight) {

    super(GMRFSkyrideLikelihoodParser.SKYLINE_LIKELIHOOD);

    this.popSizeParameter = popParameter;
    this.groupSizeParameter = groupParameter;
    this.precisionParameter = precParameter;
    this.lambdaParameter = lambda;
    this.betaParameter = beta;
    this.dMatrix = dMatrix;
    this.timeAwareSmoothing = timeAwareSmoothing;
    this.rescaleByRootHeight = rescaleByRootHeight;

    addVariable(popSizeParameter);
    addVariable(precisionParameter);
    addVariable(lambdaParameter);
    if (betaParameter != null) {
      addVariable(betaParameter);
    }

    setTree(treeList);

    int correctFieldLength = getCorrectFieldLength();

    if (popSizeParameter.getDimension() <= 1) {
      // popSize dimension hasn't been set yet, set it here:
      popSizeParameter.setDimension(correctFieldLength);
    }

    fieldLength = popSizeParameter.getDimension();
    if (correctFieldLength != fieldLength) {
      throw new IllegalArgumentException(
          "Population size parameter should have length " + correctFieldLength);
    }

    // Field length must be set by this point
    wrapSetupIntervals();
    coalescentIntervals = new double[fieldLength];
    storedCoalescentIntervals = new double[fieldLength];
    sufficientStatistics = new double[fieldLength];
    storedSufficientStatistics = new double[fieldLength];

    setupGMRFWeights();

    addStatistic(new DeltaStatistic());

    initializationReport();

    /* Force all entries in groupSizeParameter = 1 for compatibility with Tracer */
    if (groupSizeParameter != null) {
      for (int i = 0; i < groupSizeParameter.getDimension(); i++)
        groupSizeParameter.setParameterValue(i, 1.0);
    }
  }
 public String getOperatorName() {
   return "independentNormalDistribution(" + parameter.getVariableName() + ")";
 }
  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 (xo.hasAttribute(DataType.DATA_TYPE)) {
    //            String dataTypeStr = xo.getStringAttribute(DataType.DATA_TYPE);
    //            if (dataTypeStr.equals(Nucleotides.DESCRIPTION)) {
    //                dataType = Nucleotides.INSTANCE;
    //            } else if (dataTypeStr.equals(AminoAcids.DESCRIPTION)) {
    //                dataType = AminoAcids.INSTANCE;
    //            } else if (dataTypeStr.equals(Codons.DESCRIPTION)) {
    //                dataType = Codons.UNIVERSAL;
    //            } else if (dataTypeStr.equals(TwoStates.DESCRIPTION)) {
    //                dataType = TwoStates.INSTANCE;
    //            }
    //        }

    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(
                dr.evomodelxml.substmodel.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 we have relativeTo attribute then we use the old GeneralSubstitutionModel

      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);
    }
  }
 public double getBranchRate(final Tree tree, final NodeRef node) {
   return rateParameter.getParameterValue(0);
 }