C++ TopologyNode::getIndex方法代码示例

double RateMap_Biogeography::getRate(const TopologyNode& node, std::vector<CharacterEvent*> from, CharacterEvent* to, unsigned* count, double age) const
{
    double rate = 0.0;
    int s = to->getState();
    
    if (from[ to->getIndex() ]->getState() == to->getState())
    {
        std::cout << count[0] << " " << count[1] << "\n";
        std::cout << node.getIndex() << " problem...\n";
        ;
    }
    
    // rate to extinction cfg is 0
    if (count[1] == 1 && s == 0 && forbidExtinction)
        return 0.0;
    
    // rate according to binary rate matrix Q(node)
    if (branchHeterogeneousGainLossRates)
        rate = heterogeneousGainLossRates[node.getIndex()][s];
    else
        rate = homogeneousGainLossRates[s];
    
    if (branchHeterogeneousClockRates)
        rate *= heterogeneousClockRates[node.getIndex()];
    else
        rate *= homogeneousClockRate;
    
    // apply rate modifiers
    if (useGeographyRateModifier) // want this to take in age as an argument...
        rate *= geographyRateModifier->computeRateModifier(node, from, to, age);
    
    return rate;

}

double RateMap_Biogeography::getSiteRate(const TopologyNode& node, CharacterEvent* from, CharacterEvent* to, double age) const
{
    double rate = 0.0;
    int s = to->getState();
//    int charIdx = to->getIndex();
//    int epochIdx = getEpochIndex(age);
    
    // rate according to binary rate matrix Q(node)
    if (branchHeterogeneousGainLossRates)
        rate = heterogeneousGainLossRates[node.getIndex()][s];
    else
        rate = homogeneousGainLossRates[s];
    
    if (branchHeterogeneousClockRates)
        rate *= heterogeneousClockRates[node.getIndex()];
    else
        rate *= homogeneousClockRate;
    
    // area effects
    if (useGeographyRateModifier)
        rate *= geographyRateModifier->computeSiteRateModifier(node,from,to,age);

    
    return rate;
}

void AutocorrelatedLognormalRateBranchwiseVarDistribution::recursiveSimulate(const TopologyNode& node, double parentRate) {
    
    // get the index
    size_t nodeIndex = node.getIndex();
    
    // compute the variance along the branch
	double scale = scaleValue->getValue();
    double variance = sigma->getValue()[nodeIndex] * node.getBranchLength() * scale;
	double mu = log(parentRate) - (variance * 0.5);
	double stDev = sqrt(variance);
    
    // simulate a new rate
    RandomNumberGenerator* rng = GLOBAL_RNG;
    double nodeRate = RbStatistics::Lognormal::rv( mu, stDev, *rng );
    
    // we store this rate here
    (*value)[nodeIndex] = nodeRate;
    
    // simulate the rate for each child (if any)
    size_t numChildren = node.getNumberOfChildren();
    for (size_t i = 0; i < numChildren; ++i) {
        const TopologyNode& child = node.getChild(i);
        recursiveSimulate(child,nodeRate);
    }
}

void BrownianPhyloProcess::recursiveSimulate(const TopologyNode& from)  {
    
    size_t index = from.getIndex();
    
    if (! from.isRoot())    {
        
        // x ~ normal(x_up, sigma^2 * branchLength)
        
        size_t upindex = from.getParent().getIndex();
        double standDev = sigma->getValue() * sqrt(from.getBranchLength());
        double mean = (*value)[upindex] + drift->getValue() * from.getBranchLength();

        // simulate the new Val
        RandomNumberGenerator* rng = GLOBAL_RNG;
        (*value)[index] = RbStatistics::Normal::rv( mean, standDev, *rng);
     
    }
    
    // propagate forward
    size_t numChildren = from.getNumberOfChildren();
    for (size_t i = 0; i < numChildren; ++i) {
        recursiveSimulate(from.getChild(i));
    }
    
}

void RateAgeACLNMixingMove::rejectCompoundMove( void ) {
    	
    // undo the proposal
	TimeTree& tau = tree->getValue();
	std::vector<double>& nrates = rates->getValue();
	double &rootR = rootRate->getValue();
	
	size_t nn = tau.getNumberOfNodes();
	double c = storedC;
	
	for(size_t i=0; i<nn; i++){
		TopologyNode* node = &tau.getNode(i);
		if(node->isTip() == false){
			double curAge = node->getAge();
			double undoAge = curAge / c;
			tau.setAge( node->getIndex(), undoAge );
		}
	}
	
	size_t nr = nrates.size();
	rootR = rootR * c;
	for(size_t i=0; i<nr; i++){
        double curRt = nrates[i];
        double undoRt = curRt * c;
        nrates[i] = undoRt;
	}
	
#ifdef ASSERTIONS_TREE
    if ( fabs(storedRootAge - tau.getRoot().getAge()) > 1E-8 ) {
        throw RbException("Error while rejecting RateAgeACLNMixingMove proposal: Node ages were not correctly restored!");
    }
#endif
}

void PhyloWhiteNoiseProcess::recursiveSimulate(const TopologyNode& from)
{
    
    if (! from.isRoot())
    {
        // get the index
        size_t index = from.getIndex();
    
        // compute the variance along the branch
        double mean = 1.0;
        double stdev = sigma->getValue() / sqrt(from.getBranchLength());
        double alpha = mean * mean / (stdev * stdev);
        double beta = mean / (stdev * stdev);
    
        // simulate a new Val
        RandomNumberGenerator* rng = GLOBAL_RNG;
        double v = RbStatistics::Gamma::rv( alpha,beta, *rng);
    
        // we store this val here
        (*value)[index] = v;
    
    }
    
    // simulate the val for each child (if any)
    size_t numChildren = from.getNumberOfChildren();
    for (size_t i = 0; i < numChildren; ++i)
    {
        const TopologyNode& child = from.getChild(i);
        recursiveSimulate(child);
    }
    
}

void MultivariateBrownianPhyloProcess::recursiveSimulate(const TopologyNode& from)  {
    
    size_t index = from.getIndex();
    if (from.isRoot())    {
        
        std::vector<double>& val = (*value)[index];
        for (size_t i=0; i<getDim(); i++)   {
            val[i] = 0;
        }
    }
    
    else    {
        
        // x ~ normal(x_up, sigma^2 * branchLength)

        std::vector<double>& val = (*value)[index];
                
        sigma->getValue().drawNormalSampleCovariance((*value)[index]);

        size_t upindex = from.getParent().getIndex();
        std::vector<double>& upval = (*value)[upindex];

        for (size_t i=0; i<getDim(); i++)   {
            val[i] += upval[i];
        }        
    }
    
    // propagate forward
    size_t numChildren = from.getNumberOfChildren();
    for (size_t i = 0; i < numChildren; ++i) {
        recursiveSimulate(from.getChild(i));
    }
    
}

void MultivariateBrownianPhyloProcess::recursiveCorruptAll(const TopologyNode& from)    {
    
    dirtyNodes[from.getIndex()] = true;
    for (size_t i = 0; i < from.getNumberOfChildren(); ++i) {
        recursiveCorruptAll(from.getChild(i));
    }    
}

void PhyloBrownianProcessREML::recursivelyFlagNodeDirty( const TopologyNode &n )
{
    
    // we need to flag this node and all ancestral nodes for recomputation
    size_t index = n.getIndex();
    
    // if this node is already dirty, the also all the ancestral nodes must have been flagged as dirty
    if ( !dirtyNodes[index] )
    {
        // the root doesn't have an ancestor
        if ( !n.isRoot() )
        {
            recursivelyFlagNodeDirty( n.getParent() );
        }
        
        // set the flag
        dirtyNodes[index] = true;
        
        // if we previously haven't touched this node, then we need to change the active likelihood pointer
        if ( changedNodes[index] == false )
        {
            activeLikelihood[index] = (activeLikelihood[index] == 0 ? 1 : 0);
            changedNodes[index] = true;
        }
        
    }
    
}

double BrownianPhyloProcess::recursiveLnProb( const TopologyNode& from ) {
    
    double lnProb = 0.0;
    size_t index = from.getIndex();
    double val = (*value)[index];

    if (! from.isRoot()) {
        
        // x ~ normal(x_up, sigma^2 * branchLength)
        
        size_t upindex = from.getParent().getIndex();
        double standDev = sigma->getValue() * sqrt(from.getBranchLength());
        double mean = (*value)[upindex] + drift->getValue() * from.getBranchLength();
        lnProb += RbStatistics::Normal::lnPdf(val, standDev, mean);
    }
    
    // propagate forward
    size_t numChildren = from.getNumberOfChildren();
    
    for (size_t i = 0; i < numChildren; ++i) {
        lnProb += recursiveLnProb(from.getChild(i));
    }
    
    return lnProb;
    
}

void RealNodeContainer::recursiveClampAt(const TopologyNode& from, const ContinuousCharacterData* data, size_t l) {
 
    if (from.isTip())   {
        
        // get taxon index
        size_t index = from.getIndex();
        std::string taxon = tree->getTipNames()[index];
        size_t dataindex = data->getIndexOfTaxon(taxon);
        
        if (data->getCharacter(dataindex,l) != -1000) {
           (*this)[index] = data->getCharacter(dataindex,l);
            clampVector[index] = true;
            //std::cerr << "taxon : " << index << '\t' << taxon << " trait value : " << (*this)[index] << '\n';
        }
        else    {
            std::cerr << "taxon : " << taxon << " is missing for trait " << l+1 << '\n';
        }
    }

    // propagate forward
    size_t numChildren = from.getNumberOfChildren();
    for (size_t i = 0; i < numChildren; ++i) {
        recursiveClampAt(from.getChild(i),data,l);
    }    
}

double AutocorrelatedLognormalRateBranchwiseVarDistribution::recursiveLnProb( const TopologyNode& n ) {
    
    // get the index
    size_t nodeIndex = n.getIndex();
    
    double lnProb = 0.0;
    size_t numChildren = n.getNumberOfChildren();
	double scale = scaleValue->getValue();
    
    if ( numChildren > 0 ) {
        double parentRate = log( (*value)[nodeIndex] );
        
        for (size_t i = 0; i < numChildren; ++i) {
            const TopologyNode& child = n.getChild(i);
            lnProb += recursiveLnProb(child);
            
            size_t childIndex = child.getIndex();
            // compute the variance
            double variance = sigma->getValue()[childIndex] * child.getBranchLength() * scale;
            
            double childRate = (*value)[childIndex];

			// the mean of the LN dist is parentRate = exp[mu + (variance / 2)],
			// where mu is the location param of the LN dist (see Kishino & Thorne 2001)
			double mu = parentRate - (variance * 0.5);
			double stDev = sqrt(variance);
            lnProb += RbStatistics::Lognormal::lnPdf(mu, stDev, childRate);
        } 
    }
    return lnProb;
    
}

double RateMap_Biogeography::getUnnormalizedSumOfRates(const TopologyNode& node, std::vector<CharacterEvent*> from, unsigned* counts, double age) const
{
    size_t nodeIndex = node.getIndex();
    size_t epochIdx = getEpochIndex(age);
    
    // apply ctmc for branch
    const std::vector<double>& glr = ( branchHeterogeneousGainLossRates ? heterogeneousGainLossRates[nodeIndex] : homogeneousGainLossRates );
    
    // get sum of rates
    double sum = 0.0;
    for (size_t i = 0; i < from.size(); i++)
    {
        unsigned s = from[i]->getState();
        double v = availableAreaVector[ epochIdx * this->numCharacters + i ];
        
        if (forbidExtinction && s == 1 && counts[1] == 1)
            sum += 0.0;
        else if (s == 1 && v > 0)
            sum += glr[0];
        else if (s == 1 && v == 0)
            sum += 10e10;
        else  if (s == 0)
            sum += glr[1] * v;
    }
    
    // apply rate for branch
    if (branchHeterogeneousClockRates)
        sum *= heterogeneousClockRates[nodeIndex];
    else
        sum *= homogeneousClockRate;
    
    return sum;
}

void AutocorrelatedBranchMatrixDistribution::recursiveSimulate(const TopologyNode& node, RbVector< RateMatrix > *values, const std::vector< double > &scaledParent) {
    
    // get the index
    size_t nodeIndex = node.getIndex();
    
    // first we simulate our value
    RandomNumberGenerator* rng = GLOBAL_RNG;
    // do we keep our parents values?
    double u = rng->uniform01();
    if ( u < changeProbability->getValue() ) {
        // change
        
        // draw a new value for the base frequencies
        std::vector<double> newParent = RbStatistics::Dirichlet::rv(scaledParent, *rng);
        std::vector<double> newScaledParent = newParent;
        
        // compute the new scaled parent
        std::vector<double>::iterator end = newScaledParent.end();
        for (std::vector<double>::iterator it = newScaledParent.begin(); it != end; ++it) {
            (*it) *= alpha->getValue();
        }
        
        RateMatrix_GTR rm = RateMatrix_GTR( newParent.size() );
        RbPhylogenetics::Gtr::computeRateMatrix( exchangeabilityRates->getValue(), newParent, &rm );
        
        uniqueBaseFrequencies.push_back( newParent );
        uniqueMatrices.push_back( rm );
        matrixIndex[nodeIndex] = uniqueMatrices.size()-1;
        values->insert(nodeIndex, rm);
        
        size_t numChildren = node.getNumberOfChildren();
        if ( numChildren > 0 ) {
            
            for (size_t i = 0; i < numChildren; ++i) {
                const TopologyNode& child = node.getChild(i);
                recursiveSimulate(child,values,newScaledParent);
            }
        }
        
    }
    else {
        // no change
        size_t parentIndex = node.getParent().getIndex();
        values->insert(nodeIndex, uniqueMatrices[ matrixIndex[ parentIndex ] ]);
        
        size_t numChildren = node.getNumberOfChildren();
        if ( numChildren > 0 ) {
            
            for (size_t i = 0; i < numChildren; ++i) {
                const TopologyNode& child = node.getChild(i);
                recursiveSimulate(child,values,scaledParent);
            }
        }
    }
    
}

void RealNodeContainer::recursiveGetTipValues(const TopologyNode& from, ContinuousCharacterData& nameToVal) const {
    
    if(from.isTip())   {
        double tmp = (*this)[from.getIndex()];
        std::string name =  tree->getTipNames()[from.getIndex()];
        
        ContinuousTaxonData dataVec = ContinuousTaxonData(name);
        double contObs = tmp;
        dataVec.addCharacter( contObs );
        nameToVal.addTaxonData( dataVec );
        return;
    }
    // propagate forward
    size_t numChildren = from.getNumberOfChildren();
    for (size_t i = 0; i < numChildren; ++i) {
        recursiveGetTipValues(from.getChild(i), nameToVal );
    }
    
}