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/***************************************************************************
* Copyright (C) 2009-2015 by *
* BUI Quang Minh <minh.bui@univie.ac.at> *
* Lam-Tung Nguyen <nltung@gmail.com> *
* *
* *
* This program is free software; you can redistribute it and/or modify *
* it under the terms of the GNU General Public License as published by *
* the Free Software Foundation; either version 2 of the License, or *
* (at your option) any later version. *
* *
* This program is distributed in the hope that it will be useful, *
* but WITHOUT ANY WARRANTY; without even the implied warranty of *
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the *
* GNU General Public License for more details. *
* *
* You should have received a copy of the GNU General Public License *
* along with this program; if not, write to the *
* Free Software Foundation, Inc., *
* 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. *
***************************************************************************/
#ifndef IQPTREE_H
#define IQPTREE_H
#include <set>
#include <map>
#include <stack>
#include <vector>
#include "phylotree.h"
#include "phylonode.h"
#include "utils/stoprule.h"
#include "mtreeset.h"
#include "node.h"
#include "candidateset.h"
#include "utils/pllnni.h"
typedef std::map< string, double > mapString2Double;
typedef std::multiset< double, std::less< double > > multiSetDB;
typedef std::multiset< int, std::less< int > > MultiSetInt;
class RepLeaf {
public:
Node *leaf;
int height;
RepLeaf(Node *aleaf, int aheight = 0) {
leaf = aleaf;
height = aheight;
}
};
/**
nodeheightcmp, for building k-representative leaf set
*/
struct nodeheightcmp {
bool operator()(const RepLeaf* s1, const RepLeaf * s2) const {
return (s1->height) < (s2->height);
}
};
struct IntBranchInfo {
PhyloNode *node1;
PhyloNode *node2;
double lh_contribution; // log-likelihood contribution of this branch: L(T)-L(T|e=0)
};
inline int int_branch_cmp(const IntBranchInfo a, const IntBranchInfo b) {
return (a.lh_contribution < b.lh_contribution);
}
/**
Representative Leaf Set, stored as a multiset template of STL,
sorted in ascending order of leaf's height
*/
typedef multiset<RepLeaf*, nodeheightcmp> RepresentLeafSet;
/**
Main class for tree search
*/
class IQTree : public PhyloTree {
public:
/**
default constructor
*/
IQTree();
IQTree(Alignment *aln);
// EIGEN_MAKE_ALIGNED_OPERATOR_NEW
/**
destructor
*/
virtual ~IQTree();
void init();
/**
set checkpoint object
@param checkpoint
*/
virtual void setCheckpoint(Checkpoint *checkpoint);
/**
save object into the checkpoint
*/
virtual void saveCheckpoint();
/**
restore object from the checkpoint
*/
virtual void restoreCheckpoint();
/**
save UFBoot_trees.
For MPI workers only save from sample_start to sample_end
@param checkpoint Checkpoint object
*/
void saveUFBoot(Checkpoint *checkpoint);
/**
restore UFBoot_trees from sample_start to sample_end (MPI)
@param checkpoint Checkpoint object
*/
void restoreUFBoot(Checkpoint *checkpoint);
/**
* setup all necessary parameters (declared as virtual needed for phylosupertree)
*/
virtual void initSettings(Params& params);
void createPLLPartition(Params ¶ms, ostream &pllPartitionFileHandle);
void initializePLL(Params ¶ms);
bool isInitializedPLL();
virtual void initializeModel(Params ¶ms, string &model_name, ModelsBlock *models_block);
/**
print tree to .treefile
@param params program parameters, field root is taken
*/
virtual void printResultTree(string suffix = "");
/**
print tree to out
@param params program parameters, field root is taken
@param out (OUT) output stream
*/
void printResultTree(ostream &out);
void printBestCandidateTree();
/**
* print phylolib tree to a file.
* @param suffix suffix string for the tree file
*/
void printPhylolibTree(const char* suffix);
/**
* print model parameters of Phylolib(rates, base frequencies, alpha) to stdout and
* to file
*/
//void printPhylolibModelParams(const char* suffix);
/**
print intermediate tree
*/
void printIntermediateTree(int brtype);
/**
set k-representative parameter
@param k_rep k-representative
*/
// void setRepresentNum(int k_rep);
/**
set the probability of deleteing sequences for IQP algorithm
@param p_del probability of deleting sequences
*/
//void setProbDelete(double p_del);
double getProbDelete();
void resetKDelete();
void increaseKDelete();
/**
set the number of iterations for the IQPNNI algorithm
@param stop_condition stop condition (SC_FIXED_ITERATION, SC_STOP_PREDICT)
@param min_iterations the min number of iterations
@param max_iterations the maximum number of iterations
*/
// void setIQPIterations(STOP_CONDITION stop_condition, double stop_confidence, int min_iterations, int max_iterations);
/**
@param assess_quartet the quartet assessment, either IQP_DISTANCE or IQP_PARSIMONY
*/
//void setIQPAssessQuartet(IQP_ASSESS_QUARTET assess_quartet);
/**
find the k-representative leaves under the node
@param node the node at which the subtree is rooted
@param dad the dad node of the considered subtree, to direct the search
@param leaves (OUT) the k-representative leaf set
*/
RepresentLeafSet* findRepresentLeaves(vector<RepresentLeafSet*> &leaves, int nei_id,
PhyloNode *dad);
/**
clear representative leave sets iteratively, called once a leaf is re-inserted into the tree
@param node the node at which the subtree is rooted
@param dad the dad node of the considered subtree, to direct the search
@param leaves (OUT) the k-representative leaf set
*/
void clearRepresentLeaves(vector<RepresentLeafSet*> &leaves_vec, Node *node, Node *dad);
/**
remove a portion of leaves and reinsert them using the IQP algorithm
*/
void doIQP();
/**
* @brief get non-tabu branches from a set of branches
*
* @param
* allBranches[IN] the inital branches
* @param
* initTabuSplits[IN] the tabu splits
* @param
* nonTabuBranches[OUT] non-tabu branches from \a allBranches
* @param[OUT]
* tabuBranches branches that are tabu
*/
void getNonTabuBranches(Branches& allBranches, SplitGraph& tabuSplits, Branches& nonTabuBranches, Branches* tabuBranches = NULL);
/**
* @brief remove all branches corresponding to nnis
* @param nodes1 node vector containing one end of the branches
* @param nodes2 node vector containing the other end of the branches
* @param nnis
* @return
*/
int removeNNIBranches(NodeVector& nodes1, NodeVector& nodes2, unordered_map<string, NNIMove> nnis);
/**
* Perform a series of random NNI moves
* @return the perturbed newick string
*/
string doRandomNNIs(bool storeTabu = false);
/**
* Do a random NNI on splits that are shared among all the candidate trees.
* @return the perturbed newick string
*/
string perturbStableSplits(double supportValue);
/**
* input model parameters from IQ-TREE to PLL
*/
void inputModelIQTree2PLL();
/**
* input model parameters from PLL to IQ-TREE
*/
void inputModelPLL2IQTree();
/**
* get the rate parameters from PLL
* @return double array containing the 6 rates
*/
double* getModelRatesFromPLL();
/**
* get the alpha parameter from PLL for the GAMMA distribution of rate heterogenity
* @return alpha parameter
*/
double getAlphaFromPLL();
/**
* print model parameters from PLL
*/
void pllPrintModelParams();
/**
* input the tree string from IQTree kernel to PLL kernel
* @return
*/
double inputTree2PLL(string treestring, bool computeLH = true);
//bool containPosNNI(vector<NNIMove> posNNIs);
/**
* Perturb the tree for the next round of local search by swaping position of 2 random leaves
* @param nbDist The minimum distance between the 2 nodes that are swapped
* @param nbTimes Number of times that the swap operations are carried out
* @return The new loglikelihood of the tree
*/
double perturb(int times);
/**
* TODO
* @param node1
* @param node2
* @return
*/
double swapTaxa(PhyloNode *node1, PhyloNode *node2);
/**
perform tree search
@return best likelihood found
*/
double doTreeSearch();
/**
* Wrapper function that uses either PLL or IQ-TREE to optimize the branch length
* @param maxTraversal
* maximum number of tree traversal for branch length optimization
* @return NEWICK tree string
*/
string optimizeBranches(int maxTraversal = 100);
/**
* Wrapper function to compute tree log-likelihood.
* This function with call either PLL or IQ-TREE to compute tree log-likelihood
* @return current score of tree
*/
double computeLogL();
/**
* Print scores of tree used for generating offsprings
*/
void printBestScores();
/****************************************************************************
Fast Nearest Neighbor Interchange by maximum likelihood
****************************************************************************/
/**
* Optimize current tree using NNI
*
* @return
* <number of NNI steps, number of NNIs> done
*/
virtual pair<int, int> optimizeNNI(bool speedNNI = true);
/**
* Return the current best score found
*/
double getBestScore();
/**
* @brief Generate a list of internal branches on which NNI moves will be evaluated
* @param
* nonNNIBranches [OUT] Branches on which NNI evaluation will be skipped
* @param
* tabuSplits [IN] A list of splits that are considered tabu
* @param
* candidateSplitHash [IN] Lists that appear on the best 20 candidate trees
* @param
* dad [IN] for navigation
* @param
* node[IN] for navigation
* @return A list of branches for evaluating NNIs
*/
void getNNIBranches(SplitIntMap &tabuSplits, SplitIntMap &candidateSplitHash, Branches &nonNNIBranches, Branches &outBranches, Node *dad = NULL, Node *node = NULL);
/**
* Return internal branches that appear in \a candidateSplitHash
* and has support value >= \a supportValue.
* @param
* candidateSplitHash [IN] A set of splits with the number of occurences.
* @param
* supportValue [IN] Only consider split whose support value is higher than this number
* @param
* dad [IN] for navigation
* @param
* node[IN] for navigation
* @return
* A list of branches fufilling the aforementioned conditions.
*/
void getStableBranches(SplitIntMap &candSplits, double supportValue, Branches &outBranches, Node *dad = NULL, Node *node = NULL);
/**
*
* Determine whether to evaluate NNI moves on the branch corresponding to the current split
*
* @param curSplit [IN] the split that correspond to the current branch
* @param tabuSplits [IN] tabu splits
* @param candSplits [IN] splits contained in all candidate trees
* @param nonNNIBranches [OUT] branches that are not inserted to nniBranches are store here
* @param nniBranches [OUT] if the split is neither stable nor tabu it is inserted in this list
*/
bool shouldEvaluate(Split* curSplit, SplitIntMap &tabuSplits, SplitIntMap &candSplits);
/**
* @brief Only select NNI branches that are 2 branches away from the previously
* appied NNIs
* @param
* appliedNNIs List of previously applied NNIs
* @return
* List of branches to be evaluated
*/
void filterNNIBranches(vector<NNIMove> &appliedNNIs, Branches &outBranches);
/**
* @brief get branches that correspond to the splits in \a nniSplits
*/
void getSplitBranches(Branches &branches, SplitIntMap &splits, Node *dad = NULL, Node *node = NULL);
/**
* Do fastNNI using PLL
*
* @param nniCount (OUT) number of NNIs applied
* @param nniSteps (OUT) number of NNI steps done
*/
double pllOptimizeNNI(int &nniCount, int &nniSteps, SearchInfo &searchinfo);
/**
* @brief Perform NNI search on the current tree topology
* @return <number_of_NNIs, number_of_NNI_steps>
* This function will automatically use the selected kernel (either PLL or IQ-TREE)
*/
pair<int, int> doNNISearch();
/**
@brief evaluate all NNIs
@param node evaluate all NNIs of the subtree rooted at node
@param dad a neighbor of \p node which does not belong to the subtree
being considered (used for traverse direction)
*/
//void evalNNIs(PhyloNode *node = NULL, PhyloNode *dad = NULL);
/**
* @brief Evaluate all NNIs on branch defined by \a branches
*
* @param nniBranches [IN] branches the branches on which NNIs will be evaluated
* @return list positive NNIs
*/
void evaluateNNIs(Branches &nniBranches, vector<NNIMove> &outNNIMoves);
double optimizeNNIBranches(Branches &nniBranches);
/**
search all positive NNI move on the current tree and save them
on the possilbleNNIMoves list
*/
void evalNNIsSort(bool approx_nni);
/**
apply NNIs from the non-conflicting NNI list
@param compatibleNNIs vector of all compatible NNIs
@param changeBran whether or not the computed branch lengths should be applied
*/
virtual void doNNIs(vector<NNIMove> &compatibleNNIs, bool changeBran = true);
/**
* Restore the old 5 branch lengths stored in the NNI move.
* This is called after an NNI is reverted.
* @param nnimove the NNI move currently in consideration
*/
//void restoreNNIBranches(NNIMove nnimove);
/**
* @brief get a list of compatible NNIs from a list of NNIs
* @param nniMoves [IN] list of NNIs
* @return list of compatible NNIs
*/
void getCompatibleNNIs(vector<NNIMove> &nniMoves, vector<NNIMove> &compatibleNNIs);
/**
add a NNI move to the list of possible NNI moves;
*/
void addPositiveNNIMove(NNIMove &myMove);
/**
* Estimate the 95% quantile of the distribution of N (see paper for more d
details)
* @return the estimated value
*/
inline double estN95(void);
/**
* Estimate the median of the distribution of N (see paper for more d
details)
* @return the estimated value
*/
double getAvgNumNNI(void);
/**
* Estimate the median of the distribution of N (see paper for more d
details)
* @return the estimated value
*/
double estDeltaMedian(void);
/**
* Estimate the 95% quantile of the distribution of DELTA (see paper for
more detail)
* @return the estimated value
*/
inline double estDelta95(void);
/**
current parsimony score of the tree
*/
int cur_pars_score;
// bool enable_parsimony;
/**
stopping rule
*/
StopRule stop_rule;
/**
* Parsimony scores, used for linear regression
*/
double* pars_scores;
/**
Log-likelihood variastring IQPTree::bran2string(PhyloNode* node1, PhyloNode* node2)nce
*/
double logl_variance;
/**
* The coressponding log-likelihood score from computed indendently from the parsimony
* scores
*/
double* lh_scores;
Linear* linRegModel;
inline double getNNICutoff() {
return nni_cutoff;
}
/*
* Contains a sorted list of all NNIs (2n-6) evaluated for the current best tree
* The last element (nni_for_pertub.end()) is the best NNI
*/
vector<pllNNIMove> nniListOfBestTree;
/**
* information and parameters for the tree search procedure
*/
SearchInfo searchinfo;
/**
* Vector contains number of NNIs used at each iterations
*/
vector<int> vecNumNNI;
/**
* Do memory allocation and initialize parameter for UFBoot to run with PLL
*/
void pllInitUFBootData();
/**
* Do memory deallocation for UFBoot data (PLL mode)
*/
void pllDestroyUFBootData();
/**
* DTH:
* Substitute bases in seq according to PLL's rules
* This function should be updated if PLL's rules change.
* @param seq: data of some sequence to be substituted
* @param dataType: PLL_DNA_DATA or PLL_AA_DATA
*/
void pllBaseSubstitute (char *str, int dataType);
/*
* An array to map site index in pllAlignment into IQTree pattern index
* Born due to the order difference of these two
* Will be deallocated in pllDestroyUFBootData()
*/
int * pll2iqtree_pattern_index;
/*
* Build pll2iqtree_pattern_index
* Must be called AFTER initializing PLL model
*/
void pllBuildIQTreePatternIndex();
/**
* FOR TESTING:
* Write to log file the freq of pllAlignment sites, and
* freq of bootstrap site stored in pllUFBootDataPtr->boot_samples
*/
void pllLogBootSamples(int** pll_boot_samples, int nsamples, int npatterns);
/**
* Convert certain arrays in pllUFBootDataPtr
* into IQTree data structures
* to be usable in IQTree::summarizeBootstrap()
*/
void pllConvertUFBootData2IQTree();
protected:
/**
* Splits corresponding to random NNIs
*/
SplitIntMap initTabuSplits;
/**
criterion to assess important quartet
*/
IQP_ASSESS_QUARTET iqp_assess_quartet;
/**
* Taxa set
*/
NodeVector taxaSet;
/**
* Vector contains approximated improvement pro NNI at each iterations
*/
vector<double> vecImpProNNI;
/**
Optimal branch lengths
*/
// mapString2Double optBrans;
/**
* @brief get branches, on which NNIs are evaluated for the next NNI step.
* @param[out] nodes1 one ends of the branches
* @param[out] nodes2 the other ends of the branches
* @param[in] nnis NNIs that have been previously applied
*/
void generateNNIBranches(NodeVector& nodes1, NodeVector& nodes2, unordered_map<string, NNIMove>& nnis);
int k_delete, k_delete_min, k_delete_max, k_delete_stay;
/**
number of representative leaves for IQP step
*/
int k_represent;
public:
/**
* Candidate tree set (the current best N (default N = 5)
* NNI-optimal trees
*/
CandidateSet candidateTrees;
/**
* Set of all intermediate trees (initial trees, tree generated by NNI steps,
* NNI-optimal trees)
*/
CandidateSet intermediateTrees;
/**
* Update the candidate set with a new NNI-optimal tree. The maximum size of the candidate set
* is fixed to the initial setting. Thus, if the size exceed the maximum number of trees, the worse
* tree will be removed.
*
* @param treeString
* the new tree
* @param score
* the score of the new tree
* @param updateStopRule
* Whether or not to update the stop rule
* @return relative position of the new tree to the current best.
* -1 if duplicated
* -2 if the candidate set is not updated
*/
int addTreeToCandidateSet(string treeString, double score, bool updateStopRule, int sourceProcID);
/**
MPI: synchronize candidate trees between all processes
@param nTrees number of trees to broadcast
@param updateStopRule true to update stopping rule, false otherwise
*/
void syncCandidateTrees(int nTrees, bool updateStopRule);
/**
MPI: synchronize tree of current iteration with master
will update candidateset_changed
@param curTree current tree
*/
void syncCurrentTree();
/**
MPI: Master sends stop message to all workers
*/
void sendStopMessage();
/**
* Generate the initial parsimony/random trees, called by initCandidateTreeSet
* @param nParTrees number of parsimony/random trees to generate
*/
void createInitTrees(int nParTrees);
/**
* Generate the initial candidate tree set
* @param nParTrees number of parsimony trees to generate
* @param nNNITrees number of NNI locally optimal trees to generate
*/
void initCandidateTreeSet(int nParTrees, int nNNITrees);
/**
* Generate the initial tree (usually used for model parameter estimation)
*/
void computeInitialTree(LikelihoodKernel kernel);
/**
* @brief: optimize model parameters on the current tree
* either IQ-TREE or PLL
* @param printInfo to print model parameters to the screen or not
* @param epsilon likelihood epsilon for optimization
*
*/
string optimizeModelParameters(bool printInfo = false, double epsilon = -1);
/**
* variable storing the current best tree topology
*/
topol* pllBestTree;
/****** following variables are for ultra-fast bootstrap *******/
/** TRUE to save also branch lengths into treels_newick */
// bool save_all_br_lens;
/**
this keeps the list of intermediate trees.
it will be activated if params.avoid_duplicated_trees is TRUE.
*/
// StringIntMap treels;
/** pattern log-likelihood vector for each treels */
// vector<double* > treels_ptnlh;
/** OBSOLETE: tree log-likelihood for each treels */
// DoubleVector treels_logl;
/** NEWICK string for each treels */
// StrVector treels_newick;
/** OBSOLETE: maximum number of distinct candidate trees (tau parameter) */
// int max_candidate_trees;
/** log-likelihood threshold (l_min) */
double logl_cutoff;
/** vector of bootstrap alignments generated */
vector<BootValType* > boot_samples;
/** starting sample for UFBoot, used for MPI */
int sample_start;
/** end sample for UFBoot, used for MPI */
int sample_end;
/** newick string of corresponding bootstrap trees */
StrVector boot_trees;
/** bootstrap tree strings with branch lengths, for -wbtl option */
// StrVector boot_trees_brlen;
/** number of multiple optimal trees per replicate */
IntVector boot_counts;
/** corresponding RELL log-likelihood */
DoubleVector boot_logl;
/** corresponding log-likelihood on original alignment */
DoubleVector boot_orig_logl;
/** Set of splits occurring in bootstrap trees */
vector<SplitGraph*> boot_splits;
/** log-likelihood of bootstrap consensus tree */
double boot_consense_logl;
/** Robinson-Foulds distance between contree and ML tree */
int contree_rfdist;
/** Corresponding map for set of splits occurring in bootstrap trees */
//SplitIntMap boot_splits_map;
/** summarize all bootstrap trees */
void summarizeBootstrap(Params ¶ms, MTreeSet &trees);
void summarizeBootstrap(Params ¶ms);
/** summarize bootstrap trees into split set */
void summarizeBootstrap(SplitGraph &sg);
void writeUFBootTrees(Params ¶ms);
/** @return bootstrap correlation coefficient for assessing convergence */
double computeBootstrapCorrelation();
int getDelete() const;
void setDelete(int _delete);
protected:
/**** NNI cutoff heuristic *****/
/**
*/
vector<NNIInfo> nni_info;
bool estimate_nni_cutoff;
double nni_cutoff;
bool nni_sort;
bool testNNI;
ofstream outNNI;
protected:
//bool print_tree_lh;
//int write_intermediate_trees;
ofstream out_treels, out_treelh, out_sitelh, out_treebetter;
string treels_name, out_lh_file, site_lh_file;
void estimateNNICutoff(Params* params);
virtual void saveCurrentTree(double logl); // save current tree
void saveNNITrees(PhyloNode *node = NULL, PhyloNode *dad = NULL);
int duplication_counter;
// MPI: vector of size = num processes, true if master should send candidate set to worker
BoolVector candidateset_changed;
// true if best candidate tree is changed
bool bestcandidate_changed;
/**
number of IQPNNI iterations
*/
//int iqpnni_iterations;
/**
bonus values of all branches, used for IQP algorithm
*/
//double *bonus_values;
/**
delete a set of leaves from tree (with the probability p_delete), assume tree is birfucating
@param del_leaves (OUT) the list of deleted leaves
*/
void deleteLeaves(PhyloNodeVector &del_leaves);
void deleteNonTabuLeaves(PhyloNodeVector &del_leaves);
/**
* delete a set of leaves from tree
* non-cherry leaves are selected first
* @param del_leaves (OUT) the list of deleted leaves
*/
void deleteNonCherryLeaves(PhyloNodeVector &del_leaves);
/**
reinsert the whole list of leaves back into the tree
@param del_leaves the list of deleted leaves, returned by deleteLeaves() function
*/
virtual void reinsertLeaves(PhyloNodeVector &del_leaves);
void reinsertLeavesByParsimony(PhyloNodeVector &del_leaves);
void doParsimonyReinsertion();
/**
assess a quartet with four taxa. Current implementation uses the four-point condition
based on distance matrix for quick evaluation.
@param leaf0 one of the leaf in the existing sub-tree
@param leaf1 one of the leaf in the existing sub-tree
@param leaf2 one of the leaf in the existing sub-tree
@param del_leaf a leaf that was deleted (not in the existing sub-tree)
@return 0, 1, or 2 depending on del_leaf should be in subtree containing leaf0, leaf1, or leaf2, respectively
*/
int assessQuartet(Node *leaf0, Node *leaf1, Node *leaf2, Node *del_leaf);
/**
assess a quartet with four taxa using parsimony
@param leaf0 one of the leaf in the existing sub-tree
@param leaf1 one of the leaf in the existing sub-tree
@param leaf2 one of the leaf in the existing sub-tree
@param del_leaf a leaf that was deleted (not in the existing sub-tree)
@return 0, 1, or 2 depending on del_leaf should be in subtree containing leaf0, leaf1, or leaf2, respectively
*/
int assessQuartetParsimony(Node *leaf0, Node *leaf1, Node *leaf2,
Node *del_leaf);
/**
assess the important quartets around a virtual root of the tree.
This function will assign bonus points to branches by updating the variable 'bonus_values'
@param cur_root the current virtual root
@param del_leaf a leaf that was deleted (not in the existing sub-tree)
*/
void assessQuartets(vector<RepresentLeafSet*> &leaves_vec, PhyloNode *cur_root, PhyloNode *del_leaf);
/**
initialize the bonus points to ZERO
@param node the root of the sub-tree
@param dad dad of 'node', used to direct the recursion
*/
void initializeBonus(PhyloNode *node = NULL, PhyloNode *dad = NULL);
/**
raise the bonus points for all branches in the subtree rooted at a node
@param node the root of the sub-tree
@param dad dad of 'node', used to direct the recursion
*/
void raiseBonus(Neighbor *nei, Node *dad, double bonus);
/**
Bonuses are stored in a partial fashion. This function will propagate the bonus at every branch
into the subtree at this branch.
@param node the root of the sub-tree
@param dad dad of 'node', used to direct the recursion
@return the partial bonus of the branch (node -> dad)
*/
double computePartialBonus(Node *node, Node* dad);
/**
determine the list of branches with the same best bonus point
@param best_bonus the best bonus determined by findBestBonus()
@param best_nodes (OUT) vector of one ends of the branches with highest bonus point
@param best_dads (OUT) vector of the other ends of the branches with highest bonus point
@param node the root of the sub-tree
@param dad dad of 'node', used to direct the recursion
*/
void findBestBonus(double &best_score, NodeVector &best_nodes, NodeVector &best_dads, Node *node = NULL, Node *dad = NULL);
void estDeltaMin();
void convertNNI2Splits(SplitIntMap &nniSplits, int numNNIs, vector<NNIMove> &compatibleNNIs);
string generateParsimonyTree(int randomSeed);
double doTreePerturbation();
void estimateLoglCutoffBS();
//void estimateNNICutoff(Params ¶ms);
public:
/**
* Return best tree string from the candidate set
*
* @param numTrees
* Number of best trees to return
* @return
* A string vector of trees
*/
vector<string> getBestTrees(int numTrees = 0);
/**
* Print the iteration number and the tree score
*/
void printIterationInfo(int sourceProcID);
/**
* Return branches that are 2 branches away from the branches, on which NNIs were applied
* in the previous NNI steps.
* @param
* previousNNIBranches[IN] a set of branches on which NNIs were performed in the previous NNI step.
* @return
* a set of branches, on which NNIs should be evaluated for the current NNI steps
*/
Branches getReducedListOfNNIBranches(Branches &previousNNIBranches);
// Diep added for UFBoot2-Corr
void refineBootTrees();
bool on_refine_btree;
Alignment* saved_aln_on_refine_btree;
vector<IntVector> boot_samples_int;
};
#endif
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