void CleanTree(TreeType& node)
{
node.Stat().LastDistance() = 0.0;
for (size_t i = 0; i < node.NumChildren(); ++i)
CleanTree(node.Child(i));
}
void CARTTrainer::pruneTree(TreeType & tree){
//Calculate g of all the nodes
measureStrength(tree, 0, 0);
//Find the lowest g of the internal nodes
double g = std::numeric_limits<double>::max();
for(std::size_t i = 0; i != tree.size(); i++){
if(tree[i].leftNodeId > 0 && tree[i].g < g){
//Update g
g = tree[i].g;
}
}
//Prune the nodes with lowest g and make them terminal
for(std::size_t i=0; i != tree.size(); i++){
//Make the internal nodes with the smallest g terminal nodes and prune their children!
if( tree[i].leftNodeId > 0 && tree[i].g == g){
// pruneNode(tree, tree[i].leftNodeId);
// pruneNode(tree, tree[i].rightNodeId);
// //Make the node terminal
tree[i].leftNodeId = 0;
tree[i].rightNodeId = 0;
}
}
}
DTBStat(const TreeType& node) :
maxNeighborDistance(DBL_MAX),
minNeighborDistance(DBL_MAX),
bound(DBL_MAX),
componentMembership(
((node.NumPoints() == 1) && (node.NumChildren() == 0)) ?
node.Point(0) : -1) { }
void CheckHierarchy(const TreeType& tree)
{
for (size_t i = 0; i < tree.NumChildren(); i++)
{
BOOST_REQUIRE_EQUAL(&tree, tree.Child(i).Parent());
CheckHierarchy(tree.Child(i));
}
}
double FastMKSRules<KernelType, TreeType>::Rescore(TreeType& queryNode,
TreeType& /*referenceNode*/,
const double oldScore) const
{
queryNode.Stat().Bound() = CalculateBound(queryNode);
const double bestKernel = queryNode.Stat().Bound();
return ((1.0 / oldScore) > bestKernel) ? oldScore : DBL_MAX;
}
FastMKSStat(const TreeType& node) :
bound(-DBL_MAX),
lastKernel(0.0),
lastKernelNode(NULL)
{
// Do we have to calculate the centroid?
if (tree::TreeTraits<TreeType>::FirstPointIsCentroid)
{
// If this type of tree has self-children, then maybe the evaluation is
// already done. These statistics are built bottom-up, so the child stat
// should already be done.
if ((tree::TreeTraits<TreeType>::HasSelfChildren) &&
(node.NumChildren() > 0) &&
(node.Point(0) == node.Child(0).Point(0)))
{
selfKernel = node.Child(0).Stat().SelfKernel();
}
else
{
selfKernel = sqrt(node.Metric().Kernel().Evaluate(
node.Dataset().col(node.Point(0)),
node.Dataset().col(node.Point(0))));
}
}
else
{
// Calculate the centroid.
arma::vec center;
node.Center(center);
selfKernel = sqrt(node.Metric().Kernel().Evaluate(center, center));
}
}
static int GetSplitPolicy(const TreeType& child,
const size_t axis,
const typename TreeType::ElemType cut)
{
if (child.Bound()[axis].Hi() <= cut)
return AssignToFirstTree;
else if (child.Bound()[axis].Lo() >= cut)
return AssignToSecondTree;
return SplitRequired;
}
/*
Removes branch with root node id nodeId, incl. the node itself
*/
void CARTTrainer::pruneNode(TreeType & tree, std::size_t nodeId){
std::size_t i = findNode(tree,nodeId);
if(tree[i].leftNodeId>0){
//Prune left branch
pruneNode(tree, tree[i].leftNodeId);
//Prune right branch
pruneNode(tree, tree[i].rightNodeId);
}
//Remove node
tree.erase(tree.begin()+i);
}
double RangeSearchRules<MetricType, TreeType>::Score(const size_t queryIndex,
TreeType& referenceNode)
{
// We must get the minimum and maximum distances and store them in this
// object.
math::Range distances;
if (tree::TreeTraits<TreeType>::FirstPointIsCentroid)
{
// In this situation, we calculate the base case. So we should check to be
// sure we haven't already done that.
double baseCase;
if (tree::TreeTraits<TreeType>::HasSelfChildren &&
(referenceNode.Parent() != NULL) &&
(referenceNode.Point(0) == referenceNode.Parent()->Point(0)))
{
// If the tree has self-children and this is a self-child, the base case
// was already calculated.
baseCase = referenceNode.Parent()->Stat().LastDistance();
lastQueryIndex = queryIndex;
lastReferenceIndex = referenceNode.Point(0);
}
else
{
// We must calculate the base case by hand.
baseCase = BaseCase(queryIndex, referenceNode.Point(0));
}
// This may be possibly loose for non-ball bound trees.
distances.Lo() = baseCase - referenceNode.FurthestDescendantDistance();
distances.Hi() = baseCase + referenceNode.FurthestDescendantDistance();
// Update last distance calculation.
referenceNode.Stat().LastDistance() = baseCase;
}
else
{
distances = referenceNode.RangeDistance(querySet.unsafe_col(queryIndex));
}
// If the ranges do not overlap, prune this node.
if (!distances.Contains(range))
return DBL_MAX;
// In this case, all of the points in the reference node will be part of the
// results.
if ((distances.Lo() >= range.Lo()) && (distances.Hi() <= range.Hi()))
{
AddResult(queryIndex, referenceNode);
return DBL_MAX; // We don't need to go any deeper.
}
// Otherwise the score doesn't matter. Recursion order is irrelevant in
// range search.
return 0.0;
}
bool HilbertRTreeAuxiliaryInformation<TreeType, HilbertValueType>::
UpdateAuxiliaryInfo(TreeType* node)
{
if (node->IsLeaf()) // Should already be updated
return true;
TreeType* child = node->Children()[node->NumChildren() - 1];
if (hilbertValue.CompareWith(child->AuxiliaryInfo().hilbertValue()) < 0)
{
hilbertValue.Copy(node,child);
return true;
}
return false;
}
void SearchTree(TreeType<T>& tree)
{
T input;
cout << "Enter item to search: ";
cin >> input;
tree.SearchItem(input);
}
void DeleteFromTree(TreeType<T>& tree)
{
T input;
cout << "Enter item to delete: ";
cin >> input;
tree.DeleteItem(input);
}
void InsertItem(TreeType<T>& tree)
{
cout << "Enter number: ";
T num;
cin >> num;
tree.InsertItem(num);
}
int GetMaxLevel(const TreeType& tree)
{
int max = 1;
if (!tree.IsLeaf())
{
int m = 0;
for (size_t i = 0; i < tree.NumChildren(); i++)
{
int n = GetMaxLevel(*tree.Children()[i]);
if (n > m)
m = n;
}
max += m;
}
return max;
}
int GetMinLevel(const TreeType& tree)
{
int min = 1;
if (!tree.IsLeaf())
{
int m = INT_MAX;
for (size_t i = 0; i < tree.NumChildren(); i++)
{
int n = GetMinLevel(*tree.Children()[i]);
if (n < m)
m = n;
}
min += m;
}
return min;
}
void NeighborSearchRules<
SortPolicy,
MetricType,
TreeType>::
UpdateAfterRecursion(TreeType& queryNode, TreeType& /* referenceNode */)
{
// Find the worst distance that the children found (including any points), and
// update the bound accordingly.
double worstDistance = SortPolicy::BestDistance();
// First look through children nodes.
for (size_t i = 0; i < queryNode.NumChildren(); ++i)
{
if (SortPolicy::IsBetter(worstDistance, queryNode.Child(i).Stat().Bound()))
worstDistance = queryNode.Child(i).Stat().Bound();
}
// Now look through children points.
for (size_t i = 0; i < queryNode.NumPoints(); ++i)
{
if (SortPolicy::IsBetter(worstDistance,
distances(distances.n_rows - 1, queryNode.Point(i))))
worstDistance = distances(distances.n_rows - 1, queryNode.Point(i));
}
// Take the worst distance from all of these, and update our bound to reflect
// that.
queryNode.Stat().Bound() = worstDistance;
}
double DTBRules<MetricType, TreeType>::Score(TreeType& queryNode,
TreeType& referenceNode)
{
// If all the queries belong to the same component as all the references
// then we prune.
if ((queryNode.Stat().ComponentMembership() >= 0) &&
(queryNode.Stat().ComponentMembership() ==
referenceNode.Stat().ComponentMembership()))
return DBL_MAX;
++scores;
const double distance = queryNode.MinDistance(&referenceNode);
const double bound = CalculateBound(queryNode);
// If all the points in the reference node are farther than the candidate
// nearest neighbor for all queries in the node, we prune.
return (bound < distance) ? DBL_MAX : distance;
}
void CheckContainment(const TreeType& tree)
{
if (tree.NumChildren() == 0)
{
for (size_t i = 0; i < tree.Count(); i++)
BOOST_REQUIRE(tree.Bound().Contains(
tree.Dataset().unsafe_col(tree.Points()[i])));
}
else
{
for (size_t i = 0; i < tree.NumChildren(); i++)
{
for (size_t j = 0; j < tree.Bound().Dim(); j++)
BOOST_REQUIRE(tree.Bound()[j].Contains(tree.Children()[i]->Bound()[j]));
CheckContainment(*(tree.Children()[i]));
}
}
}
inline double KDERules<MetricType, KernelType, TreeType>::
Score(const size_t queryIndex, TreeType& referenceNode)
{
double score, maxKernel, minKernel, bound;
const arma::vec& queryPoint = querySet.unsafe_col(queryIndex);
const double minDistance = referenceNode.MinDistance(queryPoint);
bool newCalculations = true;
if (tree::TreeTraits<TreeType>::FirstPointIsCentroid &&
lastQueryIndex == queryIndex &&
traversalInfo.LastReferenceNode() != NULL &&
traversalInfo.LastReferenceNode()->Point(0) == referenceNode.Point(0))
{
// Don't duplicate calculations.
newCalculations = false;
lastQueryIndex = queryIndex;
lastReferenceIndex = referenceNode.Point(0);
}
else
{
// Calculations are new.
maxKernel = kernel.Evaluate(minDistance);
minKernel = kernel.Evaluate(referenceNode.MaxDistance(queryPoint));
bound = maxKernel - minKernel;
}
if (newCalculations &&
bound <= (absError + relError * minKernel) / referenceSet.n_cols)
{
// Estimate values.
double kernelValue;
// Calculate kernel value based on reference node centroid.
if (tree::TreeTraits<TreeType>::FirstPointIsCentroid)
{
kernelValue = EvaluateKernel(queryIndex, referenceNode.Point(0));
}
else
{
kde::KDEStat& referenceStat = referenceNode.Stat();
kernelValue = EvaluateKernel(queryPoint, referenceStat.Centroid());
}
densities(queryIndex) += referenceNode.NumDescendants() * kernelValue;
// Don't explore this tree branch.
score = DBL_MAX;
}
else
{
score = minDistance;
}
++scores;
traversalInfo.LastReferenceNode() = &referenceNode;
traversalInfo.LastScore() = score;
return score;
}
size_t nodeDepth(const TreeType& tree, const NodeType& node)
{
const auto* currentNode = &node;
size_t depth = 0;
while (!currentNode->isRoot()) {
currentNode = &tree.nodeAt(currentNode->parentNodeIndex);
depth++;
}
return depth;
}
inline double NeighborSearchRules<SortPolicy, MetricType, TreeType>::Score(
TreeType& queryNode,
TreeType& referenceNode) const
{
const double distance = SortPolicy::BestNodeToNodeDistance(&queryNode,
&referenceNode);
const double bestDistance = queryNode.Stat().Bound();
return (SortPolicy::IsBetter(distance, bestDistance)) ? distance : DBL_MAX;
}
inline double DTBRules<MetricType, TreeType>::CalculateBound(
TreeType& queryNode) const
{
double worstPointBound = -DBL_MAX;
double bestPointBound = DBL_MAX;
double worstChildBound = -DBL_MAX;
double bestChildBound = DBL_MAX;
// Now, find the best and worst point bounds.
for (size_t i = 0; i < queryNode.NumPoints(); ++i)
{
const size_t pointComponent = connections.Find(queryNode.Point(i));
const double bound = neighborsDistances[pointComponent];
if (bound > worstPointBound)
worstPointBound = bound;
if (bound < bestPointBound)
bestPointBound = bound;
}
// Find the best and worst child bounds.
for (size_t i = 0; i < queryNode.NumChildren(); ++i)
{
const double maxBound = queryNode.Child(i).Stat().MaxNeighborDistance();
if (maxBound > worstChildBound)
worstChildBound = maxBound;
const double minBound = queryNode.Child(i).Stat().MinNeighborDistance();
if (minBound < bestChildBound)
bestChildBound = minBound;
}
// Now calculate the actual bounds.
const double worstBound = std::max(worstPointBound, worstChildBound);
const double bestBound = std::min(bestPointBound, bestChildBound);
// We must check that bestBound != DBL_MAX; otherwise, we risk overflow.
const double bestAdjustedBound = (bestBound == DBL_MAX) ? DBL_MAX :
bestBound + 2 * queryNode.FurthestDescendantDistance();
// Update the relevant quantities in the node.
queryNode.Stat().MaxNeighborDistance() = worstBound;
queryNode.Stat().MinNeighborDistance() = bestBound;
queryNode.Stat().Bound() = std::min(worstBound, bestAdjustedBound);
return queryNode.Stat().Bound();
}
double DTBRules<MetricType, TreeType>::Score(const size_t queryIndex,
TreeType& referenceNode)
{
size_t queryComponentIndex = connections.Find(queryIndex);
// If the query belongs to the same component as all of the references,
// then prune. The cast is to stop a warning about comparing unsigned to
// signed values.
if (queryComponentIndex ==
(size_t) referenceNode.Stat().ComponentMembership())
return DBL_MAX;
const arma::vec queryPoint = dataSet.unsafe_col(queryIndex);
const double distance = referenceNode.MinDistance(queryPoint);
// If all the points in the reference node are farther than the candidate
// nearest neighbor for the query's component, we prune.
return neighborsDistances[queryComponentIndex] < distance
? DBL_MAX : distance;
}
/**
* @brief Return a list clades, each itself containing a list of edge indices of that clade.
*
* The function takes a list of clades with their taxa as input, and a reference tree.
* It then inspects all clades and findes the edges of the tree that belong into a clade.
* Furthermore, a clade "basal_branches" is added for those edges of the tree that do not
* belong to any clade.
*
* The edges of a clade are determined by finding the smalles subtree (split) of the tree that
* contains all nodes of the clade. That means, the clades should be monophyletic in order for this
* algorithm to work properly.
*/
CladeEdgeList get_clade_edges( CladeTaxaList const& clades, TreeType& tree )
{
// Prepare the result map.
CladeEdgeList clade_edges;
// Make a set of all edges that do not belong to any clade (the basal branches of the tree).
// We first fill it with all edge indices, then remove the clade-edges later,
// so that only the wanted ones remain.
std::unordered_set<size_t> basal_branches;
for( auto it = tree.begin_edges(); it != tree.end_edges(); ++it ) {
basal_branches.insert( it->index() );
}
// Process all clades.
for( auto const& clade : clades ) {
// Find the edges that are part of the subtree of this clade.
auto const subedges = get_clade_edges( tree, clade.second );
// TODO for now, we convert to an unordered map here by hand.
// this can be clean up!
auto const subedge_map = std::unordered_set<size_t>(
subedges.begin(), subedges.end()
);
// Add them to the clade edges list.
clade_edges.push_back( std::make_pair( clade.first, subedge_map ));
// Remove the edge indices of this clade from the basal branches (non-clade) edges list.
for( auto const edge : subedges ) {
basal_branches.erase( edge );
}
}
// Now that we have processed all clades, also add the non-clade edges (basal branches)
// to the list as a special clade "basal_branches". This way, all edges of the reference tree
// are used by exaclty one clade.
clade_edges.push_back( std::make_pair( "basal_branches", basal_branches ));
return clade_edges;
}
inline double NeighborSearchRules<SortPolicy, MetricType, TreeType>::Rescore(
TreeType& queryNode,
TreeType& /* referenceNode */,
const double oldScore) const
{
if (oldScore == DBL_MAX)
return oldScore;
const double bestDistance = queryNode.Stat().Bound();
return (SortPolicy::IsBetter(oldScore, bestDistance)) ? oldScore : DBL_MAX;
}
void TestRangeQuery(TreeType &a_tree, string &db_filename, string &key1, string &key2) {
SequenceMap lower_bound(key1);
SequenceMap upper_bound(key2);
ifstream rebasefile(db_filename);
string db_line;
if (!rebasefile.is_open()) { //Program exists if file is not opened or cannot be opened
exit(EXIT_FAILURE);
}
while(rebasefile.good()) {
(getline(rebasefile, db_line));
size_t pos;
string delimiter = "/";
string a_reco_seq;
string an_enz_acro;
//Enzyme acronym is copied and deleted from db_line
pos = db_line.find(delimiter);
an_enz_acro = db_line.substr(0, pos);
db_line.erase(0, pos + delimiter.length());
while((pos = db_line.find(delimiter)) != string::npos) {
a_reco_seq = db_line.substr(0, pos);
if(a_reco_seq == "") {
break;
}
else {
SequenceMap new_sequence_map(a_reco_seq, an_enz_acro);
a_tree.insert(new_sequence_map);
db_line.erase(0, pos + delimiter.length());
}
}
}
a_tree.print_range_elem(lower_bound, upper_bound);
}
double DTBRules<MetricType, TreeType>::Score(const size_t queryIndex,
TreeType& referenceNode,
const double baseCaseResult)
{
// I don't really understand the last argument here
// It just gets passed in the distance call, otherwise this function
// is the same as the one above.
size_t queryComponentIndex = connections.Find(queryIndex);
// If the query belongs to the same component as all of the references,
// then prune.
if (queryComponentIndex == referenceNode.Stat().ComponentMembership())
return DBL_MAX;
const arma::vec queryPoint = dataSet.unsafe_col(queryIndex);
const double distance = referenceNode.MinDistance(queryPoint,
baseCaseResult);
// If all the points in the reference node are farther than the candidate
// nearest neighbor for the query's component, we prune.
return (neighborsDistances[queryComponentIndex] < distance) ? DBL_MAX :
distance;
}
void FillTree (std::string db_filename, TreeType &a_tree) {
ifstream inStream(db_filename);
std::string db_line, an_enz_acro, a_reco_seq, garbage_line;
for (int i = 0; i < 10; i ++) //Skip over the header and begin reading on line 11.
getline(inStream, garbage_line);
while (std::getline (inStream, db_line)) { //inStream has reached line 11 and will begin to parse data.
if (db_line.empty()) continue;
size_t first_slash = db_line.find("/");
an_enz_acro = GetEnzymeAcronym(db_line, first_slash);
while (GetNextRecognitionSequence(db_line, a_reco_seq, first_slash)) {
SequenceMap new_sequence_map(a_reco_seq, an_enz_acro);
a_tree.insert(new_sequence_map);
}
}
}
void CheckBound(const TreeType& tree)
{
typedef typename TreeType::ElemType ElemType;
for (size_t i = 0; i < tree.NumDescendants(); i++)
{
arma::Col<ElemType> point = tree.Dataset().col(tree.Descendant(i));
// Check that the point is contained in the bound.
BOOST_REQUIRE_EQUAL(true, tree.Bound().Contains(point));
const arma::Mat<ElemType>& loBound = tree.Bound().LoBound();
const arma::Mat<ElemType>& hiBound = tree.Bound().HiBound();
// Ensure that there is a hyperrectangle that contains the point.
bool success = false;
for (size_t j = 0; j < tree.Bound().NumBounds(); j++)
{
success = true;
for (size_t k = 0; k < loBound.n_rows; k++)
{
if (point[k] < loBound(k, j) - 1e-14 * std::fabs(loBound(k, j)) ||
point[k] > hiBound(k, j) + 1e-14 * std::fabs(hiBound(k, j)))
{
success = false;
break;
}
}
if (success)
break;
}
BOOST_REQUIRE_EQUAL(success, true);
}
if (!tree.IsLeaf())
{
CheckBound(*tree.Left());
CheckBound(*tree.Right());
}
}
DualTreeKMeansStatistic(TreeType& node) :
closestQueryNode(NULL),
minQueryNodeDistance(DBL_MAX),
maxQueryNodeDistance(DBL_MAX),
clustersPruned(0),
iteration(size_t() - 1)
{
// Empirically calculate the centroid.
centroid.zeros(node.Dataset().n_rows);
for (size_t i = 0; i < node.NumPoints(); ++i)
centroid += node.Dataset().col(node.Point(i));
for (size_t i = 0; i < node.NumChildren(); ++i)
centroid += node.Child(i).NumDescendants() *
node.Child(i).Stat().Centroid();
centroid /= node.NumDescendants();
}
