int BinarySearchTree::getMaxDepth(Node *node)
{
if(node==NULL)
return 0;
else
return max(1+getMaxDepth(node->getRight()),1+getMaxDepth(node->getLeft()));
}
// 递归过程中，直接修改currentDepth, maxDepth, 因此采用引用传参
void getMaxDepth(BinaryTreeNode* data, int& currentDepth, int& maxDepth)
{
currentDepth ++;
if (data->left == NULL && data->right == NULL)
{
maxDepth = (maxDepth < currentDepth ? currentDepth : maxDepth);
//currentDepth--;
return ;
}
if (data->left != NULL)
{
getMaxDepth(data, currentDepth, maxDepth);
currentDepth--;
}
if (data->right != NULL)
{
getMaxDepth(data, currentDepth, maxDepth);
currentDepth--;
}
}
void CustomRule::apply(Builder* b) const {
int newDepth = -1;
/// If there is a maxdepth set for this object check it.
if (getMaxDepth() != -1) {
if (!b->getState().maxDepths.contains(this)) {
/// We will add a new maxdepth for this rule to the state.
newDepth = getMaxDepth()-1;
} else {
int depth = b->getState().maxDepths[this];
if (depth <= 0) {
/// This rule is retired.
if (retirementRule) {
b->getState().maxDepths[this] = maxDepth;
retirementRule->rule()->apply(b);
}
return;
} else {
/// Decrease depth.
newDepth = depth-1;
}
}
}
/// Apply all actions.
for (int i = 0; i < actions.size(); i++) {
if (getMaxDepth() != -1) {
actions[i].apply(b, this, newDepth);
} else {
actions[i].apply(b, 0 ,0);
}
}
}
void getMaxDepth(TreeNode* node, int curDepth, int& maxDepth)
{
if (!node->left && !node->right)
{
maxDepth = max(maxDepth, curDepth);
}
if (node->left) { getMaxDepth(node->left, curDepth + 1, maxDepth); }
if (node->right) { getMaxDepth(node->right, curDepth + 1, maxDepth); }
}
int KDNode::getMaxDepth(KDNode* node){
int depth = node->depth;
if(node->left != NULL){
depth = getMaxDepth(node->left);
}
if(node->right != NULL){
depth = max(depth, getMaxDepth(node->right));
}
return depth;
}
int main()
{
// Initialize the root
Node *root = NULL;
int insertElements(Node **root,int data);
int printElements(Node *root);
int getMaxDepth(Node *root,int parentLevel);
int getNodeCount(Node *root);
int hasPathSum(Node *root,int sumValue,int parentSumVal);
int printPath(Node *root);
int numElements;
printf("Please enter the number of elements to be inserted\n");
scanf("%d",&numElements);
if (numElements > MAXELEMENTS)
{
printf("Exceeded total num of elements allowed\n");
return 0;
}
// Input elements into BST
for (int i=1;i<=numElements;i++)
{
int data;
printf("Enter node data \n");
scanf("%d",&data);
if (!insertElements(&root,data))
{
printf("Unable to insert element\n");
return 0;
}
}
// print BST elements
if (!printElements(root))
{
printf("Unable to print elements\n");
return 0;
}
printf("The max depth of the tree %d \n",getMaxDepth(root,100));
printf("Number of nodes in the tree %d \n",getNodeCount(root));
int sumVal;
printf("Enter the sum value \n");
scanf("%d",&sumVal);
if (hasPathSum(root,sumVal,100))
{
printf("sum of at least one of the path is %d\n",sumVal);
} else
{
printf("There is no path with sum %d\n",sumVal);
}
return 1;
}
//This is for real octants only, pseudo-boundary octants can not be tested
//using this.
bool DA::isBoundaryOctant(unsigned char *flags) {
#ifdef __DEBUG_DA_PUBLIC__
assert(m_bIamActive);
#endif
unsigned char _flags = 0;
Point pt = getCurrentOffset();
unsigned int x = pt.xint();
unsigned int y = pt.yint();
unsigned int z = pt.zint();
unsigned int d = getLevel(curr())-1;
unsigned int maxD = getMaxDepth()-1;
unsigned int len = (unsigned int)(1u<<(maxD - d) );
unsigned int blen = (unsigned int)(1u << maxD);
if (!x) _flags |= ot::TreeNode::X_NEG_BDY;
if (!y) _flags |= ot::TreeNode::Y_NEG_BDY;
if (!z) _flags |= ot::TreeNode::Z_NEG_BDY;
if ( (x+len) == blen ) _flags |= ot::TreeNode::X_POS_BDY;
if ( (y+len) == blen ) _flags |= ot::TreeNode::Y_POS_BDY;
if ( (z+len) == blen ) _flags |= ot::TreeNode::Z_POS_BDY;
if(flags) {
*flags = _flags;
}
return _flags;
}//end function
Entry*
NameTree::findExactMatch(const Name& name, size_t prefixLen) const
{
prefixLen = std::min(name.size(), prefixLen);
if (prefixLen > getMaxDepth()) {
return nullptr;
}
const Node* node = m_ht.find(name, prefixLen);
return node == nullptr ? nullptr : &node->entry;
}
int getMaxDepth(Node *root,int currentDepth)
{
static char firstCall = 'Y';
// Set currentPath to 0 for first call
if (firstCall == 'Y')
{
currentDepth = 0;
firstCall = 'N';
}
// If no root, depth 0
if (!root)
{
return currentDepth;
}
// Increment the depth by 1
++currentDepth;
// Initialize the left & right child depth to current depth
int leftChildDepth = currentDepth;
int rightChildDepth = currentDepth;
if (root->leftChild)
{
leftChildDepth = getMaxDepth(root->leftChild, currentDepth);
}
if (root->rightChild)
{
rightChildDepth = getMaxDepth(root->rightChild, currentDepth);
}
// return the max of the child depth
return (leftChildDepth>rightChildDepth? leftChildDepth:rightChildDepth);
}
int getDepthOfBinaryTree(BinaryTreeNode* root)
{
if (root == NULL)
{
return 0;
}
int currentDepth = 0;
int maxDepth = 0;
getMaxDepth(root, currentDepth, maxDepth);
return maxDepth;
}
Entry*
NameTree::findLongestPrefixMatch(const Name& name, const EntrySelector& entrySelector) const
{
size_t depth = std::min(name.size(), getMaxDepth());
HashSequence hashes = computeHashes(name, depth);
for (ssize_t i = depth; i >= 0; --i) {
const Node* node = m_ht.find(name, i, hashes);
if (node != nullptr && entrySelector(node->entry)) {
return &node->entry;
}
}
return nullptr;
}
std::vector<bool> ot::TreeNode ::getMorton() const {
unsigned int dim = getDim();
unsigned int maxDepth = getMaxDepth();
unsigned int Ln = 1;
if(maxDepth > 0) {
Ln = binOp::binLength(maxDepth);
}
unsigned int const N = (dim*maxDepth) + Ln;
std::vector<bool> numBin(N);
std::vector<bool> xBin(maxDepth);
std::vector<bool> yBin(maxDepth);
std::vector<bool> zBin(maxDepth);
std::vector<bool> levBin(Ln);
//create Binary Representations
binOp::toBin(getX(), maxDepth, xBin);
if(dim > 1) { binOp::toBin(getY(), maxDepth, yBin); }
if(dim > 2) { binOp::toBin(getZ(), maxDepth, zBin); }
binOp::toBin(getLevel(), Ln, levBin);
//Interleave bits
if(dim > 2) {
for(unsigned int j = 0; j < maxDepth; j++) {
numBin[(j*dim)] = zBin[j];
numBin[((j*dim)+1)] = yBin[j];
numBin[((j*dim)+2)] = xBin[j];
}//end for
}else if(dim > 1) {
for(unsigned int j = 0; j < maxDepth; j++) {
numBin[(j*dim)] = yBin[j];
numBin[((j*dim)+1)] = xBin[j];
}//end for
}else {
for(unsigned int j = 0; j < maxDepth; j++) {
numBin[j] = xBin[j];
}//end for
}//end if-else
//Append level
for(unsigned int j = 0; j < Ln; j++) {
numBin[((maxDepth*dim)+j)] = levBin[j];
}
return numBin ;
}
Entry&
NameTree::lookup(const pit::Entry& pitEntry)
{
const Name& name = pitEntry.getName();
NFD_LOG_TRACE("lookup(PIT " << name << ')');
bool hasDigest = name.size() > 0 && name[-1].isImplicitSha256Digest();
if (hasDigest && name.size() <= getMaxDepth()) {
return this->lookup(name);
}
Entry* nte = this->getEntry(pitEntry);
BOOST_ASSERT(nte != nullptr);
BOOST_ASSERT(std::count_if(nte->getPitEntries().begin(), nte->getPitEntries().end(),
[&pitEntry] (const shared_ptr<pit::Entry>& pitEntry1) {
return pitEntry1.get() == &pitEntry;
}) == 1);
return *nte;
}
Entry*
NameTree::findLongestPrefixMatch(const pit::Entry& pitEntry, const EntrySelector& entrySelector) const
{
const Entry* nte = this->getEntry(pitEntry);
BOOST_ASSERT(nte != nullptr);
const Name& name = pitEntry.getName();
size_t depth = std::min(name.size(), getMaxDepth());
if (nte->getName().size() < pitEntry.getName().size()) {
// PIT entry name either exceeds depth limit or ends with an implicit digest: go deeper
for (size_t i = nte->getName().size() + 1; i <= depth; ++i) {
const Entry* exact = this->findExactMatch(name, i);
if (exact == nullptr) {
break;
}
nte = exact;
}
}
return this->findLongestPrefixMatch(*nte, entrySelector);
}
Entry&
NameTree::lookup(const Name& name, size_t prefixLen)
{
NFD_LOG_TRACE("lookup(" << name << ", " << prefixLen << ')');
BOOST_ASSERT(prefixLen <= name.size());
BOOST_ASSERT(prefixLen <= getMaxDepth());
HashSequence hashes = computeHashes(name, prefixLen);
const Node* node = nullptr;
Entry* parent = nullptr;
for (size_t i = 0; i <= prefixLen; ++i) {
bool isNew = false;
std::tie(node, isNew) = m_ht.insert(name, i, hashes);
if (isNew && parent != nullptr) {
node->entry.setParent(*parent);
}
parent = &node->entry;
}
return node->entry;
}
void SerialRandomTree::build(){
// create the attribute indices window
m_attIndicesWindow = std::vector<int>(data->numAttributes,0);
int j = 0;
for(int i = 0; i < m_attIndicesWindow.size(); i++){
m_attIndicesWindow[i] = j++;
}
data->whatGoesWhere = std::vector<int>(data->inBag.size(),0);
data->createInBagSortedIndices();
int unprocessedNodes = 1;
int newNodes = 0;
SerialTreeNodePtr currentNode = SerialTreeNodePtr(new SerialTreeNode);
currentNode->sortedIndices = (*data->sortedIndices);
// compute initial class counts
currentNode->classProbs = std::vector<double>(data->numClasses,0);
for(int i = 0; i < data->numInstances; i++) {
currentNode->classProbs[(*data->instClassValues)[i]] += (*data->instWeights)[i];
}
m_depth = 0;
m_treeNodes.push_back(currentNode);
while(unprocessedNodes != 0){
// Iterate through unprocessed nodes
while(unprocessedNodes != 0){
currentNode = m_treeNodes[m_treeNodes.size()-(unprocessedNodes+newNodes)];
// Check if node is a leaf
if((currentNode->sortedIndices.size() > 0 && currentNode->sortedIndices[0].size() < max(2, getMinNum())) // small
|| (abs(currentNode->classProbs[maxIndex(currentNode->classProbs)] - sum(currentNode->classProbs)) < 1e-6) // pure
|| ((getMaxDepth() > 0) && (m_depth >= getMaxDepth())) // deep
){
createLeafNode(currentNode);
currentNode->clean();
}
else{
if(findSplit(currentNode)){
createSplitNodes(currentNode,newNodes);
currentNode->clean();
}
else{
createLeafNode(currentNode);
currentNode->clean();
}
}
unprocessedNodes--;
}
m_depth++;
unprocessedNodes = newNodes;
newNodes = 0;
}
m_nodesInTree = m_treeNodes.size();
data.reset();
m_dists.clear();
m_splits.clear();
m_props.clear();
m_vals.clear();
m_dists.shrink_to_fit();
m_splits.shrink_to_fit();
m_props.shrink_to_fit();
m_vals.shrink_to_fit();
}
// Stop criteria:
// 1) When all the training examples agree at each leaf node: in this
// case we can just return either 1.0 or 0.0 for each leaf node.
// 2) When we have no more examples down a particular branch:
// we can then assign a default value, such as the overall fraction of
// positive examples in the entire training set, or the overall fraction
// of positive examples under the parent node
// 3) When we have no more attributes to split on: Then we let pl be the
// proportion of positive examples in that part of the tree.
DecisionTree::Node* DecisionTree::recursiveTrainTree
(const vector<TrainingExample*>& examples,
const vector<double>& weights, double sum_weights,
const set<size_t>& used_feature_indeces) {
// split on feature that gives greatest increase in the log likelihood..
// (the feature that gives greatest reduction in entropy)..
// if((used_feature_indeces.size()==m_features.size())) {
assert(examples.size()>0);
assert(examples.size()==weights.size());
size_t num_features = examples[0]->getNumberOfFeatures();
if((used_feature_indeces.size()==num_features) ||
(used_feature_indeces.size()==getMaxDepth())) { // all used up
// stop criteria 3...
double pl = countPositiveClassLabels
(m_positive_label, examples, weights)/sum_weights;
Node* T = new Node(this, pl); // leaf
// cerr << "encountered main leaf: features used up.\n";
return T;
}
size_t chosen_feature_index =
chooseFeature(m_positive_label, examples, weights, sum_weights,
m_feature_thresholds, used_feature_indeces);
if(chosen_feature_index==(size_t)(-1)) { // all examples are one or the other
double pl = countPositiveClassLabels
(m_positive_label, examples,weights)/sum_weights;
Node* T = new Node(this, pl); // leaf
// cerr << "encountered main leaf: invalid chosen_feature_index, all examples one or the other.\n";
return T;
}
set<size_t> now_used_feature_indeces = used_feature_indeces;
now_used_feature_indeces.insert(chosen_feature_index);
// cerr << "Chosen feature ["<< chosen_feature_index<< "]: " << m_features[chosen_feature_index] << endl;
// make this the root of the tree
Node* T = new Node(this, chosen_feature_index);
// instead of for loop over values v of feature, we just have two...
vector<TrainingExample*> above_examples;
vector<TrainingExample*> below_examples;
vector<double> above_weights;
vector<double> below_weights;
double sum_above_weights = 0;
double sum_below_weights = 0;
splitExamplesByThreshold(above_examples, above_weights, sum_above_weights,
below_examples, below_weights, sum_below_weights,
examples, weights, sum_weights,
chosen_feature_index,
m_feature_thresholds[chosen_feature_index]);
Node* right_child=0;
double count_positive_class_labels_for_above_examples =
countPositiveClassLabels(m_positive_label,
above_examples, above_weights);
if(above_examples.size()==0) { // stop criteria 2... (use parents examples)...
double pl = count_positive_class_labels_for_above_examples/sum_weights;
right_child = new Node(this,pl); // leaf
// cerr << "encountered right leaf: no above examples\n";
} else if(count_positive_class_labels_for_above_examples==sum_above_weights) {
// stop criteria 1... (use probability 1)...
right_child = new Node(this, 1.0); // leaf
// cerr << "encountered right leaf: no negative examples in class 'above'\n";
} else if(count_positive_class_labels_for_above_examples==0) {
// stop criteria 1... (use probability 0)...
right_child = new Node(this, 0.0); // leaf
// cerr << "encountered right leaf: no positive examples in class 'above'\n";
} else {
// cerr << "Splitting right on feature [" << chosen_feature_index << "]: \"" << m_features[chosen_feature_index];
// cerr << "\" using " << above_examples.size() << " 'above' examples\n";
right_child = recursiveTrainTree(above_examples, above_weights,
sum_above_weights, now_used_feature_indeces);
}
T->setRightChild(right_child);
Node* left_child=0;
double count_positive_class_labels_for_below_examples =
countPositiveClassLabels(m_positive_label,
below_examples, below_weights);
if(below_examples.size()==0) { // stop criteria 2... (use parents examples)...
double pl = count_positive_class_labels_for_below_examples/sum_weights;
left_child = new Node(this,pl); // leaf
// cerr << "encountered left leaf: no examples in class 'below'\n";
} else if(count_positive_class_labels_for_below_examples==sum_below_weights) {
// stop criteria 1... (use probability 1)...
left_child = new Node(this, 1.0); // leaf
// cerr << "encountered left leaf: no positive examples in class 'below'\n";
} else if(count_positive_class_labels_for_below_examples==0) {
// stop criteria 1... (use probability 0)...
left_child = new Node(this, 0.0); // leaf
// cerr << "encountered left leaf: no negative examples in class 'below'\n";
} else {
// cerr << "Splitting left on feature [" << chosen_feature_index << "]: \"" << m_features[chosen_feature_index];
// cerr << "\" using " << below_examples.size() << " 'below' examples\n";
left_child = recursiveTrainTree(below_examples, below_weights,
sum_below_weights, now_used_feature_indeces);
}
T->setLeftChild(left_child);
return T;
}
int BinarySearchTree::getMaxDepth()
{
return getMaxDepth(this->root);
}
int TreeNode ::balanceSubtree(std::vector<TreeNode > & inp,
std::vector<TreeNode > & out, bool incCorner,
bool isSortedCompleteLinear) const {
PROF_BAL_SUBTREE_BEGIN
unsigned int dim = getDim();
unsigned int maxDepth = getMaxDepth();
unsigned int maxCornerX = this->maxX();
unsigned int maxCornerY = this->maxY();
unsigned int maxCornerZ = this->maxZ();
unsigned int minLevInp = maxDepth;
unsigned int maxLevInp = 0;
out.clear();
if( (maxDepth == 0) || (getLevel() == maxDepth) || (inp.empty()) ) {
//No decendants.
PROF_BAL_SUBTREE_END
}
std::vector<TreeNode > *workVec = NULL;
unsigned int *workVecSz = NULL;
if(maxDepth - getLevel()) {
workVec = new std::vector<TreeNode >[maxDepth - getLevel()];
workVecSz = new unsigned int [maxDepth-getLevel()];
}
for(unsigned int i = 0; i < (maxDepth - getLevel()); i++) {
workVecSz[i] = 0;
}
for(unsigned int i = 0; i < inp.size(); i++) {
#ifdef __DEBUG_TN__
assert(areComparable(*this, inp[i]));
#endif
if( this->isAncestor(inp[i]) ) {
unsigned int currOctLev = inp[i].getLevel();
workVecSz[maxDepth - currOctLev]++;
if(currOctLev < minLevInp) {
minLevInp = currOctLev;
}
if(currOctLev > maxLevInp) {
maxLevInp = currOctLev;
}
}//end if decendant of block
}//end for
if(isSortedCompleteLinear && ((maxLevInp - minLevInp) < 2) ) {
//Already balanced
out = inp;
if(workVecSz) {
delete [] workVecSz;
workVecSz = NULL;
}
if(workVec) {
delete [] workVec;
workVec = NULL;
}
PROF_BAL_SUBTREE_END
}
JNIEXPORT jint JNICALL Java_FTAGUI_FTACanvas_nativeGetMaxDepth(JNIEnv *env, jobject obj){
jint maxDepth;
maxDepth = getMaxDepth();
return maxDepth;
}
int maxDepth(TreeNode* root) {
if (!root) { return 0; }
int maxDepth = 0;
getMaxDepth(root, 1, maxDepth);
return maxDepth;
}
unsigned ClusterAlg::getDist(ClusterAlg* other)
{
// construct graph
MGraph<ClusterAlg*, unsigned> *graph (constructGraph ( other ));
// compute path(s)
std::list<ClusterAlg*> path0, path1, path2;
unsigned dist0 (graph->getPathsAndDist ( this, other, path0, path1, path2 ));
unsigned dist1(std::numeric_limits<unsigned>::max()), dist2(dist1), tmp_dist;
delete graph;
// *** first level refinement
// compute refined graph for path0
unsigned max_depth (depth), refine_level(3);
getMaxDepth(max_depth);
if (max_depth - depth < refine_level) refine_level = max_depth - depth;
if (refine_level != 0) {
refine_level += depth;
graph = constructRefinedGraph (refine_level, path0);
// choose a new src and target node in the new graph
// starting from old src and target
ClusterAlg*src (*path0.begin()), *target (*(--path0.end()));
getNode (refine_level, src);
other->getNode (refine_level, target);
// compute distance in refined graph
std::list<ClusterAlg*> path00, path01, path02;
tmp_dist = graph->getPathsAndDist ( src, target, path00, path01, path02 );
if (tmp_dist < dist1) dist1 = tmp_dist;
// tidy up
if (graph) delete graph;
// *** second level refinement
// *** refinement path p00
refine_level=6;
if (max_depth - depth < refine_level) refine_level = max_depth - depth;
if (refine_level != 0 && refine_level > 3) {
refine_level += depth;
graph = constructRefinedGraph(refine_level, path00);
// choose a new src and target node in the new graph
// starting from old src and target
src = *path00.begin();
target = *(--path00.end());
getNode (refine_level, target);
other->getNode (refine_level, src);
// compute distance in refined graph
std::list<ClusterAlg*> path000, path001, path002;
tmp_dist = graph->getPathsAndDist ( src, target, path000, path001, path002 );
if (tmp_dist < dist2) dist2 = tmp_dist;
// tidy up
if (graph) delete graph;
}
// *** refinement path p01
refine_level=6;
if (max_depth - depth < refine_level) refine_level = max_depth - depth;
if (refine_level != 0 && refine_level > 3 && path01.size() != 0) {
refine_level += depth;
graph = constructRefinedGraph (refine_level, path01);
// choose a new src and target node in the new graph
// starting from old src and target
src = *path01.begin();
target = *(--path01.end());
getNode (refine_level, target);
other->getNode (refine_level, src);
// compute distance in refined graph
std::list<ClusterAlg*> path010, path011, path012;
tmp_dist = graph->getPathsAndDist ( src, target, path010, path011, path012 );
if (tmp_dist < dist2) dist2 = tmp_dist;
// tidy up
if (graph) delete graph;
}
// *** refinement path p02
refine_level=6;
if (max_depth - depth < refine_level) refine_level = max_depth - depth;
if (refine_level != 0 && refine_level > 3 && path02.size() != 0) {
refine_level += depth;
graph = constructRefinedGraph (refine_level, path02);
// choose a new src and target node in the new graph
// starting from old src and target
src = *path02.begin();
target = *(--path02.end());
getNode (refine_level, target);
other->getNode (refine_level, src);
// compute distance in refined graph
std::list<ClusterAlg*> path020, path021, path022;
tmp_dist = graph->getPathsAndDist ( src, target, path020, path021, path022 );
if (tmp_dist < dist2) dist2 = tmp_dist;
// tidy up
if (graph) delete graph;
}
}
// *** first level refinement
// compute refined graph for path1
refine_level=3;
if (max_depth - depth < refine_level) refine_level = max_depth - depth;
if (refine_level != 0 && path1.size() > 0) {
refine_level += depth;
graph = constructRefinedGraph (refine_level, path1);
// choose a new src and target node in the new graph
// starting from old src and target
ClusterAlg *src (*path1.begin()), *target (*(--path1.end()));
other->getNode (refine_level, target);
getNode (refine_level, src);
// std::cout << "\033[32m |p1| " << dist << " \033[0m" << std::flush;
// compute distance in refined graph
std::list<ClusterAlg*> path10, path11, path12;
tmp_dist = graph->getPathsAndDist ( src, target, path10, path11, path12 );
if (tmp_dist < dist1) dist1 = tmp_dist;
// tidy up
if (graph) delete graph;
// *** second level refinement
// *** refinement path p10
refine_level=6;
if (max_depth - depth < refine_level) refine_level = max_depth - depth;
if (refine_level != 0 && refine_level > 3) {
refine_level += depth;
graph = constructRefinedGraph (refine_level, path10);
// choose a new src and target node in the new graph
// starting from old src and target
src = *path10.begin();
target = *(--path10.end());
getNode (refine_level, target);
other->getNode (refine_level, src);
// compute distance in refined graph
std::list<ClusterAlg*> path100, path101, path102;
tmp_dist = graph->getPathsAndDist ( src, target, path100, path101, path102 );
if (tmp_dist < dist2) dist2 = tmp_dist;
// tidy up
if (graph) delete graph;
}
// *** refinement path p11
refine_level=6;
if (max_depth - depth < refine_level) refine_level = max_depth - depth;
if (refine_level != 0 && refine_level > 3 && path11.size() != 0) {
refine_level += depth;
graph = constructRefinedGraph (refine_level, path11);
// choose a new src and target node in the new graph
// starting from old src and target
src = *path11.begin();
target = *(--path11.end());
getNode (refine_level, target);
other->getNode (refine_level, src);
// compute distance in refined graph
std::list<ClusterAlg*> path110, path111, path112;
tmp_dist = graph->getPathsAndDist ( src, target, path110, path111, path112 );
if (tmp_dist < dist2) dist2 = tmp_dist;
// tidy up
if (graph) delete graph;
}
// *** refinement path p12
refine_level=6;
if (max_depth - depth < refine_level) refine_level = max_depth - depth;
if (refine_level != 0 && refine_level > 3 && path12.size() != 0) {
refine_level += depth;
graph = constructRefinedGraph (refine_level, path12);
// choose a new src and target node in the new graph
// starting from old src and target
src = *path12.begin();
target = *(--path12.end());
getNode (refine_level, target);
other->getNode (refine_level, src);
// compute distance in refined graph
std::list<ClusterAlg*> path120, path121, path122;
tmp_dist = graph->getPathsAndDist ( src, target, path120, path121, path122 );
if (tmp_dist < dist2) dist2 = tmp_dist;
// tidy up
if (graph) delete graph;
}
}
// *** first level refinement
// compute refined graph for path2
refine_level=3;
if (max_depth - depth < refine_level) refine_level = max_depth - depth;
if (refine_level != 0 && path2.size() > 0) {
refine_level += depth;
graph = constructRefinedGraph (refine_level, path2);
// choose a new src and target node in the new graph
// starting from old src and target
ClusterAlg *src (*path2.begin()), *target (*(--path2.end()));
other->getNode (refine_level, target);
getNode (refine_level, src);
// compute distance in refined graph
std::list<ClusterAlg*> path20, path21, path22;
tmp_dist = graph->getPathsAndDist ( src, target, path20, path21, path22 );
if (tmp_dist < dist1) dist1 = tmp_dist;
// tidy up
if (graph) delete graph;
// *** second level refinement
// *** refinement path p20
refine_level=6;
if (max_depth - depth < refine_level) refine_level = max_depth - depth;
if (refine_level != 0 && refine_level > 3) {
refine_level += depth;
graph = constructRefinedGraph (refine_level, path20);
// choose a new src and target node in the new graph
// starting from old src and target
src = *path20.begin();
target = *(--path20.end());
getNode (refine_level, target);
other->getNode (refine_level, src);
// compute distance in refined graph
std::list<ClusterAlg*> path200, path201, path202;
tmp_dist = graph->getPathsAndDist ( src, target, path200, path201, path202 );
if (tmp_dist < dist2) dist2 = tmp_dist;
// tidy up
if (graph) delete graph;
}
// *** refinement path p21
refine_level=6;
if (max_depth - depth < refine_level) refine_level = max_depth - depth;
if (refine_level != 0 && refine_level > 3 && path21.size() != 0) {
refine_level += depth;
graph = constructRefinedGraph (refine_level, path21);
// choose a new src and target node in the new graph
// starting from old src and target
src = *path21.begin();
target = *(--path21.end());
getNode (refine_level, target);
other->getNode (refine_level, src);
// compute distance in refined graph
std::list<ClusterAlg*> path210, path211, path212;
tmp_dist = graph->getPathsAndDist ( src, target, path210, path211, path212 );
if (tmp_dist < dist2) dist2 = tmp_dist;
// tidy up
if (graph) delete graph;
}
// *** refinement path p22
refine_level=6;
if (max_depth - depth < refine_level) refine_level = max_depth - depth;
if (refine_level != 0 && refine_level > 3 && path22.size() != 0) {
refine_level += depth;
graph = constructRefinedGraph (refine_level, path22);
// choose a new src and target node in the new graph
// starting from old src and target
src = *path22.begin();
target = *(--path22.end());
getNode (refine_level, target);
other->getNode (refine_level, src);
// compute distance in refined graph
std::list<ClusterAlg*> path220, path221, path222;
tmp_dist = graph->getPathsAndDist ( src, target, path220, path221, path222 );
if (tmp_dist < dist2) dist2 = tmp_dist;
// tidy up
if (graph) delete graph;
}
}
if (dist2 < std::numeric_limits<unsigned>::max())
return dist2;
if (dist1 < std::numeric_limits<unsigned>::max())
return dist1;
return dist0;
}
