[mlpack-git] master: R tree clean up. Change Child() to match BSP tree. Copy constructor takes a second argument to specify whether it is a deep copy. (d740ec5)
gitdub at big.cc.gt.atl.ga.us
gitdub at big.cc.gt.atl.ga.us
Thu Mar 5 21:55:12 EST 2015
Repository : https://github.com/mlpack/mlpack
On branch : master
Link : https://github.com/mlpack/mlpack/compare/904762495c039e345beba14c1142fd719b3bd50e...f94823c800ad6f7266995c700b1b630d5ffdcf40
>---------------------------------------------------------------
commit d740ec5c3b96a0b4f67cf9db059a962d0fa595b3
Author: andrewmw94 <andrewmw94 at gmail.com>
Date: Mon Jul 28 21:05:47 2014 +0000
R tree clean up. Change Child() to match BSP tree. Copy constructor takes a second argument to specify whether it is a deep copy.
>---------------------------------------------------------------
d740ec5c3b96a0b4f67cf9db059a962d0fa595b3
.../r_star_tree_descent_heuristic_impl.hpp | 24 ++--
.../tree/rectangle_tree/r_star_tree_split_impl.hpp | 110 ++++++++---------
.../r_tree_descent_heuristic_impl.hpp | 16 +--
.../core/tree/rectangle_tree/r_tree_split_impl.hpp | 72 ++++++------
.../core/tree/rectangle_tree/rectangle_tree.hpp | 33 ++++--
.../tree/rectangle_tree/rectangle_tree_impl.hpp | 42 ++++++-
src/mlpack/tests/rectangle_tree_test.cpp | 130 +++++++++++----------
7 files changed, 241 insertions(+), 186 deletions(-)
diff --git a/src/mlpack/core/tree/rectangle_tree/r_star_tree_descent_heuristic_impl.hpp b/src/mlpack/core/tree/rectangle_tree/r_star_tree_descent_heuristic_impl.hpp
index da6d425..15e5bc5 100644
--- a/src/mlpack/core/tree/rectangle_tree/r_star_tree_descent_heuristic_impl.hpp
+++ b/src/mlpack/core/tree/rectangle_tree/r_star_tree_descent_heuristic_impl.hpp
@@ -20,7 +20,7 @@ inline size_t RStarTreeDescentHeuristic::ChooseDescentNode(const TreeType* node,
std::vector<double> originalScores(node->NumChildren());
double origMinScore = DBL_MAX;
- if (node->Child(0)->IsLeaf()) { // If its children are leaf nodes, use minimum overlap to choose.
+ if (node->Children()[0]->IsLeaf()) { // If its children are leaf nodes, use minimum overlap to choose.
double bestIndex = 0;
for (size_t i = 0; i < node->NumChildren(); i++) {
@@ -30,10 +30,10 @@ inline size_t RStarTreeDescentHeuristic::ChooseDescentNode(const TreeType* node,
double overlap = 1.0;
double newOverlap = 1.0;
for (size_t k = 0; k < node->Bound().Dim(); k++) {
- double newHigh = std::max(point[k], node->Child(i)->Bound()[k].Hi());
- double newLow = std::min(point[k], node->Child(i)->Bound()[k].Lo());
- overlap *= node->Child(i)->Bound()[k].Hi() < node->Child(j)->Bound()[k].Lo() || node->Child(i)->Bound()[k].Lo() > node->Child(j)->Bound()[k].Hi() ? 0 : std::min(node->Child(i)->Bound()[k].Hi(), node->Child(j)->Bound()[k].Hi()) - std::max(node->Child(i)->Bound()[k].Lo(), node->Child(j)->Bound()[k].Lo());
- newOverlap *= newHigh < node->Child(j)->Bound()[k].Lo() || newLow > node->Child(j)->Bound()[k].Hi() ? 0 : std::min(newHigh, node->Child(j)->Bound()[k].Hi()) - std::max(newLow, node->Child(j)->Bound()[k].Lo());
+ double newHigh = std::max(point[k], node->Children()[i]->Bound()[k].Hi());
+ double newLow = std::min(point[k], node->Children()[i]->Bound()[k].Lo());
+ overlap *= node->Children()[i]->Bound()[k].Hi() < node->Children()[j]->Bound()[k].Lo() || node->Children()[i]->Bound()[k].Lo() > node->Children()[j]->Bound()[k].Hi() ? 0 : std::min(node->Children()[i]->Bound()[k].Hi(), node->Children()[j]->Bound()[k].Hi()) - std::max(node->Children()[i]->Bound()[k].Lo(), node->Children()[j]->Bound()[k].Lo());
+ newOverlap *= newHigh < node->Children()[j]->Bound()[k].Lo() || newLow > node->Children()[j]->Bound()[k].Hi() ? 0 : std::min(newHigh, node->Children()[j]->Bound()[k].Hi()) - std::max(newLow, node->Children()[j]->Bound()[k].Lo());
}
sc += newOverlap - overlap;
}
@@ -66,9 +66,9 @@ inline size_t RStarTreeDescentHeuristic::ChooseDescentNode(const TreeType* node,
double v1 = 1.0;
double v2 = 1.0;
for (size_t j = 0; j < node->Bound().Dim(); j++) {
- v1 *= node->Child(i)->Bound()[j].Width();
- v2 *= node->Child(i)->Bound()[j].Contains(point[j]) ? node->Child(i)->Bound()[j].Width() : (node->Child(i)->Bound()[j].Hi() < point[j] ? (point[j] - node->Child(i)->Bound()[j].Lo()) :
- (node->Child(i)->Bound()[j].Hi() - point[j]));
+ v1 *= node->Children()[i]->Bound()[j].Width();
+ v2 *= node->Children()[i]->Bound()[j].Contains(point[j]) ? node->Children()[i]->Bound()[j].Width() : (node->Children()[i]->Bound()[j].Hi() < point[j] ? (point[j] - node->Children()[i]->Bound()[j].Lo()) :
+ (node->Children()[i]->Bound()[j].Hi() - point[j]));
}
assert(v2 - v1 >= 0);
vols[i] = v1;
@@ -113,10 +113,10 @@ inline size_t RStarTreeDescentHeuristic::ChooseDescentNode(const TreeType* node,
for (size_t i = 0; i < node->NumChildren(); i++) {
double v1 = 1.0;
double v2 = 1.0;
- for (size_t j = 0; j < node->Child(i)->Bound().Dim(); j++) {
- v1 *= node->Child(i)->Bound()[j].Width();
- v2 *= node->Child(i)->Bound()[j].Contains(insertedNode->Bound()[j]) ? node->Child(i)->Bound()[j].Width() :
- (insertedNode->Bound()[j].Contains(node->Child(i)->Bound()[j]) ? insertedNode->Bound()[j].Width() : (insertedNode->Bound()[j].Lo() < node->Child(i)->Bound()[j].Lo() ? (node->Child(i)->Bound()[j].Hi() - insertedNode->Bound()[j].Lo()) : (insertedNode->Bound()[j].Hi() - node->Child(i)->Bound()[j].Lo())));
+ for (size_t j = 0; j < node->Children()[i]->Bound().Dim(); j++) {
+ v1 *= node->Children()[i]->Bound()[j].Width();
+ v2 *= node->Children()[i]->Bound()[j].Contains(insertedNode->Bound()[j]) ? node->Children()[i]->Bound()[j].Width() :
+ (insertedNode->Bound()[j].Contains(node->Children()[i]->Bound()[j]) ? insertedNode->Bound()[j].Width() : (insertedNode->Bound()[j].Lo() < node->Children()[i]->Bound()[j].Lo() ? (node->Children()[i]->Bound()[j].Hi() - insertedNode->Bound()[j].Lo()) : (insertedNode->Bound()[j].Hi() - node->Children()[i]->Bound()[j].Lo())));
}
assert(v2 - v1 >= 0);
vols[i] = v1;
diff --git a/src/mlpack/core/tree/rectangle_tree/r_star_tree_split_impl.hpp b/src/mlpack/core/tree/rectangle_tree/r_star_tree_split_impl.hpp
index b59dfb9..26d6e1d 100644
--- a/src/mlpack/core/tree/rectangle_tree/r_star_tree_split_impl.hpp
+++ b/src/mlpack/core/tree/rectangle_tree/r_star_tree_split_impl.hpp
@@ -31,26 +31,17 @@ void RStarTreeSplit<DescentType, StatisticType, MatType>::SplitLeafNode(
// and other methods don't confuse the end user by giving an address of another node.
if (tree->Parent() == NULL) {
RectangleTree<RStarTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>* copy =
- new RectangleTree<RStarTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>(*tree); // We actually want to copy this way. Pointers and everything.
+ new RectangleTree<RStarTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>(*tree, false); // We actually want to copy this way. Pointers and everything.
+
copy->Parent() = tree;
tree->Count() = 0;
tree->NullifyData();
- tree->Child((tree->NumChildren())++) = copy; // Because this was a leaf node, numChildren must be 0.
+ tree->Children()[(tree->NumChildren())++] = copy; // Because this was a leaf node, numChildren must be 0.
assert(tree->NumChildren() == 1);
RStarTreeSplit<DescentType, StatisticType, MatType>::SplitLeafNode(copy, relevels);
return;
}
- bool foundTreeChildParent = false;
- for(int nou=0; nou < tree->Parent()->NumChildren(); nou++) {
- if(tree->Parent()->Child(nou) == tree) {
- foundTreeChildParent = true;
- break;
- }
- }
- assert(foundTreeChildParent == true);
-
-
// If we haven't yet reinserted on this level, we try doing so now.
if(relevels[tree->TreeDepth()]) {
relevels[tree->TreeDepth()] = false;
@@ -216,14 +207,14 @@ void RStarTreeSplit<DescentType, StatisticType, MatType>::SplitLeafNode(
RectangleTree<RStarTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>* par = tree->Parent();
int index = -1;
for (int i = 0; i < par->NumChildren(); i++) {
- if (par->Child(i) == tree) {
+ if (par->Children()[i] == tree) {
index = i;
break;
}
}
assert(index != -1);
- par->Child(index) = treeOne;
- par->Child(par->NumChildren()++) = treeTwo;
+ par->Children()[index] = treeOne;
+ par->Children()[par->NumChildren()++] = treeTwo;
// we only add one at a time, so we should only need to test for equality
// just in case, we use an assert.
@@ -260,11 +251,12 @@ bool RStarTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
// and other methods don't confuse the end user by giving an address of another node.
if (tree->Parent() == NULL) {
RectangleTree<RStarTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>* copy =
- new RectangleTree<RStarTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>(*tree); // We actually want to copy this way. Pointers and everything.
+ new RectangleTree<RStarTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>(*tree, false); // We actually want to copy this way. Pointers and everything.
+
copy->Parent() = tree;
tree->NumChildren() = 0;
tree->NullifyData();
- tree->Child((tree->NumChildren())++) = copy;
+ tree->Children()[(tree->NumChildren())++] = copy;
RStarTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(copy, relevels);
return true;
}
@@ -288,7 +280,7 @@ bool RStarTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
tree->Bound().Centroid(c1); // Modifies c1.
for(size_t i = 0; i < sorted.size(); i++) {
arma::vec c2;
- tree->Child(i)->Bound().Centroid(c2); // Modifies c2.
+ tree->Children()[i]->Bound().Centroid(c2); // Modifies c2.
sorted[i].d = tree->Bound().Metric().Evaluate(c1,c2);
sorted[i].n = i;
}
@@ -298,8 +290,8 @@ bool RStarTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
std::vector<RectangleTree<RStarTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>*> removedNodes(p);
for(size_t i =0; i < p; i++) {
- removedNodes[i] = tree->Child(sorted[sorted.size()-1-i].n); // We start from the end of sorted.
- root->RemoveNode(tree->Child(sorted[sorted.size()-1-i].n), relevels);
+ removedNodes[i] = tree->Children()[sorted[sorted.size()-1-i].n]; // We start from the end of sorted.
+ root->RemoveNode(tree->Children()[sorted[sorted.size()-1-i].n], relevels);
}
for(size_t i = 0; i < p; i++) {
@@ -314,7 +306,7 @@ bool RStarTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
// if(tree->NumChildren() < tree->MinNumChildren()) {
// std::vector<RectangleTree<RStarTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>*> rmNodes(tree->NumChildren());
// for(size_t i = 0; i < rmNodes.size(); i++) {
-// rmNodes[i] = tree->Child(i);
+// rmNodes[i] = tree->Children()[i];
// }
// root->RemoveNode(tree, relevels);
// for(size_t i = 0; i < rmNodes.size(); i++) {
@@ -340,7 +332,7 @@ bool RStarTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
// We'll do Bound().Lo() now and use Bound().Hi() later.
std::vector<sortStruct> sorted(tree->NumChildren());
for (int i = 0; i < sorted.size(); i++) {
- sorted[i].d = tree->Child(i)->Bound()[j].Lo();
+ sorted[i].d = tree->Children()[i]->Bound()[j].Lo();
sorted[i].n = i;
}
@@ -368,21 +360,21 @@ bool RStarTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
std::vector<double> maxG2(maxG1.size());
std::vector<double> minG2(maxG1.size());
for (int k = 0; k < tree->Bound().Dim(); k++) {
- minG1[k] = tree->Child(sorted[0].n)->Bound()[k].Lo();
- maxG1[k] = tree->Child(sorted[0].n)->Bound()[k].Hi();
- minG2[k] = tree->Child(sorted[sorted.size() - 1].n)->Bound()[k].Lo();
- maxG2[k] = tree->Child(sorted[sorted.size() - 1].n)->Bound()[k].Hi();
+ minG1[k] = tree->Children()[sorted[0].n]->Bound()[k].Lo();
+ maxG1[k] = tree->Children()[sorted[0].n]->Bound()[k].Hi();
+ minG2[k] = tree->Children()[sorted[sorted.size() - 1].n]->Bound()[k].Lo();
+ maxG2[k] = tree->Children()[sorted[sorted.size() - 1].n]->Bound()[k].Hi();
for (int l = 1; l < tree->NumChildren() - 1; l++) {
if (l < cutOff) {
- if (tree->Child(sorted[l].n)->Bound()[k].Lo() < minG1[k])
- minG1[k] = tree->Child(sorted[l].n)->Bound()[k].Lo();
- else if (tree->Child(sorted[l].n)->Bound()[k].Hi() > maxG1[k])
- maxG1[k] = tree->Child(sorted[l].n)->Bound()[k].Hi();
+ if (tree->Children()[sorted[l].n]->Bound()[k].Lo() < minG1[k])
+ minG1[k] = tree->Children()[sorted[l].n]->Bound()[k].Lo();
+ else if (tree->Children()[sorted[l].n]->Bound()[k].Hi() > maxG1[k])
+ maxG1[k] = tree->Children()[sorted[l].n]->Bound()[k].Hi();
} else {
- if (tree->Child(sorted[l].n)->Bound()[k].Lo() < minG2[k])
- minG2[k] = tree->Child(sorted[l].n)->Bound()[k].Lo();
- else if (tree->Child(sorted[l].n)->Bound()[k].Hi() > maxG2[k])
- maxG2[k] = tree->Child(sorted[l].n)->Bound()[k].Hi();
+ if (tree->Children()[sorted[l].n]->Bound()[k].Lo() < minG2[k])
+ minG2[k] = tree->Children()[sorted[l].n]->Bound()[k].Lo();
+ else if (tree->Children()[sorted[l].n]->Bound()[k].Hi() > maxG2[k])
+ maxG2[k] = tree->Children()[sorted[l].n]->Bound()[k].Hi();
}
}
}
@@ -424,7 +416,7 @@ bool RStarTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
// We'll do Bound().Lo() now and use Bound().Hi() later.
std::vector<sortStruct> sorted(tree->NumChildren());
for (int i = 0; i < sorted.size(); i++) {
- sorted[i].d = tree->Child(i)->Bound()[j].Hi();
+ sorted[i].d = tree->Children()[i]->Bound()[j].Hi();
sorted[i].n = i;
}
@@ -452,21 +444,21 @@ bool RStarTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
std::vector<double> maxG2(maxG1.size());
std::vector<double> minG2(maxG1.size());
for (int k = 0; k < tree->Bound().Dim(); k++) {
- minG1[k] = tree->Child(sorted[0].n)->Bound()[k].Lo();
- maxG1[k] = tree->Child(sorted[0].n)->Bound()[k].Hi();
- minG2[k] = tree->Child(sorted[sorted.size() - 1].n)->Bound()[k].Lo();
- maxG2[k] = tree->Child(sorted[sorted.size() - 1].n)->Bound()[k].Hi();
+ minG1[k] = tree->Children()[sorted[0].n]->Bound()[k].Lo();
+ maxG1[k] = tree->Children()[sorted[0].n]->Bound()[k].Hi();
+ minG2[k] = tree->Children()[sorted[sorted.size() - 1].n]->Bound()[k].Lo();
+ maxG2[k] = tree->Children()[sorted[sorted.size() - 1].n]->Bound()[k].Hi();
for (int l = 1; l < tree->NumChildren() - 1; l++) {
if (l < cutOff) {
- if (tree->Child(sorted[l].n)->Bound()[k].Lo() < minG1[k])
- minG1[k] = tree->Child(sorted[l].n)->Bound()[k].Lo();
- else if (tree->Child(sorted[l].n)->Bound()[k].Hi() > maxG1[k])
- maxG1[k] = tree->Child(sorted[l].n)->Bound()[k].Hi();
+ if (tree->Children()[sorted[l].n]->Bound()[k].Lo() < minG1[k])
+ minG1[k] = tree->Children()[sorted[l].n]->Bound()[k].Lo();
+ else if (tree->Children()[sorted[l].n]->Bound()[k].Hi() > maxG1[k])
+ maxG1[k] = tree->Children()[sorted[l].n]->Bound()[k].Hi();
} else {
- if (tree->Child(sorted[l].n)->Bound()[k].Lo() < minG2[k])
- minG2[k] = tree->Child(sorted[l].n)->Bound()[k].Lo();
- else if (tree->Child(sorted[l].n)->Bound()[k].Hi() > maxG2[k])
- maxG2[k] = tree->Child(sorted[l].n)->Bound()[k].Hi();
+ if (tree->Children()[sorted[l].n]->Bound()[k].Lo() < minG2[k])
+ minG2[k] = tree->Children()[sorted[l].n]->Bound()[k].Lo();
+ else if (tree->Children()[sorted[l].n]->Bound()[k].Hi() > maxG2[k])
+ maxG2[k] = tree->Children()[sorted[l].n]->Bound()[k].Hi();
}
}
}
@@ -505,12 +497,12 @@ bool RStarTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
std::vector<sortStruct> sorted(tree->NumChildren());
if (lowIsBest) {
for (int i = 0; i < sorted.size(); i++) {
- sorted[i].d = tree->Child(i)->Bound()[bestAxis].Lo();
+ sorted[i].d = tree->Children()[i]->Bound()[bestAxis].Lo();
sorted[i].n = i;
}
} else {
for (int i = 0; i < sorted.size(); i++) {
- sorted[i].d = tree->Child(i)->Bound()[bestAxis].Hi();
+ sorted[i].d = tree->Children()[i]->Bound()[bestAxis].Hi();
sorted[i].n = i;
}
}
@@ -525,16 +517,16 @@ bool RStarTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
if (tiedOnOverlap) {
for (int i = 0; i < tree->NumChildren(); i++) {
if (i < bestAreaIndexOnBestAxis + tree->MinNumChildren())
- InsertNodeIntoTree(treeOne, tree->Child(sorted[i].n));
+ InsertNodeIntoTree(treeOne, tree->Children()[sorted[i].n]);
else
- InsertNodeIntoTree(treeTwo, tree->Child(sorted[i].n));
+ InsertNodeIntoTree(treeTwo, tree->Children()[sorted[i].n]);
}
} else {
for (int i = 0; i < tree->NumChildren(); i++) {
if (i < bestOverlapIndexOnBestAxis + tree->MinNumChildren())
- InsertNodeIntoTree(treeOne, tree->Child(sorted[i].n));
+ InsertNodeIntoTree(treeOne, tree->Children()[sorted[i].n]);
else
- InsertNodeIntoTree(treeTwo, tree->Child(sorted[i].n));
+ InsertNodeIntoTree(treeTwo, tree->Children()[sorted[i].n]);
}
}
@@ -542,13 +534,13 @@ bool RStarTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
RectangleTree<RStarTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>* par = tree->Parent();
int index = 0;
for (int i = 0; i < par->NumChildren(); i++) {
- if (par->Child(i) == tree) {
+ if (par->Children()[i] == tree) {
index = i;
break;
}
}
- par->Child(index) = treeOne;
- par->Child(par->NumChildren()++) = treeTwo;
+ par->Children()[index] = treeOne;
+ par->Children()[par->NumChildren()++] = treeTwo;
// we only add one at a time, so we should only need to test for equality
// just in case, we use an assert.
@@ -560,10 +552,10 @@ bool RStarTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
// We have to update the children of each of these new nodes so that they record the
// correct parent.
for (int i = 0; i < treeOne->NumChildren(); i++) {
- treeOne->Child(i)->Parent() = treeOne;
+ treeOne->Children()[i]->Parent() = treeOne;
}
for (int i = 0; i < treeTwo->NumChildren(); i++) {
- treeTwo->Child(i)->Parent() = treeTwo;
+ treeTwo->Children()[i]->Parent() = treeTwo;
}
assert(treeOne->Parent()->NumChildren() <= treeOne->MaxNumChildren());
@@ -590,7 +582,7 @@ void RStarTreeSplit<DescentType, StatisticType, MatType>::InsertNodeIntoTree(
RectangleTree<RStarTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>* srcNode)
{
destTree->Bound() |= srcNode->Bound();
- destTree->Child(destTree->NumChildren()++) = srcNode;
+ destTree->Children()[destTree->NumChildren()++] = srcNode;
}
}; // namespace tree
diff --git a/src/mlpack/core/tree/rectangle_tree/r_tree_descent_heuristic_impl.hpp b/src/mlpack/core/tree/rectangle_tree/r_tree_descent_heuristic_impl.hpp
index f30d676..5767062 100644
--- a/src/mlpack/core/tree/rectangle_tree/r_tree_descent_heuristic_impl.hpp
+++ b/src/mlpack/core/tree/rectangle_tree/r_tree_descent_heuristic_impl.hpp
@@ -25,10 +25,10 @@ inline size_t RTreeDescentHeuristic::ChooseDescentNode(const TreeType* node, con
for (size_t i = 0; i < node->NumChildren(); i++) {
double v1 = 1.0;
double v2 = 1.0;
- for (size_t j = 0; j < node->Child(i)->Bound().Dim(); j++) {
- v1 *= node->Child(i)->Bound()[j].Width();
- v2 *= node->Child(i)->Bound()[j].Contains(point[j]) ? node->Child(i)->Bound()[j].Width() : (node->Child(i)->Bound()[j].Hi() < point[j] ? (point[j] - node->Child(i)->Bound()[j].Lo()) :
- (node->Child(i)->Bound()[j].Hi() - point[j]));
+ for (size_t j = 0; j < node->Children()[i]->Bound().Dim(); j++) {
+ v1 *= node->Children()[i]->Bound()[j].Width();
+ v2 *= node->Children()[i]->Bound()[j].Contains(point[j]) ? node->Children()[i]->Bound()[j].Width() : (node->Children()[i]->Bound()[j].Hi() < point[j] ? (point[j] - node->Children()[i]->Bound()[j].Lo()) :
+ (node->Children()[i]->Bound()[j].Hi() - point[j]));
}
assert(v2 - v1 >= 0);
vols[i] = v1;
@@ -66,10 +66,10 @@ inline size_t RTreeDescentHeuristic::ChooseDescentNode(const TreeType* node, con
for (size_t i = 0; i < node->NumChildren(); i++) {
double v1 = 1.0;
double v2 = 1.0;
- for (size_t j = 0; j < node->Child(i)->Bound().Dim(); j++) {
- v1 *= node->Child(i)->Bound()[j].Width();
- v2 *= node->Child(i)->Bound()[j].Contains(insertedNode->Bound()[j]) ? node->Child(i)->Bound()[j].Width() :
- (insertedNode->Bound()[j].Contains(node->Child(i)->Bound()[j]) ? insertedNode->Bound()[j].Width() : (insertedNode->Bound()[j].Lo() < node->Child(i)->Bound()[j].Lo() ? (node->Child(i)->Bound()[j].Hi() - insertedNode->Bound()[j].Lo()) : (insertedNode->Bound()[j].Hi() - node->Child(i)->Bound()[j].Lo())));
+ for (size_t j = 0; j < node->Children()[i]->Bound().Dim(); j++) {
+ v1 *= node->Children()[i]->Bound()[j].Width();
+ v2 *= node->Children()[i]->Bound()[j].Contains(insertedNode->Bound()[j]) ? node->Children()[i]->Bound()[j].Width() :
+ (insertedNode->Bound()[j].Contains(node->Children()[i]->Bound()[j]) ? insertedNode->Bound()[j].Width() : (insertedNode->Bound()[j].Lo() < node->Children()[i]->Bound()[j].Lo() ? (node->Children()[i]->Bound()[j].Hi() - insertedNode->Bound()[j].Lo()) : (insertedNode->Bound()[j].Hi() - node->Children()[i]->Bound()[j].Lo())));
}
assert(v2 - v1 >= 0);
vols[i] = v1;
diff --git a/src/mlpack/core/tree/rectangle_tree/r_tree_split_impl.hpp b/src/mlpack/core/tree/rectangle_tree/r_tree_split_impl.hpp
index 6dd41eb..18cfcec 100644
--- a/src/mlpack/core/tree/rectangle_tree/r_tree_split_impl.hpp
+++ b/src/mlpack/core/tree/rectangle_tree/r_tree_split_impl.hpp
@@ -30,12 +30,15 @@ void RTreeSplit<DescentType, StatisticType, MatType>::SplitLeafNode(
// If we are splitting the root node, we need will do things differently so that the constructor
// and other methods don't confuse the end user by giving an address of another node.
if (tree->Parent() == NULL) {
+
+// RectangleTree<RTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>* copy = tree->ExactClone(); // We actually want to copy this way. Pointers and everything.
RectangleTree<RTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>* copy =
- new RectangleTree<RTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>(*tree); // We actually want to copy this way. Pointers and everything.
+ new RectangleTree<RTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>(*tree, false); // We actually want to copy this way. Pointers and everything.
+
copy->Parent() = tree;
tree->Count() = 0;
tree->NullifyData();
- tree->Child((tree->NumChildren())++) = copy; // Because this was a leaf node, numChildren must be 0.
+ tree->Children()[(tree->NumChildren())++] = copy; // Because this was a leaf node, numChildren must be 0.
RTreeSplit<DescentType, StatisticType, MatType>::SplitLeafNode(copy, relevels);
return;
}
@@ -60,13 +63,13 @@ void RTreeSplit<DescentType, StatisticType, MatType>::SplitLeafNode(
RectangleTree<RTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>* par = tree->Parent();
int index = 0;
for (int i = 0; i < par->NumChildren(); i++) {
- if (par->Child(i) == tree) {
+ if (par->Children()[i] == tree) {
index = i;
break;
}
}
- par->Child(index) = treeOne;
- par->Child(par->NumChildren()++) = treeTwo;
+ par->Children()[index] = treeOne;
+ par->Children()[par->NumChildren()++] = treeTwo;
// we only add one at a time, so we should only need to test for equality
// just in case, we use an assert.
@@ -104,11 +107,12 @@ bool RTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
// and other methods don't confuse the end user by giving an address of another node.
if (tree->Parent() == NULL) {
RectangleTree<RTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>* copy =
- new RectangleTree<RTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>(*tree); // We actually want to copy this way. Pointers and everything.
+ new RectangleTree<RTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>(*tree, false); // We actually want to copy this way. Pointers and everything.
+
copy->Parent() = tree;
tree->NumChildren() = 0;
tree->NullifyData();
- tree->Child((tree->NumChildren())++) = copy;
+ tree->Children()[(tree->NumChildren())++] = copy;
RTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(copy, relevels);
return true;
}
@@ -131,17 +135,17 @@ bool RTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
RectangleTree<RTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>* par = tree->Parent();
int index = -1;
for (int i = 0; i < par->NumChildren(); i++) {
- if (par->Child(i) == tree) {
+ if (par->Children()[i] == tree) {
index = i;
break;
}
}
assert(index != -1);
- par->Child(index) = treeOne;
- par->Child(par->NumChildren()++) = treeTwo;
+ par->Children()[index] = treeOne;
+ par->Children()[par->NumChildren()++] = treeTwo;
for (int i = 0; i < par->NumChildren(); i++) {
- assert(par->Child(i) != tree);
+ assert(par->Children()[i] != tree);
}
// we only add one at a time, so should only need to test for equality
@@ -155,10 +159,10 @@ bool RTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
// We have to update the children of each of these new nodes so that they record the
// correct parent.
for (int i = 0; i < treeOne->NumChildren(); i++) {
- treeOne->Child(i)->Parent() = treeOne;
+ treeOne->Children()[i]->Parent() = treeOne;
}
for (int i = 0; i < treeTwo->NumChildren(); i++) {
- treeTwo->Child(i)->Parent() = treeTwo;
+ treeTwo->Children()[i]->Parent() = treeTwo;
}
assert(treeOne->NumChildren() <= treeOne->MaxNumChildren());
@@ -228,8 +232,8 @@ void RTreeSplit<DescentType, StatisticType, MatType>::GetBoundSeeds(
for (int j = i + 1; j < tree.NumChildren(); j++) {
double score = 1.0;
for (int k = 0; k < tree.Bound().Dim(); k++) {
- score *= std::max(tree.Child(i)->Bound()[k].Hi(), tree.Child(j)->Bound()[k].Hi()) -
- std::min(tree.Child(i)->Bound()[k].Lo(), tree.Child(j)->Bound()[k].Lo());
+ score *= std::max(tree.Children()[i]->Bound()[k].Hi(), tree.Children()[j]->Bound()[k].Hi()) -
+ std::min(tree.Children()[i]->Bound()[k].Lo(), tree.Children()[j]->Bound()[k].Lo());
}
if (score > worstPairScore) {
worstPairScore = score;
@@ -380,20 +384,20 @@ void RTreeSplit<DescentType, StatisticType, MatType>::AssignNodeDestNode(
for (int i = 0; i < oldTree->NumChildren(); i++) {
for (int j = i + 1; j < oldTree->NumChildren(); j++) {
- assert(oldTree->Child(i) != oldTree->Child(j));
+ assert(oldTree->Children()[i] != oldTree->Children()[j]);
}
}
- InsertNodeIntoTree(treeOne, oldTree->Child(intI));
- InsertNodeIntoTree(treeTwo, oldTree->Child(intJ));
+ InsertNodeIntoTree(treeOne, oldTree->Children()[intI]);
+ InsertNodeIntoTree(treeTwo, oldTree->Children()[intJ]);
// If intJ is the last node in the tree, we need to switch the order so that we remove the correct nodes.
if (intI > intJ) {
- oldTree->Child(intI) = oldTree->Child(--end); // decrement end
- oldTree->Child(intJ) = oldTree->Child(--end); // decrement end
+ oldTree->Children()[intI] = oldTree->Children()[--end]; // decrement end
+ oldTree->Children()[intJ] = oldTree->Children()[--end]; // decrement end
} else {
- oldTree->Child(intJ) = oldTree->Child(--end); // decrement end
- oldTree->Child(intI) = oldTree->Child(--end); // decrement end
+ oldTree->Children()[intJ] = oldTree->Children()[--end]; // decrement end
+ oldTree->Children()[intI] = oldTree->Children()[--end]; // decrement end
}
assert(treeOne->NumChildren() == 1);
@@ -401,16 +405,16 @@ void RTreeSplit<DescentType, StatisticType, MatType>::AssignNodeDestNode(
for (int i = 0; i < end; i++) {
for (int j = i + 1; j < end; j++) {
- assert(oldTree->Child(i) != oldTree->Child(j));
+ assert(oldTree->Children()[i] != oldTree->Children()[j]);
}
}
for (int i = 0; i < end; i++) {
- assert(oldTree->Child(i) != treeOne->Child(0));
+ assert(oldTree->Children()[i] != treeOne->Children()[0]);
}
for (int i = 0; i < end; i++) {
- assert(oldTree->Child(i) != treeTwo->Child(0));
+ assert(oldTree->Children()[i] != treeTwo->Children()[0]);
}
@@ -439,7 +443,7 @@ void RTreeSplit<DescentType, StatisticType, MatType>::AssignNodeDestNode(
for (int i = 0; i < oldTree->Bound().Dim(); i++) {
// For each of the new rectangles, find the width in this dimension if we add the rectangle at index to
// the new rectangle.
- math::Range range = oldTree->Child(index)->Bound()[i];
+ math::Range range = oldTree->Children()[index]->Bound()[i];
newVolOne *= treeOne->Bound()[i].Contains(range) ? treeOne->Bound()[i].Width() :
(range.Contains(treeOne->Bound()[i]) ? range.Width() : (range.Lo() < treeOne->Bound()[i].Lo() ? (treeOne->Bound()[i].Hi() - range.Lo()) :
(range.Hi() - treeOne->Bound()[i].Lo())));
@@ -467,25 +471,25 @@ void RTreeSplit<DescentType, StatisticType, MatType>::AssignNodeDestNode(
// Assign the rectangle that causes the least increase in volume
// to the appropriate rectangle.
if (bestRect == 1) {
- InsertNodeIntoTree(treeOne, oldTree->Child(bestIndex));
+ InsertNodeIntoTree(treeOne, oldTree->Children()[bestIndex]);
numAssignTreeOne++;
} else {
- InsertNodeIntoTree(treeTwo, oldTree->Child(bestIndex));
+ InsertNodeIntoTree(treeTwo, oldTree->Children()[bestIndex]);
numAssignTreeTwo++;
}
- oldTree->Child(bestIndex) = oldTree->Child(--end); // Decrement end.
+ oldTree->Children()[bestIndex] = oldTree->Children()[--end]; // Decrement end.
}
// See if we need to satisfy the minimum fill.
if (end > 0) {
if (numAssignTreeOne < numAssignTreeTwo) {
for (int i = 0; i < end; i++) {
- InsertNodeIntoTree(treeOne, oldTree->Child(i));
+ InsertNodeIntoTree(treeOne, oldTree->Children()[i]);
numAssignTreeOne++;
}
} else {
for (int i = 0; i < end; i++) {
- InsertNodeIntoTree(treeTwo, oldTree->Child(i));
+ InsertNodeIntoTree(treeTwo, oldTree->Children()[i]);
numAssignTreeTwo++;
}
}
@@ -493,12 +497,12 @@ void RTreeSplit<DescentType, StatisticType, MatType>::AssignNodeDestNode(
for (int i = 0; i < treeOne->NumChildren(); i++) {
for (int j = i + 1; j < treeOne->NumChildren(); j++) {
- assert(treeOne->Child(i) != treeOne->Child(j));
+ assert(treeOne->Children()[i] != treeOne->Children()[j]);
}
}
for (int i = 0; i < treeTwo->NumChildren(); i++) {
for (int j = i + 1; j < treeTwo->NumChildren(); j++) {
- assert(treeTwo->Child(i) != treeTwo->Child(j));
+ assert(treeTwo->Children()[i] != treeTwo->Children()[j]);
}
}
}
@@ -514,7 +518,7 @@ void RTreeSplit<DescentType, StatisticType, MatType>::InsertNodeIntoTree(
RectangleTree<RTreeSplit<DescentType, StatisticType, MatType>, DescentType, StatisticType, MatType>* srcNode)
{
destTree->Bound() |= srcNode->Bound();
- destTree->Child(destTree->NumChildren()++) = srcNode;
+ destTree->Children()[destTree->NumChildren()++] = srcNode;
}
diff --git a/src/mlpack/core/tree/rectangle_tree/rectangle_tree.hpp b/src/mlpack/core/tree/rectangle_tree/rectangle_tree.hpp
index 32c888c..cc5d205 100644
--- a/src/mlpack/core/tree/rectangle_tree/rectangle_tree.hpp
+++ b/src/mlpack/core/tree/rectangle_tree/rectangle_tree.hpp
@@ -102,12 +102,12 @@ class RectangleTree
* "CENTERAL" DATA MATRIX.
*/
RectangleTree(MatType& data,
- const size_t maxLeafSize = 20,
- const size_t minLeafSize = 8,
- const size_t maxNumChildren = 5,
- const size_t minNumChildren = 2,
- const size_t firstDataIndex = 0
- );
+ const size_t maxLeafSize = 20,
+ const size_t minLeafSize = 8,
+ const size_t maxNumChildren = 5,
+ const size_t minNumChildren = 2,
+ const size_t firstDataIndex = 0
+ );
/**
* Construct this as an empty node with the specified parent. Copying the parameters
@@ -118,6 +118,14 @@ class RectangleTree
explicit RectangleTree(RectangleTree<SplitType, DescentType, StatisticType, MatType>* parentNode);
/**
+ * Create a rectangle tree by copying the other tree. Be careful! This can
+ * take a long time and use a lot of memory.
+ *
+ * @param other The tree to be copied.
+ */
+ RectangleTree(const RectangleTree& other, const bool deepCopy = true);
+
+ /**
* Deletes this node, deallocating the memory for the children and calling
* their destructors in turn. This will invalidate any younters or references
* to any nodes which are children of this one.
@@ -317,10 +325,10 @@ class RectangleTree
*
* @param child Index of child to return.
*/
- inline RectangleTree<SplitType, DescentType, StatisticType, MatType>*
+ inline RectangleTree<SplitType, DescentType, StatisticType, MatType>&
Child(const size_t child) const
{
- return children[child];
+ return *children[child];
}
/**
@@ -328,10 +336,10 @@ class RectangleTree
*
* @param child Index of child to return.
*/
- inline RectangleTree<SplitType, DescentType, StatisticType, MatType>*&
+ inline RectangleTree<SplitType, DescentType, StatisticType, MatType>&
Child(const size_t child)
{
- return children[child];
+ return *children[child];
}
//! Return the number of points in this node (returns 0 if this node is not a leaf).
@@ -494,6 +502,11 @@ class RectangleTree
bool ShrinkBoundForBound(const HRectBound<>& changedBound);
/**
+ * Make an exact copy of this node, pointers and everything.
+ */
+ RectangleTree* ExactClone();
+
+ /**
* Returns a string representation of this object.
*/
std::string ToString() const;
diff --git a/src/mlpack/core/tree/rectangle_tree/rectangle_tree_impl.hpp b/src/mlpack/core/tree/rectangle_tree/rectangle_tree_impl.hpp
index 392458b..44d95d8 100644
--- a/src/mlpack/core/tree/rectangle_tree/rectangle_tree_impl.hpp
+++ b/src/mlpack/core/tree/rectangle_tree/rectangle_tree_impl.hpp
@@ -78,6 +78,46 @@ localDataset(new MatType(static_cast<int> (parentNode->Bound().Dim()), static_ca
}
/**
+ * Create a rectangle tree by copying the other tree. Be careful! This can
+ * take a long time and use a lot of memory.
+ */
+template<typename SplitType,
+typename DescentType,
+typename StatisticType,
+typename MatType>
+RectangleTree<SplitType, DescentType, StatisticType, MatType>::RectangleTree(
+ const RectangleTree& other, const bool deepCopy) :
+ maxNumChildren(other.MaxNumChildren()),
+ minNumChildren(other.MinNumChildren()),
+ numChildren(other.NumChildren()),
+ children(maxNumChildren + 1),
+ parent(other.Parent()),
+ begin(other.Begin()),
+ count(other.Count()),
+ maxLeafSize(other.MaxLeafSize()),
+ minLeafSize(other.MinLeafSize()),
+ bound(other.bound),
+ parentDistance(other.ParentDistance()),
+ dataset(other.dataset),
+ points(other.Points()),
+ localDataset(NULL)
+{
+ if(deepCopy) {
+ if(numChildren > 0) {
+ for(size_t i = 0; i < numChildren; i++) {
+ children[i] = new RectangleTree(*(other.Children()[i]));
+ }
+ } else {
+ localDataset = new MatType(other.LocalDataset());
+ }
+ } else {
+ children = other.Children();
+ arma::mat& otherData = const_cast<arma::mat&> (other.LocalDataset());
+ localDataset = &otherData;
+ }
+}
+
+/**
* Deletes this node, deallocating the memory for the children and calling
* their destructors in turn. This will invalidate any pointers or references
* to any nodes which are children of this one.
@@ -292,7 +332,7 @@ RemoveNode(const RectangleTree* node, std::vector<bool>& relevels)
}
bool contains = true;
for (size_t j = 0; j < node->Bound().Dim(); j++) {
- contains &= Child(i)->Bound()[j].Contains(node->Bound()[j]);
+ contains &= Children()[i]->Bound()[j].Contains(node->Bound()[j]);
}
if (contains)
if (children[i]->RemoveNode(node, relevels))
diff --git a/src/mlpack/tests/rectangle_tree_test.cpp b/src/mlpack/tests/rectangle_tree_test.cpp
index eb1738d..1755864 100644
--- a/src/mlpack/tests/rectangle_tree_test.cpp
+++ b/src/mlpack/tests/rectangle_tree_test.cpp
@@ -56,7 +56,13 @@ BOOST_AUTO_TEST_CASE(RectangleTreeConstructionCountTest) {
tree::RTreeDescentHeuristic,
NeighborSearchStat<NearestNeighborSort>,
arma::mat> tree(dataset, 20, 6, 5, 2, 0);
+ RectangleTree<tree::RTreeSplit<tree::RTreeDescentHeuristic, NeighborSearchStat<NearestNeighborSort>, arma::mat>,
+ tree::RTreeDescentHeuristic,
+ NeighborSearchStat<NearestNeighborSort>,
+ arma::mat> tree2 = tree;
+
BOOST_REQUIRE_EQUAL(tree.NumDescendants(), 1000);
+ BOOST_REQUIRE_EQUAL(tree2.NumDescendants(), 1000);
}
/**
@@ -71,7 +77,7 @@ std::vector<arma::vec*> getAllPointsInTree(const RectangleTree<tree::RTreeSplit<
std::vector<arma::vec*> vec;
if (tree.NumChildren() > 0) {
for (size_t i = 0; i < tree.NumChildren(); i++) {
- std::vector<arma::vec*> tmp = getAllPointsInTree(*(tree.Child(i)));
+ std::vector<arma::vec*> tmp = getAllPointsInTree(*(tree.Children()[i]));
vec.insert(vec.begin(), tmp.begin(), tmp.end());
}
} else {
@@ -131,7 +137,7 @@ void checkContainment(const RectangleTree<tree::RTreeSplit<tree::RTreeDescentHeu
for (size_t j = 0; j < tree.Bound().Dim(); j++) {
BOOST_REQUIRE_EQUAL(tree.Bound()[j].Contains(tree.Children()[i]->Bound()[j]), true);
}
- checkContainment(*(tree.Child(i)));
+ checkContainment(*(tree.Children()[i]));
}
}
return;
@@ -167,7 +173,7 @@ void checkSync(const RectangleTree<tree::RTreeSplit<tree::RTreeDescentHeuristic,
}
} else {
for (size_t i = 0; i < tree.NumChildren(); i++) {
- checkSync(*tree.Child(i));
+ checkSync(*tree.Children()[i]);
}
}
return;
@@ -204,7 +210,7 @@ void checkFills(const RectangleTree<tree::RTreeSplit<tree::RTreeDescentHeuristic
} else {
for (size_t i = 0; i < tree.NumChildren(); i++) {
BOOST_REQUIRE_EQUAL((tree.NumChildren() >= tree.MinNumChildren() || tree.Parent() == NULL) && tree.NumChildren() <= tree.MaxNumChildren(), true);
- checkFills(*tree.Child(i));
+ checkFills(*tree.Children()[i]);
}
}
return;
@@ -236,7 +242,7 @@ int getMaxLevel(const RectangleTree<tree::RTreeSplit<tree::RTreeDescentHeuristic
if (!tree.IsLeaf()) {
int m = 0;
for (size_t i = 0; i < tree.NumChildren(); i++) {
- int n = getMaxLevel(*tree.Child(i));
+ int n = getMaxLevel(*tree.Children()[i]);
if (n > m)
m = n;
}
@@ -259,7 +265,7 @@ int getMinLevel(const RectangleTree<tree::RTreeSplit<tree::RTreeDescentHeuristic
if (!tree.IsLeaf()) {
int m = INT_MAX;
for (size_t i = 0; i < tree.NumChildren(); i++) {
- int n = getMinLevel(*tree.Child(i));
+ int n = getMinLevel(*tree.Children()[i]);
if (n < m)
m = n;
}
@@ -449,7 +455,7 @@ void checkContainment(const RectangleTree<tree::RStarTreeSplit<tree::RStarTreeDe
for (size_t j = 0; j < tree.Bound().Dim(); j++) {
BOOST_REQUIRE_EQUAL(tree.Bound()[j].Contains(tree.Children()[i]->Bound()[j]), true);
}
- checkContainment(*(tree.Child(i)));
+ checkContainment(*(tree.Children()[i]));
}
}
return;
@@ -472,7 +478,7 @@ void checkSync(const RectangleTree<tree::RStarTreeSplit<tree::RStarTreeDescentHe
}
} else {
for (size_t i = 0; i < tree.NumChildren(); i++) {
- checkSync(*tree.Child(i));
+ checkSync(*tree.Children()[i]);
}
}
return;
@@ -547,44 +553,44 @@ BOOST_AUTO_TEST_CASE(RTreeSplitTest) {
BOOST_REQUIRE_EQUAL(RTree.TreeDepth(), 3);
int firstChild = 0, secondChild = 1;
- if(RTree.Child(firstChild)->NumChildren() == 2) {
+ if(RTree.Children()[firstChild]->NumChildren() == 2) {
firstChild = 1;
secondChild = 0;
}
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->Bound()[0].Lo(), 0);
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->Bound()[0].Hi(), 0.1);
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->Bound()[1].Lo(), 0);
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->Bound()[1].Hi(), 1.0);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->Bound()[0].Lo(), 0);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->Bound()[0].Hi(), 0.1);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->Bound()[1].Lo(), 0);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->Bound()[1].Hi(), 1.0);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Bound()[0].Lo(), 0.3);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Bound()[0].Hi(), 1.0);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Bound()[1].Lo(), 0.1);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Bound()[1].Hi(), 0.9);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Bound()[0].Lo(), 0.3);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Bound()[0].Hi(), 1.0);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Bound()[1].Lo(), 0.1);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Bound()[1].Hi(), 0.9);
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->NumChildren(), 1);
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->Child(0)->Bound()[0].Lo(), 0);
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->Child(0)->Bound()[0].Hi(), 0.1);
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->Child(0)->Bound()[1].Lo(), 0);
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->Child(0)->Bound()[1].Hi(), 1.0);
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->Child(0)->Count(), 3);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->NumChildren(), 1);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->Children()[0]->Bound()[0].Lo(), 0);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->Children()[0]->Bound()[0].Hi(), 0.1);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->Children()[0]->Bound()[1].Lo(), 0);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->Children()[0]->Bound()[1].Hi(), 1.0);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->Children()[0]->Count(), 3);
int firstPrime = 0, secondPrime = 1;
- if(RTree.Child(secondChild)->Child(firstPrime)->Count() == 3) {
+ if(RTree.Children()[secondChild]->Children()[firstPrime]->Count() == 3) {
firstPrime = 1;
secondPrime = 0;
}
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->NumChildren(), 2);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(firstPrime)->Count(), 4);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(firstPrime)->Bound()[0].Lo(), 0.3);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(firstPrime)->Bound()[0].Hi(), 0.7);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(firstPrime)->Bound()[1].Lo(), 0.3);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(firstPrime)->Bound()[1].Hi(), 0.7);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(secondPrime)->Count(), 3);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(secondPrime)->Bound()[0].Lo(), 0.9);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(secondPrime)->Bound()[0].Hi(), 1.0);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(secondPrime)->Bound()[1].Lo(), 0.1);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(secondPrime)->Bound()[1].Hi(), 0.9);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->NumChildren(), 2);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[firstPrime]->Count(), 4);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[firstPrime]->Bound()[0].Lo(), 0.3);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[firstPrime]->Bound()[0].Hi(), 0.7);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[firstPrime]->Bound()[1].Lo(), 0.3);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[firstPrime]->Bound()[1].Hi(), 0.7);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[secondPrime]->Count(), 3);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[secondPrime]->Bound()[0].Lo(), 0.9);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[secondPrime]->Bound()[0].Hi(), 1.0);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[secondPrime]->Bound()[1].Lo(), 0.1);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[secondPrime]->Bound()[1].Hi(), 0.9);
}
@@ -615,44 +621,44 @@ BOOST_AUTO_TEST_CASE(RStarTreeSplitTest) {
BOOST_REQUIRE_EQUAL(RTree.TreeDepth(), 3);
int firstChild = 0, secondChild = 1;
- if(RTree.Child(firstChild)->NumChildren() == 2) {
+ if(RTree.Children()[firstChild]->NumChildren() == 2) {
firstChild = 1;
secondChild = 0;
}
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->Bound()[0].Lo(), 0);
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->Bound()[0].Hi(), 0.1);
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->Bound()[1].Lo(), 0);
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->Bound()[1].Hi(), 1.0);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->Bound()[0].Lo(), 0);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->Bound()[0].Hi(), 0.1);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->Bound()[1].Lo(), 0);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->Bound()[1].Hi(), 1.0);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Bound()[0].Lo(), 0.3);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Bound()[0].Hi(), 1.0);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Bound()[1].Lo(), 0.1);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Bound()[1].Hi(), 0.9);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Bound()[0].Lo(), 0.3);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Bound()[0].Hi(), 1.0);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Bound()[1].Lo(), 0.1);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Bound()[1].Hi(), 0.9);
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->NumChildren(), 1);
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->Child(0)->Bound()[0].Lo(), 0);
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->Child(0)->Bound()[0].Hi(), 0.1);
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->Child(0)->Bound()[1].Lo(), 0);
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->Child(0)->Bound()[1].Hi(), 1.0);
- BOOST_REQUIRE_EQUAL(RTree.Child(firstChild)->Child(0)->Count(), 3);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->NumChildren(), 1);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->Children()[0]->Bound()[0].Lo(), 0);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->Children()[0]->Bound()[0].Hi(), 0.1);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->Children()[0]->Bound()[1].Lo(), 0);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->Children()[0]->Bound()[1].Hi(), 1.0);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[firstChild]->Children()[0]->Count(), 3);
int firstPrime = 0, secondPrime = 1;
- if(RTree.Child(secondChild)->Child(firstPrime)->Count() == 3) {
+ if(RTree.Children()[secondChild]->Children()[firstPrime]->Count() == 3) {
firstPrime = 1;
secondPrime = 0;
}
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->NumChildren(), 2);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(firstPrime)->Count(), 4);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(firstPrime)->Bound()[0].Lo(), 0.3);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(firstPrime)->Bound()[0].Hi(), 0.7);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(firstPrime)->Bound()[1].Lo(), 0.3);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(firstPrime)->Bound()[1].Hi(), 0.7);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(secondPrime)->Count(), 3);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(secondPrime)->Bound()[0].Lo(), 0.9);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(secondPrime)->Bound()[0].Hi(), 1.0);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(secondPrime)->Bound()[1].Lo(), 0.1);
- BOOST_REQUIRE_EQUAL(RTree.Child(secondChild)->Child(secondPrime)->Bound()[1].Hi(), 0.9);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->NumChildren(), 2);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[firstPrime]->Count(), 4);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[firstPrime]->Bound()[0].Lo(), 0.3);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[firstPrime]->Bound()[0].Hi(), 0.7);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[firstPrime]->Bound()[1].Lo(), 0.3);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[firstPrime]->Bound()[1].Hi(), 0.7);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[secondPrime]->Count(), 3);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[secondPrime]->Bound()[0].Lo(), 0.9);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[secondPrime]->Bound()[0].Hi(), 1.0);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[secondPrime]->Bound()[1].Lo(), 0.1);
+ BOOST_REQUIRE_EQUAL(RTree.Children()[secondChild]->Children()[secondPrime]->Bound()[1].Hi(), 0.9);
}
BOOST_AUTO_TEST_SUITE_END();
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