[mlpack] 04/149: Fix some more signed/unsigned comparison warnings that I introduced with the previous revision.
Barak A. Pearlmutter
barak+git at pearlmutter.net
Sat May 2 09:11:03 UTC 2015
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bap pushed a commit to branch svn-trunk
in repository mlpack.
commit bb74e399013594d65ab2062976103fd63c821f5e
Author: rcurtin <rcurtin at 9d5b8971-822b-0410-80eb-d18c1038ef23>
Date: Fri Sep 12 19:02:56 2014 +0000
Fix some more signed/unsigned comparison warnings that I introduced with the
previous revision.
git-svn-id: http://svn.cc.gatech.edu/fastlab/mlpack/trunk@17177 9d5b8971-822b-0410-80eb-d18c1038ef23
---
src/mlpack/core/tree/rectangle_tree/r_star_tree_split_impl.hpp | 6 +++---
src/mlpack/core/tree/rectangle_tree/r_tree_split_impl.hpp | 6 +++---
src/mlpack/core/tree/rectangle_tree/x_tree_split_impl.hpp | 10 ++++++----
3 files changed, 12 insertions(+), 10 deletions(-)
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 2d771c0..8aef253 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
@@ -132,7 +132,7 @@ void RStarTreeSplit<DescentType, StatisticType, MatType>::SplitLeafNode(
// tree->MinLeafSize() + i points in one rectangle and the rest in
// another. Then we calculate the three scores for that distribution.
- int cutOff = tree->MinLeafSize() + i;
+ size_t cutOff = tree->MinLeafSize() + i;
// We'll calculate the max and min in each dimension by hand to save time.
std::vector<double> maxG1(tree->Bound().Dim());
@@ -396,7 +396,7 @@ bool RStarTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
// tree->MinNumChildren() + i points in one rectangle and the rest in
// another. Then we calculate the three scores for that distribution.
- int cutOff = tree->MinNumChildren() + i;
+ size_t cutOff = tree->MinNumChildren() + i;
// We'll calculate the max and min in each dimension by hand to save time.
std::vector<double> maxG1(tree->Bound().Dim());
@@ -508,7 +508,7 @@ bool RStarTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
// tree->MinNumChildren() + i points in one rectangle and the rest in
// another. Then we calculate the three scores for that distribution.
- int cutOff = tree->MinNumChildren() + i;
+ size_t cutOff = tree->MinNumChildren() + i;
// We'll calculate the max and min in each dimension by hand to save time.
std::vector<double> maxG1(tree->Bound().Dim());
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 22aa7a1..417b5ff 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
@@ -240,7 +240,7 @@ void RTreeSplit<DescentType, StatisticType, MatType>::AssignPointDestNode(
const int intI,
const int intJ)
{
- int end = oldTree->Count();
+ size_t end = oldTree->Count();
assert(end > 1); // If this isn't true, the tree is really weird.
@@ -294,7 +294,7 @@ void RTreeSplit<DescentType, StatisticType, MatType>::AssignPointDestNode(
// First, calculate the starting volume.
double volOne = 1.0;
double volTwo = 1.0;
- for (int i = 0; i < oldTree->Bound().Dim(); i++)
+ for (size_t i = 0; i < oldTree->Bound().Dim(); i++)
{
volOne *= treeOne->Bound()[i].Width();
volTwo *= treeTwo->Bound()[i].Width();
@@ -382,7 +382,7 @@ void RTreeSplit<DescentType, StatisticType, MatType>::AssignNodeDestNode(
const int intJ)
{
- int end = oldTree->NumChildren();
+ size_t end = oldTree->NumChildren();
assert(end > 1); // If this isn't true, the tree is really weird.
assert(intI != intJ);
diff --git a/src/mlpack/core/tree/rectangle_tree/x_tree_split_impl.hpp b/src/mlpack/core/tree/rectangle_tree/x_tree_split_impl.hpp
index 5ab24ff..3f527cf 100644
--- a/src/mlpack/core/tree/rectangle_tree/x_tree_split_impl.hpp
+++ b/src/mlpack/core/tree/rectangle_tree/x_tree_split_impl.hpp
@@ -415,7 +415,8 @@ bool XTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
}
// Track the minOverlapSplit data
- if(minOverlapSplitDimension != -1 && j == minOverlapSplitDimension) {
+ if(minOverlapSplitDimension != tree->Bound().Dim() &&
+ j == minOverlapSplitDimension) {
for(size_t i = 0; i < overlapedAreas.size(); i++) {
if(overlapedAreas[i] < bestScoreMinOverlapSplit) {
bestScoreMinOverlapSplit = overlapedAreas[i];
@@ -516,7 +517,8 @@ bool XTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
}
// Track the minOverlapSplit data
- if(minOverlapSplitDimension != -1 && j == minOverlapSplitDimension) {
+ if(minOverlapSplitDimension != tree->Bound().Dim() &&
+ j == minOverlapSplitDimension) {
for(size_t i = 0; i < overlapedAreas.size(); i++) {
if(overlapedAreas[i] < bestScoreMinOverlapSplit) {
minOverlapSplitUsesHi = true;
@@ -576,7 +578,7 @@ bool XTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
// a "super node" (more accurately we resize this one to make it a super node).
if(useMinOverlapSplit) {
// If there is a dimension that might work, try that.
- if(minOverlapSplitDimension != -1 && bestScoreMinOverlapSplit / areaOfBestMinOverlapSplit < MAX_OVERLAP) {
+ if(minOverlapSplitDimension != tree->Bound().Dim() && bestScoreMinOverlapSplit / areaOfBestMinOverlapSplit < MAX_OVERLAP) {
std::vector<sortStruct> sorted2(tree->NumChildren());
if (minOverlapSplitUsesHi) {
for (size_t i = 0; i < sorted2.size(); i++) {
@@ -584,7 +586,7 @@ bool XTreeSplit<DescentType, StatisticType, MatType>::SplitNonLeafNode(
sorted2[i].n = i;
}
} else {
- for (int i = 0; i < sorted2.size(); i++) {
+ for (size_t i = 0; i < sorted2.size(); i++) {
sorted2[i].d = tree->Children()[i]->Bound()[bestAxis].Lo();
sorted2[i].n = i;
}
--
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