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Refactor to use BoundTraits. Remove nextDimension.

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The extra overhead of tracking the number of tied dimensions is overkill, when that situation will happen so infrequently in practice.  It used to happen with the BallBound where all dimensions are always the same, but the BoundTraits solution is better.
This commit is contained in:
Ryan Curtin
2015-05-04 11:12:39 -04:00
parent acbf832a79
commit b9d571606d
2 changed files with 92 additions and 100 deletions
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src/mlpack/core/tree/binary_space_tree/mean_split.hpp
+22 -32
View File
@@ -23,13 +23,6 @@ template<typename BoundType, typename MatType = arma::mat>
class MeanSplit
{
public:
/**
* Instantiate a MeanSplit object. This will initialize the nextDimension
* object, which will determine which dimension to use next, in the instance
* of a tie.
*/
MeanSplit() : nextDimension(0) { /* Nothing to do. */ }
/**
* Split the node according to the mean value in the dimension with maximum
* width.
@@ -44,11 +37,11 @@ class MeanSplit
* @param splitCol The index at which the dataset is divided into two parts
* after the rearrangement.
*/
bool SplitNode(const BoundType& bound,
MatType& data,
const size_t begin,
const size_t count,
size_t& splitCol);
static bool SplitNode(const BoundType& bound,
MatType& data,
const size_t begin,
const size_t count,
size_t& splitCol);
/**
* Split the node according to the mean value in the dimension with maximum
@@ -66,12 +59,12 @@ class MeanSplit
* @param oldFromNew Vector which will be filled with the old positions for
* each new point.
*/
bool SplitNode(const BoundType& bound,
MatType& data,
const size_t begin,
const size_t count,
size_t& splitCol,
std::vector<size_t>& oldFromNew);
static bool SplitNode(const BoundType& bound,
MatType& data,
const size_t begin,
const size_t count,
size_t& splitCol,
std::vector<size_t>& oldFromNew);
private:
/**
@@ -86,11 +79,11 @@ class MeanSplit
* @param splitVal The split in dimension splitDimension is based on this
* value.
*/
size_t PerformSplit(MatType& data,
const size_t begin,
const size_t count,
const size_t splitDimension,
const double splitVal);
static size_t PerformSplit(MatType& data,
const size_t begin,
const size_t count,
const size_t splitDimension,
const double splitVal);
/**
* Reorder the dataset into two parts such that they lie on either side of
@@ -106,15 +99,12 @@ class MeanSplit
* @param oldFromNew Vector which will be filled with the old positions for
* each new point.
*/
size_t PerformSplit(MatType& data,
const size_t begin,
const size_t count,
const size_t splitDimension,
const double splitVal,
std::vector<size_t>& oldFromNew);
//! Tracks the next dimension to use in case of a tie.
size_t nextDimension;
static size_t PerformSplit(MatType& data,
const size_t begin,
const size_t count,
const size_t splitDimension,
const double splitVal,
std::vector<size_t>& oldFromNew);
};
}; // namespace tree
src/mlpack/core/tree/binary_space_tree/mean_split_impl.hpp
+70 -68
View File
@@ -23,40 +23,47 @@ bool MeanSplit<BoundType, MatType>::SplitNode(const BoundType& bound,
size_t splitDimension = data.n_rows; // Indicate invalid.
double maxWidth = -1;
// Find the split dimension.
size_t ties = 0;
for (size_t d = 0; d < data.n_rows; d++)
// Find the split dimension. If the bound is tight, we only need to consult
// the bound's width.
if (bound::BoundTraits<BoundType>::HasTightBounds)
{
const double width = bound[d].Width();
if (width > maxWidth)
for (size_t d = 0; d < data.n_rows; d++)
{
maxWidth = width;
splitDimension = d;
}
if (width == maxWidth)
{
// There's a tie. Record that.
++ties;
const double width = bound[d].Width();
if (width > maxWidth)
{
maxWidth = width;
splitDimension = d;
}
}
}
if (ties > 0)
else
{
// Look through a second time, and determine the correct dimension.
size_t tieIndex = 0;
for (size_t d = 0; d < data.n_rows; ++d)
// We must individually calculate bounding boxes.
math::Range* ranges = new math::Range[data.n_rows];
for (size_t i = begin; i < begin + count; ++i)
{
// Expand each dimension as necessary.
for (size_t d = 0; d < data.n_rows; ++d)
{
const double val = data(d, i);
if (val < ranges[d].Lo())
ranges[d].Lo() = val;
if (val > ranges[d].Hi())
ranges[d].Hi() = val;
}
}
// Now, which is the widest?
for (size_t d = 0; d < data.n_rows; d++)
{
const double width = bound[d].Width();
if (width == maxWidth)
if (width > maxWidth)
{
if (tieIndex == (nextDimension % ties))
{
splitDimension = d;
break;
}
++tieIndex;
maxWidth = width;
splitDimension = d;
}
}
}
@@ -79,9 +86,6 @@ bool MeanSplit<BoundType, MatType>::SplitNode(const BoundType& bound,
// greater than splitVal are on the right side of splitCol.
splitCol = PerformSplit(data, begin, count, splitDimension, splitVal);
// Increment the tie-breaker counter.
++nextDimension;
return true;
}
@@ -96,40 +100,47 @@ bool MeanSplit<BoundType, MatType>::SplitNode(const BoundType& bound,
size_t splitDimension = data.n_rows; // Indicate invalid.
double maxWidth = -1;
// Find the split dimension.
size_t ties = 0;
for (size_t d = 0; d < data.n_rows; d++)
// Find the split dimension. If the bound is tight, we only need to consult
// the bound's width.
if (bound::BoundTraits<BoundType>::HasTightBounds)
{
const double width = bound[d].Width();
if (width > maxWidth)
{
maxWidth = width;
splitDimension = d;
}
if (width == maxWidth)
{
// There's a tie. Record that.
++ties;
}
}
if (ties > 0)
{
// Look through a second time, and determine the correct dimension.
size_t tieIndex = 0;
for (size_t d = 0; d < data.n_rows; ++d)
for (size_t d = 0; d < data.n_rows; d++)
{
const double width = bound[d].Width();
if (width == maxWidth)
if (width > maxWidth)
{
if (tieIndex == (nextDimension % ties))
{
splitDimension = d;
break;
}
++tieIndex;
maxWidth = width;
splitDimension = d;
}
}
}
else
{
// We must individually calculate bounding boxes.
math::Range* ranges = new math::Range[data.n_rows];
for (size_t i = begin; i < begin + count; ++i)
{
// Expand each dimension as necessary.
for (size_t d = 0; d < data.n_rows; ++d)
{
const double val = data(d, i);
if (val < ranges[d].Lo())
ranges[d].Lo() = val;
if (val > ranges[d].Hi())
ranges[d].Hi() = val;
}
}
// Now, which is the widest?
for (size_t d = 0; d < data.n_rows; d++)
{
const double width = bound[d].Width();
if (width > maxWidth)
{
maxWidth = width;
splitDimension = d;
}
}
}
@@ -153,9 +164,6 @@ bool MeanSplit<BoundType, MatType>::SplitNode(const BoundType& bound,
splitCol = PerformSplit(data, begin, count, splitDimension, splitVal,
oldFromNew);
// Increment the tie-breaker counter.
++nextDimension;
return true;
}
@@ -178,7 +186,7 @@ size_t MeanSplit<BoundType, MatType>::
// condition is in the middle.
while ((data(splitDimension, left) < splitVal) && (left <= right))
left++;
while ((data(splitDimension, right) >= splitVal) && (left <= right))
while ((data(splitDimension, right) >= splitVal) && (left <= right) && (right > 0))
right--;
while (left <= right)
@@ -202,9 +210,6 @@ size_t MeanSplit<BoundType, MatType>::
Log::Assert(left == right + 1);
Log::Assert(left > begin);
Log::Assert(left < begin + count - 1);
return left;
}
@@ -228,7 +233,7 @@ size_t MeanSplit<BoundType, MatType>::
// condition is in the middle.
while ((data(splitDimension, left) < splitVal) && (left <= right))
left++;
while ((data(splitDimension, right) >= splitVal) && (left <= right))
while ((data(splitDimension, right) >= splitVal) && (left <= right) && (right > 0))
right--;
while (left <= right)
@@ -257,9 +262,6 @@ size_t MeanSplit<BoundType, MatType>::
Log::Assert(left == right + 1);
Log::Assert(left > begin);
Log::Assert(left < begin + count - 1);
return left;
}