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DTree< MatType, TagType > Class Template Reference

A density estimation tree is similar to both a decision tree and a space partitioning tree (like a kd-tree). More...

Public Types

typedef MatType::elem_type ElemType
 The actual, underlying type we're working with. More...

 
typedef arma::Col< ElemTypeStatType
 
typedef MatType::vec_type VecType
 

Public Member Functions

 DTree ()
 Create an empty density estimation tree. More...

 
 DTree (const DTree &obj)
 Create a tree that is the copy of the given tree. More...

 
 DTree (DTree &&obj)
 Create a tree by taking ownership of another tree (move constructor). More...

 
 DTree (const StatType &maxVals, const StatType &minVals, const size_t totalPoints)
 Create a density estimation tree with the given bounds and the given number of total points. More...

 
 DTree (MatType &data)
 Create a density estimation tree on the given data. More...

 
 DTree (const StatType &maxVals, const StatType &minVals, const size_t start, const size_t end, const double logNegError)
 Create a child node of a density estimation tree given the bounding box specified by maxVals and minVals, using the size given in start and end and the specified error. More...

 
 DTree (const StatType &maxVals, const StatType &minVals, const size_t totalPoints, const size_t start, const size_t end)
 Create a child node of a density estimation tree given the bounding box specified by maxVals and minVals, using the size given in start and end, and calculating the error with the total number of points given. More...

 
 ~DTree ()
 Clean up memory allocated by the tree. More...

 
double AlphaUpper () const
 Return the upper part of the alpha sum. More...

 
TagType BucketTag () const
 Return the current bucket's ID, if leaf, or -1 otherwise. More...

 
double ComputeValue (const VecType &query) const
 Compute the logarithm of the density estimate of a given query point. More...

 
void ComputeVariableImportance (arma::vec &importances) const
 Compute the variable importance of each dimension in the learned tree. More...

 
size_t End () const
 Return the first index of a point not contained in this node. More...

 
TagType FindBucket (const VecType &query) const
 Return the tag of the leaf containing the query. More...

 
double Grow (MatType &data, arma::Col< size_t > &oldFromNew, const bool useVolReg=false, const size_t maxLeafSize=10, const size_t minLeafSize=5)
 Greedily expand the tree. More...

 
DTreeLeft () const
 Return the left child. More...

 
double LogNegativeError (const size_t totalPoints) const
 Compute the log-negative-error for this point, given the total number of points in the dataset. More...

 
double LogNegError () const
 Return the log negative error of this node. More...

 
double LogVolume () const
 Return the inverse of the volume of this node. More...

 
const StatTypeMaxVals () const
 Return the maximum values. More...

 
const StatTypeMinVals () const
 Return the minimum values. More...

 
DTreeoperator= (const DTree &obj)
 Copy the given tree. More...

 
DTreeoperator= (DTree &&obj)
 Take ownership of the given tree (move operator). More...

 
double PruneAndUpdate (const double oldAlpha, const size_t points, const bool useVolReg=false)
 Perform alpha pruning on a tree. More...

 
double Ratio () const
 Return the ratio of points in this node to the points in the whole dataset. More...

 
DTreeRight () const
 Return the right child. More...

 
bool Root () const
 Return whether or not this is the root of the tree. More...

 
template
<
typename
Archive
>
void Serialize (Archive &ar, const unsigned int)
 Serialize the density estimation tree. More...

 
size_t SplitDim () const
 Return the split dimension of this node. More...

 
ElemType SplitValue () const
 Return the split value of this node. More...

 
size_t Start () const
 Return the starting index of points contained in this node. More...

 
size_t SubtreeLeaves () const
 Return the number of leaves which are descendants of this node. More...

 
double SubtreeLeavesLogNegError () const
 Return the log negative error of all descendants of this node. More...

 
TagType TagTree (const TagType &tag=0)
 Index the buckets for possible usage later; this results in every leaf in the tree having a specific tag (accessible with BucketTag()). More...

 
bool WithinRange (const VecType &query) const
 Return whether a query point is within the range of this node. More...

 

Detailed Description


template
<
typename
MatType
=
arma::mat
,
typename
TagType
=
int
>

class mlpack::det::DTree< MatType, TagType >

A density estimation tree is similar to both a decision tree and a space partitioning tree (like a kd-tree).

Each leaf represents a constant-density hyper-rectangle. The tree is constructed in such a way as to minimize the integrated square error between the probability distribution of the tree and the observed probability distribution of the data. Because the tree is similar to a decision tree, the density estimation tree can provide very fast density estimates for a given point.

For more information, see the following paper:

@incollection{ram2011,
author = {Ram, Parikshit and Gray, Alexander G.},
title = {Density estimation trees},
booktitle = {{Proceedings of the 17th ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining}},
series = {KDD '11},
year = {2011},
pages = {627--635}
}

Definition at line 46 of file dtree.hpp.

Member Typedef Documentation

◆ ElemType

typedef MatType::elem_type ElemType

The actual, underlying type we're working with.

Definition at line 52 of file dtree.hpp.

◆ StatType

typedef arma::Col<ElemType> StatType

Definition at line 54 of file dtree.hpp.

◆ VecType

typedef MatType::vec_type VecType

Definition at line 53 of file dtree.hpp.

Constructor & Destructor Documentation

◆ DTree() [1/7]

DTree ( )

Create an empty density estimation tree.

◆ DTree() [2/7]

DTree ( const DTree< MatType, TagType > &  obj)

Create a tree that is the copy of the given tree.

Parameters
objTree to copy.

◆ DTree() [3/7]

DTree ( DTree< MatType, TagType > &&  obj)

Create a tree by taking ownership of another tree (move constructor).

Parameters
objTree to take ownership of.

◆ DTree() [4/7]

DTree ( const StatType maxVals,
const StatType minVals,
const size_t  totalPoints 
)

Create a density estimation tree with the given bounds and the given number of total points.

Children will not be created.

Parameters
maxValsMaximum values of the bounding box.
minValsMinimum values of the bounding box.
totalPointsTotal number of points in the dataset.

◆ DTree() [5/7]

DTree ( MatType &  data)

Create a density estimation tree on the given data.

Children will be created following the procedure outlined in the paper. The data will be modified; it will be reordered similar to the way BinarySpaceTree modifies datasets.

Parameters
dataDataset to build tree on.

◆ DTree() [6/7]

DTree ( const StatType maxVals,
const StatType minVals,
const size_t  start,
const size_t  end,
const double  logNegError 
)

Create a child node of a density estimation tree given the bounding box specified by maxVals and minVals, using the size given in start and end and the specified error.

Children of this node will not be created recursively.

Parameters
maxValsUpper bound of bounding box.
minValsLower bound of bounding box.
startStart of points represented by this node in the data matrix.
endEnd of points represented by this node in the data matrix.
errorlog-negative error of this node.

◆ DTree() [7/7]

DTree ( const StatType maxVals,
const StatType minVals,
const size_t  totalPoints,
const size_t  start,
const size_t  end 
)

Create a child node of a density estimation tree given the bounding box specified by maxVals and minVals, using the size given in start and end, and calculating the error with the total number of points given.

Children of this node will not be created recursively.

Parameters
maxValsUpper bound of bounding box.
minValsLower bound of bounding box.
startStart of points represented by this node in the data matrix.
endEnd of points represented by this node in the data matrix.

◆ ~DTree()

~DTree ( )

Clean up memory allocated by the tree.

Member Function Documentation

◆ AlphaUpper()

double AlphaUpper ( ) const
inline

Return the upper part of the alpha sum.

Definition at line 302 of file dtree.hpp.

◆ BucketTag()

TagType BucketTag ( ) const
inline

Return the current bucket's ID, if leaf, or -1 otherwise.

Definition at line 304 of file dtree.hpp.

◆ ComputeValue()

double ComputeValue ( const VecType query) const

Compute the logarithm of the density estimate of a given query point.

Parameters
queryPoint to estimate density of.

◆ ComputeVariableImportance()

void ComputeVariableImportance ( arma::vec &  importances) const

Compute the variable importance of each dimension in the learned tree.

Parameters
importancesVector to store the calculated importances in.

◆ End()

size_t End ( ) const
inline

Return the first index of a point not contained in this node.

Definition at line 279 of file dtree.hpp.

◆ FindBucket()

TagType FindBucket ( const VecType query) const

Return the tag of the leaf containing the query.

This is useful for generating class memberships.

Parameters
queryQuery to search for.

◆ Grow()

double Grow ( MatType &  data,
arma::Col< size_t > &  oldFromNew,
const bool  useVolReg = false,
const size_t  maxLeafSize = 10,
const size_t  minLeafSize = 5 
)

Greedily expand the tree.

The points in the dataset will be reordered during tree growth.

Parameters
dataDataset to build tree on.
oldFromNewMappings from old points to new points.
useVolRegIf true, volume regularization is used.
maxLeafSizeMaximum size of a leaf.
minLeafSizeMinimum size of a leaf.

◆ Left()

DTree* Left ( ) const
inline

Return the left child.

Definition at line 296 of file dtree.hpp.

◆ LogNegativeError()

double LogNegativeError ( const size_t  totalPoints) const

Compute the log-negative-error for this point, given the total number of points in the dataset.

Parameters
totalPointsTotal number of points in the dataset.

◆ LogNegError()

double LogNegError ( ) const
inline

Return the log negative error of this node.

Definition at line 285 of file dtree.hpp.

◆ LogVolume()

double LogVolume ( ) const
inline

Return the inverse of the volume of this node.

Definition at line 294 of file dtree.hpp.

◆ MaxVals()

const StatType& MaxVals ( ) const
inline

Return the maximum values.

Definition at line 307 of file dtree.hpp.

◆ MinVals()

const StatType& MinVals ( ) const
inline

Return the minimum values.

Definition at line 310 of file dtree.hpp.

References DTree< MatType, TagType >::Serialize().

◆ operator=() [1/2]

DTree& operator= ( const DTree< MatType, TagType > &  obj)

Copy the given tree.

Parameters
objTree to copy.

◆ operator=() [2/2]

DTree& operator= ( DTree< MatType, TagType > &&  obj)

Take ownership of the given tree (move operator).

Parameters
objTree to take ownership of.

◆ PruneAndUpdate()

double PruneAndUpdate ( const double  oldAlpha,
const size_t  points,
const bool  useVolReg = false 
)

Perform alpha pruning on a tree.

Returns the new value of alpha.

Parameters
oldAlphaOld value of alpha.
pointsTotal number of points in dataset.
useVolRegIf true, volume regularization is used.
Returns
New value of alpha.

◆ Ratio()

double Ratio ( ) const
inline

Return the ratio of points in this node to the points in the whole dataset.

Definition at line 292 of file dtree.hpp.

◆ Right()

DTree* Right ( ) const
inline

Return the right child.

Definition at line 298 of file dtree.hpp.

◆ Root()

bool Root ( ) const
inline

Return whether or not this is the root of the tree.

Definition at line 300 of file dtree.hpp.

◆ Serialize()

void Serialize ( Archive &  ar,
const unsigned  int 
)

Serialize the density estimation tree.

Referenced by DTree< MatType, TagType >::MinVals().

◆ SplitDim()

size_t SplitDim ( ) const
inline

Return the split dimension of this node.

Definition at line 281 of file dtree.hpp.

◆ SplitValue()

ElemType SplitValue ( ) const
inline

Return the split value of this node.

Definition at line 283 of file dtree.hpp.

◆ Start()

size_t Start ( ) const
inline

Return the starting index of points contained in this node.

Definition at line 277 of file dtree.hpp.

◆ SubtreeLeaves()

size_t SubtreeLeaves ( ) const
inline

Return the number of leaves which are descendants of this node.

Definition at line 289 of file dtree.hpp.

◆ SubtreeLeavesLogNegError()

double SubtreeLeavesLogNegError ( ) const
inline

Return the log negative error of all descendants of this node.

Definition at line 287 of file dtree.hpp.

◆ TagTree()

TagType TagTree ( const TagType &  tag = 0)

Index the buckets for possible usage later; this results in every leaf in the tree having a specific tag (accessible with BucketTag()).

This function calls itself recursively.

Parameters
tagTag for the next leaf; leave at 0 for the initial call.

◆ WithinRange()

bool WithinRange ( const VecType query) const

Return whether a query point is within the range of this node.


The documentation for this class was generated from the following file: