SYNOPSIS

Classes

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

Functions

void PrintLeafMembership (DTree *dtree, const arma::mat &data, const arma::Mat< size_t > &labels, const size_t numClasses, const std::string leafClassMembershipFile='')
Print the membership of leaves of a density estimation tree given the labels and number of classes.
void PrintVariableImportance (const DTree *dtree, const std::string viFile='')
Print the variable importance of each dimension of a density estimation tree.
DTree * Trainer (arma::mat &dataset, const size_t folds, const bool useVolumeReg=false, const size_t maxLeafSize=10, const size_t minLeafSize=5, const std::string unprunedTreeOutput='')
Train the optimal decision tree using cross-validation with the given number of folds.

Detailed Description

Density Estimation Trees.

Function Documentation

void mlpack::det::PrintLeafMembership (DTree *dtree, const arma::mat &data, const arma::Mat< size_t > &labels, const size_tnumClasses, const std::stringleafClassMembershipFile = '')

Print the membership of leaves of a density estimation tree given the labels and number of classes. Optionally, pass the name of a file to print this information to (otherwise stdout is used).

Parameters:

dtree Tree to print membership of.
data Dataset tree is built upon.
labels Class labels of dataset.
numClasses Number of classes in dataset.
leafClassMembershipFile Name of file to print to (optional).

void mlpack::det::PrintVariableImportance (const DTree *dtree, const std::stringviFile = '')

Print the variable importance of each dimension of a density estimation tree. Optionally, pass the name of a file to print this information to (otherwise stdout is used).

Parameters:

dtree Density tree to use.
viFile Name of file to print to (optional).

DTree* mlpack::det::Trainer (arma::mat &dataset, const size_tfolds, const booluseVolumeReg = false, const size_tmaxLeafSize = 10, const size_tminLeafSize = 5, const std::stringunprunedTreeOutput = '')

Train the optimal decision tree using cross-validation with the given number of folds. Optionally, give a filename to print the unpruned tree to. This initializes a tree on the heap, so you are responsible for deleting it.

Parameters:

dataset Dataset for the tree to use.
folds Number of folds to use for cross-validation.
useVolumeReg If true, use volume regularization.
maxLeafSize Maximum number of points allowed in a leaf.
minLeafSize Minimum number of points allowed in a leaf.
unprunedTreeOutput Filename to print unpruned tree to (optional).

Author

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