MLPACK  1.0.4
Classes | Functions
mlpack::det Namespace Reference

Density Estimation Trees. More...

Classes

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

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.
DTreeTrainer (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_t  numClasses,
const std::string  leafClassMembershipFile = "" 
)

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:
dtreeTree to print membership of.
dataDataset tree is built upon.
labelsClass labels of dataset.
numClassesNumber of classes in dataset.
leafClassMembershipFileName of file to print to (optional).
void mlpack::det::PrintVariableImportance ( const DTree *  dtree,
const std::string  viFile = "" 
)

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:
dtreeDensity tree to use.
viFileName of file to print to (optional).
DTree* mlpack::det::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.

Optionally, give a filename to print the unpruned tree to.

Parameters:
datasetDataset for the tree to use.
foldsNumber of folds to use for cross-validation.
useVolumeRegIf true, use volume regularization.
maxLeafSizeMaximum number of points allowed in a leaf.
minLeafSizeMinimum number of points allowed in a leaf.
unprunedTreeOutputFilename to print unpruned tree to (optional).