mlpack
2.2.5
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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. More... | |
void | PrintVariableImportance (const DTree *dtree, const std::string viFile="") |
Print the variable importance of each dimension of a density estimation tree. More... | |
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. More... | |
Density Estimation Trees.
void mlpack::det::PrintLeafMembership | ( | DTree * | dtree, |
const arma::mat & | data, | ||
const arma::Mat< size_t > & | labels, | ||
const size_t | numClasses, | ||
const std::string | leafClassMembershipFile = "" |
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) |
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).
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::string | viFile = "" |
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) |
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).
dtree | Density tree to use. |
viFile | Name of file to print to (optional). |
DTree* mlpack::det::Trainer | ( | arma::mat & | dataset, |
const size_t | folds, | ||
const bool | useVolumeReg = false , |
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const size_t | maxLeafSize = 10 , |
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const size_t | minLeafSize = 5 , |
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const std::string | unprunedTreeOutput = "" |
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) |
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.
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). |