13 #ifndef MLPACK_METHODS_HOEFFDING_TREES_HOEFFDING_CATEGORICAL_SPLIT_HPP 14 #define MLPACK_METHODS_HOEFFDING_TREES_HOEFFDING_CATEGORICAL_SPLIT_HPP 43 template<
typename FitnessFunction>
58 const size_t numClasses);
67 const size_t numClasses,
77 void Train(eT value,
const size_t label);
102 void Split(arma::Col<size_t>& childMajorities, SplitInfo& splitInfo);
110 template<
typename Archive>
127 #include "hoeffding_categorical_split_impl.hpp" void Serialize(Archive &ar, const unsigned int)
Serialize the categorical split.
Linear algebra utility functions, generally performed on matrices or vectors.
The core includes that mlpack expects; standard C++ includes and Armadillo.
FirstShim< T > CreateNVP(T &t, const std::string &name, typename std::enable_if_t< HasSerialize< T >::value > *=0)
Call this function to produce a name-value pair; this is similar to BOOST_SERIALIZATION_NVP(), but should be used for types that have a Serialize() function (or contain a type that has a Serialize() function) instead of a serialize() function.
double MajorityProbability() const
Get the probability of the majority class given the points seen so far.
HoeffdingCategoricalSplit(const size_t numCategories, const size_t numClasses)
Create the HoeffdingCategoricalSplit given a number of categories for this dimension and a number of ...
arma::Mat< size_t > sufficientStatistics
The sufficient statistics for all points seen so far.
void EvaluateFitnessFunction(double &bestFitness, double &secondBestFitness) const
Given the points seen so far, evaluate the fitness function, returning the gain for the best possible...
size_t NumChildren() const
Return the number of children, if the node were to split.
void Split(arma::Col< size_t > &childMajorities, SplitInfo &splitInfo)
Gather the information for a split: get the labels of the child majorities, and initialize the SplitI...
CategoricalSplitInfo SplitInfo
The type of split information required by the HoeffdingCategoricalSplit.
size_t MajorityClass() const
Get the majority class seen so far.
This is the standard Hoeffding-bound categorical feature proposed in the paper below: ...
void Train(eT value, const size_t label)
Train on the given value with the given label.