binary_numeric_split.hpp
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1 
13 #ifndef MLPACK_METHODS_HOEFFDING_SPLIT_BINARY_NUMERIC_SPLIT_HPP
14 #define MLPACK_METHODS_HOEFFDING_SPLIT_BINARY_NUMERIC_SPLIT_HPP
15 
17 
18 namespace mlpack {
19 namespace tree {
20 
45 template<typename FitnessFunction,
46  typename ObservationType = double>
48 {
49  public:
52 
58  BinaryNumericSplit(const size_t numClasses = 0);
59 
66  BinaryNumericSplit(const size_t numClasses, const BinaryNumericSplit& other);
67 
74  void Train(ObservationType value, const size_t label);
75 
89  void EvaluateFitnessFunction(double& bestFitness,
90  double& secondBestFitness);
91 
92  // Return the number of children if this node were to split on this feature.
93  size_t NumChildren() const { return 2; }
94 
102  void Split(arma::Col<size_t>& childMajorities, SplitInfo& splitInfo);
103 
105  size_t MajorityClass() const;
107  double MajorityProbability() const;
108 
110  template<typename Archive>
111  void serialize(Archive& ar, const unsigned int /* version */);
112 
113  private:
115  std::multimap<ObservationType, size_t> sortedElements;
117  arma::Col<size_t> classCounts;
118 
120  ObservationType bestSplit;
123  bool isAccurate;
124 };
125 
126 // Convenience typedef.
127 template<typename FitnessFunction>
129 
130 } // namespace tree
131 } // namespace mlpack
132 
133 // Include implementation.
134 #include "binary_numeric_split_impl.hpp"
135 
136 #endif
double MajorityProbability() const
The probability of the majority class given the points seen so far.
strip_type.hpp
Definition: add_to_po.hpp:21
void serialize(Archive &ar, const unsigned int)
Serialize the object.
BinaryNumericSplitInfo< ObservationType > SplitInfo
The splitting information required by the BinaryNumericSplit.
void Train(ObservationType value, const size_t label)
Train on the given value with the given label.
The BinaryNumericSplit class implements the numeric feature splitting strategy devised by Gama...
size_t MajorityClass() const
The majority class of the points seen so far.
BinaryNumericSplit(const size_t numClasses=0)
Create the BinaryNumericSplit object with the given number of classes.
void EvaluateFitnessFunction(double &bestFitness, double &secondBestFitness)
Given the points seen so far, evaluate the fitness function, returning the best possible gain of a bi...
void Split(arma::Col< size_t > &childMajorities, SplitInfo &splitInfo)
Given that a split should happen, return the majority classes of the (two) children and an initialize...