The HoeffdingNumericSplit class implements the numeric feature splitting strategy alluded to by Domingos and Hulten in the following paper: More...
Public Types  
typedef NumericSplitInfo< ObservationType >  SplitInfo 
The splitting information type required by the HoeffdingNumericSplit. More...  
Public Member Functions  
HoeffdingNumericSplit (const size_t numClasses=0, const size_t bins=10, const size_t observationsBeforeBinning=100)  
Create the HoeffdingNumericSplit class, and specify some basic parameters about how the binning should take place. More...  
HoeffdingNumericSplit (const size_t numClasses, const HoeffdingNumericSplit &other)  
Create the HoeffdingNumericSplit class, using the parameters from the given other split object. More...  
size_t  Bins () const 
Return the number of bins. More...  
void  EvaluateFitnessFunction (double &bestFitness, double &secondBestFitness) const 
Evaluate the fitness function given what has been calculated so far. More...  
size_t  MajorityClass () const 
Return the majority class. More...  
double  MajorityProbability () const 
Return the probability of the majority class. More...  
size_t  NumChildren () const 
Return the number of children if this node splits on this feature. More...  
template < typename Archive >  
void  serialize (Archive &ar, const unsigned int) 
Serialize the object. More...  
void  Split (arma::Col< size_t > &childMajorities, SplitInfo &splitInfo) const 
Return the majority class of each child to be created, if a split on this dimension was performed. More...  
void  Train (ObservationType value, const size_t label) 
Train the HoeffdingNumericSplit on the given observed value (remember that this object only cares about the information for a single feature, not an entire point). More...  
The HoeffdingNumericSplit class implements the numeric feature splitting strategy alluded to by Domingos and Hulten in the following paper:
The strategy alluded to is very simple: we discretize the numeric features that we see. But in this case, we don't know how many bins we have, which makes things a little difficult. This class only makes binary splits, and has a maximum number of bins. The binning strategy is simple: the split caches the minimum and maximum value of points seen so far, and when the number of points hits a predefined threshold, the cached minimummaximum range is equally split into bins, and splitting proceeds in the same way as with the categorical splits. This is a simple and stupid strategy, so don't expect it to be the best possible thing you can do.
FitnessFunction  Fitness function to use for calculating gain. 
ObservationType  Type of observations in this dimension. 
Definition at line 53 of file hoeffding_numeric_split.hpp.
typedef NumericSplitInfo<ObservationType> SplitInfo 
The splitting information type required by the HoeffdingNumericSplit.
Definition at line 57 of file hoeffding_numeric_split.hpp.
HoeffdingNumericSplit  (  const size_t  numClasses = 0 , 
const size_t  bins = 10 , 

const size_t  observationsBeforeBinning = 100 

) 
Create the HoeffdingNumericSplit class, and specify some basic parameters about how the binning should take place.
numClasses  Number of classes. 
bins  Number of bins. 
observationsBeforeBinning  Number of points to see before binning is performed. 
HoeffdingNumericSplit  (  const size_t  numClasses, 
const HoeffdingNumericSplit< FitnessFunction, ObservationType > &  other  
) 
Create the HoeffdingNumericSplit class, using the parameters from the given other split object.

inline 
Return the number of bins.
Definition at line 120 of file hoeffding_numeric_split.hpp.
References HoeffdingNumericSplit< FitnessFunction, ObservationType >::serialize().
void EvaluateFitnessFunction  (  double &  bestFitness, 
double &  secondBestFitness  
)  const 
Evaluate the fitness function given what has been calculated so far.
In this case, if binning has not yet been performed, 0 will be returned (i.e., no gain). Because this split can only split one possible way, secondBestFitness (the fitness function for the second best possible split) will be set to 0.
bestFitness  Value of the fitness function for the best possible split. 
secondBestFitness  Value of the fitness function for the second best possible split (always 0 for this split). 
size_t MajorityClass  (  )  const 
Return the majority class.
Referenced by HoeffdingNumericSplit< FitnessFunction, ObservationType >::NumChildren().
double MajorityProbability  (  )  const 
Return the probability of the majority class.
Referenced by HoeffdingNumericSplit< FitnessFunction, ObservationType >::NumChildren().

inline 
Return the number of children if this node splits on this feature.
Definition at line 106 of file hoeffding_numeric_split.hpp.
References HoeffdingNumericSplit< FitnessFunction, ObservationType >::MajorityClass(), HoeffdingNumericSplit< FitnessFunction, ObservationType >::MajorityProbability(), and HoeffdingNumericSplit< FitnessFunction, ObservationType >::Split().
void serialize  (  Archive &  ar, 
const unsigned  int  
) 
Serialize the object.
Referenced by HoeffdingNumericSplit< FitnessFunction, ObservationType >::Bins().
void Split  (  arma::Col< size_t > &  childMajorities, 
SplitInfo &  splitInfo  
)  const 
Return the majority class of each child to be created, if a split on this dimension was performed.
Also create the split object.
Referenced by HoeffdingNumericSplit< FitnessFunction, ObservationType >::NumChildren().
void Train  (  ObservationType  value, 
const size_t  label  
) 
Train the HoeffdingNumericSplit on the given observed value (remember that this object only cares about the information for a single feature, not an entire point).
value  Value in the dimension that this HoeffdingNumericSplit refers to. 
label  Label of the given point. 