DecisionTreeLearner.h File Reference

method to train a DecisionTree More...

#include "Pattern/Pattern.h"
#include "DataSet.h"
#include "InformationMetrics.h"
#include "Classifier/DecisionTree.h"
#include "Test.h"

Go to the source code of this file.

Classes
struct	DTResponse
class	IntegerPolicy
	discrete policy More...
class	WeightedPolicy
	weighted policy More...
struct	DecisionTreeLearner< Aggregator >
	A Decision Tree builder. More...

Functions
bool	operator< (const DTResponse &a, const DTResponse &b)
template<class T >
std::pair< int, float >	FindThreshold (const DTResponse *data, int n, int A, int B)
template<>
std::pair< int, float >	FindThreshold< GiniIndex > (const DTResponse *data, int n, int A, int B)
template<class FeatureExtractor , class PatternType >
float	TestThreshold (const std::vector< PatternType > &patterns, long stride, const FeatureExtractor &h, int th)
template<class FeatureExtractor , class PatternType , class Param >
void	build_response (DTResponse *resp, const PatternType *patterns, Param param, int n, const FeatureExtractor &op)
template<class FeatureExtractor , class FeatureGenerator , class PatternType , class Param1 >
bool	BuildDecisionTree (DecisionTree< FeatureExtractor > &root, const std::vector< PatternType > &patterns, Param1 param, FeatureGenerator &f, int max_depth)

Detailed Description

method to train a DecisionTree

Function Documentation

template<class FeatureExtractor , class FeatureGenerator , class PatternType , class Param1 >

bool BuildDecisionTree	(	DecisionTree< FeatureExtractor > &	root,
		const std::vector< PatternType > &	patterns,
		Param1	param,
		FeatureGenerator &	f,
		int	max_depth
	)

Build a Recursive Decision Tree