TQRF_Forest Class Reference

Inheritance diagram for TQRF_Forest:

Public Member Functions
int	init (map< string, string > &map)
	Virtual to init object from parsed fields.
int	init_from_string (string init_string)
void	init_tables (Quantized_Feat &qfeat)
int	Train (const MedFeatures &medf, const MedMat< float > &Y)
	The basic train matrix for TQRF is MedFeatures (!!) the reason is that it contains everything in one place: that is: the X features, the Y outcome, the weights and the samples for each row.
int	Train (const MedFeatures &medf)
int	Train_AdaBoost (const MedFeatures &medf, const MedMat< float > &Y)
int	update_counts (vector< vector< float > > &sample_counts, MedMat< float > &x, Quantized_Feat &qf, int zero_counts, int round)
int	tune_betas (Quantized_Feat &qfeat)
	tuning : solving a gd problem of finding the optimal betas for nodes at some certain chosen depth in the trees on a kept-a-side set of samples.
int	solve_betas_gd (MedMat< float > &C, MedMat< float > &S, vector< float > &b)
int	Predict (MedMat< float > &x, vector< float > &preds) const
	However - the basic predict for this model is MedMat !! , as here it is much simpler : we only need to find the terminal nodes in the trees and calculate our scores.
int	n_preds_per_sample () const
int	Predict_Categorial (MedMat< float > &x, vector< float > &preds) const
void	print_average_bagging (int _n_time_slices, int _n_categ)
Public Member Functions inherited from SerializableObject
virtual int	version () const
	Relevant for serializations.
virtual string	my_class_name () const
	For better handling of serializations it is highly recommended that each SerializableObject inheriting class will implement the next method.
virtual void	serialized_fields_name (vector< string > &field_names) const
	The names of the serialized fields.
virtual void *	new_polymorphic (string derived_name)
	for polymorphic classes that want to be able to serialize/deserialize a pointer * to the derived class given its type one needs to implement this function to return a new to the derived class given its type (as in my_type)
virtual void	pre_serialization ()
virtual void	post_deserialization ()
virtual size_t	get_size ()
	Gets bytes sizes for serializations.
virtual size_t	serialize (unsigned char *blob)
	Serialiazing object to blob memory. return number ob bytes wrote to memory.
virtual size_t	deserialize (unsigned char *blob)
	Deserialiazing blob to object. returns number of bytes read.
size_t	serialize_vec (vector< unsigned char > &blob)
size_t	deserialize_vec (vector< unsigned char > &blob)
virtual size_t	serialize (vector< unsigned char > &blob)
virtual size_t	deserialize (vector< unsigned char > &blob)
virtual int	read_from_file (const string &fname)
	read and deserialize model
virtual int	write_to_file (const string &fname)
	serialize model and write to file
virtual int	read_from_file_unsafe (const string &fname)
	read and deserialize model without checking version number - unsafe read
int	init_from_string (string init_string)
	Init from string.
int	init_params_from_file (string init_file)
int	init_param_from_file (string file_str, string &param)
int	update_from_string (const string &init_string)
virtual int	update (map< string, string > &map)
	Virtual to update object from parsed fields.
virtual string	object_json () const

Static Public Member Functions
static int	get_tree_type (const string &str)
static int	get_missing_value_method (const string &str)

Data Fields
TQRF_Params	params
vector< TQRF_Tree >	trees
vector< float >	alphas
vector< float >	betas

Member Function Documentation

init()

int TQRF_Forest::init ( map< string, string > &  map )

inlinevirtual

Virtual to init object from parsed fields.

Reimplemented from SerializableObject.

Train()

int TQRF_Forest::Train	(	const MedFeatures &	medf,
		const MedMat< float > &	Y
	)

The basic train matrix for TQRF is MedFeatures (!!) the reason is that it contains everything in one place: that is: the X features, the Y outcome, the weights and the samples for each row.

All of these are needed when calculating a logrank score for example The y matrix is added since we may want to use regression with y values given for every time slice ...

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The documentation for this class was generated from the following files:

• Internal/TQRF/TQRF/TQRF.h
• Internal/TQRF/TQRF/TQRF.cpp