A gibbs sampler - has learn and create sample based on mask. More...

#include <GibbsSampler.h>

Inheritance diagram for GibbsSampler< T >:

Public Member Functions
void	learn_gibbs (const map< string, vector< T > > &cohort_data)
	learn gibbs sample - for each feature creates predictors

void	learn_gibbs (const map< string, vector< T > > &cohort_data, const vector< string > &learn_features, bool skip_missing)
	learn gibbs sample - for each feature creates predictors

void	prepare_predictors ()
	Should be called before first get_samples when used in parallel manner.

void	get_samples (map< string, vector< T > > &results, const GibbsSamplingParams &sampling_params, const vector< bool > mask=NULL, const vector< T > mask_values=NULL, bool print_progress=false)
	generates samples based on gibbs sampling process

void	get_samples (map< string, vector< T > > &results, const GibbsSamplingParams &sampling_params, mt19937 &rnd_gen, const vector< bool > mask=NULL, const vector< T > mask_values=NULL, bool print_progress=false)
	generates samples based on gibbs sampling process.

void	get_parallel_samples (map< string, vector< T > > &results, const GibbsSamplingParams &sampling_params, const vector< bool > mask=NULL, const vector< T > mask_values=NULL)
	generates samples based on gibbs sampling process - uses only burn rate and creates one sample and exits

void	filter_samples (const map< string, vector< float > > &cohort_data, map< string, vector< T > > &results, const string &predictor_type, const string &predictor_args, float filter_sens)
	takes original cohort and results samples - filters and keep only samples that are similar to original population

int	init (map< string, string > &map)
	initialized params init function. reffer to that

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

Data Fields
Gibbs_Params	params
	gibbs params

vector< PredictorOrEmpty< T > >	feats_predictors
	gibbs_feature generators based on predictors

vector< string >	all_feat_names
	all features names (saved in learn)

vector< string >	impute_feat_names
	all features names (saved in learn)

vector< vector< T > >	uniqu_value_bins
	to round samples to those resoultions! - important for no leak!

Detailed Description

template<typename T>
class GibbsSampler< T >

A gibbs sampler - has learn and create sample based on mask.

Member Function Documentation

◆ get_samples()

template<typename T >

void GibbsSampler< T >::get_samples	(	map< string, vector< T > > &	results,
		const GibbsSamplingParams &	sampling_params,
		mt19937 &	rnd_gen,
		const vector< bool > *	mask = `NULL`,
		const vector< T > *	mask_values = `NULL`,
		bool	print_progress = `false`
	)

generates samples based on gibbs sampling process.

const and can be called parallel

◆ init()

template<typename T >

int GibbsSampler< T >::init ( map< string, string > & map )

virtual

initialized params init function. reffer to that

Reimplemented from SerializableObject.

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

Internal/MedStat/MedStat/GibbsSampler.h
Internal/MedStat/MedStat/GibbsSampler.cpp

Public Member Functions

Data Fields

Detailed Description

Member Function Documentation

◆ get_samples()

◆ init()