<<<<<<< HEAD:library/doc/html/classbdm_1_1MixEF.html
#include <mixtures.h>
=======
#include <mixtures.h>
>>>>>>> doc:library/doc/html/classbdm_1_1MixEF.html
Public Member Functions | |
| MixEF (const Array< BMEF * > &Coms0, const vec &alpha0) | |
| Full constructor. | |
| MixEF () | |
| Constructor of empty mixture. | |
| MixEF (const MixEF &M2) | |
| Copy constructor. | |
| void | init (BMEF *Com0, const mat &Data, int c=5) |
| void | bayes (const vec &dt) |
| Recursive EM-like algorithm (QB-variant), see Karny et. al, 2006. | |
| void | bayes (const mat &dt) |
| EM algorithm. | |
| void | bayesB (const mat &dt, const vec &wData) |
| double | logpred (const vec &dt) const |
| const epdf & | posterior () const |
| const eprod * | _e () const |
| emix * | epredictor () const |
Constructs a predictive density . | |
| void | flatten (const BMEF *M2) |
| Flatten the density as if it was not estimated from the data. | |
| BMEF * | _Coms (int i) |
| Access function. | |
| void | set_method (MixEF_METHOD M) |
| Set which method is to be used. | |
| virtual void | set_statistics (const BMEF *BM0) |
| get statistics from another model | |
| virtual void | bayes (const vec &data, const double w) |
| Weighted update of sufficient statistics (Bayes rule). | |
| BMEF * | _copy_ () const |
| Flatten the posterior as if to keep nu0 data. | |
| virtual string | to_string () |
| This method returns a basic info about the current instance. | |
| virtual void | from_setting (const Setting &set) |
| This method arrange instance properties according the data stored in the Setting structure. | |
| virtual void | to_setting (Setting &set) const |
| This method save all the instance properties into the Setting structure. | |
| virtual void | validate () |
| This method TODO. | |
Mathematical operations | |
| virtual void | bayesB (const mat &Dt) |
| Batch Bayes rule (columns of Dt are observations). | |
| vec | logpred_m (const mat &dt) const |
| Matrix version of logpred. | |
| virtual mpdf * | predictor () const |
| Constructs a conditional density 1-step ahead predictor. | |
Access to attributes | |
| const RV & | _drv () const |
| void | set_drv (const RV &rv) |
| void | set_rv (const RV &rv) |
| double | _ll () const |
| void | set_evalll (bool evl0) |
Protected Member Functions | |
| void | build_est () |
| Auxiliary function for use in constructors. | |
Protected Attributes | |
| int | n |
| Number of components. | |
| Array< BMEF * > | Coms |
Models for Components of . | |
| multiBM | weights |
| Statistics for weights. | |
| eprod * | est |
| Posterior on component parameters. | |
| MixEF_METHOD | method |
| Flag for a method that is used in the inference. | |
| double | frg |
| forgetting factor | |
| double | last_lognc |
cached value of lognc() in the previous step (used in evaluation of ll ) | |
| RV | drv |
| Random variable of the data (optional). | |
| double | ll |
| Logarithm of marginalized data likelihood. | |
| bool | evalll |
If true, the filter will compute likelihood of the data record and store it in ll . Set to false if you want to save computational time. | |
Extension to conditional BM | |
| This extension is useful e.g. in Marginalized Particle Filter (bdm::MPF). Alternatively, it can be used for automated connection to DS when the condition is observed | |
| const RV & | _rvc () const |
| access function | |
| virtual void | condition (const vec &val) |
Substitute val for rvc. | |
| RV | rvc |
| Name of extension variable. | |
Logging of results | |
| virtual void | set_options (const string &opt) |
| Set boolean options from a string recognized are: "logbounds,logll". | |
| virtual void | log_add (logger &L, const string &name="") |
| Add all logged variables to a logger. | |
| virtual void | logit (logger &L) |
| ivec | LIDs |
| IDs of storages in loggers 4:[1=mean,2=lb,3=ub,4=ll]. | |
| ivec | LFlags |
| Flags for logging - same size as LIDs, each entry correspond to the same in LIDs. | |
An approximate estimation method for models with latent discrete variable, such as mixture models of the following kind:
where
is a known function of past outputs,
are component weights, and component parameters
are assumed to be mutually independent.
is an aggregation af all component parameters and weights, i.e.
.
The characteristic feature of this model is that if the exact values of the latent variable were known, estimation of the parameters can be handled by a single model. For example, for the case of mixture models, posterior density for each component parameters would be a BayesianModel from Exponential Family.
This class uses EM-style type algorithms for estimation of its parameters. Under this simplification, the posterior density is a product of exponential family members, hence under EM-style approximate estimation this class itself belongs to the exponential family.
TODO: Extend BM to use rvc.
| void bdm::MixEF::init | ( | BMEF * | Com0, | |
| const mat & | Data, | |||
| int | c = 5 | |||
| ) |
Initializing the mixture by a random pick of centroids from data
| Com0 | Initial component - necessary to determine its type. | |
| Data | Data on which the initialization will be done | |
| c | Initial number of components, default=5 |
References bdm::BMEF::_copy_(), build_est(), Coms, est, n, and weights.
=======References bdm::BMEF::_copy_(), build_est(), Coms, est, n, bdm::multiBM::set_parameters(), and weights.
>>>>>>> doc:library/doc/html/classbdm_1_1MixEF.html| double bdm::MixEF::logpred | ( | const vec & | dt | ) | const [virtual] |
Evaluates predictive log-likelihood of the given data record I.e. marginal likelihood of the data with the posterior integrated out.
Reimplemented from bdm::BM.
<<<<<<< HEAD:library/doc/html/classbdm_1_1MixEF.htmlReferences Coms, bdm::epdf::mean(), and weights.
=======References Coms, bdm::epdf::mean(), bdm::multiBM::posterior(), and weights.
>>>>>>> doc:library/doc/html/classbdm_1_1MixEF.html
1.5.9