BinaryCovariatesModule Class Reference

Module for binary covariate-related computations within clustering processes. More...

#include <binary_covariate_module.hpp>

Inheritance diagram for BinaryCovariatesModule:

Public Member Functions
	BinaryCovariatesModule (const Data &data_, const Eigen::VectorXi binary_covariate, double beta_prior_alpha_, double beta_prior_beta_, const Eigen::VectorXi *old_alloc_provider=nullptr, const std::unordered_map< int, std::vector< int > > *old_cluster_members_provider_=nullptr)
	Constructor for BinaryCovariatesModule.
Similarity Computation Methods
double	compute_similarity_cls (int cls_idx, bool old_allo=false) const override __attribute__((hot))
	Compute covariate similarity contribution for a cluster.
double	compute_similarity_obs (int obs_idx, int cls_idx) const override __attribute__((hot))
	Compute covariate similarity for a single observation in a cluster.
Eigen::VectorXd	compute_similarity_obs (int obs_idx) const override __attribute__((hot))
	Compute covariate similarity contributions for all existing clusters.
Public Member Functions inherited from Module
	Module (const Eigen::VectorXi *old_allocations_provider_=nullptr, const std::unordered_map< int, std::vector< int > > *old_cluster_members_provider_=nullptr)
void	set_old_allocations_provider (const Eigen::VectorXi *provider)
void	set_old_cluster_members_provider (const std::unordered_map< int, std::vector< int > > *provider)
virtual	~Module ()=default

Protected Attributes
Module References
const double	beta_prior_alpha = 1.0
const double	beta_prior_beta = 1.0
const Data &	data
	Reference to data object with cluster assignments.
const Eigen::VectorXi	binary_covariate_data
Precomputed Values
const double	log_beta_prior
Protected Attributes inherited from Module
const Eigen::VectorXi *	old_allocations_provider
	Provider function for accessing old allocation state.
const std::unordered_map< int, std::vector< int > > *	old_cluster_members_provider
	Provider function for accessing old cluster members map.

Detailed Description

Module for binary covariate-related computations within clustering processes.

This class implements covariate-related computations for clustering processes where covariates are binary (0/1). It computes similarity measures based on the distribution of binary covariates within clusters using a Beta-Binomial model.

alpha is associated with the number of successes (1s) and beta with the number of failures (0s).

Constructor & Destructor Documentation

BinaryCovariatesModule()

BinaryCovariatesModule::BinaryCovariatesModule ( const Data & data_, const Eigen::VectorXi binary_covariate, double beta_prior_alpha_, double beta_prior_beta_, const Eigen::VectorXi * old_alloc_provider = nullptr, const std::unordered_map< int, std::vector< int > > * old_cluster_members_provider_ = nullptr )

inline

Constructor for BinaryCovariatesModule.

Parameters

data_	Reference to data object with cluster assignments
binary_covariate	Covariate values (1D vector of 0s and 1s)
beta_prior_alpha_	Prior alpha parameter for Beta-Binomial model
beta_prior_beta_	Prior beta parameter for Beta-Binomial model
old_alloc_provider	Optional pointer to old allocations for split-merge moves
old_cluster_members_provider_	Optional pointer to old cluster members for split-merge moves

Member Function Documentation

compute_similarity_cls()

double BinaryCovariatesModule::compute_similarity_cls ( int cls_idx, bool old_allo = false ) const

overridevirtual

Compute covariate similarity contribution for a cluster.

Computes the log marginal likelihood of the covariates within a cluster under the Beta-Binomial conjugate model. Higher values indicate that observations in the cluster have similar covariate values. ...

The computation follows Müller et al. (2011):

1. Count successes (1s) and failures (0s)
2. Compute log marginal likelihood using log-gamma functions

Implements Module.

compute_similarity_obs() [1/2]

Eigen::VectorXd BinaryCovariatesModule::compute_similarity_obs ( int obs_idx ) const

overridevirtual

Compute covariate similarity contributions for all existing clusters.

Computes the predictive contributions for adding observation obs_idx to each existing cluster, considering covariate values.

Parameters

obs_idx

Index of the observation

Returns

Vector of log predictive density contributions for each cluster

Implements Module.

compute_similarity_obs() [2/2]

double BinaryCovariatesModule::compute_similarity_obs ( int obs_idx, int cls_idx ) const

overridevirtual

Compute covariate similarity for a single observation in a cluster.

Computes the predictive contribution when adding observation obs_idx to cluster cls_idx, considering the covariate values.

Parameters

obs_idx	Index of the observation
cls_idx	Index of the cluster

Returns

Log predictive density contribution

Used in Gibbs sampling to compute the probability of assigning an observation to a cluster based on covariate similarity.

Implements Module.

Member Data Documentation

beta_prior_alpha

const double BinaryCovariatesModule::beta_prior_alpha = 1.0

protected

Prior alpha parameter for Beta-Binomial model

beta_prior_beta

const double BinaryCovariatesModule::beta_prior_beta = 1.0

protected

Prior beta parameter for Beta-Binomial model

binary_covariate_data

const Eigen::VectorXi BinaryCovariatesModule::binary_covariate_data

protected

Binary covariate values for observations

data

const Data& BinaryCovariatesModule::data

protected

Reference to data object with cluster assignments.

log_beta_prior

const double BinaryCovariatesModule::log_beta_prior

protected

Initial value:

=
        std::lgamma(beta_prior_alpha) + std::lgamma(beta_prior_beta) -
        std::lgamma(beta_prior_alpha + beta_prior_beta)

Log Beta function for prior parameters

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

• src/processes/module/binary_covariate_module.hpp
• src/processes/module/binary_covariate_module.cpp