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#' @include mmpcaClustcontrol_class.R
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#' @title mmpcaClust class
#' @description An S4 class representing a fitted mmpca model.
#' @name mmpcaClust-class
#'
#' @section Objects from the class:
#' Object of class "\code{mmpcaClust}" are returned by
#' \code{\link{mmpca_clust}}()
#'
#' @slot call A \code{\link{call}} object specifying the call
#' @slot method The method used in the call
#' @slot clustering The final partition found by the algorithm
#' @slot controls An object of class \code{\linkS4class{mmpcaClustcontrol}}
#' containing the controls used in the VEM algorithm on the aggregated DTM
#' during the loop. The slots \code{controls@@control_lda_init} where only use
#' when init.beta == 'lda'.
#' @slot K An integer specifying the number of topics.
#' @slot Q An integer specifying he number of clusters.
#' @slot N An integer specifying the number of observations.
#' @slot V An integer specifying the number of variables.
#' @slot beta The (KxV) topic matrix.
#' @slot gamma A (QxK) matrix containing the variational paramaters of the
#' variational distribution of each $\\theta_q$ in its rows.
#' @slot lda_algo An object of class "\code{LDA}" (cf.
#' \code{\linkS4class{TopicModel}}) containing the results of the
#' \code{\link{LDA}}() function applied to the aggregated DTM,
#' with control \code{controls@@control_lda_loop}
#' @slot max.epochs The maximum number of pass through the whole dataset in the
#' algorithm.
#' @slot logLikelihoods A numeric vector containing the evolution of the
#' variational bound every \code{keep} iteration.
#' @slot keep An integer specifying the . Mostly useful for the plot function.
#' @slot n_epochs The number of pass through the datasets before convergence.
#' see details
#' @slot llhood The final value of the variational lower bound.
#' @slot Yinit The value of the initial partition.
#' @slot icl The Integrated Classification Likelihood value.
#'
#' @details The BB-CVEM method is the branch & bound greedy procedure proposed in
#' the original paper of Jouvin et. al. \url{https://arxiv.org/abs/1909.00721}. The number of epochs in the \code{n_epochs} slot
#' is actually the true number of pass minus 1 (unless \code{max.epochs} was
#' reached). Indeed, the last pass before convergence does not change either
#' the bound or the \code{clustering}, hence it is removed of the counter.
#'
#' @importClassesFrom topicmodels LDA
#' @export
setClass("mmpcaClust",
slots = list(call = "call",
method = "character",
clustering = "ANY",
K = "integer",
Q = "integer",
N = "integer",
V = "integer",
beta = "matrix",
gamma = "matrix",
lda_algo = "LDA_VEM",
max.epochs = "integer",
logLikelihoods = "vector",
keep = "integer",
n_epochs = "integer",
llhood = "numeric",
Yinit = "vector",
icl = "numeric",
controls = "mmpcaClustcontrol"
)
)
## ****************************
## plot methods
setMethod("show",
signature = signature(object = "mmpcaClust"),
definition = function(object) {
cat("A", class(object), " model with", object@Q, "clusters and", object@K, "topics.\n")
}
)
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