R/ProbKMA-export.R

In funMoDisco: Motif Discovery in Functional Data

#' @docType class
#' @title ProbKMA Class
#' @description 
#' The `ProbKMA` class is an R wrapper for the C++ implementation of the 
#' Probabilistic K-means Algorithm (ProbKMA) with local alignment. This class 
#' facilitates local clustering of functional data and functional motif discovery, 
#' as proposed in the paper 
#' `Probabilistic K-means with local alignment for clustering and motif discovery in functional data`, 
#' authored by Marzia A. Cremona and Francesca Chiaromonte.
#' 
#' @section Constructor:
#' Create a `ProbKMA` object using the following constructor:
#' 
#' \preformatted{
#' prok <- new(ProbKMA, data$Y, data$V, params, data$P0, data$S0, "H1")
#' }
#' 
#' @return 
#' A `ProbKMA` object from the C++ ProbKMA class.
#' 
#' @section Parameters:
#' \describe{
#'   \item{Y}{A list containing functional data and possibly derivatives.}
#'   \item{params}{An instance of the Parameters class, containing algorithm settings.}
#'   \item{P0}{A matrix representing the initial membership probabilities.}
#'   \item{S0}{A matrix representing the initial shift warping parameters.}
#'   \item{diss}{A character string specifying the dissimilarity measure. Possible choices are: 
#'     \itemize{
#'       \item `'d0_L2'`
#'       \item `'d1_L2'`
#'       \item `'d0_d1_L2'`
#'     }}
#' }
#' 
#' @section Usage:
#' You can access and modify the `ProbKMA` object with the following methods:
#' \describe{
#'   \item{Getters:}{
#'     \describe{
#'       \item{prok$get_parameters()}{Returns a list of parameters.}
#'       \item{prok$get_motifs()}{Returns a list containing the motifs found.}
#'     }
#'   }
#'   \item{Setters:}{
#'     \describe{
#'       \item{prok$set_P0(P)}{Sets the membership matrix.}
#'       \item{prok$set_S0(S)}{Sets the shift warping matrix.}
#'       \item{prok$set_parameters(param)}{Sets parameters field by passing a list of parameters.}
#'     }
#'   }
#'   \item{Initialize Motifs:}{
#'     \describe{
#'       \item{prok$reinit_motifs(c, d)}{Reinitializes (empty) K motifs with dimension c_k x d.}
#'     }
#'   }
#'   \item{Run ProbKMA algorithm:}{
#'     \describe{
#'       \item{prok$probKMA_run()}{Runs the algorithm.}
#'     }
#'   }
#' }
#' 
#' @author Niccolò Feresini and Riccardo Lazzarini
#' @name ProbKMA
#' @export ProbKMA
loadModule(module = "ProbKMAModule", TRUE)