R/mixture.R

In BayesForge: Bayesian Inference using 'numpyro' and 'XLA'

#' @title A marginalized finite mixture of component distributions.
#' @description
#' This distribution represents a mixture of component distributions, where the
#' mixing weights are determined by a Categorical distribution. The resulting
#' distribution can be either a MixtureGeneral (when component distributions
#' are a list) or a MixtureSameFamily (when component distributions are a single
#' distribution).
#'
#' @param mixing_distribution A `Categorical` distribution specifying the weights for each mixture component.
#'   The size of this distribution specifies the number of components in the mixture.
#' @param component_distributions A list of distributions representing the components of the mixture.
#' @param shape A numeric vector specifying the shape of the distribution.
#' @param event An integer representing the number of batch dimensions to reinterpret as event dimensions.
#' @param mask A logical vector used to mask observations.
#' @param create_obj A logical value. If `TRUE`, returns the raw BI distribution object.
#' @param validate_args Logical: Whether to validate parameter values.  Defaults to `reticulate::py_none()`.
#' @param sample A logical value that controls the function's behavior. If `TRUE`,
#'   the function will directly draw samples from the distribution. If `FALSE`,
#'   it will create a random variable within a model. Defaults to `FALSE`.
#' @param seed An integer used to set the random seed for reproducibility when
#'   `sample = TRUE`. This argument has no effect when `sample = FALSE`, as
#'   randomness is handled by the model's inference engine. Defaults to 0.
#' @param obs A numeric vector or array of observed values. If provided, the
#'   random variable is conditioned on these values. If `NULL`, the variable is
#'   treated as a latent (unobserved) variable. Defaults to `NULL`.
#' @param name A character string representing the name of the random variable
#'   within a model. This is used to uniquely identify the variable. Defaults to 'x'.
#' @param to_jax Boolean. Indicates whether to return a JAX array or not.
#'
#' @return When \code{sample=FALSE}: A BI Mixture distribution object (for model building).
#'         When \code{sample=TRUE}: A JAX array of samples drawn from the Mixture distribution (for direct sampling).
#'         When \code{create_obj=TRUE}: The raw BI distribution object (for advanced use cases).
#'
#' @seealso
#'  - When \code{sample=FALSE}, a BI marginalized finite mixture distribution object (for model building).
#'
#'  - When \code{sample=TRUE}, a JAX array of samples drawn from the marginalized finite mixture distribution (for direct sampling).
#'
#'  - When \code{create_obj=TRUE}, the raw BI distribution object (for advanced use cases).
#'
#' @examples
#' \donttest{
#' library(BayesForge)
#' m=importBF(platform='cpu')
#' bf.dist.mixture(
#'   mixing_distribution = bf.dist.categorical(probs = c(0.3, 0, 7),create_obj = TRUE),
#'   component_distributions = c(
#'   bf.dist.normal(0,1,create_obj = TRUE),
#'   bf.dist.normal(0,1,create_obj = TRUE),
#'   bf.dist.normal(0,1,create_obj = TRUE)
#'   ),
#'   sample = TRUE
#' )
#' }
#' @export

bf.dist.mixture=function(mixing_distribution, component_distributions, validate_args=py_none(), name='x', obs=py_none(), mask=py_none(), sample=FALSE, seed = py_none(), shape=c(), event=0, create_obj=FALSE, to_jax = TRUE) {
     shape=do.call(tuple, as.list(as.integer(shape)))
     reticulate::py_run_string("def is_none(x): return x is None");
     if (!.BF_env$.py$is_none(seed)){seed=as.integer(seed);}
     .BF_env$.bf_instance$dist$mixture(
       mixing_distribution,
       component_distributions = reticulate::r_to_py(component_distributions, convert = TRUE),
       validate_args= validate_args,  name= name,  obs= obs,  mask= mask,  sample= sample,  seed= seed,  shape= shape,  event= event,  create_obj= create_obj,   to_jax = to_jax)
}