R/mixture_same_family.R

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

#' @title A finite mixture of component distributions from the same family.
#' @description
#' A *Mixture (Same-Family)* distribution is a finite mixture in which **all components come from the same parametric family**
#' (for example, all Normal distributions but with different parameters), and are combined via mixing weights.
#' @param mixing_distribution A distribution specifying the weights for each mixture component.
#'   The size of this distribution specifies the number of components in the mixture.
#' @param component_distribution A list of distributions representing the components of the mixture.
#' @param shape A numeric vector specifying the shape of the distribution.
#' @param event Integer representing the number of batch dimensions to reinterpret as event dimensions (used in model building).
#' @param mask A logical vector, matrix, or array to mask observations.
#' @param create_obj Logical; If TRUE, returns the raw BI distribution object instead of creating a sample site.
#' @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 MixtureSameFamily distribution object (for model building).
#'
#'    - When \code{sample=TRUE}, a JAX array of samples drawn from the MixtureSameFamily distribution (for direct sampling).
#'
#'    - When \code{create_obj=TRUE}, the raw BI distribution object (for advanced use cases).
#'
#' @seealso This is a wrapper of  \url{https://num.pyro.ai/en/stable/distributions.html#mixture-same-family}
#'
#' @examples
#' \donttest{
#' library(BayesForge)
#' m <- importBF(platform = "cpu")
#' bf.dist.mixture_same_family(
#'   mixing_distribution = bf.dist.categorical(probs = c(0.3, 0.7), create_obj = TRUE),
#'   component_distribution = bf.dist.normal(0, 1, shape = c(2), create_obj = TRUE),
#'   sample = TRUE
#' )
#' }
#' @export
bf.dist.mixture_same_family <- function(mixing_distribution, component_distribution, 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_same_family(
    mixing_distribution,
    component_distribution,
    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
  )
}