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#' @title A finite mixture of component distributions from different families.
#'
#' @description
#' A mixture distribution is a probability distribution constructed by selecting one of several component
#' distributions according to specified weights, and then drawing a sample from the chosen component.
#' It allows modelling of heterogeneous populations and multimodal data.
#'
#' @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 support A constraint object specifying the support of the mixture distribution.
#' If not provided, the support will be inferred from the component distributions.
#' @param shape Numeric vector. A multi-purpose argument for shaping. When `sample=False` (model building), this is used with `.expand(shape)` to set the distribution's batch shape. When `sample=True` (direct sampling), this is used as `sample_shape` to draw a raw JAX array of the given shape.
#' @param event Integer. The number of batch dimensions to reinterpret as event dimensions (used in model building).
#' @param mask Logical vector. Optional boolean array to mask observations.
#' @param create_obj Logical. If True, returns the raw BI distribution object instead of creating a sample site. This is essential for building complex distributions like `MixtureSameFamily`.
#' @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 MixtureGeneral distribution object (for model building).
#'
#' - When \code{sample=TRUE}, a JAX array of samples drawn from the MixtureGeneral 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_general(
#' 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_general=function(mixing_distribution, component_distributions, support=py_none(), 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_general(
mixing_distribution,
component_distributions = reticulate::r_to_py(component_distributions, convert = TRUE),
support= support, 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)
}
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