R/projected_normal.R

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

#' @title Samples from a Projected Normal distribution.
#' @description
#' The projected normal distribution arises by taking a multivariate normal vector
#' \deqn{\mathbf X \sim \mathcal N_n (\mu, \Sigma) } in \deqn{\mathbb R^n}
#' and projecting it to the unit sphere. This distribution is commonly used
#' in directional statistics (data on circles or spheres) and supports
#' asymmetric and even multimodal behaviours depending on parameters.
#'
#' @param concentration A numeric vector representing the concentration parameter,
#'   representing the direction towards which the samples are concentrated.
#' @param shape A numeric vector used for shaping. When \code{sample=FALSE} (model
#'   building), this is used with `.expand(shape)` to set the distribution's
#'   batch shape. When \code{sample=TRUE} (direct sampling), this is used as
#'   `sample_shape` to draw a raw JAX array of the given shape.
#' @param event The number of batch dimensions to reinterpret as event
#'   dimensions (used in model building).
#' @param mask An 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 Projected Normal distribution object (for model building).
#'
#'  - When \code{sample=TRUE}, a JAX array of samples drawn from the Projected Normal distribution (for direct sampling).
#'
#'  - When \code{create_obj=TRUE}, the raw BI distribution object (for advanced use cases).
#'
#' @seealso \url{https://num.pyro.ai/en/stable/distributions.html#projectednormal}
#'
#' @examples
#' \donttest{
#' library(BayesForge)
#' m=importBF(platform='cpu')
#' bf.dist.projected_normal(concentration = c(1.0, 3.0, 2.0), sample = TRUE)
#' }
#' @export

bf.dist.projected_normal=function(concentration, 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)))
     if (!.BF_env$.py$is_none(seed)){seed=as.integer(seed);}
     .BF_env$.bf_instance$dist$projected_normal(
       concentration = .BF_env$jnp$array(concentration),
       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)
}