R/laplace.R

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

#' @title Laplace Distribution
#'
#' @description
#' Samples from a Laplace distribution, also known as the double exponential distribution.
#' The Laplace distribution is defined by its location parameter (loc) and scale parameter (scale).
#' @param loc A numeric vector representing the location parameter of the Laplace distribution.
#' @param scale A numeric vector representing the scale parameter of the Laplace distribution. Must be positive.
#' @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 Integer representing the number of batch dimensions to reinterpret as event dimensions (used in model building).
#' @param mask A logical vector, optionally used 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 Logical. Defaults to TRUE.
#'
#' @return
#'    - When \code{sample=FALSE}: A BI Laplace distribution object (for model building).
#'
#'    - When \code{sample=TRUE}: A JAX array of samples drawn from the Laplace 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#laplace}
#'
#' @examples
#' \donttest{
#' library(BayesForge)
#' m <- importBF(platform = "cpu")
#' bf.dist.laplace(sample = TRUE)
#' }
#' @export


bf.dist.laplace <- function(
    loc = 0.0,
    scale = 1.0,
    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$laplace(
    loc = .BF_env$jnp$array(loc),
    scale = .BF_env$jnp$array(scale),
    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
  )
}