R/log_uniform.R

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

#' @title Samples from a Log Uniform distribution.
#'
#' @description
#' A random variable $X$ is **log-uniform** on \deqn{[a, b]}, with $0 < a < b$, if \deqn{\ln X} is uniformly distributed on \deqn{[\ln a, \ln b]}.
#' Equivalently, the density of $X$ is proportional to $1/x$ over that interval. This distribution is sometimes called the *reciprocal distribution*.
#' It is useful in modeling scales spanning several orders of magnitude, where you want every decade (or log-interval) to have equal weight.

#' @param low A numeric vector representing the lower bound of the uniform distribution's log-space. Must be positive.
#' @param high A numeric vector representing the upper bound of the uniform distribution's log-space. Must be positive.
#' @param shape A numeric vector specifying the shape of the output. 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 specifying the number of batch dimensions to reinterpret as event dimensions (used in model building).
#' @param mask A logical vector 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 Log Uniform distribution object (for model building).
#'
#'  - When \code{sample=TRUE}, a JAX array of samples drawn from the Log Uniform 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#loguniform}
#'
#' @examples
#' \donttest{
#' library(BayesForge)
#' m=importBF(platform='cpu')
#' bf.dist.log_uniform(1,2, sample = TRUE)
#' }
#' @export
bf.dist.log_uniform=function(low, high, 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$log_uniform(
       low = .BF_env$jnp$array(low),
       high = .BF_env$jnp$array(low),
       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)
}