R/multi_variate_normal.R

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

#' @title Samples from a Multivariate Normal distribution.
#'
#' @description
#' The Multivariate Normal distribution, also known as the Gaussian distribution in multiple dimensions,
#' is a probability distribution that arises frequently in statistics and machine learning. It is
#' defined by its mean vector and covariance matrix, which describe the central tendency and
#' spread of the distribution, respectively.
#'
#' @export
#' @importFrom reticulate py_none tuple
#' @param loc A numeric vector representing the mean vector of the distribution.
#' @param covariance_matrix A numeric vector, matrix, or array representing the covariance matrix of the distribution. Must be positive definite.
#' @param precision_matrix A numeric vector, matrix, or array representing the precision matrix (inverse of the covariance matrix) of the distribution. Must be positive definite.
#' @param scale_tril A numeric vector, matrix, or array representing the lower triangular Cholesky decomposition of the covariance matrix.
#' @param shape A numeric vector representing 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 representing 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.
#' @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 Multivariate Normal distribution object (for model building).
#'
#'  - When \code{sample=TRUE}, a JAX array of samples drawn from the Multivariate 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#multivariate-normal}
#'
#' @examples
#' \donttest{
#' library(BayesForge)
#' m=importBF(platform='cpu')
#' bf.dist.multivariate_normal(
#' loc =  c(1.0, 0.0, -2.0),
#' covariance_matrix = matrix(
#' c( 2.0,  0.7, -0.3, 0.7,  1.0,  0.5, -0.3,  0.5,  1.5),
#' nrow = 3, byrow = TRUE),
#' sample = TRUE)
#' }
#' @export
bf.dist.multivariate_normal=function(loc=0.0, covariance_matrix=py_none(), precision_matrix=py_none(), scale_tril=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(covariance_matrix)){covariance_matrix = .BF_env$jnp$array(covariance_matrix)}
     if(!.BF_env$.py$is_none(precision_matrix)){precision_matrix = .BF_env$jnp$array(precision_matrix)}
     if(!.BF_env$.py$is_none(scale_tril)){scale_tril = .BF_env$jnp$array(scale_tril)}

     if (!.BF_env$.py$is_none(seed)){seed=as.integer(seed);}

     .BF_env$.bf_instance$dist$multivariate_normal(
       loc = .BF_env$jnp$array(loc),
       covariance_matrix = covariance_matrix,
       precision_matrix = precision_matrix,
       scale_tril = scale_tril,
       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)
}