#-----------------------------------------------------------------------
#' Initialize the parameters in a basic way (zero for the conditional mean,
#'  uniform for weights, identity for covariance matrices, and uniformly distributed forthe clustering)
#' @param n sample size
#' @param p number of covariates
#' @param m size of the response
#' @param k number of clusters
#' @return list with phiInit, rhoInit,piInit,gamInit
#' @export
#-----------------------------------------------------------------------
basic_Init_Parameters = function(n,p,m,k)
{
  phiInit = array(0, dim=c(p,m,k))
  
  piInit = (1./k)*rep.int(1,k)
  
  rhoInit = array(0, dim=c(m,m,k))
  for(i in 1:k)
    rhoInit[,,i] = diag(m)
  
  gamInit = 0.1*array(1, dim=c(n,k))
  R = sample(1:k,n, replace=TRUE)
  for(i in 1:n)
    gamInit[i,R[i]] = 0.9
  gamInit = gamInit/sum(gamInit[1,])
  
  return (data = list(phiInit = phiInit, rhoInit = rhoInit, piInit = piInit, gamInit = gamInit))
}