X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=R%2FbasicInitParameters.R;h=3583e6829ab721e14b43ab1be4687635a55ec0db;hb=c3bc47052f3ccb659659c59a82e9a99ea842398d;hp=b70edab719936405469463d78996f82c41436ec4;hpb=493a35bfea6d1210c94ced8fbfe3e572f0389ea5;p=valse.git

diff --git a/R/basicInitParameters.R b/R/basicInitParameters.R
index b70edab..3583e68 100644
--- a/R/basicInitParameters.R
+++ b/R/basicInitParameters.R
@@ -1,29 +1,28 @@
-basic_Init_Parameters = function(n,p,m,k){
-  phiInit = array(0, dim=c(p,m,k))
-  
-  piInit = (1.0/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(list(phiInit, rhoInit, piInit, gamInit))
-}
+#-----------------------------------------------------------------------
+#' 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)
 
-n= 10
-p = 10
-m = 5
-k = 5
-list_param = basic_Init_Parameters(n,p,m,k)
+	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))
+}