X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=R%2FbasicInitParameters.R;h=980122924725ba3dfed07747c77022ce52cdcd67;hp=6090d0aa775d393e85d9856602742f826710ae1c;hb=d1531659214edd6eaef0ac9ec835455614bba16c;hpb=6bd2e869a17f3980d52820643c1c1b5f3725738e

diff --git a/R/basicInitParameters.R b/R/basicInitParameters.R
index 6090d0a..9801229 100644
--- a/R/basicInitParameters.R
+++ b/R/basicInitParameters.R
@@ -1,18 +1,28 @@
+#-----------------------------------------------------------------------
+#' 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 (list(phiInit, rhoInit, piInit, gamInit))
+  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))
 }