X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FgenerateSampleInputs.R;h=c7aa3c6a78bd2c0b033467b3fa317d00af0dd5a4;hb=390625126a7ca58dafd2b4834f2f1d7a527d019f;hp=8edd0312c1201008950ae6f4cf1da6211477bc67;hpb=1c45d8e4f6fd4209d26709f17a58920218ee828d;p=valse.git

diff --git a/pkg/R/generateSampleInputs.R b/pkg/R/generateSampleInputs.R
index 8edd031..c7aa3c6 100644
--- a/pkg/R/generateSampleInputs.R
+++ b/pkg/R/generateSampleInputs.R
@@ -13,21 +13,19 @@ generateXY = function(meanX, covX, covY, pi, beta, n)
 	p = dim(covX)[1]
 	m = dim(covY)[1]
 	k = dim(covY)[3]
-
+	
 	X = matrix(nrow=n,ncol=p)
 	Y = matrix(nrow=n,ncol=m)
 	class = matrix(nrow = n)
-
+	
 	require(MASS) #simulate from a multivariate normal distribution
 	for (i in 1:n)
 	{
 		class[i] = sample(1:k, 1, prob=pi)
 		X[i,] = mvrnorm(1, meanX, covX)
-		print(X[i,])
-		print(beta[,,class[i]])
 		Y[i,] = mvrnorm(1, X[i,] %*% beta[,,class[i]], covY[,,class[i]])
 	}
-
+	
 	return (list(X=X,Y=Y, class = class))
 }
 
@@ -40,13 +38,11 @@ generateXY = function(meanX, covX, covY, pi, beta, n)
 #' @export
 generateXYdefault = function(n, p, m, k)
 {
-	rangeX = 100
-	meanX = rangeX * matrix(1 - 2*runif(p*k), ncol=k)
-	covX = array(dim=c(p,p,k))
+	meanX = rep(0, p)
+	covX = diag(p)
 	covY = array(dim=c(m,m,k))
 	for(r in 1:k)
 	{
-		covX[,,r] = diag(p)
 		covY[,,r] = diag(m)
 	}
 	pi = rep(1./k,k)