re-indent and remove prints in generateSampleInputs

[valse.git] / pkg / R / generateSampleInputs.R

diff --git a/pkg/R/generateSampleInputs.R b/pkg/R/generateSampleInputs.R
index 8edd031..c7aa3c6 100644 (file)
--- 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)