X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=test%2Fgenerate_test_data%2Fhelper.R;fp=test%2Fgenerate_test_data%2Fhelper.R;h=49cd1b5743e66448d6b39fda03474991d3850190;hp=0000000000000000000000000000000000000000;hb=086ca318ed5580e961ceda3f1e122a2da58e4427;hpb=4e8267487c83c27273305b1379e44bc7abebf4b5

diff --git a/test/generate_test_data/helper.R b/test/generate_test_data/helper.R
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+#' Generate a sample of (X,Y) of size n with default values
+#' @param n sample size
+#' @param p number of covariates
+#' @param m size of the response
+#' @param k number of clusters
+#' @return list with X and Y
+#' @export
+generateXYdefault = function(n, p, m, k)
+{
+	meanX = rep(0, p)
+	covX = diag(p)
+	covY = array(dim=c(m,m,k))
+	for(r in 1:k)
+		covY[,,r] = diag(m)
+	pi = rep(1./k,k)
+	#initialize beta to a random number of non-zero random value
+	beta = array(0, dim=c(p,m,k))
+	for (j in 1:p)
+	{
+		nonZeroCount = sample(1:m, 1)
+		beta[j,1:nonZeroCount,] = matrix(runif(nonZeroCount*k), ncol=k)
+	}
+
+	sample_IO = generateXY(meanX, covX, covY, pi, beta, n)
+	return (list(X=sample_IO$X,Y=sample_IO$Y))
+}
+
+#' Initialize the parameters in a basic way (zero for the conditional mean, uniform for weights,
+#' identity for covariance matrices, and uniformly distributed for the 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
+basicInitParameters = function(n,p,m,k)
+{
+	phiInit = array(0, dim=c(p,m,k))
+
+	piInit = (1./k)*rep(1,k)
+
+	rhoInit = array(dim=c(m,m,k))
+	for (i in 1:k)
+		rhoInit[,,i] = diag(m)
+
+	gamInit = 0.1 * matrix(1, nrow=n, ncol=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" = phiInit, "rhoInit" = rhoInit, "piInit" = piInit, "gamInit" = gamInit))
+}