Add 'fast' argument to select C code or R code

[valse.git] / test / generate_test_data / helper.R

diff --git a/test/generate_test_data/helper.R b/test/generate_test_data/helper.R
deleted file mode 100644 (file)
index 8ec122b..0000000
--- a/test/generate_test_data/helper.R
+++ /dev/null
@@ -1,58 +0,0 @@
-#' 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
-#'
-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)
-       π = rep(1./k,k)
-       #initialize beta to a random number of non-zero random value
-       β = array(0, dim=c(p,m,k))
-       for (j in 1:p)
-       {
-               nonZeroCount = sample(1:m, 1)
-               β[j,1:nonZeroCount,] = matrix(runif(nonZeroCount*k), ncol=k)
-       }
-
-       sample_IO = generateXY(n, π, meanX, β, covX, covY)
-       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
-#'
-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))
-}