From: Benjamin Auder <benjamin.auder@somewhere>
Date: Thu, 23 Mar 2017 16:00:39 +0000 (+0100)
Subject: re-indent and remove prints in generateSampleInputs
X-Git-Url: https://git.auder.net/?p=valse.git;a=commitdiff_plain;h=390625126a7ca58dafd2b4834f2f1d7a527d019f

re-indent and remove prints in generateSampleInputs
---

diff --git a/pkg/R/generateSampleInputs.R b/pkg/R/generateSampleInputs.R
index 4da1ea5..c7aa3c6 100644
--- a/pkg/R/generateSampleInputs.R
+++ b/pkg/R/generateSampleInputs.R
@@ -10,25 +10,23 @@
 #' @export
 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))
+	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)
+		Y[i,] = mvrnorm(1, X[i,] %*% beta[,,class[i]], covY[,,class[i]])
+	}
+	
+	return (list(X=X,Y=Y, class = class))
 }
 
 #' Generate a sample of (X,Y) of size n with default values
@@ -40,24 +38,24 @@ generateXY = function(meanX, covX, covY, pi, beta, n)
 #' @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))
+	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,
@@ -70,19 +68,19 @@ generateXYdefault = function(n, p, m, k)
 #' @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))
+	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))
 }