From: emilie Date: Thu, 23 Mar 2017 15:42:44 +0000 (+0100) Subject: update generateBlabla X-Git-Url: https://git.auder.net/variants/Chakart/pieces/current/git-logo.png?a=commitdiff_plain;h=c1eca3d7a9affe8703c4ce8a098f9637ed71fd21;p=valse.git update generateBlabla --- diff --git a/data/LLF b/data/LLF new file mode 100644 index 0000000..bfbcab7 --- /dev/null +++ b/data/LLF @@ -0,0 +1,10 @@ +19.2502013654606 +19.3196781868653 +19.2385076140179 +19.215019582298 +19.2076426063067 +19.204697321211 +19.2024707674756 +19.2019384300132 +19.2020803032321 +19.2028073508542 diff --git a/data/S b/data/S new file mode 100644 index 0000000..48bdc35 --- /dev/null +++ b/data/S @@ -0,0 +1,450 @@ +-3.6499980423214 +-11.7888506096169 +-12.1896838059207 +-11.6104218735289 +-13.6043160580275 +-13.8439420777605 +-18.006698691302 +-22.0503908254597 +-26.5233269013949 +-21.8359942414232 +-14.739680916542 +-3.27374694943559 +-4.61438462262547 +-16.5368552474927 +-6.38421677372035 +-17.1033355142445 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file mode 100644 index 0000000..7ed6ff8 --- /dev/null +++ b/data/mini @@ -0,0 +1 @@ +5 diff --git a/data/phi b/data/phi new file mode 100644 index 0000000..f971ea9 --- /dev/null +++ b/data/phi @@ -0,0 +1,450 @@ +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0.0481116020907517 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0.054436299351563 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0.38444372430829 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0.0920630728655972 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0.0840195423057433 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0.266510280406599 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0.0131202643979129 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0.100433276589961 +0 +0 +0 +0 +0 +0 +0 +0 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+#' @param n sample size #' #' @return list with X and Y #' @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[,class[i]], covX[,,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) + 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)) } #' Generate a sample of (X,Y) of size n with default values @@ -38,26 +40,24 @@ 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)) - 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) - #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 +70,19 @@ 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)) }