From: Benjamin Auder Date: Tue, 6 Dec 2016 13:07:39 +0000 (+0100) Subject: replace spaces by tabs X-Git-Url: https://git.auder.net/assets/css/current/doc/%7B%7B%20targetUrl%20%7D%7D?a=commitdiff_plain;h=e166ed4e1370aa7961f0d8609936591cfc6808cc;p=valse.git replace spaces by tabs --- diff --git a/R/basicInitParameters.R b/R/basicInitParameters.R index 9801229..3583e68 100644 --- a/R/basicInitParameters.R +++ b/R/basicInitParameters.R @@ -1,6 +1,6 @@ #----------------------------------------------------------------------- #' Initialize the parameters in a basic way (zero for the conditional mean, -#' uniform for weights, identity for covariance matrices, and uniformly distributed forthe clustering) +#' uniform for weights, identity for covariance matrices, and uniformly distributed forthe clustering) #' @param n sample size #' @param p number of covariates #' @param m size of the response @@ -10,19 +10,19 @@ #----------------------------------------------------------------------- basic_Init_Parameters = function(n,p,m,k) { - phiInit = array(0, dim=c(p,m,k)) - - piInit = (1./k)*rep.int(1,k) - - rhoInit = array(0, dim=c(m,m,k)) - for(i in 1:k) - rhoInit[,,i] = diag(m) - - gamInit = 0.1*array(1, dim=c(n,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 (data = list(phiInit = phiInit, rhoInit = rhoInit, piInit = piInit, gamInit = gamInit)) + phiInit = array(0, dim=c(p,m,k)) + + piInit = (1./k)*rep.int(1,k) + + rhoInit = array(0, dim=c(m,m,k)) + for(i in 1:k) + rhoInit[,,i] = diag(m) + + gamInit = 0.1*array(1, dim=c(n,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 (data = list(phiInit = phiInit, rhoInit = rhoInit, piInit = piInit, gamInit = gamInit)) } diff --git a/R/discardSimilarModels.R b/R/discardSimilarModels.R index 8b12077..29d8f10 100644 --- a/R/discardSimilarModels.R +++ b/R/discardSimilarModels.R @@ -7,25 +7,25 @@ #' @param pi weight parameters (of size K*size(gridLambda)) #' #' @return a list with update B1, B2, glambda, rho and pi, and ind the vector of indices -#' of selected models. +#' of selected models. #' @export discardSimilarModels = function(B1,B2,glambda,rho,pi) { - ind = c() - for (j in 1:length(glambda)) - { - for (ll in 1:(l-1)) - { - if(B1[,,l] == B1[,,ll]) - ind = c(ind, l) - } - } - ind = unique(ind) - B1 = B1[,,-ind] - glambda = glambda[-ind] - B2 = B2[,,-ind] - rho = rho[,,,-ind] - pi = pi[,-ind] - - return (list(B1=B1,B2=B2,glambda=glambda,rho=rho,pi=pi,ind=ind)) + ind = c() + for (j in 1:length(glambda)) + { + for (ll in 1:(l-1)) + { + if(B1[,,l] == B1[,,ll]) + ind = c(ind, l) + } + } + ind = unique(ind) + B1 = B1[,,-ind] + glambda = glambda[-ind] + B2 = B2[,,-ind] + rho = rho[,,,-ind] + pi = pi[,-ind] + + return (list(B1=B1,B2=B2,glambda=glambda,rho=rho,pi=pi,ind=ind)) } diff --git a/R/generateIO.R b/R/generateIO.R index 4527f08..5f19488 100644 --- a/R/generateIO.R +++ b/R/generateIO.R @@ -1,36 +1,36 @@ #' Generate a sample of (X,Y) of size n #' @param covX covariance for covariates (of size p*p*K) #' @param covY covariance for the response vector (of size m*m*K) -#' @param pi proportion for each cluster +#' @param pi proportion for each cluster #' @param beta regression matrix -#' @param n sample size +#' @param n sample size #' #' @return list with X and Y #' @export #----------------------------------------------------------------------- generateIO = function(covX, covY, pi, beta, n) { - p = dim(covX)[1] - - m = dim(covY)[1] - k = dim(covY)[3] - - Y = matrix(0,n,m) - require(mvtnorm) - X = rmvnorm(n, mean = rep(0,p), sigma = covX) - - require(MASS) #simulate from a multivariate normal distribution - for (i in 1:n) - { - - for (r in 1:k) - { - BXir = rep(0,m) - for (mm in 1:m) - BXir[mm] = X[i,] %*% beta[,mm,r] - Y[i,] = Y[i,] + pi[r] * mvrnorm(1,BXir, covY[,,r]) - } - } - - return (list(X=X,Y=Y)) + p = dim(covX)[1] + + m = dim(covY)[1] + k = dim(covY)[3] + + Y = matrix(0,n,m) + require(mvtnorm) + X = rmvnorm(n, mean = rep(0,p), sigma = covX) + + require(MASS) #simulate from a multivariate normal distribution + for (i in 1:n) + { + + for (r in 1:k) + { + BXir = rep(0,m) + for (mm in 1:m) + BXir[mm] = X[i,] %*% beta[,mm,r] + Y[i,] = Y[i,] + pi[r] * mvrnorm(1,BXir, covY[,,r]) + } + } + + return (list(X=X,Y=Y)) } diff --git a/R/generateIOdefault.R b/R/generateIOdefault.R index 3613f2b..b0d748a 100644 --- a/R/generateIOdefault.R +++ b/R/generateIOdefault.R @@ -8,22 +8,22 @@ #----------------------------------------------------------------------- generateIOdefault = function(n, p, m, k) { - covX = diag(p) - covY = array(0, dim=c(m,m,k)) - for(r in 1:k) - { - covY[,,r] = diag(m) - } - - pi = rep(1./k,k) - - beta = array(0, dim=c(p,m,k)) - for(j in 1:p) - { - nonZeroCount = ceiling(m * runif(1)) - beta[j,1:nonZeroCount,] = matrix(runif(nonZeroCount*k), ncol=k) - } - - sample_IO = generateIO(covX, covY, pi, beta, n) - return (list(X=sample_IO$X,Y=sample_IO$Y)) + covX = diag(p) + covY = array(0, dim=c(m,m,k)) + for(r in 1:k) + { + covY[,,r] = diag(m) + } + + pi = rep(1./k,k) + + beta = array(0, dim=c(p,m,k)) + for(j in 1:p) + { + nonZeroCount = ceiling(m * runif(1)) + beta[j,1:nonZeroCount,] = matrix(runif(nonZeroCount*k), ncol=k) + } + + sample_IO = generateIO(covX, covY, pi, beta, n) + return (list(X=sample_IO$X,Y=sample_IO$Y)) } diff --git a/R/gridLambda.R b/R/gridLambda.R index 7b82f63..2c66e4c 100644 --- a/R/gridLambda.R +++ b/R/gridLambda.R @@ -1,31 +1,31 @@ #' Construct the data-driven grid for the regularization parameters used for the Lasso estimator #' @param phiInit value for phi -#' @param rhoInt value for rho -#' @param piInit value for pi +#' @param rhoInt value for rho +#' @param piInit value for pi #' @param gamInit value for gamma -#' @param mini minimum number of iterations in EM algorithm -#' @param maxi maximum number of iterations in EM algorithm -#' @param tau threshold to stop EM algorithm +#' @param mini minimum number of iterations in EM algorithm +#' @param maxi maximum number of iterations in EM algorithm +#' @param tau threshold to stop EM algorithm #' @return the grid of regularization parameters #' @export #----------------------------------------------------------------------- gridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, tau) { - n = nrow(X) - p = dim(phiInit)[1] - m = dim(phiInit)[2] - k = dim(phiInit)[3] - - list_EMG = .Call("EMGLLF",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau) - - grid = array(0, dim=c(p,m,k)) - for (i in 1:p) - { - for (j in 1:m) - grid[i,j,] = abs(list_EMG$S[i,j,]) / (n*list_EMG$pi^gamma) - } - grid = unique(grid) - grid = grid[grid <=1] - - return(grid) + n = nrow(X) + p = dim(phiInit)[1] + m = dim(phiInit)[2] + k = dim(phiInit)[3] + + list_EMG = .Call("EMGLLF",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau) + + grid = array(0, dim=c(p,m,k)) + for (i in 1:p) + { + for (j in 1:m) + grid[i,j,] = abs(list_EMG$S[i,j,]) / (n*list_EMG$pi^gamma) + } + grid = unique(grid) + grid = grid[grid <=1] + + return(grid) } diff --git a/R/indicesSelection.R b/R/indicesSelection.R index 1adece6..7835430 100644 --- a/R/indicesSelection.R +++ b/R/indicesSelection.R @@ -4,33 +4,33 @@ #' @param thresh threshold to say a cofficient is equal to zero #' #' @return a list with A, a matrix with relevant indices (size = p*m) and B, a -#' matrix with irrelevant indices (size = p*m) +#' matrix with irrelevant indices (size = p*m) #' @export indicesSelection = function(phi, thresh = 1e-6) { - dim_phi = dim(phi) - p = dim_phi[1] - m = dim_phi[2] - - A = matrix(0, p, m) - B = matrix(0, p, m) - - for(j in 1:p) - { - cpt1 = 0 - cpt2 = 0 - for(mm in 1:m) - { - if(max(phi[j,mm,]) > thresh) - { - cpt1 = cpt1 + 1 - A[j,cpt] = mm - } else - { - cpt2 = cpt2+1 - B[j, cpt2] = mm - } - } - } - return (list(A=A,B=B)) + dim_phi = dim(phi) + p = dim_phi[1] + m = dim_phi[2] + + A = matrix(0, p, m) + B = matrix(0, p, m) + + for(j in 1:p) + { + cpt1 = 0 + cpt2 = 0 + for(mm in 1:m) + { + if(max(phi[j,mm,]) > thresh) + { + cpt1 = cpt1 + 1 + A[j,cpt] = mm + } else + { + cpt2 = cpt2+1 + B[j, cpt2] = mm + } + } + } + return (list(A=A,B=B)) } diff --git a/R/initSmallEM.R b/R/initSmallEM.R index 8cfb7e8..5044a38 100644 --- a/R/initSmallEM.R +++ b/R/initSmallEM.R @@ -9,66 +9,66 @@ #' @export initSmallEM = function(k,X,Y,tau) { - n = nrow(Y) - m = ncol(Y) - p = ncol(X) - - betaInit1 = array(0, dim=c(p,m,k,20)) - sigmaInit1 = array(0, dim = c(m,m,k,20)) - phiInit1 = array(0, dim = c(p,m,k,20)) - rhoInit1 = array(0, dim = c(m,m,k,20)) - piInit1 = matrix(0,20,k) - gamInit1 = array(0, dim=c(n,k,20)) - LLFinit1 = list() - - require(MASS) #Moore-Penrose generalized inverse of matrix - require(mclust) # K-means with selection of K - for(repet in 1:20) - { - clusters = Mclust(matrix(c(X,Y),nrow=n),k) #default distance : euclidean - Zinit1[,repet] = clusters$classification - - for(r in 1:k) - { - Z = Zinit1[,repet] - Z_bin = vec_bin(Z,r) - Z_vec = Z_bin$Z #vecteur 0 et 1 aux endroits o? Z==r - Z_indice = Z_bin$indice #renvoit les indices o? Z==r - - betaInit1[,,r,repet] = - ginv(t(x[Z_indice,])%*%x[Z_indice,])%*%t(x[Z_indice,])%*%y[Z_indice,] - sigmaInit1[,,r,repet] = diag(m) - phiInit1[,,r,repet] = betaInit1[,,r,repet]/sigmaInit1[,,r,repet] - rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet]) - piInit1[repet,r] = sum(Z_vec)/n - } - - for(i in 1:n) - { - for(r in 1:k) - { - dotProduct = (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) %*% - (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) - Gam[i,r] = piInit1[repet,r]*det(rhoInit1[,,r,repet])*exp(-0.5*dotProduct) - } - sumGamI = sum(gam[i,]) - gamInit1[i,,repet]= Gam[i,] / sumGamI - } - - miniInit = 10 - maxiInit = 11 - - new_EMG = .Call("EMGLLF",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,], - gamInit1[,,repet],miniInit,maxiInit,1,0,x,y,tau) - LLFEessai = new_EMG$LLF - LLFinit1[repet] = LLFEessai[length(LLFEessai)] - } - - b = which.max(LLFinit1) - phiInit = phiInit1[,,,b] - rhoInit = rhoInit1[,,,b] - piInit = piInit1[b,] - gamInit = gamInit1[,,b] - - return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit)) + n = nrow(Y) + m = ncol(Y) + p = ncol(X) + + betaInit1 = array(0, dim=c(p,m,k,20)) + sigmaInit1 = array(0, dim = c(m,m,k,20)) + phiInit1 = array(0, dim = c(p,m,k,20)) + rhoInit1 = array(0, dim = c(m,m,k,20)) + piInit1 = matrix(0,20,k) + gamInit1 = array(0, dim=c(n,k,20)) + LLFinit1 = list() + + require(MASS) #Moore-Penrose generalized inverse of matrix + require(mclust) # K-means with selection of K + for(repet in 1:20) + { + clusters = Mclust(matrix(c(X,Y),nrow=n),k) #default distance : euclidean + Zinit1[,repet] = clusters$classification + + for(r in 1:k) + { + Z = Zinit1[,repet] + Z_bin = vec_bin(Z,r) + Z_vec = Z_bin$Z #vecteur 0 et 1 aux endroits o? Z==r + Z_indice = Z_bin$indice #renvoit les indices o? Z==r + + betaInit1[,,r,repet] = + ginv(t(x[Z_indice,])%*%x[Z_indice,])%*%t(x[Z_indice,])%*%y[Z_indice,] + sigmaInit1[,,r,repet] = diag(m) + phiInit1[,,r,repet] = betaInit1[,,r,repet]/sigmaInit1[,,r,repet] + rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet]) + piInit1[repet,r] = sum(Z_vec)/n + } + + for(i in 1:n) + { + for(r in 1:k) + { + dotProduct = (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) %*% + (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) + Gam[i,r] = piInit1[repet,r]*det(rhoInit1[,,r,repet])*exp(-0.5*dotProduct) + } + sumGamI = sum(gam[i,]) + gamInit1[i,,repet]= Gam[i,] / sumGamI + } + + miniInit = 10 + maxiInit = 11 + + new_EMG = .Call("EMGLLF",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,], + gamInit1[,,repet],miniInit,maxiInit,1,0,x,y,tau) + LLFEessai = new_EMG$LLF + LLFinit1[repet] = LLFEessai[length(LLFEessai)] + } + + b = which.max(LLFinit1) + phiInit = phiInit1[,,,b] + rhoInit = rhoInit1[,,,b] + piInit = piInit1[b,] + gamInit = gamInit1[,,b] + + return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit)) } diff --git a/R/modelSelection.R b/R/modelSelection.R index 5a79bb6..bc7eeae 100644 --- a/R/modelSelection.R +++ b/R/modelSelection.R @@ -3,34 +3,34 @@ #' the likelihood if there were several with the same dimension #' #' @param LLF a matrix, the first column corresponds to likelihoods for several models -#' the second column corresponds to the dimensions of the corresponding models. +#' the second column corresponds to the dimensions of the corresponding models. #' #' @return a list with indices, a vector of indices selected models, -#' and D1, a vector of corresponding dimensions +#' and D1, a vector of corresponding dimensions #' @export #' modelSelection = function(LLF) { - D = LLF[,2] - D1 = unique(D) - - indices = rep(1, length(D1)) - #select argmax MLE - if (length(D1)>2) - { - for (i in 1:length(D1)) - { - A = c() - for (j in 1:length(D)) - { - if(D[[j]]==D1[[i]]) - a = c(a, LLF[j,1]) - } - b = max(a) - #indices[i] : first indices of the binary vector where u_i ==1 - indices[i] = which.max(vec_bin(LLF,b)[[1]]) - } - } - - return (list(indices=indices,D1=D1)) + D = LLF[,2] + D1 = unique(D) + + indices = rep(1, length(D1)) + #select argmax MLE + if (length(D1)>2) + { + for (i in 1:length(D1)) + { + A = c() + for (j in 1:length(D)) + { + if(D[[j]]==D1[[i]]) + a = c(a, LLF[j,1]) + } + b = max(a) + #indices[i] : first indices of the binary vector where u_i ==1 + indices[i] = which.max(vec_bin(LLF,b)[[1]]) + } + } + + return (list(indices=indices,D1=D1)) } diff --git a/R/vec_bin.R b/R/vec_bin.R index 01dbfe1..ece7280 100644 --- a/R/vec_bin.R +++ b/R/vec_bin.R @@ -6,18 +6,18 @@ #' @return a list with Z (a binary vector of size the size of X) and indices where Z is equal to 1 vec_bin = function(X,r) { - Z = rep(0,length(X)) - indice = c() - j = 1 - for (i in 1:length(X)) - { - if(X[i] == r) - { - Z[i] = 1 - indice[j] = i - j=j+1 - } else - Z[i] = 0 - } - return (list(Z=Z,indice=indice)) + Z = rep(0,length(X)) + indice = c() + j = 1 + for (i in 1:length(X)) + { + if(X[i] == r) + { + Z[i] = 1 + indice[j] = i + j=j+1 + } else + Z[i] = 0 + } + return (list(Z=Z,indice=indice)) } \ No newline at end of file