X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FEMGLLF.R;fp=pkg%2FR%2FEMGLLF.R;h=0000000000000000000000000000000000000000;hp=03f0a75b47246a9a2d33be68082031267fe4a68c;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=ea5860f1b4fc91f06e371a0b26915198474a849d diff --git a/pkg/R/EMGLLF.R b/pkg/R/EMGLLF.R deleted file mode 100644 index 03f0a75..0000000 --- a/pkg/R/EMGLLF.R +++ /dev/null @@ -1,194 +0,0 @@ -#' EMGLLF -#' -#' Description de EMGLLF -#' -#' @param phiInit an initialization for phi -#' @param rhoInit an initialization for rho -#' @param piInit an initialization for pi -#' @param gamInit initialization for the a posteriori probabilities -#' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10 -#' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100 -#' @param gamma integer for the power in the penaly, by default = 1 -#' @param lambda regularization parameter in the Lasso estimation -#' @param X matrix of covariates (of size n*p) -#' @param Y matrix of responses (of size n*m) -#' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4 -#' -#' @return A list ... phi,rho,pi,LLF,S,affec: -#' phi : parametre de moyenne renormalisé, calculé par l'EM -#' rho : parametre de variance renormalisé, calculé par l'EM -#' pi : parametre des proportions renormalisé, calculé par l'EM -#' LLF : log vraisemblance associée à cet échantillon, pour les valeurs estimées des paramètres -#' S : ... affec : ... -#' -#' @export -EMGLLF <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, - X, Y, eps, fast) -{ - if (!fast) - { - # Function in R - return(.EMGLLF_R(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, - X, Y, eps)) - } - - # Function in C - n <- nrow(X) #nombre d'echantillons - p <- ncol(X) #nombre de covariables - m <- ncol(Y) #taille de Y (multivarié) - k <- length(piInit) #nombre de composantes dans le mélange - .Call("EMGLLF", phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, - X, Y, eps, phi = double(p * m * k), rho = double(m * m * k), pi = double(k), - LLF = double(maxi), S = double(p * m * k), affec = integer(n), n, p, m, k, - PACKAGE = "valse") -} - -# R version - slow but easy to read -.EMGLLF_R <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, - X, Y, eps) -{ - # Matrix dimensions - n <- nrow(X) - p <- ncol(X) - m <- ncol(Y) - k <- length(piInit) - - # Adjustments required when p==1 or m==1 (var.sel. or output dim 1) - if (p==1 || m==1) - phiInit <- array(phiInit, dim=c(p,m,k)) - if (m==1) - rhoInit <- array(rhoInit, dim=c(m,m,k)) - - # Outputs - phi <- phiInit - rho <- rhoInit - pi <- piInit - llh <- -Inf - S <- array(0, dim = c(p, m, k)) - - # Algorithm variables - gam <- gamInit - Gram2 <- array(0, dim = c(p, p, k)) - ps2 <- array(0, dim = c(p, m, k)) - X2 <- array(0, dim = c(n, p, k)) - Y2 <- array(0, dim = c(n, m, k)) - EPS <- 1e-15 - - for (ite in 1:maxi) - { - # Remember last pi,rho,phi values for exit condition in the end of loop - Phi <- phi - Rho <- rho - Pi <- pi - - # Computations associated to X and Y - for (r in 1:k) - { - for (mm in 1:m) - Y2[, mm, r] <- sqrt(gam[, r]) * Y[, mm] - for (i in 1:n) - X2[i, , r] <- sqrt(gam[i, r]) * X[i, ] - for (mm in 1:m) - ps2[, mm, r] <- crossprod(X2[, , r], Y2[, mm, r]) - for (j in 1:p) - { - for (s in 1:p) - Gram2[j, s, r] <- crossprod(X2[, j, r], X2[, s, r]) - } - } - - ## M step - - # For pi - b <- sapply(1:k, function(r) sum(abs(phi[, , r]))) - gam2 <- colSums(gam) - a <- sum(gam %*% log(pi)) - - # While the proportions are nonpositive - kk <- 0 - pi2AllPositive <- FALSE - while (!pi2AllPositive) - { - pi2 <- pi + 0.1^kk * ((1/n) * gam2 - pi) - pi2AllPositive <- all(pi2 >= 0) - kk <- kk + 1 - } - - # t(m) is the largest value in the grid O.1^k such that it is nonincreasing - while (kk < 1000 && -a/n + lambda * sum(pi^gamma * b) < - # na.rm=TRUE to handle 0*log(0) - -sum(gam2 * log(pi2), na.rm=TRUE)/n + lambda * sum(pi2^gamma * b)) - { - pi2 <- pi + 0.1^kk * (1/n * gam2 - pi) - kk <- kk + 1 - } - t <- 0.1^kk - pi <- (pi + t * (pi2 - pi))/sum(pi + t * (pi2 - pi)) - - # For phi and rho - for (r in 1:k) - { - for (mm in 1:m) - { - ps <- 0 - for (i in 1:n) - ps <- ps + Y2[i, mm, r] * sum(X2[i, , r] * phi[, mm, r]) - nY2 <- sum(Y2[, mm, r]^2) - rho[mm, mm, r] <- (ps + sqrt(ps^2 + 4 * nY2 * gam2[r]))/(2 * nY2) - } - } - - for (r in 1:k) - { - for (j in 1:p) - { - for (mm in 1:m) - { - S[j, mm, r] <- -rho[mm, mm, r] * ps2[j, mm, r] + - sum(phi[-j, mm, r] * Gram2[j, -j, r]) - if (abs(S[j, mm, r]) <= n * lambda * (pi[r]^gamma)) { - phi[j, mm, r] <- 0 - } else if (S[j, mm, r] > n * lambda * (pi[r]^gamma)) { - phi[j, mm, r] <- (n * lambda * (pi[r]^gamma) - S[j, mm, r])/Gram2[j, j, r] - } else { - phi[j, mm, r] <- -(n * lambda * (pi[r]^gamma) + S[j, mm, r])/Gram2[j, j, r] - } - } - } - } - - ## E step - - # Precompute det(rho[,,r]) for r in 1...k - detRho <- sapply(1:k, function(r) gdet(rho[, , r])) - sumLogLLH <- 0 - for (i in 1:n) - { - # Update gam[,]; use log to avoid numerical problems - logGam <- sapply(1:k, function(r) { - log(pi[r]) + log(detRho[r]) - 0.5 * - sum((Y[i, ] %*% rho[, , r] - X[i, ] %*% phi[, , r])^2) - }) - - logGam <- logGam - max(logGam) #adjust without changing proportions - gam[i, ] <- exp(logGam) - norm_fact <- sum(gam[i, ]) - gam[i, ] <- gam[i, ] / norm_fact - sumLogLLH <- sumLogLLH + log(norm_fact) - log((2 * base::pi)^(m/2)) - } - - sumPen <- sum(pi^gamma * b) - last_llh <- llh - llh <- -sumLogLLH/n + lambda * sumPen - dist <- ifelse(ite == 1, llh, (llh - last_llh)/(1 + abs(llh))) - Dist1 <- max((abs(phi - Phi))/(1 + abs(phi))) - Dist2 <- max((abs(rho - Rho))/(1 + abs(rho))) - Dist3 <- max((abs(pi - Pi))/(1 + abs(Pi))) - dist2 <- max(Dist1, Dist2, Dist3) - - if (ite >= mini && (dist >= eps || dist2 >= sqrt(eps))) - break - } - - list(phi = phi, rho = rho, pi = pi, llh = llh, S = S) -}