X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FinitSmallEM.R;fp=pkg%2FR%2FinitSmallEM.R;h=0000000000000000000000000000000000000000;hp=056d7e70e89fb08e7072ac63820c9c5f6365dc8a;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=ca277ac5ab51fef149014eb5e4610403fdb3227b

diff --git a/pkg/R/initSmallEM.R b/pkg/R/initSmallEM.R
deleted file mode 100644
index 056d7e7..0000000
--- a/pkg/R/initSmallEM.R
+++ /dev/null
@@ -1,79 +0,0 @@
-#' initialization of the EM algorithm 
-#'
-#' @param k number of components
-#' @param X matrix of covariates (of size n*p)
-#' @param Y matrix of responses (of size n*m)
-#'
-#' @return a list with phiInit, rhoInit, piInit, gamInit
-#' @export
-#' @importFrom methods new
-#' @importFrom stats cutree dist hclust runif
-initSmallEM <- function(k, X, Y, fast)
-{
-  n <- nrow(Y)
-  m <- ncol(Y)
-  p <- ncol(X)
-  nIte <- 20
-  Zinit1 <- array(0, dim = c(n, nIte))
-  betaInit1 <- array(0, dim = c(p, m, k, nIte))
-  sigmaInit1 <- array(0, dim = c(m, m, k, nIte))
-  phiInit1 <- array(0, dim = c(p, m, k, nIte))
-  rhoInit1 <- array(0, dim = c(m, m, k, nIte))
-  Gam <- matrix(0, n, k)
-  piInit1 <- matrix(0, nIte, k)
-  gamInit1 <- array(0, dim = c(n, k, nIte))
-  LLFinit1 <- list()
-
-  # require(MASS) #Moore-Penrose generalized inverse of matrix
-  for (repet in 1:nIte)
-  {
-    distance_clus <- dist(cbind(X, Y))
-    tree_hier <- hclust(distance_clus)
-    Zinit1[, repet] <- cutree(tree_hier, k)
-
-    for (r in 1:k)
-    {
-      Z <- Zinit1[, repet]
-      Z_indice <- seq_len(n)[Z == r]  #renvoit les indices où Z==r
-      if (length(Z_indice) == 1) {
-        betaInit1[, , r, repet] <- MASS::ginv(crossprod(t(X[Z_indice, ]))) %*% 
-          crossprod(t(X[Z_indice, ]), Y[Z_indice, ])
-      } else {
-        betaInit1[, , r, repet] <- MASS::ginv(crossprod(X[Z_indice, ])) %*% 
-          crossprod(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] <- mean(Z == r)
-    }
-
-    for (i in 1:n)
-    {
-      for (r in 1:k)
-      {
-        dotProduct <- tcrossprod(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
-
-    init_EMG <- EMGLLF(phiInit1[, , , repet], rhoInit1[, , , repet], piInit1[repet, ],
-      gamInit1[, , repet], miniInit, maxiInit, gamma = 1, lambda = 0, X, Y,
-      eps = 1e-04, fast)
-    LLFinit1[[repet]] <- init_EMG$llh
-  }
-  b <- which.min(LLFinit1)
-  phiInit <- phiInit1[, , , b]
-  rhoInit <- rhoInit1[, , , b]
-  piInit <- piInit1[b, ]
-  gamInit <- gamInit1[, , b]
-
-  return(list(phiInit = phiInit, rhoInit = rhoInit, piInit = piInit, gamInit = gamInit))
-}