#' @param ncores_outer Number of cores for the outer loop on k
#' @param ncores_inner Number of cores for the inner loop on lambda
#' @param thresh real, threshold to say a variable is relevant, by default = 1e-8
+#' @param grid_lambda, a vector with regularization parameters if known, by default numeric(0)
#' @param size_coll_mod (Maximum) size of a collection of models
#' @param fast TRUE to use compiled C code, FALSE for R code only
#' @param verbose TRUE to show some execution traces
#' @export
valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mini = 10,
maxi = 50, eps = 1e-04, kmin = 2, kmax = 3, rank.min = 1, rank.max = 5, ncores_outer = 1,
- ncores_inner = 1, thresh = 1e-08, size_coll_mod = 10, fast = TRUE, verbose = FALSE,
- plot = TRUE)
+ ncores_inner = 1, thresh = 1e-08, grid_lambda = numeric(0), size_coll_mod = 10,
+ fast = TRUE, verbose = FALSE, plot = TRUE)
{
- p <- dim(X)[2]
- m <- dim(Y)[2]
- n <- dim(X)[1]
+ n <- nrow(X)
+ p <- ncol(X)
+ m <- ncol(Y)
if (verbose)
print("main loop: over all k and all lambda")
# smallEM initializes parameters by k-means and regression model in each
# component, doing this 20 times, and keeping the values maximizing the
# likelihood after 10 iterations of the EM algorithm.
- P <- initSmallEM(k, X, Y)
- grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
- X, Y, gamma, mini, maxi, eps, fast)
+ P <- initSmallEM(k, X, Y, fast)
+ if (length(grid_lambda) == 0)
+ {
+ grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
+ X, Y, gamma, mini, maxi, eps, fast)
+ }
if (length(grid_lambda) > size_coll_mod)
grid_lambda <- grid_lambda[seq(1, length(grid_lambda), length.out = size_coll_mod)]
complexity = sumPen, contrast = -LLH)
}))
tableauRecap <- tableauRecap[which(tableauRecap[, 4] != Inf), ]
-
+
+
+ if (verbose == TRUE)
+ print(tableauRecap)
modSel <- capushe::capushe(tableauRecap, n)
indModSel <- if (selecMod == "DDSE")
- as.numeric(modSel@DDSE@model) else if (selecMod == "Djump")
- as.numeric(modSel@Djump@model) else if (selecMod == "BIC")
- modSel@BIC_capushe$model else if (selecMod == "AIC")
- modSel@AIC_capushe$model
-
- mod <- as.character(tableauRecap[indModSel, 1])
- listMod <- as.integer(unlist(strsplit(mod, "[.]")))
- modelSel <- models_list[[listMod[1]]][[listMod[2]]]
-
- ## Affectations
- Gam <- matrix(0, ncol = length(modelSel$pi), nrow = n)
- for (i in 1:n)
{
- for (r in 1:length(modelSel$pi))
- {
- sqNorm2 <- sum((Y[i, ] %*% modelSel$rho[, , r] - X[i, ] %*% modelSel$phi[, , r])^2)
- Gam[i, r] <- modelSel$pi[r] * exp(-0.5 * sqNorm2) * det(modelSel$rho[, , r])
- }
+ as.numeric(modSel@DDSE@model)
+ } else if (selecMod == "Djump")
+ {
+ as.numeric(modSel@Djump@model)
+ } else if (selecMod == "BIC")
+ {
+ modSel@BIC_capushe$model
+ } else if (selecMod == "AIC")
+ {
+ modSel@AIC_capushe$model
}
- Gam <- Gam/rowSums(Gam)
- modelSel$affec <- apply(Gam, 1, which.max)
- modelSel$proba <- Gam
-
+
+ listMod <- as.integer(unlist(strsplit(as.character(indModSel), "[.]")))
+ modelSel <- models_list[[listMod[1]]][[listMod[2]]]
+ modelSel$tableau <- tableauRecap
+
if (plot)
print(plot_valse(X, Y, modelSel, n))