diff --git a/multipartite_fungustree.R b/multipartite_fungustree.R
index 870b537..45ed8e9 100644
--- a/multipartite_fungustree.R
+++ b/multipartite_fungustree.R
@@ -1,6 +1,7 @@
 # library(GREMLINS) # Load the custom GREMLINS
 devtools::load_all("../GREMLINS/")
-library(sbm)
+# library(sbm)
+devtools::load_all("../sbm/")
 
 data(fungusTreeNetwork)
 tree_genetic_dist_matrix <- fungusTreeNetwork$covar_tree$genetic_dist
@@ -26,34 +27,159 @@ v_distrib <- c("gaussian", "bernoulli")
 namesFG <- c("Tree", "Fungus")
 givenclassif <- list(tree_geo_sbm$memberships, fungus_tree_sbm$memberships[["row"]])
 
-geo_multi_noinit <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = TRUE, verbose = TRUE)
+# geo_multi_noinit <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = TRUE, verbose = TRUE)
 
 Tree_Geo_dist_sbm <- defineSBM(-fungusTreeNetwork$covar_tree$geographic_dist, model = "gaussian", type = "simple", dimLabels = c("Tree"))
 Tree_Taxo_dist_sbm <- defineSBM(-fungusTreeNetwork$covar_tree$taxonomic_dist, model = "gaussian", type = "simple", dimLabels = c("Tree"))
-Tree_Genetic_dist_sbm <- defineSBM(fungusTreeNetwork$covar_tree$genetic_dist, model = "gaussian", type = "simple", dimLabels = c("Tree"))
+Tree_Genetic_dist_sbm <- defineSBM(-fungusTreeNetwork$covar_tree$genetic_dist, model = "gaussian", type = "simple", dimLabels = c("Tree"))
 FungusTree_sbm <- defineSBM(fungusTreeNetwork$fungus_tree, dimLabels = c("Fungus", "Tree"), model = "bernoulli", type = "bipartite")
 
-geo_multi_noinit_sbm <- estimateMultipartiteSBM(list(Tree_Geo_dist_sbm, FungusTree_sbm), estimOptions = list(initBM = TRUE, ))
+# geo_multi_noinit_sbm <- estimateMultipartiteSBM(list(Tree_Geo_dist_sbm, FungusTree_sbm), estimOptions = list(initBM = TRUE))
 
-geo_multi_noinit_nobm <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = FALSE, verbose = TRUE)
-# Les kmins donnent le même résultat ICL -449.21
-geo_multi_noinit_nobm_kmin <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = FALSE, verbose = TRUE, v_Kmin = c(7, 2))
-geo_multi_noinit_kmin <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = TRUE, verbose = TRUE, v_Kmin = c(7, 2))
-geo_multi_init_kmin <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = givenclassif, v_Kmin = c(7, 2)) # Ne s'autorise pas à descendre
-##
+# geo_multi_noinit_nobm <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = FALSE, verbose = TRUE)
+# # Les kmins donnent le même résultat ICL -449.21
+# geo_multi_noinit_nobm_kmin <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = FALSE, verbose = TRUE, v_Kmin = c(7, 2))
+# geo_multi_noinit_kmin <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = TRUE, verbose = TRUE, v_Kmin = c(7, 2))
+# geo_multi_init_kmin <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = givenclassif, v_Kmin = c(7, 2)) # Ne s'autorise pas à descendre
+# ##
 
-# Le meilleur ICL est ici : -408.05 avec une classif de base issue de blockmodels indépendants
-geo_multi_init <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = givenclassif)
+# # Le meilleur ICL est ici : -408.05 avec une classif de base issue de blockmodels indépendants
+# geo_multi_init <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = givenclassif)
 
-library(knitr)
+# library(knitr)
 
-kable(data.frame("Given classif" = c(FALSE, FALSE, FALSE, FALSE, TRUE, TRUE), "InitBM" = c(TRUE, FALSE, FALSE, TRUE, TRUE, TRUE), "Kmin given" = c(FALSE, FALSE, TRUE, TRUE, TRUE, FALSE), ICL = c(
-    geo_multi_noinit$fittedModel[[1]]$ICL,
-    geo_multi_noinit_nobm$fittedModel[[1]]$ICL,
-    geo_multi_noinit_nobm_kmin$fittedModel[[1]]$ICL,
-    geo_multi_noinit_kmin$fittedModel[[1]]$ICL,
-    geo_multi_init_kmin$fittedModel[[1]]$ICL,
-    geo_multi_init$fittedModel[[1]]$ICL
-)), format = "markdown")
+# kable(data.frame("Given classif" = c(FALSE, FALSE, FALSE, FALSE, TRUE, TRUE), "InitBM" = c(TRUE, FALSE, FALSE, TRUE, TRUE, TRUE), "Kmin given" = c(FALSE, FALSE, TRUE, TRUE, TRUE, FALSE), ICL = c(
+#     geo_multi_noinit$fittedModel[[1]]$ICL,
+#     geo_multi_noinit_nobm$fittedModel[[1]]$ICL,
+#     geo_multi_noinit_nobm_kmin$fittedModel[[1]]$ICL,
+#     geo_multi_noinit_kmin$fittedModel[[1]]$ICL,
+#     geo_multi_init_kmin$fittedModel[[1]]$ICL,
+#     geo_multi_init$fittedModel[[1]]$ICL
+# )), format = "markdown")
 
-dataR6 <- formattingData(list_Net, v_distrib)
+conditions <- expand.grid(given = c(TRUE, FALSE), initBM = c(TRUE, FALSE), Kmin = c(TRUE, FALSE), rep = seq(3))
+
+library(future.apply)
+library(future.callr)
+
+plan("multisession")
+
+results_geo_df <- do.call("rbind", future_lapply(seq_len(nrow(conditions)), function(row_idx) {
+    initBM <- conditions[row_idx, "initBM"]
+    is_given <- conditions[row_idx, "given"]
+    is_Kmin <- conditions[row_idx, "Kmin"]
+    rep <- conditions[row_idx, "rep"]
+
+    if (is_given) {
+        the_givenclassif <- givenclassif
+    } else {
+        the_givenclassif <- NULL
+    }
+
+    if (is_Kmin) {
+        the_Kmin <- list(seq(7, 10), seq(2, 10))
+    } else {
+        the_Kmin <- list(seq(10), seq(10))
+    }
+
+    names(the_Kmin) <- c("Tree", "Fungus")
+
+    fit <- estimateMultipartiteSBM(
+        listSBM = list(Tree_Geo_dist_sbm, FungusTree_sbm),
+        estimOptions = list(
+            "initBM" = initBM,
+            "givenclassif" = the_givenclassif,
+            "nbBlocksRange" = the_Kmin,
+            maxVEM = 1000,
+            maxVE = 1000
+        )
+    )
+    out <- data.frame(
+        InitBM = initBM,
+        Kmin = is_Kmin,
+        GivenClassif = is_given,
+        rep = rep,
+        ICL = fit$ICL,
+        nbBlocksTree = fit$nbBlocks["Tree"], nbBlocksFungus = fit$nbBlocks["Fungus"]
+    )
+}, future.seed = TRUE))
+# future_
+results_taxo_df <- do.call("rbind", lapply(seq_len(nrow(conditions)), function(row_idx) {
+    initBM <- conditions[row_idx, "initBM"]
+    is_given <- conditions[row_idx, "given"]
+    is_Kmin <- conditions[row_idx, "Kmin"]
+    rep <- conditions[row_idx, "rep"]
+
+    if (is_given) {
+        the_givenclassif <- givenclassif
+    } else {
+        the_givenclassif <- NULL
+    }
+
+    if (is_Kmin) {
+        the_Kmin <- list(seq(1, 10), seq(1, 10))
+    } else {
+        the_Kmin <- list(seq(10), seq(10))
+    }
+
+    names(the_Kmin) <- c("Tree", "Fungus")
+
+    fit <- estimateMultipartiteSBM(
+        listSBM = list(Tree_Taxo_dist_sbm, FungusTree_sbm),
+        estimOptions = list(
+            "initBM" = initBM,
+            "givenclassif" = the_givenclassif,
+            "nbBlocksRange" = the_Kmin,
+            maxVEM = 100000,
+            maxVE = 100000
+        )
+    )
+    out <- data.frame(
+        InitBM = initBM,
+        Kmin = is_Kmin,
+        GivenClassif = is_given,
+        rep = rep,
+        ICL = fit$ICL,
+        nbBlocksTree = fit$nbBlocks["Tree"], nbBlocksFungus = fit$nbBlocks["Fungus"]
+    )
+})) # , future.seed = TRUE))
+
+results_genetic_df <- do.call("rbind", lapply(seq_len(nrow(conditions)), function(row_idx) {
+    initBM <- conditions[row_idx, "initBM"]
+    is_given <- conditions[row_idx, "given"]
+    is_Kmin <- conditions[row_idx, "Kmin"]
+    rep <- conditions[row_idx, "rep"]
+
+    if (is_given) {
+        the_givenclassif <- givenclassif
+    } else {
+        the_givenclassif <- NULL
+    }
+
+    if (is_Kmin) {
+        the_Kmin <- list(seq(4, 10), seq(2, 10))
+    } else {
+        the_Kmin <- list(seq(10), seq(10))
+    }
+
+    names(the_Kmin) <- c("Tree", "Fungus")
+
+    fit <- estimateMultipartiteSBM(
+        listSBM = list(Tree_Genetic_dist_sbm, FungusTree_sbm),
+        estimOptions = list(
+            "initBM" = initBM,
+            "givenclassif" = the_givenclassif,
+            "nbBlocksRange" = the_Kmin,
+            maxVEM = 100000,
+            maxVE = 100000
+        )
+    )
+    out <- data.frame(
+        InitBM = initBM,
+        Kmin = is_Kmin,
+        GivenClassif = is_given,
+        rep = rep,
+        ICL = fit$ICL,
+        nbBlocksTree = fit$nbBlocks["Tree"], nbBlocksFungus = fit$nbBlocks["Fungus"]
+    )
+})) # , future.seed = TRUE))