# library(GREMLINS) # Load the custom GREMLINS
devtools::load_all("../GREMLINS/")
library(sbm)

data(fungusTreeNetwork)
tree_genetic_dist_matrix <- fungusTreeNetwork$covar_tree$genetic_dist

tree_taxonomic_dist_matrix <- (fungusTreeNetwork$covar_tree$taxonomic_dist)


tree_genet_sbm <- estimateSimpleSBM(netMat = tree_genetic_dist_matrix, model = "gaussian", estimOptions = list(plot = FALSE))
plot(tree_genet_sbm)
tree_taxonomic_sbm <- estimateSimpleSBM(netMat = tree_taxonomic_dist_matrix, model = "gaussian", estimOptions = list(plot = FALSE))
plot(tree_taxonomic_sbm)
tree_geo_sbm <- estimateSimpleSBM(netMat = fungusTreeNetwork$covar_tree$geographic_dist, model = "gaussian", estimOptions = list(plot = FALSE))
plot(tree_geo_sbm)

fungus_tree_sbm <- estimateBipartiteSBM(netMat = fungusTreeNetwork$fungus_tree, model = "bernoulli", estimOptions = list(plot = FALSE))

Tree_Geo_dist <- defineNetwork(fungusTreeNetwork$covar_tree$geographic_dist, type = "adj", "Tree", "Tree")
Tree_Genet_dist <- defineNetwork(fungusTreeNetwork$covar_tree$genetic_dist, type = "adj", "Tree", "Tree")
FungusTree_Count <- defineNetwork(fungusTreeNetwork$fungus_tree, type = "inc", "Fungus", "Tree")

list_Net <- list(Tree_Geo_dist, FungusTree_Count)
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)

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"))
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_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)

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")

dataR6 <- formattingData(list_Net, v_distrib)