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# GAM-clustering analysis
results <- gamClustering(E.prep = matrix.prep,
                         network.prep = network.prep,
                         cur.centers = cur.centers,                     
                         start.base = 0.5,
                         base.dec = 0.05,
                         max.module.size = 50,                         
                         cor.threshold = 0.7,
                         p.adj.val.threshold = 0.05,                         
                         batch.solver = seq_batch_solver(solver),
                         work.dir = work.dir,                         
                         show.intermediate.clustering = FALSE,
                         verbose = FALSE,
                         collect.stats = TRUE)



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Max cor exceeded 0.7: 0.75
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Error: Trying to fix variable with value that is out of possible range.

[*] Iteration 1

Error in FUN(X[[i]], ...): Not all edge weights are finite numbers
Traceback:

1. gamClustering(E.prep = E.prep, network.prep = network.prep, cur.centers = cur.centers, 
 .     start.base = 0.5, base.dec = 0.05, max.module.size = 30, 
 .     cor.threshold = 0.9, p.adj.val.threshold = 0.001, batch.solver = seq_batch_solver(solver), 
 .     work.dir = work.dir, show.intermediate.clustering = T, verbose = T, 
 .     collect.stats = TRUE)
2. lapply(nets, mwcsr::normalize_sgmwcs_instance, edges.weight.column = "score", 
 .     nodes.weight.column = "score", edges.group.by = "gene", nodes.group.by = NULL, 
 .     group.only.positive = TRUE)   # at line 89-94 of file <text>
3. lapply(nets, mwcsr::normalize_sgmwcs_instance, edges.weight.column = "score", 
 .     nodes.weight.column = "score", edges.group.by = "gene", nodes.group.by = NULL, 
 .     group.only.positive = TRUE)
4. FUN(X[[i]], ...)
5. stop(sprintf("Not all edge weights are finite numbers"))

gamClustering <- function(E.prep,
                          network.prep,
                          cur.centers,
                          
                          start.base = 0.5,
                          base.dec = 0.05,
                          max.module.size = 50,
                          
                          cor.threshold = 0.8,
                          p.adj.val.threshold = 0.001,
                          
                          batch.solver = seq_batch_solver(solver),
                          work.dir,
                          
                          show.intermediate.clustering = TRUE,
                          verbose = TRUE,
                          collect.stats = TRUE
                          ){
  
  iteration <- 1

 [...]
      nets <- lapply(idxs, function(i) {

        minOther <- pmin(apply(dist.to.centers[-i, , drop=F], 2, min), base)
        score <- log2(minOther) - log2(dist.to.centers[i, ])
        score[score == Inf] <- 0
        score <- pmax(score, -1000)
        posScores_keeping_var <<- c(posScores_keeping_var, length(which(score>0)))

        EdgeTable <- data.table::as.data.table(data.table::copy(network.prep))
        EdgeTable[, score := score[gene]]
        EdgeTable[from > to, c("from", "to") := list(to, from)]
        EdgeTable <- EdgeTable[order(score, decreasing = T)]
        EdgeTable <- unique(EdgeTable, by=c("from", "to"))
        # we still keep loops here

        ##Two line , I added
        
        Iscore <- EdgeTable$score[is.finite(EdgeTable$score)]
        EdgeTable <- EdgeTable %>% dplyr::filter(score %in% Iscore)
        

        scored_graph <- igraph::graph_from_data_frame(EdgeTable, directed = F)
        igraph::V(scored_graph)$score <- 0
        scored_graph
      })

      nets_attr <- lapply(nets, mwcsr::normalize_sgmwcs_instance,
                          edges.weight.column = "score",
                          nodes.weight.column = "score",
                          edges.group.by = "gene",
                          nodes.group.by = NULL,
                          group.only.positive = TRUE)
      
     [..]

[*] Iteration 1

>> base was equal to: 0.5;

>> number of modules was equal to: 32;

>> sizes of modules (unique genes) were in range: 0-4

Max diff: 0.42

[*] Iteration 2

Error in eval(expr, envir, enclos): Trying to fix variable with value that is out of possible range.
Traceback:

1. gamClustering(E.prep = E.prep, network.prep = network.prep, cur.centers = cur.centers, 
 .     start.base = 0.5, base.dec = 0.05, max.module.size = 30, 
 .     cor.threshold = 0.9, p.adj.val.threshold = 0.001, batch.solver = seq_batch_solver(solver), 
 .     work.dir = work.dir, show.intermediate.clustering = T, verbose = T, 
 .     collect.stats = TRUE)
2. batch.solver(nets_attr)   # at line 99 of file <text>
3. lapply(instances, mwcsr::solve_mwcsp, solver = mwcs_solver)
4. FUN(X[[i]], ...)
5. solve_mwcsp.rnc_solver(X[[i]], ...)
6. sgmwcs_solve(inst_rep, solver)