comp_kernels_train: Computes CMF kernel matrices for training and saves to file
In conmolfields: Continuous molecular fields

Computes CMF kernel matrices for training and saves to file

1	comp_kernels_train(train_fname = "ligands-train.mol2", kernels_train_fname = "ligands-kernels-train.RData", mfields = c("q", "vdw", "logp", "abra", "abrb"), print_comp_kernels = TRUE, ...)

`train_fname`
`kernels_train_fname`
`mfields`
`print_comp_kernels`
`...`

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (train_fname = "ligands-train.mol2", kernels_train_fname = "ligands-kernels-train.RData", 
    mfields = c("q", "vdw", "logp", "abra", "abrb"), print_comp_kernels = TRUE, 
    ...) 
{
    mdb0 <- read_mol2(train_fname)
    mdb <- cmf_params_tripos(mdb0)
    nfields <- length(mfields)
    syb_types <- get_syb_types_list(mdb)
    kernels <- list()
    kernels$alphas <- alphas
    for (f in 1:nfields) {
        kernels[[mfields[f]]] <- list()
    }
    for (ialpha in 1:length(alphas)) {
        alpha <- alphas[ialpha]
        for (f in 1:nfields) {
            field <- mfields[f]
            if (print_comp_kernels) {
                cat(sprintf("computing kernel_%s for alpha=%g\n", 
                  field, alpha))
                flush.console()
            }
            if (field == "ind") {
                Km <- 0
                for (type in syb_types) {
                  if (print_comp_kernels) 
                    cat(type)
                  Km <- Km + cmf_indicator_kernel_matrix(mdb, 
                    alpha, type, verbose = print_comp_kernels)
                }
            }
            else {
                Km <- cmf_kernel_matrix_tt(field, mdb, alpha, 
                  verbose = print_comp_kernels)
            }
            kernels[[field]][[ialpha]] <- Km
        }
    }
    save(kernels, file = kernels_train_fname)
  }