comp_ind_kernels_pred: Computes indicator kernel matrices for prediction and saves...
In conmolfields: Continuous molecular fields

Computes indicator kernel matrices for prediction and saves to file

1	comp_ind_kernels_pred(train_fname = "ligands-train.mol2", pred_fname = "ligands-pred.mol2", ind_kernels_pred_fname = "ligands-ind-kernels-pred.RData", print_comp_kernels = TRUE, ...)

`train_fname`
`pred_fname`
`ind_kernels_pred_fname`
`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", pred_fname = "ligands-pred.mol2", 
    ind_kernels_pred_fname = "ligands-ind-kernels-pred.RData", 
    print_comp_kernels = TRUE, ...) 
{
    mdb0_train <- read_mol2(train_fname)
    mdb0_pred <- read_mol2(pred_fname)
    mdb_train <- cmf_params_tripos(mdb0_train)
    mdb_pred <- cmf_params_tripos(mdb0_pred)
    syb_types <- get_syb_types_list(mdb_train)
    kernels_pred <- list()
    kernels_pred$alphas <- alphas
    for (type in syb_types) {
        kernels_pred[[type]] <- list()
    }
    for (ialpha in 1:length(alphas)) {
        alpha <- alphas[ialpha]
        if (print_comp_kernels) {
            cat(sprintf("computing indicator kernels for alpha=%g\n", 
                alpha))
            flush.console()
        }
        for (type in syb_types) {
            if (print_comp_kernels) 
                cat(type)
            kernels_pred[[type]][[ialpha]] <- cmf_indicator_kernel_matrix_pred(mdb_pred, 
                mdb_train, alpha, type, verbose = print_comp_kernels)
        }
    }
    save(kernels_pred, file = ind_kernels_pred_fname)
  }