Computes CMF kernel matrices for prediction and saves them to file

comp_kernels_pred(train_fname = "ligands-train.mol2", pred_fname = "ligands-pred.mol2", kernels_pred_fname = "ligands-kernels-pred.RData", mfields = c("q", "vdw", "logp", "abra", "abrb"), print_comp_kernels = TRUE, ...)

Arguments

train_fname

pred_fname

kernels_pred_fname

mfields

print_comp_kernels

Details

Value

References

Note

See also

Examples

##---- 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", kernels_pred_fname = "ligands-kernels-pred.RData", mfields = c("q", "vdw", "logp", "abra", "abrb"), 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) nfields <- length(mfields) syb_types <- get_syb_types_list(mdb_train) kernels_pred <- list() kernels_pred$alphas <- alphas for (f in 1:nfields) { kernels_pred[[mfields[f]]] <- list() } for (ialpha in 1:length(alphas)) { alpha <- alphas[ialpha] for (f in 1:nfields) { field <- mfields[f] if (print_comp_kernels) { cat(sprintf("computing kernel_%s for alpha=%g\n", field, alpha)) flush.console() } if (field == "ind") { Km <- 0 for (type in syb_types) { if (print_comp_kernels) cat(type) Km <- Km + cmf_indicator_kernel_matrix_pred(mdb_pred, mdb_train, alpha, type, verbose = print_comp_kernels) } } else { Km <- cmf_kernel_matrix_tp(field, mdb_pred, mdb_train, alpha, verbose = print_comp_kernels) } kernels_pred[[field]][[ialpha]] <- Km } } save(kernels_pred, file = kernels_pred_fname) }
#> function (train_fname = "ligands-train.mol2", pred_fname = "ligands-pred.mol2", #> kernels_pred_fname = "ligands-kernels-pred.RData", mfields = c("q", #> "vdw", "logp", "abra", "abrb"), print_comp_kernels = TRUE, #> ...) #> { #> mdb0_train <- read_mol2(train_fname) #> mdb0_pred <- read_mol2(pred_fname) #> mdb_train <- cmf_params_tripos(mdb0_train) #> mdb_pred <- cmf_params_tripos(mdb0_pred) #> nfields <- length(mfields) #> syb_types <- get_syb_types_list(mdb_train) #> kernels_pred <- list() #> kernels_pred$alphas <- alphas #> for (f in 1:nfields) { #> kernels_pred[[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_pred(mdb_pred, #> mdb_train, alpha, type, verbose = print_comp_kernels) #> } #> } #> else { #> Km <- cmf_kernel_matrix_tp(field, mdb_pred, mdb_train, #> alpha, verbose = print_comp_kernels) #> } #> kernels_pred[[field]][[ialpha]] <- Km #> } #> } #> save(kernels_pred, file = kernels_pred_fname) #> } #> <environment: 0x102568948>