comp_kernels_pred {conmolfields} | R Documentation |
Computes CMF kernel matrices for prediction and saves them to file
Description
Computes CMF kernel matrices for prediction and saves them to file
Usage
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 |
|
... |
|
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)
}
[Package
conmolfields version 0.0-19
Index]