R/make-sim-features.R

In nateaff/eeg-complex: Analyses for "Complexity-based seizure prediction"

#' Generate a set of features on two sets of simulations 
#' 
#' Simulations are generated by sim_suite() and the 
#' features in the features argument are computed.a
#' A dataframe of features, feature name and id are returned.
#'
#' @param n Number of functions for each group
#' @param len Length of simulations
#' @param features Features to compute
#' @param seed Random generator seed.
#'
#' @return A dataframe of features, id and function names 
make_sim_features <- function(n, len, features, seed = 1) {
  #----------------------------------------------------------
  gs <- tssims::sim_suite()
  fnames <- tssims::sim_names()
  names(gs$group1)
  set.seed(seed)
  for(k in 1:length(gs$group1)){
    f1 <- gs$group1[[k]]
    f2 <- gs$group2[[k]]

    ts_mat1 <- replicate(n, tssims::gen(tssims::jitter_params(f1))(len)) %>% 
               apply(., 2, ecomplex::normalize)  
    ts_mat2 <- replicate(n, tssims::gen(tssims::jitter_params(f2))(len)) %>% 
               apply(., 2, ecomplex::normalize)

    df1 <- get_features(ts_mat1, features, id = "1", ncores = 4)
    df2 <- get_features(ts_mat2, features, id = "2", ncores = 4)

    temp <-rbind(df1, df2)
    temp$fnames <- rep(fnames[k], n)
    if(k == 1){ 
      comb <- temp
    } else {
      comb <- rbind(comb, temp)
    }
  }

  combn <- data.frame(apply(subset(comb, select = c(-id, -fnames)), 2, ecomplex::normalize))
  combn$id <- as.factor(comb$id)
  combn$fnames <- comb$fnames 
  combn
}