#' Function to predict prababilities for each sampled data set
#' Take a model and a predicted set of training data and calculate the probabilities of fishing
#' in each site given a set choice set.
#' @param train_dat Training data, sampled from training_data function
#' @param the_model Output from sampled_rums function
#' @param ncores Number of cores to run in parallel, defaults to 6
#' @export
add_probs <- function(train_dat, the_model, ncores = 6){
#add probabilities to the first tows
first_tows <- train_dat[[1]]
first_tows <- mclapply(unique(first_tows$fished_haul), FUN = function(xx){
temp <- first_tows %>% filter(fished_haul == xx)
probs <- predict(the_model[[3]], temp)
temp$probs <- probs
return(temp)
}, mc.cores = ncores)
first_tows <- ldply(first_tows)
#Add probabilities to the second tows
second_tows <- train_dat[[2]]
second_tows <- mclapply(unique(second_tows$fished_haul), FUN = function(xx){
temp <- second_tows %>% filter(fished_haul == xx)
probs <- predict(the_model[[4]], newdata = temp)
temp$probs <- probs
return(temp)
}, mc.cores = ncores)
second_tows <- ldply(second_tows)
return(list(first_tows = first_tows, second_tows = second_tows))
}
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