Nothing
######################################################################
# Euclidean distance split criteria
######################################################################
eucliDist <- function(n_data,
s.n_mat,
var_ind,
pr.y1_ct1,
pr.y1_ct0,
pr.l,
pr.r,
pr.y1_l.ct1,
pr.y1_l.ct0,
pr.y1_r.ct1,
pr.y1_r.ct0,
pr.ct1,
pr.ct0,
pr.l_ct1,
pr.l_ct0,
minbucket.ok) {
### Euclidean gain
eucli.node <- (pr.y1_ct1 - pr.y1_ct0) ^ 2 + ((1 - pr.y1_ct1) - (1 - pr.y1_ct0)) ^ 2
eucli.l <- (pr.y1_l.ct1 - pr.y1_l.ct0) ^ 2 + ((1 - pr.y1_l.ct1) - (1 - pr.y1_l.ct0)) ^ 2
eucli.r <- (pr.y1_r.ct1 - pr.y1_r.ct0) ^ 2 + ((1 - pr.y1_r.ct1) - (1 - pr.y1_r.ct0)) ^ 2
eucli.lr <- pr.l * eucli.l + pr.r * eucli.r
eucli.gain <- eucli.lr - eucli.node
### Euclidean Normalization factor
gini.ct <- 2 * pr.ct1 * (1 - pr.ct1)
eucli.ct <- (pr.l_ct1 - pr.l_ct0) ^ 2 + ((1 - pr.l_ct1) - (1 - pr.l_ct0)) ^ 2
gini.ct1 <- 2 * pr.l_ct1 * (1 - pr.l_ct1)
gini.ct0 <- 2 * pr.l_ct0 * (1 - pr.l_ct0)
eucli.norm <- gini.ct * eucli.ct + gini.ct1 * pr.ct1 + gini.ct0 * pr.ct0 + 0.5
### Output
s.value.t <- eucli.gain / eucli.norm
s.value <- max(s.value.t[minbucket.ok])
wh.max <- which(s.value.t == s.value)
wh.max <- wh.max[minbucket.ok[wh.max]] #Ensures the max selected satisfies the constraint (in case of duplicates)
### break ties randomly
if (length(wh.max) > 1) {
wh.max <- sample(wh.max, 1)
}
if (is.numeric(n_data[, var_ind])) {
x.value = s.n_mat[wh.max, 1]
} else x.value = s.n_mat[, 1] <= wh.max
criteria.res <- list(s.value = s.value,
x.value = x.value)
return(criteria.res)
}
### END FUN
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.