Tunes 2D event detection using labeled data

This function finds best parameters for 2D event detection using labeled data.

tune_cpdbee_2D(
  x,
  cl,
  alpha_min = 0.95,
  alpha_max = 0.98,
  alpha_step = 0.01,
  epsilon_min = 2,
  epsilon_max = 12,
  epsilon_step = 2,
  minPts_min = 4,
  minPts_max = 12,
  minPts_step = 2
)

Arguments

x

The data in an mxn matrix or dataframe.

cl

The actual locations of the events.

alpha_min

The minimum threshold value.

alpha_max

The maximum threshold value.

alpha_step

The incremental step size for alpha.

epsilon_min

The minimum epsilon value for DBSCAN clustering.

epsilon_max

The maximum epsilon value for DBSCAN clustering.

epsilon_step

The incremental step size for epsilon for DBSCAN clustering.

minPts_min

The minimum minPts value for for DBSCAN clustering.

minPts_max

The maximum minPts value for for DBSCAN clustering.

minPts_step

The incremental step size for minPts for DBSCAN clustering.

Value

A list with following components

best

The best threshold, epsilon and MinPts for 2D event detection and the associated Jaccard Index.

all

All parameter values used and the associated Jaccard Index values.

Examples

if (FALSE) {
out <- gen_stream(1, sd=15)
zz <- as.matrix(out$data)
clst <- get_clusters(zz, filename = NULL, thres = 0.95, 
                    vis = TRUE, epsilon = 5, miniPts = 10, 
                    rolling = FALSE)
clst_loc <- clst$data[ ,1:2]
out <- tune_cpdbee_2D(zz, clst_loc)
out$best
}