tune_cpdbee_2D function

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.

Returns

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

## Not run:

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
## End(Not run)