View source: R/clusteringTypicalDaysForOneClass.R
clusterTypicalDaysForOneClass | R Documentation |
Run a clustering algorithm for a given historical period. It creates clusters by gathering the most similar days and chooses among them the best representative: it will be a so-called typical day. The metric used to determine the similarity of two days is a weighted sum of 24 hourly distances, meaning the distances between the domains of the two days at the same hour.
clusterTypicalDaysForOneClass(dates, PLAN, VERT = NULL,
hubDrop = list(NL = c("BE", "DE", "FR", "AT")), nbCluster = NULL,
hourWeight = rep(1, 24), className = NULL, idStart = 1,
maxDomainSize = 20000, ponderate = FALSE)
dates |
|
PLAN |
PLAN is generated in this format with the function getPreprocPlan |
VERT |
|
hubDrop |
|
nbCluster |
|
hourWeight |
|
className |
|
idStart |
|
maxDomainSize |
|
ponderate |
|
## Not run:
library(data.table)
library(quadprog)
PLAN <- readRDS(system.file("testdata/plan_test2.rds", package = "fbClust"))
dates <- seq(as.Date("2018-10-02"), as.Date("2018-10-04"), by = "day")
hourWeight = rep(1, 24)
nbcluster <- 2
maxDomainSize <- 20000
idStart <- 1
hubDrop <- list(NL = c("BE", "DE", "FR", "AT"))
clusterTypicalDaysForOneClass(
dates = dates, PLAN = PLAN, VERT = NULL, maxDomainSize = maxDomainSize,
hubDrop = hubDrop,
nbCluster = nbcluster,report = F, hourWeight = hourWeight, idStart = idStart)
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.