getDataTest = function(n)
{
data = Data$new()
- x = seq(0,10,0.1)
- L = length(x)
+ x = seq(0,9.5,0.1)
+ L = length(x) #96 1/4h
s1 = cos(x)
s2 = sin(x)
s3 = c( s1[1:(L%/%2)] , s2[(L%/%2+1):L] )
- #sum((s1-s2)^2) == 97.59381
- #sum((s1-s3)^2) == 57.03051
- #sum((s2-s3)^2) == 40.5633
- s = list( s1, s2, s3 )
+ #sum((s1-s2)^2) == 96
+ #sum((s1-s3)^2) == 58
+ #sum((s2-s3)^2) == 38
+ s = list(s1, s2, s3)
series = list()
for (i in seq_len(n))
{
# 10 series with NAs for index 2
if (I(i) == 2 && i >= 60 && i<= 90)
serie[sample(seq_len(L),1)] = NA
- data$append(c(), serie, level, c(), c()) #no need for more
+ time = as.POSIXct(i*15*60, origin="2007-01-01", tz="GMT")
+ exo = runif(4)
+ exo_hat = runif(4)
+ data$append(time, serie, level, exo, exo_hat)
}
data
}
data = getDataTest(150)
# index 143 : serie type 2
- f = computeFilaments(data, 143, limit=60, plot=FALSE)
+ pred = computeForecast(data, 143, "Neighbors", "Zero",
+ horizon=length(data$getSerie(1)), simtype="endo", h_window=1)
+ f = computeFilaments(data, pred, 1, limit=60, plot=FALSE)
# Expected output: 50-3-10 series of type 2, then 23 series of type 3 (closest next)
expect_identical(length(f$neighb_indices), as.integer(60))
expect_match(f$colors[38], "#E*")
# index 142 : serie type 1
- f = computeFilaments(data, 142, limit=50, plot=FALSE)
+ pred = computeForecast(data, 142, "Neighbors", "Zero",
+ horizon=length(data$getSerie(1)), simtype="endo", h_window=1)
+ f = computeFilaments(data, pred, 1, limit=50, plot=FALSE)
# Expected output: 50-10-3 series of type 1, then 13 series of type 3 (closest next)
# NOTE: -10 because only past days with no-NAs tomorrow => exclude type 1 in [60,90[