stop("Bad timezone (see ?timezone)")
input_tz = input_tz[1]
working_tz = working_tz[1]
- if (!is.data.frame(ts_data) && !is.character(ts_data))
- stop("Bad time-series input (data frame or CSV file)")
+ if ( (!is.data.frame(ts_data) && !is.character(ts_data)) ||
+ (!is.data.frame(exo_data) && !is.character(exo_data)) )
+ stop("Bad time-series / exogenous input (data [frame] or CSV file)")
if (is.character(ts_data))
ts_data = ts_data[1]
+ if (is.character(exo_data))
+ exo_data = exo_data[1]
predict_at = as.integer(predict_at)[1]
if (predict_at<0 || predict_at>23)
stop("Bad predict_at (0-23)")
ts_df =
if (is.character(ts_data)) {
- read.csv(ts_data)
+ if (ts_data %in% data(package="talweg")$results[,"Item"])
+ ts_data =
+
+
+
+
+ ############CONTINUE: http://r-pkgs.had.co.nz/data.html
+
+
+
+
+
+ read.csv(ts_data)
} else {
ts_data
}
--- /dev/null
+context("Check that forecasters behave as expected")
+
+test_that("Average+Zero method behave as expected",
+{
+
+test_that("Persistence+Zero method behave as expected",
+{
+
+test_that("Neighbors+Zero method behave as expected",
+{
+
+test_that("Neighbors+Neighbors method behave as expected",
+{
+
+
+
+#TODO: with and without shift at origin (so series values at least forst ones are required)
+
+
+ n = 1500
+ series = list()
+ for (i in seq_len(n))
+ {
+ index = (i%%3) + 1
+ level = mean(s[[index]])
+ serie = s[[index]] - level + rnorm(L,sd=0.05)
+ # 10 series with NAs for index 2
+ if (index == 2 && i >= 60 && i<= 90)
+ serie[sample(seq_len(L),1)] = NA
+ series[[i]] = list("level"=level,"serie"=serie) #no need for more :: si : time !!!
+ }
+ data = new("Data", data=series)
+
+ dateIndexToInteger = function(index, data)
+})
context("Check that computeFilaments behaves as expected")
-test_that("output is as expected on simulated series",
+getDataTest = function(n, shift)
{
x = seq(0,10,0.1)
L = length(x)
#sum((s1-s3)^2) == 57.03051
#sum((s2-s3)^2) == 40.5633
s = list( s1, s2, s3 )
- n = 150
series = list()
for (i in seq_len(n))
{
serie[sample(seq_len(L),1)] = NA
series[[i]] = list("level"=level,"serie"=serie) #no need for more
}
- data = new("Data", data=series)
+ if (shift)
+ {
+ # Simulate shift at origin when predict_at > 0
+ series[2:(n+1)] = series[1:n]
+ series[[1]] = list("level"=0, "serie"=s[[1]][1:(L%/%2)])
+ }
+ new("Data", data=series)
+}
+
+test_that("output is as expected on simulated series",
+{
+ data = getDataTest(150, FALSE)
# index 142 : serie type 2
- f2 = computeFilaments(data, 142, limit=60, plot=FALSE)
+ f = computeFilaments(data, 142, limit=60, plot=FALSE)
# Expected output: 22 series of type 3 (closer), then 50-2-10 series of type 2
- #
- #
- #
- #
- #
- #
- # Simulate shift at origin when predict_at > 0
- series[2:(n+1)] = series[1:n]
- series[[1]] = list("level"=0, "serie"=s[[1]][1:(L%/%2)])
+ expect_identical(length(f$indices), 60)
+ expect_identical(length(f$colors), 60)
+ for (i in 1:22)
+ {
+ expect_identical((f$indices[i] %% 3) + 1, 3)
+ expect_match(f2$colors[i], f$colors[1])
+ }
+ for (i in 23:60)
+ {
+ expect_identical((f$indices[i] %% 3) + 1, 2)
+ expect_match(f2$colors[i], f$colors[23])
+ }
+ expect_match(colors[1], "...")
+ expect_match(colors[23], "...")
+})
+
+test_that("output is as expected on simulated series",
+{
+ data = getDataTest(150, TRUE)
+
# index 143 : serie type 3
- f3 = computeFilaments(data, 143, limit=70, plot=FALSE)
+ f = computeFilaments(data, 143, limit=70, plot=FALSE)
# Expected output: 22 series of type 2 (closer) then 50-2 series of type 3
- # ATTENTION au shift
- #
- #
+ expect_identical(length(f$indices), 70)
+ expect_identical(length(f$colors), 70)
+ for (i in 1:22)
+ {
+ # -1 because of the initial shift
+ expect_identical(( (f$indices[i]-1) %% 3 ) + 1, 2)
+ expect_match(f$colors[i], f$colors[1])
+ }
+ for (i in 23:70)
+ {
+ expect_identical(( (f$indices[i]-1) %% 3 ) + 1, 3)
+ expect_match(f$colors[i], f$colors[23])
+ }
+ expect_match(colors[1], "...")
+ expect_match(colors[23], "...")
+})
+
+test_that("output is as expected on simulated series",
+{
+ data = getDataTest(150, TRUE)
+
# index 144 : serie type 1
- f1 = computeFilaments(data, 144, limit=50, plot=FALSE)
+ f = computeFilaments(data, 144, limit=50, plot=FALSE)
# Expected output: 2 series of type 3 (closer), then 50-2 series of type 1
- #
- expect_that( diff_norm, is_less_than(0.5) )
+ expect_identical(length(f$indices), 50)
+ expect_identical(length(f$colors), 50)
+ for (i in 1:2)
+ {
+ # -1 because of the initial shift
+ expect_identical(( (f$indices[i]-1) %% 3 ) + 1, 3)
+ expect_match(f$colors[i], f$colors[1])
+ }
+ for (i in 3:50)
+ {
+ expect_identical(( (f$indices[i]-1) %% 3 ) + 1, 1)
+ expect_match(f$colors[i], f$colors[3])
+ }
+ expect_match(colors[1], "...")
+ expect_match(colors[3], "...")
})
context("Check that dateIndexToInteger behaves as expected")
-test_that("integer index matches date in data",
+getDataTest = function(n, shift)
{
-
-
-
-#TODO: with and without shift at origin (so series values at least forst ones are required)
-
-
n = 1500
series = list()
+ s = rep(0, 24)
+
for (i in seq_len(n))
{
- index = (i%%3) + 1
- level = mean(s[[index]])
- serie = s[[index]] - level + rnorm(L,sd=0.05)
+ level = i %% 3mean(s[[index]])
+ serie = s
# 10 series with NAs for index 2
if (index == 2 && i >= 60 && i<= 90)
serie[sample(seq_len(L),1)] = NA
- series[[i]] = list("level"=level,"serie"=serie) #no need for more :: si : time !!!
+ series[[i]] = list("level"=i%%3, "serie"=s, "time"=)
}
data = new("Data", data=series)
+}
+test_that("integer index matches date in data, predict_at == 0",
+{
+ data = getData(
dateIndexToInteger = function(index, data)
})
+
+test_that("integer index matches date in data, predict_at > 0",
+{
+
+
+
+
+ ####TODO: CSV as raw data in inst/extdata http://r-pkgs.had.co.nz/data.html
projects / talweg.git / commitdiff