inherit = Forecaster,
public = list(
- predictSerie = function(data, today, memory, horizon, ...)
- {
- # Parameters (potentially) computed during shape prediction stage
- predicted_shape = self$predictShape(data, today, memory, horizon, ...)
-# predicted_delta = private$.pjump(data,today,memory,horizon,private$.params,...)
- # Predicted shape is aligned it on the end of current day + jump
-# predicted_shape+tail(data$getSerie(today),1)-predicted_shape[1]+predicted_delta
- predicted_shape
- },
+# predictSerie = function(data, today, memory, horizon, ...)
+# {
+# # Parameters (potentially) computed during shape prediction stage
+# predicted_shape = self$predictShape(data, today, memory, horizon, ...)
+## predicted_delta = private$.pjump(data,today,memory,horizon,private$.params,...)
+# # Predicted shape is aligned it on the end of current day + jump
+## predicted_shape+tail(data$getSerie(today),1)-predicted_shape[1]+predicted_delta
+# predicted_shape
+# },
predictShape = function(data, today, memory, horizon, ...)
{
# (re)initialize computed parameters
{
fdays = fdays[ fdays < today ]
# TODO: 3 = magic number
- if (length(fdays) < 3)
+ if (length(fdays) < 1)
return (NA)
# Neighbors: days in "same season"
levelToday = data$getLevel(today)
distances = sapply(indices, function(i) abs(data$getLevel(i)-levelToday))
same_pollution = (distances <= 2)
- if (sum(same_pollution) < 3) #TODO: 3 == magic number
+ if (sum(same_pollution) < 1) #TODO: 3 == magic number
{
same_pollution = (distances <= 5)
- if (sum(same_pollution) < 3)
+ if (sum(same_pollution) < 1)
return (NA)
}
indices = indices[same_pollution]
+ #TODO: we shouldn't need that block
+ if (length(indices) == 1)
+ {
+ if (final_call)
+ {
+ private$.params$weights <- 1
+ private$.params$indices <- indices
+ private$.params$window <- 1
+ }
+ return ( data$getSerie(indices[1])[1:horizon] ) #what else?!
+ }
+
if (simtype != "exo")
{
h_endo = ifelse(simtype=="mix", h[1], h)
sigma = cov(M) #NOTE: robust covariance is way too slow
# sigma_inv = solve(sigma) #TODO: use pseudo-inverse if needed?
sigma_inv = MASS::ginv(sigma)
-#if (final_call) browser()
+
# Distances from last observed day to days in the past
distances2 = sapply(seq_along(indices), function(i) {
delta = M[1,] - M[i+1,]
if (final_call)
{
private$.params$weights <- similarities
- private$.params$indices <- fdays
+ private$.params$indices <- indices
private$.params$window <-
if (simtype=="endo")
h_endo
#oo = forecaster$predictSerie(data, integer_indices[1], memory, horizon, ...)
#browser()
- library(parallel)
- ppp <- parallel::mclapply(seq_along(integer_indices), function(i) {
- list(
- "forecast" = forecaster$predictSerie(data, integer_indices[i], memory, horizon, ...),
- "params"= forecaster$getParameters(),
- "index" = integer_indices[i] )
- }, mc.cores=3)
-
+ parll=TRUE #FALSE
+ if (parll)
+ {
+ library(parallel)
+ ppp <- parallel::mclapply(seq_along(integer_indices), function(i) {
+ list(
+ "forecast" = forecaster$predictSerie(data, integer_indices[i], memory, horizon, ...),
+ "params"= forecaster$getParameters(),
+ "index" = integer_indices[i] )
+ }, mc.cores=3)
+ }
+ else
+ {
+ ppp <- lapply(seq_along(integer_indices), function(i) {
+ list(
+ "forecast" = forecaster$predictSerie(data, integer_indices[i], memory, horizon, ...),
+ "params"= forecaster$getParameters(),
+ "index" = integer_indices[i] )
+ })
+ }
#browser()
for (i in seq_along(integer_indices))
{
index = dateIndexToInteger(index)
- #TODO: mardi similaire à lundi mercredi jeudi aussi ...etc
+ #TODO: mardi similaire à lundi mercredi jeudi aussi ...etc ==> "isSimilarDay()..."
if (!same_season)
{
#take all similar days in recent past
return ( rep(index,nb_days) - 7*seq_len(nb_days) )
}
-
- #TODO: use data... 12-12-1-2 CH, 3-4-9-10 EP et le reste NP
#Look for similar days in similar season
+ nb_days = min( (index-1) %/% 7, limit)
+ i = index - 7
days = c()
- i = index
+ month_ref = as.POSIXlt(data$getTime(index)[1])$mon + 1
while (i >= 1 && length(days) < limit)
{
- if (i < index)
- {
+ if (isSameSeason(as.POSIXlt(data$getTime(i)[1])$mon + 1, month_ref))
days = c(days, i)
- #look in the "future of the past"
- for (j in 1:4)
- days = c(days, i+7*j)
- }
- #...and in the "past of the past"
- for (j in 1:4)
- {
- if (i - 7*j >= 1)
- days = c(days, i-7*j)
- }
- # TODO: exact computation instead of -364
- # 364 = closest multiple of 7 to 365 - drift along the years... but not so many years so OK
- i = i - 364
-
-
+ i = i-7
}
+ return ( days )
+}
- return ( days[1:min(limit,length(days))] )
+#TODO: use data... 12-12-1-2 CH, 3-4-9-10 EP et le reste NP
+isSameSeason = function(month, month_ref)
+{
+ if (month_ref %in% c(11,12,1,2))
+ return (month %in% c(11,12,1,2))
+ if (month_ref %in% c(3,4,9,10))
+ return (month %in% c(3,4,9,10))
+ return (month %in% c(5,6,7,8))
}
+#TODO:
+#distinction lun-jeudi, puis ven, sam, dim
+#isSameDay = function(day, day_ref)
+#{
+# if (day_ref ==
+
#' getNoNA2
#'
#' Get indices in data of no-NA series followed by no-NA, within [first,last] range.
"editable": true
},
"source": [
- "\n",
- "\n",
"<h2 style=\"color:blue;font-size:2em\">Pollution par chauffage</h2>"
]
},
"outputs": [],
"source": [
"reload(\"../pkg\")\n",
- "p1 = computeForecast(data, indices_ch, \"Neighbors\", \"Zero\", horizon=H, simtype=\"exo\")\n",
- "p2 = computeForecast(data, indices_ch, \"Neighbors\", \"Zero\", horizon=H, simtype=\"endo\")\n",
- "p3 = computeForecast(data, indices_ch, \"Neighbors\", \"Zero\", horizon=H, simtype=\"mix\")\n",
+ "#p1 = computeForecast(data, indices_ch, \"Neighbors\", \"Zero\", horizon=H, simtype=\"exo\")\n",
+ "#p2 = computeForecast(data, indices_ch, \"Neighbors\", \"Zero\", horizon=H, simtype=\"endo\")\n",
+ "#p3 = computeForecast(data, indices_ch, \"Neighbors\", \"Zero\", horizon=H, simtype=\"mix\")\n",
"p4 = computeForecast(data, indices_ch, \"Neighbors2\", \"Zero\", horizon=H, simtype=\"exo\")\n",
"p5 = computeForecast(data, indices_ch, \"Neighbors2\", \"Zero\", horizon=H, simtype=\"endo\")\n",
- "p6 = computeForecast(data, indices_ch, \"Neighbors2\", \"Zero\", horizon=H, simtype=\"mix\")\n"
+ "p6 = computeForecast(data, indices_ch, \"Neighbors2\", \"Zero\", horizon=H, simtype=\"mix\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "getSimilarDaysIndices(1000,10,TRUE,data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "as.POSIXlt(data$getTime(1000)[1])"
]
},
{
},
"outputs": [],
"source": [
- "e_nn_exo = computeError(data, p_nn_exo, 3)\n",
- "e_nn_mix = computeError(data, p_nn_mix, 3)\n",
- "e_az = computeError(data, p_az, 3)\n",
- "e_pz = computeError(data, p_pz, 3)\n",
- "options(repr.plot.width=9, repr.plot.height=7)\n",
- "plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4))\n",
- "\n",
- "# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence\n",
- "\n",
- "i_np = which.min(e_nn_exo$abs$indices)\n",
- "i_p = which.max(e_nn_exo$abs$indices)"
+ "plotError(list(e4,e1,e2,e3, e5,e6), cols=c(1,2,3,4,5,6))"
]
},
{
projects / talweg.git / commitdiff