R6,
methods
Suggests:
+ parallel,
devtools,
roxygen2,
testthat,
LazyData: yes
URL: http://git.auder.net/?p=talweg.git
License: MIT + file LICENSE
-RoxygenNote: 5.0.1
+RoxygenNote: 6.0.1
Collate:
'A_NAMESPACE.R'
'Data.R'
}
# Indices of similar days for cross-validation; TODO: 45 = magic number
- sdays = getSimilarDaysIndices(today, limit=45, same_season=FALSE)
+ sdays = getSimilarDaysIndices(today, data, limit=45, same_season=FALSE)
cv_days = intersect(fdays,sdays)
# Limit to 20 most recent matching days (TODO: 20 == magic number)
M[i+1,] = c( data$getLevel(fdays[i]), as.double(data$getExo(fdays[i])) )
sigma = cov(M) #NOTE: robust covariance is way too slow
- sigma_inv = solve(sigma) #TODO: use pseudo-inverse if needed?
+ # TODO: 10 == magic number; more robust way == det, or always ginv()
+ sigma_inv =
+ if (length(fdays) > 10)
+ solve(sigma)
+ else
+ MASS::ginv(sigma)
# Distances from last observed day to days in the past
distances2 = sapply(seq_along(fdays), function(i) {
})
sd_dist = sd(distances2)
- if (sd_dist < .Machine$double.eps)
+ if (sd_dist < .25 * sqrt(.Machine$double.eps))
{
# warning("All computed distances are very close: stdev too small")
sd_dist = 1 #mostly for tests... FIXME:
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, ...)
+ {
+ # This method predict shape + level at the same time, all in next call
+ self$predictShape(data, today, memory, horizon, ...)
+ },
predictShape = function(data, today, memory, horizon, ...)
{
# (re)initialize computed parameters
}
# Indices of similar days for cross-validation; TODO: 45 = magic number
- sdays = getSimilarDaysIndices(today, limit=45, same_season=FALSE)
+ sdays = getSimilarDaysIndices(today, data, limit=45, same_season=FALSE)
cv_days = intersect(fdays,sdays)
# Limit to 20 most recent matching days (TODO: 20 == magic number)
{
fdays = fdays[ fdays < today ]
# TODO: 3 = magic number
- if (length(fdays) < 1)
+ if (length(fdays) < 3)
return (NA)
# Neighbors: days in "same season"
- sdays = getSimilarDaysIndices(today, limit=45, same_season=TRUE, data)
+ sdays = getSimilarDaysIndices(today, data, limit=45, same_season=TRUE)
indices = intersect(fdays,sdays)
+ if (length(indices) <= 1)
+ return (NA)
levelToday = data$getLevel(today)
distances = sapply(indices, function(i) abs(data$getLevel(i)-levelToday))
+ # 2 and 5 below == magic numbers (determined by Bruno & Michel)
same_pollution = (distances <= 2)
- if (sum(same_pollution) < 1) #TODO: 3 == magic number
+ if (sum(same_pollution) == 0)
{
same_pollution = (distances <= 5)
- if (sum(same_pollution) < 1)
+ if (sum(same_pollution) == 0)
return (NA)
}
indices = indices[same_pollution]
-
- #TODO: we shouldn't need that block
if (length(indices) == 1)
{
if (final_call)
M[i+1,] = c( data$getLevel(indices[i]), as.double(data$getExo(indices[i])) )
sigma = cov(M) #NOTE: robust covariance is way too slow
-# sigma_inv = solve(sigma) #TODO: use pseudo-inverse if needed?
- sigma_inv = MASS::ginv(sigma)
+ # TODO: 10 == magic number; more robust way == det, or always ginv()
+ sigma_inv =
+ if (length(indices) > 10)
+ solve(sigma)
+ else
+ MASS::ginv(sigma)
# Distances from last observed day to days in the past
distances2 = sapply(seq_along(indices), function(i) {
getNeighborsJumpPredict = function(data, today, memory, horizon, params, ...)
{
first_day = max(1, today-memory)
- filter = params$indices >= first_day
+ filter = (params$indices >= first_day)
indices = params$indices[filter]
weights = params$weights[filter]
#' }
#' @param memory Data depth (in days) to be used for prediction
#' @param horizon Number of time steps to predict
+#' @param ncores Number of cores for parallel execution (1 to disable)
#' @param ... Additional parameters for the forecasting models
#'
#' @return An object of class Forecast
#' }}
#' @export
computeForecast = function(data, indices, forecaster, pjump,
- memory=Inf, horizon=data$getStdHorizon(), ...)
+ memory=Inf, horizon=data$getStdHorizon(), ncores=3, ...)
{
# (basic) Arguments sanity checks
horizon = as.integer(horizon)[1]
forecaster = forecaster_class_name$new( #.pjump =
getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
-#oo = forecaster$predictSerie(data, integer_indices[1], memory, horizon, ...)
-#browser()
-
- parll=TRUE #FALSE
- if (parll)
+ if (ncores > 1 && requireNamespace("parallel",quietly=TRUE))
{
- library(parallel)
- ppp <- parallel::mclapply(seq_along(integer_indices), function(i) {
+ p <- parallel::mclapply(seq_along(integer_indices), function(i) {
list(
"forecast" = forecaster$predictSerie(data, integer_indices[i], memory, horizon, ...),
"params"= forecaster$getParameters(),
"index" = integer_indices[i] )
- }, mc.cores=3)
+ }, mc.cores=ncores)
}
else
{
- ppp <- lapply(seq_along(integer_indices), function(i) {
+ p <- lapply(seq_along(integer_indices), function(i) {
list(
"forecast" = forecaster$predictSerie(data, integer_indices[i], memory, horizon, ...),
"params"= forecaster$getParameters(),
"index" = integer_indices[i] )
})
}
-#browser()
-
-for (i in seq_along(integer_indices))
-{
- pred$append(
- new_serie = ppp[[i]]$forecast,
- new_params = ppp[[i]]$params,
- new_index_in_data = ppp[[i]]$index
- )
-}
+ # TODO: find a way to fill pred in //...
+ for (i in seq_along(integer_indices))
+ {
+ pred$append(
+ new_serie = p[[i]]$forecast,
+ new_params = p[[i]]$params,
+ new_index_in_data = p[[i]]$index
+ )
+ }
pred
}
#' Find similar days indices in the past.
#'
#' @param index Day index (numeric or date)
+#' @param data Reference dataset, object output of \code{getData}
#' @param limit Maximum number of indices to return
#' @param same_season Should the indices correspond to day in same season?
-#' @param data Dataset is required for a search in same season
#'
#' @export
-getSimilarDaysIndices = function(index, limit, same_season, data=NULL)
+getSimilarDaysIndices = function(index, data, limit, same_season)
{
- index = dateIndexToInteger(index)
+ index = dateIndexToInteger(index, data)
- #TODO: mardi similaire à lundi mercredi jeudi aussi ...etc ==> "isSimilarDay()..."
- if (!same_season)
- {
- #take all similar days in recent past
- nb_days = min( (index-1) %/% 7, limit)
- return ( rep(index,nb_days) - 7*seq_len(nb_days) )
- }
-
- #Look for similar days in similar season
- nb_days = min( (index-1) %/% 7, limit)
- i = index - 7
+ # Look for similar days (optionally in same season)
+ i = index - 1
days = c()
- month_ref = as.POSIXlt(data$getTime(index)[1])$mon + 1
+ dt_ref = as.POSIXlt(data$getTime(index)[1]) #first date-time of current day
+ day_ref = dt_ref$wday #1=monday, ..., 6=saturday, 0=sunday
+ month_ref = as.POSIXlt(data$getTime(index)[1])$mon+1 #month in 1...12
while (i >= 1 && length(days) < limit)
{
- if (isSameSeason(as.POSIXlt(data$getTime(i)[1])$mon + 1, month_ref))
- days = c(days, i)
- i = i-7
+ dt = as.POSIXlt(data$getTime(i)[1])
+ if (.isSameDay(dt$wday, day_ref))
+ {
+ if (!same_season || .isSameSeason(dt$mon+1, month_ref))
+ days = c(days, i)
+ }
+ i = i - 1
}
return ( days )
}
-#TODO: use data... 12-12-1-2 CH, 3-4-9-10 EP et le reste NP
-isSameSeason = function(month, month_ref)
+# isSameSeason
+#
+# Check if two months fall in the same "season" (defined by estimated pollution rate)
+#
+# @param month month index to test
+# @param month_ref month to compare to
+#
+.isSameSeason = function(month, month_ref)
{
- if (month_ref %in% c(11,12,1,2))
+ if (month_ref %in% c(11,12,1,2)) #~= mid-polluted
return (month %in% c(11,12,1,2))
- if (month_ref %in% c(3,4,9,10))
+ if (month_ref %in% c(3,4,9,10)) #~= high-polluted
return (month %in% c(3,4,9,10))
- return (month %in% c(5,6,7,8))
+ return (month %in% c(5,6,7,8)) #~= non polluted
}
-#TODO:
-#distinction lun-jeudi, puis ven, sam, dim
-#isSameDay = function(day, day_ref)
-#{
-# if (day_ref ==
+# isSameDay
+#
+# Monday=Tuesday=Wednesday=Thursday ; Friday, Saturday, Sunday: specials
+#
+# @param day day index to test
+# @param day_ref day index to compare to
+#
+.isSameDay = function(day, day_ref)
+{
+ if (day_ref == 0)
+ return (day==0)
+ if (day_ref <= 4)
+ return (day <= 4)
+ return (day == day_ref)
+}
#' getNoNA2
#'
inputfile = sys.argv[1]
with open(inputfile, 'r') as f:
text = f.read()
- outputfile = '-' if len(sys.argv) <= 2 else sys.argv[2]
+ # Assuming file extension .gj (generate Jupyter); TODO: less strict
+ outputfile = inputfile[:-3]+'.ipynb' if (len(sys.argv)<=2 or sys.argv[2]=='-') \
+ else sys.argv[2]
except (IndexError, IOError) as e:
print('Usage: %s inputfile [outputfile|- [Mako args]]' % (sys.argv[0]))
print(e)
sys.exit(1)
cells = read(text, argv=sys.argv[3:])
filestr = write(cells)
- # Assuming file extension .gj (generate Jupyter); TODO: less strict
- outputfile = inputfile[:-3]+'.ipynb' if outputfile == '-' else outputfile
with open(outputfile, 'w') as f:
f.write(filestr)
<h2>Introduction</h2>
J'ai fait quelques essais dans différentes configurations pour la méthode "Neighbors"
-(la seule dont on a parlé) et sa variante récente appelée pour l'instant "Neighbors2".<br>
+(la seule dont on a parlé) et sa variante récente appelée pour l'instant "Neighbors2",
+avec simtype="mix" : deux types de similarités prises en compte, puis multiplication des poids.
+Pour Neighbors on prédit le saut (par la moyenne pondérée des sauts passés), et pour Neighbors2
+on n'effectue aucun raccordement (prévision directe).
- * simtype="exo", "endo" ou "mix" : type de similarités (fenêtre optimisée par VC)
- * same_season=FALSE : les indices pour la validation croisée ne tiennent pas compte des saisons
- * mix_strategy="mult" : on multiplie les poids (au lieu d'en éteindre)
-
-J'ai systématiquement comparé à une approche naïve : la moyennes des lendemains des jours
-"similaires" dans tout le passé ; à chaque fois sans prédiction du saut (sauf pour Neighbors :
-prédiction basée sur les poids calculés).
+J'ai systématiquement comparé à une approche naïve : la moyenne des lendemains des jours
+"similaires" dans tout le passé, ainsi qu'à la persistence -- reproduisant le jour courant ou
+allant chercher le futur similaire une semaine avant.
Ensuite j'affiche les erreurs, quelques courbes prévues/mesurées, quelques filaments puis les
histogrammes de quelques poids. Concernant les graphes de filaments, la moitié gauche du graphe
ts_data = read.csv(system.file("extdata","pm10_mesures_H_loc_report.csv",package="talweg"))
exo_data = read.csv(system.file("extdata","meteo_extra_noNAs.csv",package="talweg"))
-data = getData(ts_data, exo_data, input_tz = "Europe/Paris", working_tz="Europe/Paris",
- predict_at=P) #predict from P+1 to P+H included
+# NOTE: 'GMT' because DST gaps are filled and multiple values merged in above dataset.
+# Prediction from P+1 to P+H included.
+data = getData(ts_data, exo_data, input_tz = "GMT", working_tz="GMT", predict_at=P)
indices_ch = seq(as.Date("2015-01-18"),as.Date("2015-01-24"),"days")
indices_ep = seq(as.Date("2015-03-15"),as.Date("2015-03-21"),"days")
-----
<h2 style="color:blue;font-size:2em">${list_titles[i]}</h2>
-----r
-p_nn_exo = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors",
- horizon=H, simtype="exo")
-p_nn_mix = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors",
- horizon=H, simtype="mix")
-p_az = computeForecast(data, ${list_indices[i]}, "Average", "Zero",
- horizon=H)
-p_pz = computeForecast(data, ${list_indices[i]}, "Persistence", "Zero",
- horizon=${H}, same_day=${'TRUE' if loop.index < 2 else 'FALSE'})
+p_nn = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors", horizon=H)
+p_nn2 = computeForecast(data, ${list_indices[i]}, "Neighbors2", "Zero", horizon=H)
+p_az = computeForecast(data, ${list_indices[i]}, "Average", "Zero", horizon=H)
+p_pz = computeForecast(data, ${list_indices[i]}, "Persistence", "Zero", horizon=H, same_day=${'TRUE' if loop.index < 2 else 'FALSE'})
-----r
-e_nn_exo = computeError(data, p_nn_exo, H)
-e_nn_mix = computeError(data, p_nn_mix, H)
+e_nn = computeError(data, p_nn, H)
+e_nn2 = computeError(data, p_nn2, H)
e_az = computeError(data, p_az, H)
e_pz = computeError(data, p_pz, H)
options(repr.plot.width=9, repr.plot.height=7)
-plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4))
+plotError(list(e_nn, e_pz, e_az, e_nn2), cols=c(1,2,colors()[258], 4))
-# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence
+# Noir: Neighbors, bleu: Neighbors2, vert: moyenne, rouge: persistence
-i_np = which.min(e_nn_exo$abs$indices)
-i_p = which.max(e_nn_exo$abs$indices)
+i_np = which.min(e_nn$abs$indices)
+i_p = which.max(e_nn$abs$indices)
-----r
options(repr.plot.width=9, repr.plot.height=4)
par(mfrow=c(1,2))
-plotPredReal(data, p_nn_exo, i_np); title(paste("PredReal nn exo day",i_np))
-plotPredReal(data, p_nn_exo, i_p); title(paste("PredReal nn exo day",i_p))
+plotPredReal(data, p_nn, i_np); title(paste("PredReal nn day",i_np))
+plotPredReal(data, p_nn2, i_p); title(paste("PredReal nn day",i_p))
-plotPredReal(data, p_nn_mix, i_np); title(paste("PredReal nn mix day",i_np))
-plotPredReal(data, p_nn_mix, i_p); title(paste("PredReal nn mix day",i_p))
+plotPredReal(data, p_nn2, i_np); title(paste("PredReal nn2 day",i_np))
+plotPredReal(data, p_nn2, i_p); title(paste("PredReal nn2 day",i_p))
plotPredReal(data, p_az, i_np); title(paste("PredReal az day",i_np))
plotPredReal(data, p_az, i_p); title(paste("PredReal az day",i_p))
# Bleu: prévue, noir: réalisée
-----r
par(mfrow=c(1,2))
-f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste("Filaments nn exo day",i_np))
-f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste("Filaments nn exo day",i_p))
+f_np = computeFilaments(data, p_nn, i_np, plot=TRUE); title(paste("Filaments nn day",i_np))
+f_p = computeFilaments(data, p_nn, i_p, plot=TRUE); title(paste("Filaments nn day",i_p))
-f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste("Filaments nn mix day",i_np))
-f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste("Filaments nn mix day",i_p))
+f_np2 = computeFilaments(data, p_nn2, i_np, plot=TRUE); title(paste("Filaments nn2 day",i_np))
+f_p2 = computeFilaments(data, p_nn2, i_p, plot=TRUE); title(paste("Filaments nn2 day",i_p))
-----r
par(mfrow=c(1,2))
-plotFilamentsBox(data, f_np_exo); title(paste("FilBox nn exo day",i_np))
-plotFilamentsBox(data, f_p_exo); title(paste("FilBox nn exo day",i_p))
+plotFilamentsBox(data, f_np); title(paste("FilBox nn day",i_np))
+plotFilamentsBox(data, f_p); title(paste("FilBox nn day",i_p))
-plotFilamentsBox(data, f_np_mix); title(paste("FilBox nn mix day",i_np))
-plotFilamentsBox(data, f_p_mix); title(paste("FilBox nn mix day",i_p))
+plotFilamentsBox(data, f_np2); title(paste("FilBox nn2 day",i_np))
+plotFilamentsBox(data, f_p2); title(paste("FilBox nn2 day",i_p))
-----r
par(mfrow=c(1,2))
-plotRelVar(data, f_np_exo); title(paste("StdDev nn exo day",i_np))
-plotRelVar(data, f_p_exo); title(paste("StdDev nn exo day",i_p))
+plotRelVar(data, f_np); title(paste("StdDev nn day",i_np))
+plotRelVar(data, f_p); title(paste("StdDev nn day",i_p))
-plotRelVar(data, f_np_mix); title(paste("StdDev nn mix day",i_np))
-plotRelVar(data, f_p_mix); title(paste("StdDev nn mix day",i_p))
+plotRelVar(data, f_np2); title(paste("StdDev nn2 day",i_np))
+plotRelVar(data, f_p2); title(paste("StdDev nn2 day",i_p))
# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir
-----r
par(mfrow=c(1,2))
-plotSimils(p_nn_exo, i_np); title(paste("Weights nn exo day",i_np))
-plotSimils(p_nn_exo, i_p); title(paste("Weights nn exo day",i_p))
+plotSimils(p_nn, i_np); title(paste("Weights nn day",i_np))
+plotSimils(p_nn, i_p); title(paste("Weights nn day",i_p))
-plotSimils(p_nn_mix, i_np); title(paste("Weights nn mix day",i_np))
-plotSimils(p_nn_mix, i_p); title(paste("Weights nn mix day",i_p))
+plotSimils(p_nn2, i_np); title(paste("Weights nn2 day",i_np))
+plotSimils(p_nn2, i_p); title(paste("Weights nn2 day",i_p))
# - pollué à gauche, + pollué à droite
-----r
-# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite
-p_nn_exo$getParams(i_np)$window
-p_nn_exo$getParams(i_p)$window
+# Fenêtres sélectionnées dans ]0,7] / nn à gauche, nn2 à droite
+p_nn$getParams(i_np)$window
+p_nn$getParams(i_p)$window
-p_nn_mix$getParams(i_np)$window
-p_nn_mix$getParams(i_p)$window
+p_nn2$getParams(i_np)$window
+p_nn2$getParams(i_p)$window
% endfor
------
-<h2>Bilan</h2>
-
-Problème difficile : on ne fait guère mieux qu'une naïve moyenne des lendemains des jours
-similaires dans le passé, ce qui n'est pas loin de prédire une série constante égale à la
-dernière valeur observée (méthode "zéro"). La persistence donne parfois de bons résultats
-mais est trop instable (sensibilité à l'argument <code>same_day</code>).
-
-Comment améliorer la méthode ?
"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",
- "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\")"
+ "p3 = computeForecast(data, indices_ch, \"Neighbors\", \"Zero\", horizon=H, simtype=\"mix\")\n",
+ "p4 = computeForecast(data, indices_ch, \"Neighbors\", \"Neighbors\", 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\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
},
"outputs": [],
"source": [
"cell_type": "code",
"execution_count": null,
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
},
"outputs": [],
"source": [
"cell_type": "code",
"execution_count": null,
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
},
"outputs": [],
"source": [
- "e1 = computeError(data, p1, H)\n",
- "e2 = computeError(data, p2, H)\n",
+ "#e1 = computeError(data, p1, H)\n",
+ "#e2 = computeError(data, p2, H)\n",
"e3 = computeError(data, p3, H)\n",
"e4 = computeError(data, p4, H)\n",
- "e5 = computeError(data, p5, H)\n",
- "e6 = computeError(data, p6, H)\n",
- "plotError(list(e1,e2,e3,e4,e5,e6), cols=c(1,2,colors()[258], 4,5,6))"
+ "#e5 = computeError(data, p5, H)\n",
+ "#e6 = computeError(data, p6, H)\n",
+ "plotError(list(e3,e4), cols=c(1,2))"
]
},
{
},
"outputs": [],
"source": [
- "plotError(list(e4,e1,e2,e3, e5,e6), cols=c(1,2,3,4,5,6))"
+ "\tfirst_day = 1\n",
+ "params=p3$getParams(3)\n",
+ "\tfilter = (params$indices >= first_day)\n",
+ "\tindices = params$indices[filter]\n",
+ "\tweights = params$weights[filter]\n",
+ "\n",
+ "\n",
+ "\tgaps = sapply(indices, function(i) {\n",
+ "\t\tdata$getSerie(i+1)[1] - tail(data$getSerie(i), 1)\n",
+ "\t})\n",
+ "\tscal_product = weights * gaps\n",
+ "\tnorm_fact = sum( weights[!is.na(scal_product)] )\n",
+ "\tsum(scal_product, na.rm=TRUE) / norm_fact\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "hist(weights)"
]
},
{
"options(repr.plot.width=9, repr.plot.height=4)\n",
"par(mfrow=c(1,2))\n",
"\n",
- "plotPredReal(data, p_nn_exo, i_np); title(paste(\"PredReal nn exo day\",i_np))\n",
- "plotPredReal(data, p_nn_exo, i_p); title(paste(\"PredReal nn exo day\",i_p))\n",
+ "plotPredReal(data, p3, 3); title(paste(\"PredReal nn exo day\",3))\n",
+ "plotPredReal(data, p3, 5); title(paste(\"PredReal nn exo day\",5))\n",
"\n",
- "plotPredReal(data, p_nn_mix, i_np); title(paste(\"PredReal nn mix day\",i_np))\n",
- "plotPredReal(data, p_nn_mix, i_p); title(paste(\"PredReal nn mix day\",i_p))\n",
- "\n",
- "plotPredReal(data, p_az, i_np); title(paste(\"PredReal az day\",i_np))\n",
- "plotPredReal(data, p_az, i_p); title(paste(\"PredReal az day\",i_p))\n",
+ "plotPredReal(data, p4, 3); title(paste(\"PredReal nn mix day\",3))\n",
+ "plotPredReal(data, p4, 5); title(paste(\"PredReal nn mix day\",5))\n",
"\n",
"# Bleu: prévue, noir: réalisée"
]
projects / talweg.git / commitdiff