'update'

diff --git a/pkg/DESCRIPTION b/pkg/DESCRIPTION
index 849ff92..b1697df 100644 (file)
--- a/pkg/DESCRIPTION
+++ b/pkg/DESCRIPTION
@@ -23,14 +23,13 @@ Suggests:
 LazyData: yes
 URL: http://git.auder.net/?p=talweg.git
 License: MIT + file LICENSE
-RoxygenNote: 5.0.1
-Collate:
+RoxygenNote: 6.0.1
+Collate: 
     'A_NAMESPACE.R'
     'Data.R'
     'Forecaster.R'
     'F_Average.R'
     'F_Neighbors.R'
-    'F_Neighbors2.R'
     'F_Persistence.R'
     'F_Zero.R'
     'Forecast.R'

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index c55291a..9ba72b8 100644 (file)
--- a/pkg/R/F_Neighbors.R
+++ b/pkg/R/F_Neighbors.R
@@ -22,8 +22,8 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
                        fdays = getNoNA2(data, max(today-memory,1), today-1)
 
                        # Get optional args
-                       local = ifelse(hasArg("local"), list(...)$local, FALSE) #same level + season?
-                       simtype = ifelse(hasArg("simtype"), list(...)$simtype, "mix") #or "endo", or "exo"
+                       local = ifelse(hasArg("local"), list(...)$local, TRUE) #same level + season?
+                       simtype = ifelse(hasArg("simtype"), list(...)$simtype, "none") #or "endo", or "exo"
                        if (hasArg("window"))
                        {
                                return ( private$.predictShapeAux(data,
@@ -34,7 +34,7 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
                        cv_days = getSimilarDaysIndices(today, data, limit=20, same_season=FALSE,
                                days_in=fdays)
 
-                       # Optimize h : h |--> sum of prediction errors on last 45 "similar" days
+                       # Optimize h : h |--> sum of prediction errors on last N "similar" days
                        errorOnLastNdays = function(window, simtype)
                        {
                                error = 0
@@ -54,6 +54,7 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
                                return (error / nb_jours)
                        }
 
+                       # TODO: 7 == magic number
                        if (simtype != "endo")
                        {
                                best_window_exo = optimize(
@@ -100,9 +101,9 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
                                        return (NA)
                                levelToday = data$getLevel(today)
                                distances = sapply(fdays, function(i) abs(data$getLevel(i)-levelToday))
-                               #TODO: 2, 3, 5, 10 magic numbers here...
+                               #TODO: 2, 10, 3, 12 magic numbers here...
                                dist_thresh = 2
-                               min_neighbs = min(3,length(fdays))
+                               min_neighbs = min(10,length(fdays))
                                repeat
                                {
                                        same_pollution = (distances <= dist_thresh)
@@ -112,7 +113,7 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
                                        dist_thresh = dist_thresh + 3
                                }
                                fdays = fdays[same_pollution]
-                               max_neighbs = 10
+                               max_neighbs = 12
                                if (nb_neighbs > max_neighbs)
                                {
                                        # Keep only max_neighbs closest neighbors
@@ -133,7 +134,7 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
                        else
                                fdays = fdays_cut #no conditioning
 
-                       if (simtype != "exo")
+                       if (simtype == "endo" || simtype == "mix")
                        {
                                # Compute endogen similarities using given window
                                window_endo = ifelse(simtype=="mix", window[1], window)
@@ -154,10 +155,10 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
                                simils_endo = exp(-distances2/(sd_dist*window_endo^2))
                        }
 
-                       if (simtype != "endo")
+                       if (simtype == "exo" || simtype == "mix")
                        {
                                # Compute exogen similarities using given window
-                               h_exo = ifelse(simtype=="mix", window[2], window)
+                               window_exo = ifelse(simtype=="mix", window[2], window)
 
                                M = matrix( nrow=1+length(fdays), ncol=1+length(data$getExo(today)) )
                                M[1,] = c( data$getLevel(today), as.double(data$getExo(today)) )
@@ -192,8 +193,10 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
                                        simils_exo
                                else if (simtype == "endo")
                                        simils_endo
-                               else #mix
+                               else if (simtype == "mix")
                                        simils_endo * simils_exo
+                               else #none
+                                       rep(1, length(fdays))
                        similarities = similarities / sum(similarities)
 
                        prediction = rep(0, horizon)

diff --git a/pkg/R/computeForecast.R b/pkg/R/computeForecast.R
index d635560..24f114e 100644 (file)
--- a/pkg/R/computeForecast.R
+++ b/pkg/R/computeForecast.R
@@ -27,8 +27,7 @@
 #' @examples
 #' ts_data = system.file("extdata","pm10_mesures_H_loc.csv",package="talweg")
 #' exo_data = 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=7)
+#' data = getData(ts_data, exo_data, input_tz="GMT", working_tz="GMT", predict_at=7)
 #' pred = computeForecast(data, 2200:2230, "Persistence", "Persistence", 500, 12)
 #' \dontrun{#Sketch for real-time mode:
 #' data = new("Data", ...)

diff --git a/reports/report.gj b/reports/report.gj
index 5e57660..8e0f9ea 100644 (file)
--- a/reports/report.gj
+++ b/reports/report.gj
@@ -4,15 +4,15 @@
 J'ai fait quelques essais dans deux configurations pour la méthode "Neighbors"
 (la seule dont on a parlé, incorporant désormais la "variante Bruno/Michel").
 
- * avec simtype="mix" et raccordement simple ("Zero") dans le cas "non local", i.e. on va
-   chercher des voisins n'importe où du moment qu'ils correspondent à deux jours consécutifs sans
-   valeurs manquantes.
- * avec simtype="endo" et raccordement "Neighbor" dans le cas "local" : voisins de même niveau de
-   pollution et même saison.
+ * avec simtype="mix" et raccordement "Neighbors" (p1) dans le cas "non local", i.e. on va
+   chercher des voisins n'importe où du moment qu'ils correspondent au premier élément d'un
+        couple de deux jours consécutifs sans valeurs manquantes.
+ * avec simtype="endo" + raccordement "Neighbors" (p2) puis "none" (p3, moyenne simple) + raccordement
+   "Zero" (sans ajustement) dans le cas "local" : voisins de même niveau de pollution et même saison.
 
 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.
+"similaires" dans tout le passé (p4), ainsi qu'à la persistence (p5) -- reproduisant le jour courant ou
+allant chercher le futur similaire une semaine avant (argument "same_day").
 
 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
@@ -43,22 +43,25 @@ indices_np = seq(as.Date("2015-04-26"),as.Date("2015-05-02"),"days")
 -----
 <h2 style="color:blue;font-size:2em">${list_titles[i]}</h2>
 -----r
-p_n = computeForecast(data, ${list_indices[i]}, "Neighbors", "Zero", horizon=H,
+p1 = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors", horizon=H,
        simtype="mix", local=FALSE)
-p_l = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors", horizon=H,
+p2 = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors", horizon=H,
        simtype="endo", local=TRUE)
-p_a = computeForecast(data, ${list_indices[i]}, "Average", "Zero", horizon=H)
-p_p = computeForecast(data, ${list_indices[i]}, "Persistence", "Zero", horizon=H,
+p3 = computeForecast(data, ${list_indices[i]}, "Neighbors", "Zero", horizon=H,
+       simtype="none", local=TRUE)
+p4 = computeForecast(data, ${list_indices[i]}, "Average", "Zero", horizon=H)
+p5 = computeForecast(data, ${list_indices[i]}, "Persistence", "Zero", horizon=H,
        same_day=${'TRUE' if loop.index < 2 else 'FALSE'})
 -----r
-e_n = computeError(data, p_n, H)
-e_l = computeError(data, p_nl, H)
-e_a = computeError(data, p_a, H)
-e_p = computeError(data, p_p, H)
+e1 = computeError(data, p_nz_mf, H)
+e2 = computeError(data, p_nz_mfl, H)
+e3 = computeError(data, p_a, H)
+e4 = computeError(data, p_p, H)
 options(repr.plot.width=9, repr.plot.height=7)
 plotError(list(e_n, e_p, e_a, e_l), cols=c(1,2,colors()[258], 4))
 
-# Noir: Neighbors non-local, bleu: Neighbors local, vert: moyenne, rouge: persistence
+# noir: Neighbors non-local (p1), bleu: Neighbors local endo (p2), mauve: Neighbors local none (p3),
+# vert: moyenne (p4), rouge: persistence (p5)
 
 i_np = which.min(e_n$abs$indices)
 i_p = which.max(e_n$abs$indices)
@@ -66,54 +69,52 @@ i_p = which.max(e_n$abs$indices)
 options(repr.plot.width=9, repr.plot.height=4)
 par(mfrow=c(1,2))
 
-plotPredReal(data, p_n, i_np); title(paste("PredReal non-loc day",i_np))
-plotPredReal(data, p_n, i_p); title(paste("PredReal non-loc day",i_p))
+plotPredReal(data, p1, i_np); title(paste("PredReal p1 day",i_np))
+plotPredReal(data, p1, i_p); title(paste("PredReal p1 day",i_p))
 
-plotPredReal(data, p_l, i_np); title(paste("PredReal loc day",i_np))
-plotPredReal(data, p_l, i_p); title(paste("PredReal loc day",i_p))
+plotPredReal(data, p2, i_np); title(paste("PredReal p2 day",i_np))
+plotPredReal(data, p2, i_p); title(paste("PredReal p2 day",i_p))
 
-plotPredReal(data, p_a, i_np); title(paste("PredReal avg day",i_np))
-plotPredReal(data, p_a, i_p); title(paste("PredReal avg day",i_p))
+plotPredReal(data, p3, i_np); title(paste("PredReal p3 day",i_np))
+plotPredReal(data, p3, i_p); title(paste("PredReal p3 day",i_p))
 
 # Bleu: prévue, noir: réalisée
 -----r
 par(mfrow=c(1,2))
-f_np_n = computeFilaments(data, p_n, i_np, plot=TRUE); title(paste("Filaments non-loc day",i_np))
-f_p_n = computeFilaments(data, p_n, i_p, plot=TRUE); title(paste("Filaments non-loc day",i_p))
+f_np1 = computeFilaments(data, p1, i_np, plot=TRUE); title(paste("Filaments p1 day",i_np))
+f_p1 = computeFilaments(data, p1, i_p, plot=TRUE); title(paste("Filaments p1 day",i_p))
 
-f_np_l = computeFilaments(data, p_l, i_np, plot=TRUE); title(paste("Filaments loc day",i_np))
-f_p_l = computeFilaments(data, p_l, i_p, plot=TRUE); title(paste("Filaments loc day",i_p))
+f_np2 = computeFilaments(data, p2, i_np, plot=TRUE); title(paste("Filaments p2 day",i_np))
+f_p2 = computeFilaments(data, p2, i_p, plot=TRUE); title(paste("Filaments p2 day",i_p))
 -----r
 par(mfrow=c(1,2))
-plotFilamentsBox(data, f_np_n); title(paste("FilBox non-loc day",i_np))
-plotFilamentsBox(data, f_p_n); title(paste("FilBox non-loc day",i_p))
+plotFilamentsBox(data, f_np1); title(paste("FilBox p1 day",i_np))
+plotFilamentsBox(data, f_p1); title(paste("FilBox p1 day",i_p))
 
-# Generally too few neighbors:
-#plotFilamentsBox(data, f_np_l); title(paste("FilBox loc day",i_np))
-#plotFilamentsBox(data, f_p_l); title(paste("FilBox loc day",i_p))
+# Too few neighbors in the local case for this plot
 -----r
 par(mfrow=c(1,2))
-plotRelVar(data, f_np_n); title(paste("StdDev non-loc day",i_np))
-plotRelVar(data, f_p_n); title(paste("StdDev non-loc day",i_p))
+plotRelVar(data, f_np1); title(paste("StdDev p1 day",i_np))
+plotRelVar(data, f_p1); title(paste("StdDev p1 day",i_p))
 
-plotRelVar(data, f_np_l); title(paste("StdDev loc day",i_np))
-plotRelVar(data, f_p_l); title(paste("StdDev loc day",i_p))
+plotRelVar(data, f_np2); title(paste("StdDev p2 day",i_np))
+plotRelVar(data, f_p2); title(paste("StdDev p2 day",i_p))
 
 # Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir
 -----r
 par(mfrow=c(1,2))
-plotSimils(p_n, i_np); title(paste("Weights non-loc day",i_np))
-plotSimils(p_n, i_p); title(paste("Weights non-loc day",i_p))
+plotSimils(p1, i_np); title(paste("Weights p1 day",i_np))
+plotSimils(p1, i_p); title(paste("Weights p1 day",i_p))
 
-plotSimils(p_l, i_np); title(paste("Weights loc day",i_np))
-plotSimils(p_l, i_p); title(paste("Weights loc day",i_p))
+plotSimils(p2, i_np); title(paste("Weights p2 day",i_np))
+plotSimils(p2, i_p); title(paste("Weights p2 day",i_p))
 
 # - pollué à gauche, + pollué à droite
 -----r
 # Fenêtres sélectionnées dans ]0,7] / non-loc à gauche, loc à droite
-p_n$getParams(i_np)$window
-p_n$getParams(i_p)$window
+p1$getParams(i_np)$window
+p1$getParams(i_p)$window
 
-p_l$getParams(i_np)$window
-p_l$getParams(i_p)$window
+p2$getParams(i_np)$window
+p2$getParams(i_p)$window
 % endfor