first tests for Neighbors2 after debug; TODO: some missing forecasts

[talweg.git] / reports / report.gj

diff --git a/reports/report.gj b/reports/report.gj
index dc4cbd4..aee6ad4 100644 (file)
--- a/reports/report.gj
+++ b/reports/report.gj
@@ -2,9 +2,9 @@
 <h2>Introduction</h2>
 
 J'ai fait quelques essais dans différentes configurations pour la méthode "Neighbors"
-(la seule dont on a parlé).<br>Il semble que le mieux soit
+(la seule dont on a parlé) et sa variante récente appelée pour l'instant "Neighbors2".<br>
 
- * simtype="exo" ou "mix" : similarités exogènes avec/sans endogènes (fenêtre optimisée par VC)
+ * 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)
 
@@ -24,23 +24,30 @@ list_indices = ['indices_ch', 'indices_ep', 'indices_np']
 -----r
 library(talweg)
 
+P = ${P} #instant de prévision
+H = ${H} #horizon (en heures)
+
 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=13)
+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
 
 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")
 indices_np = seq(as.Date("2015-04-26"),as.Date("2015-05-02"),"days")
 
-H = 17 #predict from 8am to 12pm
 % for i in range(3):
 -----
 <h2 style="color:blue;font-size:2em">${list_titles[i]}</h2>
 -----r
-p_nn_exo = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors", simtype="exo", horizon=H)
-p_nn_mix = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors", simtype="mix", horizon=H)
-p_az = computeForecast(data, ${list_indices[i]}, "Average", "Zero", horizon=H) #, memory=183)
-p_pz = computeForecast(data, ${list_indices[i]}, "Persistence", "Zero", horizon=H, same_day=TRUE)
+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'})
 -----r
 e_nn_exo = computeError(data, p_nn_exo, H)
 e_nn_mix = computeError(data, p_nn_mix, H)