X-Git-Url: https://git.auder.net/?p=talweg.git;a=blobdiff_plain;f=reports%2Freport.gj;h=aee6ad45598ac2c63489469651be28f3164ea752;hp=088b43efb9ccd0435c2f61d62acd8de8339b0557;hb=5e838b3e17465c376ca075b766cf2543c82e9864;hpb=55639673dd1510a02671c4646813ae346cdca4d6

diff --git a/reports/report.gj b/reports/report.gj
index 088b43e..aee6ad4 100644
--- 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)
 
@@ -24,10 +24,13 @@ list_indices = ['indices_ch', 'indices_ep', 'indices_np']
 -----r
 library(talweg)
 
+P = ${P} #instant de prévision
+H = ${H} #horizon (en heures)
+
 ts_data = read.csv(system.file("extdata","pm10_mesures_H_loc_report.csv",package="talweg"))
 exo_data = read.csv(system.file("extdata","meteo_extra_noNAs.csv",package="talweg"))
 data = getData(ts_data, exo_data, input_tz = "Europe/Paris", working_tz="Europe/Paris",
-	predict_at=${P}) #predict from P+1 to P+H included
+	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")
@@ -38,18 +41,18 @@ 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_nn_exo = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors",
-	horizon=${H}, simtype="exo")
+	horizon=H, simtype="exo")
 p_nn_mix = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors",
-	horizon=${H}, simtype="mix")
+	horizon=H, simtype="mix")
 p_az = computeForecast(data, ${list_indices[i]}, "Average", "Zero",
-	horizon=${H})
+	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_az = computeError(data, p_az, ${H})
-e_pz = computeError(data, p_pz, ${H})
+e_nn_exo = computeError(data, p_nn_exo, H)
+e_nn_mix = computeError(data, p_nn_mix, 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))