X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FF_Neighbors.R;h=ffb6d371df57a910451f0a79ffeed340886132ff;hb=99f83c9af27492f6fb9b10f51fb8704ed588f5c1;hp=f1aecb5ccb637a581e1984f444564786ce561ff6;hpb=469529710f56c790ae932b45d13fed2e34bcabf2;p=talweg.git

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index f1aecb5..ffb6d37 100644
--- a/pkg/R/F_Neighbors.R
+++ b/pkg/R/F_Neighbors.R
@@ -61,19 +61,20 @@ NeighborsForecaster = setRefClass(
 
 			#GET OPTIONAL PARAMS
 			# Similarity computed with exogenous variables ? endogenous ? both ? ("exo","endo","mix")
-			simtype = ifelse(hasArg("simtype"), list(...)$simtype, "exo")
+			simtype = ifelse(hasArg("simtype"), list(...)$simtype, "mix")
 			simthresh = ifelse(hasArg("simthresh"), list(...)$simthresh, 0.)
-			kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss")
-			mix_strategy = ifelse(hasArg("mix_strategy"), list(...)$mix_strategy, "neighb") #or "mult"
-			same_season = ifelse(hasArg("same_season"), list(...)$same_season, TRUE)
+			kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss") #or "Epan"
+			mix_strategy = ifelse(hasArg("mix_strategy"), list(...)$mix_strategy, "mult") #or "neighb"
+			same_season = ifelse(hasArg("same_season"), list(...)$same_season, FALSE)
 			if (hasArg(h_window))
 				return (.predictShapeAux(fdays_indices, today, horizon, list(...)$h_window, kernel,
-					simtype, simthresh, mix_strategy, FALSE))
+					simtype, simthresh, mix_strategy, TRUE))
 			#END GET
 
 			# Indices for cross-validation; TODO: 45 = magic number
 			indices = getSimilarDaysIndices(today, limit=45, same_season=same_season)
-			#indices = (end_index-45):(end_index-1)
+			if (tail(indices,1) == 1)
+				indices = head(indices,-1)
 
 			# Function to optimize h : h |--> sum of prediction errors on last 45 "similar" days
 			errorOnLastNdays = function(h, kernel, simtype)
@@ -150,10 +151,12 @@ NeighborsForecaster = setRefClass(
 				}
 
 				sd_dist = sd(distances2)
+				if (sd_dist < .Machine$double.eps)
+					sd_dist = 1 #mostly for tests... FIXME:
 				simils_endo =
-					if (kernel=="Gauss") {
+					if (kernel=="Gauss")
 						exp(-distances2/(sd_dist*h_endo^2))
-					} else { #Epanechnikov
+					else { #Epanechnikov
 						u = 1 - distances2/(sd_dist*h_endo^2)
 						u[abs(u)>1] = 0.
 						u
@@ -164,17 +167,16 @@ NeighborsForecaster = setRefClass(
 			{
 				h_exo = ifelse(simtype=="mix", h[2], h)
 
-				# TODO: [rnormand] if predict_at == 0h then we should use measures from day minus 1
-				M = matrix( nrow=1+length(fdays_indices), ncol=1+length(dat[[today]]$exo_hat) )
-				M[1,] = c( dat[[today]]$level, as.double(dat[[today]]$exo_hat) )
+				M = matrix( nrow=1+length(fdays_indices), ncol=1+length(dat[[today]]$exo) )
+				M[1,] = c( dat[[today]]$level, as.double(dat[[today]]$exo) )
 				for (i in seq_along(fdays_indices))
 				{
 					M[i+1,] = c( dat[[ fdays_indices[i] ]]$level,
-						as.double(dat[[ fdays_indices[i] ]]$exo_hat) )
+						as.double(dat[[ fdays_indices[i] ]]$exo) )
 				}
 
 				sigma = cov(M) #NOTE: robust covariance is way too slow
-				sigma_inv = qr.solve(sigma)
+				sigma_inv = solve(sigma) #TODO: use pseudo-inverse if needed?
 
 				# Distances from last observed day to days in the past
 				distances2 = rep(NA, nrow(M)-1)
@@ -203,10 +205,9 @@ NeighborsForecaster = setRefClass(
 					#TODO: 60 = magic number
 					keep_indices = sort(simils_exo, index.return=TRUE)$ix[1:(min(60,length(simils_exo)))]
 					simils_endo[-keep_indices] = 0.
-				} else #mix_strategy == "mult"
-				{
-					simils_endo = simils_endo * simils_exo
 				}
+				else #mix_strategy == "mult"
+					simils_endo = simils_endo * simils_exo
 			}
 
 			similarities =
@@ -236,8 +237,8 @@ NeighborsForecaster = setRefClass(
 			prediction = rep(0, horizon)
 			for (i in seq_along(fdays_indices))
 				prediction = prediction + similarities[i] * dat[[ fdays_indices[i]+1 ]]$serie[1:horizon]
-
 			prediction = prediction / sum(similarities, na.rm=TRUE)
+
 			if (final_call)
 			{
 				params$weights <<- similarities
@@ -251,6 +252,7 @@ NeighborsForecaster = setRefClass(
 						c(h_endo,h_exo)
 					}
 			}
+
 			return (prediction)
 		}
 	)