X-Git-Url: https://git.auder.net/?p=talweg.git;a=blobdiff_plain;f=pkg%2FR%2FF_Neighbors.R;h=ac0df0414c7f79ef671d9e8dd62b4fe13286b2b7;hp=43a6a13a1d82d7d15d7ec9c6519aeb1084f9309c;hb=25b75559e2d9bf84e2de35b851d93fefdae36e17;hpb=66877df35f2fc9561728537c713c963230b0de45

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index 43a6a13..ac0df04 100644
--- a/pkg/R/F_Neighbors.R
+++ b/pkg/R/F_Neighbors.R
@@ -1,18 +1,13 @@
 #' @include Forecaster.R
 #'
-#' @title Neighbors Forecaster
+#' Neighbors Forecaster
 #'
-#' @description Predict tomorrow as a weighted combination of "futures of the past" days.
-#'   Inherits \code{\link{Forecaster}}
-NeighborsForecaster = setRefClass(
-	Class = "NeighborsForecaster",
-	contains = "Forecaster",
-
-	methods = list(
-		initialize = function(...)
-		{
-			callSuper(...)
-		},
+#' Predict tomorrow as a weighted combination of "futures of the past" days.
+#' Inherits \code{\link{Forecaster}}
+NeighborsForecaster = R6::R6Class("NeighborsForecaster",
+	inherit = "Forecaster",
+
+	public = list(
 		predictShape = function(today, memory, horizon, ...)
 		{
 			# (re)initialize computed parameters
@@ -24,33 +19,6 @@ NeighborsForecaster = setRefClass(
 			if (hasArg(h_window))
 				return (.predictShapeAux(fdays,today,horizon,list(...)$h_window,kernel,simtype,TRUE))
 
-			# HACK for test reports: complete some days with a few NAs, for nicer graphics
-			nas_in_serie = is.na(data$getSerie(today))
-			if (any(nas_in_serie))
-			{
-				if (sum(nas_in_serie) >= length(nas_in_serie) / 2)
-					return (NA)
-				for (i in seq_along(nas_in_serie))
-				{
-					if (nas_in_serie[i])
-					{
-						#look left
-						left = i-1
-						while (left>=1 && nas_in_serie[left])
-							left = left-1
-						#look right
-						right = i+1
-						while (right<=length(nas_in_serie) && nas_in_serie[right])
-							right = right+1
-						#HACK: modify by-reference Data object...
-						data$data[[today]]$serie[i] <<-
-							if (left==0) data$data[[today]]$serie[right]
-							else if (right==0) data$data[[today]]$serie[left]
-							else (data$data[[today]]$serie[left] + data$data[[today]]$serie[right]) / 2.
-					}
-				}
-			}
-
 			# Determine indices of no-NAs days followed by no-NAs tomorrows
 			first_day = max(today - memory, 1)
 			fdays = (first_day:(today-1))[ sapply(first_day:(today-1), function(i) {
@@ -92,12 +60,12 @@ NeighborsForecaster = setRefClass(
 				h_best_mix = c(h_best_endo,h_best_exo)
 				return (.predictShapeAux(fdays, today, horizon, h_best_mix,  kernel, "mix",  TRUE))
 			}
-		},
+		}
+	),
+	private = list(
 		# Precondition: "today" is full (no NAs)
 		.predictShapeAux = function(fdays, today, horizon, h, kernel, simtype, final_call)
 		{
-			dat = data$data #HACK: faster this way...
-
 			fdays = fdays[ fdays < today ]
 			# TODO: 3 = magic number
 			if (length(fdays) < 3)
@@ -111,7 +79,7 @@ NeighborsForecaster = setRefClass(
 				distances2 = rep(NA, length(fdays))
 				for (i in seq_along(fdays))
 				{
-					delta = dat[[today]]$serie - dat[[ fdays[i] ]]$serie
+					delta = data$getCenteredSerie(today) - data$getCenteredSerie(fdays[i])
 					# Require at least half of non-NA common values to compute the distance
 					if (sum(is.na(delta)) <= 0) #length(delta)/2)
 						distances2[i] = mean(delta^2) #, na.rm=TRUE)
@@ -134,10 +102,10 @@ NeighborsForecaster = setRefClass(
 			{
 				h_exo = ifelse(simtype=="mix", h[2], h)
 
-				M = matrix( nrow=1+length(fdays), ncol=1+length(dat[[today]]$exo) )
-				M[1,] = c( dat[[today]]$level, as.double(dat[[today]]$exo) )
+				M = matrix( nrow=1+length(fdays), ncol=1+length(data$getExo(today)) )
+				M[1,] = c( data$getLevel(today), as.double(data$getExo(today)) )
 				for (i in seq_along(fdays))
-					M[i+1,] = c( dat[[ fdays[i] ]]$level, as.double(dat[[ fdays[i] ]]$exo) )
+					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?
@@ -171,7 +139,7 @@ NeighborsForecaster = setRefClass(
 
 			prediction = rep(0, horizon)
 			for (i in seq_along(fdays_indices))
-				prediction = prediction + similarities[i] * dat[[ fdays_indices[i]+1 ]]$serie[1:horizon]
+				prediction = prediction + similarities[i] * data$getSerie(fdays_indices[i]+1)[1:horizon]
 			prediction = prediction / sum(similarities, na.rm=TRUE)
 
 			if (final_call)