X-Git-Url: https://git.auder.net/?p=talweg.git;a=blobdiff_plain;f=pkg%2FR%2FF_Neighbors2.R;h=787dd2b62b69af25c01cfca2de541a6939955f30;hp=83ef453093ed5b4430c1ef1cbbf5ace2ebc95902;hb=ee8b1b4e3c13f8dcf13a2c8da6a3bef1520c8252;hpb=a866acb3c0ae138b22df9dae9ec576b866794417

diff --git a/pkg/R/F_Neighbors2.R b/pkg/R/F_Neighbors2.R
index 83ef453..787dd2b 100644
--- a/pkg/R/F_Neighbors2.R
+++ b/pkg/R/F_Neighbors2.R
@@ -9,15 +9,11 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 	inherit = Forecaster,
 
 	public = list(
-#		predictSerie = function(data, today, memory, horizon, ...)
-#		{
-#			# Parameters (potentially) computed during shape prediction stage
-#			predicted_shape = self$predictShape(data, today, memory, horizon, ...)
-##			predicted_delta = private$.pjump(data,today,memory,horizon,private$.params,...)
-#			# Predicted shape is aligned it on the end of current day + jump
-##			predicted_shape+tail(data$getSerie(today),1)-predicted_shape[1]+predicted_delta
-#			predicted_shape
-#		},
+		predictSerie = function(data, today, memory, horizon, ...)
+		{
+			# This method predict shape + level at the same time, all in next call
+			self$predictShape(data, today, memory, horizon, ...)
+		},
 		predictShape = function(data, today, memory, horizon, ...)
 		{
 			# (re)initialize computed parameters
@@ -40,7 +36,7 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 			}
 
 			# Indices of similar days for cross-validation; TODO: 45 = magic number
-			sdays = getSimilarDaysIndices(today, limit=45, same_season=FALSE)
+			sdays = getSimilarDaysIndices(today, data, limit=45, same_season=FALSE)
 
 			cv_days = intersect(fdays,sdays)
 			# Limit to 20 most recent matching days (TODO: 20 == magic number)
@@ -101,24 +97,25 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 		{
 			fdays = fdays[ fdays < today ]
 			# TODO: 3 = magic number
-			if (length(fdays) < 1)
+			if (length(fdays) < 3)
 				return (NA)
 
 			# Neighbors: days in "same season"
-			sdays = getSimilarDaysIndices(today, limit=45, same_season=TRUE, data)
+			sdays = getSimilarDaysIndices(today, data, limit=45, same_season=TRUE)
 			indices = intersect(fdays,sdays)
+			if (length(indices) <= 1)
+				return (NA)
 			levelToday = data$getLevel(today)
 			distances = sapply(indices, function(i) abs(data$getLevel(i)-levelToday))
+			# 2 and 5 below == magic numbers (determined by Bruno & Michel)
 			same_pollution = (distances <= 2)
-			if (sum(same_pollution) < 1) #TODO: 3 == magic number
+			if (sum(same_pollution) == 0)
 			{
 				same_pollution = (distances <= 5)
-				if (sum(same_pollution) < 1)
+				if (sum(same_pollution) == 0)
 					return (NA)
 			}
 			indices = indices[same_pollution]
-
-			#TODO: we shouldn't need that block
 			if (length(indices) == 1)
 			{
 				if (final_call)
@@ -169,8 +166,12 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
 					M[i+1,] = c( data$getLevel(indices[i]), as.double(data$getExo(indices[i])) )
 
 				sigma = cov(M) #NOTE: robust covariance is way too slow
-#				sigma_inv = solve(sigma) #TODO: use pseudo-inverse if needed?
-				sigma_inv = MASS::ginv(sigma)
+				# TODO: 10 == magic number; more robust way == det, or always ginv()
+				sigma_inv =
+					if (length(indices) > 10)
+						solve(sigma)
+					else
+						MASS::ginv(sigma)
 
 				# Distances from last observed day to days in the past
 				distances2 = sapply(seq_along(indices), function(i) {