fix window bounds

[talweg.git] / pkg / R / F_Neighbors.R

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index 5b2c899..27cd23a 100644 (file)
--- a/pkg/R/F_Neighbors.R
+++ b/pkg/R/F_Neighbors.R
@@ -30,12 +30,8 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
                                        fdays, today, horizon, list(...)$h_window, kernel, simtype, TRUE) )
                        }
 
-                       # Indices of similar days for cross-validation; TODO: 45 = magic number
-                       sdays = getSimilarDaysIndices(today, limit=45, same_season=FALSE)
-
-                       cv_days = intersect(fdays,sdays)
-                       # Limit to 20 most recent matching days (TODO: 20 == magic number)
-                       cv_days = sort(cv_days,decreasing=TRUE)[1:min(20,length(cv_days))]
+                       # Indices of similar days for cross-validation; TODO: 20 = magic number
+                       cv_days = getSimilarDaysIndices(today, data, limit=20, same_season=FALSE, days_in=fdays)
 
                        # Function to optimize h : h |--> sum of prediction errors on last 45 "similar" days
                        errorOnLastNdays = function(h, kernel, simtype)
@@ -60,12 +56,12 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
                        if (simtype != "endo")
                        {
                                h_best_exo = optimize(
-                                       errorOnLastNdays, c(0,10), kernel=kernel, simtype="exo")$minimum
+                                       errorOnLastNdays, c(0,7), kernel=kernel, simtype="exo")$minimum
                        }
                        if (simtype != "exo")
                        {
                                h_best_endo = optimize(
-                                       errorOnLastNdays, c(0,10), kernel=kernel, simtype="endo")$minimum
+                                       errorOnLastNdays, c(0,7), kernel=kernel, simtype="endo")$minimum
                        }
 
                        if (simtype == "endo")
@@ -134,7 +130,12 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
                                        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?
+                               # TODO: 10 == magic number; more robust way == det, or always ginv()
+                               sigma_inv =
+                                       if (length(fdays) > 10)
+                                               solve(sigma)
+                                       else
+                                               MASS::ginv(sigma)
 
                                # Distances from last observed day to days in the past
                                distances2 = sapply(seq_along(fdays), function(i) {
@@ -143,7 +144,7 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
                                })
 
                                sd_dist = sd(distances2)
-                               if (sd_dist < .Machine$double.eps)
+                               if (sd_dist < .25 * sqrt(.Machine$double.eps))
                                {
 #                                      warning("All computed distances are very close: stdev too small")
                                        sd_dist = 1 #mostly for tests... FIXME:
@@ -167,11 +168,11 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
                                        simils_endo
                                else #mix
                                        simils_endo * simils_exo
+                       similarities = similarities / sum(similarities)
 
                        prediction = rep(0, horizon)
                        for (i in seq_along(fdays))
                                prediction = prediction + similarities[i] * data$getCenteredSerie(fdays[i]+1)[1:horizon]
-                       prediction = prediction / sum(similarities, na.rm=TRUE)
 
                        if (final_call)
                        {