intermediate: R6, too slow

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

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index 43a6a13..ac0df04 100644 (file)
--- 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)