From: Benjamin Auder Date: Mon, 27 Mar 2017 10:34:47 +0000 (+0200) Subject: 'update' X-Git-Url: https://git.auder.net/variants/Bario/assets/current/rpsls.js?a=commitdiff_plain;h=2ae382665c51cdebbe3f29d58da0e69c494c4c91;p=talweg.git 'update' --- diff --git a/pkg/R/F_Neighbors2.R b/pkg/R/F_Neighbors2.R index e6addde..bf9422a 100644 --- a/pkg/R/F_Neighbors2.R +++ b/pkg/R/F_Neighbors2.R @@ -19,7 +19,10 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster", return (NA) # Determine indices of no-NAs days followed by no-NAs tomorrows - fdays = getNoNA2(data, max(today-memory,1), today-1) + # Indices of similar days for cross-validation; TODO: 45 = magic number + fdays = intersect( + getNoNA2(data, max(today-memory,1), today-1) + getSimilarDaysIndices(today, limit=45, same_season=TRUE) ) # Get optional args kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss") #or "Epan" @@ -29,21 +32,15 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster", fdays, today, horizon, list(...)$h_window, kernel, TRUE) ) } - - # Indices of similar days for cross-validation; TODO: 45 = magic number - # TODO: ici faut une sorte de "same_season==TRUE" --> mois similaires epandage - sdays = getSimilarDaysIndices(today, limit=45, same_season=FALSE) - - # Function to optimize h : h |--> sum of prediction errors on last 45 "similar" days errorOnLastNdays = function(h, kernel) { error = 0 nb_jours = 0 - for (i in intersect(fdays,sdays)) + for (day in fdays) { # mix_strategy is never used here (simtype != "mix"), therefore left blank - prediction = private$.predictShapeAux(data, fdays, i, horizon, h, kernel, FALSE) + prediction = private$.predictShapeAux(data,fdays,day,horizon,h,kernel,FALSE) if (!is.na(prediction[1])) { nb_jours = nb_jours + 1 @@ -55,9 +52,8 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster", } # h :: only for endo in this variation - h_best_endo = optimize(errorOnLastNdays, c(0,10), kernel=kernel)$minimum - - return (private$.predictShapeAux(data, fdays, today, horizon, h_best, kernel, TRUE)) + h_best = optimize(errorOnLastNdays, c(0,10), kernel=kernel)$minimum + return (private$.predictShapeAux(data,fdays,today,horizon,h_best,kernel,TRUE)) } ), private = list( @@ -96,6 +92,7 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster", u } + # EXOGENS: distances computations are enough # TODO: search among similar concentrations....... at this stage ?! M = matrix( nrow=1+length(fdays), ncol=1+length(data$getExo(today)) ) @@ -117,6 +114,7 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster", ppv <- sort(distances2, index.return=TRUE)$ix[1:10] #.............. #PPV pour endo ? + similarities = if (simtype == "exo") simils_exo @@ -134,13 +132,7 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster", { private$.params$weights <- similarities private$.params$indices <- fdays - private$.params$window <- - if (simtype=="endo") - h_endo - else if (simtype=="exo") - h_exo - else #mix - c(h_endo,h_exo) + private$.params$window <- h } return (prediction)