From: Benjamin Auder Date: Mon, 27 Mar 2017 16:45:28 +0000 (+0200) Subject: written my understanding of Neighbors2; TODO: sameSeasonSimilarIndices X-Git-Url: https://git.auder.net/variants/Chakart/doc/scripts/assets/img/cross.svg?a=commitdiff_plain;h=9db234c56c330bb3f652718c5ee1eb16bc1f6fc7;p=talweg.git written my understanding of Neighbors2; TODO: sameSeasonSimilarIndices --- diff --git a/pkg/R/F_Neighbors2.R b/pkg/R/F_Neighbors2.R index bf9422a..7267661 100644 --- a/pkg/R/F_Neighbors2.R +++ b/pkg/R/F_Neighbors2.R @@ -19,10 +19,7 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster", return (NA) # Determine indices of no-NAs days followed by no-NAs tomorrows - # 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) ) + fdays = getNoNA2(data, max(today-memory,1), today-1) # Get optional args kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss") #or "Epan" @@ -32,12 +29,15 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster", fdays, today, horizon, list(...)$h_window, kernel, TRUE) ) } + # Indices of similar days for cross-validation; TODO: 45 = magic number + 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 (day in fdays) + for (day in intersect(fdays,sdays)) { # mix_strategy is never used here (simtype != "mix"), therefore left blank prediction = private$.predictShapeAux(data,fdays,day,horizon,h,kernel,FALSE) @@ -52,7 +52,7 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster", } # h :: only for endo in this variation - h_best = optimize(errorOnLastNdays, c(0,10), kernel=kernel)$minimum + h_best = optimize(errorOnLastNdays, c(0,7), kernel=kernel)$minimum return (private$.predictShapeAux(data,fdays,today,horizon,h_best,kernel,TRUE)) } ), @@ -65,15 +65,30 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster", if (length(fdays) < 3) return (NA) - # ENDO:: Distances from last observed day to days in the past - distances2 = rep(NA, length(fdays)) - for (i in seq_along(fdays)) + # Neighbors: days in "same season" + sdays = getSimilarDaysIndices(today, limit=45, same_season=TRUE, data) + indices = intersect(fdays,sdays) + levelToday = data$getLevel(today) + distances = sapply(seq_along(indices), function(i) abs(data$getLevel(i)-levelToday)) + same_pollution = (distances <= 2) + if (sum(same_pollution) < 3) #TODO: 3 == magic number { - delta = data$getSerie(today) - data$getSerie(fdays[i]) - # Require at least half of non-NA common values to compute the distance - if ( !any( is.na(delta) ) ) - distances2[i] = mean(delta^2) + same_pollution = (distances <= 5) + if (sum(same_pollution) < 3) + return (NA) } + indices = indices[same_pollution] + + # Now OK: indices same season, same pollution level + # ........... + + + # ENDO:: Distances from last observed day to days in the past + serieToday = data$getSerie(today) + distances2 = sapply(indices, function(i) { + delta = serieToday - data$getSerie(i) + distances2[i] = mean(delta^2) + }) sd_dist = sd(distances2) if (sd_dist < .Machine$double.eps) @@ -92,46 +107,35 @@ Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster", u } - - # EXOGENS: distances computations are enough - # TODO: search among similar concentrations....... at this stage ?! - M = matrix( nrow=1+length(fdays), ncol=1+length(data$getExo(today)) ) - M[1,] = c( data$getLevel(today), as.double(data$getExo(today)) ) - for (i in seq_along(fdays)) - 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? - - # Distances from last observed day to days in the past - distances2 = rep(NA, nrow(M)-1) - for (i in 2:nrow(M)) - { - delta = M[1,] - M[i,] - distances2[i-1] = delta %*% sigma_inv %*% delta - } - - ppv <- sort(distances2, index.return=TRUE)$ix[1:10] #.............. -#PPV pour endo ? - - - similarities = - if (simtype == "exo") - simils_exo - else if (simtype == "endo") - simils_endo - else #mix - simils_endo * simils_exo +# # EXOGENS: distances computations are enough +# # TODO: search among similar concentrations....... at this stage ?! +# M = matrix( nrow=1+length(fdays), ncol=1+length(data$getExo(today)) ) +# M[1,] = c( data$getLevel(today), as.double(data$getExo(today)) ) +# for (i in seq_along(fdays)) +# 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? +# +# # Distances from last observed day to days in the past +# distances2 = rep(NA, nrow(M)-1) +# for (i in 2:nrow(M)) +# { +# delta = M[1,] - M[i,] +# distances2[i-1] = delta %*% sigma_inv %*% delta +# } + + similarities = simils_endo prediction = rep(0, horizon) - for (i in seq_along(fdays)) - prediction = prediction + similarities[i] * data$getSerie(fdays[i]+1)[1:horizon] + for (i in seq_along(indices)) + prediction = prediction + similarities[i] * data$getSerie(indices[i]+1)[1:horizon] prediction = prediction / sum(similarities, na.rm=TRUE) if (final_call) { private$.params$weights <- similarities - private$.params$indices <- fdays + private$.params$indices <- indices private$.params$window <- h } diff --git a/pkg/R/utils.R b/pkg/R/utils.R index 712a4f8..f79c300 100644 --- a/pkg/R/utils.R +++ b/pkg/R/utils.R @@ -58,9 +58,10 @@ integerIndexToDate = function(index, data) #' @param index Day index (numeric or date) #' @param limit Maximum number of indices to return #' @param same_season Should the indices correspond to day in same season? +#' @param data Dataset is required for a search in same season #' #' @export -getSimilarDaysIndices = function(index, limit, same_season) +getSimilarDaysIndices = function(index, limit, same_season, data=NULL) { index = dateIndexToInteger(index) @@ -72,7 +73,9 @@ getSimilarDaysIndices = function(index, limit, same_season) return ( rep(index,nb_days) - 7*seq_len(nb_days) ) } - #Look for similar days in similar season (+/- 30 days) + + #TODO: use data... 12-12-1-2 CH, 3-4-9-10 EP et le reste NP + #Look for similar days in similar season days = c() i = index while (i >= 1 && length(days) < limit) @@ -93,6 +96,8 @@ getSimilarDaysIndices = function(index, limit, same_season) # TODO: exact computation instead of -364 # 364 = closest multiple of 7 to 365 - drift along the years... but not so many years so OK i = i - 364 + + } return ( days[1:min(limit,length(days))] )