#' obtain the final prediction.
#' }
#'
+#' @usage # NeighborsForecaster$new(pjump)
+#'
#' @docType class
#' @format R6 class, inherits Forecaster
#' @aliases F_Neighbors
)
)
-#' getNoNA2
-#'
-#' Get indices in data of no-NA series followed by no-NA, within [first,last] range.
-#'
-#' @inheritParams dateIndexToInteger
-#' @param first First index (included)
-#' @param last Last index (included)
-#'
-.getNoNA2 = function(data, first, last)
-{
- (first:last)[ sapply(first:last, function(i)
- !any( is.na(data$getCenteredSerie(i)) | is.na(data$getCenteredSerie(i+1)) )
- ) ]
-}
-
-#' getConstrainedNeighbs
-#'
-#' Get indices of neighbors of similar pollution level (among same season + day type).
-#'
-#' @param today Index of current day
-#' @param data Object of class Data
-#' @param fdays Current set of "first days" (no-NA pairs)
-#' @param min_neighbs Minimum number of points in a neighborhood
-#' @param max_neighbs Maximum number of points in a neighborhood
-#'
+# getConstrainedNeighbs
+#
+# Get indices of neighbors of similar pollution level (among same season + day type).
+#
+# @param today Index of current day
+# @param data Object of class Data
+# @param fdays Current set of "first days" (no-NA pairs)
+# @param min_neighbs Minimum number of points in a neighborhood
+# @param max_neighbs Maximum number of points in a neighborhood
+#
.getConstrainedNeighbs = function(today, data, fdays, min_neighbs=10, max_neighbs=12)
{
levelToday = data$getLevel(today)
fdays
}
-#' compute similarities
-#'
-#' Apply the gaussian kernel on computed squared distances.
-#'
-#' @param distances2 Squared distances
-#' @param window Window parameter for the kernel
-#'
+# compute similarities
+#
+# Apply the gaussian kernel on computed squared distances.
+#
+# @param distances2 Squared distances
+# @param window Window parameter for the kernel
+#
.computeSimils <- function(distances2, window)
{
sd_dist = sd(distances2)