X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FF_Neighbors.R;h=51b57304347c66faeb90528fa15428a5ec3b726b;hb=689aa1d3c58e8b1e9fdfa9b895ca38c7228f56cc;hp=ffb068f9f248eec2e02aad151ed5b7dc2d460213;hpb=3ddf1c12af0c167fe7d3bb59e63258550270cfc5;p=talweg.git

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index ffb068f..51b5730 100644
--- a/pkg/R/F_Neighbors.R
+++ b/pkg/R/F_Neighbors.R
@@ -28,6 +28,8 @@
 #'     obtain the final prediction.
 #' }
 #'
+#' @usage f <- NeighborsForecaster$new(pjump)
+#'
 #' @docType class
 #' @format R6 class, inherits Forecaster
 #' @aliases F_Neighbors
@@ -216,31 +218,16 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 	)
 )
 
-#' 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$getLevel(today)
@@ -264,16 +251,16 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 		fdays = fdays[
 			sort(distances[same_pollution],index.return=TRUE)$ix[1:max_neighbs] ]
 	}
-	fdsays
+	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)