Version 2.0.0 upload

DESCRIPTION

@@ -1,25 +1,20 @@
 Package: knnp
-Version: 1.0.0
-Date: 2018-06-18
-Title: Time Series Prediction using K-Nearest Neighbors Algorithm
-        (Parallel)
+Version: 2.0.0
+Date: 2020-01-08
+Title: Time Series Prediction using K-Nearest Neighbors Algorithm (Parallel)
 Authors@R: c(
-  person("Daniel", "Bastarrica Lacalle", email="[email protected]", role=c("aut")),
-  person("Javier", "Berdecio Trigueros", email="[email protected]", role=c("aut", "cre"))
+  person("Daniel", "Bastarrica Lacalle", email="[email protected]", role=c("aut", "cre")),
+  person("Javier", "Berdecio Trigueros", email="[email protected]", role=c("aut")),
+  person("Javier", "Arroyo Gallardo", email="[email protected]", role=c("aut")),
+  person("Albert", "Meco Alias", email="[email protected]", role=c("aut"))
   )
-Depends: R (>= 3.3.3)
-Imports: parallelDist, forecast, stats, utils, doParallel, foreach
+Depends: R (>= 3.6.1)
+Imports: parallelDist, forecast, stats, utils, doParallel, foreach, plyr
+Suggests: tseries, tsibble
 Description: Two main functionalities are provided. One of them is predicting values with 
     k-nearest neighbors algorithm and the other is optimizing the parameters k and d of the algorithm.
     These are carried out in parallel using multiple threads.
 License: AGPL-3
-RoxygenNote: 6.0.1
 URL: https://github.com/Dani-Basta/TFG
 BugReports: https://github.com/Dani-Basta/TFG/issues
-NeedsCompilation: no
-Packaged: 2018-06-18 04:15:01 UTC; javier
-Author: Daniel Bastarrica Lacalle [aut],
-  Javier Berdecio Trigueros [aut, cre]
-Maintainer: Javier Berdecio Trigueros <[email protected]>
-Repository: CRAN
-Date/Publication: 2018-07-01 15:00:02 UTC
+RoxygenNote: 6.1.1

NAMESPACE

@@ -1,10 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
-export(knn_distances)
-export(knn_next)
-export(knn_optim)
-export(knn_optim_parallel)
-export(knn_optim_parallel2)
-export(knn_optim_parallelf)
+export(knn)
+export(knn_forecast)
+export(knn_param_search)
 export(knn_past)
-import(foreach)

NEWS.md

@@ -1,3 +1,12 @@
+# knnp 2.0.0
+* Simplified package use with less functions
+* Improved result return with information about kNN forecast process
+* Added unified wrapper to parameters search and prediction
+* Improved parallelization 
+* Improved compatibility with forecast package
+* Added tsibble objects support 
+* Changes in parameters names
+
 # knnp 1.0.0
 * Initial release
 

R/knn.R

@@ -0,0 +1,73 @@
+#' Generic function to make a prediction for a time series.
+#' If a knn model is provided as the first argument, knn_forecast
+#' will be directly called. If single values are provided as k and d
+#' as no parameter search can be perfomed, knn_forecast will be
+#' called automatically. If no values are provided for k and/or d,
+#' values 1 to 50 will be used by default.
+#'
+#' @param y A time series or a trained kNN model generated by the
+#' knn_param_search function. In case that a model is provided the knn_forecast
+#' function will be automatically called.
+#' @param k Values of k's to be analyzed or chosen k for knn forecasting.
+#' Default value is 1 to 50.
+#' @param d Values of d's to be analyzed or chosen d for knn forecasting.
+#' Default value is 1 to 50.
+#' @param initial Variable that determines the limit of the known past for
+#' the first instant predicted.
+#' @param distance Type of metric to evaluate the distance between points.
+#' Many metrics are supported: euclidean, manhattan, dynamic time warping,
+#' camberra and others. For more information about the supported metrics check
+#' the values that 'method' argument of function parDist (from parallelDist
+#' package) can take as this is the function used to calculate the distances.
+#' Link to package info: https://cran.r-project.org/web/packages/parallelDist
+#' Some of the values that this argument can take are "euclidean", "manhattan",
+#' "dtw", "camberra", "chord".
+#' @param error_measure Type of metric to evaluate the prediction error.
+#' Five metrics supported:
+#' \describe{
+#'   \item{ME}{Mean Error}
+#'   \item{RMSE}{Root Mean Squared Error}
+#'   \item{MAE}{Mean Absolute Error}
+#'   \item{MPE}{Mean Percentage Error}
+#'   \item{MAPE}{Mean Absolute Percentage Error}
+#' }
+#' @param weight Type of weight to be used at the time of calculating the
+#' predicted value with a weighted mean. Three supported: proportional,
+#' average, linear.
+#' \describe{
+#'   \item{proportional}{the weight assigned to each neighbor is inversely
+#'                       proportional to its distance}
+#'   \item{average}{all neighbors are assigned with the same weight}
+#'   \item{linear}{nearest neighbor is assigned with weight k, second closest
+#'                 neighbor with weight k-1, and so on until the least nearest
+#'                 neighbor which is assigned with a weight of 1.}
+#' }
+#' @param v Variable to be predicted if given multivariate time series.
+#' @param threads Number of threads to be used when parallelizing, default is 1
+#' @return A matrix of errors, optimal k and d. All tested ks and ks and all
+#' the used metrics.
+#' @examples
+#' knn(AirPassengers, 1:5, 1:3)
+#' knn(LakeHuron, 1:10, 1:6)
+#' @export
+knn <- function(y, k = 1:50, d = 1:50, distance = "euclidean", error_measure =
+                    "MAE", weight = "proportional", v = 1, threads = 1) {
+    if (any(class(y) == "kNN")) {
+        warning("kNN model provided, simple prediction carried out",
+                immediate. = TRUE)
+        knn_forecast(y)
+    }
+    else if (length(k) == 1 && length(d) == 1) {
+        warning(paste0("k and d are single integers: supposing simple ",
+                       "prediction is required"), immediate. = TRUE)
+        knn_forecast(y = y, k = k, d = d, distance = distance, weight = weight,
+                     v = v, threads = threads)
+    }
+    else {
+        warning(paste0("Beginning parameter search process. This may take a ",
+                       "while"), immediate. = TRUE)
+        knn_forecast(knn_param_search(y = y, k = k, d = d, distance = distance,
+                            error_measure = error_measure, weight = weight,
+                            v = v, threads = threads))
+    }
+}

R/knn_elements.R

@@ -1,5 +1,3 @@
-#' 'Elements' matrix computation
-#'
 #' Creates a matrix to be used for calculating distances. The most
 #' recent 'element' is put in the first row of the matrix, the
 #' second most recent 'element' in the second row and so on. Therefore,
@@ -8,13 +6,17 @@
 #' @param y A matrix.
 #' @param d Length of each of the 'elements'.
 #' @return A matrix to be used for calculating distances.
+#' @examples
+#' knn_elements(matrix(AirPassengers), 2)
+#' knn_elements(matrix(LakeHuron), 6)
 knn_elements <- function(y, d) {
   n <- NROW(y)
   m <- NCOL(y)
   last_elem <- n - d
 
-  # Fill matrix as described above, it is done vertically for efficiency reasons
+  # Fill matrix as described above, done vertically for efficiency reasons
   elements_matrix <- matrix(nrow = last_elem + 1, ncol = d * m)
+
   col <- 1
   for (i in 1:m) {
     for (j in 1:d) {