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#######################################################################
# dbscan - Density Based Clustering of Applications with Noise
# and Related Algorithms
# Copyright (C) 2015 Michael Hahsler
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#' NN --- Nearest Neighbors Superclass
#'
#' NN is an abstract S3 superclass for the classes of the objects returned
#' by [kNN()], [frNN()] and [sNN()]. Methods for sorting, plotting and getting an
#' adjacency list are defined.
#'
#' @name NN
#' @aliases NN
#' @family NN functions
#'
#' @param x a `NN` object
#' @param pch plotting character.
#' @param col color used for the data points (nodes).
#' @param linecol color used for edges.
#' @param ... further parameters past on to [plot()].
#' @param decreasing sort in decreasing order?
#' @param data that was used to create `x`
#' @param main title
#'
#' @section Subclasses:
#' [kNN], [frNN] and [sNN]
#'
#' @author Michael Hahsler
#' @keywords model
#' @examples
#' data(iris)
#' x <- iris[, -5]
#'
#' # finding kNN directly in data (using a kd-tree)
#' nn <- kNN(x, k=5)
#' nn
#'
#' # plot the kNN where NN are shown as line conecting points.
#' plot(nn, x)
#'
#' # show the first few elements of the adjacency list
#' head(adjacencylist(nn))
#'
#' \dontrun{
#' # create a graph and find connected components (if igraph is installed)
#' library("igraph")
#' g <- graph_from_adj_list(adjacencylist(nn))
#' comp <- components(g)
#' plot(x, col = comp$membership)
#'
#' # detect clusters (communities) with the label propagation algorithm
#' cl <- membership(cluster_label_prop(g))
#' plot(x, col = cl)
#' }
NULL
#' @rdname NN
#' @export
adjacencylist <- function (x, ...)
UseMethod("adjacencylist", x)
#' @rdname NN
#' @export
adjacencylist.NN <- function (x, ...) {
stop("needs to be implemented by a subclass")
}
#' @rdname NN
#' @export
sort.NN <- function(x, decreasing = FALSE, ...) {
stop("needs to be implemented by a subclass")
}
#' @rdname NN
#' @export
plot.NN <- function(x, data, main = NULL, pch = 16, col = NULL, linecol = "gray", ...) {
if (is.null(main)) {
if (inherits(x, "frNN"))
main <- paste0("frNN graph (eps = ", x$eps, ")")
if (inherits(x, "kNN"))
main <- paste0(x$k, "-NN graph")
if (inherits(x, "sNN"))
main <- paste0("Shared NN graph (k=", x$k,
ifelse(is.null(x$kt), "", paste0(", kt=", x$kt)), ")")
}
## create an empty plot
plot(data[, 1:2], main = main, type = "n", pch = pch, col = col, ...)
id <- adjacencylist(x)
## use lines if it is from the same data
## FIXME: this test is not perfect, maybe we should have a parameter here or add the query points...
if (length(id) == nrow(data)) {
for (i in seq_along(id)) {
for (j in seq_along(id[[i]]))
lines(x = c(data[i, 1], data[id[[i]][j], 1]),
y = c(data[i, 2], data[id[[i]][j], 2]), col = linecol,
...)
}
## ad vertices
points(data[, 1:2], main = main, pch = pch, col = col, ...)
} else {
## ad vertices
points(data[, 1:2], main = main, pch = pch, ...)
## use colors if it was from a query
for (i in seq_along(id)) {
points(data[id[[i]], ], pch = pch, col = i + 1L)
}
}
}
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