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########################### Developer Notice ###########################
# Description:
# This file holds all the DynComm main algorithms. It also holds the lists of
# available algorithms (ALGORITHM) and criterion (CRITERION).
#
# Internally, this object, dispatches calls to objects that do the actual work.
#
# New algorithms should have their name added to the list of algorithms
# (ALGORITHM).
#
# New criterion should have their name added to the list of criterion
# (CRITERION).
#
# New main algorithms source code must be added to their corresponding files as
# stated in the developer documentation.
#
# More developer information can be found in the project source page on GitHub at
# https://github.com/softskillsgroup/DynComm-R-package
#
#
# Author: poltergeist0
# Date: 2019-01-01
#include main algorithms implemented in R
source("R/DynCommMainR.R")
#include algorithms documentation
source("R/ALGORITHM.R")
#include criterion documentation
source("R/CRITERION.R")
########################### API Documentation ###########################
#' @name ALGORITHM
#'
#' @aliases algorithm Algorithm
#'
#' @title List of available algorithms.
#'
#' @author poltergeist0
#'
#' @description
#' An algorithm mainly defines how vertices and/or communities are processed,
#' when criterion is applyed (quality measurements occur) and what happens
#' to the communities depending on the value of the quality obtained.
#'
#' @usage ALGORITHM
#'
########## document new algorithms here #############
#' @format A named list with the names of the available algorithms:
#' \describe{
#' \item{LOUVAIN}{
#' is a greedy optimization method to extract communities from large networks
#' by optimizing the density of edges inside communities to edges outside
#' communities. \cr
#' See \code{\link{ALGORITHM_LOUVAIN}}\cr
#' @references \insertRef{cordeiro2016dynamic}{DynComm}
#' }
#' }
#'
#' @seealso \code{\link{DynComm}}
#'
#' @examples
#' ALGORITHM$LOUVAIN
# ALGORITHM$TILES
#'
# @export DynComm::ALGORITHM
#'
#' @export
#'
########## list new algorithms here #############
ALGORITHM <- list(
#### C++ algorithms are listed from 1 to 10000
LOUVAIN=1L
# ,SHAKEN=2L
#### Python algorithms are listed from 10001 to 20000
# ,TILES=10001L
# ,ETILES=10002L
#### R algorithms are listed from 20001 to 30000
)
#' @name CRITERION
#'
#' @aliases criterion Criterion
#'
#' @title List of available CRITERION (quality measurement functions).
#'
#' @author poltergeist0
#'
#' @description
#' A criterion is used to indicate the proximity of the current grouping
#' of vertices (communities) to the optimum one.
#'
#' @details
#' Theoretically, the bigger the value returned by the criterion, the closer the
#' current grouping is to the best possible grouping.
#'
#' Each CRITERION internally defines two functions. One is used to
#' evaluate if moving a vertex from one group (community) to another
#' possibly yields a better overall result. The other is used to measure
#' the actual overall quality of the entire grouping (current community
#' mapping).
#'
#' Not all criterion might be available for all algorithms. See each algorithms'
#' help to find which criterion is supported
#'
#' @usage CRITERION
#'
########## document new criterion here #############
#' @format A named list with the names of the available CRITERION:
#' \describe{
#' \item{MODULARITY}{
#' Newman-Girvan \cr
#' See \code{\link{CRITERION_MODULARITY}}
#' }
# \item{BALMOD}{Balanced Modularity}
#'}
#'
#' @seealso \code{\link{DynComm}}
#'
#' @examples
#' CRITERION$MODULARITY
# CRITERION$BALMOD
#'
#' @export
#'
########## list new criterions here #############
CRITERION <- list(
# C++ criterion are listed from 1 to 10000
MODULARITY=1L
# ,BALMOD=2L
# Python criterion are listed from 10001 to 20000
)
########################### Main Algorithm Documentation ###########################
#' @name DynCommMain
#'
#' @keywords internal
#'
# @aliases Dyncommmain dyncommmain
#'
#' @title DynCommMain
#'
#' @author poltergeist0
#'
#' @description
#' Provides a single interface for all main algorithms in the different
#' languages.
#'
#' @details
#' Includes methods to get results of processing and to interact with the
#' vertices, edges and communities.
#'
#' @rdname DynCommMain
#'
# @docType class
#'
#' @usage DynCommMain(Algorithm,Criterion,Parameters)
#'
#' @param Algorithm One of the available ALGORITHM See \code{\link{ALGORITHM}}
#'
#' @param Criterion One of the available CRITERION. See \code{\link{CRITERION}}
#'
#' @param Parameters A two column matrix defining additional parameters. See
#' the PARAMETERS section on this page
#'
#' @return \code{DynCommMain} object
#'
#' @seealso \code{\link{DynComm}}
#'
# @export
#'
#' @examples
#' \dontrun{
#' Parameters<-matrix(c("-e","0.1"),1,2,TRUE)
#' dc<-DynCommMain(ALGORITHM$LOUVAIN,CRITERION$MODULARITY,Parameters)
#' dc$addRemoveEdgesFile("initial_graph.txt")
#' dc$communityCount()
#' dc$communities()
#' dc$communityNodeCount(1)
#' dc$vertices(1)
#' dc$communityMapping(TRUE)
#' dc$time()
#' dc$addRemoveEdgesFile("s0000000000.txt")
#' }
#'
#' @section PARAMETERS:
#' A two column matrix defining additional parameters to be passed to the
#' selected ALGORITHM and CRITERION.
#' The first column names the parameter and the second defines its value.
#' \describe{
#' \item{
#' -c
#' }{
#' Owsinski-Zadrozny quality function parameter. Values [0.0:1.0]. Default: 0.5
#' }
#' \item{
#' -k
#' }{
#' Shi-Malik quality function kappa_min value. Value > 0 . Default 1
#' }
#' \item{
#' -w
#' }{
#' Treat graph as weighted. In other words, do not ignore weights for edges
#' that define them when inserting edges in the graph.
#' A weight of exactly zero removes the edge instead of inserting so its
#' weight is never ignored.
#' Without this parameter defined or for edges that do not have a weight defined,
#' edges are assigned the default value of 1 (one).
#' As an example, reading from a file may define weights (a third column) for
#' some edges (defined in rows, one per row) and not for others. With this
#' parameter defined, the edges that have weights that are not exactly zero,
#' have their weight replaced by the default value.
#' }
#' \item{
#' -e
#' }{
#' Stops when, on a cycle of the algorithm, the quality is increased by less
#' than the value given in this parameter.
#' }
#' \item{
#' cv
#' }{
#' Community-Vertex.
#' Boolean parameter that indicates if sending community mapping to a file
#' prints the community first, if true, or the vertex first, if false. See
#' \code{\link{communityMapping}} for details.
#' Default TRUE
#' }
#' }
#'
#' @section Methods:
#' \describe{
#'
# derived from example in https://www.cyclismo.org/tutorial/R/s3Classes.html
DynCommMain <- function(Algorithm,Criterion,Parameters)
{
## Get the environment for this
## instance of the function.
thisEnv <- environment()
########## constructor #############
alg <- Algorithm
qlt <- Criterion
prm <- Parameters
if(alg>=1 & alg<=10000){
dc <- new(DynCommRcpp,alg,qlt,prm)
# print(dc)
# TO DO: check for errors
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# dc <- reticulate::import_from_path("DynCommPython","../src/base/Python")
# # calls to python should be equal to calls to c++ but may need separate processing of outputs
# }
else if(alg>=20001 & alg<=30000){
# print("R algorithm")
dc <- DynCommMainR(alg,qlt,prm)
}
else{
dc<-NULL
print("Unknown algorithm :(")
}
## Create the list used to represent an
## object for this class
me <- list(
## Define the environment where this list is defined so
## that I can refer to it later.
thisEnv = thisEnv,
#'
#' \item{results(differential)}{Get additional results of the algorithm or the currently selected post processing steps. See \code{\link{results}}}
#'
results = function(differential=TRUE)
{
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$results(differential))
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$results(differential))
# }
else{
return(matrix(nrow=0,ncol=2,byrow=TRUE,dimnames = list(c(),c("name","value"))))
}
},
#'
#' \item{addRemoveEdges(graphAddRemove)}{Add and remove edges read from a file. See \code{\link{addRemoveEdges}}}
#'
addRemoveEdgesFile = function(graphAddRemoveFile){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$addRemoveEdgesFile(graphAddRemoveFile))
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$addRemoveEdgesFile(graphAddRemoveFile))
# }
else{
return(FALSE)
}
},
#'
#' \item{addRemoveEdges(graphAddRemove)}{Add and remove edges read from a matrix. See \code{\link{addRemoveEdges}}}
#'
addRemoveEdgesMatrix = function(graphAddRemoveMatrix){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$addRemoveEdgesMatrix(graphAddRemoveMatrix))
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$addRemoveEdgesMatrix(graphAddRemoveMatrix))
# }
else{
return(FALSE)
}
},
#'
#' \item{quality()}{Get the quality measurement of the graph after the last iteration. See \code{\link{quality}}}
#'
quality=function(){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$quality())
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$quality())
# }
else{
return(NA)
}
},
#'
#' \item{communityCount()}{Get the number of communities after the last iteration. See \code{\link{communityCount}}}
#'
communityCount=function(){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$communityCount())
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$communityCount())
# }
else{
return(NA)
}
},
#'
#' \item{communities()}{Get all communities after the last iteration. See \code{\link{communities}}}
#'
communities=function(){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$communities())
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$communities())
# }
else{
return(list())
}
},
#'
#' \item{communitiesEdgeCount()}{Get the number of community to community edges in the graph. See \code{\link{communitiesEdgeCount}}}
#'
communitiesEdgeCount=function() {
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$communitiesEdgeCount())
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$communitiesEdgeCount())
# }
else{
return(NA)
}
},
#'
#' \item{communityNeighbours(community)}{Get the neighbours of the given community after the last iteration. See \code{\link{communityNeighbours}}}
#'
communityNeighbours=function(community){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$communityNeighbours(community))
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$communityNeighbours(community))
# }
else{
return(matrix(nrow=0,ncol=2,byrow=TRUE,dimnames = list(c(),c("neighbour","weight"))))
}
},
#'
#' \item{communityInnerEdgesWeight(community)}{Get the sum of weights of the inner edges of the given community after the last iteration. See \code{\link{communityInnerEdgesWeight}}}
#'
communityInnerEdgesWeight=function(community){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$communityInnerEdgesWeight(community))
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$communityInnerEdgesWeight(community))
# }
else{
return(NA)
}
},
#'
#' \item{communityTotalWeight(community)}{Get the sum of weights of all edges of the given community after the last iteration. See \code{\link{communityTotalWeight}}}
#'
communityTotalWeight=function(community){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$communityTotalWeight(community))
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$communityTotalWeight(community))
# }
else{
return(NA)
}
},
#'
#' \item{communityEdgeWeight(source,destination)}{Get the weight of the edge that goes from source to destination after the last iteration. See \code{\link{communityEdgeWeight}}}
#'
communityEdgeWeight=function(source,destination){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$communityEdgeWeight(source,destination))
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$communityEdgeWeight(source,destination))
# }
else{
return(NA)
}
},
#'
#' \item{communityVertexCount(community)}{Get the amount of vertices in the given community after the last iteration. See \code{\link{communityVertexCount}}}
#'
communityVertexCount=function(community){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$communityVertexCount(community))
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$communityVertexCount(community))
# }
else{
return(NA)
}
},
#'
#' \item{community(vertex)}{Get the community of the given vertex after the last iteration. See \code{\link{community}}}
#'
community=function(vertex){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$community(vertex))
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$community(vertex))
# }
else{
return(NA)
}
},
#'
#' \item{vertexCount()}{Get the total number of vertices after the last iteration. See \code{\link{vertexCount}}}
#'
vertexCount=function(){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$vertexCount())
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$vertexCount())
# }
else{
return(NA)
}
},
#'
#' \item{verticesAll()}{Get all vertices in the graph after the last iteration. See \code{\link{verticesAll}}}
#'
verticesAll=function(){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$verticesAll())
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$verticesAll())
# }
else{
return(list())
}
},
#'
#' \item{neighbours(vertex)}{Get the neighbours of the given vertex after the last iteration. See \code{\link{neighbours}}}
#'
neighbours=function(vertex){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$neighbours(vertex))
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$neighbours(vertex))
# }
else{
return(matrix(nrow=0,ncol=2,byrow=TRUE,dimnames = list(c(),c("neighbour","weight"))))
}
},
#'
#' \item{edgeWeight(source,destination)}{Get the weight of the edge that goes from source vertex to destination vertex after the last iteration. See \code{\link{edgeWeight}}}
#'
edgeWeight=function(source,destination){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$edgeWeight(source,destination))
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$edgeWeight(source,destination))
# }
else{
return(NA)
}
},
#'
#' \item{vertices(community)}{Get all vertices belonging to the given community after the last iteration. See \code{\link{vertices}}}
#'
vertices=function(community){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$vertices(community))
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$vertices(community))
# }
else{
return(list())
}
},
#'
#' \item{edgeCount()}{Get the number of vertex to vertex edges in the graph. See \code{\link{edgeCount}}}
#'
edgeCount=function() {
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$edgeCount())
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$edgeCount())
# }
else{
return(NA)
}
},
#'
#' \item{communityMapping(differential)}{Get the community mapping for all communities after the last iteration.See \code{\link{communityMapping}}}
#'
communityMappingMatrix = function(differential=TRUE){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$communityMappingMatrix(differential))
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$communityMappingMatrix(differential))
# }
else{
return(matrix(nrow=0,ncol=2,byrow=TRUE,dimnames = list(c(),c("vertex","community"))))
}
},
#'
#' \item{communityMapping(differential)}{Get the community mapping for all communities after the last iteration.See \code{\link{communityMapping}}}
#'
communityMappingFile = function(differential=TRUE,file=""){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$communityMappingFile(prm[which(prm=="cv"),2], differential,file))
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$communityMappingFile(prm[which(prm=="cv"),2],differential,file))
# }
else{
return(matrix(nrow=0,ncol=1,byrow=TRUE,dimnames = list(c(),c("reply"))))
}
},
#'
#' \item{time()}{Get the cumulative time spent on processing after the last iteration. See \code{\link{time}}}
#'
time=function(differential=FALSE){
if((alg>=1 & alg<=10000) | (alg>=20001 & alg<=30000)){ # R and C++ calls are identical
return(dc$time(differential))
}
# else if(alg>=10001 & alg<=20000){
# # print("Python algorithm")
# return(dc$time(differential))
# }
else{
return(NA)
}
}
)
# close methods section of the documentation
#'
#' }
#'
## Define the value of the list within the current environment.
assign('this',me,envir=thisEnv)
## Set the name for the class
class(me) <- append(class(me),"DynCommMain")
return(me)
}
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