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##copyright 2002 R. Gentleman, all rights reserved
## a simple implementation of the notions of cluster graphs
## and clustering using distance measures
## for the adjacency matrix view of undirected graphs the
## storage of the graph in lower triangular form is appealing
## The lower triangle of the distance matrix stored by columns in a
## single vector. The vector has the attributes `"Size"', `"Diag"',
## `"Upper"', `"Labels"' and `"class"' equal to `"dist"'.
## the lower triangle stored by columns
## for i < j <= n the dissimilarity between row i and j is
## x[n*(i-1)-i*(i-1)/2+j-i]
## size is the number of obs
## labels are their labels, Diag and Upper indicate how to print
##
##a function for Martin -- if it ever gets good enough.
##FIXME: should look at other matrix packages; this is just subsetting
##from a lower (or upper) triangular matrix representation -- which
##should be easier...but maybe it needs to be done in C
"[.dist" <- function(x, i, j, drop=TRUE) {
lend <- attr(x, "Size")
if( missing(i) )
if( missing(j) )
return(x)
else { ## return columns
i <- seq_len(lend)
}
if( missing(j) ) {
j <- seq_len(lend)
}
## we have both -- return a matrix
leni <- length(i)
lenj <- length(j)
outl <- leni*lenj
iuse <- rep(i, length.out=outl)
juse <- rep(j, rep(leni, lenj), length.out=outl)
igtj <- iuse > juse
newi <- ifelse(!igtj, iuse, juse)
newj <- ifelse(igtj, iuse, juse)
lend <- attr(x, "Size")
subs <- lend*(newi-1)-newi*(newi-1)/2+newj-newi
zeros <- newi==newj
rval <- rep(0, length(subs))
subs <- subs[!zeros]
sdata <- unclass(x)[subs]
rval[!zeros]<-sdata
labels <- attr(x, "Labels")
if( drop && (lenj == 1 || leni==1) ) {
out<-rval
if( leni == 1 )
names(out) <- labels[j]
else
names(out) <- labels[i]
}
else {
out <- matrix(rval, ncol=lenj, nrow=leni)
dimnames(out) <- list(labels[i], labels[j])
}
out
}
setMethod("initialize", "distGraph", function(.Object, Dist) {
if( is.null( attr(Dist, "Labels") ) )
attr(Dist, "Labels") = as.character(seq_len(attr(Dist, "Size")))
else
checkValidNodeName(attr(Dist, "Labels"))
.Object@Dist = Dist
.Object
})
setMethod("Dist", "distGraph", function(object)
object@Dist)
setMethod("show", "distGraph", function(object) {
cat("distGraph with ", attr(object@Dist, "Size"),
" nodes \n", sep="")})
setMethod("threshold", "distGraph", function(object, k, value=0) {
nd <- object@Dist
nd[nd > k ] <- value
new("distGraph", Dist=nd)
})
setMethod("addNode", c("character", "distGraph", "list"),
function(node, object, edges) {
gN = nodes(object)
nNode = length(gN)
nAdd = length(node)
numE = sapply(edges, length)
if( any(numE != nNode+nAdd) )
stop("must supply all internode distances")
newN <- c(gN, node)
nmE <- names(edges)
if( length(nmE) != nAdd || any(!(names(edges) %in% node)))
stop("edges must be named")
edges <- edges[node]
lapply(edges, function(x) if(any( !(names(x) %in%
newN) ) )
stop("bad edge specification"))
ordE <- lapply(edges, function(x) x[newN])
subM <- sapply(ordE, function(x) x) ##should be a matrix!
oldD <- as.matrix(Dist(object))
f1 <- cbind(oldD, subM[seq_len(nNode),])
f2 <- rbind(f1, t(subM))
rv <- new("distGraph", Dist = as.dist(f2))
})
setMethod("numNodes", "distGraph", function(object)
attr(Dist(object), "Size"))
setMethod("adj", c("distGraph", "ANY"), function(object, index) {
nodenames<- nodes(object)
if( is.character(index) )
index <- match(index, nodenames)
if( !is.numeric(index) )
stop("'index' not recognized")
adjM <- object@Dist[index,]
if( is.matrix(adjM) )
adjL <- split(adjM, row(adjM))
else
adjL <- list(adjM)
for(i in seq_along(adjL))
adjL[[i]] <- names(adjL[[i]])[adjL[[i]] > 0 ]
return(adjL) })
setMethod("edges", c("distGraph", "missing"), function(object, which) {
nN <- nodes(object)
eL <- lapply(nN, function(x) adj(object, x)[[1]])
names(eL) <- nN
return(eL) })
setMethod("edges", c("distGraph", "character"), function(object, which) {
nN <- nodes(object)
wh<-match(which, nN)
if( any(is.na(wh)) )
stop("'which' nodes not all in graph")
eL <- lapply(which, function(x) adj(object, x)[[1]])
names(eL) <- which
eL})
## FIXME: update for new attribute storage
setMethod("edgeWeights", "distGraph",
function(object, index, attr, default, type.checker) {
edg <- edges(object)
if( !missing(index) )
edg <- edg[index]
NODES <- nodes(object)
edLocs <- match(names(edg), NODES)
edE <- lapply(edg, function(x) match(x, NODES))
for( i in seq(along=edLocs) )
edE[[i]] <- object@Dist[edLocs[i], edE[[i]]]
names(edE) <- names(edg)
edE
})
setMethod("subGraph", c("character", "distGraph"),
function(snodes, graph) {
gN <- nodes(graph)
whN <- match(snodes, gN)
if( any(is.na(whN) ) )
stop("'snodes' not all in graph")
nD <- as.matrix(Dist(graph))[whN, whN]
new("distGraph", Dist=as.dist(nD))
})
setMethod("edgeL", "distGraph", function(graph, index) {
edges <- edges(graph)
edgeL <- mapply(function(x, y, nodes) {
out <- list(edges=match(x, nodes), weights=y)
}, edges, edgeWeights(graph), MoreArgs=list(nodes=nodes(graph)),
SIMPLIFY=FALSE)
names(edgeL) <- names(edges)
if (! missing(index))
edgeL <- edgeL[[index]]
edgeL
})
####################################
##clusterGraph code here
####################################
# setMethod("initialize", "clusterGraph", function(.Object, clusters) {
# if( is.factor(clusters) )
# clusters = split(names(clusters), clusters)
# .Object@clusters = clusters
# .Object
# })
setMethod("edges", c("clusterGraph", "missing"), function(object, which) {
edges<-list()
for(clust in object@clusters) {
cc <- as.character(clust)
for(i in seq(along=cc) )
edges[[cc[i]]] <- cc[-i]
}
edges})
setMethod("edges", c("clusterGraph", "character"), function(object, which) {
nN <- nodes(object)
wh <- match(which, nN)
if( any(is.na(wh)) )
stop("'which' nodes not all in graph")
edges<-list()
for(clust in object@clusters) {
cc <- intersect(as.character(clust), which)
for(i in seq(along=cc) )
edges[[cc[i]]] <- cc[-i]
}
edges[which]})
setMethod("edgeL", "clusterGraph", function(graph, index) {
clusters <- connComp(graph)
nodes <- nodes(graph)
edgeL <- list()
cur <- 1
for (i in seq(along=clusters)) {
curClust <- clusters[[i]]
for (j in seq(along = curClust)) {
edgeL[[cur]] <- list(edges=match(curClust[-j], nodes))
cur <- cur + 1
}
}
names(edgeL) <- nodes
if (! missing(index))
edgeL <- edgeL[[index]]
edgeL
})
##FIXME: this should be done from distances, but for now...)
##eg, if a distance matrix was supplied we could use that to define
##edge weights -- as that seems appropriate
## FIXME: update for new attr storage
setMethod("edgeWeights", "clusterGraph",
function(object, index, attr, default, type.checker) {
edg <- edges(object)
if( !missing(index) )
edg <- edg[index]
ans <- lapply(edg, function(x) { ans <- rep(1, length(x));
names(ans) <- x; ans})
ans})
setMethod("subGraph", c("character", "clusterGraph"),
function(snodes, graph) {
cList <- graph@clusters
cL <- lapply(cList, function(x) intersect(x, snodes))
graph@clusters <- cL
graph})
setMethod("numNodes", "clusterGraph", function(object)
sum(sapply(object@clusters, length)))
setMethod("adj", c("clusterGraph", "ANY"), function(object, index) {
nIndex <- length(index)
if( any(is.na(match(index, nodes(object)))) )
stop("invalid node label")
rval <- vector("list", length=nIndex)
names(rval) <- index
for(i in seq_len(nIndex)) {
for(cl in object@clusters)
if( index[i] %in% cl )
rval[[i]] <- cl
}
return(rval)})
##for cluster graphs, acc and adj are the same
setMethod("acc", c("clusterGraph", "character"),
function(object, index) {
nIndex <- length(index)
if( any(is.na(match(index, nodes(object)))) )
stop("invalid node label")
rval <- vector("list", length=nIndex)
names(rval) <- index
for(i in seq_len(nIndex)) {
for(cl in object@clusters)
if( index[i] %in% cl )
rval[[i]] <- cl
}
return(rval)})
setMethod("connComp", "clusterGraph", function(object)
object@clusters)
setMethod("show", "clusterGraph",
function(object)
{
numNull<-numNoEdges(object)
numNodes<- numNodes(object)
numEdge<-numEdges(object)
cat("A graph with ", edgemode(object), " edges\n")
cat("Number of Nodes = ",numNodes,"\n",sep="")
cat("Number of Edges = ",numEdge,"\n",sep="")
})
setAs(from="clusterGraph", to="matrix",
function(from, to) {
theNodes <- nodes(from) # will be grouped by cluster!
numNodes <- length(theNodes)
m <- matrix(0, nrow=numNodes, ncol=numNodes,
dimnames=list(theNodes, theNodes))
for (clust in from@clusters) {
idx <- match(clust, theNodes)
m[idx, idx] <- 1
}
diag(m) <- 0 # eliminate self-loops
m
})
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