1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
|
#
.is.wholenumber <-function(x, tol = .Machine$double.eps^0.5) abs(x - round(x)) < tol
.propensityClustering.internal <-function(adjacency, initialClusters, l2bool, nClusters, initbool,
fastUpdates, accelerated = TRUE){
#if(l2bool>0){
# print("Using L2 updates")
#}else{
# print("Using Poisson updates")
# }
# for(i in 1:length(initialClusters)){
# if(initialClusters[i]<1){
# stop("Initial Cluster values must be integers greater than 0.")
# }
# }
# if(!.is.wholenumber(initialClusters[1])){
# print("Converting Cluster values to integers and storing in initialClusters.norm")
# initialClusters.norm = as.numeric(as.factor(initialClusters))
# }else{
# initialClusters.norm=initialClusters
# }
nodes=length(initialClusters)
# if(nodes!=length(adjacency[1,])|nodes!=length(adjacency[,1])){
# stop("Adjacency must have same length as initial clustering")
# }
# if(nClusters<2){
# stop("Number of clusters must be > 1. Use propensityDecomposition for 1 cluster.")
# }
Phat=rep(0.7, nodes)
Ahat=matrix(0.5, nClusters,nClusters)
diag(Ahat) = 1;
fact=0.
lnorm=0.
if (accelerated)
{
if (fastUpdates)
{
results = .Fortran(.C_propclusttrial,
ADJ=as.single(adjacency),
Clustering=as.integer(initialClusters),Propensity=as.double(Phat),
IntermodularAdjacency=as.double(Ahat),Factorizability=as.double(fact),
Criteria=as.double(lnorm),Nodes=as.integer(nodes),Clusters=as.integer(nClusters),
L2=as.integer(l2bool),Init=as.integer(initbool))
} else {
results = .Fortran(.C_propclustaccel,
ADJ=as.single(adjacency),
Clustering=as.integer(initialClusters),Propensity=as.double(Phat),
IntermodularAdjacency=as.double(Ahat),Factorizability=as.double(fact),
Criteria=as.double(lnorm),Nodes=as.integer(nodes),Clusters=as.integer(nClusters),
L2=as.integer(l2bool),Init=as.integer(initbool))
}
} else {
results = .Fortran(.C_propensityclustering,
ADJ=as.single(adjacency),
Clustering=as.integer(initialClusters),Propensity=as.double(Phat),
IntermodularAdjacency=as.double(Ahat),Factorizability=as.double(fact),
Criteria=as.double(lnorm),Nodes=as.integer(nodes),Clusters=as.integer(nClusters),
L2=as.integer(l2bool),Init=as.integer(initbool))
}
resultsmod=results[2:6]
if(l2bool>0){
resultsmod$L2Norm=results$Criteria
}else{
resultsmod$Loglik=results$Criteria
}
dim ( resultsmod$IntermodularAdjacency ) = c(nClusters, nClusters)
# if(!.is.wholenumber(initialClusters[1])){
# finalClusters = .translateUsingTable(results$Clustering,
# .translationTable(initialClusters.norm, initialClusters))
# }else{
# finalClusters = results$Clustering
# }
#
# resultsmod$Clustering=finalClusters
#
meanMat = matrix((results$ADJ),ncol=nodes);
resultsmod$MeanValues=as.dist(meanMat);
resultsmod$TailPvalues = as.dist(t(meanMat));
return(resultsmod)
}
CPBADecomposition<-function(adjacency,
clustering,
nClusters = NULL,
objectiveFunction = c("Poisson", "L2norm"),
dropUnassigned = TRUE,
unassignedLabel = 0,
unassignedMethod = "average",
accelerated = TRUE,
parallel = FALSE)
{
.checkAdjMat(adjacency, min = 0, max = max(adjacency, na.rm = TRUE));
objectiveFunction = match.arg(objectiveFunction);
nAllNodes = nNodes = ncol(adjacency);
useNodes = rep(TRUE, nNodes);
if (parallel)
{
warning("Parallel version does not work yet... using standard accelerated calculations.");
parallel = FALSE;
}
if (length(clustering)!=nNodes)
stop("Length of 'clustering' must be the same as the number of nodes (columns) in 'adjacency'.");
if (is.null(nClusters))
{
if (any(is.na(clustering))) stop("All entries in 'clustering' must be present (non-NA)");
if (all(clustering==unassignedLabel))
stop("All entries in 'clustering' are unassigned.");
if (dropUnassigned)
{
useNodes = clustering != unassignedLabel;
adjacency = adjacency[useNodes, useNodes];
clustering = clustering[useNodes];
nNodes = ncol(adjacency);
} else {
clustering = .assignToNearestCluster(1-adjacency, labels = clustering,
method = unassignedMethod, unassignedLabel = unassignedLabel);
}
} else {
if (nClusters != 1) stop("If given, number of clusters must be 1.");
clustering = rep(1, nNodes);
}
nClusters = length(unique(clustering));
clustering.norm = as.numeric(as.factor(clustering))
propensity.all = rep(NA, nAllNodes);
Phat=rep(0.7, nNodes)
fact=0
lnorm=0
if(nClusters>1)
{
Ahat=matrix(0.5, nClusters,nClusters)
diag(Ahat) = 1;
if (accelerated)
{
results=.Fortran(.C_propdecompaccel,
ADJ=as.single(adjacency),
Clustering=as.integer(clustering.norm),
Propensity=as.double(Phat),
IntermodularAdjacency=as.double(Ahat),
Factorizability=as.double(fact),
Criteria=as.double(lnorm),Nodes=as.integer(nNodes),
Clusters=as.integer(nClusters),L2=as.integer(objectiveFunction=="L2norm"))
} else {
results=.Fortran(.C_propensitydecomposition,
ADJ=as.single(adjacency),
Clustering=as.integer(clustering.norm),
Propensity=as.double(Phat),
IntermodularAdjacency=as.double(Ahat),
Factorizability=as.double(fact),
Criteria=as.double(lnorm),Nodes=as.integer(nNodes),
Clusters=as.integer(nClusters),L2=as.integer(objectiveFunction=="L2norm"))
}
resultsmod=results[3:5]
resultsmod$IntermodularAdjacency=matrix((resultsmod$IntermodularAdj),ncol=nClusters)
finalClusters = .translateUsingTable(results$Clustering, .translationTable(clustering.norm, clustering))
} else {
results=.Fortran(.C_singleclusterupdate, ADJ=as.single(adjacency),
Propensity=as.double(Phat),Factorizability=as.double(fact),
Criteria=as.double(lnorm),Nodes=as.integer(nNodes),
L2=as.integer(objectiveFunction=="L2norm"))
resultsmod=results[2:3]
}
propensity.all[useNodes] = resultsmod$Propensity
resultsmod$Propensity = propensity.all
if (objectiveFunction=="L2norm")
{
resultsmod$L2Norm=results$Criteria
} else {
resultsmod$Loglik=results$Criteria
}
meanMat = matrix(NA, nAllNodes, nAllNodes)
meanMat[useNodes, useNodes] = matrix((results$ADJ),ncol=nNodes);
resultsmod$ExpectedAdjacency=as.dist(meanMat);
resultsmod$EdgePvalues = as.dist(t(meanMat));
return(resultsmod)
}
|
