Nothing
# auxiliary class that allows for classical matrices and matrices from the
# 'Matrix' package; note that, previously the class 'mMatrix' was used
setClassUnion("AnyMatrix",
members = c("matrix", "Matrix"))
# S4 class definition for exemplar-based clustering
setClass("ExClust",
representation = representation
(
l = "numeric",
sel = "numeric",
exemplars = "numeric",
clusters = "list",
idx = "numeric",
sim = "AnyMatrix",
call = "character"
),
prototype = prototype
(
l = 0,
sel = numeric(0),
exemplars = numeric(0),
clusters = list(),
idx = numeric(0),
sim = matrix(nrow=0, ncol=0),
call = character(0)
)
)
# S4 class definition for the result object of affinity propagation clustering
setClass("APResult",
representation = representation
(
sweeps = "numeric",
it = "numeric",
p = "numeric",
netsim = "numeric",
dpsim = "numeric",
expref = "numeric",
netsimLev = "numeric",
netsimAll = "numeric",
dpsimAll = "numeric",
exprefAll = "numeric",
idxAll = "matrix"
),
prototype = prototype
(
sweeps = 0,
it = 0,
p = 0,
netsim = NaN,
dpsim = NaN,
expref = NaN,
netsimLev = numeric(0),
netsimAll = NaN,
dpsimAll = NaN,
exprefAll = NaN,
idxAll = matrix(nrow=0, ncol=0)
),
contains = "ExClust"
)
# S4 class definition for the result object of the aggExCluster algorithm
setClass("AggExResult",
representation = representation
(
l = "numeric",
sel = "numeric",
maxNoClusters = "numeric",
clusters = "list",
exemplars = "list",
merge = "matrix",
height = "numeric",
order = "numeric",
labels = "character",
sim = "matrix",
call = "character"
),
prototype = prototype
(
l = 0,
sel = numeric(0),
maxNoClusters = 0,
clusters = list(),
exemplars = list(),
merge = matrix(NA, 1, 1),
height = numeric(0),
order = numeric(0),
labels = c(),
sim = matrix(NA, 1, 1),
call = character(0)
)
)
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