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#### Methods for the virtual class 'triangularMatrix' of triangular matrices
#### Note that specific methods are in (8 different) ./?t?Matrix.R
setAs("triangularMatrix", "symmetricMatrix",
function(from) as(as(from, "generalMatrix"), "symmetricMatrix"))
setAs("dgeMatrix", "triangularMatrix", function(from) asTri(from, "dtrMatrix"))
setAs("lgeMatrix", "triangularMatrix", function(from) asTri(from, "ltrMatrix"))
setAs("ngeMatrix", "triangularMatrix", function(from) asTri(from, "ntrMatrix"))
.tril.tr <- function(x, k = 0, ...) { # are always square
k <- as.integer(k[1])
dd <- dim(x)
stopifnot(-dd[1] <= k, k <= dd[1]) # had k <= 0
if(k == 0 && x@uplo == "L") x
else { ## more to do
if(x@diag == "U") x <- .diagU2N(x, class(x), checkDense = TRUE)
callNextMethod()
}
}
.triu.tr <- function(x, k = 0, ...) { # are always square
k <- as.integer(k[1])
dd <- dim(x)
stopifnot(-dd[1] <= k, k <= dd[1]) # had k >= 0
if(k == 0 && x@uplo == "U") x
else { ## more to do
if(x@diag == "U") x <- .diagU2N(x, class(x), checkDense = TRUE)
callNextMethod()
}
}
## In order to evade method dispatch ambiguity (with [CTR]sparse* and ddense*),
## but still remain "general"
## we use this hack instead of signature x = "triangularMatrix" :
trCls <- names(getClass("triangularMatrix")@subclasses)
trCls. <- trCls[grep(".t.Matrix", trCls)] # not "*Cholesky", "*Kaufman" ..
for(cls in trCls.) {
setMethod("tril", cls, .tril.tr)
setMethod("triu", cls, .triu.tr)
}
## ditto here:
for(cls in trCls)
setMethod("isTriangular", signature(object = cls),
function(object, ...) TRUE)
## instead of just for .... signature(object = "triangularMatrix")
rm(trCls)
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