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
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
|
#### Methods for the virtual class 'CsparseMatrix' of sparse matrices stored in
#### "column compressed" format.
#### -- many more specific things are e.g. in ./dgCMatrix.R
setAs("CsparseMatrix", "TsparseMatrix",
function(from)
## |-> cholmod_C -> cholmod_T -> chm_triplet_to_SEXP
## modified to support triangular (../src/Csparse.c)
.Call(Csparse_to_Tsparse, from, is(from, "triangularMatrix")))
## special cases (when a specific "to" class is specified)
setAs("dgCMatrix", "dgTMatrix",
function(from) .Call(Csparse_to_Tsparse, from, FALSE))
setAs("dsCMatrix", "dsTMatrix",
function(from) .Call(Csparse_to_Tsparse, from, FALSE))
setAs("dsCMatrix", "dgCMatrix",
function(from) .Call(Csparse_symmetric_to_general, from))
setAs("dtCMatrix", "dtTMatrix",
function(from) .Call(Csparse_to_Tsparse, from, TRUE))
## Current code loses symmetry and triangularity properties. With suitable
## changes to chm_dense_to_SEXP (../src/chm_common.c) we can avoid this.
setAs("CsparseMatrix", "denseMatrix",
function(from) {
## |-> cholmod_C -> cholmod_dense -> chm_dense_to_dense
if (is(from, "triangularMatrix") && from@diag == "U")
from <- .Call(Csparse_diagU2N, from)
.Call(Csparse_to_dense, from)
})
## special cases (when a specific "to" class is specified)
setAs("dgCMatrix", "dgeMatrix",
function(from) .Call(Csparse_to_dense, from))
## cholmod_sparse_to_dense converts symmetric storage to general
## storage so symmetric classes are ok for conversion to matrix.
## unit triangular needs special handling
setAs("CsparseMatrix", "matrix",
function(from) {
## |-> cholmod_C -> cholmod_dense -> chm_dense_to_matrix
if (is(from, "triangularMatrix") && from@diag == "U")
from <- .Call(Csparse_diagU2N, from)
.Call(Csparse_to_matrix, from)
})
### Some group methods:
## TODO : Consider going a level up, and do this for all "Ops"
setMethod("Arith",
signature(e1 = "CsparseMatrix", e2 = "CsparseMatrix"),
function(e1, e2) callGeneric(as(e1, "dgCMatrix"),
as(e2, "dgCMatrix")))
setMethod("Arith",
signature(e1 = "CsparseMatrix", e2 = "numeric"),
function(e1, e2) {
if(length(e2) == 1) { ## e.g., Mat ^ a
f0 <- callGeneric(0, e2)
if(is0(f0)) { # remain sparse, symm., tri.,...
e1@x <- callGeneric(e1@x, e2)
return(e1)
}
}
## all other (potentially non-sparse) cases: give up symm, tri,..
callGeneric(as(e1, paste(.M.kind(e1), "gCMatrix", sep='')), e2)
})
## The same, e1 <-> e2 :
setMethod("Arith",
signature(e1 = "numeric", e2 = "CsparseMatrix"),
function(e1, e2) {
if(length(e1) == 1) {
f0 <- callGeneric(e1, 0)
if(is0(f0)) {
e2@x <- callGeneric(e1, e2@x)
return(e2)
}
}
callGeneric(e1, as(e2, paste(.M.kind(e2), "gCMatrix", sep='')))
})
setMethod("Math",
signature(x = "CsparseMatrix"),
function(x) {
f0 <- callGeneric(0.)
if(is0(f0)) {
## sparseness, symm., triang.,... preserved
x@x <- callGeneric(x@x)
x
} else { ## no sparseness
callGeneric(as_dense(x))
}
})
### workhorse for "[<-" -- both for d* and l* C-sparse matrices :
replCmat <- function (x, i, j, value)
{
di <- dim(x)
dn <- dimnames(x)
i1 <- if(missing(i)) 0:(di[1] - 1:1) else .ind.prep2(i, 1, di, dn)
i2 <- if(missing(j)) 0:(di[2] - 1:1) else .ind.prep2(j, 2, di, dn)
dind <- c(length(i1), length(i2)) # dimension of replacement region
lenRepl <- prod(dind)
lenV <- length(value)
if(lenV == 0) {
if(lenRepl != 0)
stop("nothing to replace with")
else return(x)
}
## else: lenV := length(value) is > 0
if(lenRepl %% lenV != 0)
stop("number of items to replace is not a multiple of replacement length")
if(lenV > lenRepl)
stop("too many replacement values")
clx <- c(class(x)) # keep "symmetry" if changed here:
x.sym <- is(x, "symmetricMatrix")
if(x.sym) { ## only half the indices are there..
x.sym <-
(dind[1] == dind[2] && i1 == i2 &&
(lenRepl == 1 || isSymmetric(array(value, dim=dind))))
## x.sym : result is *still* symmetric
x <- .Call(Csparse_symmetric_to_general, x)
}
xj <- .Call(Matrix_expand_pointers, x@p)
sel <- (!is.na(match(x@i, i1)) &
!is.na(match( xj, i2)))
has.x <- any("x" == slotNames(x)) # i.e. *not* nonzero-pattern
if(has.x && sum(sel) == lenRepl) { ## all entries to be replaced are non-zero:
value <- rep(value, length = lenRepl)
## Ideally we only replace them where value != 0 and drop the value==0
## ones; but that would have to (?) go through dgT*
## v0 <- 0 == value
## if (lenRepl == 1) and v0 is TRUE, the following is not doing anything
##- --> ./dgTMatrix.R and its replTmat()
## x@x[sel[!v0]] <- value[!v0]
x@x[sel] <- value
return(if(x.sym) as_CspClass(x, clx) else x)
}
## else go via Tsparse.. {FIXME: a waste! - we already have 'xj' ..}
x <- as(x, "TsparseMatrix")
if(missing(i))
x[ ,j] <- value
else if(missing(j))
x[i, ] <- value
else
x[i,j] <- value
if(any(is0(x@x))) ## drop all values that "happen to be 0"
drop0(x, clx)
else as_CspClass(x, clx)
}
setReplaceMethod("[", signature(x = "CsparseMatrix", i = "index", j = "missing",
value = "replValue"),
function (x, i, value) replCmat(x, i=i, value=value))
setReplaceMethod("[", signature(x = "CsparseMatrix", i = "missing", j = "index",
value = "replValue"),
function (x, j, value) replCmat(x, j=j, value=value))
setReplaceMethod("[", signature(x = "CsparseMatrix", i = "index", j = "index",
value = "replValue"),
replCmat)
setMethod("crossprod", signature(x = "CsparseMatrix", y = "missing"),
function(x, y = NULL) {
if (is(x, "symmetricMatrix")) {
warning("crossprod(x) calculated as x %*% x for sparse, symmetric x")
return(x %*% x)
}
.Call(Csparse_crossprod, x, trans = FALSE, triplet = FALSE)
})
setMethod("crossprod", signature(x = "CsparseMatrix", y = "CsparseMatrix"),
function(x, y = NULL)
.Call(Csparse_Csparse_crossprod, x, y, trans = FALSE))
setMethod("tcrossprod", signature(x = "CsparseMatrix", y = "CsparseMatrix"),
function(x, y = NULL)
.Call(Csparse_Csparse_crossprod, x, y, trans = TRUE))
## FIXME: Generalize the class of y. This specific method is to replace one
## in dgCMatrix.R
setMethod("crossprod", signature(x = "CsparseMatrix", y = "ddenseMatrix"),
function(x, y = NULL) .Call(Csparse_dense_crossprod, x, y))
setMethod("crossprod", signature(x = "CsparseMatrix", y = "matrix"),
function(x, y = NULL) .Call(Csparse_dense_crossprod, x, y))
setMethod("crossprod", signature(x = "CsparseMatrix", y = "numeric"),
function(x, y = NULL) .Call(Csparse_dense_crossprod, x, y))
setMethod("tcrossprod", signature(x = "CsparseMatrix", y = "missing"),
function(x, y = NULL) {
if (is(x, "symmetricMatrix")) {
warning("tcrossprod(x) calculated as x %*% x for sparse, symmetric x")
return(x %*% x)
}
.Call(Csparse_crossprod, x, trans = TRUE, triplet = FALSE)
})
setMethod("t", signature(x = "CsparseMatrix"),
function(x) .Call(Csparse_transpose, x, is(x, "triangularMatrix")))
setMethod("%*%", signature(x = "CsparseMatrix", y = "CsparseMatrix"),
function(x, y) .Call(Csparse_Csparse_prod, x, y))
setMethod("%*%", signature(x = "CsparseMatrix", y = "ddenseMatrix"),
function(x, y) .Call(Csparse_dense_prod, x, y))
setMethod("%*%", signature(x = "CsparseMatrix", y = "matrix"),
function(x, y) .Call(Csparse_dense_prod, x, y))
## Not needed because of c("Matrix", "numeric") method
##setMethod("%*%", signature(x = "CsparseMatrix", y = "numeric"),
## function(x, y) .Call(Csparse_dense_prod, x, y))
## FIXME(2): These two are sub-optimal : has 2 x t(<dense>) :
setMethod("%*%", signature(x = "ddenseMatrix", y = "CsparseMatrix"),
function(x, y) t(.Call(Csparse_dense_crossprod, y, t(x))),
valueClass = "dgeMatrix")
setMethod("%*%", signature(x = "matrix", y = "CsparseMatrix"),
function(x, y) t(.Call(Csparse_dense_crossprod, y, t(x))),
valueClass = "dgeMatrix")
## Not needed because of c("numeric", "Matrix") method
##setMethod("%*%", signature(x = "numeric", y = "CsparseMatrix"),
## function(x, y) t(.Call(Csparse_dense_crossprod, y, x)),
## valueClass = "dgeMatrix")
## NB: have extra tril(), triu() methods for symmetric ["dsC" and "lsC"]
setMethod("tril", "CsparseMatrix",
function(x, k = 0, ...) {
k <- as.integer(k[1])
dd <- dim(x); sqr <- dd[1] == dd[2]
stopifnot(-dd[1] <= k, k <= dd[1]) # had k <= 0
r <- .Call(Csparse_band, x, -dd[1], k)
## return "lower triangular" if k <= 0
if(sqr && k <= 0)
as(r, paste(.M.kind(x), "tCMatrix", sep='')) else r
})
setMethod("triu", "CsparseMatrix",
function(x, k = 0, ...) {
k <- as.integer(k[1])
dd <- dim(x); sqr <- dd[1] == dd[2]
stopifnot(-dd[1] <= k, k <= dd[1]) # had k >= 0
r <- .Call(Csparse_band, x, k, dd[2])
## return "upper triangular" if k >= 0
if(sqr && k >= 0)
as(r, paste(.M.kind(x), "tCMatrix", sep='')) else r
})
setMethod("band", "CsparseMatrix",
function(x, k1, k2, ...) {
k1 <- as.integer(k1[1])
k2 <- as.integer(k2[1])
dd <- dim(x); sqr <- dd[1] == dd[2]
stopifnot(-dd[1] <= k1, k1 <= k2, k2 <= dd[1])
r <- .Call(Csparse_band, x, k1, k2)
if(sqr && k1 * k2 >= 0) ## triangular
as(r, paste(.M.kind(x), "tCMatrix", sep=''))
else if (k1 < 0 && k1 == -k2 && isSymmetric(x)) ## symmetric
as(r, paste(.M.kind(x), "sCMatrix", sep=''))
else
r
})
setMethod("diag", "CsparseMatrix",
function(x, nrow, ncol = n) {
dm <- .Call(Csparse_band, x, 0, 0)
dlen <- min(dm@Dim)
ind1 <- dm@i + 1:1 # 1-based index vector
if (is(dm, "nMatrix")) {
val <- rep.int(FALSE, dlen)
val[ind1] <- TRUE
}
else if (is(dm, "lMatrix")) {
val <- rep.int(FALSE, dlen)
val[ind1] <- as.logical(dm@x)
}
else {
val <- rep.int(0, dlen)
## cMatrix not yet active but for future expansion
if (is(dm, "cMatrix")) val <- as.complex(val)
val[ind1] <- dm@x
}
val
})
setMethod("colSums", signature(x = "CsparseMatrix"), .as.dgC.Fun,
valueClass = "numeric")
setMethod("colMeans", signature(x = "CsparseMatrix"), .as.dgC.Fun,
valueClass = "numeric")
setMethod("rowSums", signature(x = "CsparseMatrix"), .as.dgC.Fun,
valueClass = "numeric")
setMethod("rowMeans", signature(x = "CsparseMatrix"), .as.dgC.Fun,
valueClass = "numeric")
|
