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Diff of /pkg/TODO

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revision 530, Mon Feb 7 10:58:27 2005 UTC revision 2048, Tue Aug 14 15:09:10 2007 UTC
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1    Check for DimNames propagation in coercion and other operations.
2    ------
3  - Report the problem in the Linux ldexp manual page.  The second and  - Report the problem in the Linux ldexp manual page.  The second and
4    third calls in the Synopsis should be to ldexpf and ldexpl.    third calls in the Synopsis should be to ldexpf and ldexpl.
5    
6  - [,] indexing  - [,] indexing: for sparse "works", but not yet for negative indices!
7    
8  - group generics: "Arith", but also "Ops" and "Math";  see  - consider moving alloc3Darray from ./src/Mutils.c to
9    ?Math.data.frame  and the examples in    $(RSRC)/src/base/array.c
10    ?SetGeneric  ------
11    - provide methods for "dspMatrix" and "dppMatrix"!
12    
13  - bCrosstab(): do we really want the diagonal "V:V" crosstabs?  - implement (more) methods for supporting "packed" (symmetric / triangular)
14                 (if so or in any case: add to  man/bCrosstab.Rd )    matrices; particularly something like pack() and unpack()  [to/from our
15      classes from/to "numeric"] --- have already man/unpack.Rd but no method yet!
16    
17  - src/Metis/ : one of the two Makefiles needs fixing, as changing    (have some dtr* <-> dtp*)
              src/Metis/*.c  does not lead to recompilation.  
   --DB - it seems both Makefiles need fixing.  I think I have the  
              src/Metis/Makefile fixed but not src/Makefile  
18    
19  - man/Matrix.Rd :  has example with dimnames, but we just drop them!  -----
                 MM thinks dimnames should be supported (but then ...)  
20    
21  - data/ : names 'mm' and even more 'y' are  ``too short''.  - combine the C functions for multiplication by special forms and
22          If we really want to keep them, don't use "LazyData"    solution wrt special forms by using a 'right' argument and a
23          (such that one needs  data(*) explicitly);    'classed' argument.
24          But MM would rather want something like  ex.mm and ex.y     [done with dgeMatrix_matrix_mm();  not yet for other classes;
25        and for _crossprod()]
26    
27  - "factors" maybe should move up to "Matrix"  - add more comprehensive examples / tests for Schur decomposition
28    
29  -------  - arithmetic for sparse matrices:
30                 <sparseMatrix>  o  <same-dim-sparseMatrix>
31      should return a sparse matrix  for at least "+" and "*" , also %%,
32      and "/" and "%/%" at least when the RHS is non-zero a scalar.
33      Challenge: nice implementation (``common non-0''; but Tsparse* is not uniq).
34    
35  We have a (at least one) basic problem :  -----
    Currently the show() method fail sometime after coercion:  
    e.g. 'sy' show()s wrongly, even though it "str()" fine :  
36    
37        (po <- crossprod(Matrix(0:3, 2))) # ok  - "Math2" , "Math", "Arith":
38        (ge <- as(po, "dgeMatrix")) # ok     keep triangular and symmetric Matrices when appropriate:
39        (sy <- as(po, "dsyMatrix")) # BAD     particularly desirable for  "Math2": round(), signif()
       str(sy) # looks fine  
40    
41    or  - "d" <-> "l" coercion for all "[TCR]" sparse matrices is really trivial:
42        example(expand) # -> ex$L and ex$U look bad, however    "d" -> "l" : drops the 'x' slot
43        as(ex$L, "dgeMatrix") # `works'    "l" -> "d" : construct an 'x' slot of all '1'
44      We currently have many of these conversions explicitly, e.g.
45       setAs("dsTMatrix", "lsTMatrix",
46          function(from) new("lsTMatrix", i = from@i, j = from@j, uplo = from@uplo,
47                             Dim = from@Dim, Dimnames = from@Dimnames))
48      but I would rather want to automatically construct all these coercion
49      methods at once by a ``method constructor'', i.e.,
50      for all  "dsparse*" -> "lsparse*" and vice versa.
51      How can one do this {in a documented way} ?
52    
53    {Of course, we don't need a workaround but must understand  - Think of constructing  setAs(...) calls automatically in order to
54     and solve the problem}    basically enable all ``sensible'' as(fromMatrix, toMatrix)  calls,
55      possibly using canCoerce(.)
56    
57    - setAs(<Mcl>,  "[dln]Matrix") for <Mcl> in {Matrix or denseMatrix + sparseMatrix}
58    
59    - When we have a packed matrix, it's a waste to go through "full" to "sparse":
60      ==> implement
61            setAs("dspMatrix", "sparseMatrix")
62            setAs("dppMatrix", "sparseMatrix")
63            setAs("dtpMatrix", "sparseMatrix")
64      and the same for "lsp" , "ltp"  and  "nsp" , "ntp" !
65    
66    - tcrossprod(x, y) : do provide methods for y != NULL
67      calling Lapack's DGEMM for "dense"
68      [2005-12-xx: done for dgeMatrix at least]
69    
70    - BUGlet:  Shouldn't lose factorization here:
71      h6 <- Hilbert(6); chol(h6) ; str(h6) # has factor
72      str(H6 <- as(h6, "dspMatrix"))       # has lost factor
73      ## and the same in a similar situation involving  "dpo", "dpp"
74    
75    - Things like  M[upper.tri(M)] are not really most useful for  sparse
76      matrices.  --> provide generic functions
77      upperTriMatrix(), lowerTriMatrix()  both with argument  'diag = TRUE'
78      (which can be set to FALSE of course) which are used to extract a
79      triangle from an arbitrary sparse matrix and  return a  "dtCMatrix".
80    
81    - Factorizations: LU done; also Schur()  for  *sparse*  Matrices.
82    
83    - band(), triu(), tril() for *all* including "matrix", not just sparse matrices
84    
85    - is.na() method for all our matrices [ ==> which(*, arr.ind=TRUE) might work ]
86    
87    - use  .Call(Csparse_drop, M, tol) in more places,
88      both with 'tol = 0.' to drop "values that happen to be 0" and for
89      zapsmall() methods for Csparse*
90    
91    - implement .Call(Csparse_scale, ....) interfacing to cholmod_scale()
92      in src/CHOLMOD/Include/cholmod_matrixops.h : for another function
93      specifically for multiplying a cholmod_sparse object by a diagonal matrix.
94      Use it in %*% and [t]crossprod methods.
95    
96    - chol() and determinant() should ``work'': proper result or "good" error
97      message.
98    
99    - make sure *all* group methods have (maybe "bail-out") setMethod for "Matrix".
100      e.g. zapsmall(<pMatrix>) fails "badly"
101    
102    - speedup: pass class definition to non0ind() [check all calls ..]
103    
104    - sum(): implement methods which work for *all* our matrices.
105    
106    - Implement  expand(.) for the Cholesky() results
107      "dCHMsimpl" and  "dCHMsuper"  -- currently have no *decent* way to get at
108      the matrix factors of the corresponding matrix factorization !!
109    
110    - rbind(<sparse>, <dense>) does not work  (e.g. <dgC>, <dge>)
111    
112    - ensure that  M[0], M[FALSE], M[1:2]  works as for traditional Matrices
113    
114    - make sure  M[FALSE, FALSE]  works for all Matrices
115      {e.g. fails for M <- Diagonal(4)}
116    
117    - <sparse> %*% <dense>  {also in crossprod/tcrossprod}  currently always
118      returns <dense>, since --> Csparse_dense_prod --> cholmod_sdmult
119      and that does only return dense.
120      When the sparse matrix is very sparse, i.e. has many rows with only zero
121      entries, it would make much sense to return sparse.
122    
123    - sparse-symmetric + diagonal should stay sparse-symmetric
124      (only stays sparse): Matrix(0, 4, 4) + Diagonal(4, 1:4)
125      --> R/diagMatrix.R ('FIXME')
126      but also R/Ops.R  to ensure  sp-sym. + sp-sym. |-> sp-sym.  etc
127    
128    - Diagonal(n) %*% A ---  too slow!! --> ~/R/MM/Pkg-ex/Matrix/diag-Tamas-ex.R
129    
130    - For a square sparse matrix 'b' {typically dgCMatrix or dgTMatrix},
131      we'd want a function  "Mat_plus_t_Mat" <- function(b) {....}
132      which computes the symmetric sparse matrix   b + t(b)
133      in way that never works with size-doubled vectors from  b@i etc..
134    
135    - ! <symmetricMatrix>  loses symmetry, both for dense and sparse matrices.
136      !M  where M is "sparseMatrix", currently always gives dense. This only
137      makes sense when M is ``really sparse''.
138    
139    - msy <- as(matrix(c(2:1,1:2),2), "dsyMatrix"); str(msy)
140    
141      shows that the Cholesky factorization is computed ``too quickly''.
142      Can be a big pain for largish matrices, when it is unneeded.
143    
144    - example(Cholesky, echo=FALSE) ; cm <- chol(mtm); str(cm); str(mtm)
145    
146      shows that chol() does not seems to make use of an already
147      present factorization and rather uses one with more '0' in x slot.
148    
149    - diag(m) <- val    currently automatically works via  m[cbind(i,i)] <- val
150      However,
151      we need methods for 'diag<-' at least for diagonalMatrix,
152      triangularMatrix, and probably also "dense*general*Matrix" since the
153      above currently goes via "matrix" and back instead of using the 'x' slot
154      directly.
155    
156    - image(M, ..): Think about an optional smart option which keeps
157       "0 |-> transparent" and allows colors to differentiate negative and
158       positive entries.
159    
160    - examples for solve( Cholesky(.), b, system = c("A", "LDLt"....))
161      probably rather in man/CHMfactor-class.Rd than man/Cholesky.Rd

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