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Diff of /pkg/R/Matrix.R

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revision 2110, Sat Jan 26 20:59:26 2008 UTC revision 2144, Tue Mar 18 23:08:12 2008 UTC
# Line 45  Line 45 
45            function(x, ...) as.logical(as.vector(x)))            function(x, ...) as.logical(as.vector(x)))
46    
47  setMethod("cov2cor", signature(V = "Matrix"),  setMethod("cov2cor", signature(V = "Matrix"),
48            function(V) as(cov2cor(as(V, "matrix")), "dpoMatrix"))            function(V) { ## was as(cov2cor(as(V, "matrix")), "dpoMatrix"))
49                  r <- V
50                  p <- (d <- dim(V))[1]
51                  if(p != d[2]) stop("'V' is not a square matrix")
52                  Is <- sqrt(1/diag(V)) # diag( 1/sigma_i )
53                  if(any(!is.finite(Is)))
54                      warning("diag(.) had 0 or NA entries; non-finite result is doubtful")
55                  Is <- Diagonal(x = Is)
56                  r <- Is %*% V %*% Is
57                  r[cbind(1L:p,1L:p)] <- 1 # exact in diagonal
58                  as(forceSymmetric(r), "dpoMatrix")
59              })
60    
61  ## "base" has an isSymmetric() S3-generic since R 2.3.0  ## "base" has an isSymmetric() S3-generic since R 2.3.0
62  setMethod("isSymmetric", signature(object = "symmetricMatrix"),  setMethod("isSymmetric", signature(object = "symmetricMatrix"),
63            function(object,tol) TRUE)            function(object, ...) TRUE)
64  setMethod("isSymmetric", signature(object = "triangularMatrix"),  setMethod("isSymmetric", signature(object = "triangularMatrix"),
65            ## TRUE iff diagonal:            ## TRUE iff diagonal:
66            function(object,tol) isDiagonal(object))            function(object, ...) isDiagonal(object))
   
 setMethod("isTriangular", signature(object = "triangularMatrix"),  
           function(object, ...) TRUE)  
67    
68  setMethod("isTriangular", signature(object = "matrix"), isTriMat)  setMethod("isTriangular", signature(object = "matrix"), isTriMat)
69    
70  setMethod("isDiagonal", signature(object = "matrix"), .is.diagonal)  setMethod("isDiagonal", signature(object = "matrix"), .is.diagonal)
71    
72    ## The "catch all" methods -- far from optimal:
73    setMethod("symmpart", signature(x = "Matrix"),
74              function(x) as((x + t(x))/2, "symmetricMatrix"))
75    setMethod("skewpart", signature(x = "Matrix"),
76              function(x) (x - t(x))/2)
77    
78    ## FIXME: do this (similarly as for "ddense.." in C
79    setMethod("symmpart", signature(x = "matrix"), function(x) (x + t(x))/2)
80    setMethod("skewpart", signature(x = "matrix"), function(x) (x - t(x))/2)
81    
82    
83    
84    
85  setMethod("dim", signature(x = "Matrix"),  setMethod("dim", signature(x = "Matrix"),
# Line 101  Line 120 
120  ##        "!" is in ./not.R  ##        "!" is in ./not.R
121    
122    
123  Matrix <-  Matrix <- function (data = NA, nrow = 1, ncol = 1, byrow = FALSE,
124      function (data = NA, nrow = 1, ncol = 1, byrow = FALSE, dimnames = NULL,                      dimnames = NULL, sparse = NULL, forceCheck = FALSE)
               sparse = NULL, forceCheck = FALSE)  
125  {  {
126      sparseDefault <- function(m) prod(dim(m)) > 2*sum(isN0(as(m, "matrix")))      sparseDefault <- function(m) prod(dim(m)) > 2*sum(isN0(as(m, "matrix")))
127    
# Line 112  Line 130 
130          class(data) <- "matrix" # "matrix" first for S4 dispatch          class(data) <- "matrix" # "matrix" first for S4 dispatch
131      if(is.null(sparse1 <- sparse) && (i.M || is(data, "matrix")))      if(is.null(sparse1 <- sparse) && (i.M || is(data, "matrix")))
132          sparse <- sparseDefault(data)          sparse <- sparseDefault(data)
133        sM <- FALSE
134      doDN <- TRUE      doDN <- TRUE
135      if (i.M) {      if (i.M) {
136          if(!missing(nrow) || !missing(ncol)|| !missing(byrow))          if(!missing(nrow) || !missing(ncol)|| !missing(byrow))
# Line 131  Line 149 
149              ## Matrix(0, ...) : always sparse unless "sparse = FALSE":              ## Matrix(0, ...) : always sparse unless "sparse = FALSE":
150              if(is.null(sparse)) sparse1 <- sparse <- TRUE              if(is.null(sparse)) sparse1 <- sparse <- TRUE
151              i.M <- sM <- TRUE              i.M <- sM <- TRUE
152                isSym <- nrow == ncol
153              ## will be sparse: do NOT construct full matrix!              ## will be sparse: do NOT construct full matrix!
154              data <- new(if(is.numeric(data)) "dgTMatrix" else              data <- new(paste(if(is.numeric(data)) "d" else
155                          if(is.logical(data)) "lgTMatrix" else                                if(is.logical(data)) "l" else
156                          stop("invalid 'data'"),                          stop("invalid 'data'"),
157                                  if(isSym) "s" else "g", "CMatrix", sep=''),
158                            p = rep.int(0L, ncol+1L),
159                          Dim = as.integer(c(nrow,ncol)),                          Dim = as.integer(c(nrow,ncol)),
160                          Dimnames = if(is.null(dimnames)) list(NULL,NULL)                          Dimnames = if(is.null(dimnames)) list(NULL,NULL)
161                          else dimnames)                          else dimnames)
# Line 162  Line 183 
183          isTri <- isTriangular(data)          isTri <- isTriangular(data)
184      isDiag <- isSym # cannot be diagonal if it isn't symmetric      isDiag <- isSym # cannot be diagonal if it isn't symmetric
185      if(isDiag)      if(isDiag)
186          isDiag <- isDiagonal(data)          isDiag <- !isTRUE(sparse1) && isDiagonal(data)
187    
188      ## Find proper matrix class 'cl'      ## try to coerce ``via'' virtual classes
189      cl <-      if(isDiag) { ## diagonal is preferred to sparse !
190          if(isDiag && !isTRUE(sparse1))          data <- as(data, "diagonalMatrix")
191              "diagonalMatrix" # -> will automatically check for type          isSym <- FALSE
192          else {      } else if(sparse && !sM)
             ## consider it's type  
             ctype <-  
                 if(is(data,"Matrix")) class(data)  
                 else {  
                     if("complex" == (ctype <- typeof(data)))  
                         "z" else ctype  
                 }  
             ctype <- substr(ctype, 1,1) # "d", "l", "i" or "z"  
             if(ctype == "z")  
                 stop("complex matrices not yet implemented in Matrix package")  
             if(ctype == "i") {  
                 warning("integer matrices not yet implemented in 'Matrix'; ",  
                         "using 'double' ones'")  
                 ctype <- "d"  
             }  
             paste(ctype,  
                   if(sparse) {  
                       if(isSym) "sCMatrix" else  
                       if(isTri) "tCMatrix" else "gCMatrix"  
                   } else { ## dense  
                       if(isSym) "syMatrix" else  
                       if(isTri) "trMatrix" else "geMatrix"  
                   }, sep="")  
         }  
   
     ## Can we coerce and be done?  
     if(!canCoerce(data,cl)) { ## try to coerce ``via'' virtual classes  
         if(sparse && !sM)  
193              data <- as(data, "sparseMatrix")              data <- as(data, "sparseMatrix")
194          else if(!sparse && !is(data, "denseMatrix"))      else if(!sparse) {
195            if(i.M) { ## data is 'Matrix'
196                if(!is(data, "denseMatrix"))
197              data <- as(data, "denseMatrix")              data <- as(data, "denseMatrix")
198          if(isTri && !is(data, "triangularMatrix"))          } else { ## data is "matrix" (and result "dense" -> go via "general"
199              data <- as(data, "triangularMatrix")              ctype <- typeof(data)
200          else if(isSym && !is(data, "symmetricMatrix"))              if (ctype == "complex")
201              data <- as(data, "symmetricMatrix")                  stop("complex matrices not yet implemented in Matrix package")
202                if (ctype == "integer") ## integer Matrices not yet implemented
203                    storage.mode(data) <- "double"
204                data <- new(paste(.M.kind(data), "geMatrix", sep=''),
205                            Dim = dim(data),
206                            Dimnames = .M.DN(data),
207                            x = c(data))
208      }      }
209      ## now coerce in any case .. maybe producing sensible error message:      }
210      as(data, cl)  
211        if(isTri && !is(data, "triangularMatrix")) {
212            data <- if(attr(isTri,"kind") == "L") tril(data) else triu(data)
213                                            #was as(data, "triangularMatrix")
214        } else if(isSym && !is(data, "symmetricMatrix"))
215            data <- forceSymmetric(data) #was as(data, "symmetricMatrix")
216    
217        data
218  }  }
219    
220  ## Methods for operations where one argument is numeric  ## Methods for operations where one argument is numeric
# Line 300  Line 308 
308    
309  ## FIXME: All of these should never be called  ## FIXME: All of these should never be called
310  setMethod("chol", signature(x = "Matrix"),  setMethod("chol", signature(x = "Matrix"),
311            function(x, pivot = FALSE) .bail.out.1(.Generic, class(x)))            function(x, pivot = FALSE, ...) .bail.out.1(.Generic, class(x)))
312  setMethod("determinant", signature(x = "Matrix"),  setMethod("determinant", signature(x = "Matrix"),
313            function(x, logarithm = TRUE) .bail.out.1(.Generic, class(x)))            function(x, logarithm = TRUE, ...) .bail.out.1(.Generic, class(x)))
314    
315  setMethod("diag", signature(x = "Matrix"),  setMethod("diag", signature(x = "Matrix"),
316            function(x, nrow, ncol) .bail.out.1(.Generic, class(x)))            function(x, nrow, ncol) .bail.out.1(.Generic, class(x)))
# Line 376  Line 384 
384    
385  ## missing 'drop' --> 'drop = TRUE'  ## missing 'drop' --> 'drop = TRUE'
386  ##                     -----------  ##                     -----------
387  ## select rows  ## select rows __ or __ vector indexing:
388  setMethod("[", signature(x = "Matrix", i = "index", j = "missing",  setMethod("[", signature(x = "Matrix", i = "index", j = "missing",
389                           drop = "missing"),                           drop = "missing"),
390            function(x,i,j, ..., drop) {            function(x,i,j, ..., drop) {
391                if(nargs() == 2) { ## e.g. M[0] , M[TRUE],  M[1:2]                if(nargs() == 2) { ## e.g. M[0] , M[TRUE],  M[1:2]
392                    if(any(i) || prod(dim(x)) == 0)                    if(any(as.logical(i)) || prod(dim(x)) == 0)
393                          ## FIXME: for *large sparse*, use sparseVector !
394                        as.vector(x)[i]                        as.vector(x)[i]
395                    else ## save memory                    else ## save memory (for large sparse M):
396                        as.vector(x[1,1])[FALSE]                        as.vector(x[1,1])[FALSE]
397                } else {                } else {
398                    callGeneric(x, i=i, , drop=TRUE)                    callGeneric(x, i=i, , drop=TRUE)
# Line 412  Line 421 
421      nA <- nargs()      nA <- nargs()
422      if(nA == 2) { ##  M [ M >= 7 ]      if(nA == 2) { ##  M [ M >= 7 ]
423          ## FIXME: when both 'x' and 'i' are sparse, this can be very inefficient          ## FIXME: when both 'x' and 'i' are sparse, this can be very inefficient
424          as(x, geClass(x))@x[as.vector(i)]          if(is(x, "sparseMatrix"))
425                message("<sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient")
426            toC <- geClass(x)
427            if(canCoerce(x, toC)) as(x, toC)@x[as.vector(i)]
428            else as(as(as(x, "generalMatrix"), "denseMatrix"), toC)@x[as.vector(i)]
429          ## -> error when lengths don't match          ## -> error when lengths don't match
430      } else if(nA == 3) { ##  M [ M[,1, drop=FALSE] >= 7, ]      } else if(nA == 3) { ##  M [ M[,1, drop=FALSE] >= 7, ]
431          stop("not-yet-implemented 'Matrix' subsetting") ## FIXME          stop("not-yet-implemented 'Matrix' subsetting") ## FIXME
# Line 428  Line 441 
441            .M.sub.i.logical)            .M.sub.i.logical)
442    
443    
444    subset.ij <- function(x, ij) {
445        m <- nrow(ij)
446        if(m > 3) {
447            cld <- getClassDef(class(x))
448            sym.x <- extends(cld, "symmetricMatrix")
449            if(sym.x) {
450                W <- if(x@uplo == "U") # stored only [i,j] with i <= j
451                    ij[,1] > ij[,2] else ij[,1] < ij[,2]
452                if(any(W))
453                    ij[W,] <- ij[W, 2:1]
454            }
455            if(extends(cld, "sparseMatrix")) {
456                ## do something smarter:
457                nr <- nrow(x)
458                if(!extends(cld, "CsparseMatrix")) {
459                    x <- as(x, "CsparseMatrix") # simpler; our standard
460                    cld <- getClassDef(class(x))
461                }
462                tri.x <- extends(cld, "triangularMatrix")
463                if(tri.x) {
464                    ## need these for the 'x' slot in any case
465                    if (x@diag == "U") x <- .Call(Csparse_diagU2N, x)
466                    ## slightly more efficient than non0.i() or non0ind():
467                    ij.x <- .Call(compressed_non_0_ij, x, isC=TRUE)
468                } else { ## symmetric / general : for symmetric, only "existing"b
469                    ij.x <- non0.i(x, cld)
470                }
471    
472                mi <- match(encodeInd(ij.x,   nr),
473                            encodeInd(ij -1L, nr), nomatch=0)
474                mmi <- mi != 0
475                ## Result:
476                ans <- vector(mode = .type.kind[.M.kindC(cld)], length = m)
477                ## those that are *not* zero:
478                ans[mi[mmi]] <-
479                    if(extends(cld, "nsparseMatrix")) TRUE else x@x[mmi]
480                ans
481    
482            } else { ## non-sparse : dense
483                ##---- NEVER happens:  'denseMatrix' has its own setMethod(.) !
484                message("m[ <ij-matrix> ]: inefficiently indexing single elements")
485                i1 <- ij[,1]
486                i2 <- ij[,2]
487                ## very inefficient for large m
488                unlist(lapply(seq_len(m), function(j) x[i1[j], i2[j]]))
489            }
490        } else { # 1 <= m <= 3
491            i1 <- ij[,1]
492            i2 <- ij[,2]
493            unlist(lapply(seq_len(m), function(j) x[i1[j], i2[j]]))
494        }
495    }
496    
497  ## A[ ij ]  where ij is (i,j) 2-column matrix -- but also when that is logical mat!  ## A[ ij ]  where ij is (i,j) 2-column matrix -- but also when that is logical mat!
498  .M.sub.i.2col <- function (x, i, j, ..., drop)  .M.sub.i.2col <- function (x, i, j, ..., drop)
499  {  {
# Line 443  Line 509 
509          m <- nrow(i)          m <- nrow(i)
510          if(m == 0) return(vector(mode = .type.kind[.M.kind(x)]))          if(m == 0) return(vector(mode = .type.kind[.M.kind(x)]))
511          ## else          ## else
512          i1 <- i[,1]          subset.ij(x, i)
         i2 <- i[,2]  
         ## potentially inefficient -- FIXME --  
         unlist(lapply(seq_len(m), function(j) x[i1[j], i2[j]]))  
513    
514      } else stop("nargs() = ", nA,      } else stop("nargs() = ", nA,
515                  ".  Extraneous illegal arguments inside '[ .. ]' (i.2col)?")                  ".  Extraneous illegal arguments inside '[ .. ]' (i.2col)?")
# Line 502  Line 565 
565          value <- rep(value, length = m)          value <- rep(value, length = m)
566          i1 <- i[,1]          i1 <- i[,1]
567          i2 <- i[,2]          i2 <- i[,2]
568            if(m > 2)
569                message("m[ <ij-matrix> ] <- v: inefficiently treating single elements")
570          ## inefficient -- FIXME -- (also loses "symmetry" unnecessarily)          ## inefficient -- FIXME -- (also loses "symmetry" unnecessarily)
571          for(k in seq_len(m))          for(k in seq_len(m))
572              x[i1[k], i2[k]] <- value[k]              x[i1[k], i2[k]] <- value[k]

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