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[matrix] Diff of /pkg/tests/indexing.R
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Diff of /pkg/tests/indexing.R

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revision 1724, Sat Jan 13 21:06:51 2007 UTC revision 2110, Sat Jan 26 20:59:26 2008 UTC
# Line 81  Line 81 
81  mC[1:2,]  mC[1:2,]
82  mC[7,  drop = FALSE]  mC[7,  drop = FALSE]
83  assert.EQ.mat(mC[1:2,], mm[1:2,])  assert.EQ.mat(mC[1:2,], mm[1:2,])
84    
85    ## *repeated* (aka 'duplicated') indices - did not work at all ...
86    i <- rep(8:10,2)
87    j <- c(2:4, 4:3)
88    assert.EQ.mat(mC[i,], mm[i,])
89    assert.EQ.mat(mC[,j], mm[,j])
90    assert.EQ.mat(mC[i, 2:1], mm[i, 2:1])
91    assert.EQ.mat(mC[c(4,1,2:1), j], mm[c(4,1,2:1), j])
92    assert.EQ.mat(mC[i,j], mm[i,j])
93    set.seed(7)
94    for(n in 1:50) {
95        i <- sample(sample(nrow(mC), 7), 20, replace = TRUE)
96        j <- sample(sample(ncol(mC), 6), 17, replace = TRUE)
97        assert.EQ.mat(mC[i,j], mm[i,j])
98    }
99    
100    ##---- Symmetric indexing of symmetric Matrix ----------
101    m. <- mC; m.[, c(2, 7:12)] <- 0
102    validObject(S <- crossprod(add.simpleDimnames(m.) %% 100))
103    ss <- as(S, "matrix")
104    T <- as(S, "TsparseMatrix")
105    ## non-repeated indices:
106    i <- c(7:5, 2:4);assert.EQ.mat(T[i,i], ss[i,i])
107    N <- nrow(T)
108    set.seed(11)
109    for(n in 1:50) {
110        i <- sample(N, max(2, sample(N,1)), replace = FALSE)
111        validObject(Tii <- T[i,i])
112        stopifnot(is(Tii, "dsTMatrix"), # remained symmetric Tsparse
113                  identical(t(Tii), t(T)[i,i]))
114        assert.EQ.mat(Tii, ss[i,i])
115    }
116    
117    ## repeated ones ``the challenge'' (to do smartly):
118    j <- c(4, 4, 9, 12, 9, 4, 17, 3, 18, 4, 12, 18, 4, 9)
119    assert.EQ.mat(T[j,j], ss[j,j])
120    ## and another two sets  (a, A) &  (a., A.) :
121    a <- matrix(0, 6,6)
122    a[upper.tri(a)] <- (utr <- c(2, 0,-1, 0,0,5, 7,0,0,0, 0,0,-2,0,8))
123    ta <- t(a); ta[upper.tri(a)] <- utr; a <- t(ta)
124    diag(a) <- c(0,3,0,4,6,0)
125    A <- as(Matrix(a), "TsparseMatrix")
126    A. <- A
127    diag(A.) <- 10 * (1:6)
128    a. <- as(A., "matrix")
129    ## More testing {this was not working for a long time..}
130    set.seed(1)
131    for(n in 1:100) {
132        i <- sample(1:nrow(A), 3+2*rpois(1, lam=3), replace=TRUE)
133        Aii  <- A[i,i]
134        A.ii <- A.[i,i]
135        stopifnot(class(Aii) == class(A),
136                  class(A.ii) == class(A.))
137        assert.EQ.mat(Aii , a [i,i])
138        assert.EQ.mat(A.ii, a.[i,i])
139        assert.EQ.mat(T[i,i], ss[i,i])
140    }
141    
142    
143  stopifnot(all.equal(mC[,3], mm[,3]),  stopifnot(all.equal(mC[,3], mm[,3]),
144            identical(mC[ij], mm[ij]))            identical(mC[ij], mm[ij]))
145  assert.EQ.mat(mC[7, , drop=FALSE], mm[7, , drop=FALSE])  assert.EQ.mat(mC[7, , drop=FALSE], mm[7, , drop=FALSE])
# Line 125  Line 184 
184            identical(as.mat(l3 ), m.x[1:3, ] != 0)            identical(as.mat(l3 ), m.x[1:3, ] != 0)
185            )            )
186    
187    ##-- Sub*assignment* with repeated / duplicated index:
188    A <- Matrix(0,4,3) ; A[c(1,2,1), 2] <- 1 ; A
189    B <- A;              B[c(1,2,1), 2] <- 1:3; B; B. <- B
190    B.[3,] <- rbind(4:2)
191    diag(B.) <- 10 * diag(B.)
192    C <- B.; C[,2] <- C[,2];  C[1,] <- C[1,]; C[2:3,2:1] <- C[2:3,2:1]
193    stopifnot(identical(unname(as.matrix(A)),
194                        local({a <- matrix(0,4,3); a[c(1,2,1), 2] <-  1 ; a})),
195              identical(unname(as.matrix(B)),
196                        local({a <- matrix(0,4,3); a[c(1,2,1), 2] <- 1:3; a})),
197              identical(C, drop0(B.)))
198    
199    
200  ## used to fail  ## used to fail
201  n <- 5 ## or much larger  n <- 5 ## or much larger
202  sm <- new("dsTMatrix", i=as.integer(1),j=as.integer(1),  sm <- new("dsTMatrix", i=as.integer(1),j=as.integer(1),
# Line 142  Line 214 
214  m0 <- Diagonal(5)  m0 <- Diagonal(5)
215  (m1 <- as(m0, "sparseMatrix"))  # dtTMatrix  (m1 <- as(m0, "sparseMatrix"))  # dtTMatrix
216  (m2 <- as(m0, "CsparseMatrix")) # dtCMatrix (with an irrelevant warning)  (m2 <- as(m0, "CsparseMatrix")) # dtCMatrix (with an irrelevant warning)
217    m1g <- as(m1, "generalMatrix")
218    stopifnot(is(m1g, "dgTMatrix"))
219    assert.EQ.mat(m2[1:3,],    diag(5)[1:3,])
220    assert.EQ.mat(m2[,c(4,1)], diag(5)[,c(4,1)])
221    stopifnot(identical(m2[1:3,], as(m1[1:3,], "CsparseMatrix")),
222              identical(Matrix:::uniqTsparse(m1[, c(4,2)]),
223                        Matrix:::uniqTsparse(as(m2[, c(4,2)], "TsparseMatrix")))
224              )## failed in 0.9975-11
225    
226  M <- m0; M[1,] <- 0  M <- m0; M[1,] <- 0
227  stopifnot(identical(M, Diagonal(x=c(0, rep(1,4)))))  stopifnot(identical(M, Diagonal(x=c(0, rep(1,4)))))
# Line 169  Line 249 
249  stopifnot(is(T, "dtCMatrix"), identical(T[,3], c(10,10,10,0,0)))  stopifnot(is(T, "dtCMatrix"), identical(T[,3], c(10,10,10,0,0)))
250    
251    
252    ## "Vector indices" -------------------
253    D <- Diagonal(6)
254    M <- as(D,"dgeMatrix")
255    m <- as(D,"matrix")
256    s <- as(D,"TsparseMatrix")
257    S <- as(s,"CsparseMatrix")
258    i <- c(3,1,6); v <- c(10,15,20)
259    ## (logical,value) which both are recycled:
260    L <- c(TRUE, rep(FALSE,8)) ; z <- c(50,99)
261    
262    ## vector subassignment, both with integer & logical
263    ## these now work correctly {though not very efficiently; hence warnings}
264    m[i] <- v # the role model: only first column is affected
265    M[i] <- v; assert.EQ.mat(M,m) # dge
266    D[i] <- v; assert.EQ.mat(D,m) # ddi -> dtT -> dgT
267    s[i] <- v; assert.EQ.mat(s,m) # dtT -> dgT
268    S[i] <- v; assert.EQ.mat(S,m); S # dtC -> dtT -> dgT -> dgC
269    ## logical
270    m[L] <- z
271    M[L] <- z; assert.EQ.mat(M,m)
272    D[L] <- z; assert.EQ.mat(D,m)
273    s[L] <- z; assert.EQ.mat(s,m)
274    S[L] <- z; assert.EQ.mat(S,m) ; S
275    
276    ## indexing [i]  vs  [i,] --- now ok
277    stopifnot(identical4(m[i], M[i], D[i], s[i]), identical(s[i],S[i]))
278    stopifnot(identical4(m[L], M[L], D[L], s[L]), identical(s[L],S[L]))
279    assert.EQ.mat(D[i,], m[i,])
280    assert.EQ.mat(M[i,], m[i,])
281    assert.EQ.mat(s[i,], m[i,])
282    assert.EQ.mat(S[i,], m[i,])
283    
284    assert.EQ.mat(D[,i], m[,i])
285    assert.EQ.mat(M[,i], m[,i])
286    assert.EQ.mat(s[,i], m[,i])
287    assert.EQ.mat(S[,i], m[,i])
288    
289    
290  ## --- negative indices ----------  ## --- negative indices ----------
291  mc <- mC[1:5, 1:7]  mc <- mC[1:5, 1:7]
292  mt <- mT[1:5, 1:7]  mt <- mT[1:5, 1:7]
# Line 255  Line 373 
373  mc[4,] <- 0  mc[4,] <- 0
374  mc  mc
375    
376    S <- as(Diagonal(5),"sparseMatrix")
377  H <- Hilbert(9)  H <- Hilbert(9)
378  Hc <- as(round(H, 3), "dsCMatrix")# a sparse matrix with no 0 ...  Hc <- as(round(H, 3), "dsCMatrix")# a sparse matrix with no 0 ...
379  (trH <- tril(Hc[1:5, 1:5]))  (trH <- tril(Hc[1:5, 1:5]))
380  stopifnot(is(trH, "triangularMatrix"), trH@uplo == "L")  stopifnot(is(trH, "triangularMatrix"), trH@uplo == "L",
381              is(S, "triangularMatrix"))
382    
383    ## triangular assignment
384    ## the slick (but inefficient in case of sparse!) way to assign sub-diagonals:
385    ## equivalent to tmp <- `diag<-`(S[,-1], -2:1); S[,-1] <- tmp
386    ## which dispatches to (x="TsparseMatrix", i="missing",j="index", value="replValue")
387    diag(S[,-1]) <- -2:1 # used to give a wrong warning
388    ## FIXME? the above *could* return triangular -- but for that
389    
390    trH[c(1:2,4), c(2:3,5)] <- 0 # gave an *error* upto Jan.2008
391    trH[ lower.tri(trH) ] <- 0   # ditto, because of callNextMethod()
392    
393    m <- Matrix(0+1:28, nrow = 4)
394    m[-3,c(2,4:5,7)] <- m[ 3, 1:4] <- m[1:3, 6] <- 0
395    mT <- as(m, "dgTMatrix")
396    stopifnot(identical(mT[lower.tri(mT)],
397                        m [lower.tri(m) ]))
398    lM <- upper.tri(mT, diag=TRUE)
399    mT[lM] <- 0
400     m[lM] <- 0
401    assert.EQ.mat(mT, as(m,"matrix"))
402    mT[lM] <- -1:0
403     m[lM] <- -1:0
404    assert.EQ.mat(mT, as(m,"matrix"))
405    (mT <- drop0(mT))
406    
407  i <- c(1:2, 4, 6:7); j <- c(2:4,6)  i <- c(1:2, 4, 6:7); j <- c(2:4,6)
408  H[i,j] <- 0  H[i,j] <- 0
# Line 270  Line 414 
414    
415  ## an example that failed for a long time  ## an example that failed for a long time
416  sy3 <- new("dsyMatrix", Dim = as.integer(c(2, 2)), x = c(14, -1, 2, -7))  sy3 <- new("dsyMatrix", Dim = as.integer(c(2, 2)), x = c(14, -1, 2, -7))
417  validObject(dm <- kronecker(Diagonal(2), sy3))  validObject(dm <- kronecker(Diagonal(2), sy3))# now sparse with new kronecker
418    dm <- Matrix(as.matrix(dm))# -> "dsyMatrix"
419  (s2 <- as(dm, "sparseMatrix"))  (s2 <- as(dm, "sparseMatrix"))
420  validObject(st <- as(s2, "TsparseMatrix"))  validObject(st <- as(s2, "TsparseMatrix"))
421    stopifnot(is(s2, "symmetricMatrix"),
422              is(st, "symmetricMatrix"))
423  validObject(s.32  <- st[1:3,1:2]) ## 3 x 2 - and *not* dsTMatrix  validObject(s.32  <- st[1:3,1:2]) ## 3 x 2 - and *not* dsTMatrix
424  validObject(s2.32 <- s2[1:3,1:2])  validObject(s2.32 <- s2[1:3,1:2])
425  I <- c(1,4:3)  I <- c(1,4:3)
# Line 325  Line 472 
472  validObject(x.x)  validObject(x.x)
473  stopifnot(class(x.x) == "dsCMatrix",  stopifnot(class(x.x) == "dsCMatrix",
474            12:16 == as.mat(x.x)[cbind(2:6, 2:6)])            12:16 == as.mat(x.x)[cbind(2:6, 2:6)])
475    (ne1 <- (mc - m.) != 0)
476    stopifnot(identical(ne1, 0 != abs(mc - m.)))
477    (ge <- m. >= mc) # contains "=" -> result is dense
478    ne. <- mc != m.  # was wrong (+ warning)
479    stopifnot(identical(!(m. < mc), m. >= mc),
480              identical(m. < mc, as(!ge, "sparseMatrix")),
481              identical(ne., Matrix:::drop0(ne1)))
482    
483    (M3 <- Matrix(upper.tri(matrix(, 3, 3)))) # ltC; indexing used to fail
484    T3 <- as(M3, "TsparseMatrix")
485    stopifnot(identical(drop(M3), M3),
486              identical4(drop(M3[,2, drop = FALSE]), M3[,2, drop = TRUE],
487                         drop(T3[,2, drop = FALSE]), T3[,2, drop = TRUE]),
488              is(T3, "triangularMatrix"),
489              !is(T3[,2, drop=FALSE], "triangularMatrix")
490              )
491    
492  cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons''  cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons''

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