####  eval / parse / deparse / substitute ...

####  Part 2
####  ======  Recommended packages allowed  .. output tests *sloppily*

#### This file is skipped without recommended packages.

srcdir <- file.path(Sys.getenv("SRCDIR"), "eval-fns.R")
source(if(file.exists(srcdir)) srcdir else "./eval-fns.R", echo = TRUE)
rm("srcdir")

require("Matrix")
D5. <- Diagonal(x = 5:1)
D5N <- D5.; D5N[5,5] <- NA
## a subset/version of example(Matrix) : --------------------------------

(Z32 <- Matrix(0, 3, 2))              # 3 by 2 matrix of zeros -> sparse
(z32 <- Matrix(0, 3, 2, sparse=FALSE))# -> 'dense'

## 4 cases - 3 different results :
## TODO (Z22  <- Matrix(0, 2, 2))              # diagonal from Matrix 1.3.* on
(Z22. <- Matrix(0, 2, 2, sparse=FALSE))# (ditto)
(Z22s <- Matrix(0, 2, 2,               doDiag=FALSE))# -> sparse symm. "dsCMatrix"
(Z22d <- Matrix(0, 2, 2, sparse=FALSE, doDiag=FALSE))# -> dense  symm. "dsyMatrix"

## logical ones:
(L4  <- Matrix(diag(4) >  0)) # -> "ldiMatrix" with diag = "U"
## TODO (L4. <- Matrix(diag(4) >  0, sparse=TRUE)) #  ditto, from Matrix 1.3.* on
(L4d <- Matrix(diag(4) >= 0)) # -> "lsyMatrix" (of all 'TRUE')
## triangular
l3 <- upper.tri(matrix(,3,3))
(M <- Matrix(l3))               # "ltCMatrix"
(Nl3 <- Matrix(! l3))           # "ltrMatrix"
(l3s <- as(l3, "CsparseMatrix"))# "lgCMatrix"

(I3 <- Matrix(diag(3)))# identity, i.e., unit "diagonalMatrix"

(ad <- cbind(a=c(2,1), b=1:2))# symmetric *apart* from dimnames
(As <- Matrix(ad, dimnames = list(NULL,NULL)))# -> symmetric
forceSymmetric(ad) # also symmetric, w/ symm. dimnames
stopifnot(is(As, "symmetricMatrix"),
          is(Matrix(0, 3,3), "sparseMatrix"),
          is(Matrix(FALSE, 1,1), "sparseMatrix"))

## a subset from  example(sparseMatrix) : -------------------------------
i <- c(1,3:8); j <- c(2,9,6:10); x <- 7 * (1:7)
A <- sparseMatrix(i, j, x = x)
sA <- sparseMatrix(i, j, x = x, symmetric = TRUE)
tA <- sparseMatrix(i, j, x = x, triangular= TRUE)
## dims can be larger than the maximum row or column indices
AA <- sparseMatrix(c(1,3:8), c(2,9,6:10), x = 7 * (1:7), dims = c(10,20))
## i, j and x can be in an arbitrary order, as long as they are consistent
set.seed(1); (perm <- sample(1:7))
A1 <- sparseMatrix(i[perm], j[perm], x = x[perm])
## the (i,j) pairs can be repeated, in which case the x's are summed
args <- data.frame(i = c(i, 1), j = c(j, 2), x = c(x, 2))
Aa <- do.call(sparseMatrix, args)
A. <- do.call(sparseMatrix, c(args, list(use.last.ij = TRUE)))
## for a pattern matrix, of course there is no "summing":
nA <- do.call(sparseMatrix, args[c("i","j")])
dn <- list(LETTERS[1:3], letters[1:5])
## pointer vectors can be used, and the (i,x) slots are sorted if necessary:
m <- sparseMatrix(i = c(3,1, 3:2, 2:1), p= c(0:2, 4,4,6), x = 1:6, dimnames = dn)
## no 'x' --> patter*n* matrix:
n <- sparseMatrix(i=1:6, j=rev(2:7))
## an empty sparse matrix:
e <- sparseMatrix(dims = c(4,6), i={}, j={})
## a symmetric one:
sy <- sparseMatrix(i= c(2,4,3:5), j= c(4,7:5,5), x = 1:5,
                   dims = c(7,7), symmetric=TRUE)


runEPD_checks() # Action!

summary(warnings())
## at the very end
cat('Time elapsed: ', proc.time(), "\n")