sparseVector-class(Matrix)
sparseVector-class()所属R语言包：Matrix
Sparse Vector Classes
稀疏向量类
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Sparse Vector Classes: The virtual mother class "sparseVector" has the five actual daughter classes "dsparseVector", "isparseVector", "lsparseVector", "nsparseVector", and "zsparseVector", where we've mainly implemented methods for the d*, l* and n* ones.
稀疏向量类：虚拟母亲类"sparseVector"有实际的五个女儿类"dsparseVector"，"isparseVector"，"lsparseVector"，"nsparseVector"，"zsparseVector"我们主要实施d*，l*和n*的方法。
插槽----------Slots----------
length: class "numeric" - the length of the sparse vector. Note that "numeric" can be considerably larger than the maximal "integer",
length：类"numeric" - length稀疏向量。注意"numeric"可以大大超过最大"integer"，
i: class "numeric" - the (1-based) indices of the non-zero entries. Must not be NA and strictly sorted increasingly.
i：类"numeric" - 非零项（1）指数。绝不能NA和越来越严格排序。
Note that "integer" is “part of” "numeric",
注意"integer""numeric"“一部分”，
x: (for all but "nsparseVector"): the non-zero entries. This is of class "numeric" for class "dsparseVector", "logical" for class "lsparseVector", etc.
x：（为所有，但"nsparseVector"）：非零项。这是类"numeric"类"dsparseVector"，"logical"为类的"lsparseVector"，等。
Note that "nsparseVector"s have no x slot. Further, mainly for ease of method definitions, we've defined the class union (see setClassUnion) of all sparse vector classes which have an x slot, as class "xsparseVector".
请注意，"nsparseVector"S有没有x插槽。此外，主要用于方便的方法定义，我们定义的类工会（见setClassUnion）的所有稀疏向量类，其中有一个x插槽，类"xsparseVector"。
方法----------Methods----------
length signature(x = "sparseVector"): simply extracts
长度signature(x = "sparseVector")：只需提取物
show signature(object = "sparseVector"): The show method for sparse vectors prints “structural” zeroes as "." using the non-exported prSpVector function which allows further customization such as replacing "." by " " (blank).
显示signature(object = "sparseVector")：show稀疏向量的方法，打印的“结构性”零"."使用非出口prSpVector功能，可进一步定制，如更换<X >"."（空）。
Note that options(max.print) will influence how many
请注意，options(max.print)会影响多少
as.vector signature(x = "sparseVector", mode = "character") coerces sparse vectors to “regular”, i.e., atomic vectors.
as.vectorsignature(x = "sparseVector", mode = "character")胁迫“常规”，即原子向量的稀疏向量。
as ..: see coerce below
如..：看到coerce下面
coerce signature(from = "sparseVector", to = "sparseMatrix"), and
强制signature(from = "sparseVector", to = "sparseMatrix")，
coerce signature(from = "sparseMatrix", to = "sparseVector"), etc: coercions to and from sparse matrices (sparseMatrix) are provided and work analogously as in standard R, i.e., a vector is
强制signature(from = "sparseMatrix", to = "sparseVector")等强制（sparseMatrix）和工作标准的R，即类似于一个向量的稀疏矩阵
dim<- signature(x = "sparseVector", value = "integer") coerces a sparse vector to a sparse Matrix, i.e., an object inheriting from sparseMatrix, of the
昏暗< - signature(x = "sparseVector", value = "integer")胁迫稀疏向量的稀疏矩阵，即从sparseMatrix继承的对象，
head signature(x = "sparseVector"): as with R's (package util) head, head(x,n) (for n >= 1) is equivalent to x[1:n], but here can be much
头signature(x = "sparseVector")：R的（包util）head，head(x,n)（为n >= 1）x[1:n]相当于，但在这里可以多
tail signature(x = "sparseVector"): analogous to
尾巴signature(x = "sparseVector")：类似于
rep signature(x = "sparseVector") repeat x, with the same argument list (x, times, length.out, each,
代表signature(x = "sparseVector")重复x，用相同的参数列表的<code>（X，时间，length.out，每个
Ops signature(e1 = "sparseVector", e2 = "*"): define arithmetic, compare and logic operations, (see
OPSsignature(e1 = "sparseVector", e2 = "*")：定义运算，比较和逻辑运算，（见
Summary signature(x = "sparseVector"): define
摘要signature(x = "sparseVector")：定义
[ signature(x = "atomicVector", i = ...): not only can you subset (aka “index into”) sparseVectors x[i] using sparseVectors i, but we also support efficient subsetting of traditional vectors x by logical sparse vectors (i.e., i of class "nsparseVector" or
[signature(x = "atomicVector", i = ...)：你不仅可以子集（又名“索引”）sparseVectorsx[i]使用sparseVectors i，但我们也支持传统向量的高效子集x逻辑稀疏向量（即i类"nsparseVector"
举例----------Examples----------
getClass("sparseVector")
getClass("dsparseVector")
getClass("xsparseVector")# those with an 'x' slot[那些有一个“x”槽]
sx <- c(0,0,3, 3.2, 0,0,0,-3:1,0,0,2,0,0,5,0,0)
(ss <- as(sx, "sparseVector"))
ix <- as.integer(round(sx))
(is <- as(ix, "sparseVector"))
## an "isparseVector" (!)[＃“isparseVector”的（！）]
## rep() works too:[＃代表（）工作：]
(ri <- rep(is, length.out= 25))
## Using `dim<-` as in base R :[＃昏暗< - 基地ŕ：]
r <- ss
dim(r) <- c(4,5) # becomes a sparse Matrix:[成为一个稀疏矩阵：]
r
## or coercion (as as.matrix() in base R):[＃胁迫（如as.matrix（在基地ŕ））：]
as(ss, "Matrix")
stopifnot(all(ss == print(as(ss, "CsparseMatrix"))))
## currently has "non-structural" FALSE -- printing as ":"[＃目前有“非结构性”假 - 印刷“：”]
(lis <- is & FALSE)
(nn <- is[is == 0]) # all "structural" FALSE[所有的“结构性”假]
## NA-case[＃不适用的情况]
sN <- sx; sN[4] <- NA
(svN <- as(sN, "sparseVector"))
v <- as(c(0,0,3, 3.2, rep(0,9),-3,0,-1, rep(0,20),5,0),
"sparseVector")
v <- rep(rep(v, 50), 5000)
set.seed(1); v[sample(v@i, 1e6)] <- 0
str(v)
system.time(for(i in 1:4) hv <- head(v, 1e6))
## user system elapsed[＃用户系统经过]
## 0.033 0.000 0.032[＃0.033 0.000 0.032]
system.time(for(i in 1:4) h2 <- v[1:1e6])
## user system elapsed[＃用户系统经过]
## 1.317 0.000 1.319[＃1.317 0.000 1.319]
stopifnot(identical(hv, h2),
identical(is | FALSE, is != 0),
validObject(svN), validObject(lis), as.logical(is.na(svN[4])),
identical(is^2 > 0, is & TRUE),
all(!lis), !any(lis), length(nn@i) == 0, !any(nn), all(!nn),
sum(lis) == 0, !prod(lis), range(lis) == c(0,0))
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注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-2-16 17:47:56
