wavBestBasis(wmtsa)
wavBestBasis()所属R语言包：wmtsa
DWPT Best basis selection
DWPT选择最好的基础
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The discrete wavelet packet transform (DWPT) contains a multitude of disjoint dyadic decompositions representing an ensemble of different bases. Best basis selection is an attempt to isloate one such basis
离散小波包变换（DWPT）的包含了多个不相交的二进分解合奏不同的碱基。选择最好的基础是企图，isloate这样一个基础
用法----------Usage----------
wavBestBasis(costs)
参数----------Arguments----------
参数：costs
a numeric vector containing the costs for each crystal in a DWPT in C(W(0,0)), C(W(1,0)), C(W(1,1)), C(W(2,0)), ..., C(W(J,2^J-1)) order where C() is the additive cost functional and W(j,n) is the DWPT crystal at level j and oscillation (local node) index n for j=1,...,J.
一个数字向量的成本，每一个水晶C(W(0,0)), C(W(1,0)), C(W(1,1)), C(W(2,0)), ..., C(W(J,2^J-1))为了在DWPTC()是功能性的添加剂成本和W(j,n)是DWPT的水晶级j和振荡（本地节点）指数nj=1,...,J的。
参考文献----------References----------
Ronald R. Coifman and Mladen Victor Wickerhauser, “Entropy-Based Algorithms for Best Basis Selection", IEEE Transactions on Information Theory, 38(2), pp. 713–718, 1992.
D. B. Percival and A. T. Walden, Wavelet Methods for Time Series Analysis, Cambridge University Press, 2000.
参见----------See Also----------
实例----------Examples----------
## calculate the DWPT of the difference of the [＃计算的DWPT，差异化的]
## atomic clock series [＃原子钟系列]
W <- wavDWPT(diff(atomclock), n.level=6)
## define an entropy cost functional [＃定义一个熵成本的功能]
"entropy" <- function(x){
iz <- which(x==0)
z <- -x^2 * log(x^2)
if (length(iz))
z[iz] <- 0
sum(z)
}
## create the cost vector [＃创建成本向量]
C <- unlist(lapply(W$data, entropy))
## calculate the bets basis [＃计算的赌注基础]
z <- wavBestBasis(C)
## print the crystals of the best basis [＃打印晶体的最佳基础]
paste("W(", z$level, ",", z$osc, ")", sep="")
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-10-1 22:51:48
