pcaNet(pcaMethods)
pcaNet()所属R语言包：pcaMethods
Class representation of the NLPCA neural net
类表示NLPCA神经网络
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This is a class representation of a non-linear PCA neural network. The nlpcaNet class is not meant for user-level
这是一个非线性PCA神经网络的类表示。 nlpcaNet类并不意味着用户级
Details
详情----------Details----------
Creating Objects
创建对象
new("nlpcaNet", net=[the network structure], hierarchic=[hierarchic design], fct=[the functions at each layer], fkt=[the functions used for forward propagation], weightDecay=[incremental decrease of weight changes over iterations (between 0 and 1)], featureSorting=[sort features or not], dataDist=[represents the present values], inverse=[net is inverse mode or not], fCount=[amount of times features were sorted], componentLayer=[which layer is the 'bottleneck' (principal components)], erro=[the used error function], gradient=[the used gradient method], weights=[the present weights], maxIter=[the amount of iterations that was done], scalingFactor=[the scale of the original matrix])
new("nlpcaNet", net=[the network structure], hierarchic=[hierarchic design], fct=[the functions at each layer], fkt=[the functions used for forward propagation], weightDecay=[incremental decrease of weight changes over iterations (between 0 and 1)], featureSorting=[sort features or not], dataDist=[represents the present values], inverse=[net is inverse mode or not], fCount=[amount of times features were sorted], componentLayer=[which layer is the 'bottleneck' (principal components)], erro=[the used error function], gradient=[the used gradient method], weights=[the present weights], maxIter=[the amount of iterations that was done], scalingFactor=[the scale of the original matrix])
Slots
插槽
net "matrix", matrix showing the representation of the neural network, e.g. (2,4,6) for a network with two features, a
净“矩阵”，矩阵显示神经网络的代表，例如： （2,4,6）有两个功能，网络
hierarchic "list", the hierarchic design of the network, holds 'idx' (), 'var' () and layer (which layer is the principal
层次“列表”，网络的层次设计，拥有IDX（）VAR（）和层（哪一层是主要的
fct "character", a vector naming the functions that will be applied on each layer. "linr" is linear (i.e.) standard matrix products and "tanh" means that the arcus tangens is applied on the
FCT的“性格”，矢量命名，将适用于每一层的功能。 “linr”是线性（IE）的标准矩阵产品“的tanh”上的应用，弓tangens
fkt "character", same as fct but the functions used during
FKT“性格”，作为FCT相同，但使用过程中的功能
weightDecay "numeric", the value that is used to
weightDecay“数字”，用来值
featureSorting "logical", indicates if features will be sorted or not. This is used to make the NLPCA assume properties closer to those of standard PCA were the first component is more
featureSorting“逻辑”，表示如果功能进行排序或没有。这是用来做NLPCA承担物业接近标准PCA的第一部分是
dataDist "matrix", a matrix of ones and zeroes indicating
dataDist“矩阵”的，并表示零矩阵
inverse "logical", network is inverse mode (currently only inverse is supported) or not. Eg. the case when we have truly
逆“逻辑”，网络是反模式（目前只支持逆）或不。例如。当我们有真正的情况下，
fCount "integer", Counter for the amount of times features
FCOUNT“整数”，时代特征量计数器
componentLayer "numeric", the index of 'net' that is the
componentLayer“数字”，“净”的指数，是
error "function", the used error function. Currently only one
错误“功能”，使用的误差函数。目前只有一个
gradient "function", the used gradient function. Currently
梯度“功能”，用于梯度功能。目前
weights "list", A list holding managements of the weights. The list has two functions, weights$current() and weights$set() which access a matrix in the local environment of
砝码“列表”，权重列表管理。列表中有两个功能，重量为（）和重量访问在当地环境中的矩阵元一套（）
maxIter "integer", the amount of iterations used to train
maxIter“整数”，用来训练量迭代
scalingFactor "numeric", training the network is best made with 'small' values so the original data is scaled down to a
scalingFactor“数字”，是最好的培训网络与小的价值观，使原始数据缩减到
Methods
方法
vector2matrices Returns the
vector2matrices返回
作者（S）----------Author(s)----------
Henning Redestig
参见----------See Also----------
nlpca
nlpca
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。
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发表于 2012-2-26 10:51:34
