som(som)
som()所属R语言包：som
Function to train a Self-Organizing Map
自组织映射功能训练
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Produces an object of class "som" which is a Self-Organizing Map fit of the data.
生成一个对象类的“SOM”这是一个自组织映射适合的数据。
用法----------Usage----------
som.init(data, xdim, ydim, init="linear")
som(data, xdim, ydim, init="linear", alpha=NULL, alphaType="inverse",
neigh="gaussian", topol="rect", radius=NULL, rlen=NULL, err.radius=1,
inv.alp.c=NULL)
som.train(data, code, xdim, ydim, alpha=NULL, alphaType="inverse",
neigh="gaussian", topol="rect", radius=NULL, rlen=NULL, err.radius=1, inv.alp.c=NULL)
som.update(obj, alpha = NULL, radius = NULL, rlen = NULL, err.radius =
1, inv.alp.c = NULL)
som.project(obj, newdat)
参数----------Arguments----------
参数：obj
a "som" object.
“SOM的对象。
参数：newdat
a new dataset needs to be projected onto the map.
一个新的数据集需要被投影到图。
参数：code
a matrix of initial code vector in the map.
图中的一个矩阵的初始代码矢量。
参数：data
a data frame or matrix of input data.
一个数据框或矩阵的输入数据。
参数：xdim
an integer specifying the x-dimension of the map.
的整数指定x维的图。
参数：ydim
an integer specifying the y-dimension of the map.
一个整数指定的y维度的图。
参数：init
a character string specifying the initializing method. The following are permitted: "sample" uses a radom sample from the data; "random" uses random draws from N(0,1); "linear" uses the linear grids upon the first two principle components directin.
一个字符串，指定初始化方法。以下是允许的："sample"使用一个无规则从数据样本;"random"使用随机从N（0,1）;"linear"使用线性网格时的前两个主成分directin 。
参数：alpha
a vector of initial learning rate parameter for the two training phases. Decreases linearly to zero during training.
一个向量的两个训练阶段的初始学习率参数。呈线性下降到零在训练中。
参数：alphaType
a character string specifying learning rate funciton type. Possible choices are linear function ("linear") and inverse-time type function ("inverse").
一个字符的字符串，指定学习速率功能可按类型。可能的选择是线性函数（"linear"）和反时限类型的函数（"inverse"“）。
参数：neigh
a character string specifying the neighborhood function type. The following are permitted:
一个字符串指定的邻域函数的类型。以下是允许的：
"bubble" "gaussian"
"bubble""gaussian"
参数：topol
a character string specifying the topology type when measuring distance in the map. The following are permitted:
在图上测量距离时，指定的拓扑结构类型的字符串。以下是允许的：
"hexa" "rect"
"hexa""rect"
参数：radius
a vector of initial radius of the training area in som-algorithm for the two training phases. Decreases linearly to one during training.
一个向量的初始半径在SOM算法的两个训练阶段的训练区。线性减小，在训练。
参数：rlen
a vector of running length (number of steps) in the two training phases.
在两个训练阶段的运行长度（步数）的向量。
参数：err.radius
a numeric value specifying the radius when calculating average distortion measure.
一个数字值，该值指定半径计算平均失真测量时。
参数：inv.alp.c
the constant C in the inverse learning rate function: alpha0 * C / (C + t);
逆学习率函数的常数C：alpha0 * C /（C + T）;
值----------Value----------
"som.init" initializes a map and returns the code matrix. "som" does the two-step som training in a batch fashion and return a "som" object. "som.train" takes data, code, and traing parameters and perform the requested som training. "som.update" takes a "som" object and further train it with updated paramters. "som.project" projects new data onto the map.
“som.init初始化图和返回码矩阵。 “索姆”做两步SOM的培训，一批时尚，并返回一个SOM的对象。 “som.train需要的数据，代码和训练探索参数，并执行所请求的SOM训练。 “som.update”SOM的对象，并进一步培养与更新的参数研究。 “som.project”项目新的数据到图上。
An object of class "som" representing the fit, which is a list containing the following components:
类的一个对象"som"的配合，这是一个列表，其中包含以下组件：
<table summary="R valueblock"> <tr valign="top"><td>data</td> <td> the dataset on which som was applied.</td></tr> <tr valign="top"><td>init</td> <td> a character string indicating the initializing method.</td></tr> <tr valign="top"><td>xdim</td> <td> an integer specifying the x-dimension of the map.</td></tr> <tr valign="top"><td>ydim</td> <td> an integer specifying the y-dimension of the map.</td></tr> <tr valign="top"><td>code</td> <td> a metrix with nrow = xdim*ydim, each row corresponding to a code vector of a cell in the map. The mapping from cell coordinate (x, y) to the row index in the code matrix is: rownumber = x + y * xdim </td></tr> <tr valign="top"><td>visual</td> <td> a data frame of three columns, with the same number of rows as in data: x and y are the coordinate of the corresponding observation in the map, and qerror is the quantization error computed as the squared distance (depends topol) between the observation vector and its coding vector. </td></tr> <tr valign="top"><td>alpha0</td> <td> a vector of initial learning rate parameter for the two training phases.</td></tr> <tr valign="top"><td>alpha</td> <td> a character string specifying learning rate funciton type.</td></tr> <tr valign="top"><td>neigh</td> <td> a character string specifying the neighborhood function type.</td></tr> <tr valign="top"><td>topol</td> <td> a character string specifying the topology type when measuring distance in the map.</td></tr> <tr valign="top"><td>radius0</td> <td> a vector of initial radius of the training area in som-algorithm for the two training phases.</td></tr> <tr valign="top"><td>rlen</td> <td> a vector of running length in the two training phases.</td></tr> <tr valign="top"><td>qerror</td> <td> a numeric value of average distortion measure.</td></tr> <tr valign="top"><td>code.sum</td> <td> a dataframe summaries the number of observations in each map cell.</td></tr> </table>
<table summary="R valueblock"> <tr valign="top"> <TD>data </ TD> <TD>索姆应用的数据集。</ TD> </ TR> <TR VALIGN =“”> <TD>init </ TD> <td>一个字符串，表示初始化方法。</ TD> </ TR> <tr valign="top"> <TD> X> </ TD> <TD>一个整数，指定x方向的图。</ TD> </ TR> <tr valign="top"> <TD> xdim </ TD> < TD>的整数，指定y维度的图。</ TD> </ TR> <tr valign="top"> <TD> ydim</ TD> <td>一个Metrix的，与NROW = xdim * ydim，对应的码矢量图中的单元格的每一行。单元坐标（X，Y）码矩阵中的行索引的映射是：rownumber =所述+ Y * xdim </ TD> </ TR> <tr valign="top"> <TD>code， </ TD> <td>一个数据框的三列，与相同数量的行数据：x和y是在图上的相应观察的坐标，和qerror是量化误差的平方距离作为计算（取决于拓扑）之间的观察矢量和其编码矢量。 </ TD> </ TR> <tr valign="top"> <TD> visual</ TD> <td>一个矢量的初始学习率参数的两个训练阶段。</ TD> </ TR> <tr valign="top"> <TD> alpha0 </ TD> <td>一个字符串指定学习功能可按类型。</ TD> </ TR> <tr valign="top"> <TD> alpha </ TD> <td>一个指定的邻域函数类型的字符串。</ TD> </ TR> <tr valign="top"> <TD>neigh</ TD> <td>一个字符串指定的拓扑结构类型时，在图上测量距离。</ TD> </ TR> <tr valign="top"> <TD> topol</ TD> <TD>一个向量的初始半径在SOM算法的两个训练阶段的训练区。</ TD> </ TR> <tr valign="top"> <TD>radius0</ TD> <TD>在这两个训练阶段运行长度的向量。</ TD> </ TR> <tr valign="top"> <TD>rlen</ TD> <td>一个平均失真测量的数值。 </ TD> </ TR> <tr valign="top"> <TD>qerror </ TD> <td>一个数据框总结了若干意见，在每个图的单元。</ TD> </ TR > </ TABLE>
（作者）----------Author(s)----------
Jun Yan <jyan@stat.uiowa.edu>
参考文献----------References----------
Kohonen, Hynninen, Kangas, and Laaksonen (1995), SOM-PAK, the Self-Organizing Map Program Package (version 3.1).
实例----------Examples----------
data(yeast)
yeast <- yeast[, -c(1, 11)]
yeast.f <- filtering(yeast)
yeast.f.n <- normalize(yeast.f)
foo <- som(yeast.f.n, xdim=5, ydim=6)
foo <- som(yeast.f.n, xdim=5, ydim=6, topol="hexa", neigh="gaussian")
plot(foo)
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 2012-9-30 11:52:37
