ComputeM(GraphAlignment)
ComputeM()所属R语言包:GraphAlignment
Compute score matrix M
计算得分矩阵M
译者:生物统计家园网 机器人LoveR
描述----------Description----------
Compute the score matrix M.
计算得分矩阵M
用法----------Usage----------
ComputeM(A, B, R, P, linkScore, selfLinkScore, nodeScore1,
nodeScore0, lookupLink, lookupNode, clamp=TRUE)
参数----------Arguments----------
参数:A
adjacency matrix for network A
邻接矩阵为网络A
参数:B
adjacency matrix for network B
网络B的邻接矩阵
参数:R
node similarity matrix
节点相似矩阵
参数:P
permutation vector to be used as the initial alignment (see InitialAlignment)
置换向量被用来作为初始对准(见InitialAlignment)
参数:linkScore
link score matrix (see ComputeLinkParameters)
链接得分矩阵(见ComputeLinkParameters)
参数:selfLinkScore
self link score matrix (see ComputeLinkParameters)
自链接得分矩阵(见ComputeLinkParameters)
参数:nodeScore1
node score vector (s1) (see ComputeNodeParameters)
节点的得分矢量(S1)(见ComputeNodeParameters)
参数:nodeScore0
node score vector for unaligned nodes (s0) (see ComputeNodeParameters)
未对齐节点的节点得分向量(S0)(见ComputeNodeParameters)
参数:lookupLink
link bin lookup table (see GetBinNumber)
链接斌查找表(见GetBinNumber)
参数:lookupNode
node bin lookup table (see GetBinNumber)
节点槽查找表(见GetBinNumber)
参数:clamp
clamp values to range when performing bin lookups
钳位值范围时执行的bin查找
Details
详情----------Details----------
This function computes the score Matrix M from the network adjacency matrices A and B, the node similarity matrix R, an alignment P (given as a permutation vector) and the node and link scores with their associated binning information. The alignment P is either generated by the previous iterative step, or, initially, by using InitialAlignment. The matrix M is then given to the linear assignment solver to compute the new alignment.
此函数计算从网络邻接矩阵的得分矩阵M A和B,节点相似矩阵R,对齐P(下作为排列向量给出)和与他们相关的分级信息的节点和链路的分数。要么产生对齐P是由先前的迭代步骤,或者,最初使用InitialAlignment。矩阵M,然后给线性分配求解器计算新的路线。
值----------Value----------
The return value is the score matrix M.
返回值是得分矩阵M
作者(S)----------Author(s)----------
Joern P. Meier, Michal Kolar, Ville Mustonen, Michael Laessig, and Johannes Berg
举例----------Examples----------
ex<-GenerateExample(dimA=22, dimB=22, filling=.5, covariance=.6,
symmetric=TRUE, numOrths=10, correlated=seq(1,18))
pinitial<-InitialAlignment(psize=34, r=ex$r, mode="reciprocal")
lookupLink<-seq(-2,2,.5)
linkParams<-ComputeLinkParameters(ex$a, ex$b, pinitial, lookupLink)
lookupNode<-c(-.5,.5,1.5)
nodeParams<-ComputeNodeParameters(dimA=22, dimB=22, ex$r,
pinitial, lookupNode)
ComputeM(A=ex$a, B=ex$b, R=ex$r, P=pinitial,
linkScore=linkParams$ls,
selfLinkScore=linkParams$ls,
nodeScore1=nodeParams$s1, nodeScore0=nodeParams$s0,
lookupLink=lookupLink, lookupNode=lookupNode)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注:
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