Edo
2020-11-30 20:29:07
的组合cbind，并rbind会做的伎俩。我在p中添加了一个附加元素，以表明它甚至可以使用更多名称。
# your data EDITED BASED ON THE DPUT you posted!!
p <- c(Afghanistan = 2769, Albania = 93893300, Algeria = 0, Argentina=4550430, Australia=50, Ban = 2)
p <- stack(p)
names(p) <- c("Value", "Area")
Z <- matrix(c(0,138201.333333333,0,0,0,0,1162.33333333333,0,38.3333333333333,0,0,0,0,0,0,0,300238,0,9675,0), nrow = 4, ncol = 4, byrow=TRUE)
dimnames(Z) = list(c("Afghanistan","Albania","Algeria", "Argentina"),c("Afghanistan","Albania","Algeria", "Argentina")) #matrix
# solution
to_add <- setdiff(p[p$Value>0, "Area"], unlist(dimnames(Z)))
Z <- cbind(Z, matrix(0, nrow(Z), length(to_add), dimnames = list(NULL, to_add)))
Z <- rbind(Z, matrix(0, length(to_add), ncol(Z), dimnames = list(to_add, NULL)))
Z
#> Afghanistan Albania Algeria Argentina Australia Ban
#> Afghanistan 0.00000 138201.3 0.000 0 0 0
#> Albania 0.00000 0.0 1162.333 0 0 0
#> Algeria 38.33333 0.0 0.000 0 0 0
#> Argentina 0.00000 0.0 0.000 0 0 0
#> Australia 0.00000 0.0 0.000 0 0 0
#> Ban 0.00000 0.0 0.000 0 0 0
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Mamba is a new state space model architecture showing promising performance on information-dense data such as language modeling, where previous subquadratic models fall short of Transformers. It is based on the line of progress on structured state space models, with an efficient hardware-aware design and implementation in the spirit of FlashAttention.
(翻译：Mamba 是一种新的状态空间模型架构，在信息密集型数据（例如语言建模）上显示出良好的性能，而之前的二次模型在 Transformers 方面存在不足。它基于结构化状态空间模型的进展，并本着FlashAttention的精神进行高效的硬件感知设计和实现。)
谢谢，尽管我不确定如何在实际数据中使用此代码，但Z是从csv创建的大型矩阵。我尝试了您的代码，但它给了我“在Ops.factor（left，right）：'>'对因子没有意义。”
请尝试进行此编辑。
您的错误看起来像是在创建时发生的事情
to_add。请确保它class(p$p)是数字。否则，请告诉我。嗯
class(p$p)给我"NULL"请分享
dput(p)。看起来p数据帧的列未称为p。那就是产生您的问题的原因。