小编Ram*_*cha的帖子

我写了这个简单的程序,从txt文件加载矩阵并计算距离.在Windows上的visual studio中编译程序时,我遇到以下错误:

1>main.obj : error LNK2019: unresolved external symbol "void __cdecl cv::fastFree(void *)" (?fastFree@cv@@YAXPAX@Z) referenced in function "public: __thiscall     cv::Mat::~Mat(void)" (??1Mat@cv@@QAE@XZ)
1>system.obj : error LNK2001: unresolved external symbol "void __cdecl cv::fastFree(void *)" (?fastFree@cv@@YAXPAX@Z)
1>main.obj : error LNK2019: unresolved external symbol "public: void __thiscall cv::Mat::deallocate(void)" (?deallocate@Mat@cv@@QAEXXZ) referenced in function "public: void __thiscall cv::Mat::release(void)" (?release@Mat@cv@@QAEXXZ)
1>system.obj : error LNK2001: unresolved external symbol "public: void __thiscall cv::Mat::deallocate(void)" (?deallocate@Mat@cv@@QAEXXZ)
1>main.obj : error LNK2019: unresolved external symbol "int __cdecl cv::_interlockedExchangeAdd(int *,int)" (?_interlockedExchangeAdd@cv@@YAHPAHH@Z) referenced in function …

我想将大数据矩阵(500万X 512)与kmeans聚类到5000个中心.我正在使用R,以免用这个矩阵来打破我的记忆.

我编写了这段代码,将txt矩阵转换为xdf,然后转换为cluster:

rxTextToXdf(inFile = inFile, outFile = outFile)
vars <- rxGetInfo(outFile,getVarInfo=TRUE)
myformula <- as.formula(paste("~", paste(names(vars$varInfo), collapse = "+"), sep=""))

clust <- rxKmeans(formula = myformula, data = outFile,numClusters = 5000, algorithm =     "lloyd", overwrite = TRUE)
write.table(clust$centers, file = centersFiletxt, sep=",", row.names=FALSE,    col.names=FALSE)

但它已经运行了一个星期了.任何想法如何让它更快？