## ----dsetup,echo=FALSE,results="hide",include=FALSE--------------------------- suppressPackageStartupMessages({ library(BiocSklearn) library(BiocStyle) }) ## ----loadup------------------------------------------------------------------- library(BiocSklearn) ## ----doimp, eval=FALSE-------------------------------------------------------- # irloc = system.file("csv/iris.csv", package="BiocSklearn") # irismat = skels$np$genfromtxt(irloc, delimiter=',') ## ----dota, eval=FALSE--------------------------------------------------------- # skels$np$take(irismat, 0:2, 0L ) ## ----dor---------------------------------------------------------------------- fullpc = prcomp(data.matrix(iris[,1:4]))$x ## ----dopc1-------------------------------------------------------------------- ppca = skPCA(data.matrix(iris[,1:4])) ppca ## ----lk1---------------------------------------------------------------------- tx = getTransformed(ppca) dim(tx) head(tx) ## ----dopy, eval=FALSE--------------------------------------------------------- # pyobj(ppca)$fit_transform(irismat)[1:3,] ## ----lkconc------------------------------------------------------------------- round(cor(tx, fullpc),3) ## ----doincr, eval=FALSE------------------------------------------------------- # ippca = skIncrPCA(irismat) # # ippcab = skIncrPCA(irismat, batch_size=25L) # round(cor(getTransformed(ippcab), fullpc),3) ## ----dopartial, eval=FALSE---------------------------------------------------- # ta = skels$np$take # provide slicer utility # ipc = skPartialPCA_step(ta(irismat,0:49,0L)) # ipc = skPartialPCA_step(ta(irismat,50:99,0L), obj=ipc) # ipc = skPartialPCA_step(ta(irismat,100:149,0L), obj=ipc) # ipc$transform(ta(irismat,0:5,0L)) # fullpc[1:5,] ## ----lkmref,eval=FALSE-------------------------------------------------------- # fn = system.file("ban_6_17/assays.h5", package="BiocSklearn") # ban = H5matref(fn) # ban ## ----getmmm,eval=FALSE-------------------------------------------------------- # np = import("numpy", convert=FALSE) # ensure # ban$shape ## ----dotx,eval=FALSE---------------------------------------------------------- # ban2 = np$matrix(ban)$T ## ----dopart, eval=FALSE------------------------------------------------------- # st = skPartialPCA_step(ta(ban2, 0:999, 0L)) # st = skPartialPCA_step(ta(ban2, 1000:10999, 0L), obj=st) # st = skPartialPCA_step(ta(ban2, 11000:44559, 0L), obj=st) # sss = st$transform(ban2) ## ----dover, eval=FALSE-------------------------------------------------------- # iii = skPCA(ban2) # dim(getTransformed(iii)) # round(cor(sss[,1:4], getTransformed(iii)[,1:4]),3)