skPartialPCA_step {BiocSklearn} | R Documentation |
take a step in sklearn IncrementalPCA partial fit procedure
skPartialPCA_step(mat, n_components, obj)
mat |
a matrix – can be R matrix or numpy.ndarray |
n_components |
number of PCA to retrieve |
obj |
sklearn.decomposition.IncrementalPCA instance |
trained IncrementalPCA reference, to which 'transform' method can be applied to obtain projection for any compliant input
if obj is missing, the process is initialized with the matrix provided
## Not run: # these steps are not basilisk-compliant, you need to acquire references irloc = system.file("csv/iris.csv", package="BiocSklearn") irismat = SklearnEls()$np$genfromtxt(irloc, delimiter=',') ta = SklearnEls()$np$take 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) head(names(ipc)) ipc$transform(ta(irismat,0:5,0L)) fullproj = ipc$transform(irismat) fullpc = prcomp(data.matrix(iris[,1:4]))$x round(cor(fullpc,fullproj),3) ## End(Not run)