skPartialPCA_step {BiocSklearn} | R Documentation

## take a step in sklearn IncrementalPCA partial fit procedure

### Description

take a step in sklearn IncrementalPCA partial fit procedure

### Usage

    skPartialPCA_step(mat, n_components, obj)

### Arguments

`mat` | a matrix – can be R matrix or numpy.ndarray
`n_components` | number of PCA to retrieve
`obj` | sklearn.decomposition.IncrementalPCA instance

### Value

trained IncrementalPCA reference, to which 'transform' method can be applied to obtain projection for any compliant input

### Note

if obj is missing, the process is initialized with the matrix provided

### Examples

    ## 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)

[Package BiocSklearn version 1.12.0 Index]