skKMeans {BiocSklearn} | R Documentation

## interface to sklearn.cluster.KMeans with attention to direct work with HDF5

### Description

interface to sklearn.cluster.KMeans with attention to direct work with HDF5

### Usage

    skKMeans(mat, ...)

### Arguments

`mat` | a matrix-like datum or reference to such
`...` | arguments to sklearn.cluster.KMeans

### Note

You can use 'py_help(SklearnEls()$skcl$KMeans)' to get python documentation on parameters and return structure.

### Examples

    # start with numpy array reference as data
    irloc = system.file("csv/iris.csv", package="BiocSklearn")
    skels = SklearnEls()
    irismat = skels$np$genfromtxt(irloc, delimiter=',')
    ans = skKMeans(irismat, n_clusters=2L)
    names(ans) # names of available result components
    table(iris$Species, ans$labels_)
    # now use an HDF5 reference
    irh5 = system.file("hdf5/irmat.h5", package="BiocSklearn")
    fref = skels$h5py$File(irh5)
    ds = fref$`__getitem__`("quants") # thanks Samuela Pollack!
    ans2 = skKMeans(skels$np$array(ds)$T, n_clusters=2L) # HDF5 matrix is transposed relative to python array layout!  Is the np$array conversion unduly costly?
    table(ans$labels_, ans2$labels_)
    ans3 = skKMeans(skels$np$array(ds)$T,
       n_clusters=8L, max_iter=200L,
       algorithm="full", random_state=20L)

[Package BiocSklearn version 1.7.0 Index]