skKMeans {BiocSklearn} | R Documentation

## interface to sklearn.cluster.KMeans using basilisk discipline

### Description

interface to sklearn.cluster.KMeans using basilisk discipline

### Usage

    skKMeans(mat, ...)

### Arguments

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

### Value

a list with cluster assignments (integers starting with zero) and asserted cluster centers.

### Note

You can use `py_help(SklearnEls()$skcl$KMeans)` to get python documentation on parameters and return structure. This is a demonstrative interface to the resources of sklearn.cluster. In this particular interface, we are using sklearn.cluster.k_means_.KMeans. There are many other possibilities in sklearn.cluster: _dbscan_inner, feature_agglomeration, hierarchical, k_means, k_means_elkan, affinity_propagation, bicluster, birch, dbscan, hierarchical, k_means, mean_shift, setup, spectral.

### Examples

    ## Not run:
    # This blocked example shows a risky approach evading basilisk discipline and is
    # to be used at your own risk.
    # 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)

    ## End(Not run)
    dem = skKMeans(iris[,1:4], n_clusters=3L, max_iter=100L, algorithm="full",
       random_state=20L)
    str(dem)
    tab = table(iris$Species, dem$labels)
    tab
    plot(iris[,1], iris[,3], col=as.numeric(factor(iris$Species)))
    points(dem$centers[,1], dem$centers[,3], pch=19, col=apply(tab,2,which.max))

[Package BiocSklearn version 1.10.3 Index]