H5matref {BiocSklearn} | R Documentation

## obtain an HDF5 dataset reference suitable for handling as numpy matrix

### Description

obtain an HDF5 dataset reference suitable for handling as numpy matrix

### Usage

    H5matref(filename, dsname = "assay001")

### Arguments

`filename` | a pathname to an HDF5 file
`dsname` | internal name of HDF5 matrix to use, defaults to 'assay001'

### Value

instance of (S3) "h5py._hl.dataset.Dataset"

### Note

This should only be used with persistent environment discipline of basilisk. Additional support is planned in Bioc 3.12.

### Examples

    ## Not run:
    fn = system.file("ban_6_17/assays.h5", package="BiocSklearn")
    ban = H5matref(fn)
    ban
    proc = basilisk::basiliskStart(BiocSklearn:::bsklenv)
    basilisk::basiliskRun(proc, function() {
     np = import("numpy", convert=FALSE) # ensure
     print(ban$shape)
     print(np$take(ban, 0:3, 0L))
     fullpca = skPCA(ban)
     dim(getTransformed(fullpca))
     ta = np$take
     })
    basilisk::basiliskStop(proc)

    ## End(Not run)
    # project samples
    ## Not run:   # on celaya2 this code throws errors, and
    #  I have seen
    # .../lib/python2.7/site-packages/sklearn/decomposition/incremental_pca.py:271: RuntimeWarning: Mean of empty slice.
    #   explained_variance[self.n_components_:].mean()
    # .../lib/python2.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars
    #   ret = ret.dtype.type(ret / rcount)
    ta(ban, 0:20, 0L)$shape
    st = skPartialPCA_step(ta(ban, 0:20, 0L))
    st = skPartialPCA_step(ta(ban, 21:40, 0L), obj=st)
    st = skPartialPCA_step(ta(ban, 41:63, 0L), obj=st)
    oo = st$transform(ban)
    dim(oo)
    cor(oo[,1:4], getTransformed(fullpca)[,1:4])

    ## End(Not run) # so blocking this part of example for now

[Package BiocSklearn version 1.14.1 Index]