Module miscfx

Z-Score the samples of a Dataset (in-place).

mean and std can be used to pass custom values to the z-scoring. Both may be scalars or arrays.

All computations are done in place. Data upcasting is done automatically if necessary into targetdtype

If baselinelabels provided, and mean or std aren't provided, it would compute the corresponding measure based only on labels in baselinelabels

If perchunk is True samples within the same chunk are z-scored independent of samples from other chunks, e.i. mean and standard deviation are calculated individually.

Decorators:

• @datasetmethod

Apply a function to each row of the samples matrix of a dataset.

The functor given as fx has to honour an axis keyword argument in the way that NumPy used it (e.g. NumPy.mean, var).

Returns:

a new Dataset object with the aggregated feature(s).

Decorators:

• @datasetmethod

Decorators:

• @datasetmethod

Change chunking of the dataset

Group chunks into groups to match desired number of chunks. Makes sense if originally there were no strong groupping into chunks or each sample was independent, thus belonged to its own chunk

Parameters:

• source (Dataset or list of chunk ids) - dataset or list of chunk ids to operate on. If Dataset, then its chunks get modified
• nchunks (int) - desired number of chunks

Decorators:

• @datasetmethod

Returns an array with the number of samples per label in each chunk.

Array shape is (chunks x labels).

Parameters:

• dataset, Dataset - Source dataset.

Decorators:

• @datasetmethod