Column major dataset abstraction for efficiently manipulating
in memory datasets.

Dealing with categorical dataset data involves having two mapping systems.
The first is a map of category to integer within the same column.
The second is a 'one-hot' encoding where you generate more columns but those have
a reduced number of possible categories, usually one categorical value per
column.

An int-list implementation that resizes its backing store as it is required to hold
wider data.

This code provided initial by genmeblog after careful consideration
of R print code

The etl pipeline and dataset operators are built to produce a metadata options map.
Their API access to the options is centralized in this file.

This file really should be named univocity.clj.  But it is for parsing and writing
csv and tsv data.

Sequences of maps are maybe the most basic pure datastructure for data.
Converting them into a more structured form (and back) is a key component of
dealing with datatets

Spreadsheets in general are stored in a cell-based format.  This means that any cell
could have data of any type.  Commonalities around parsing spreadsheet-type systems
are captured here.

PCA and K-PCA using smile implementations.

A set of common 'pipeline' operations you probably will want to run on a dataset.

Conversion mechanisms from dataset to tensor and back