Simplified the Dataset classes to facilitate constructing custom data…

…sets. Added an example to the docs to illustrate how to construct custom datasets.

HISTORY.md

@@ -1,3 +1,8 @@
+### Version 0.4.7
+Minor updates to Dataset classes that simplify them so that
+end users can create custom datasets and wrap e.g. csv files
+or databases when setting up their training dataset.
+
 ### Version 0.4.6
 Added the Conv1dTwoLayer kernel. Removed the experimental simplex
 rffs feature which is of uncertain usefulness. Fixed a bug occuring

docs/basic_tutorial.rst

@@ -4,7 +4,14 @@ xGPR quickstart
 Build your training set
 -------------------------
 
-Start by building a Dataset, which is similar to a DataLoader in PyTorch:::
+Start by building a Dataset, which is similar to a DataLoader in PyTorch. If
+your data is organized in one of a couple fairly common ways, you can
+use a built-in xGPR function to build this dataset. If your data is in
+some other form(e.g. a fasta file, an SQLite db or an HDF5 file) and you
+don't want to make a copy of it, you can instead
+subclass xGPR's ``DatasetBaseclass`` and build a
+custom ``Dataset`` object. We'll look at the more common
+situations first:::
 
   from xGPR import build_regression_dataset
 
@@ -56,6 +63,11 @@ When you create the dataset, xGPR will do some checks to make sure that
 what you fed it makes sense. If the dataset is very large, these may take a
 second.
 
+Finally, let's say your data is a fasta file, a csv file, an HDF5 file or some
+other format. You can create your own Dataset that loads the data in minibatches
+during training and does any preprocessing you want to do on each minibatch.
+To see how to do this, check out :doc:`notebooks/custom_dataset_example`.
+
 Fit your model and make predictions
 -------------------------------------