doc build

Author: andycasey

docs/api.rst

@@ -8,6 +8,5 @@ MCFA
 
 .. autoclass:: mcfa.mcfa.MCFA
    :members:
-   :private-members:
 
 

docs/conf.py

@@ -185,3 +185,5 @@
 
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {'https://docs.python.org/': None}
+
+autodoc_mock_imports = ['numpy', 'scipy', 'sklearn', 'tqdm']

docs/index.rst

@@ -2,16 +2,29 @@
 Mixture of Common Factor Analysers
 ==================================
 
+A mixture of common factor analysers (MCFA) is a method for modelling multi-dimensional data.
+The model assumes that the data are generated by a set of mutually orthogonal latent factors
+that are common to all data, but the scoring (or extent) of those factors is different for
+each data point. It also assumes that the scoring in latent space can be modelled as a mixture
+of multivariate Gaussian distributions. The latent space is assumed to be lower dimensional than the data. 
+
+
+Model parameters are estimated using the expectation-maximization algorithm, given some fixed number
+of latent factors and components. If the number of latent factors and components is not known then
+these are found through a grid search, where the minimum message length is adopted as the objective
+function.
+
+
+:ref:`getting-started` Getting started guide.
+
+
 .. toctree::
    :maxdepth: 2
    :caption: Contents:
 
    api
 
 
-Indices and tables
-==================
 
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
+
+:ref:`genindex` | :ref:`modindex` | :ref:`search`