Merge pull request #149 from sashafrey/master

Documentation whitepaper - BigARTM as a Service [skip ci]

docs/stories/cloud_service.txt

@@ -0,0 +1,22 @@
+BigARTM as a Service
+====================
+
+The following diagram shows a suggested topology for a query service that involve topic modelling on Big Data.
+
+.. image:: _images/cloud_service.png
+   :alt: cloud_service
+
+Here the main use for Hadoop / MapReduce is to process your Big Unstructured Data into a compact bag-of-words representation.
+Due to out-of-core design and extreme performance BigARTM will be able to handle this data on a single compute-optimized node.
+The resulting topic model should be replicated on all query instances that serve user requests.
+
+To avoid query-time dependency on BigARTM component you may want to infer topic distributions ``theta_{td}`` for new documents in your code.
+This can be done as follows. Start from uniform topic assigment ``theta_{td} = 1 / |T|`` and update it in the following loop:
+
+.. image:: _images/theta_update.png
+   :alt: theta_update
+
+where ``n_dw`` is the number of word ``w`` occurences in document ``d``, ``phi_wt`` is an element of the Phi matrix.
+In BigARTM the loop is repeated :attr:`ModelConfig.inner_iterations_count` times (defaulst to ``10``).
+To precisely replicate BigARTM behavior one needs to account for class weights and include regularizers.
+Please contact us if you need more details.

docs/stories/index.txt

@@ -5,10 +5,11 @@
 
 .. _stories:
 
-Advanced experiments
-====================
+Whitepapers
+===========
 
 .. toctree::
    :maxdepth: 2
 
    experiment02_artm
+   cloud_service