Smqtk

This is a minor release that provides minor updates and fixes as well as a
new Classifier implementation, new parameters for some existing algorithms
and added scripts that were the result of a recent hackathon.

The new classifier implementation, the `IndexLabelClassifier`, was created
for the situation where the resultant vector from DescriptorGenerator is
actually classification probabilities. An example where this may be the case
is when a CNN model and configuration for the Caffe implementation yields a
class probability (or Softmax) layer.

The specific scripts added from the hackathon are related to classifying
entities based on associated image content.

This is a minor release that provides various minor updates and fixes as well
as a few new command-line tools and a new web service application.

Among the new tools include a couple classifier validation scripts for
checking the performance of a classification algorithm fundamentally as well
as against a specific test set.

A few MEMEX program specific scripts have been added in a separated directory,
defining an ingestion process from an ElasticSearch instance through
descritpor and hash code computation.

Finally, a new web service has been added that exposes the IQR process for
external tools. The existing IQR demo web application still functions as it
did before, but does not yet use this service under the hood.

This minor release primarily adds a new modular LSH nearest-neighbor index
algorithm implementation. This new implementation strictly replaces the now
deprecated and removed `ITQNearestNeighborsIndex` implementation because of
its increased modularity and flexibility. The old `ITQNearestNeighborsIndex`
implementation had been hard-coded and its previous functionality can be
reproduced with the new implementation (`ItqFunctor` + `LinearHashIndex`).

The `CodeIndex` representation interface has also been depricated as its
function has been replaced by the combination of the
`LSHNearestNeighborIndex` implementation.

This minor release primarily adds classifier algorithm and classification
representation support, a new service web application for nearest-neighbors
algorithms, as well as additional documentation.

Also, this release adds a few more command line tools, especially of note is
`iqrTrainClassifier.py` that can train a classifier based on the saved state
of the IQR demonstration application (also a new feature).

This is a minor release with a necessary bug fix for installing SMQTK. This
release also has a minor documentation update regarding Caffe AlexNet default
model files and how/where to get them.

This is a minor release if SMQTK that provides both new functionality and
fixes over the previous version v0.1.

The highlights of this release are new and updated interface classes, an
updated plugin system, new HBase and PostgreSQL DataElement implementations,
and a new wrapper for Caffe CNN descriptor extraction.

Additional one-off scripts were added for reference as well as a more
generally usable utility for listing out available plugins for the running
system and environment.

Additional notes about the release are provided below.