Add documentation for Scoring Module

This change is updating the glossary with Scoring Module terms
and adding an information page about implementing scoring engine
plugin.

Partially-Implements: blueprint scoring-module
Change-Id: I411370dcc003ed837d8ce67659ecbfef3548ee11

watcher/api/controllers/v1/scoring_engine.py

@@ -16,8 +16,10 @@
 # limitations under the License.
 
 """
-A :ref:`Scoring Engine <scoring_engine_definition>` is an instance of a data
-model, to which a learning data was applied.
+A :ref:`Scoring Engine <scoring_engine_definition>` is an executable that has
+a well-defined input, a well-defined output, and performs a purely mathematical
+task. That is, the calculation does not depend on the environment in which it
+is running - it would produce the same result anywhere.
 
 Because there might be multiple algorithms used to build a particular data
 model (and therefore a scoring engine), the usage of scoring engine might

watcher/decision_engine/scoring/dummy_scorer.py

@@ -15,7 +15,6 @@
 # implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#
 
 from oslo_log import log
 from oslo_serialization import jsonutils
@@ -33,6 +32,7 @@ class DummyScorer(base.ScoringEngine):
     Typically a scoring engine would be implemented using machine learning
     techniques. For example, for workload classification problem the solution
     could consist of the following steps:
+
     1. Define a problem to solve: we want to detect the workload on the
        machine based on the collected metrics like power consumption,
        temperature, CPU load, memory usage, disk usage, network usage, etc.