Neurotune Architecture
===========================

Neurotune provides three important classes: Optimizers, Evaluators
and Controllers. Any optimization of a model must utilise (usually) one
instance of each of these classes.

Optimizer Class
---------------
The optimizer controls the progress of the optimization - the mating, survival 
and death of individuals (each individaul in this case being a simulation).
The optimizer generally acheives this by starting with an initial population 
chosen randomly (Say a population of 100, with 3 free paramaters x,y,z, 
the population is therefore 100 members [x1,y1,z1]. . . [x100,y100,z100]).
Seeds chosen by the user may also be present in the initial population. 
In order to evaluate this population, the optimizer passes every member 
to the evaluator which assigns each indivudal a fitness value based on 
the simulation output.

Evaluator Class
---------------
But how does the Evaluator do this?
The evaluator does the above by
looking at simulation output and targets and assigning a fitness (via its
cost function). The evaluator needs a way to run each simulation - this is
why it needs a controller.
Hence there are things like ClampEvalutor -
This evaluator specifially evaluates the finess of a current clamp simulation
compared to the exeperimental data. - It passes those finess results back to
the optimizer, the optimizer doesn't need to "know""what kind of simulation
is being run to obtain these finesses.

Controller Class
---------------
The controller class implements the actual running of the simulation given
the parameters. A controller will provide the evaluator with a run() method
and hide the details of the simulation implementation from the evaluator.

.. figure::  OptimalNeuronArchitecture.png
   :align:   center

Why this architecture?
---------------------
This architecture decouples the optimization from the actual assigining of
fitness and the actual control of a simulation.
The ability easily mix and
match optimizers, controllers and evaluators gives great exibility. For in-
stance, the CLI Controller used in the musclemodel doesn't need to be a
Python script- it just needs to be a program which can accept a command
line argument and run a simulation based on it.

What is a dumb evaluator?
-------------------------
Most evaluators work by receiving a trace back from the simulation and
assessing its fitness. A dumb evalutor is a bit special - The simulation itself
reports a fitness and writes it to a file. The evaluator is dumb because all it
needs to do is read this finess value and report that back to the Optimizer.
This allows a lot of flexibility (If a user finds that the given evaluators don't
match their needs but doesn't want the hassle of writing their own evaluator
(e.g doesn't feel comfortable with OOP) they don't have to).