Class MF_minimise_command

Command class for standard model-free minimisation.

Instance Methods

__init__(self)
Initialise the base class.

tuple of numpy array, float, int, int, int, int, str optimise(self)
Model-free optimisation.

run(self, processor, completed)
Setup and perform the model-free optimisation.

store_data(self, data, opt_params)
Store all the data required for model-free optimisation.

Inherited from multi.slave_commands.Slave_command: set_memo_id

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__

Properties

Inherited from object: __class__

Method Details

__init__(self)
(Constructor)

Initialise the base class.

Overrides: object.__init__

optimise(self)

Model-free optimisation.

Returns: tuple of numpy array, float, int, int, int, int, str

The optimisation results consisting of the parameter vector, function value, iteration count, function count, gradient count, Hessian count, and warnings.

run(self, processor, completed)

Setup and perform the model-free optimisation.

Parameters:

• processor - The slave processor the command is running on. Results from the command are returned via calls to processor.return_object.
• completed - The flag used in batching result returns to indicate that the sequence of batched result commands has completed. This value should be returned via the last result object retuned by this method or methods it calls. All other Result_commands should be initialised with completed=False. This is an optimisation to prevent the sending an extra batched result queue completion result command being sent, it may be an over early optimisation.

Overrides: multi.slave_commands.Slave_command.run

store_data(self, data, opt_params)

Store all the data required for model-free optimisation.

Parameters:

• data (class instance) - The data used to initialise the model-free target function class.
• opt_params (class instance) - The parameters and data required for optimisation using minfx.