mlpy.knowledgerep.cbr.engine.CaseBase

class mlpy.knowledgerep.cbr.engine.CaseBase(case_template, reuse_method=None, reuse_method_params=None, revision_method=None, revision_method_params=None, retention_method=None, retention_method_params=None, plot_retrieval=None, plot_retrieval_names=None)

Bases: object

The case base engine.

The case base engine maintains the a database of all cases entered into the case base. It manages retrieval, revision, reuse, and retention of cases.

Parameters:

case_template: dict

The template from which to create a new case.

Example:

An example template for a feature named state with the specified feature parameters. data is the data from which to extract the case from. In this example it is expected that data has a member variable state.

{
    "state": {
        "type": "float",
        "value": "data.state",
        "is_index": True,
        "retrieval_method": "radius-n",
        "retrieval_method_params": 0.01
    },
    "delta_state": {
        "type": "float",
        "value": "data.next_state - data.state",
        "is_index": False,
    }
}

reuse_method : str

The reuse method name to be used during the reuse step. Default is defaultreusemethod.

reuse_method_params : dict

Non-positional initialization parameters for the reuse method instantiation.

revision_method : str

The revision method name to be used during the revision step. Default is defaultrevisionmethod.

revision_method_params : dict

Non-positional initialization parameters for the revision method instantiation.

retention_method : str

The retention method name to be used during the retention step. Default is defaultretentionmethod.

retention_method_params : dict

Non-positional initialization parameters for the retention method instantiation.

plot_retrieval : bool

Whether to plot the result or not. Default is False.

plot_retrieval_names : str or list[str]

The names of the feature which to plot.

Examples

Create a case base:

>>> from mlpy.auxiliary.io import load_from_file
>>>
>>> template = {}
>>> cb = CaseBase(template)

Fill case base with data read from file:

>>> from mlpy.mdp.stateaction import Experience, State, Action
>>>
>>> data = load_from_file("data/jointsAndActionsData.pkl")
>>> for i in xrange(len(data.itervalues().next())):
...     for j in xrange(len(data.itervalues().next()[0][i]) - 1):
...         if not j == 10:  # exclude one experience as test case
...             experience = Experience(State(data["states"][i][:, j]),
...                                     Action(data["actions"][i][:, j]),
...                                     State(data["states"][i][:, j + 1]))
...             cb.run(cb.case_from_data(experience))

Loop over all cases in the case base:

>>> for i in len(cb):
...     pass

Retrieve case with id=0:

>>> case = cb[0]

Attributes

counter

The case counter.

Methods

add(case)	Add a new case without any checks.
case_from_data(data)	Convert data into a case using the case template.
get_new_id()	Return an unused case id.
load(filename)
next()
plot_retention(case, case_matches)	Plot the retention result.
plot_retrieval(case, case_id_list[, names])	Plot the retrieved data.
plot_reuse(case, case_matches, revised_matches)	Plot the reuse result.
plot_revision(case, case_matches)	Plot revision results.
retain(case, case_matches)	Retain new case.
retrieve(case[, names, validity_check])	Retrieve cases similar to the query case.
reuse(case, case_matches)	Performs the reuse step
revision(case, case_matches)	Evaluate solution provided by problem-solving experience.
run(case)	Run the case base.
save(filename)