mlpy.mdp.continuous.casml.CASML¶
-
class
mlpy.mdp.continuous.casml.
CASML
(case_template, rho=None, tau=None, sigma=None, ncomponents=1, revision_method_params=None, retention_method_params=None, case_base_params=None, hmm_params=None, proba_calc_method=None)[source]¶ Bases:
mlpy.mdp.IMDPModel
Continuous Action and State Model Learner (CASML).
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, } }
rho : float, optional
The maximum permitted error when comparing cosine similarity of actions. Default is 0.99.
tau : float, optional
The maximum permitted error when comparing most similar solution. Default is 0.8.
sigma : float, optional
The maximum permitted error when comparing actual with estimated transitions. Default is 0.2.
ncomponents : int, optional
Number of states of the hidden Markov model. Default is 1.
revision_method_params : dict, optional
Additional initialization parameters for
CASMLRevisionMethod
.retention_method_params : dict, optional
Additional initialization parameters for
CASMLRetentionMethod
.case_base_params : dict, optional
Initialization parameters for
CaseBase
.hmm_params : dict, optional
Additional initialization parameters for
GaussianHMM
.proba_calc_method : str, optional
The method used to calculate the probability distribution for the initial states. Default is DefaultProbaCalcMethod.
Attributes
mid
The module’s unique identifier. Methods
fit
(obs, actions[, n_init])Fit the CaseBase
and theHMM
.load
(filename)Load the state of the module from file. predict_proba
(state, action)Predict the probability distribution. sample
([state, action])Sample from the probability distribution. save
(filename)Save the current state of the module to file. update
(experience)Update the model with the agent’s experience.