maxProb

model.decision.discrete.maxProb(task_responses=(0, 1))[source]

Decisions for an arbitrary number of choices

Choice made by choosing the most likely

Parameters:task_responses (tuple) – Provides the action responses expected by the tasks for each probability estimate.
Returns:
  • decision_function (function) – Calculates the decisions based on the probabilities and returns the decision and the probability of that decision
  • decision (int or None) – The action to be taken by the model
  • probDict (OrderedDict of valid responses) – A dictionary of considered actions as keys and their associated probabilities as values

See also

models.QLearn(), models.QLearn2(), models.OpAL()

Examples

>>> np.random.seed(100)
>>> d = maxProb([1,2,3])
>>> d([0.6, 0.3, 0.5])
(1, OrderedDict([(1, 0.6), (2, 0.3), (3, 0.5)]))
>>> d([0.2, 0.3, 0.5], trial_responses=[1, 2])
(2, OrderedDict([(1, 0.2), (2, 0.3), (3, 0.5)]))
>>> d([0.2, 0.3, 0.5], trial_responses=[])
(None, OrderedDict([(1, 0.2), (2, 0.3), (3, 0.5)]))
>>> d = maxProb(["A", "B", "C"])
>>> d([0.6, 0.3, 0.5], trial_responses=["A", "B"])
('A', OrderedDict([('A', 0.6), ('B', 0.3), ('C', 0.5)]))