weightProb

model.decision.discrete.weightProb(task_responses=(0, 1))

Decisions for an arbitrary number of choices

Choice made by choosing randomly based on which are valid and what their associated probabilities are

Parameters:task_responses (tuple) – Provides the action responses expected by the task 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 = weightProb([0, 1, 2, 3])
>>> d([0.4, 0.8, 0.3, 0.5])
(1, OrderedDict([(0, 0.2), (1, 0.4), (2, 0.15), (3, 0.25)]))
>>> d([0.1, 0.3, 0.4, 0.2])
(1, OrderedDict([(0, 0.1), (1, 0.3), (2, 0.4), (3, 0.2)]))
>>> d([0.2, 0.5, 0.3, 0.5], trial_responses=[0, 2])
(2, OrderedDict([(0, 0.4), (1, 0), (2, 0.6), (3, 0)]))
>>> d = weightProb(["A", "B", "C"])
>>> d([0.2, 0.3, 0.5], trial_responses=["A", "B"])
(u'B', OrderedDict([(u'A', 0.4), (u'B', 0.6), (u'C', 0)]))
>>> d([0.2, 0.3, 0.5], trial_responses=[])
(None, OrderedDict([(u'A', 0.2), (u'B', 0.3), (u'C', 0.5)]))