model.randomBias module¶
Author: | Dominic Hunt |
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class
model.randomBias.
RandomBias
(expect=None, **kwargs)[source]¶ Bases:
model.modelTemplate.Model
A model replicating a participant who chooses randomly, but with a bias towards certain actions
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Name
¶ The name of the class used when recording what has been used.
Type: string
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currAction
¶ The current action chosen by the model. Used to pass participant action to model when fitting
Type: int
Parameters: - prob* (float, optional) – The probabilities for each action. Can be un-normalised. The parameter names are
prob
followed by a number e.g.prob1
,prob2
. It is expected that there will be same number asnumber_actions
. - number_actions (integer, optional) – The maximum number of valid actions the model can expect to receive. Default 2.
- action_codes (dict with string or int as keys and int values, optional) – A dictionary used to convert between the action references used by the task or dataset and references used in the models to describe the order in which the action information is stored.
- stimFunc (function, optional) – The function that transforms the stimulus into a form the model can understand and a string to identify it later. Default is blankStim
- rewFunc (function, optional) – The function that transforms the reward into a form the model can understand. Default is blankRew
- decFunc (function, optional) – The function that takes the internal values of the model and turns them in to a decision. Default is model.decision.discrete.weightProb
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actorStimulusProbs
()[source]¶ Calculates in the model-appropriate way the probability of each action.
Returns: probabilities – The probabilities associated with the action choices Return type: 1D ndArray of floats
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calcProbabilities
()[source]¶ Calculate the probabilities associated with the actions
Parameters: actionValues (1D ndArray of floats) – Returns: probArray – The probabilities associated with the actionValues Return type: 1D ndArray of floats
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delta
(reward, expectation, action, stimuli)[source]¶ Calculates the comparison between the reward and the expectation
Parameters: Returns: Return type: delta
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parameter_patterns
= [u'^prob\\d+$']¶
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returnTaskState
()[source]¶ Returns all the relevant data for this model
Returns: results – The dictionary contains a series of keys including Name, Probabilities, Actions and Events. Return type: dict
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rewardExpectation
(observation)[source]¶ Calculate the estimated reward based on the action and stimuli
This contains parts that are task dependent
Parameters: observation ({int | float | tuple}) – The set of stimuli Returns: - actionExpectations (array of floats) – The expected rewards for each action
- stimuli (list of floats) – The processed observations
- activeStimuli (list of [0, 1] mapping to [False, True]) – A list of the stimuli that were or were not present
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storeState
()[source]¶ Stores the state of all the important variables so that they can be accessed later
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updateModel
(delta, action, stimuli, stimuliFilter)[source]¶ Parameters: - delta (float) – The difference between the reward and the expected reward
- action (int) – The action chosen by the model in this trialstep
- stimuli (list of float) – The weights of the different stimuli in this trialstep
- stimuliFilter (list of bool) – A list describing if a stimulus cue is present in this trialstep
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