quail.OptimalPresenter¶

class quail.OptimalPresenter(strategy='random', features=None, params=None, fingerprint=None)[source]¶

A class that reorders stimuli to optimize memory performance

A memory fingerprint can be defined as a subject’s tendency to cluster their recall responses with respect to more than one stimulus feature dimensions. What is a ‘stimulus feature dimension’ you ask? It is simply an attribute of the stimulus, such as its color, category, spatial location etc.

Parameters:

Parameters:	init : quail.Egg Data to initialize the fingerprint instance features : list Features to consider for fingerprint analyses, defaults to all. state : np.array The current fingerprint (an array of real numbers between 0 and 1, inclusive) initialized to all 0.5 n : int a counter specifying how many lists went into estimating the current fingerprint (initialize to 0) permute : bool A boolean flag specifying whether to use permutations to compute the fingerprint (default: True) dist_funcs : dict (optional) A dictionary of custom distance functions for stimulus features. Each key should be the name of a feature and each value should be an inline distance function (e.g. dist_funcs[‘feature_n’] = lambda a, b: abs(a-b)) meta : dict (optional) Meta data about the study (i.e. version, description, date, etc.) can be saved here.

init : quail.Egg: Data to initialize the fingerprint instance
features : list: Features to consider for fingerprint analyses, defaults to all.
state : np.array: The current fingerprint (an array of real numbers between 0 and 1, inclusive) initialized to all 0.5
n : int: a counter specifying how many lists went into estimating the current fingerprint (initialize to 0)
permute : bool: A boolean flag specifying whether to use permutations to compute the fingerprint (default: True)
dist_funcs : dict (optional): A dictionary of custom distance functions for stimulus features. Each key should be the name of a feature and each value should be an inline distance function (e.g. dist_funcs[‘feature_n’] = lambda a, b: abs(a-b))
meta : dict (optional): Meta data about the study (i.e. version, description, date, etc.) can be saved here.

Methods

`get_params`(name)	Sets a parameter to a particular value
`order`(egg[, method, nperms, strategy, ...])	Reorders a list of stimuli to match a fingerprint
`set_params`(name, value)	Sets a parameter to a particular value
`set_strategy`([strategy])	Sets a reordering strategy

next

__init__(strategy='random', features=None, params=None, fingerprint=None)[source]¶

Methods

`__init__`([strategy, features, params, ...])
`get_params`(name)	Sets a parameter to a particular value
`order`(egg[, method, nperms, strategy, ...])	Reorders a list of stimuli to match a fingerprint
`set_params`(name, value)	Sets a parameter to a particular value
`set_strategy`([strategy])	Sets a reordering strategy