quail.Fingerprint

class quail.Fingerprint(init=None, features='all', state=None, n=0, permute=False, nperms=1000, parallel=False)[source]

Class for the memory fingerprint

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:
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

update(egg[, permute, nperms, parallel]) In-place method that updates fingerprint with new data
get_features  
next  
__init__(init=None, features='all', state=None, n=0, permute=False, nperms=1000, parallel=False)[source]

Methods

__init__([init, features, state, n, ...])
get_features()
update(egg[, permute, nperms, parallel]) In-place method that updates fingerprint with new data