'starting letter': 'C',
'length': 3
}, {
'category': 'animal',
'size': 'bigger',
'starting letter': 'D',
'length': 3
}, {
'category': 'object',
'size': 'smaller',
'starting letter': 'S',
'length': 4
}, {
'category': 'animal',
'size': 'bigger',
'starting letter': 'H',
'length': 5
}]
dist_funcs = {
'category': 'lambda a, b: int(a!=b)',
'size': 'lambda a, b: int(a!=b)',
'starting letter': 'lambda a, b: int(a!=b)',
'length': 'lambda a, b: np.linalg.norm(np.subtract(a,b))'
}
egg = quail.Egg(pres=[next_presented],
rec=[next_recalled],
features=[next_features],
dist_funcs=dist_funcs)
egg.analyze('lagcrp').plot()
'starting letter': 'D',
'length': 3
}, {
'category': 'object',
'size': 'smaller',
'starting letter': 'S',
'length': 4
}, {
'category': 'animal',
'size': 'bigger',
'starting letter': 'B',
'length': 3
}]
egg = quail.Egg(pres=[next_presented],
rec=[next_recalled],
features=[next_features])
# initialize fingerprint
fingerprint = Fingerprint(init=egg)
# initialize presenter
params = {'fingerprint': fingerprint}
presenter = OptimalPresenter(params=params, strategy='stabilize')
# update the fingerprint
fingerprint.update(egg)
# reorder next list
reordered_egg = presenter.order(egg, method='permute', nperms=100)
# -*- coding: utf-8 -*-
"""
=============================
Create an egg
=============================
An egg is made up of two primary pieces of data: `pres`, which are the
words/stimuli that were presented to a subject and `rec`, which are the
words/stimuli that were recalled by the subject.
"""
# Code source: Andrew Heusser
# License: MIT
import quail
# presented words
presented_words = [['cat', 'bat', 'hat', 'goat'],
['zoo', 'animal', 'zebra', 'horse']]
# recalled words
recalled_words = [['bat', 'cat', 'goat', 'hat'], ['animal', 'horse', 'zoo']]
# create egg
egg = quail.Egg(pres=presented_words, rec=recalled_words)
'size': 'bigger',
'starting letter': 'H',
'length': 5
}, {
'item': 'DOG',
'category': 'animal',
'size': 'bigger',
'starting letter': 'D',
'length': 3
}, {
'item': 'CAT',
'category': 'animal',
'size': 'bigger',
'starting letter': 'C',
'length': 3
}]
# set some custom distance functions
dist_funcs = {
'category': 'lambda a, b: int(a!=b)',
'size': 'lambda a, b: int(a!=b)',
'starting letter': 'lambda a, b: int(a!=b)',
'length': 'lambda a, b: np.linalg.norm(np.subtract(a,b))'
}
egg = quail.Egg(pres=[presented], rec=[recalled], dist_funcs=dist_funcs)
fegg = egg.analyze('lagcrp')
fegg.plot()
pres1.append(l2)
# Make recall words list of lists
rec1 = np.load(subj_dir[s] + '/data/recall_data.npy')
# This line repeats subjects recall twice to match the word presentations structure. Specifically, since words from their recall (which was one long recall of all 24 words) may be contained in either L1 or L2.
rep_recalls = list(
itertools.chain.from_iterable(
itertools.repeat(x, 2) for x in rec1))
cond_list = condition_list(subj_dir, s)
cond_list_all.append(cond_list)
pres_subj = np.squeeze(pres1)
presented_words.append(pres_subj)
recalled_words.append(rep_recalls)
pres2 = np.squeeze(presented_words)
multisubject_egg = quail.Egg(pres=pres2.tolist(), rec=recalled_words)
acc = multisubject_egg.analyze('accuracy')
list_num = np.tile([0, 1], 12)
ABA = {}
ABA['L1'] = []
ABA['L2'] = []
AAA = {}
AAA['L1'] = []
AAA['L2'] = []
AAB = {}
AAB['L1'] = []
AAB['L2'] = []