def _demo_decision_maker():
alp = ['T', 'H', 'I', 'S', 'I', 'S', 'D', 'E', 'M', 'O']
len_alp = len(alp)
evidence_names = ['LM', 'ERP', 'FRP']
num_series = 10
conjugator = EvidenceFusion(evidence_names, len_dist=len_alp)
decision_maker = DecisionMaker(
min_num_inq=1,
max_num_inq=10,
state='',
alphabet=alp)
for idx_series in range(num_series):
while True:
# Generate random inquiries
evidence_erp = np.abs(np.random.randn(len_alp))
evidence_erp[idx_series] += 1
evidence_frp = np.abs(np.random.randn(len_alp))
evidence_frp[idx_series] += 3
p = conjugator.update_and_fuse(
{'ERP': evidence_erp, 'FRP': evidence_frp})
d, arg = decision_maker.decide(p)
if d:
break
# Reset the conjugator before starting a new series for clear history
conjugator.reset_history()
print('State:{}'.format(decision_maker.state))
print('Displayed State: {}'.format(decision_maker.displayed_state))
def __init__(self,
min_num_seq,
max_num_seq,
signal_model=None,
fs=300,
k=2,
alp=None,
evidence_names=['LM', 'ERP'],
task_list=[('I_LOVE_COOKIES', 'I_LOVE_')],
lmodel=None,
is_txt_stim=True,
device_name='LSL',
device_channels=None,
stimuli_timing=[1, .2],
decision_threshold=0.8,
backspace_prob=0.05,
backspace_always_shown=False,
filter_high=45,
filter_low=2,
filter_order=2,
notch_filter_frequency=60):
self.conjugator = EvidenceFusion(evidence_names, len_dist=len(alp))
seq_constants = []
if backspace_always_shown and BACKSPACE_CHAR in alp:
seq_constants.append(BACKSPACE_CHAR)
self.decision_maker = DecisionMaker(
min_num_seq,
max_num_seq,
decision_threshold=decision_threshold,
state=task_list[0][1],
alphabet=alp,
is_txt_stim=is_txt_stim,
stimuli_timing=stimuli_timing,
seq_constants=seq_constants)
self.alp = alp
# non-letter target labels include the fixation cross and calibration.
self.nonletters = ['+', 'PLUS', 'calibration_trigger']
self.valid_targets = set(self.alp)
self.signal_model = signal_model
self.sampling_rate = fs
self.downsample_rate = k
self.filter_high = filter_high
self.filter_low = filter_low
self.filter_order = filter_order
self.notch_filter_frequency = notch_filter_frequency
self.mode = 'copy_phrase'
self.task_list = task_list
self.lmodel = lmodel
self.channel_map = analysis_channels(device_channels, device_name)
self.backspace_prob = backspace_prob
def setUp(self):
"""Set up decision maker object for testing """
self.decision_maker = DecisionMaker(
1,
3,
state='',
alphabet=list(string.ascii_uppercase) + ['<'] + [SPACE_CHAR],
is_txt_stim=True,
stimuli_timing=[1, .2],
seq_constants=None)
self.evidence_fusion = EvidenceFusion(list_name_evidence=['A', 'B'],
len_dist=2)
def _demo_fusion():
len_alp = 4
evidence_names = ['LM', 'ERP', 'FRP']
num_series = 4
num_inquiries = 10
conjugator = EvidenceFusion(evidence_names, len_dist=len_alp)
print('Random Series!')
for idx_ep in range(num_series):
prior = np.abs(np.random.randn(len_alp))
prior = prior / np.sum(prior)
conjugator.update_and_fuse({'LM': prior})
for idx in range(num_inquiries):
# Generate random inquiries
evidence_erp = 10 * np.abs(np.random.randn(len_alp))
evidence_frp = 10 * np.abs(np.random.randn(len_alp))
conjugator.update_and_fuse(
{'ERP': evidence_erp, 'FRP': evidence_frp})
print('Series: {}'.format(idx_ep))
print(conjugator.evidence_history['ERP'])
print(conjugator.evidence_history['FRP'])
print(conjugator.evidence_history['LM'])
print('Posterior:{}'.format(conjugator.likelihood))
# Reset the conjugator before starting a new series for clear history
conjugator.reset_history()
def setUp(self):
"""Set up decision maker object for testing """
alphabet = list(string.ascii_uppercase) + ['<'] + [SPACE_CHAR]
stopping_criteria = CriteriaEvaluator(
continue_criteria=[MinIterationsCriteria(min_num_inq=1)],
commit_criteria=[
MaxIterationsCriteria(max_num_inq=10),
ProbThresholdCriteria(threshold=0.8)
])
stimuli_agent = NBestStimuliAgent(alphabet=alphabet, len_query=10)
self.decision_maker = DecisionMaker(
stimuli_agent=stimuli_agent,
stopping_evaluator=stopping_criteria,
state='',
alphabet=alphabet,
is_txt_stim=True,
stimuli_timing=[1, .2],
inq_constants=None)
self.evidence_fusion = EvidenceFusion(list_name_evidence=['A', 'B'],
len_dist=2)
class CopyPhraseWrapper:
"""Basic copy phrase task duty cycle wrapper.
Given the phrases once operate() is called performs the task.
Attr:
min_num_seq: The minimum number of sequences to be displayed
max_num_seq: The maximum number of sequences to be displayed
model(pipeline): model trained using a calibration session of the
same user.
fs(int): sampling frequency
k(int): down sampling rate
alp(list[str]): symbol set of the task
task_list(list[tuple(str,str)]): list[(phrases, initial_states)] for
the copy phrase task
is_txt_stim: Whether or not the stimuli are text objects
conjugator(EvidenceFusion): fuses evidences in the task
decision_maker(DecisionMaker): mastermind of the task
mode(str): mode of thet task (should be copy phrase)
d(binary): decision flag
sti(list(tuple)): stimuli for the display
decision_threshold: Minimum likelihood value required for a decision
backspace_prob(float): default language model probability for the
backspace character.
backspace_always_shown(bool): whether or not the backspace should
always be presented.
"""
def __init__(self,
min_num_seq,
max_num_seq,
signal_model=None,
fs=300,
k=2,
alp=None,
evidence_names=['LM', 'ERP'],
task_list=[('I_LOVE_COOKIES', 'I_LOVE_')],
lmodel=None,
is_txt_stim=True,
device_name='LSL',
device_channels=None,
stimuli_timing=[1, .2],
decision_threshold=0.8,
backspace_prob=0.05,
backspace_always_shown=False,
filter_high=45,
filter_low=2,
filter_order=2,
notch_filter_frequency=60):
self.conjugator = EvidenceFusion(evidence_names, len_dist=len(alp))
seq_constants = []
if backspace_always_shown and BACKSPACE_CHAR in alp:
seq_constants.append(BACKSPACE_CHAR)
self.decision_maker = DecisionMaker(
min_num_seq,
max_num_seq,
decision_threshold=decision_threshold,
state=task_list[0][1],
alphabet=alp,
is_txt_stim=is_txt_stim,
stimuli_timing=stimuli_timing,
seq_constants=seq_constants)
self.alp = alp
# non-letter target labels include the fixation cross and calibration.
self.nonletters = ['+', 'PLUS', 'calibration_trigger']
self.valid_targets = set(self.alp)
self.signal_model = signal_model
self.sampling_rate = fs
self.downsample_rate = k
self.filter_high = filter_high
self.filter_low = filter_low
self.filter_order = filter_order
self.notch_filter_frequency = notch_filter_frequency
self.mode = 'copy_phrase'
self.task_list = task_list
self.lmodel = lmodel
self.channel_map = analysis_channels(device_channels, device_name)
self.backspace_prob = backspace_prob
def evaluate_sequence(self, raw_data, triggers, target_info,
window_length):
"""Once data is collected, infers meaning from the data.
Args:
raw_data(ndarray[float]): C x L eeg data where C is number of
channels and L is the signal length
triggers(list[tuple(str,float)]): triggers e.g. ('A', 1)
as letter and flash time for the letter
target_info(list[str]): target information about the stimuli
window_length(int): The length of the time between stimuli presentation
"""
letters, times, target_info = self.letter_info(triggers, target_info)
# Remove 60hz noise with a notch filter
notch_filter_data = notch.notch_filter(
raw_data,
self.sampling_rate,
frequency_to_remove=self.notch_filter_frequency)
# bandpass filter from 2-45hz
filtered_data = bandpass.butter_bandpass_filter(
notch_filter_data,
self.filter_low,
self.filter_high,
self.sampling_rate,
order=self.filter_order)
# downsample
data = downsample.downsample(filtered_data,
factor=self.downsample_rate)
x, _, _, _ = trial_reshaper(target_info,
times,
data,
fs=self.sampling_rate,
k=self.downsample_rate,
mode=self.mode,
channel_map=self.channel_map,
trial_length=window_length)
lik_r = inference(x, letters, self.signal_model, self.alp)
prob = self.conjugator.update_and_fuse({'ERP': lik_r})
decision, sti = self.decision_maker.decide(prob)
return decision, sti
def letter_info(
self, triggers: List[Tuple[str, float]], target_info: List[str]
) -> Tuple[List[str], List[float], List[str]]:
"""
Filters out non-letters and separates timings from letters.
Parameters:
-----------
triggers: triggers e.g. [['A', 0.5], ...]
as letter and flash time for the letter
target_info: target information about the stimuli;
ex. ['nontarget', 'nontarget', ...]
Returns:
--------
(letters, times, target_info)
"""
letters = []
times = []
target_types = []
for i, (letter, stamp) in enumerate(triggers):
if letter not in self.nonletters:
letters.append(letter)
times.append(stamp)
target_types.append(target_info[i])
# Raise an error if the stimuli includes unexpected terms
if not set(letters).issubset(self.valid_targets):
invalid = set(letters).difference(self.valid_targets)
raise Exception(
f'unexpected letters received in copy phrase: {invalid}')
return letters, times, target_types
def initialize_epoch(self):
"""If a decision is made initializes the next epoch."""
try:
# First, reset the history for this new epoch
self.conjugator.reset_history()
# If there is no language model specified, mock the LM prior
# TODO: is the probability domain correct? ERP evidence is in
# the log domain; LM by default returns negative log domain.
if not self.lmodel:
# mock probabilities to be equally likely for all letters.
overrides = {BACKSPACE_CHAR: self.backspace_prob}
prior = equally_probable(self.alp, overrides)
# Else, let's query the lmodel for priors
else:
# Get the displayed state
# TODO: for oclm this should be a list of (sym, prob)
update = self.decision_maker.displayed_state
# update the lmodel and get back the priors
lm_prior = self.lmodel.state_update(update)
# normalize to probability domain if needed
if getattr(self.lmodel, 'normalized', False):
lm_letter_prior = lm_prior['letter']
else:
lm_letter_prior = norm_domain(lm_prior['letter'])
if BACKSPACE_CHAR in self.alp:
# Append backspace if missing.
sym = (BACKSPACE_CHAR, self.backspace_prob)
lm_letter_prior = sym_appended(lm_letter_prior, sym)
# convert to format needed for evidence fusion;
# probability value only in alphabet order.
# TODO: ensure that probabilities still add to 1.0
prior = [
prior_prob for alp_letter in self.alp
for prior_sym, prior_prob in lm_letter_prior
if alp_letter == prior_sym
]
# Try fusing the lmodel evidence
try:
prob_dist = self.conjugator.update_and_fuse(
{'LM': np.array(prior)})
except Exception as lm_exception:
print("Error updating language model!")
raise lm_exception
# Get decision maker to give us back some decisions and stimuli
is_accepted, sti = self.decision_maker.decide(prob_dist)
except Exception as init_exception:
print("Error in initialize_epoch: %s" % (init_exception))
raise init_exception
return is_accepted, sti
def __init__(self,
min_num_inq,
max_num_inq,
signal_model=None,
fs=300,
k=2,
alp=None,
evidence_names=['LM', 'ERP'],
task_list=[('I_LOVE_COOKIES', 'I_LOVE_')],
lmodel=None,
is_txt_stim=True,
device_name='LSL',
device_channels=None,
stimuli_timing=[1, .2],
decision_threshold=0.8,
backspace_prob=0.05,
backspace_always_shown=False,
filter_high=45,
filter_low=2,
filter_order=2,
notch_filter_frequency=60):
self.conjugator = EvidenceFusion(evidence_names, len_dist=len(alp))
inq_constants = []
if backspace_always_shown and BACKSPACE_CHAR in alp:
inq_constants.append(BACKSPACE_CHAR)
# Stimuli Selection Module
stopping_criteria = CriteriaEvaluator(
continue_criteria=[MinIterationsCriteria(min_num_inq)],
commit_criteria=[
MaxIterationsCriteria(max_num_inq),
ProbThresholdCriteria(decision_threshold)
])
# TODO: Parametrize len_query in the future releases!
stimuli_agent = NBestStimuliAgent(alphabet=alp, len_query=10)
self.decision_maker = DecisionMaker(
stimuli_agent=stimuli_agent,
stopping_evaluator=stopping_criteria,
state=task_list[0][1],
alphabet=alp,
is_txt_stim=is_txt_stim,
stimuli_timing=stimuli_timing,
inq_constants=inq_constants)
self.alp = alp
# non-letter target labels include the fixation cross and calibration.
self.nonletters = ['+', 'PLUS', 'calibration_trigger']
self.valid_targets = set(self.alp)
self.signal_model = signal_model
self.sampling_rate = fs
self.downsample_rate = k
self.filter_high = filter_high
self.filter_low = filter_low
self.filter_order = filter_order
self.notch_filter_frequency = notch_filter_frequency
self.mode = 'copy_phrase'
self.task_list = task_list
self.lmodel = lmodel
self.channel_map = analysis_channels(device_channels, device_name)
self.backspace_prob = backspace_prob
class TestDecisionMaker(unittest.TestCase):
"""Test for decision maker class """
def setUp(self):
"""Set up decision maker object for testing """
self.decision_maker = DecisionMaker(
1,
3,
state='',
alphabet=list(string.ascii_uppercase) + ['<'] + [SPACE_CHAR],
is_txt_stim=True,
stimuli_timing=[1, .2],
seq_constants=None)
self.evidence_fusion = EvidenceFusion(list_name_evidence=['A', 'B'],
len_dist=2)
def tearDown(self):
"""Reset decision maker and evidence fusion at the
end of each test. """
self.decision_maker.reset()
self.evidence_fusion.reset_history()
def test_evidence_fusion_init(self):
self.assertEqual(self.evidence_fusion.evidence_history, {
'A': [],
'B': []
})
self.assertEqual(self.evidence_fusion.likelihood[0], [0.5])
self.assertEqual(self.evidence_fusion.likelihood[1], [0.5])
def test_reset_history(self):
self.evidence_fusion.reset_history()
self.assertEqual(self.evidence_fusion.evidence_history, {
'A': [],
'B': []
})
self.assertEqual(self.evidence_fusion.likelihood[0], [0.5])
self.assertEqual(self.evidence_fusion.likelihood[1], [0.5])
def test_update_and_fuse_with_float_evidence(self):
dict_evidence = {'A': [0.5], 'B': [0.5]}
self.evidence_fusion.update_and_fuse(dict_evidence)
self.assertEqual(self.evidence_fusion.evidence_history['A'][0],
dict_evidence['A'])
self.assertEqual(self.evidence_fusion.evidence_history['B'][0],
dict_evidence['B'])
self.assertEqual(self.evidence_fusion.likelihood[0], [[0.5]])
self.assertEqual(self.evidence_fusion.likelihood[1], [[0.5]])
def test_update_and_fuse_with_inf_evidence(self):
dict_evidence = {'A': [0.5], 'B': [math.inf]}
self.evidence_fusion.update_and_fuse(dict_evidence)
self.assertEqual(self.evidence_fusion.evidence_history['A'][0],
dict_evidence['A'])
self.assertEqual(self.evidence_fusion.evidence_history['B'][0],
dict_evidence['B'])
self.assertEqual(self.evidence_fusion.likelihood[0], [1.0])
self.assertEqual(self.evidence_fusion.likelihood[1], [0.0])
def test_save_history(self):
history = self.evidence_fusion.save_history()
self.assertEqual(0, history)
def test_decision_maker_init(self):
"""Test initialization"""
self.assertEqual(self.decision_maker.min_num_seq, 1)
self.assertEqual(self.decision_maker.max_num_seq, 3)
self.assertEqual(self.decision_maker.state, '')
self.assertEqual(self.decision_maker.displayed_state, '')
def test_decide_without_commit(self):
"""
Test decide method with case of no commit
using a fake probability distribution
"""
probability_distribution = np.ones(len(
self.decision_maker.alphabet)) / 8
decision, chosen_stimuli = self.decision_maker.decide(
probability_distribution)
self.assertTrue(
np.all(self.decision_maker.list_epoch[-1]['list_distribution'][-1]
== probability_distribution))
self.assertFalse(decision)
self.decision_maker.do_epoch()
self.assertEqual(self.decision_maker.sequence_counter, 0)
def test_decide_with_commit(self):
"""Test decide method with case of commit"""
probability_distribution = np.ones(len(self.decision_maker.alphabet))
self.decision_maker.sequence_counter = self.decision_maker.min_num_seq
decision, chosen_stimuli = self.decision_maker.decide(
probability_distribution)
self.assertTrue(decision)
self.assertEqual(chosen_stimuli, None)
def test_update_with_letter(self):
"""Test update method with letter being the new state"""
old_displayed_state = self.decision_maker.displayed_state
old_state = self.decision_maker.state
new_state = 'E'
self.decision_maker.update(state=new_state)
self.assertEqual(self.decision_maker.state, old_state + new_state)
self.assertEqual(self.decision_maker.displayed_state,
old_displayed_state + 'E')
def test_update_with_backspace(self):
"""Test update method with backspace being the new state"""
old_displayed_state = self.decision_maker.displayed_state
old_state = self.decision_maker.state
new_state = '<'
self.decision_maker.update(state=new_state)
self.assertEqual(self.decision_maker.state, old_state + new_state)
self.assertEqual(self.decision_maker.displayed_state,
old_displayed_state[0:-1])
self.assertLess(len(self.decision_maker.displayed_state),
len(self.decision_maker.state))
def test_reset(self):
"""Test reset of decision maker state"""
self.decision_maker.reset()
self.assertEqual(self.decision_maker.state, '')
self.assertEqual(self.decision_maker.displayed_state, '')
self.assertEqual(self.decision_maker.time, 0)
self.assertEqual(self.decision_maker.sequence_counter, 0)
def test_form_display_state(self):
"""Test form display state method with a dummy state"""
self.decision_maker.update(state='ABC<.E')
self.decision_maker.form_display_state(self.decision_maker.state)
self.assertEqual(self.decision_maker.displayed_state, 'ABE')
self.decision_maker.reset()
def test_do_epoch(self):
"""Test do_epoch method"""
probability_distribution = np.ones(len(
self.decision_maker.alphabet)) / 8
decision, chosen_stimuli = self.decision_maker.decide(
probability_distribution)
self.decision_maker.do_epoch()
self.assertEqual(self.decision_maker.sequence_counter, 0)
def test_decide_state_update(self):
"""Tests decide state update method"""
probability_distribution = np.ones(len(self.decision_maker.alphabet))
self.decision_maker.list_epoch[-1]['list_distribution'].append(
probability_distribution)
decision = self.decision_maker.decide_state_update()
expected = 'A' # expect to commit to first letter in sequence, due to uniform probability
self.assertEqual(decision, 'A')
def test_schedule_sequence(self):
"""Test sequence scheduling. Should return new stimuli list, at random."""
probability_distribution = np.ones(len(self.decision_maker.alphabet))
old_counter = self.decision_maker.sequence_counter
self.decision_maker.list_epoch[-1]['list_distribution'].append(
probability_distribution)
stimuli = self.decision_maker.schedule_sequence()
self.assertEqual(self.decision_maker.state, '.')
self.assertEqual(stimuli[0],
self.decision_maker.list_epoch[-1]['list_sti'][-1])
self.assertLess(old_counter, self.decision_maker.sequence_counter)
def test_prepare_stimuli(self):
"""Test that stimuli are prepared as expected"""
probability_distribution = np.ones(len(self.decision_maker.alphabet))
self.decision_maker.list_epoch[-1]['list_distribution'].append(
probability_distribution)
stimuli = self.decision_maker.prepare_stimuli()
self.assertEqual(11, len(stimuli[0][0]))
for i in range(1, len(stimuli[0][0])):
self.assertIn(stimuli[0][0][i], self.decision_maker.alphabet)
self.assertEqual(stimuli[1][0][0:2],
self.decision_maker.stimuli_timing)
class TestDecisionMaker(unittest.TestCase):
"""Test for decision maker class """
def setUp(self):
"""Set up decision maker object for testing """
alphabet = list(string.ascii_uppercase) + ['<'] + [SPACE_CHAR]
stopping_criteria = CriteriaEvaluator(
continue_criteria=[MinIterationsCriteria(min_num_inq=1)],
commit_criteria=[
MaxIterationsCriteria(max_num_inq=10),
ProbThresholdCriteria(threshold=0.8)
])
stimuli_agent = NBestStimuliAgent(alphabet=alphabet, len_query=10)
self.decision_maker = DecisionMaker(
stimuli_agent=stimuli_agent,
stopping_evaluator=stopping_criteria,
state='',
alphabet=alphabet,
is_txt_stim=True,
stimuli_timing=[1, .2],
inq_constants=None)
self.evidence_fusion = EvidenceFusion(list_name_evidence=['A', 'B'],
len_dist=2)
def tearDown(self):
"""Reset decision maker and evidence fusion at the
end of each test. """
self.decision_maker.reset()
self.evidence_fusion.reset_history()
def test_evidence_fusion_init(self):
self.assertEqual(self.evidence_fusion.evidence_history, {
'A': [],
'B': []
})
self.assertEqual(self.evidence_fusion.likelihood[0], [0.5])
self.assertEqual(self.evidence_fusion.likelihood[1], [0.5])
def test_reset_history(self):
self.evidence_fusion.reset_history()
self.assertEqual(self.evidence_fusion.evidence_history, {
'A': [],
'B': []
})
self.assertEqual(self.evidence_fusion.likelihood[0], [0.5])
self.assertEqual(self.evidence_fusion.likelihood[1], [0.5])
def test_update_and_fuse_with_float_evidence(self):
dict_evidence = {'A': [0.5], 'B': [0.5]}
self.evidence_fusion.update_and_fuse(dict_evidence)
self.assertEqual(self.evidence_fusion.evidence_history['A'][0],
dict_evidence['A'])
self.assertEqual(self.evidence_fusion.evidence_history['B'][0],
dict_evidence['B'])
self.assertEqual(self.evidence_fusion.likelihood[0], [[0.5]])
self.assertEqual(self.evidence_fusion.likelihood[1], [[0.5]])
def test_update_and_fuse_with_inf_evidence(self):
dict_evidence = {'A': [0.5], 'B': [math.inf]}
self.evidence_fusion.update_and_fuse(dict_evidence)
self.assertEqual(self.evidence_fusion.evidence_history['A'][0],
dict_evidence['A'])
self.assertEqual(self.evidence_fusion.evidence_history['B'][0],
dict_evidence['B'])
self.assertEqual(self.evidence_fusion.likelihood[0], [1.0])
self.assertEqual(self.evidence_fusion.likelihood[1], [0.0])
def test_save_history(self):
history = self.evidence_fusion.save_history()
self.assertEqual(0, history)
def test_decision_maker_init(self):
"""Test initialization"""
# TODO: Update that test part
# self.assertEqual(self.decision_maker.min_num_inq, 1)
# self.assertEqual(self.decision_maker.max_num_inq, 3)
self.assertEqual(self.decision_maker.state, '')
self.assertEqual(self.decision_maker.displayed_state, '')
def test_update_with_letter(self):
"""Test update method with letter being the new state"""
old_displayed_state = self.decision_maker.displayed_state
old_state = self.decision_maker.state
new_state = 'E'
self.decision_maker.update(state=new_state)
self.assertEqual(self.decision_maker.state, old_state + new_state)
self.assertEqual(self.decision_maker.displayed_state,
old_displayed_state + 'E')
def test_update_with_backspace(self):
"""Test update method with backspace being the new state"""
old_displayed_state = self.decision_maker.displayed_state
old_state = self.decision_maker.state
new_state = '<'
self.decision_maker.update(state=new_state)
self.assertEqual(self.decision_maker.state, old_state + new_state)
self.assertEqual(self.decision_maker.displayed_state,
old_displayed_state[0:-1])
self.assertLess(len(self.decision_maker.displayed_state),
len(self.decision_maker.state))
def test_reset(self):
"""Test reset of decision maker state"""
self.decision_maker.reset()
self.assertEqual(self.decision_maker.state, '')
self.assertEqual(self.decision_maker.displayed_state, '')
self.assertEqual(self.decision_maker.time, 0)
self.assertEqual(self.decision_maker.inquiry_counter, 0)
def test_form_display_state(self):
"""Test form display state method with a dummy state"""
self.decision_maker.update(state='ABC<.E')
self.decision_maker.form_display_state(self.decision_maker.state)
self.assertEqual(self.decision_maker.displayed_state, 'ABE')
self.decision_maker.reset()
def test_do_series(self):
"""Test do_series method"""
probability_distribution = np.ones(len(
self.decision_maker.alphabet)) / 8
decision, chosen_stimuli = self.decision_maker.decide(
probability_distribution)
self.decision_maker.do_series()
self.assertEqual(self.decision_maker.inquiry_counter, 0)
def test_decide_state_update(self):
"""Tests decide state update method"""
probability_distribution = np.ones(len(self.decision_maker.alphabet))
self.decision_maker.list_series[-1]['list_distribution'].append(
probability_distribution)
decision = self.decision_maker.decide_state_update()
expected = 'A' # expect to commit to first letter in inquiry, due to uniform probability
self.assertEqual(decision, 'A')
def test_schedule_inquiry(self):
"""Test inquiry scheduling. Should return new stimuli list, at random."""
probability_distribution = np.ones(len(self.decision_maker.alphabet))
old_counter = self.decision_maker.inquiry_counter
self.decision_maker.list_series[-1]['list_distribution'].append(
probability_distribution)
stimuli = self.decision_maker.schedule_inquiry()
self.assertEqual(self.decision_maker.state, '.')
self.assertEqual(stimuli[0],
self.decision_maker.list_series[-1]['list_sti'][-1])
self.assertLess(old_counter, self.decision_maker.inquiry_counter)
def test_prepare_stimuli(self):
"""Test that stimuli are prepared as expected"""
probability_distribution = np.ones(len(self.decision_maker.alphabet))
self.decision_maker.list_series[-1]['list_distribution'].append(
probability_distribution)
stimuli = self.decision_maker.prepare_stimuli()
self.assertEqual(self.decision_maker.stimuli_agent.len_query + 1,
len(stimuli[0][0]))
for i in range(1, len(stimuli[0][0])):
self.assertIn(stimuli[0][0][i], self.decision_maker.alphabet)
self.assertEqual(stimuli[1][0][0:2],
self.decision_maker.stimuli_timing)