def test_insert_sym_when_already_exists(self):
"""Test insertion of an additional symbol to a normalized list of
symbols."""
syms = [('A', 0.25), ('B', 0.25), ('C', 0.25), ('D', 0.25)]
self.assertEqual(1.0, sum([prob for _, prob in syms]))
new_list = sym_appended(syms, ('D', 0.25))
self.assertEqual(syms, new_list, "Value already present")
new_list = sym_appended(syms, ('D', 0.2))
self.assertEqual(
syms, new_list, msg="Changing the probability does not matter")
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 test_insert_sym_with_non_zero_prob(self):
"""Test insertion of an additional symbol to a normalized list of
symbols with a non-zero probability."""
syms = [('A', 0.25), ('B', 0.25), ('C', 0.25), ('D', 0.25)]
self.assertEqual(1.0, sum([prob for _, prob in syms]))
new_sym = ('<', 0.2)
new_list = sym_appended(syms, new_sym)
self.assertEqual(len(syms) + 1, len(new_list))
self.assertAlmostEqual(1.0, sum([prob for _, prob in new_list]))
new_list_dict = dict(new_list)
prev_list_dict = dict(syms)
for s, _ in syms:
self.assertTrue(s in new_list_dict)
self.assertTrue(new_list_dict[s] < prev_list_dict[s])
self.assertEqual(0.2, new_list_dict[s])
self.assertTrue(new_sym[0] in new_list_dict)
self.assertEqual(new_sym[1], new_list_dict[new_sym[0]])
def test_insert_sym_with_zero_prob(self):
"""Test insertion of an additional symbol (with zero probability) to a
normalized list of symbols."""
syms = [
('S', 0.21999999999999953), ('U', 0.03), ('O', 0.03), ('M', 0.03),
('W', 0.03), ('T', 0.03), ('P', 0.03), ('R', 0.03), ('L', 0.03),
('N', 0.03), ('_', 0.03), ('C', 0.03), ('E', 0.03), ('B', 0.03),
('A', 0.03), ('D', 0.03), ('H', 0.03), ('G', 0.03), ('F', 0.03),
('V', 0.03), ('K', 0.03), ('I', 0.03), ('Y', 0.03), ('J', 0.03),
('X', 0.03), ('Z', 0.03), ('Q', 0.03)
]
self.assertEqual(1.0, sum([prob for _, prob in syms]))
new_list = sym_appended(syms, ('<', 0.0))
self.assertEqual(len(syms) + 1, len(new_list))
self.assertEqual(1.0, sum([prob for _, prob in new_list]))
for pair in syms:
self.assertTrue(pair in new_list)
self.assertTrue('<' in dict(new_list))
def test_small_probs(self):
"""When very small values are returned from the LM, inserting a letter
should still result in all positive values"""
probs = [('_', 0.8137718053286306), ('R', 0.04917114015944412),
('Y', 0.04375449276342169), ('I', 0.03125895356629575),
('M', 0.023673042636520744), ('S', 0.018415576386909806),
('N', 0.014673750822550981), ('O', 0.003311888694636908),
('A', 0.0015325727808248953), ('E', 0.00020663161460758318),
('F', 0.0001271103705188304), ('L', 7.17785373200501e-05),
('T', 1.9445808941289728e-05), ('V', 8.947029414950125e-06),
('D', 1.3287314209822164e-06), ('W', 5.781802939202195e-07),
('C', 4.956713702874677e-07), ('U', 1.3950738615826266e-07),
('J', 6.925441151532328e-08), ('H', 6.067034614011934e-08),
('B', 4.83364892878174e-08), ('K', 4.424005264637171e-08),
('P', 3.319195566216423e-08), ('Z', 2.9048107858874575e-08),
('X', 1.904356496190087e-08), ('Q', 1.0477572781302604e-08),
('G', 7.146978265955833e-09)]
syms = sym_appended(probs, ('<', 0.05))
self.assertAlmostEqual(1.0, sum([sym[1] for sym in syms]))
for sym in syms:
self.assertTrue(sym[1] >= 0)
