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_norm_domain(self):
"""Test conversion from negative log likelihood to prob."""
letters = [('S', 0.25179717717251715), ('U', 1.656395297172517),
('O', 4.719718077172517), ('M', 4.824790097172517),
('W', 4.846891977172517), ('T', 6.100397207172517),
('P', 6.8986402471725174), ('R', 7.081149197172517),
('L', 7.108869167172517), ('N', 7.508945087172517),
('_', 8.251687627172517), ('C', 8.670805547172517),
('E', 8.820671657172516), ('B', 8.838797187172517),
('A', 9.040823557172518), ('D', 9.134643177172517),
('H', 9.134643177172517), ('G', 9.193730927172517),
('F', 9.265835427172517), ('V', 9.374314927172517),
('K', 9.569215427172518), ('I', 9.648203427172517),
('Y', 10.942930827172518), ('J', 11.299606927172517),
('X', 12.329225127172517), ('Z', 12.329227027172518),
('Q', 13.245515427172517)]
expected = [
('S', 0.7774023970322453), ('U', 0.19082561142814908),
('O', 0.008917692295108082), ('M', 0.008028238843581626),
('W', 0.00785274617485694), ('T', 0.0022419770132497793),
('P', 0.0010091567002187994), ('R', 0.0008408063406892647),
('L', 0.0008178192862913141), ('N', 0.0005481590438212282),
('_', 0.0002608180220954618), ('C', 0.00017152088039886618),
('E', 0.0001476491573050645), ('B', 0.0001449970461439091),
('A', 0.0001184732267906119), ('D', 0.00010786359081437584),
('H', 0.00010786359081437584), ('G', 0.00010167481484983796),
('F', 9.460167015257232e-05), ('V', 8.487636182290733e-05),
('K', 6.984616150492476e-05), ('I', 6.454141629213861e-05),
('Y', 1.7682575696535268e-05), ('J', 1.2377788678084351e-05),
('X', 4.420644194323101e-06), ('Z', 4.420635795107107e-06),
('Q', 1.7682584413941958e-06)
]
for i, pair in enumerate(norm_domain(letters)):
self.assertEqual(expected[i][0], pair[0])
self.assertAlmostEqual(expected[i][1], pair[1])
def main():
"""Runs the demo"""
# init LMWrapper
lmodel = LangModel(logfile="lmwrap.log")
# init LM
lmodel.init()
print('\nNo History\n')
# get initial priors
print(lmodel.recent_priors())
# get priors
print('\nHistory: T\n')
priors = lmodel.state_update(['T'])
# display priors
print(lmodel.recent_priors())
print('\nHistory: TH\n')
priors = lmodel.state_update(['H'])
print(lmodel.recent_priors())
print('\nHistory: THE\n')
priors = lmodel.state_update(['E'])
print(lmodel.recent_priors())
# reset history al together
lmodel.reset()
print("\n--------------RESET-------------\n")
print('\nHistory: THE (fed as a single string)\n')
priors = lmodel.state_update(list('THE'))
print(lmodel.recent_priors())
print('\n\nLetters in the probability domain:\n')
print(norm_domain(lmodel.recent_priors()['letter']))