def update_posterior(posterior, signal, context):
new_posterior = np.zeros(len(posterior))
for i in range(len(posterior)): # for each hypothesis
language = lp_pairs[i][0]
perspective = lp_pairs[i][1]
ref_distribution = calc_mental_state(perspective, context)
marginalize = np.zeros(len(meanings))
for m in meanings:
# get the list of signals which can be used for the given meaning
signals_for_r = [
signals[s] for s in range(len(meanings))
if language[m][s] == '1'
]
num_signals_for_r = len(signals_for_r)
# compute the product of the probability that the speaker chooses referent r and that signal s is produced
if signal in signals_for_r:
if num_signals_for_r == num_signals:
in_language = log(1 / num_signals_for_r)
else:
in_language = log((1 - noise) / num_signals_for_r)
marginalize[m] = ref_distribution[m] + in_language
else:
out_of_language = log(noise /
(num_signals - num_signals_for_r))
marginalize[m] = ref_distribution[m] + out_of_language
new_posterior[i] = posterior[i] + logsumexp(marginalize)
return utilities.normalize_logprobs(new_posterior)
def list1_perception_matrix(language, ref_distribution):
mat = np.zeros((len(signals), len(meanings)))
for s in range(len(signals)):
row = np.zeros(len(meanings))
for m in meanings:
row[m] = list1_lit_spkr(signals[s], m, language, ref_distribution)
mat[s] = utilities.normalize_logprobs(row)
return mat
def calc_mental_state(perspective, context):
""" Given speaker's perspective and the context,
compute a probability distribution over the referents
of how likely the speaker is to speak about each referent
p. 88 Equation 3.1 """
distribution = np.zeros(len(context))
for o in range(len(context)):
distribution[o] = log(1 - abs(perspective - context[o]))
return utilities.normalize_logprobs(distribution)
def list1_perception_matrix(language, ref_distribution):
""" Turn the level-1 learner's model of the literal
speaker into a listener's perception matrix """
mat = np.zeros((len(signals), len(meanings)))
for s in range(len(signals)):
row = np.zeros(len(meanings))
for m in meanings:
row[m] = learn1_lit_spkr(signals[s], m, language, ref_distribution)
mat[s] = utilities.normalize_logprobs(row)
return mat
def update_posterior(posterior, signal, context):
new_posterior = np.zeros(len(posterior))
for i in range(len(posterior)): # for each hypothesis
language = languages[lp_pairs[i][0]]
perspective = perspectives[lp_pairs[i][1]]
ref_distribution = calc_mental_state(perspective, context)
marginalize = np.zeros(len(meanings))
for m in meanings:
marginalize[m] = list2_spkr1(signal, m, language, ref_distribution)
# marginalize[m] = list1_lit_spkr(signal, meaning, language, ref_distribution) # level-1 listener
new_posterior[i] = posterior[i] + logsumexp(marginalize)
return utilities.normalize_logprobs(new_posterior)
def update_posterior(posterior, signal, context):
""" Update the posterior probabilities the learner has assigned to
each lexicon/perspective pair based on the observed signal
and context """
new_posterior = np.zeros(len(posterior))
for i in range(len(posterior)): # for each hypothesis
language = languages[hypotheses[i][0]]
perspective = perspectives[hypotheses[i][1]]
pragmatic_lvl = pragmatic_levels[hypotheses[i][2]]
ref_distribution = calc_mental_state(perspective, context)
marginalize = np.zeros(len(meanings))
if pragmatic_lvl == 0:
for m in meanings:
marginalize[m] = learn1_lit_spkr(signal, m, language, ref_distribution) # level-1 learner
elif pragmatic_lvl == 1:
for m in meanings:
marginalize[m] = learn2_spkr1(signal, m, language, ref_distribution) # level-2 learner
new_posterior[i] = posterior[i] + logsumexp(marginalize)
return utilities.normalize_logprobs(new_posterior)
def calc_mental_state(perspective, context):
distribution = np.zeros(len(context))
for o in range(len(context)):
distribution[o] = log(1 - abs(perspective - context[o]))
return utilities.normalize_logprobs(distribution)