def generic_disjunction_example(alpha=1.0,
beta=1.0,
disjunction_cost=1.0,
n=2,
fulldisplay=False,
unknown_word=None):
"""Common code for our two illustrative examples, which
differ only in the above keyword parameters. Increase n to see
greater depths of recursion. use fulldisplay=True to see more
details."""
# Use the lexicon generation convenience function to
# generate all the join-closure lexica and calculate
# the necessary message costs:
lexica = Lexica(baselexicon={
'A': ['1'],
'B': ['2'],
'X': ['1', '2']
},
costs={
'A': 0.0,
'B': 0.0,
'X': 0.0
},
join_closure=True,
nullsem=True,
nullcost=5.0,
disjunction_cost=disjunction_cost,
unknown_word=unknown_word)
# Lexical matrices:
lexmats = lexica.lexica2matrices()
# Pragmatic models for the above lexical space.
mod = Pragmod(lexica=lexmats,
messages=lexica.messages,
states=lexica.states,
costs=lexica.cost_vector(),
prior=np.repeat(1.0 / len(lexica.states),
len(lexica.states)),
lexprior=np.repeat(1.0 / len(lexmats), len(lexmats)),
temperature=1.0,
alpha=alpha,
beta=beta)
if fulldisplay:
lexica.display()
# Run the base model on the individual lexica so we can show
# those lower steps:
for lex in lexmats:
print("=" * 70)
print(mod.lex2str(lex))
mod.run_base_model(lex, n=2, display=True, digits=2)
## Run the anxious experts model - fulldisplay=True for a fuller picture:
langs = mod.run_expertise_model(n=n, display=fulldisplay, digits=2)
# Look at the specific table we care about:
msg_index = mod.messages.index('A v X')
final_listener = langs[-1]
mod.display_joint_listener(final_listener[msg_index], digits=2)
return langs
def simple_disjunction():
lexicon = np.array([[1.0, 1.0, 0.0], [1.0, 0.0, 1.0], [1.0, 0.0, 0.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
mod = Pragmod(lexica=None,
messages=[p, q, pandq, porq, NULL_MSG],
states=[w1, w2, w3],
costs=np.array([0.0, 0.0, 1.0, 1.0, 5.0]),
prior=np.repeat(1.0 / 4.0, 3),
lexprior=None,
temperature=1.0,
alpha=1.0,
beta=0.0)
mod.run_base_model(lexicon, n=2, display=True, digits=2)
def generic_disjunction_example(
alpha=1.0,
beta=1.0,
disjunction_cost=1.0,
n=2,
fulldisplay=False,
unknown_word=None):
"""Common code for our two illustrative examples, which
differ only in the above keyword parameters. Increase n to see
greater depths of recursion. use fulldisplay=True to see more
details."""
# Use the lexicon generation convenience function to
# generate all the join-closure lexica and calculate
# the necessary message costs:
lexica = Lexica(
baselexicon={'A': ['1'], 'B': ['2'], 'X':['1', '2']},
costs={'A':0.0, 'B':0.0, 'X':0.0},
join_closure=True,
nullsem=True,
nullcost=5.0,
disjunction_cost=disjunction_cost,
unknown_word=unknown_word)
# Lexical matrices:
lexmats = lexica.lexica2matrices()
# Pragmatic models for the above lexical space.
mod = Pragmod(
lexica=lexmats,
messages=lexica.messages,
states=lexica.states,
costs=lexica.cost_vector(),
prior=np.repeat(1.0/len(lexica.states), len(lexica.states)),
lexprior=np.repeat(1.0/len(lexmats), len(lexmats)),
temperature=1.0,
alpha=alpha,
beta=beta)
if fulldisplay:
lexica.display()
# Run the base model on the individual lexica so we can show
# those lower steps:
for lex in lexmats:
print("=" * 70)
print(mod.lex2str(lex))
mod.run_base_model(lex, n=2, display=True, digits=2)
## Run the anxious experts model - fulldisplay=True for a fuller picture:
langs = mod.run_expertise_model(n=n, display=fulldisplay, digits=2)
# Look at the specific table we care about:
msg_index = mod.messages.index('A v X')
final_listener = langs[-1]
mod.display_joint_listener(final_listener[msg_index], digits=2)
return langs
def simple_disjunction():
lexicon = np.array([[1.0, 1.0, 0.0],
[1.0, 0.0, 1.0],
[1.0, 0.0, 0.0],
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0]])
mod = Pragmod(
lexica=None,
messages=[p, q, pandq, porq, NULL_MSG],
states=[w1, w2, w3],
costs=np.array([0.0, 0.0, 1.0, 1.0, 5.0]),
prior=np.repeat(1.0/4.0, 3),
lexprior=None,
temperature=1.0,
alpha=1.0,
beta=0.0)
mod.run_base_model(lexicon, n=2, display=True, digits=2)
def basic_scalar():
lexica = [np.array([[1.0,1.0], [1.0, 0.0], [1.0, 1.0]]),
np.array([[1.0,0.0], [1.0, 0.0], [1.0, 1.0]]),
np.array([[0.0,1.0], [1.0, 0.0], [1.0, 1.0]])]
mod = Pragmod(lexica=lexica,
messages=['cheap', 'free', NULL_MSG],
states=[w1, w2],
costs=np.array([0.0, 0.0, 5.0]),
prior=np.repeat(1.0/2.0, 2),
lexprior=np.repeat(1.0/3.0, 3),
temperature=1.0,
alpha=1.0,
beta=2.0)
for lex in lexica:
print("=" * 70)
print(mod.lex2str(lex))
mod.run_base_model(lex, n=2, display=True, digits=2)
mod.run_expertise_model(n=3, display=True, digits=2)
def basic_scalar():
lexica = [
np.array([[1.0, 1.0], [1.0, 0.0], [1.0, 1.0]]),
np.array([[1.0, 0.0], [1.0, 0.0], [1.0, 1.0]]),
np.array([[0.0, 1.0], [1.0, 0.0], [1.0, 1.0]])
]
mod = Pragmod(lexica=lexica,
messages=['cheap', 'free', NULL_MSG],
states=[w1, w2],
costs=np.array([0.0, 0.0, 5.0]),
prior=np.repeat(1.0 / 2.0, 2),
lexprior=np.repeat(1.0 / 3.0, 3),
temperature=1.0,
alpha=1.0,
beta=2.0)
for lex in lexica:
print("=" * 70)
print(mod.lex2str(lex))
mod.run_base_model(lex, n=2, display=True, digits=2)
mod.run_expertise_model(n=3, display=True, digits=2)