def scalars(n=3):
"""Scalar example without and with disjunction; compare with Bergen et al.'s figure 5 and figure 9"""
baselexicon = {'some': [r'$w_{\exists\neg\forall}$', r'$w_{\forall}$'], 'all': [r'$w_{\forall}$']}
basic_lexica = Lexica(baselexicon=baselexicon)
lexica = Lexica(baselexicon=baselexicon, join_closure=True, disjunction_cost=0.1)
lexica.display()
mod = Pragmod(lexica=lexica.lexica2matrices(),
messages=lexica.messages,
meanings=lexica.states,
costs=lexica.cost_vector(),
prior=np.repeat(1.0/len(lexica.states), len(lexica.states)),
lexprior=np.repeat(1.0/len(lexica), len(lexica)),
temperature=1.0)
#mod.run_expertise_model(n=n, display=True, digits=4)
#mod.plot_expertise_listener(output_filename='../paper/fig/scalar-expertise-listener-marginalized.pdf', n=n)
mod.plot_expertise_speaker(output_filename='../paper/fig/scalar-expertise-speaker.pdf', n=n)
def manner(n=3):
"""Settings for Bergen et al.'s figure 6. Seems to reproduce the effects they report."""
lexica = Lexica(baselexicon={'SHORT': [r'$w_{RARE}$', r'$w_{FREQ}$'], r'long': [r'$w_{RARE}$', r'$w_{FREQ}$']},
costs={'SHORT':1.0, r'long':2.0},
null_cost=5.0,
join_closure=False,
disjunction_cost=0.1)
lexica.display()
mod = Pragmod(lexica=lexica.lexica2matrices(),
messages=lexica.messages,
meanings=lexica.states,
costs=lexica.cost_vector(),
prior=np.array([2.0/3.0, 1.0/3.0]),
lexprior=np.repeat(1.0/len(lexica), len(lexica)),
temperature=1.0,
alpha=3.0)
mod.plot_expertise_listener(output_filename='../paper/fig/manner-expertise-listener-marginalized.pdf', n=n)
mod.plot_expertise_speaker(output_filename='../paper/fig/manner-expertise-speaker.pdf', n=n)
mod.plot_expertise_speaker(output_filename='../paper/fig/manner-expertise-speaker-lexsum.pdf', n=n, lexsum=True)
