def name_cpd(aid):
from pgmpy.factors import TabularCPD
cpd = TabularCPD(
variable='N' + aid,
variable_card=num_names,
values=[[1.0 / num_names] * num_names])
cpd.semtype = 'name'
return cpd
def name_cpd(aid):
from pgmpy.factors import TabularCPD
cpd = TabularCPD(
variable='N' + aid,
variable_card=num_names,
values=[[1.0 / num_names] * num_names])
cpd.semtype = 'name'
return cpd
def score_cpd(aid1, aid2):
cpd = TabularCPD(
variable='S' + aid1 + aid2,
variable_card=num_scores,
values=score_values,
evidence=['N' + aid1, 'N' + aid2],
evidence_card=[num_names, num_names])
cpd.semtype = 'score'
return cpd
def score_cpd(aid1, aid2):
cpd = TabularCPD(
variable='S' + aid1 + aid2,
variable_card=num_scores,
values=score_values,
evidence=['A' + aid1 + aid2], # [::-1],
evidence_card=[num_same_diff]) # [::-1])
cpd.semtype = 'score'
return cpd
def samediff_cpd(aid1, aid2):
cpd = TabularCPD(
variable='A' + aid1 + aid2,
variable_card=num_same_diff,
values=samediff_vals,
evidence=['N' + aid1, 'N' + aid2], # [::-1],
evidence_card=[num_names, num_names]) # [::-1])
cpd.semtype = 'match'
return cpd
def score_cpd(aid1, aid2):
cpd = TabularCPD(
variable='S' + aid1 + aid2,
variable_card=num_scores,
values=score_values,
evidence=['N' + aid1, 'N' + aid2],
evidence_card=[num_names, num_names])
cpd.semtype = 'score'
return cpd
def score_cpd(aid1, aid2):
cpd = TabularCPD(
variable='S' + aid1 + aid2,
variable_card=num_scores,
values=score_values,
evidence=['A' + aid1 + aid2], # [::-1],
evidence_card=[num_same_diff]) # [::-1])
cpd.semtype = 'score'
return cpd
def samediff_cpd(aid1, aid2):
cpd = TabularCPD(
variable='A' + aid1 + aid2,
variable_card=num_same_diff,
values=samediff_vals,
evidence=['N' + aid1, 'N' + aid2], # [::-1],
evidence_card=[num_names, num_names]) # [::-1])
cpd.semtype = 'match'
return cpd
def score_cpd(aid1, aid2):
semtype = 'score'
evidence = ['A' + aid1 + aid2, 'N' + aid1, 'N' + aid2]
evidence_cpds = [var2_cpd[key] for key in evidence]
evidence_nice = [semtype2_nice[cpd.semtype] for cpd in evidence_cpds]
evidence_card = list(map(len, evidence_nice))
evidence_states = list(ut.iprod(*evidence_nice))
variable_basis = semtype2_nice[semtype]
variable_values = []
for mystate in variable_basis:
row = []
for state in evidence_states:
if state[0] == state[1]:
if state[2] == 'same':
val = .2 if mystate == 'low' else .8
else:
val = 1
# val = .5 if mystate == 'low' else .5
elif state[0] != state[1]:
if state[2] == 'same':
val = .5 if mystate == 'low' else .5
else:
val = 1
# val = .9 if mystate == 'low' else .1
row.append(val)
variable_values.append(row)
cpd = TabularCPD(
variable='S' + aid1 + aid2,
variable_card=len(variable_basis),
values=variable_values,
evidence=evidence, # [::-1],
evidence_card=evidence_card) # [::-1])
cpd.semtype = semtype
return cpd
def score_cpd(aid1, aid2):
semtype = 'score'
evidence = ['A' + aid1 + aid2, 'N' + aid1, 'N' + aid2]
evidence_cpds = [var2_cpd[key] for key in evidence]
evidence_nice = [semtype2_nice[cpd.semtype] for cpd in evidence_cpds]
evidence_card = list(map(len, evidence_nice))
evidence_states = list(ut.iprod(*evidence_nice))
variable_basis = semtype2_nice[semtype]
variable_values = []
for mystate in variable_basis:
row = []
for state in evidence_states:
if state[0] == state[1]:
if state[2] == 'same':
val = .2 if mystate == 'low' else .8
else:
val = 1
# val = .5 if mystate == 'low' else .5
elif state[0] != state[1]:
if state[2] == 'same':
val = .5 if mystate == 'low' else .5
else:
val = 1
# val = .9 if mystate == 'low' else .1
row.append(val)
variable_values.append(row)
cpd = TabularCPD(
variable='S' + aid1 + aid2,
variable_card=len(variable_basis),
values=variable_values,
evidence=evidence, # [::-1],
evidence_card=evidence_card) # [::-1])
cpd.semtype = semtype
return cpd