def example_cpd(self, id_=0):
kw = dict()
if self.evidence_ttypes is None:
kw['parents'] = ut.chr_range(id_, id_ + 1)[0]
else:
kw['parents'] = [
tcpd.example_cpd(i)
for i, tcpd in enumerate(self.evidence_ttypes)
]
example_cpd = self.new_cpd(**kw)
return example_cpd
def example_cpd(self, id_=0):
kw = dict()
if self.evidence_ttypes is None:
kw['parents'] = ut.chr_range(id_, id_ + 1)[0]
else:
kw['parents'] = [
tcpd.example_cpd(i)
for i, tcpd in enumerate(self.evidence_ttypes)
]
example_cpd = self.new_cpd(**kw)
return example_cpd
def vim_popup_menu():
""" http://stackoverflow.com/questions/13537521/custom-popup-menu-in-vim """
import vim
import utool as ut
vim.command('echohl Title')
vim.command("echo 'Code fragments:'")
vim.command("echohl None")
options = ['foo', 'bar']
id_list = ut.chr_range(len(options), base='1')
for id_, opt in zip(id_list, options):
vim.command("echo '%s. %s'" % (id_, opt))
choice = chr(int(vim.eval('getchar()')))
print('choice = %r' % (choice,))
pass
def vim_popup_menu(options):
""" http://stackoverflow.com/questions/13537521/custom-popup-menu-in-vim """
import vim
import utool as ut
vim.command('echohl Title')
vim.command("echo 'Code fragments:'")
vim.command("echohl None")
id_list = ut.chr_range(len(options), base='1')
for id_, opt in zip(id_list, options):
vim.command("echo '%s. %s'" % (id_, opt))
vim.command("echo 'Enter the number of your choice '")
choice = chr(int(vim.eval('getchar()')))
print('choice = %r' % (choice,))
try:
chosen = options[int(choice) - 1]
except TypeError:
chosen = None
print('chosen = %r' % (chosen,))
return chosen
def test_avl_split(verbose=1):
for num in range(0, 20):
for index in range(num):
if verbose:
print('------')
print('num = %r' % (num,))
print('index = %r' % (index,))
try:
tree0 = EulerTourTree(ut.chr_range(num))
tour = list(tree0)
tree0._assert_nodes()
if verbose >= 2:
tree0.print_tree()
if verbose:
print('tree0 = %r' % (tree0,))
node = tree0.get_node(index)
if verbose:
print('node = %s' % (node,))
part1, part2, bnode = avl_split(tree0.root, node)
tree1 = EulerTourTree(root=part1)
tree2 = EulerTourTree(root=part2)
if verbose >= 2:
tree1.print_tree(), tree2.print_tree()
if verbose:
print('tree1 = %r' % (tree1,))
print('tree2 = %r' % (tree2,))
# Should correspond to a split in the tour list
assert bnode.left is None, 'bnode must be split'
assert bnode.right is None, 'bnode must be split'
assert bnode.parent is None, 'bnode must be split'
assert bnode is node, 'node must be same'
ut.assert_eq(list(tree1), tour[:index])
ut.assert_eq(list(tree2), tour[index + 1:])
tree1._assert_nodes(), tree2._assert_nodes()
except Exception:
print('num = %r' % (num,))
print('index = %r' % (index,))
raise
def make_name_model(num_annots, num_names=None, verbose=True, mode=1):
"""
Defines the general name model
CommandLine:
python -m wbia.algo.hots.bayes --exec-make_name_model --show
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.hots.bayes import * # NOQA
>>> defaults = dict(num_annots=2, num_names=2, verbose=True, mode=2)
>>> kw = ut.argparse_funckw(make_name_model, defaults)
>>> model = make_name_model(**kw)
>>> ut.quit_if_noshow()
>>> show_model(model, show_prior=True)
>>> ut.show_if_requested()
"""
# annots = ut.chr_range(num_annots, base='a')
mode = ut.get_argval('--mode', default=mode)
annots = ut.chr_range(num_annots,
base=ut.get_argval('--base', default='a'))
# The indexes of match CPDs will not change if another annotation is added
upper_diag_idxs = ut.colwise_diag_idxs(num_annots, 2)
if num_names is None:
num_names = num_annots
# -- Define CPD Templates
def match_pmf(match_type, n1, n2):
if n1 == n2:
val = 1.0 if match_type == 'same' else 0.0
# val = .999 if match_type == 'same' else 0.001
elif n1 != n2:
# val = 0.01 if match_type == 'same' else .99
val = 0.0 if match_type == 'same' else 1.0
return val
def score_pmf(score_type, match_type):
score_lookup = {
'same': {
'low': 0.1,
'high': 0.9,
'veryhigh': 0.9
},
'diff': {
'low': 0.9,
'high': 0.09,
'veryhigh': 0.01
}
#'same': {'low': .1, 'high': .9},
#'diff': {'low': .9, 'high': .1}
}
val = score_lookup[match_type][score_type]
return val
def score_pmf3(score_type, match_type, isdup='False'):
score_lookup = {
'False': {
'same': {
'low': 0.1,
'high': 0.5,
'veryhigh': 0.4
},
'diff': {
'low': 0.9,
'high': 0.09,
'veryhigh': 0.01
},
},
'True': {
'same': {
'low': 0.01,
'high': 0.2,
'veryhigh': 0.79
},
'diff': {
'low': 0.4,
'high': 0.4,
'veryhigh': 0.2
},
},
}
val = score_lookup[isdup][match_type][score_type]
return val
def score_pmf2(score_type, n1, n2):
score_lookup = {
True: {
'low': 0.1,
'high': 0.4,
'veryhigh': 0.5
},
False: {
'low': 0.9,
'high': 0.09,
'veryhigh': 0.01
},
}
val = score_lookup[n1 == n2][score_type]
return val
def dup_pmf(dupstate, match_type):
lookup = {
'same': {
'True': 0.5,
'False': 0.5
},
'diff': {
'True': 0.0,
'False': 1.0
},
}
return lookup[match_type][dupstate]
def check_pmf(n0, n1, match_type):
pass
def trimatch_pmf(match_ab, match_bc, match_ca):
lookup = {
'same': {
'same': {
'same': 1,
'diff': 0
},
'diff': {
'same': 0,
'diff': 1
},
},
'diff': {
'same': {
'same': 0,
'diff': 1
},
'diff': {
'same': 0.5,
'diff': 0.5
},
},
}
return lookup[match_ca][match_bc][match_ab]
name_cpd_t = pgm_ext.TemplateCPD('name', ('n', num_names),
varpref='N',
special_basis_pool=SPECIAL_BASIS_POOL)
if mode == 1 or mode == 5:
match_cpd_t = pgm_ext.TemplateCPD(
'match',
['diff', 'same'],
varpref='M',
evidence_ttypes=[name_cpd_t, name_cpd_t],
pmf_func=match_pmf,
)
if mode == 5:
trimatch_cpd_t = pgm_ext.TemplateCPD(
'tri_match',
['diff', 'same'],
varpref='T',
# evidence_ttypes=[match_cpd_t, match_cpd_t, match_cpd_t],
evidence_ttypes=[match_cpd_t, match_cpd_t],
pmf_func=trimatch_pmf,
)
score_cpd_t = pgm_ext.TemplateCPD(
#'score', ['low', 'high', 'veryhigh'],
'score',
['low', 'high'],
varpref='S',
evidence_ttypes=[match_cpd_t],
pmf_func=score_pmf,
)
else:
score_cpd_t = pgm_ext.TemplateCPD(
#'score', ['low', 'high', 'veryhigh'],
'score',
['low', 'high'],
varpref='S',
evidence_ttypes=[match_cpd_t],
pmf_func=score_pmf,
)
elif mode == 2:
name_cpd_t = pgm_ext.TemplateCPD('name', ('n', num_names),
varpref='N',
special_basis_pool=SPECIAL_BASIS_POOL)
score_cpd_t = pgm_ext.TemplateCPD(
#'score', ['low', 'high', 'veryhigh'],
'score',
['low', 'high'],
varpref='S',
evidence_ttypes=[name_cpd_t, name_cpd_t],
pmf_func=score_pmf2,
)
elif mode == 3 or mode == 4:
match_cpd_t = pgm_ext.TemplateCPD(
'match',
['diff', 'same'],
varpref='M',
evidence_ttypes=[name_cpd_t, name_cpd_t],
pmf_func=match_pmf,
)
if mode == 3:
dup_cpd_t = pgm_ext.TemplateCPD('dup', ['False', 'True'],
varpref='D')
else:
dup_cpd_t = pgm_ext.TemplateCPD(
'dup',
['False', 'True'],
varpref='D',
evidence_ttypes=[match_cpd_t],
pmf_func=dup_pmf,
)
score_cpd_t = pgm_ext.TemplateCPD(
'score',
['low', 'high', 'veryhigh'],
varpref='S',
evidence_ttypes=[match_cpd_t, dup_cpd_t],
pmf_func=score_pmf3,
)
# Instanciate templates
if mode == 1 or mode == 5:
name_cpds = [name_cpd_t.new_cpd(parents=aid) for aid in annots]
namepair_cpds = ut.list_unflat_take(name_cpds, upper_diag_idxs)
match_cpds = [
match_cpd_t.new_cpd(parents=cpds) for cpds in namepair_cpds
]
score_cpds = [
score_cpd_t.new_cpd(parents=cpds) for cpds in zip(match_cpds)
]
if mode == 5:
# triple_idxs = ut.colwise_diag_idxs(num_annots, 3)
tid2_match = {cpd._template_id: cpd for cpd in match_cpds}
trimatch_cpds = []
# such hack
for cpd in match_cpds:
parents = []
this_ = list(cpd._template_id)
for aid in annots:
if aid in this_:
continue
for aid2 in this_:
key = aid2 + aid
if key not in tid2_match:
key = aid + aid2
parents += [tid2_match[key]]
trimatch_cpds += [trimatch_cpd_t.new_cpd(parents=parents)]
# score_cpds = [score_cpd_t.new_cpd(parents=cpds)
# for cpds in zip(trimatch_cpds)]
cpd_list = name_cpds + score_cpds + match_cpds + trimatch_cpds
else:
cpd_list = name_cpds + score_cpds + match_cpds
elif mode == 2:
name_cpds = [name_cpd_t.new_cpd(parents=aid) for aid in annots]
namepair_cpds = ut.list_unflat_take(name_cpds, upper_diag_idxs)
score_cpds = [
score_cpd_t.new_cpd(parents=cpds) for cpds in namepair_cpds
]
cpd_list = name_cpds + score_cpds
elif mode == 3 or mode == 4:
name_cpds = [name_cpd_t.new_cpd(parents=aid) for aid in annots]
namepair_cpds = ut.list_unflat_take(name_cpds, upper_diag_idxs)
match_cpds = [
match_cpd_t.new_cpd(parents=cpds) for cpds in namepair_cpds
]
if mode == 3:
dup_cpds = [
dup_cpd_t.new_cpd(parents=''.join(map(str, aids)))
for aids in ut.list_unflat_take(annots, upper_diag_idxs)
]
else:
dup_cpds = [
dup_cpd_t.new_cpd(parents=[mcpds]) for mcpds in match_cpds
]
score_cpds = [
score_cpd_t.new_cpd(parents=([mcpds] + [dcpd]))
for mcpds, dcpd in zip(match_cpds, dup_cpds)
]
cpd_list = name_cpds + score_cpds + match_cpds + dup_cpds
# logger.info('upper_diag_idxs = %r' % (upper_diag_idxs,))
logger.info('score_cpds = %r' %
(ut.list_getattr(score_cpds, 'variable'), ))
# import sys
# sys.exit(1)
# Make Model
model = pgm_ext.define_model(cpd_list)
model.num_names = num_names
if verbose:
model.print_templates()
# ut.colorprint('\n --- CPD Templates ---', 'blue')
# for temp_cpd in templates:
# ut.colorprint(temp_cpd._cpdstr('psql'), 'cyan')
# print_ascii_graph(model)
return model
def name_model_mode1(num_annots, num_names=None, verbose=True):
r"""
spaghettii
CommandLine:
python -m wbia.algo.hots.bayes --exec-name_model_mode1 --show
python -m wbia.algo.hots.bayes --exec-name_model_mode1
python -m wbia.algo.hots.bayes --exec-name_model_mode1 --num-annots=3
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.hots.bayes import * # NOQA
>>> defaults = dict(num_annots=2, num_names=2, verbose=True)
>>> kw = ut.argparse_funckw(name_model_mode1, defaults)
>>> model = name_model_mode1(**kw)
>>> ut.quit_if_noshow()
>>> show_model(model, show_prior=False, show_title=False)
>>> ut.show_if_requested()
Ignore:
import nx2tikz
logger.info(nx2tikz.dumps_tikz(model, layout='layered', use_label=True))
"""
annots = ut.chr_range(num_annots,
base=ut.get_argval('--base', default='a'))
# The indexes of match CPDs will not change if another annotation is added
upper_diag_idxs = ut.colwise_diag_idxs(num_annots, 2)
if num_names is None:
num_names = num_annots
# +--- Define CPD Templates ---
# +-- Name Factor ---
name_cpd_t = pgm_ext.TemplateCPD('name', ('n', num_names),
varpref='N',
special_basis_pool=SPECIAL_BASIS_POOL)
name_cpds = [name_cpd_t.new_cpd(parents=aid) for aid in annots]
# +-- Match Factor ---
def match_pmf(match_type, n1, n2):
return {
True: {
'same': 1.0,
'diff': 0.0
},
False: {
'same': 0.0,
'diff': 1.0
},
}[n1 == n2][match_type]
match_cpd_t = pgm_ext.TemplateCPD(
'match',
['diff', 'same'],
varpref='M',
evidence_ttypes=[name_cpd_t, name_cpd_t],
pmf_func=match_pmf,
)
namepair_cpds = ut.list_unflat_take(name_cpds, upper_diag_idxs)
match_cpds = [match_cpd_t.new_cpd(parents=cpds) for cpds in namepair_cpds]
# +-- Score Factor ---
def score_pmf(score_type, match_type):
score_lookup = {
'same': {
'low': 0.1,
'high': 0.9,
'veryhigh': 0.9
},
'diff': {
'low': 0.9,
'high': 0.09,
'veryhigh': 0.01
},
}
val = score_lookup[match_type][score_type]
return val
score_cpd_t = pgm_ext.TemplateCPD(
'score',
['low', 'high'],
varpref='S',
evidence_ttypes=[match_cpd_t],
pmf_func=score_pmf,
)
score_cpds = [
score_cpd_t.new_cpd(parents=cpds) for cpds in zip(match_cpds)
]
# L___ End CPD Definitions ___
cpd_list = name_cpds + score_cpds + match_cpds
logger.info('score_cpds = %r' %
(ut.list_getattr(score_cpds, 'variable'), ))
# Make Model
model = pgm_ext.define_model(cpd_list)
model.num_names = num_names
if verbose:
model.print_templates()
return model
def name_model_mode5(num_annots, num_names=None, verbose=True, mode=1):
mode = ut.get_argval('--mode', default=mode)
annots = ut.chr_range(num_annots,
base=ut.get_argval('--base', default='a'))
# The indexes of match CPDs will not change if another annotation is added
upper_diag_idxs = ut.colwise_diag_idxs(num_annots, 2)
if num_names is None:
num_names = num_annots
# -- Define CPD Templates
name_cpd_t = pgm_ext.TemplateCPD('name', ('n', num_names),
varpref='N',
special_basis_pool=SPECIAL_BASIS_POOL)
name_cpds = [name_cpd_t.new_cpd(parents=aid) for aid in annots]
def match_pmf(match_type, n1, n2):
return {
True: {
'same': 1.0,
'diff': 0.0
},
False: {
'same': 0.0,
'diff': 1.0
},
}[n1 == n2][match_type]
match_cpd_t = pgm_ext.TemplateCPD(
'match',
['diff', 'same'],
varpref='M',
evidence_ttypes=[name_cpd_t, name_cpd_t],
pmf_func=match_pmf,
)
namepair_cpds = ut.list_unflat_take(name_cpds, upper_diag_idxs)
match_cpds = [match_cpd_t.new_cpd(parents=cpds) for cpds in namepair_cpds]
def trimatch_pmf(match_ab, match_bc, match_ca):
lookup = {
'same': {
'same': {
'same': 1,
'diff': 0
},
'diff': {
'same': 0,
'diff': 1
},
},
'diff': {
'same': {
'same': 0,
'diff': 1
},
'diff': {
'same': 0.5,
'diff': 0.5
},
},
}
return lookup[match_ca][match_bc][match_ab]
trimatch_cpd_t = pgm_ext.TemplateCPD(
'tri_match',
['diff', 'same'],
varpref='T',
evidence_ttypes=[match_cpd_t, match_cpd_t],
pmf_func=trimatch_pmf,
)
# triple_idxs = ut.colwise_diag_idxs(num_annots, 3)
tid2_match = {cpd._template_id: cpd for cpd in match_cpds}
trimatch_cpds = []
# such hack
for cpd in match_cpds:
parents = []
this_ = list(cpd._template_id)
for aid in annots:
if aid in this_:
continue
for aid2 in this_:
key = aid2 + aid
if key not in tid2_match:
key = aid + aid2
parents += [tid2_match[key]]
trimatch_cpds += [trimatch_cpd_t.new_cpd(parents=parents)]
def score_pmf(score_type, match_type):
score_lookup = {
'same': {
'low': 0.1,
'high': 0.9,
'veryhigh': 0.9
},
'diff': {
'low': 0.9,
'high': 0.09,
'veryhigh': 0.01
},
}
val = score_lookup[match_type][score_type]
return val
score_cpd_t = pgm_ext.TemplateCPD(
'score',
['low', 'high'],
varpref='S',
evidence_ttypes=[match_cpd_t],
pmf_func=score_pmf,
)
score_cpds = [
score_cpd_t.new_cpd(parents=cpds) for cpds in zip(match_cpds)
]
# score_cpds = [score_cpd_t.new_cpd(parents=cpds)
# for cpds in zip(trimatch_cpds)]
cpd_list = name_cpds + score_cpds + match_cpds + trimatch_cpds
logger.info('score_cpds = %r' %
(ut.list_getattr(score_cpds, 'variable'), ))
# Make Model
model = pgm_ext.define_model(cpd_list)
model.num_names = num_names
if verbose:
model.print_templates()
return model
def make_name_model(num_annots, num_names=None, verbose=True, mode=1,
num_scores=2, p_score_given_same=None,
hack_score_only=False, score_basis=None,
special_names=None):
r"""
CommandLine:
python -m ibeis.algo.hots.bayes --exec-make_name_model --show
python -m ibeis.algo.hots.bayes --exec-make_name_model
python -m ibeis.algo.hots.bayes --exec-make_name_model --num-annots=3
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.bayes import * # NOQA
>>> defaults = dict(num_annots=2, num_names=2, verbose=True)
>>> modeltype = ut.get_argval('--modeltype', default='bayes')
>>> kw = ut.argparse_funckw(make_name_model, defaults)
>>> model = make_name_model(**kw)
>>> ut.quit_if_noshow()
>>> model.show_model(show_prior=False, show_title=False, modeltype=modeltype)
>>> ut.show_if_requested()
"""
if special_names is None:
special_names = SPECIAL_BASIS_POOL
assert mode == 1, 'only can do mode 1'
base = ut.get_argval('--base', type_=str, default='a')
annots = ut.chr_range(num_annots, base=base)
# The indexes of match CPDs will not change if another annotation is added
upper_diag_idxs = ut.colwise_diag_idxs(num_annots, 2)
if hack_score_only:
upper_diag_idxs = upper_diag_idxs[-hack_score_only:]
if num_names is None:
num_names = num_annots
# +--- Define CPD Templates and Instantiation ---
cpd_list = []
# Name Factor
name_cpd_t = pgm_ext.TemplateCPD(
'name', ('n', num_names),
special_basis_pool=special_names)
name_cpds = [name_cpd_t.new_cpd(parents=aid) for aid in annots]
#name_cpds = [name_cpd_t.new_cpd(parents=aid, constrain_state=count)
# for count, aid in enumerate(annots, start=1)]
cpd_list.extend(name_cpds)
# Match Factor
def match_pmf(match_type, n1, n2):
return {
True: {'same': 1.0, 'diff': 0.0},
False: {'same': 0.0, 'diff': 1.0},
}[n1 == n2][match_type]
match_states = ['diff', 'same']
match_cpd_t = pgm_ext.TemplateCPD(
'match', match_states,
evidence_ttypes=[name_cpd_t, name_cpd_t], pmf_func=match_pmf)
namepair_cpds = ut.list_unflat_take(name_cpds, upper_diag_idxs)
match_cpds = [match_cpd_t.new_cpd(parents=cpds)
for cpds in namepair_cpds]
cpd_list.extend(match_cpds)
# Score Factor
score_states = list(range(num_scores))
if score_basis is not None:
score_states = ['%.2f' % (s,) for s in score_basis]
if p_score_given_same is None:
tmp = np.arange(num_scores + 1)[1:]
tmp = np.cumsum(tmp)
tmp = (tmp / tmp.sum())
p_score_given_same = tmp
def score_pmf(score_type, match_type):
if isinstance(score_type, six.string_types):
score_type = score_states.index(score_type)
if match_type == 'same':
return p_score_given_same[score_type]
else:
return p_score_given_same[-(score_type + 1)]
score_cpd_t = pgm_ext.TemplateCPD(
'score', score_states,
evidence_ttypes=[match_cpd_t], pmf_func=score_pmf)
score_cpds = [score_cpd_t.new_cpd(parents=cpds)
for cpds in zip(match_cpds)]
cpd_list.extend(score_cpds)
with_humans = False
if with_humans:
human_states = ['diff', 'same']
human_cpd_t = pgm_ext.TemplateCPD(
'human', human_states,
evidence_ttypes=[match_cpd_t], pmf_func=[[.9, .1], [.1, .9]])
human_cpds = [human_cpd_t.new_cpd(parents=cpds)
for cpds in zip(match_cpds)]
cpd_list.extend(human_cpds)
with_rank = False # Rank depends on dependant scores
if with_rank:
rank_states = ['0', '1', '2', '3']
rank_cpd_t = pgm_ext.TemplateCPD(
'rank', rank_states,
evidence_ttypes=[match_cpd_t], pmf_func=None)
rank_cpds = [rank_cpd_t.new_cpd(parents=cpds)
for cpds in zip(match_cpds)]
cpd_list.extend(rank_cpds)
# L___ End CPD Definitions ___
print('score_cpds = %r' % (ut.list_getattr(score_cpds, 'variable'),))
# Make Model
model = pgm_ext.define_model(cpd_list)
model.num_names = num_names
if verbose:
model.print_templates(ignore_ttypes=['match'])
return model
def make_name_model(
num_annots,
num_names=None,
verbose=True,
mode=1,
num_scores=2,
p_score_given_same=None,
hack_score_only=False,
score_basis=None,
special_names=None,
):
r"""
CommandLine:
python -m wbia.algo.hots.bayes --exec-make_name_model --no-cnn
python -m wbia.algo.hots.bayes --exec-make_name_model --show --no-cnn
python -m wbia.algo.hots.bayes --exec-make_name_model --num-annots=3
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.hots.bayes import * # NOQA
>>> defaults = dict(num_annots=2, num_names=2, verbose=True)
>>> modeltype = ut.get_argval('--modeltype', default='bayes')
>>> kw = ut.argparse_funckw(make_name_model, defaults)
>>> model = make_name_model(**kw)
>>> ut.quit_if_noshow()
>>> model.show_model(show_prior=False, show_title=False, modeltype=modeltype)
>>> ut.show_if_requested()
"""
if special_names is None:
special_names = SPECIAL_BASIS_POOL
assert mode == 1, 'only can do mode 1'
base = ut.get_argval('--base', type_=str, default='a')
annots = ut.chr_range(num_annots, base=base)
# The indexes of match CPDs will not change if another annotation is added
upper_diag_idxs = ut.colwise_diag_idxs(num_annots, 2)
if hack_score_only:
upper_diag_idxs = upper_diag_idxs[-hack_score_only:]
if num_names is None:
num_names = num_annots
# +--- Define CPD Templates and Instantiation ---
cpd_list = []
# Name Factor
name_cpd_t = pgm_ext.TemplateCPD(NAME_TTYPE, ('n', num_names),
special_basis_pool=special_names)
name_cpds = [name_cpd_t.new_cpd(parents=aid) for aid in annots]
# name_cpds = [name_cpd_t.new_cpd(parents=aid, constrain_state=count)
# for count, aid in enumerate(annots, start=1)]
cpd_list.extend(name_cpds)
# Match Factor
def match_pmf(match_type, n1, n2):
return {
True: {
'same': 1.0,
'diff': 0.0
},
False: {
'same': 0.0,
'diff': 1.0
}
}[n1 == n2][match_type]
match_states = ['diff', 'same']
match_cpd_t = pgm_ext.TemplateCPD(
MATCH_TTYPE,
match_states,
evidence_ttypes=[name_cpd_t, name_cpd_t],
pmf_func=match_pmf,
)
# match_cpd_t.varpref = 'S'
namepair_cpds = ut.unflat_take(name_cpds, upper_diag_idxs)
match_cpds = [match_cpd_t.new_cpd(parents=cpds) for cpds in namepair_cpds]
cpd_list.extend(match_cpds)
# Score Factor
score_states = list(range(num_scores))
if score_basis is not None:
score_states = ['%.2f' % (s, ) for s in score_basis]
if p_score_given_same is None:
tmp = np.arange(num_scores + 1)[1:]
tmp = np.cumsum(tmp)
tmp = tmp / tmp.sum()
p_score_given_same = tmp
def score_pmf(score_type, match_type):
if isinstance(score_type, six.string_types):
score_type = score_states.index(score_type)
if match_type == 'same':
return p_score_given_same[score_type]
else:
return p_score_given_same[-(score_type + 1)]
score_cpd_t = pgm_ext.TemplateCPD(SCORE_TTYPE,
score_states,
evidence_ttypes=[match_cpd_t],
pmf_func=score_pmf)
# match_cpd_t.varpref = 'P'
score_cpds = [
score_cpd_t.new_cpd(parents=cpds) for cpds in zip(match_cpds)
]
cpd_list.extend(score_cpds)
with_humans = False
if with_humans:
human_states = ['diff', 'same']
human_cpd_t = pgm_ext.TemplateCPD(
'human',
human_states,
evidence_ttypes=[match_cpd_t],
pmf_func=[[0.9, 0.1], [0.1, 0.9]],
)
human_cpds = [
human_cpd_t.new_cpd(parents=cpds) for cpds in zip(match_cpds)
]
cpd_list.extend(human_cpds)
with_rank = False # Rank depends on dependant scores
if with_rank:
rank_states = ['0', '1', '2', '3']
rank_cpd_t = pgm_ext.TemplateCPD('rank',
rank_states,
evidence_ttypes=[match_cpd_t],
pmf_func=None)
rank_cpds = [
rank_cpd_t.new_cpd(parents=cpds) for cpds in zip(match_cpds)
]
cpd_list.extend(rank_cpds)
# L___ End CPD Definitions ___
logger.info('score_cpds = %r' %
(ut.list_getattr(score_cpds, 'variable'), ))
# Make Model
model = pgm_ext.define_model(cpd_list)
model.num_names = num_names
if verbose:
model.print_templates(ignore_ttypes=[MATCH_TTYPE])
return model
