def build_gtg_model(T, evidence_cpds, internal_cpds):
dag, observed_species, ancestors = build_dag(T, use_tree = True, blast = False, gtg = True)
bnet = BayesianNetwork()
bnet.set_dag(dag)
# set cpds for internal nodes
set_internal_cpds(bnet, internal_cpds)
# set cpds for evidence nodes
blastcpd, gtgcpd = evidence_cpds
for u in observed_species:
bnet.set_pd(get_gtg_node_name(u), [u], gtgcpd)
return bnet, observed_species, ancestors
def build_naive_model(T, cpds):
dag, observed_species, ancestors = build_dag(T,
use_tree=False,
blast=True,
gtg=True)
bnet = BayesianNetwork()
bnet.set_dag(dag)
# set cpds for internal nodes
for u in bnet.dag.children:
if len(bnet.dag.children[u]) > 0 and len(bnet.dag.parents[u]) == 0:
bnet.set_pd(u, [], {"0": 0.5, "1": 0.5})
# set cpds for evidence nodes
blastcpd, gtgcpd = cpds
for u in observed_species:
bnet.set_pd(get_blast_node_name(u), [u], blastcpd)
bnet.set_pd(get_gtg_node_name(u), [u], gtgcpd)
return bnet, observed_species, ancestors
def build_naive_model(T, cpds):
dag, observed_species, ancestors = build_dag(T, use_tree = False, blast = True, gtg = True)
bnet = BayesianNetwork()
bnet.set_dag(dag)
# set cpds for internal nodes
for u in bnet.dag.children:
if len(bnet.dag.children[u]) > 0 and len(bnet.dag.parents[u]) == 0:
bnet.set_pd(u, [], {"0" : 0.5, "1" : 0.5})
# set cpds for evidence nodes
blastcpd, gtgcpd = cpds
for u in observed_species:
bnet.set_pd(get_blast_node_name(u), [u], blastcpd)
bnet.set_pd(get_gtg_node_name(u), [u], gtgcpd)
return bnet, observed_species, ancestors
def build_model(T, evidence_cpds, internal_cpds):
dag, observed_species, ancestors = build_dag(T,
use_tree=True,
blast=True,
gtg=True)
bnet = BayesianNetwork()
bnet.set_dag(dag)
# set cpds for internal nodes
set_internal_cpds(bnet, internal_cpds)
# set cpds for evidence nodes
blastcpd, gtgcpd = evidence_cpds
for u in observed_species:
bnet.set_pd(get_blast_node_name(u), [u], blastcpd)
bnet.set_pd(get_gtg_node_name(u), [u], gtgcpd)
return bnet, observed_species, ancestors
