def create(self, network_factory: bayesianpy.network.NetworkFactory):
network = self._template.create(network_factory)
for i, var in enumerate(self._discretised_variables.columns.tolist()):
node = builder.get_node(network, str(var))
if node is not None:
links_from = [
link.getFrom() for link in node.getLinks()
if link.getFrom().getName() != var
]
links_to = [
link.getTo() for link in node.getLinks()
if link.getTo().getName() != var
]
network.getNodes().remove(node)
for node in builder.create_discretised_variables(
network,
self._continuous,
self._discretised_variables.columns.tolist(),
bin_count=self._default_bin_count,
mode=self._mode,
zero_crossing=self._zero_crossing,
defined_bins=self._bins):
if node is not None:
for l in links_from:
builder.create_link(network, l, node)
for l in links_to:
builder.create_link(network, node, l)
return network
def create(self, network_factory: bayesianpy.network.NetworkFactory):
network = self._template.create(network_factory)
cluster = builder.create_cluster_variable(network, self._latent_states)
for node in bayesianpy.network.get_nodes(network):
if node == cluster:
continue
builder.create_link(network, cluster, node)
if self._target_nodes is not None:
for target_node in self._target_nodes:
target = builder.get_node(network, target_node)
builder.delete_links_from(network, target)
if self._remove_target_node:
bayesianpy.network.remove_node(network, self._target_nodes)
return network
