def create(self, network_factory: bayesianpy.network.NetworkFactory):
network = self._template.create(network_factory)
for node in network.getNodes():
if bayesianpy.network.is_variable_continuous(node.getVariables().get(0)):
n = builder.create_discretised_variable(
network, self._template.get_network_factory().get_data(), node.getName() + "_0Node",
bins=[(jp.java.lang.Double.NEGATIVE_INFINITY, 0.5, "closed", "open"),
(0.5, jp.java.lang.Double.POSITIVE_INFINITY, "closed", "closed")])
builder.create_link(network, n, node)
return network
def create(self, network_factory: bayesianpy.network.NetworkFactory):
network = network_factory.create()
cluster = builder.create_cluster_variable(network, self._latent_states)
if not dk.empty(self._continuous):
for c_name in self._continuous.columns:
c = builder.create_discretised_variable(network, self._continuous, c_name, bin_count=self._bin_count,
mode=self._binning_mode, zero_crossing=self._zero_crossing)
builder.create_link(network, cluster, c)
if not dk.empty(self._discrete):
for d_name in self._discrete.columns:
states = dk.compute(self._discrete[d_name].dropna().unique())
c = builder.create_discrete_variable(network, self._discrete, d_name, states)
builder.create_link(network, cluster, c)
return network
