From a6d76f4c41d0295ccf590a23322b42e52b9f14dc Mon Sep 17 00:00:00 2001
From: Hendrik Buschmeier <hbuschme@uni-bielefeld.de>
Date: Sat, 5 Apr 2014 00:05:46 +0200
Subject: [PATCH] Refactoring: Moved individual network modules to networks.py.
---
primo/core/BayesNet.py | 138 ----------
primo/core/BayesianDecisionNetwork.py | 105 --------
primo/core/DynamicBayesNet.py | 67 -----
primo/core/TwoTBN.py | 81 ------
primo/core/__init__.py | 5 -
primo/networks.py | 374 ++++++++++++++++++++++++++
6 files changed, 374 insertions(+), 396 deletions(-)
delete mode 100644 primo/core/BayesNet.py
delete mode 100644 primo/core/BayesianDecisionNetwork.py
delete mode 100644 primo/core/DynamicBayesNet.py
delete mode 100644 primo/core/TwoTBN.py
delete mode 100644 primo/core/__init__.py
create mode 100644 primo/networks.py
diff --git a/primo/core/BayesNet.py b/primo/core/BayesNet.py
deleted file mode 100644
index 82b6f2f..0000000
--- a/primo/core/BayesNet.py
+++ /dev/null
@@ -1,138 +0,0 @@
-import networkx as nx
-from primo.core import Node
-
-
-
-class BayesNet(object):
-
- def __init__(self):
- self.graph = nx.DiGraph()
- self.node_lookup = {}
-
- def add_node(self, node):
- if isinstance(node, Node):
- if node.name in self.node_lookup.keys():
- raise Exception("Node name already exists in Bayesnet: "+node.name)
- self.node_lookup[node.name]=node
- self.graph.add_node(node)
- else:
- raise Exception("Can only add 'Node' and its subclasses as nodes into the BayesNet")
-
- def add_edge(self, node_from, node_to):
- if node_from in self.graph.nodes() and node_to in self.graph.nodes():
- self.graph.add_edge(node_from, node_to)
- node_to.announce_parent(node_from)
- else:
- raise Exception("Tried to add an Edge between two Nodes of which at least one was not contained in the Bayesnet")
-
- def remove_node(self, node):
- if node.name not in self.node_lookup.keys():
- raise Exception("Node " + node.name + "does not exists")
- else :
- try:
- self.graph.remove_node(node)
- except nx.exception.NetworkXError:
- raise Exception("Tried to remove a node which does not exist.")
- del self.node_lookup[node.name]
-
- def remove_edge(self, node_from, node_to):
- try:
- self.graph.remove_edge(node_from, node_to)
- except nx.exception.NetworkXError:
- raise Exception("Tried to remove an edge which does not exist in the BayesNet")
- #raise Exception("Fixme: Adapt CPD of child-node")
-
- def get_node(self, node_name):
- try:
- return self.node_lookup[node_name]
- except KeyError:
- raise Exception("There is no node with name "+node_name+" in the BayesNet")
-
- def get_all_nodes(self):
- return self.graph.nodes()
-
- def get_nodes(self, node_names=[]):
- nodes = []
- if not node_names:
- nodes = self.graph.nodes()
- else:
- for node_name in node_names:
- nodes.append(self.get_node(node_name))
- return nodes
-
- def get_nodes_in_topological_sort(self):
- return nx.topological_sort(self.graph)
-
- def get_parents(self, node):
- if node.name not in self.node_lookup.keys():
- raise Exception("Node " + node.name + "does not exists")
- else:
- return self.graph.predecessors(node)
-
-
- def get_children(self, node):
- if node.name not in self.node_lookup.keys():
- raise Exception("Node " + node.name + "does not exists")
- else:
- return self.graph.successors(node)
-
-
- def get_markov_blanket(self, node):
- raise Exception("Called unimplemented function")
-
- def is_dag(self):
- raise Exception("Called unimplemented function")
-
- def draw(self):
- import matplotlib.pyplot as plt
- nx.draw_circular(self.graph)
- plt.show()
-
- def draw_graphviz(self):
- import matplotlib.pyplot as plt
- nx.draw_graphviz(self.graph)
- plt.show()
-
- def is_valid(self):
- '''Check if graph structure is valid.
- Returns true if graph is directed and acyclic, false otherwiese'''
-
- if self.graph.number_of_selfloops() > 0:
- return False
-
- for node in self.graph.nodes():
- if self.has_loop(node):
- return False
-
- return True
-
- def has_loop(self, node, origin=None):
- '''Check if any path from node leads back to node.
-
- Keyword arguments:
- node -- the start node
- origin -- for internal recursive loop (default: None)
-
- Returns true on succes, false otherwise.'''
- if not origin:
- origin = node
-
- for successor in self.graph.successors(node):
- if successor == origin:
- return True
- else:
- return self.has_loop(successor, origin)
-
- def clear(self):
- '''Remove all nodes and edges from the graph.
- This also removes the name, and all graph, node and edge attributes.'''
- self.graph.clear()
- self.node_lookup.clear()
-
- def number_of_nodes(self):
- '''Return the number of nodes in the graph.'''
- return len(self)
-
- def __len__(self):
- '''Return the number of nodes in the graph.'''
- return len(self.graph)
\ No newline at end of file
diff --git a/primo/core/BayesianDecisionNetwork.py b/primo/core/BayesianDecisionNetwork.py
deleted file mode 100644
index 3b190c4..0000000
--- a/primo/core/BayesianDecisionNetwork.py
+++ /dev/null
@@ -1,105 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import networkx as nx
-
-from primo.core import BayesNet
-from primo.core import Node
-from primo.decision import DecisionNode
-from primo.decision import UtilityNode
-from primo.reasoning import DiscreteNode
-
-class BayesianDecisionNetwork(BayesNet):
-
- def __init__(self):
- super(BayesianDecisionNetwork, self).__init__()
- self.partialOrdering = []
- self.random_nodes = []
- self.decision_nodes = []
- self.utility_nodes = []
-
- def is_valid(self):
- '''Check if graph structure is valid.
- Returns true if graph is directed, acyclic and if there is a path that connects every decision node(consistency check),
- false otherwise'''
-
- if self.graph.number_of_selfloops() > 0:
- return False
-
- for node in self.graph.nodes():
- if self.has_loop(node):
- return False
-
- decisionNodeList = []
- for node in self.get_all_nodes():
- if isinstance(node, DecisionNode):
- decisionNodeList.append(node)
-
- return all([nx.has_path(self.graph, x, y) == True for x in decisionNodeList for y in decisionNodeList])
-
- def add_node(self, node):
- if isinstance(node, Node):
- if node.name in self.node_lookup.keys():
- raise Exception("Node name already exists in Bayesnet: "+node.name)
- if isinstance(node, DiscreteNode):
- self.random_nodes.append(node)
- elif isinstance(node, UtilityNode):
- self.utility_nodes.append(node)
- elif isinstance(node, DecisionNode):
- self.decision_nodes.append(node)
- else:
- raise Exception("Tried to add a node which the Bayesian Decision Network can not work with")
- self.node_lookup[node.name]=node
- self.graph.add_node(node)
- else:
- raise Exception("Can only add 'Node' and its subclasses as nodes into the BayesNet")
-
- def get_all_nodes(self):
- '''Returns all RandomNodes'''
- return self.random_nodes
-
- def get_all_decision_nodes(self):
- return self.decision_nodes
-
- def get_all_utility_nodes(self):
- return self.utility_nodes
-
- def add_edge(self, node_from, node_to):
- """
- Adds an edge between two nodes. It is impossible to create en edge between two decision nodes and between two
- utility nodes.
-
- keyword arguments:
- node_from -- Node from where the edge shall begin
- node_to -- Node where the edge shall end
- """
- if isinstance(node_from, DecisionNode) and isinstance(node_to, DecisionNode):
- raise Exception("Tried to add an edge from a DecisionNode to a DecisionNode")
- if isinstance(node_from, UtilityNode) and isinstance(node_to, UtilityNode):
- raise Exception("Tried to add an edge from a UtilityNode to a UtilityNode")
- if node_from in self.graph.nodes() and node_to in self.graph.nodes():
- self.graph.add_edge(node_from, node_to)
- node_to.announce_parent(node_from)
- else:
- raise Exception("Tried to add an Edge between two Nodes of which at least one was not contained in the Bayesnet")
-
- def reset_Decisions(self):
- """
- Resets all decisions in the Bayesian Decision Network
- """
- for node in self.decision_nodes:
- node.set_state(None)
-
- def get_partialOrdering(self):
- """
- Getter for the partial ordere
- """
- return self.partialOrdering
-
- def set_partialOrdering(self, partialOrder):
- """
- Sets the partial ordering for this Bayesian Decision Network
-
- partialOrder -- ordered list of RandomNodes and Decision Nodes
- example: [decisionNode1, [randomNode1,randomNode2], decisionNode2, [randomNode3]]
- """
- self.partialOrdering = partialOrder
\ No newline at end of file
diff --git a/primo/core/DynamicBayesNet.py b/primo/core/DynamicBayesNet.py
deleted file mode 100644
index b196b87..0000000
--- a/primo/core/DynamicBayesNet.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from primo.core import BayesNet
-from primo.core import TwoTBN
-
-
-class DynamicBayesNet(BayesNet):
- ''' This is the implementation of a dynamic Bayesian network (also called
- temporal Bayesian network).
-
- Definition: DBN is a pair (B0, TwoTBN), where B0 is a BN over X(0),
- representing the initial distribution over states, and TwoTBN is a
- 2-TBN for the process.
- See Koller, Friedman - "Probabilistic Graphical Models" (p. 204)
-
- Properties: Markov property, stationary, directed, discrete,
- acyclic (within a slice)
- '''
-
- def __init__(self):
- super(DynamicBayesNet, self).__init__()
- self._B0 = BayesNet()
- self._twoTBN = TwoTBN()
-
- @property
- def B0(self):
- ''' Get the Bayesian network representing the initial distribution.'''
- return self._B0
-
- @B0.setter
- def B0(self, value):
- ''' Set the Bayesian network representing the initial distribution.'''
- if isinstance(value, BayesNet):
- if not value.is_valid():
- raise Exception("BayesNet is not valid.")
- self._B0 = value
- else:
- raise Exception("Can only set 'BayesNet' and its subclasses as " +
- "B0 of a DBN.")
-
- @property
- def twoTBN(self):
- ''' Get the 2-time-slice Bayesian network.'''
- return self._twoTBN
-
- @twoTBN.setter
- def twoTBN(self, value):
- ''' Set the 2-time-slice Bayesian network.'''
- if isinstance(value, TwoTBN):
- if not value.is_valid():
- raise Exception("BayesNet is not valid.")
- self._twoTBN = value
- else:
- raise Exception("Can only set 'TwoTBN' and its subclasses as " +
- "twoTBN of a DBN.")
-
- def is_valid(self):
- '''Check if graph structure is valid. And if there is a same-named
- inital node in towTBN for every node in BO.
- Returns true if graph is directed and acyclic, false otherwiese'''
- for node in self._B0.get_nodes():
- if not self._twoTBN.has_initial_node_by_name(node.name):
- print("Node with name " + str(node.name) +
- " not found in TwoTBN!")
- return False;
-
- return super(DynamicBayesNet, self).is_valid()
\ No newline at end of file
diff --git a/primo/core/TwoTBN.py b/primo/core/TwoTBN.py
deleted file mode 100644
index 6c5e33e..0000000
--- a/primo/core/TwoTBN.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from primo.core import BayesNet
-from primo.reasoning.density import ProbabilityTable
-
-
-class TwoTBN(BayesNet):
- ''' This is the implementation of a 2-time-slice Bayesian network (2-TBN).
- '''
-
- def __init__(self, bayesnet=None):
- BayesNet.__init__(self)
- if bayesnet:
- if not isinstance(bayesnet, BayesNet):
- raise Exception("Parameter 'bayesnet' is not a instance of class BayesNet.")
- self.graph = bayesnet.graph
- self.node_lookup = bayesnet.node_lookup
- self.__initial_nodes = []
-
- def create_timeslice(self, state, initial=False):
- '''
- Set all initial nodes to the value of their corresponding nodes
- in state (previous time slice).
-
- Keyword arguments:
- state -- Current state of the network (previous time slice).
- initial -- Set initial to true if this will be the first time slice
- and state only contains nodes of the initial distribution.
-
- Returns this instance with all initial nodes set to their
- new value.
- '''
- for (node, node_t) in self.__initial_nodes:
- cpd = ProbabilityTable()
- cpd.add_variable(node)
- node.set_cpd(cpd)
- if not initial:
- node.set_probability(1., [(node, state[node_t])])
- else:
- for node0 in state:
- if node0.name == node.name:
- node.set_probability(1., [(node, state[node0])])
- continue
- return self
-
-
- def add_node(self, node, initial=False, node_t=None):
- '''
- Add a node to the TwoTBN.
-
- Keyword arguments:
- node -- Node to be added.
- initial -- If true node is marked as initial node.
- node_t -- If initial is true this is the corresponding node in the time slice.
- '''
- super(TwoTBN, self).add_node(node)
- if initial:
- self.set_initial_node(node, node_t)
-
- def set_initial_node(self, node_name, node_name_t):
- '''
- Mark a node as initial node.
-
- Keyword arguments:
- node_name -- Name of the initial node.
- node_name_t -- Name of the corresponding node in the time slice.
- '''
- node0 = self.get_node(node_name)
- node1 = self.get_node(node_name_t)
- self.__initial_nodes.append((node0, node1))
-
- def has_initial_node_by_name(self, node_name):
- '''
- Check if this instance has an inital node with name node_name.
-
- Returns true on success, false otherwise.
- '''
- for (node, node_t) in self.__initial_nodes:
- if node.name == node_name:
- return True
- return False
\ No newline at end of file
diff --git a/primo/core/__init__.py b/primo/core/__init__.py
deleted file mode 100644
index c9b8166..0000000
--- a/primo/core/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from Node import Node
-from BayesNet import BayesNet
-from BayesianDecisionNetwork import BayesianDecisionNetwork
-from TwoTBN import TwoTBN
-from DynamicBayesNet import DynamicBayesNet
diff --git a/primo/networks.py b/primo/networks.py
new file mode 100644
index 0000000..74ee197
--- /dev/null
+++ b/primo/networks.py
@@ -0,0 +1,374 @@
+import networkx as nx
+
+import primo.nodes
+
+class BayesNet(object):
+
+ def __init__(self):
+ self.graph = nx.DiGraph()
+ self.node_lookup = {}
+
+ def add_node(self, node):
+ if isinstance(node, primo.nodes.Node):
+ if node.name in self.node_lookup.keys():
+ raise Exception("Node name already exists in Bayesnet: "+node.name)
+ self.node_lookup[node.name]=node
+ self.graph.add_node(node)
+ else:
+ raise Exception("Can only add 'Node' and its subclasses as nodes into the BayesNet")
+
+ def add_edge(self, node_from, node_to):
+ if node_from in self.graph.nodes() and node_to in self.graph.nodes():
+ self.graph.add_edge(node_from, node_to)
+ node_to.announce_parent(node_from)
+ else:
+ raise Exception("Tried to add an Edge between two Nodes of which at least one was not contained in the Bayesnet")
+
+ def remove_node(self, node):
+ if node.name not in self.node_lookup.keys():
+ raise Exception("Node " + node.name + "does not exists")
+ else :
+ try:
+ self.graph.remove_node(node)
+ except nx.exception.NetworkXError:
+ raise Exception("Tried to remove a node which does not exist.")
+ del self.node_lookup[node.name]
+
+ def remove_edge(self, node_from, node_to):
+ try:
+ self.graph.remove_edge(node_from, node_to)
+ except nx.exception.NetworkXError:
+ raise Exception("Tried to remove an edge which does not exist in the BayesNet")
+ #raise Exception("Fixme: Adapt CPD of child-node")
+
+ def get_node(self, node_name):
+ try:
+ return self.node_lookup[node_name]
+ except KeyError:
+ raise Exception("There is no node with name "+node_name+" in the BayesNet")
+
+ def get_all_nodes(self):
+ return self.graph.nodes()
+
+ def get_nodes(self, node_names=[]):
+ nodes = []
+ if not node_names:
+ nodes = self.graph.nodes()
+ else:
+ for node_name in node_names:
+ nodes.append(self.get_node(node_name))
+ return nodes
+
+ def get_nodes_in_topological_sort(self):
+ return nx.topological_sort(self.graph)
+
+ def get_parents(self, node):
+ if node.name not in self.node_lookup.keys():
+ raise Exception("Node " + node.name + "does not exists")
+ else:
+ return self.graph.predecessors(node)
+
+
+ def get_children(self, node):
+ if node.name not in self.node_lookup.keys():
+ raise Exception("Node " + node.name + "does not exists")
+ else:
+ return self.graph.successors(node)
+
+
+ def get_markov_blanket(self, node):
+ raise Exception("Called unimplemented function")
+
+ def is_dag(self):
+ raise Exception("Called unimplemented function")
+
+ def draw(self):
+ import matplotlib.pyplot as plt
+ nx.draw_circular(self.graph)
+ plt.show()
+
+ def draw_graphviz(self):
+ import matplotlib.pyplot as plt
+ nx.draw_graphviz(self.graph)
+ plt.show()
+
+ def is_valid(self):
+ '''Check if graph structure is valid.
+ Returns true if graph is directed and acyclic, false otherwiese'''
+
+ if self.graph.number_of_selfloops() > 0:
+ return False
+
+ for node in self.graph.nodes():
+ if self.has_loop(node):
+ return False
+
+ return True
+
+ def has_loop(self, node, origin=None):
+ '''Check if any path from node leads back to node.
+
+ Keyword arguments:
+ node -- the start node
+ origin -- for internal recursive loop (default: None)
+
+ Returns true on succes, false otherwise.'''
+ if not origin:
+ origin = node
+
+ for successor in self.graph.successors(node):
+ if successor == origin:
+ return True
+ else:
+ return self.has_loop(successor, origin)
+
+ def clear(self):
+ '''Remove all nodes and edges from the graph.
+ This also removes the name, and all graph, node and edge attributes.'''
+ self.graph.clear()
+ self.node_lookup.clear()
+
+ def number_of_nodes(self):
+ '''Return the number of nodes in the graph.'''
+ return len(self)
+
+ def __len__(self):
+ '''Return the number of nodes in the graph.'''
+ return len(self.graph)
+
+
+class BayesianDecisionNetwork(BayesNet):
+
+ def __init__(self):
+ super(BayesianDecisionNetwork, self).__init__()
+ self.partialOrdering = []
+ self.random_nodes = []
+ self.decision_nodes = []
+ self.utility_nodes = []
+
+ def is_valid(self):
+ '''Check if graph structure is valid.
+ Returns true if graph is directed, acyclic and if there is a path that connects every decision node(consistency check),
+ false otherwise'''
+
+ if self.graph.number_of_selfloops() > 0:
+ return False
+
+ for node in self.graph.nodes():
+ if self.has_loop(node):
+ return False
+
+ decisionNodeList = []
+ for node in self.get_all_nodes():
+ if isinstance(node, DecisionNode):
+ decisionNodeList.append(node)
+
+ return all([nx.has_path(self.graph, x, y) == True for x in decisionNodeList for y in decisionNodeList])
+
+ def add_node(self, node):
+ if isinstance(node, Node):
+ if node.name in self.node_lookup.keys():
+ raise Exception("Node name already exists in Bayesnet: "+node.name)
+ if isinstance(node, DiscreteNode):
+ self.random_nodes.append(node)
+ elif isinstance(node, UtilityNode):
+ self.utility_nodes.append(node)
+ elif isinstance(node, DecisionNode):
+ self.decision_nodes.append(node)
+ else:
+ raise Exception("Tried to add a node which the Bayesian Decision Network can not work with")
+ self.node_lookup[node.name]=node
+ self.graph.add_node(node)
+ else:
+ raise Exception("Can only add 'Node' and its subclasses as nodes into the BayesNet")
+
+ def get_all_nodes(self):
+ '''Returns all RandomNodes'''
+ return self.random_nodes
+
+ def get_all_decision_nodes(self):
+ return self.decision_nodes
+
+ def get_all_utility_nodes(self):
+ return self.utility_nodes
+
+ def add_edge(self, node_from, node_to):
+ """
+ Adds an edge between two nodes. It is impossible to create en edge between two decision nodes and between two
+ utility nodes.
+
+ keyword arguments:
+ node_from -- Node from where the edge shall begin
+ node_to -- Node where the edge shall end
+ """
+ if isinstance(node_from, DecisionNode) and isinstance(node_to, DecisionNode):
+ raise Exception("Tried to add an edge from a DecisionNode to a DecisionNode")
+ if isinstance(node_from, UtilityNode) and isinstance(node_to, UtilityNode):
+ raise Exception("Tried to add an edge from a UtilityNode to a UtilityNode")
+ if node_from in self.graph.nodes() and node_to in self.graph.nodes():
+ self.graph.add_edge(node_from, node_to)
+ node_to.announce_parent(node_from)
+ else:
+ raise Exception("Tried to add an Edge between two Nodes of which at least one was not contained in the Bayesnet")
+
+ def reset_Decisions(self):
+ """
+ Resets all decisions in the Bayesian Decision Network
+ """
+ for node in self.decision_nodes:
+ node.set_state(None)
+
+ def get_partialOrdering(self):
+ """
+ Getter for the partial ordere
+ """
+ return self.partialOrdering
+
+ def set_partialOrdering(self, partialOrder):
+ """
+ Sets the partial ordering for this Bayesian Decision Network
+
+ partialOrder -- ordered list of RandomNodes and Decision Nodes
+ example: [decisionNode1, [randomNode1,randomNode2], decisionNode2, [randomNode3]]
+ """
+ self.partialOrdering = partialOrder
+
+
+class DynamicBayesNet(BayesNet):
+ ''' This is the implementation of a dynamic Bayesian network (also called
+ temporal Bayesian network).
+
+ Definition: DBN is a pair (B0, TwoTBN), where B0 is a BN over X(0),
+ representing the initial distribution over states, and TwoTBN is a
+ 2-TBN for the process.
+ See Koller, Friedman - "Probabilistic Graphical Models" (p. 204)
+
+ Properties: Markov property, stationary, directed, discrete,
+ acyclic (within a slice)
+ '''
+
+ def __init__(self):
+ super(DynamicBayesNet, self).__init__()
+ self._B0 = BayesNet()
+ self._twoTBN = TwoTBN()
+
+ @property
+ def B0(self):
+ ''' Get the Bayesian network representing the initial distribution.'''
+ return self._B0
+
+ @B0.setter
+ def B0(self, value):
+ ''' Set the Bayesian network representing the initial distribution.'''
+ if isinstance(value, BayesNet):
+ if not value.is_valid():
+ raise Exception("BayesNet is not valid.")
+ self._B0 = value
+ else:
+ raise Exception("Can only set 'BayesNet' and its subclasses as " +
+ "B0 of a DBN.")
+
+ @property
+ def twoTBN(self):
+ ''' Get the 2-time-slice Bayesian network.'''
+ return self._twoTBN
+
+ @twoTBN.setter
+ def twoTBN(self, value):
+ ''' Set the 2-time-slice Bayesian network.'''
+ if isinstance(value, TwoTBN):
+ if not value.is_valid():
+ raise Exception("BayesNet is not valid.")
+ self._twoTBN = value
+ else:
+ raise Exception("Can only set 'TwoTBN' and its subclasses as " +
+ "twoTBN of a DBN.")
+
+ def is_valid(self):
+ '''Check if graph structure is valid. And if there is a same-named
+ inital node in towTBN for every node in BO.
+ Returns true if graph is directed and acyclic, false otherwiese'''
+ for node in self._B0.get_nodes():
+ if not self._twoTBN.has_initial_node_by_name(node.name):
+ print("Node with name " + str(node.name) +
+ " not found in TwoTBN!")
+ return False;
+
+ return super(DynamicBayesNet, self).is_valid()
+
+
+class TwoTBN(BayesNet):
+ ''' This is the implementation of a 2-time-slice Bayesian network (2-TBN).
+ '''
+
+ def __init__(self, bayesnet=None):
+ BayesNet.__init__(self)
+ if bayesnet:
+ if not isinstance(bayesnet, BayesNet):
+ raise Exception("Parameter 'bayesnet' is not a instance of class BayesNet.")
+ self.graph = bayesnet.graph
+ self.node_lookup = bayesnet.node_lookup
+ self.__initial_nodes = []
+
+ def create_timeslice(self, state, initial=False):
+ '''
+ Set all initial nodes to the value of their corresponding nodes
+ in state (previous time slice).
+
+ Keyword arguments:
+ state -- Current state of the network (previous time slice).
+ initial -- Set initial to true if this will be the first time slice
+ and state only contains nodes of the initial distribution.
+
+ Returns this instance with all initial nodes set to their
+ new value.
+ '''
+ for (node, node_t) in self.__initial_nodes:
+ cpd = ProbabilityTable()
+ cpd.add_variable(node)
+ node.set_cpd(cpd)
+ if not initial:
+ node.set_probability(1., [(node, state[node_t])])
+ else:
+ for node0 in state:
+ if node0.name == node.name:
+ node.set_probability(1., [(node, state[node0])])
+ continue
+ return self
+
+
+ def add_node(self, node, initial=False, node_t=None):
+ '''
+ Add a node to the TwoTBN.
+
+ Keyword arguments:
+ node -- Node to be added.
+ initial -- If true node is marked as initial node.
+ node_t -- If initial is true this is the corresponding node in the time slice.
+ '''
+ super(TwoTBN, self).add_node(node)
+ if initial:
+ self.set_initial_node(node, node_t)
+
+ def set_initial_node(self, node_name, node_name_t):
+ '''
+ Mark a node as initial node.
+
+ Keyword arguments:
+ node_name -- Name of the initial node.
+ node_name_t -- Name of the corresponding node in the time slice.
+ '''
+ node0 = self.get_node(node_name)
+ node1 = self.get_node(node_name_t)
+ self.__initial_nodes.append((node0, node1))
+
+ def has_initial_node_by_name(self, node_name):
+ '''
+ Check if this instance has an inital node with name node_name.
+
+ Returns true on success, false otherwise.
+ '''
+ for (node, node_t) in self.__initial_nodes:
+ if node.name == node_name:
+ return True
+ return False
--
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