From eab3033de78c816837cf4d1a0659befc7d83ea4f Mon Sep 17 00:00:00 2001
From: Hendrik Buschmeier <hbuschme@uni-bielefeld.de>
Date: Sat, 5 Apr 2014 00:26:15 +0200
Subject: [PATCH] Refactoring: Merged factor elimination, particlefiltering in
one file, respectively.
---
primo/decision/UtilityTable.py | 65 ---
primo/decision/__init__.py | 3 -
primo/decision/make_decision/__init__.py | 2 -
primo/reasoning/factor.py | 421 ++++++++++++++++++
.../EasiestFactorElimination.py | 105 -----
primo/reasoning/factorelemination/Factor.py | 36 --
.../reasoning/factorelemination/FactorTree.py | 141 ------
.../factorelemination/FactorTreeFactory.py | 166 -------
primo/reasoning/factorelemination/__init__.py | 4 -
primo/reasoning/particlebased/__init__.py | 2 -
...ParticleFilterDBN.py => particlefilter.py} | 8 +-
11 files changed, 426 insertions(+), 527 deletions(-)
delete mode 100644 primo/decision/UtilityTable.py
delete mode 100644 primo/decision/__init__.py
delete mode 100644 primo/decision/make_decision/__init__.py
create mode 100644 primo/reasoning/factor.py
delete mode 100644 primo/reasoning/factorelemination/EasiestFactorElimination.py
delete mode 100644 primo/reasoning/factorelemination/Factor.py
delete mode 100644 primo/reasoning/factorelemination/FactorTree.py
delete mode 100644 primo/reasoning/factorelemination/FactorTreeFactory.py
delete mode 100644 primo/reasoning/factorelemination/__init__.py
delete mode 100644 primo/reasoning/particlebased/__init__.py
rename primo/reasoning/{particlebased/ParticleFilterDBN.py => particlefilter.py} (98%)
diff --git a/primo/decision/UtilityTable.py b/primo/decision/UtilityTable.py
deleted file mode 100644
index 26dcfb3..0000000
--- a/primo/decision/UtilityTable.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import numpy
-import operator
-
-class UtilityTable(object):
- '''
- self.variables -- list of the parent nodes
- self.table -- utility table which contains the utility
- '''
-
- def __init__(self):
- super(UtilityTable, self).__init__()
- self.table = numpy.array(0)
- self.variables = []
-
- def add_variable(self, variable):
- self.variables.append(variable)
-
- ax = self.table.ndim
- self.table=numpy.expand_dims(self.table,ax)
- self.table=numpy.repeat(self.table,len(variable.value_range),axis = ax)
-
- def get_ut_index(self, node_value_pairs):
- nodes, values = zip(*node_value_pairs)
- index = []
- for node in self.variables:
- index_in_values_list = nodes.index(node)
- value = values[index_in_values_list]
- index.append(node.value_range.index(value))
- return tuple(index)
-
- def set_utility_table(self, table, nodes):
- if not set(nodes) == set(self.variables):
- raise Exception("The list which should define the ordering of the variables does not match"
- " the variables that this cpt depends on (plus the node itself)")
- if not self.table.ndim == table.ndim:
- raise Exception("The provided probability table does not have the right number of dimensions")
- for d,node in enumerate(nodes):
- if len(node.value_range) != table.shape[d]:
- raise Exception("The size of the provided probability table does not match the number of possible values of the node "+node.name+" in dimension "+str(d))
-
- self.table = table
- self.variables = nodes
-
- def set_utility(self, value, node_value_pairs):
- index = self.get_ut_index(node_value_pairs)
- self.table[index]=value
-
- def get_utility_table(self):
- return self.table
-
- def get_variables(self):
- return self.variables
-
- def get_utility(self, node_value_pairs):
- index = self.get_ut_index(node_value_pairs)
- return self.table[index]
-
- def __str__(self):
- return str(self.table)
-
-
-
-
\ No newline at end of file
diff --git a/primo/decision/__init__.py b/primo/decision/__init__.py
deleted file mode 100644
index d454ced..0000000
--- a/primo/decision/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from UtilityNode import UtilityNode
-from DecisionNode import DecisionNode
-from UtilityTable import UtilityTable
\ No newline at end of file
diff --git a/primo/decision/make_decision/__init__.py b/primo/decision/make_decision/__init__.py
deleted file mode 100644
index 82eee2b..0000000
--- a/primo/decision/make_decision/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from MakeDecision import MakeDecision
-
diff --git a/primo/reasoning/factor.py b/primo/reasoning/factor.py
new file mode 100644
index 0000000..18cc9a9
--- /dev/null
+++ b/primo/reasoning/factor.py
@@ -0,0 +1,421 @@
+import networkx as nx
+
+import primo.densities
+
+class Factor(object):
+
+ def __init__(self,node):
+ self.node = node
+ self.calCPD = node.get_cpd().copy()
+ self.cluster = set()
+ self.isEvidence = False
+
+ def __str__(self):
+ return self.node.name
+
+ def set_evidence(self,evd):
+ self.calCPD = self.node.get_cpd().copy()
+ self.calCPD = self.calCPD.set_evidence(evd)
+ self.isEvidene = True
+
+ def clear_evidence(self):
+ self.calCPD = self.node.get_cpd().copy()
+ self.isEvidence = False
+
+ def set_cluster(self,cluster):
+ self.cluster = cluster
+
+ def get_variables(self):
+ return self.node.get_cpd().variables
+
+ def get_calculation_CDP(self):
+ return self.calCPD;
+
+ def get_node(self):
+ return self.node
+
+ def contains_node(self,node):
+ return self.node == node
+
+class FactorTree(object):
+ '''The factor tree contains for each node of the BayesNet a factor. It
+ is a directed graph with one root node. To speed up the reasoning it uses
+ a message based approach which stores calculated intermediate results at
+ edges. Thus, the first query is expensive and all following are easy calculated.
+ The speed of the first message calculation depends on how the tree was build.
+ Literature: Modeling and Reasoning with Bayesian Networks - Adnan Darwiche
+ Chapter 7
+ '''
+
+
+ def __init__(self,graph,rootNode):
+ self.graph = graph
+ self.rootNode = rootNode
+
+ def calculate_PoE(self):
+ '''Calculates the probability of evidence with the set evidence'''
+ if not self.graph.graph['messagesValid']:
+ self.calculate_messages()
+
+ cpd = self.calculate_marginal_forOne(self.rootNode)
+
+ for v in cpd.get_variables()[:]:
+ cpd = cpd.marginalization(v)
+
+ return cpd
+
+ def calculate_marginal(self,variables):
+ ''' If evidence is set, then this methods calculates the posterior marginal.
+ With an empty evidence this is automatically the prior marginal.'''
+ if not self.graph.graph['messagesValid']:
+ self.calculate_messages()
+
+
+ resPT = primo.densities.ProbabilityTable.get_neutral_multiplication_PT()
+
+
+ for f in self.graph.nodes():
+ if f.get_node() in variables:
+ resPT = resPT.multiplication(self.calculate_marginal_forOne(f))
+
+ resPT = resPT.normalize_as_jpt()
+
+ return resPT
+
+ def calculate_marginal_forOne(self,factor):
+ curCPD = factor.get_calculation_CDP().copy()
+
+ for p in self.graph.predecessors(factor):
+ tmpCPD = self.graph[p][factor]['msgRightWay']
+ curCPD = curCPD.multiplication(tmpCPD)
+
+ for p in self.graph.neighbors(factor):
+ tmpCPD = self.graph[factor][p]['msgAgainstWay']
+ curCPD = curCPD.multiplication(tmpCPD)
+
+ for v in curCPD.get_variables()[:]:
+ if v != factor.get_node():
+ curCPD = curCPD.marginalization(v)
+
+ return curCPD
+
+
+ def draw(self):
+ '''Draws the FactorTree'''
+ import matplotlib.pyplot as plt
+ nx.draw_circular(self.graph)
+ plt.show()
+
+ def calculate_messages(self):
+ ''' Calculates the messages and stores the intermediate results.'''
+ self.pull_phase(self.rootNode,self.graph)
+ self.push_phase(self.rootNode,self.graph,primo.densities.ProbabilityTable.get_neutral_multiplication_PT())
+ self.graph.graph['messagesValid'] = True
+
+
+ def set_evidences(self,evidences):
+ self.graph.graph['messagesValid'] = False
+
+ evNodes = zip(*evidences)
+
+ for factor in self.graph.nodes():
+ if factor.get_node() in evNodes[0]:
+ idx = evNodes[0].index(factor.get_node())
+ factor.set_evidence(evidences[idx])
+
+
+
+
+ def pull_phase(self,factor,graph):
+
+ calCPD = factor.get_calculation_CDP()
+ #calculate the messages of the children
+ for child in graph.neighbors(factor):
+ tmpInput = self.pull_phase(child,graph)
+
+
+ #project each factor on the specific separator
+ separator = graph[factor][child]['separator']
+ for var in tmpInput.variables[:]:
+ if var not in separator:
+ tmpInput = tmpInput.marginalization(var)
+
+
+ #save message on edge: it's the opposite of the direction of the edge
+ graph[factor][child]['msgAgainstWay'] = tmpInput
+ #calculate the new message
+ calCPD = calCPD.multiplication(tmpInput)
+
+ return calCPD
+
+ def push_phase(self,factor,graph,inCPD):
+
+ for child in graph.neighbors(factor):
+ tmpCPD = inCPD.multiplication(factor.get_calculation_CDP())
+ for child2 in graph.neighbors(factor):
+ if (child != child2):
+ tmpCPD = tmpCPD.multiplication(graph[factor][child2]['msgAgainstWay'])
+
+ separator = graph[factor][child]['separator']
+ #project on outgoing edge separator
+ for var in tmpCPD.variables:
+ if var not in separator:
+ tmpCPD = tmpCPD.marginalization(var)
+
+ #add setOut to outgoing vars from child
+ #Message with the direction of the edge
+ graph[factor][child]['msgRightWay'] = tmpCPD
+
+
+ self.push_phase(child,graph,tmpCPD)
+
+
+class FactorTreeFactory(object):
+ '''The FactorTreeFactory creates the FactorTree out of a BayesNet.'''
+
+ def create_random_factortree(self,bayesNet):
+ ''' Creates a randomly structured FactorTree. This method is useful for testing
+ if reasoning works for arbitrary trees.'''
+ allNodes = bayesNet.get_all_nodes()
+
+ if len(allNodes) == 0:
+ raise Exception("createRandomFactorTree: No nodes in given BayesNet")
+
+ tn = allNodes.pop()
+ rootFactor = Factor(tn)
+
+ graph = nx.DiGraph(messagesValid=False)
+ graph.add_node(rootFactor)
+
+ usedNodes = [rootFactor]
+
+ for n in allNodes[:]:
+ parentNode = choice(usedNodes[:])
+ newFactor = Factor(n)
+ graph.add_edge(parentNode,newFactor, inVars=set(),outVars=set())
+ usedNodes.append(newFactor)
+
+ self.calculate_seperators_pull(rootFactor,graph)
+ self.calculate_seperators_push(rootFactor,graph,set())
+ self.intersect_seperators(graph)
+
+ self.calculate_clusters(rootFactor,graph,set())
+
+
+ return FactorTree(graph,rootFactor)
+
+ def create_greedy_factortree(self,bayesNet):
+ '''This method creates a factor the after the following algorithm:
+
+ 1. Sort factors after containing variables (descending).
+ 2. For each node in the sorted list insert at it's best position.
+
+ The best position is the node with the most joint variables.'''
+
+ allNodes = bayesNet.get_all_nodes()
+
+ if len(allNodes) == 0:
+ raise Exception("createRandomFactorTree: No nodes in given BayesNet")
+
+ sortNodeList = []
+
+ for n in allNodes:
+ sortNodeList.append((len(n.get_cpd().get_variables()),n))
+
+ #sort node list
+ sortNodeList = sorted(sortNodeList,key=itemgetter(0),reverse=True)
+
+ sortNodeList = zip(*sortNodeList)
+ sortNodeList = list(sortNodeList[1])
+
+ #root node with the most variables
+ rootFactor = Factor(sortNodeList.pop(0))
+
+ #create new graph for factor tree
+ graph = nx.DiGraph(messagesValid=False)
+ graph.add_node(rootFactor)
+
+ #All nodes are added
+ for nd in sortNodeList[:]:
+ (ct,insFactor) = self.find_best_node_for_insertion(graph,rootFactor,set(nd.get_cpd().get_variables()))
+ nFactor = Factor(nd)
+ graph.add_edge(insFactor,nFactor, inVars=set(),outVars=set())
+
+ #For the later calculation the seperators are needed
+ self.calculate_seperators_pull(rootFactor,graph)
+ self.calculate_seperators_push(rootFactor,graph,set())
+ self.intersect_seperators(graph)
+
+ #the cluster are not necessarily needed but indicate how good the calculation of messages performs
+ self.calculate_clusters(rootFactor,graph,set())
+
+
+ return FactorTree(graph,rootFactor)
+
+
+ def find_best_node_for_insertion(self,graph,factor,nodeSet):
+ '''finds the node in the graph with the most common variables to the given node'''
+
+ curJointCount = len(set(factor.get_variables()) & nodeSet)
+ curInsertFactor = factor
+
+ for nbs in graph.neighbors(factor):
+ (count,retFactor) = self.find_best_node_for_insertion(graph,nbs,nodeSet)
+ if count >= curJointCount:
+ curJointCount = count
+ curInsertFactor = retFactor
+
+ return (curJointCount,curInsertFactor)
+
+
+ def calculate_seperators_pull(self,factor,graph):
+
+ s = set()
+ pullSet = set(factor.get_variables())
+
+ #find all variables in outgoing edges for factor
+ for child in graph.neighbors(factor):
+ s = self.calculate_seperators_pull(child,graph)
+ graph[factor][child]['inVars'] = s
+
+ pullSet = s | pullSet
+
+ return pullSet
+
+ def calculate_seperators_push(self,factor,graph,setOut):
+
+ #add local vars to set
+ setOut = set(factor.get_variables()) | setOut
+
+
+ for child in graph.neighbors(factor):
+ tmpSet = copy.copy(setOut)
+ for child2 in graph.neighbors(factor):
+ if (child != child2):
+ tmpSet = tmpSet | graph[factor][child2]['inVars']
+
+ #add setOut to outgoing variables from the child
+ tmp = graph[factor][child]['outVars']
+ graph[factor][child]['outVars'] = tmp | tmpSet
+
+
+ self.calculate_seperators_push(child,graph,tmpSet)
+
+
+ def intersect_seperators(self,graph):
+
+ for n,nbrs in graph.adjacency_iter():
+ for nbr,eattr in nbrs.items():
+ eattr['separator'] = eattr['inVars'] & eattr['outVars']
+
+ def calculate_clusters(self,factor,graph,parent_seperator):
+
+ localCluster = parent_seperator | set(factor.get_variables())
+
+ for n in graph.neighbors(factor):
+ tmpSeparator = graph[factor][n]['separator']
+ localCluster = localCluster | tmpSeparator
+ self.calculate_clusters(n,graph,tmpSeparator)
+
+ factor.set_cluster(localCluster)
+
+
+class EasiestFactorElimination(object):
+ '''This is the easiest way for factor elimination.It's has the worst runtime because:
+ 1. Needed evidences are set (optional).
+ 2. All nodes are multiplied.
+ 3. The redundant variables are summed out
+
+ Literature: Modeling and Reasoning with Bayesian Networks - Adnan Darwiche
+ Chapter 6-7
+ '''
+
+
+
+ def __init__(self,bayesNet):
+ self.bn= bayesNet
+
+
+ def calculate_PriorMarginal(self,variables):
+ '''Calculates the prior marignal for the given variables. The resulting
+ CPD is returned.'''
+ nodes = self.bn.get_all_nodes()
+
+ finCpd = nodes.pop().get_cpd()
+
+ for n in nodes:
+ finCpd = finCpd.multiplication(n.get_cpd())
+
+ for v in finCpd.get_variables():
+ if v not in variables:
+ finCpd = finCpd.marginalization(v)
+
+ return finCpd
+
+ def calculate_PosteriorMarginal(self,variables,evidence):
+ '''Calculates the posterior marginal for given variables and evidence.
+ It returns the resulting cpd.'''
+ nodes = self.bn.get_all_nodes()
+
+ #List of evidences
+ ev_list = zip(*evidence)
+ # Special Case: First Node
+ node1 = nodes.pop()
+ if node1 in ev_list[0]:
+ ind = ev_list[0].index(node1)
+ finCpd = node1.get_cpd().set_evidence(evidence[ind])
+
+ else:
+ finCpd = node1.get_cpd()
+
+
+ # For all other nodes
+ for n in nodes:
+ if n in ev_list[0]:
+ #Set evidence and multiply
+ ind = ev_list[0].index(n)
+ nCPD = n.get_cpd().set_evidence(evidence[ind])
+ finCpd = finCpd.multiplication(nCPD)
+ else:
+ #only multiply
+ finCpd = finCpd.multiplication(n.get_cpd())
+
+
+ for v in finCpd.get_variables():
+ if v not in variables:
+ finCpd = finCpd.marginalization(v)
+
+ finCpd = finCpd.normalize_as_jpt()
+
+
+ return finCpd
+
+ def calculate_PoE(self,evidence):
+ ''' Calculates the probabilty of evidence for the given evidence and returns the result.'''
+
+ nodes = self.bn.get_all_nodes()
+
+ unzipped_list = zip(*evidence)
+
+ node1 = nodes.pop()
+ if node1 in unzipped_list[0]:
+ ind = unzipped_list[0].index(node1)
+ finCpd = node1.get_cpd().set_evidence(evidence[ind])
+
+ else:
+ finCpd = node1.get_cpd()
+
+ for n in nodes:
+ if n in unzipped_list[0]:
+ ind = unzipped_list[0].index(n)
+ nCPD = n.get_cpd().set_evidence(evidence[ind])
+ finCpd = finCpd.multiplication(nCPD)
+ else:
+ finCpd = finCpd.multiplication(n.get_cpd())
+
+ for v in finCpd.get_variables():
+ finCpd = finCpd.marginalization(v)
+
+ return finCpd
+
+
diff --git a/primo/reasoning/factorelemination/EasiestFactorElimination.py b/primo/reasoning/factorelemination/EasiestFactorElimination.py
deleted file mode 100644
index 74b9f14..0000000
--- a/primo/reasoning/factorelemination/EasiestFactorElimination.py
+++ /dev/null
@@ -1,105 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-from primo.core import BayesNet
-from primo.reasoning import DiscreteNode
-import numpy
-
-class EasiestFactorElimination(object):
- '''This is the easiest way for factor elimination.It's has the worst runtime because:
- 1. Needed evidences are set (optional).
- 2. All nodes are multiplied.
- 3. The redundant variables are summed out
-
- Literature: Modeling and Reasoning with Bayesian Networks - Adnan Darwiche
- Chapter 6-7
- '''
-
-
-
- def __init__(self,bayesNet):
- self.bn= bayesNet
-
-
- def calculate_PriorMarginal(self,variables):
- '''Calculates the prior marignal for the given variables. The resulting
- CPD is returned.'''
- nodes = self.bn.get_all_nodes()
-
- finCpd = nodes.pop().get_cpd()
-
- for n in nodes:
- finCpd = finCpd.multiplication(n.get_cpd())
-
- for v in finCpd.get_variables():
- if v not in variables:
- finCpd = finCpd.marginalization(v)
-
- return finCpd
-
- def calculate_PosteriorMarginal(self,variables,evidence):
- '''Calculates the posterior marginal for given variables and evidence.
- It returns the resulting cpd.'''
- nodes = self.bn.get_all_nodes()
-
- #List of evidences
- ev_list = zip(*evidence)
- # Special Case: First Node
- node1 = nodes.pop()
- if node1 in ev_list[0]:
- ind = ev_list[0].index(node1)
- finCpd = node1.get_cpd().set_evidence(evidence[ind])
-
- else:
- finCpd = node1.get_cpd()
-
-
- # For all other nodes
- for n in nodes:
- if n in ev_list[0]:
- #Set evidence and multiply
- ind = ev_list[0].index(n)
- nCPD = n.get_cpd().set_evidence(evidence[ind])
- finCpd = finCpd.multiplication(nCPD)
- else:
- #only multiply
- finCpd = finCpd.multiplication(n.get_cpd())
-
-
- for v in finCpd.get_variables():
- if v not in variables:
- finCpd = finCpd.marginalization(v)
-
- finCpd = finCpd.normalize_as_jpt()
-
-
- return finCpd
-
-
-
- def calculate_PoE(self,evidence):
- ''' Calculates the probabilty of evidence for the given evidence and returns the result.'''
-
- nodes = self.bn.get_all_nodes()
-
- unzipped_list = zip(*evidence)
-
- node1 = nodes.pop()
- if node1 in unzipped_list[0]:
- ind = unzipped_list[0].index(node1)
- finCpd = node1.get_cpd().set_evidence(evidence[ind])
-
- else:
- finCpd = node1.get_cpd()
-
- for n in nodes:
- if n in unzipped_list[0]:
- ind = unzipped_list[0].index(n)
- nCPD = n.get_cpd().set_evidence(evidence[ind])
- finCpd = finCpd.multiplication(nCPD)
- else:
- finCpd = finCpd.multiplication(n.get_cpd())
-
- for v in finCpd.get_variables():
- finCpd = finCpd.marginalization(v)
-
- return finCpd
diff --git a/primo/reasoning/factorelemination/Factor.py b/primo/reasoning/factorelemination/Factor.py
deleted file mode 100644
index 2f7c6ad..0000000
--- a/primo/reasoning/factorelemination/Factor.py
+++ /dev/null
@@ -1,36 +0,0 @@
-class Factor(object):
-
- def __init__(self,node):
- self.node = node
- self.calCPD = node.get_cpd().copy()
- self.cluster = set()
- self.isEvidence = False
-
- def __str__(self):
- return self.node.name
-
- def set_evidence(self,evd):
- self.calCPD = self.node.get_cpd().copy()
- self.calCPD = self.calCPD.set_evidence(evd)
- self.isEvidene = True
-
- def clear_evidence(self):
- self.calCPD = self.node.get_cpd().copy()
- self.isEvidence = False
-
- def set_cluster(self,cluster):
- self.cluster = cluster
-
- def get_variables(self):
- return self.node.get_cpd().variables
-
- def get_calculation_CDP(self):
- return self.calCPD;
-
- def get_node(self):
- return self.node
-
- def contains_node(self,node):
- return self.node == node
-
-
diff --git a/primo/reasoning/factorelemination/FactorTree.py b/primo/reasoning/factorelemination/FactorTree.py
deleted file mode 100644
index 892da14..0000000
--- a/primo/reasoning/factorelemination/FactorTree.py
+++ /dev/null
@@ -1,141 +0,0 @@
-
-import networkx as nx
-import primo.reasoning.density.ProbabilityTable as ProbabilityTable
-
-
-class FactorTree(object):
- '''The factor tree contains for each node of the BayesNet a factor. It
- is a directed graph with one root node. To speed up the reasoning it uses
- a message based approach which stores calculated intermediate results at
- edges. Thus, the first query is expensive and all following are easy calculated.
- The speed of the first message calculation depends on how the tree was build.
- Literature: Modeling and Reasoning with Bayesian Networks - Adnan Darwiche
- Chapter 7
- '''
-
-
- def __init__(self,graph,rootNode):
- self.graph = graph
- self.rootNode = rootNode
-
- def calculate_PoE(self):
- '''Calculates the probability of evidence with the set evidence'''
- if not self.graph.graph['messagesValid']:
- self.calculate_messages()
-
- cpd = self.calculate_marginal_forOne(self.rootNode)
-
- for v in cpd.get_variables()[:]:
- cpd = cpd.marginalization(v)
-
- return cpd
-
- def calculate_marginal(self,variables):
- ''' If evidence is set, then this methods calculates the posterior marginal.
- With an empty evidence this is automatically the prior marginal.'''
- if not self.graph.graph['messagesValid']:
- self.calculate_messages()
-
-
- resPT = ProbabilityTable.get_neutral_multiplication_PT()
-
-
- for f in self.graph.nodes():
- if f.get_node() in variables:
- resPT = resPT.multiplication(self.calculate_marginal_forOne(f))
-
- resPT = resPT.normalize_as_jpt()
-
- return resPT
-
- def calculate_marginal_forOne(self,factor):
- curCPD = factor.get_calculation_CDP().copy()
-
- for p in self.graph.predecessors(factor):
- tmpCPD = self.graph[p][factor]['msgRightWay']
- curCPD = curCPD.multiplication(tmpCPD)
-
- for p in self.graph.neighbors(factor):
- tmpCPD = self.graph[factor][p]['msgAgainstWay']
- curCPD = curCPD.multiplication(tmpCPD)
-
- for v in curCPD.get_variables()[:]:
- if v != factor.get_node():
- curCPD = curCPD.marginalization(v)
-
- return curCPD
-
-
- def draw(self):
- '''Draws the FactorTree'''
- import matplotlib.pyplot as plt
- nx.draw_circular(self.graph)
- plt.show()
-
- def calculate_messages(self):
- ''' Calculates the messages and stores the intermediate results.'''
- self.pull_phase(self.rootNode,self.graph)
- self.push_phase(self.rootNode,self.graph,ProbabilityTable.get_neutral_multiplication_PT())
- self.graph.graph['messagesValid'] = True
-
-
- def set_evidences(self,evidences):
- self.graph.graph['messagesValid'] = False
-
- evNodes = zip(*evidences)
-
- for factor in self.graph.nodes():
- if factor.get_node() in evNodes[0]:
- idx = evNodes[0].index(factor.get_node())
- factor.set_evidence(evidences[idx])
-
-
-
-
- def pull_phase(self,factor,graph):
-
- calCPD = factor.get_calculation_CDP()
- #calculate the messages of the children
- for child in graph.neighbors(factor):
- tmpInput = self.pull_phase(child,graph)
-
-
- #project each factor on the specific separator
- separator = graph[factor][child]['separator']
- for var in tmpInput.variables[:]:
- if var not in separator:
- tmpInput = tmpInput.marginalization(var)
-
-
- #save message on edge: it's the opposite of the direction of the edge
- graph[factor][child]['msgAgainstWay'] = tmpInput
- #calculate the new message
- calCPD = calCPD.multiplication(tmpInput)
-
- return calCPD
-
- def push_phase(self,factor,graph,inCPD):
-
- for child in graph.neighbors(factor):
- tmpCPD = inCPD.multiplication(factor.get_calculation_CDP())
- for child2 in graph.neighbors(factor):
- if (child != child2):
- tmpCPD = tmpCPD.multiplication(graph[factor][child2]['msgAgainstWay'])
-
- separator = graph[factor][child]['separator']
- #project on outgoing edge separator
- for var in tmpCPD.variables:
- if var not in separator:
- tmpCPD = tmpCPD.marginalization(var)
-
- #add setOut to outgoing vars from child
- #Message with the direction of the edge
- graph[factor][child]['msgRightWay'] = tmpCPD
-
-
- self.push_phase(child,graph,tmpCPD)
-
-
-
-
-
diff --git a/primo/reasoning/factorelemination/FactorTreeFactory.py b/primo/reasoning/factorelemination/FactorTreeFactory.py
deleted file mode 100644
index 2302c4b..0000000
--- a/primo/reasoning/factorelemination/FactorTreeFactory.py
+++ /dev/null
@@ -1,166 +0,0 @@
-
-import networkx as nx
-import copy
-from primo.reasoning.factorelemination import Factor
-from primo.reasoning.factorelemination import FactorTree
-from random import choice
-from operator import itemgetter
-
-class FactorTreeFactory(object):
- '''The FactorTreeFactory creates the FactorTree out of a BayesNet.'''
-
- def create_random_factortree(self,bayesNet):
- ''' Creates a randomly structured FactorTree. This method is useful for testing
- if reasoning works for arbitrary trees.'''
- allNodes = bayesNet.get_all_nodes()
-
- if len(allNodes) == 0:
- raise Exception("createRandomFactorTree: No nodes in given BayesNet")
-
- tn = allNodes.pop()
- rootFactor = Factor(tn)
-
- graph = nx.DiGraph(messagesValid=False)
- graph.add_node(rootFactor)
-
- usedNodes = [rootFactor]
-
- for n in allNodes[:]:
- parentNode = choice(usedNodes[:])
- newFactor = Factor(n)
- graph.add_edge(parentNode,newFactor, inVars=set(),outVars=set())
- usedNodes.append(newFactor)
-
- self.calculate_seperators_pull(rootFactor,graph)
- self.calculate_seperators_push(rootFactor,graph,set())
- self.intersect_seperators(graph)
-
- self.calculate_clusters(rootFactor,graph,set())
-
-
- return FactorTree(graph,rootFactor)
-
- def create_greedy_factortree(self,bayesNet):
- '''This method creates a factor the after the following algorithm:
-
- 1. Sort factors after containing variables (descending).
- 2. For each node in the sorted list insert at it's best position.
-
- The best position is the node with the most joint variables.'''
-
- allNodes = bayesNet.get_all_nodes()
-
- if len(allNodes) == 0:
- raise Exception("createRandomFactorTree: No nodes in given BayesNet")
-
- sortNodeList = []
-
- for n in allNodes:
- sortNodeList.append((len(n.get_cpd().get_variables()),n))
-
- #sort node list
- sortNodeList = sorted(sortNodeList,key=itemgetter(0),reverse=True)
-
- sortNodeList = zip(*sortNodeList)
- sortNodeList = list(sortNodeList[1])
-
- #root node with the most variables
- rootFactor = Factor(sortNodeList.pop(0))
-
- #create new graph for factor tree
- graph = nx.DiGraph(messagesValid=False)
- graph.add_node(rootFactor)
-
- #All nodes are added
- for nd in sortNodeList[:]:
- (ct,insFactor) = self.find_best_node_for_insertion(graph,rootFactor,set(nd.get_cpd().get_variables()))
- nFactor = Factor(nd)
- graph.add_edge(insFactor,nFactor, inVars=set(),outVars=set())
-
- #For the later calculation the seperators are needed
- self.calculate_seperators_pull(rootFactor,graph)
- self.calculate_seperators_push(rootFactor,graph,set())
- self.intersect_seperators(graph)
-
- #the cluster are not necessarily needed but indicate how good the calculation of messages performs
- self.calculate_clusters(rootFactor,graph,set())
-
-
- return FactorTree(graph,rootFactor)
-
-
- def find_best_node_for_insertion(self,graph,factor,nodeSet):
- '''finds the node in the graph with the most common variables to the given node'''
-
- curJointCount = len(set(factor.get_variables()) & nodeSet)
- curInsertFactor = factor
-
- for nbs in graph.neighbors(factor):
- (count,retFactor) = self.find_best_node_for_insertion(graph,nbs,nodeSet)
- if count >= curJointCount:
- curJointCount = count
- curInsertFactor = retFactor
-
- return (curJointCount,curInsertFactor)
-
-
- def calculate_seperators_pull(self,factor,graph):
-
- s = set()
- pullSet = set(factor.get_variables())
-
- #find all variables in outgoing edges for factor
- for child in graph.neighbors(factor):
- s = self.calculate_seperators_pull(child,graph)
- graph[factor][child]['inVars'] = s
-
- pullSet = s | pullSet
-
- return pullSet
-
- def calculate_seperators_push(self,factor,graph,setOut):
-
- #add local vars to set
- setOut = set(factor.get_variables()) | setOut
-
-
- for child in graph.neighbors(factor):
- tmpSet = copy.copy(setOut)
- for child2 in graph.neighbors(factor):
- if (child != child2):
- tmpSet = tmpSet | graph[factor][child2]['inVars']
-
- #add setOut to outgoing variables from the child
- tmp = graph[factor][child]['outVars']
- graph[factor][child]['outVars'] = tmp | tmpSet
-
-
- self.calculate_seperators_push(child,graph,tmpSet)
-
-
- def intersect_seperators(self,graph):
-
- for n,nbrs in graph.adjacency_iter():
- for nbr,eattr in nbrs.items():
- eattr['separator'] = eattr['inVars'] & eattr['outVars']
-
- def calculate_clusters(self,factor,graph,parent_seperator):
-
- localCluster = parent_seperator | set(factor.get_variables())
-
- for n in graph.neighbors(factor):
- tmpSeparator = graph[factor][n]['separator']
- localCluster = localCluster | tmpSeparator
- self.calculate_clusters(n,graph,tmpSeparator)
-
- factor.set_cluster(localCluster)
-
-
-
-
-
-
-
-
-
-
diff --git a/primo/reasoning/factorelemination/__init__.py b/primo/reasoning/factorelemination/__init__.py
deleted file mode 100644
index f0b37c0..0000000
--- a/primo/reasoning/factorelemination/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from EasiestFactorElimination import EasiestFactorElimination
-from Factor import Factor
-from FactorTree import FactorTree
-from FactorTreeFactory import FactorTreeFactory
\ No newline at end of file
diff --git a/primo/reasoning/particlebased/__init__.py b/primo/reasoning/particlebased/__init__.py
deleted file mode 100644
index 866e708..0000000
--- a/primo/reasoning/particlebased/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# -*- coding: utf-8 -*-
-import ParticleFilterDBN
\ No newline at end of file
diff --git a/primo/reasoning/particlebased/ParticleFilterDBN.py b/primo/reasoning/particlefilter.py
similarity index 98%
rename from primo/reasoning/particlebased/ParticleFilterDBN.py
rename to primo/reasoning/particlefilter.py
index 52fd87b..e34e6b7 100644
--- a/primo/reasoning/particlebased/ParticleFilterDBN.py
+++ b/primo/reasoning/particlefilter.py
@@ -1,10 +1,12 @@
# -*- coding: utf-8 -*-
-from primo.core import BayesNet
-from primo.core import DynamicBayesNet
-import random
+
import copy
+import random
import time
+from primo.networks import BayesNet
+from primo.networks import DynamicBayesNet
+
class Particle(object):
'''
This is the basic particle class used by the DBN particle filter.
--
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