コード例 #1
0
	def _init_BN(self, BNnodesdef):
		BNnodes = {}
		BNconnections = []
		BN = BNet()  

		# Save the nodesdef for future use (saving)
		self._BNDef = BNnodesdef

		for node in BNnodesdef:
			(nodename , isdiscrete, numstates, leafnode) = node
			BNnodes[nodename] = BN.add_v( BVertex(nodename, isdiscrete, numstates))
		
		#TODO: Find a way to improve this and avoid to have to loop a second time
		# reason is because BNnodes[leafnode] is not sure to be there when going through the first loop
		for node in BNnodesdef:
			(nodename , isdiscrete, numstates, leafnode) = node
			if type(leafnode)==type([]):
				for r in leafnode:
					if r!=None:	BNconnections.append( (BNnodes[nodename], BNnodes[r]) )
			elif leafnode!=None:
				BNconnections.append( (BNnodes[nodename], BNnodes[leafnode]) )
			else:
				#do nothing 
				pass
		
		for ep in BNconnections:
			BN.add_e( DirEdge( len( BN.e ), *ep ) )
			
		# Ok our Bnet has been created, let's save it in the controller
		self._BN = BN

		# Let's not forget to initialize the distribution
		self._BN.InitDistributions()
コード例 #2
0
    def _init_BN(self, BNName, BNnodesdef):
        BNnodes = {}
        BNconnections = []
        BN = BNet(BNName)

        # Save the nodesdef for future use (saving)
        self._BNDef = BNnodesdef

        for node in BNnodesdef:
            (nodename, isdiscrete, numstates, leafnode) = node
            BNnodes[nodename] = BN.add_v(BVertex(nodename, isdiscrete, numstates))

        # TODO: Find a way to improve this and avoid to have to loop a
        # second time
        # reason is because BNnodes[leafnode] is not sure to be there when
        # going through the first loop

        for node in BNnodesdef:
            (nodename, isdiscrete, numstates, leafnode) = node
            if type(leafnode) == type([]):
                for r in leafnode:
                    if r != None:
                        BNconnections.append((BNnodes[nodename], BNnodes[r]))
            elif leafnode != None:
                BNconnections.append((BNnodes[nodename], BNnodes[leafnode]))
            else:
                #do nothing
                pass

        for ep in BNconnections:
            BN.add_e(DirEdge(len(BN.e), *ep))

        # Ok our Bnet has been created, let's save it in the controller
        self._BN = BN

        # Let's not forget to initialize the distribution
        self._BN.InitDistributions()
コード例 #3
0
ファイル: bke_openbayes.py プロジェクト: JHSaunders/masters
def CreateOpenBayesNetwork(network):
    G = BNet( network.name )
    
    node_dict = {}

    for node in network.nodes.all():
       pynode =  BVertex( node.name, True, node.states.count() )
       node_dict[node.id] = pynode
       G.add_v(pynode)

    for edge in network.edges.all():
        pyedge = DirEdge( len( G.e ),node_dict[edge.parent_node.id] , node_dict[edge.child_node.id])
        G.add_e( pyedge )    

    G.InitDistributions()

    for node in network.nodes.all():
        if not node.is_root():        
            
            result = node.get_indexed_value_sets()[0]            
            index = node.get_indexed_value_sets()[1]
            for i in range(len(index)):
                index_dict = {}
                check_dict = {}
                result_i_parents = result[i][0]
                values_i = result[i][1]
                index_i_parents = index[i][0]
                for j in range(len(index_i_parents)):
                    index_dict[result_i_parents[j].node.name] = index_i_parents[j]
                    check_dict[result_i_parents[j].node.name] = result_i_parents[j].name
                     
                node_dict[node.id].distribution[index_dict] = [v.value for v in values_i]                 
            print "\n"    
        else:
            node_dict[node.id].setDistributionParameters([state.probability for state in node.states.all()])

    return (G,node_dict)
コード例 #4
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network = BNet('Asia Bayesian Network')

# Create a discrete node for all nodes with 2 states
visit_to_asia = network.add_v(BVertex('Visit to Asia', True, 2))  # TRUE used to indicate the data is discrete as oppose to continuous
smoking = network.add_v(BVertex('Smoking', True, 2))
tuberculosis = network.add_v(BVertex('Tuberculosis', True, 2))
lung_cancer = network.add_v(BVertex('Lung Cancer', True, 2))
bronchitis = network.add_v(BVertex('Bronchitis', True, 2))
tub_or_cancer = network.add_v(BVertex('Tuberculosis or Cancer', True, 2))
xray_result = network.add_v(BVertex('X-Ray Result', True, 2))
dyspnea = network.add_v(BVertex('Dyspnea', True, 2))

# Connect the nodes

# V -> T
network.add_e(DirEdge(len(network.e), visit_to_asia, tuberculosis))

# T -> TC
network.add_e(DirEdge(len(network.e), tuberculosis, tub_or_cancer))

# TC -> X
network.add_e(DirEdge(len(network.e), tub_or_cancer, xray_result))

# TC -> D
network.add_e(DirEdge(len(network.e), tub_or_cancer, dyspnea))

# S -> LC
network.add_e(DirEdge(len(network.e), smoking, lung_cancer))

# S -> B
network.add_e(DirEdge(len(network.e), smoking, bronchitis))
コード例 #5
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        A
       / 
      B      
       \ 
        C
all edges point downwards
A,B and C are univariate gaussian distributions
"""

from OpenBayes import BNet, BVertex, DirEdge, MCMCEngine

# create the network
G = BNet( 'Water Sprinkler Bayesian Network' )
a,b,c = [G.add_v( BVertex( nm, False, 1 ) ) for nm in 'a b c'.split()]
for ep in [( a, b ), ( b, c )]:
    G.add_e( DirEdge( len( G.e ), *ep ) )

print G

# finalize the bayesian network once all edges have been added   
G.InitDistributions()

# fill in the parameters
a.distribution.setParameters(mu=1.0, sigma=0.5)
b.distribution.setParameters(mu=2.0, sigma=1.0, wi=2.0)
c.distribution.setParameters(mu=2.0, sigma=1.0, wi=1.0)

# NOTE : for the moment only MCMCEngine can work for continuous networks
ie = MCMCEngine(G)

res = ie.MarginaliseAll()
コード例 #6
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network = BNet('Asia Bayesian Network')

# Create a discrete node for all nodes with 2 states
visit_to_asia = network.add_v(BVertex('Visit to Asia', True, 2))
smoking = network.add_v(BVertex('Smoking', True, 2))
tuberculosis = network.add_v(BVertex('Tuberculosis', True, 2))
lung_cancer = network.add_v(BVertex('Lung Cancer', True, 2))
bronchitis = network.add_v(BVertex('Bronchitis', True, 2))
tub_or_cancer = network.add_v(BVertex('Tuberculosis or Cancer', True, 2))
xray_result = network.add_v(BVertex('X-Ray Result', True, 2))
dyspnea = network.add_v(BVertex('Dyspnea', True, 2))

# Connect the nodes

# V -> T
network.add_e(DirEdge(len(network.e), visit_to_asia, tuberculosis))

# T -> TC
network.add_e(DirEdge(len(network.e), tuberculosis, tub_or_cancer))

# TC -> X
network.add_e(DirEdge(len(network.e), tub_or_cancer, xray_result))

# TC -> D
network.add_e(DirEdge(len(network.e), tub_or_cancer, dyspnea))

# S -> LC
network.add_e(DirEdge(len(network.e), smoking, lung_cancer))

# S -> B
network.add_e(DirEdge(len(network.e), smoking, bronchitis))
コード例 #7
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# create discrete node for all nodes with 2 states??
road_Density = network.add_v(BVertex('Road Density', True, 3))
unregulated_Hunting_Rate = network.add_v(BVertex('Unregulated Hunting Rate', True, 3))
cougar_risk_level = network.add_v(BVertex('Cougar Risk Level', True, 3))
wolfPresence = network.add_v(BVertex('Wolf Presence', True, 2))
predation_risk = network.add_v(BVertex('Predation Risk', True, 3))
population_hazard = network.add_v(BVertex('Population Hazard', True, 5))
proportion_of_lu_in_UWR = network.add_v(BVertex('Proportion of LU in UWR', True, 4))
uwr_hazard_rating = network.add_v(BVertex('UWR Hazard Rating', True, 5))
habitat_hazard_rating = network.add_v(BVertex('Habitat Hazard Rating', True, 5))
muleDeerHazardRating = network.add_v(BVertex('Mule Deer Hazard Rating', True, 5))
regulated_Hunting_Rate = network.add_v(BVertex('Regulated Hunting Rate', True, 3))
# now describe the connections
# population side of model
network.add_e(DirEdge(len(network.e), road_Density, unregulated_Hunting_Rate))
network.add_e(DirEdge(len(network.e), road_Density, predation_risk))
network.add_e(DirEdge(len(network.e), cougar_risk_level, predation_risk))
network.add_e(DirEdge(len(network.e), wolfPresence, predation_risk))
network.add_e(DirEdge(len(network.e), predation_risk, population_hazard))
network.add_e(DirEdge(len(network.e), unregulated_Hunting_Rate, population_hazard))
network.add_e(DirEdge(len(network.e), regulated_Hunting_Rate, population_hazard))
network.add_e(DirEdge(len(network.e), population_hazard, muleDeerHazardRating))

# habitat side of model
network.add_e(DirEdge(len(network.e), proportion_of_lu_in_UWR, habitat_hazard_rating))
network.add_e(DirEdge(len(network.e), uwr_hazard_rating, habitat_hazard_rating))
network.add_e(DirEdge(len(network.e), habitat_hazard_rating, muleDeerHazardRating))

network.InitDistributions()
#                                         <1km   /  1-2km  /   2-3km
コード例 #8
0
ファイル: structured.py プロジェクト: Anaphory/open20q
logging.getLogger("").setLevel(logging.CRITICAL)

import numpy

network = BNet('Number Guessing')

# Create a discrete node for all nodes with 2 states
numbers = network.add_v(BVertex('numbers', True, 10))
bigger_3 = network.add_v(BVertex('Is your number bigger than 3?', True, 2))
bigger_5 = network.add_v(BVertex('bigger_5', True, 2))
IsTheNumberBiggerThan7 = network.add_v(BVertex('IsTheNumberBiggerThan7', True, 2))
prime = network.add_v(BVertex('prime', True, 2))
even = network.add_v(BVertex('even', True, 2))
odd = network.add_v(BVertex('odd', True, 2))

network.add_e(DirEdge(len(network.e), numbers, bigger_3))
network.add_e(DirEdge(len(network.e), numbers, bigger_5))
network.add_e(DirEdge(len(network.e), numbers, IsTheNumberBiggerThan7))
network.add_e(DirEdge(len(network.e), numbers, even))
network.add_e(DirEdge(len(network.e), numbers, prime))

network.add_e(DirEdge(len(network.e), even, odd))

# Show the network
print network

# Initialize the distributions
network.InitDistributions()

# Set distributions for start nodes
numbers.setDistributionParameters([0.1]*10)
コード例 #9
0
ファイル: structured.py プロジェクト: hwarlley/open20q
import numpy

network = BNet('Number Guessing')

# Create a discrete node for all nodes with 2 states
numbers = network.add_v(BVertex('numbers', True, 10))
bigger_3 = network.add_v(BVertex('Is your number bigger than 3?', True, 2))
bigger_5 = network.add_v(BVertex('bigger_5', True, 2))
IsTheNumberBiggerThan7 = network.add_v(
    BVertex('IsTheNumberBiggerThan7', True, 2))
prime = network.add_v(BVertex('prime', True, 2))
even = network.add_v(BVertex('even', True, 2))
odd = network.add_v(BVertex('odd', True, 2))

network.add_e(DirEdge(len(network.e), numbers, bigger_3))
network.add_e(DirEdge(len(network.e), numbers, bigger_5))
network.add_e(DirEdge(len(network.e), numbers, IsTheNumberBiggerThan7))
network.add_e(DirEdge(len(network.e), numbers, even))
network.add_e(DirEdge(len(network.e), numbers, prime))

network.add_e(DirEdge(len(network.e), even, odd))

# Show the network
print network

# Initialize the distributions
network.InitDistributions()

# Set distributions for start nodes
numbers.setDistributionParameters([0.1] * 10)