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()
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()
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)
import site
import sys
from OpenBayes import BNet, BVertex, DirEdge, JoinTree # @UnresolvedImport
dir = r'W:\ilmb\vic\geobc\bier\p14\p14_0053_BBN_CumEffects\wrk\scripts\deps\Lib\site-packages'
site.addsitedir(dir) # @UndefinedVariable
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))
""" this examples creates a continuous bayesian network
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)
""" this examples creates a hybrid bayesian network
A
/
B
\
C
all edges point downwards
B and C are univariate gaussian distributions
A is a boolean discrete distribution
"""
from OpenBayes import BNet, BVertex, DirEdge, MCMCEngine
# create the network
G = BNet( 'Water Sprinkler Bayesian Network' )
b,c = [G.add_v( BVertex( nm, False, 1 ) ) for nm in 'b c'.split()]
a = G.add_v(BVertex('a',True,2))
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([0.7,0.3])
b.distribution.setParameters(mu=[2.0,0.0], sigma=[1.0,1.0])
c.distribution.setParameters(mu=2.0, sigma=1.0, wi=1.0)
from OpenBayes import BNet, BVertex, DirEdge, JoinTree
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))
from OpenBayes import BNet, BVertex, DirEdge
from OpenBayes import learning
from copy import deepcopy
from time import time
# create the network
G = BNet( 'A Simple Bayesian Network' )
a, b = [G.add_v( BVertex( nm, True, 2 ) ) for nm in 'a b'.split()]
for ep in [( a, b )]:
G.add_e( DirEdge( len( G.e ), *ep ) )
G2 = deepcopy(G)
print G
# finalize the bayesian network once all edges have been added
G.InitDistributions()
print a.distribution.cpt, b.distribution.cpt
print '+++++++++++++++++++++++++++++++++\n'
G2.InitDistributions()
print G2.all_v[0].distribution.cpt
engine = learning.MLLearningEngine(G)
cases = engine.ReadFile('test.xls')
print 'cases:', cases
##cases = []
t = time()
from OpenBayes import BNet, BVertex, DirEdge, JoinTree
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))
@author: kjnether
'''
# This is an attempt to figure out how to model the mule
# deer model using the openbayes module.
#
import site
import sys
from OpenBayes import BNet, BVertex, DirEdge, JoinTree # @UnresolvedImport
dir = r'W:\ilmb\vic\geobc\bier\p14\p14_0053_BBN_CumEffects\wrk\scripts\deps\Lib\site-packages'
site.addsitedir(dir) # @UndefinedVariable
network = BNet('Mule Deer Hazard') # @UndefinedVariable
# 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
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE."""
import OpenBayes
from OpenBayes import BNet, BVertex, DirEdge, JoinTree
import logging
logging.getLogger(__name__).setLevel(logging.CRITICAL)
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))
/ \
/ \
/ \
Sprinkler Rainy
\ /
\ /
\ /
Wet grass
all edges are pointing downwards
"""
from OpenBayes import BNet, BVertex, DirEdge
# create the network
G = BNet( 'Water Sprinkler Bayesian Network' )
c, s, r, w = [G.add_v( BVertex( nm, True, 2 ) ) for nm in 'c s r w'.split()]
for ep in [( c, r ), ( c, s ), ( r, w ), ( s, w )]:
G.add_e( DirEdge( len( G.e ), *ep ) )
print G
# finalize the bayesian network once all edges have been added
G.InitDistributions()
# c | Pr(c)
#---+------
# 0 | 0.5
# 1 | 0.5
c.setDistributionParameters([0.5, 0.5])
# c s | Pr(s|c)
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE."""
import OpenBayes
from OpenBayes import BNet, BVertex, DirEdge, JoinTree
import logging
logging.getLogger(__name__).setLevel(logging.CRITICAL)
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))
