def main():
"""
The main function
"""
graph = watersprinkler.main()
nbr_samples = 1000
# sample the network N times
cases = graph.sample(nbr_samples)
# create a new bayesian network with all parameters set to 1
graph_copy = deepcopy(graph)
graph_copy.init_distributions()
# Learn the parameters from the set of cases
engine = learning.MLLearningEngine(graph_copy)
# cases = engine.read_file('file.xls') #To use the data of file.xls
start_time = time()
engine.learn_ml_params(cases)
print 'Learned from %d cases in %1.3f secs' % \
(nbr_samples, (time() - start_time))
# print the learned parameters
for vertex in graph_copy.all_v:
print vertex.name, vertex.distribution.cpt,'\n'
# print the parameters
for vertex in graph.all_v:
print vertex.distribution,'\n'
def main():
"""
The main function
"""
graph = watersprinkler.main()
# create an inference Engine
# choose the one you like by commenting/uncommenting the appropriate line
engine = JoinTree(graph)
#ie = MCMCEngine(G)
# perform inference with no evidence
results = engine.marginalise_all()
print '=============================================================='
print 'Without evidence:\n'
for name, res in results.items():
print name, res, '\n'
print '=============================================================='
print 'With evidence:\n'
# add some evidence
engine.set_obs({'s':1}) # s=1
# perform inference with evidence
results = engine.marginalise_all()
# notice that the JoinTree engine does not perform a full message pass
# but only distributes the new evidence...
for res in results.values():
print res, '\n'
def main():
"""
This is the main function
"""
graph = watersprinkler.main()
nbr_samples = 2000
# sample the network N times
# cases = [{'c':0,'s':1,'r':0,'w':1},{...},...]
cases = graph.sample(nbr_samples)
# delete some observations
for i in range(500):
case = cases[3*i]
rand = random.sample(['c', 's', 'r', 'w'], 1)[0]
case[rand] = '?'
for i in range(50):
case = cases[3*i]
rand = random.sample(['c', 's', 'r', 'w'], 1)[0]
case[rand] = '?'
# copy the BN
graph_copy = deepcopy(graph)
# set all parameters to 1s
graph_copy.init_distributions()
# Learn the parameters from the set of cases
engine = learning.EMLearningEngine(graph_copy)
# cases = engine.read_file('file.xls') #To use the data of file.xls
start_time = time()
engine.em_learning(cases, 10)
print 'Learned from %d cases in %1.3f secs' % \
(nbr_samples, (time() - start_time))
# print the learned parameters
print "Learned paramters"
for vertex in graph_copy.all_v:
print vertex.name, vertex.distribution.cpt, '\n'
### print the parameters
print "Orignal Parameters"
for vertex in graph.all_v:
print vertex.name, vertex.distribution.cpt, '\n'
def main():
"""
This is the main function
"""
graph = watersprinkler.main()
nbr_samples = 2000
# sample the network several times
cases = graph.sample(nbr_samples)
# delete some observations
for i in range(500):
case = cases[3*i]
rand = random.sample(['c', 's', 'r', 'w'], 1)[0]
case[rand] = '?'
for i in range(50):
case = cases[3*i]
rand = random.sample(['c', 's', 'r', 'w'], 1)[0]
case[rand] = '?'
# Create a new BNet with no edgraphes
graph_copy = bayesnet.BNet('Water Sprinkler Bayesian Network2')
for name in "c s r w".split():
graph_copy.add_v(bayesnet.BVertex(name, True, 2))
graph_copy.init_distributions()
# Learn the structure
struct_engine = learning.SEMLearningEngine(graph_copy)
struct_engine.sem_learning(cases)
print 'learned structure: ', struct_engine.network
print 'total bic score: ', \
struct_engine.global_bic_score(nbr_samples, cases, 0)
# Learn the structure
struct_engine = learning.SEMLearningEngine(graph_copy)
struct_engine.sem_learning_approx(cases)
print 'learned structure: ', struct_engine.network
print 'total bic score: ', \
struct_engine.global_bic_score(nbr_samples, cases, 1)