def setUp(self):
self.nd = NodeData()
self.nd.load("unittestdyndict.txt")
self.skel = GraphSkeleton()
self.skel.load("unittestdyndict.txt")
self.skel.toporder()
self.d = DynDiscBayesianNetwork(self.skel, self.nd)
def setUp(self):
self.nd = NodeData()
self.nd.load("unittestdyndict.txt")
self.skel = GraphSkeleton()
self.skel.load("unittestdyndict.txt")
self.skel.toporder()
self.d = DynDiscBayesianNetwork(self.skel, self.nd)
class TestDynDiscBayesianNetwork(unittest.TestCase):
def setUp(self):
self.nd = NodeData.load("unittestdyndict.txt")
self.skel = GraphSkeleton()
self.skel.load("unittestdyndict.txt")
self.skel.toporder()
self.d = DynDiscBayesianNetwork(self.skel, self.nd)
def test_randomsample(self):
sample = self.d.randomsample(10)
for i in range(1, 10):
self.assertEqual(sample[0]['Difficulty'], sample[i]['Difficulty'])
class TestDynDiscBayesianNetwork(unittest.TestCase):
def setUp(self):
self.nd = NodeData.load("unittestdyndict.txt")
self.skel = GraphSkeleton()
self.skel.load("unittestdyndict.txt")
self.skel.toporder()
self.d = DynDiscBayesianNetwork(self.skel, self.nd)
def test_randomsample(self):
sample = self.d.randomsample(10)
for i in range(1, 10):
self.assertEqual(sample[0]['Difficulty'], sample[i]['Difficulty'])
def CreateDynDiscBN(vertices, edges, initialVdata, bnV_data):
#Creates adn returns Dynamic Discrete BN
d = DynDiscBayesianNetwork()
d.V = vertices
d.E = edges
d.initial_Vdata = initialVdata
d.twotbn_Vdata = bnV_data
return d
def ConstructDynBN(num_graph, numvalues, A, ss):
graph = conv.ian2g(num_graph)
print(graph)
V, E, initVdata = INITdata(graph, numvalues)
tfVdata = gettfVdata(num_graph, numvalues, A)
d = DynDiscBayesianNetwork()
skel = GraphSkeleton()
skel.V = V
skel.E = E
d.V = skel.V
d.E = skel.E
d.initial_Vdata = initVdata
d.twotbn_Vdata = tfVdata
print(d.V)
print(d.E)
print(d.initial_Vdata)
print(d.twotbn_Vdata)
data = sampleBN(d, ss)
return data
result = learner.discrete_estimatebn(data) # output - toggle comment to see #print json.dumps(result.E, indent=2) #print json.dumps(result.Vdata, indent=2) # (13) ----------------------------------------------------------------------- # Forward sample on dynamic Bayesian networks # read input file path = "../tests/unittestdyndict.txt" f = open(path, 'r') g = eval(f.read()) # set up dynamic BN d = DynDiscBayesianNetwork() skel = GraphSkeleton() skel.V = g["V"] skel.E = g["E"] skel.toporder() d.V = skel.V d.E = skel.E d.initial_Vdata = g["initial_Vdata"] d.twotbn_Vdata = g["twotbn_Vdata"] # forward sample seq = d.randomsample(10) # output - toggle comment to see #print json.dumps(seq, indent=2)
result = learner.discrete_estimatebn(data) # output - toggle comment to see #print json.dumps(result.E, indent=2) #print json.dumps(result.Vdata, indent=2) # (13) ----------------------------------------------------------------------- # Forward sample on dynamic Bayesian networks # read input file path = "../tests/unittestdyndict.txt" f = open(path, 'r') g = eval(f.read()) # set up dynamic BN d = DynDiscBayesianNetwork() skel = GraphSkeleton() skel.V = g["V"] skel.E = g["E"] skel.toporder() d.V = skel.V d.E = skel.E d.initial_Vdata = g["initial_Vdata"] d.twotbn_Vdata = g["twotbn_Vdata"] # forward sample seq = d.randomsample(10) # output - toggle comment to see #print json.dumps(seq, indent=2)
import json
from libpgm.nodedata import NodeData
from libpgm.graphskeleton import GraphSkeleton
from libpgm.dyndiscbayesiannetwork import DynDiscBayesianNetwork
from inference.sensor_dbn_inference import SensorDbnInference
network_file = open('test_bayesian_networks/sensor_dbn.txt', 'r')
network_file_data = eval(network_file.read())
network_skeleton = GraphSkeleton()
network_skeleton.V = network_file_data["V"]
network_skeleton.E = network_file_data["E"]
network = DynDiscBayesianNetwork()
network.V = network_skeleton.V
network.E = network_skeleton.E
network.initial_Vdata = network_file_data["initial_Vdata"]
network.twotbn_Vdata = network_file_data["twotbn_Vdata"]
inference_engine = SensorDbnInference(network)
print 'Initial belief: ', inference_engine.get_current_belief()
inference_engine.filter('1')
print 'Measurement = 1: ', inference_engine.get_current_belief()
inference_engine.filter('0')
print 'Measurement = 0: ', inference_engine.get_current_belief()
inference_engine.filter('0')
print 'Measurement = 0: ', inference_engine.get_current_belief()
# plt.scatter(test_data[cluster_2,0], test_data[cluster_2,1], c='b', marker='o')
# plt.scatter(test_data[cluster_3,0], test_data[cluster_3,1], c='k', marker='o')
# plt.scatter(means[:,0], means[:,1], c='c', marker='o')
# plt.show()
means = numpy.array([[ 2.00755688e-04, 1.65181639e-01], [ 8.37884753e-01, 9.99778286e-01], [ 9.75317567e-01, 2.46051178e-02]])
# sensor network
sensor_network_file = open('test_bayesian_networks/graph_sensor_dbn.txt', 'r')
sensor_network_file_data = eval(sensor_network_file.read())
sensor_network_skeleton = GraphSkeleton()
sensor_network_skeleton.V = sensor_network_file_data["V"]
sensor_network_skeleton.E = sensor_network_file_data["E"]
sensor_network = DynDiscBayesianNetwork()
sensor_network.V = sensor_network_skeleton.V
sensor_network.E = sensor_network_skeleton.E
sensor_network.initial_Vdata = sensor_network_file_data["initial_Vdata"]
sensor_network.twotbn_Vdata = sensor_network_file_data["twotbn_Vdata"]
# observation_network
observation_network_file = open('test_bayesian_networks/graph_transition_dbn.txt', 'r')
observation_network_file_data = eval(observation_network_file.read())
observation_network_skeleton = GraphSkeleton()
observation_network_skeleton.V = observation_network_file_data["V"]
observation_network_skeleton.E = observation_network_file_data["E"]
observation_network = DynDiscBayesianNetwork()
observation_network.V = observation_network_skeleton.V
