def test_complicated_multi(self): n1 = DiscreteNode("Some Node", [True, False]) n2 = DiscreteNode("Second Node" , [True, False,"noIdea"]) cpt1 = numpy.array([2,3]) cpt2 = numpy.array([5,7,9]) n1.set_probability_table(cpt1,[n1]) n2.set_probability_table(cpt2,[n2]) c3 = n1.get_cpd().multiplication(n2.get_cpd()) c3 = n1.get_cpd().multiplication(c3) cptN = numpy.array([[20, 28, 36],[45, 63, 81]]) numpy.testing.assert_array_equal(c3.table,cptN)
def test_easy_values(self): n1 = DiscreteNode("Some Node", [True, False]) n2 = DiscreteNode("Second Node" , [True, False]) cpt1 = numpy.array([2,3]) cpt2 = numpy.array([5,7]) n1.set_probability_table(cpt1,[n1]) n2.set_probability_table(cpt2,[n2]) s = n1.get_cpd().multiplication(n2.get_cpd()) cptN = numpy.array([[10,14],[15,21]]) numpy.testing.assert_array_equal(s.table,cptN) self.assertEqual(s.variables[0],n1)
def test_easy_marginalize(self): n1 = DiscreteNode("Some Node", [True, False]) n2 = DiscreteNode("Second Node" , [True, False, "other"]) cpt1 = numpy.array([2,3]) cpt2 = numpy.array([5,7,3]) n1.set_probability_table(cpt1,[n1]) n2.set_probability_table(cpt2,[n2]) s = n1.get_cpd().multiplication(n2.get_cpd()) s =s.marginalization(n2) print s.table cptN = numpy.array([30,45]) numpy.testing.assert_array_equal(s.table,cptN) self.assertEqual(s.variables[0],n1)
def setUp(self): # Create BayesNet self.bn = BayesNet(); # Create Nodes weather0 = DiscreteNode("Weather0", ["Sun", "Rain"]) weather = DiscreteNode("Weather", ["Sun", "Rain"]) ice_cream_eaten = DiscreteNode("Ice Cream Eaten", [True, False]) # Add nodes self.bn.add_node(weather0) self.bn.add_node(weather) self.bn.add_node(ice_cream_eaten) # Add edges self.bn.add_edge(weather, ice_cream_eaten) self.bn.add_edge(weather0, weather); # Set probabilities cpt_weather0 = numpy.array([.6, .4]) weather0.set_probability_table(cpt_weather0, [weather0]) cpt_weather = numpy.array([[.7, .5], [.3, .5]]) weather.set_probability_table(cpt_weather, [weather0, weather]) ice_cream_eaten.set_probability(.9, [(ice_cream_eaten, True), (weather, "Sun")]) ice_cream_eaten.set_probability(.1, [(ice_cream_eaten, False), (weather, "Sun")]) ice_cream_eaten.set_probability(.2, [(ice_cream_eaten, True), (weather, "Rain")]) ice_cream_eaten.set_probability(.8, [(ice_cream_eaten, False), (weather, "Rain")])
alarm = DiscreteNode("Alarm", ["Ringing", "Silent", "Kaputt"]) earthquake = DiscreteNode("Earthquake", ["Shaking", "Calm"]) john_calls = DiscreteNode("John calls", ["Calling", "Not Calling"]) mary_calls = DiscreteNode("Mary calls", ["Calling", "Not Calling"]) bn.add_node(burglary) bn.add_node(alarm) bn.add_node(earthquake) bn.add_node(john_calls) bn.add_node(mary_calls) bn.add_edge(burglary, alarm) bn.add_edge(earthquake, alarm) bn.add_edge(alarm, john_calls) bn.add_edge(alarm, mary_calls) burglary.set_probability(0.2, [(burglary, "Intruder")]) alarm.set_probability(0.1, [(alarm, "Ringing"), (burglary, "Safe"), (earthquake, "Calm")]) cpt = numpy.array([[0.1, 0.9], [0.5, 0.5], [0.4, 0.6]]) john_calls.set_probability_table(cpt, [alarm, john_calls]) print john_calls.is_valid() print alarm.is_valid() bn.draw()
import numpy #Construct some simple BayesianNetwork bn = BayesNet() burglary = DiscreteNode("Burglary", ["Intruder","Safe"]) alarm = DiscreteNode("Alarm", ["Ringing", "Silent","Kaputt"]) bn.add_node(burglary) bn.add_node(alarm) bn.add_edge(burglary,alarm) burglary_cpt=numpy.array([0.2,0.8]) burglary.set_probability_table(burglary_cpt, [burglary]) alarm_cpt=numpy.array([[0.8,0.15,0.05],[0.05,0.9,0.05]]) alarm.set_probability_table(alarm_cpt, [burglary,alarm]) #Construct a Markov Chain by sampling states from this Network transition_model = GibbsTransitionModel() mcs = MarkovChainSampler() initial_state={burglary:"Safe",alarm:"Silent"} chain = mcs.generateMarkovChain(bn, 5000, transition_model, initial_state) #for c in chain: # print c
# add Nodes to BayesNet bn.add_node(burglary) bn.add_node(alarm) bn.add_node(earthquake) bn.add_node(john_calls) bn.add_node(baum_calls) # Add edges to show dependencies bn.add_edge(burglary, alarm) bn.add_edge(earthquake, alarm) bn.add_edge(alarm, john_calls) bn.add_edge(alarm, baum_calls) # create probability tables and set them in the node cpt_burglary = numpy.array([0.001, 0.999]) burglary.set_probability_table(cpt_burglary, [burglary]) cpt_earthquake = numpy.array([0.002, 0.998]) earthquake.set_probability_table(cpt_earthquake, [earthquake]) # another possibility to set probabilities alarm.set_probability(0.95, [(alarm, "Ringing"), (burglary, "Intruder"), (earthquake, "Shaking")]) alarm.set_probability(0.05, [(alarm, "Silent"), (burglary, "Intruder"), (earthquake, "Shaking")]) alarm.set_probability(0.29, [(alarm, "Ringing"), (burglary, "Safe"), (earthquake, "Shaking")]) alarm.set_probability(0.71, [(alarm, "Silent"), (burglary, "Safe"), (earthquake, "Shaking")]) alarm.set_probability(0.94, [(alarm, "Ringing"), (burglary, "Intruder"), (earthquake, "Calm")]) alarm.set_probability(0.06, [(alarm, "Silent"), (burglary, "Intruder"), (earthquake, "Calm")]) alarm.set_probability(0.001, [(alarm, "Ringing"), (burglary, "Safe"), (earthquake, "Calm")]) alarm.set_probability(0.999, [(alarm, "Silent"), (burglary, "Safe"), (earthquake, "Calm")]) baum_calls.set_probability(0.9, [(alarm, "Ringing"), (baum_calls, "Calling")])
weather0_init = DiscreteNode("Weather0", ["Sun", "Rain"]) weather0 = DiscreteNode("Weather0", ["Sun", "Rain"]) weather = DiscreteNode("Weather", ["Sun", "Rain"]) ice_cream_eaten = DiscreteNode("Ice Cream Eaten", [True, False]) B0.add_node(weather0_init) twoTBN.add_node(weather0, True) twoTBN.add_node(weather) twoTBN.add_node(ice_cream_eaten) twoTBN.add_edge(weather, ice_cream_eaten) twoTBN.add_edge(weather0, weather); cpt_weather0_init = numpy.array([.6, .4]) weather0_init.set_probability_table(cpt_weather0_init, [weather0_init]) cpt_weather0 = numpy.array([.6, .4]) weather0.set_probability_table(cpt_weather0, [weather0]) cpt_weather = numpy.array([[.7, .5], [.3, .5]]) weather.set_probability_table(cpt_weather, [weather, weather0]) cpt_ice_cream_eaten = numpy.array([[.9, .2], [.1, .8]]) ice_cream_eaten.set_probability_table(cpt_ice_cream_eaten, [ice_cream_eaten, weather]) from primo.utils import XMLBIF xmlbif = XMLBIF(twoTBN, "Test") xmlbif.write("test.xmlbif")