def setUp(self):
nodedata = NodeData.load("unittestlgdict.txt")
skel = GraphSkeleton()
skel.load("unittestdict.txt")
skel.toporder()
self.lgb = LGBayesianNetwork(nodedata)
def net2():
nd = NodeData()
skel = GraphSkeleton()
nd.load("net.txt") # an input file
skel.load("net.txt")
# topologically order graphskeleton
skel.toporder()
# load bayesian network
lgbn = LGBayesianNetwork(skel, nd)
in_data = read_data.getdata2()
learner = PGMLearner()
bn = learner.lg_mle_estimateparams(skel, in_data)
p = cal_prob(in_data[300:500], bn)
print p
return 0
def setUp(self):
# instantiate learner
self.l = PGMLearner()
# generate graph skeleton
skel = GraphSkeleton()
skel.load("unittestdict.txt")
skel.toporder()
# generate sample sequence to try to learn from - discrete
nd = NodeData.load("unittestdict.txt")
self.samplediscbn = DiscreteBayesianNetwork(nd)
self.samplediscseq = self.samplediscbn.randomsample(5000)
# generate sample sequence to try to learn from - discrete
nda = NodeData.load("unittestlgdict.txt")
self.samplelgbn = LGBayesianNetwork(nda)
self.samplelgseq = self.samplelgbn.randomsample(10000)
self.skel = skel
def test_structure_estimation(self):
req = LinearGaussianStructureEstimationRequest()
# generate trial data
skel = GraphSkeleton()
skel.load(self.data_path)
skel.toporder()
teacher_nd = NodeData()
teacher_nd.load(self.teacher_data_path)
bn = LGBayesianNetwork(skel, teacher_nd)
data = bn.randomsample(8000)
for v in data:
gs = LinearGaussianGraphState()
for k_s, v_s in v.items():
gs.node_states.append(LinearGaussianNodeState(node=k_s, state=v_s))
req.states.append(gs)
res = self.struct_estimate(req)
self.assertIsNotNone(res.graph)
self.assertEqual(len(res.graph.nodes), 5)
self.assertEqual(len(res.graph.edges), 4)
def test_param_estimation(self):
req = LinearGaussianParameterEstimationRequest()
# load graph structure
skel = GraphSkeleton()
skel.load(self.data_path)
req.graph.nodes = skel.V
req.graph.edges = [GraphEdge(k, v) for k,v in skel.E]
skel.toporder()
# generate trial data
teacher_nd = NodeData()
teacher_nd.load(self.teacher_data_path)
bn = LGBayesianNetwork(skel, teacher_nd)
data = bn.randomsample(200)
for v in data:
gs = LinearGaussianGraphState()
for k_s, v_s in v.items():
gs.node_states.append(LinearGaussianNodeState(node=k_s, state=v_s))
req.states.append(gs)
self.assertEqual(len(self.param_estimate(req).nodes), 5)
import json
from libpgm.nodedata import NodeData
from libpgm.graphskeleton import GraphSkeleton
from libpgm.lgbayesiannetwork import LGBayesianNetwork
from libpgm.pgmlearner import PGMLearner
# generate some data to use
nd = NodeData()
nd.load("gaussGrades.txt") # an input file
skel = GraphSkeleton()
skel.load("gaussGrades.txt")
skel.toporder()
lgbn = LGBayesianNetwork(skel, nd)
data = lgbn.randomsample(8000)
print data
# instantiate my learner
learner = PGMLearner()
# estimate structure
result = learner.lg_constraint_estimatestruct(data)
# output
print json.dumps(result.E, indent=2)
if __name__ == '__main__':
rospy.init_node("pgm_learner_sample_linear_gaussian")
param_estimate = rospy.ServiceProxy("pgm_learner/linear_gaussian/parameter_estimation", LinearGaussianParameterEstimation)
req = LinearGaussianParameterEstimationRequest()
dpath = os.path.join(PKG_PATH, "test", "graph-test.txt")
tpath = os.path.join(PKG_PATH, "test", "graph-lg-test.txt")
# load graph structure
skel = GraphSkeleton()
skel.load(dpath)
req.graph.nodes = skel.V
req.graph.edges = [GraphEdge(k, v) for k,v in skel.E]
skel.toporder()
# generate trial data
teacher_nd = NodeData()
teacher_nd.load(tpath)
bn = LGBayesianNetwork(skel, teacher_nd)
data = bn.randomsample(200)
for v in data:
gs = LinearGaussianGraphState()
for k_s, v_s in v.items():
gs.node_states.append(LinearGaussianNodeState(node=k_s, state=v_s))
req.states.append(gs)
PP.pprint(param_estimate(req).nodes)
