Esempi in Python per SampleAggregator

Esempio n. 1

File: run_unit_tests.py Progetto: Anaphory/libpgm

 def setUp(self):
     skel = GraphSkeleton()
     skel.load("unittestdict.txt")
     skel.toporder()
     nodedata = NodeData.load("unittestdict.txt")
     self.bn = DiscreteBayesianNetwork(nodedata)
     agg = SampleAggregator()
     agg.aggregate(self.bn.randomsample(50))
     self.rseq = agg.seq
     self.ravg = agg.avg
     self.fn = TableCPDFactorization(self.bn)
     evidence = dict(Letter='weak')
     agg.aggregate(self.fn.gibbssample(evidence, 51))
     self.gseq = agg.seq
     self.gavg = agg.avg

Esempio n. 2

File: run_unit_tests.py Progetto: Anaphory/libpgm

 def setUp(self):
     skel = GraphSkeleton()
     skel.load("unittestdict.txt")
     skel.toporder()
     nodedata = NodeData.load("unittestdict.txt")
     self.bn = DiscreteBayesianNetwork(nodedata)
     agg = SampleAggregator()
     agg.aggregate(self.bn.randomsample(50))
     self.rseq = agg.seq
     self.ravg = agg.avg
     self.fn = TableCPDFactorization(self.bn)
     evidence = dict(Letter='weak')
     agg.aggregate(self.fn.gibbssample(evidence, 51))
     self.gseq = agg.seq
     self.gavg = agg.avg

Esempio n. 3

File: discrete_data_generation.py Progetto: sergeyplis/Undersampled_Graph_Estimation

def GenProbTable(network):
    #Generates Generic probability table for a Bayesian Network.
    #Used for initialization of Dyn. Disc. BN
    agg = SampleAggregator()
    r = agg.aggregate(network.randomsample(5000))
    return r

Esempio n. 4

# Compute the approximate probability distribution by generating samples

# load nodedata and graphskeleton
nd = NodeData()
skel = GraphSkeleton()
nd.load("../tests/unittestdict.txt")
skel.load("../tests/unittestdict.txt")

# topologically order graphskeleton
skel.toporder()

# load bayesian network
bn = DiscreteBayesianNetwork(skel, nd)

# build aggregator
agg = SampleAggregator()

# average samples
result = agg.aggregate(bn.randomsample(10))

# output - toggle comment to see
#print json.dumps(result, indent=2)

# (8) --------------------------------------------------------------------------
# Learn the CPDs of a discrete Bayesian network, given data and a structure:

# say I have some data
data = bn.randomsample(200)

# and a graphskeleton
skel = GraphSkeleton()

Esempio n. 5

File: create_struct.py Progetto: spoilr/ml-profiling

def inference(bn, evidence):
  fn = TableCPDFactorization(bn)
  result = fn.gibbssample(evidence, GIBBS_ITERATIONS)
  agg = SampleAggregator()
  result = agg.aggregate(result)
  return json.dumps(result, indent=2)

Esempio n. 6

File: examples.py Progetto: Anaphory/libpgm

# Compute the approximate probability distribution by generating samples

# load nodedata and graphskeleton
nd = NodeData()
skel = GraphSkeleton()
nd.load("../tests/unittestdict.txt")
skel.load("../tests/unittestdict.txt")

# topologically order graphskeleton
skel.toporder()

# load bayesian network
bn = DiscreteBayesianNetwork(skel, nd)

# build aggregator
agg = SampleAggregator()

# average samples
result = agg.aggregate(bn.randomsample(10))

# output - toggle comment to see
#print json.dumps(result, indent=2)

# (8) --------------------------------------------------------------------------
# Learn the CPDs of a discrete Bayesian network, given data and a structure:

# say I have some data
data = bn.randomsample(200)

# and a graphskeleton
skel = GraphSkeleton()