How to use the pomegranate.Node function in pomegranate
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DAI-Lab / SDGym / sdgym / utils / data / simulated / multivariate.py View on Github 
XRay = ConditionalProbabilityTable(
[['T','T',0.9],
['T','F',0.1],
['F','T',0.2],
['F','F',0.8],
],[Cancer])
Dyspnoea = ConditionalProbabilityTable(
[['T','T',0.65],
['T','F',0.35],
['F','T',0.3],
['F','F',0.7],
],[Cancer])
s1 = Node(Pollution, name="Pollution")
s2 = Node(Smoker, name="Smoker")
s3 = Node(Cancer, name="Cancer")
s4 = Node(XRay, name="XRay")
s5 = Node(Dyspnoea, name="Dyspnoea")
model = BayesianNetwork("Lung Cancer")
model.add_states(s1, s2, s3, s4, s5)
model.add_edge(s1, s3)
model.add_edge(s2, s3)
model.add_edge(s3, s4)
model.add_edge(s3, s5)
model.bake()
self.model = model
meta = []
name_mapper = ["Pollution", "Smoker", "Cancer", "XRay", "Dyspnoea"]
for i in range(self.model.node_count()):
meta.append({
"name": name_mapper[i],],[Pollution,Smoker])
print(Cancer)
XRay = ConditionalProbabilityTable(
[['T','T',0.9],
['T','F',0.1],
['F','T',0.2],
['F','F',0.8],
],[Cancer])
Dyspnoea = ConditionalProbabilityTable(
[['T','T',0.65],
['T','F',0.35],
['F','T',0.3],
['F','F',0.7],
],[Cancer])
s1 = Node(Pollution, name="Pollution")
s2 = Node(Smoker, name="Smoker")
s3 = Node(Cancer, name="Cancer")
s4 = Node(XRay, name="XRay")
s5 = Node(Dyspnoea, name="Dyspnoea")
model = BayesianNetwork("Lung Cancer")
model.add_states(s1, s2, s3, s4, s5)
model.add_edge(s1, s3)
model.add_edge(s2, s3)
model.add_edge(s3, s4)
model.add_edge(s3, s5)
model.bake()
self.model = model
meta = []
name_mapper = ["Pollution", "Smoker", "Cancer", "XRay", "Dyspnoea"]
for i in range(self.model.node_count()):['F','F','F',0.999],
],[Pollution,Smoker])
print(Cancer)
XRay = ConditionalProbabilityTable(
[['T','T',0.9],
['T','F',0.1],
['F','T',0.2],
['F','F',0.8],
],[Cancer])
Dyspnoea = ConditionalProbabilityTable(
[['T','T',0.65],
['T','F',0.35],
['F','T',0.3],
['F','F',0.7],
],[Cancer])
s1 = Node(Pollution, name="Pollution")
s2 = Node(Smoker, name="Smoker")
s3 = Node(Cancer, name="Cancer")
s4 = Node(XRay, name="XRay")
s5 = Node(Dyspnoea, name="Dyspnoea")
model = BayesianNetwork("Lung Cancer")
model.add_states(s1, s2, s3, s4, s5)
model.add_edge(s1, s3)
model.add_edge(s2, s3)
model.add_edge(s3, s4)
model.add_edge(s3, s5)
model.bake()
self.model = model
meta = []
name_mapper = ["Pollution", "Smoker", "Cancer", "XRay", "Dyspnoea"]for v_name in todo:
# print(v_name, type(var_nodes[v_name]))
cond_p_table, cond = var_nodes[v_name]
flag = True
for y in cond[1:]:
if y in todo:
flag = False
break
if flag:
cond_t = [var_nodes[x] for x in cond[1:]]
var_nodes[v_name] = ConditionalProbabilityTable(cond_p_table, cond_t)
todo.remove(v_name)
break
for x in var_index_to_name:
var_nodes[x] = Node(var_nodes[x], name=x)
var_nodes_list = [var_nodes[x] for x in var_index_to_name]
# print(var_nodes_list)
model = BayesianNetwork("tmp")
model.add_states(*var_nodes_list)
for edge in edges:
model.add_edge(var_nodes[edge[0]], var_nodes[edge[1]])
model.bake()
# print(model.to_json())
self.model = model['3','3',0.5],
],[A])
C = ConditionalProbabilityTable(
[['1','4',0.5],
['1','5',0.5],
['1','6',0],
['2','4',0],
['2','5',0.5],
['2','6',0.5],
['3','4',0.5],
['3','5',0],
['3','6',0.5],
],[A])
s1 = Node(A, name="A")
s2 = Node(B, name="B")
s3 = Node(C, name="C")
model = BayesianNetwork("tree")
model.add_states(s1, s2, s3)
model.add_edge(s1, s2)
model.add_edge(s1, s3)
model.bake()
self.model = model
meta = []
for i in range(self.model.node_count()-1):
meta.append({
"name": chr(ord('A') + i),
"type": "categorical",
"size": 3,
"i2s": ['1', '2', '3']print(Cancer)
XRay = ConditionalProbabilityTable(
[['T','T',0.9],
['T','F',0.1],
['F','T',0.2],
['F','F',0.8],
],[Cancer])
Dyspnoea = ConditionalProbabilityTable(
[['T','T',0.65],
['T','F',0.35],
['F','T',0.3],
['F','F',0.7],
],[Cancer])
s1 = Node(Pollution, name="Pollution")
s2 = Node(Smoker, name="Smoker")
s3 = Node(Cancer, name="Cancer")
s4 = Node(XRay, name="XRay")
s5 = Node(Dyspnoea, name="Dyspnoea")
model = BayesianNetwork("Lung Cancer")
model.add_states(s1, s2, s3, s4, s5)
model.add_edge(s1, s3)
model.add_edge(s2, s3)
model.add_edge(s3, s4)
model.add_edge(s3, s5)
model.bake()
self.model = model
meta = []
name_mapper = ["Pollution", "Smoker", "Cancer", "XRay", "Dyspnoea"]
for i in range(self.model.node_count()):
meta.append({['3','2',0],
['3','3',0.5],
],[A])
C = ConditionalProbabilityTable(
[['1','1',0.5],
['1','2',0.5],
['1','3',0],
['2','1',0],
['2','2',0.5],
['2','3',0.5],
['3','1',0.5],
['3','2',0],
['3','3',0.5],
],[B])
s1 = Node(A, name="A")
s2 = Node(B, name="B")
s3 = Node(C, name="C")
model = BayesianNetwork("ChainSampler")
model.add_states(s1, s2, s3)
model.add_edge(s1, s2)
model.add_edge(s2, s3)
model.bake()
self.model = model
meta = []
for i in range(self.model.node_count()):
meta.append({
"name": chr(ord('A') + i),
"type": "categorical",
"size": 3,['3','3',0.5],
],[A])
C = ConditionalProbabilityTable(
[['1','1',0.5],
['1','2',0.5],
['1','3',0],
['2','1',0],
['2','2',0.5],
['2','3',0.5],
['3','1',0.5],
['3','2',0],
['3','3',0.5],
],[B])
s1 = Node(A, name="A")
s2 = Node(B, name="B")
s3 = Node(C, name="C")
model = BayesianNetwork("ChainSampler")
model.add_states(s1, s2, s3)
model.add_edge(s1, s2)
model.add_edge(s2, s3)
model.bake()
self.model = model
meta = []
for i in range(self.model.node_count()):
meta.append({
"name": chr(ord('A') + i),
"type": "categorical",
"size": 3,
"i2s": ['1', '2', '3']],[A])
C = ConditionalProbabilityTable(
[['1','4',0.5],
['1','5',0.5],
['1','6',0],
['2','4',0],
['2','5',0.5],
['2','6',0.5],
['3','4',0.5],
['3','5',0],
['3','6',0.5],
],[A])
s1 = Node(A, name="A")
s2 = Node(B, name="B")
s3 = Node(C, name="C")
model = BayesianNetwork("tree")
model.add_states(s1, s2, s3)
model.add_edge(s1, s2)
model.add_edge(s1, s3)
model.bake()
self.model = model
meta = []
for i in range(self.model.node_count()-1):
meta.append({
"name": chr(ord('A') + i),
"type": "categorical",
"size": 3,
"i2s": ['1', '2', '3']
})['T','F',0.9],
],[Rain])
Wet = ConditionalProbabilityTable(
[['F','F','T',0.01],
['F','F','F',0.99],
['F','T','T',0.8],
['F','T','F',0.2],
['T','F','T',0.9],
['T','F','F',0.1],
['T','T','T',0.99],
['T','T','F',0.01],
],[Sprinkler,Rain])
s1 = Node(Rain, name="Rain")
s2 = Node(Sprinkler, name="Sprinkler")
s3 = Node(Wet, name="Wet")
model = BayesianNetwork("Simple fully connected")
model.add_states(s1, s2, s3)
model.add_edge(s1, s2)
model.add_edge(s1, s3)
model.add_edge(s2, s3)
model.bake()
self.model = model
meta = []
for i in range(self.model.node_count()):
meta.append({
"name": None,
"type": "categorical",
"size": 2,
"i2s": ['T', 'F']pomegranate
A PyTorch implementation of probabilistic models.
Package Health Score
67 / 100Popular pomegranate functions
- pomegranate.__version__
- pomegranate.BayesianNetwork
- pomegranate.BayesianNetwork.from_json
- pomegranate.BayesianNetwork.from_samples
- pomegranate.BayesianNetwork.from_structure
- pomegranate.ConditionalProbabilityTable
- pomegranate.DiscreteDistribution
- pomegranate.filters.base.Filter
- pomegranate.GeneralMixtureModel
- pomegranate.HiddenMarkovModel
- pomegranate.HiddenMarkovModel.from_matrix
- pomegranate.Node
- pomegranate.NormalDistribution
- pomegranate.State