def parseBayesNetProblem(testDict):
# needs to be able to parse in a bayes net,
# and figure out what type of operation to perform and on what
parseDict = {}
variableDomainsDict = {}
for line in testDict['variableDomainsDict'].split('\n'):
variable, domain = line.split(' : ')
variableDomainsDict[variable] = domain.split(' ')
parseDict['variableDomainsDict'] = variableDomainsDict
variables = []
for line in testDict["variables"].split('\n'):
variable = line.strip()
variables.append(variable)
edges = []
for line in testDict["edges"].split('\n'):
tokens = line.strip().split()
if len(tokens) == 2:
edges.append((tokens[0], tokens[1]))
else:
raise Exception, "[parseBayesNetProblem] Bad evaluation line: |%s|" % (line,)
# inference query args
queryVariables = testDict['queryVariables'].split(' ')
parseDict['queryVariables'] = queryVariables
evidenceDict = {}
for line in testDict['evidenceDict'].split('\n'):
if(line.count(' : ')): #so we can pass empty dicts for unnormalized variables
(evidenceVariable, evidenceValue) = line.split(' : ')
evidenceDict[evidenceVariable] = evidenceValue
parseDict['evidenceDict'] = evidenceDict
if testDict['constructRandomly'] == 'False':
# load from test file
problemBayesNet = bayesNet.constructEmptyBayesNet(variables, edges, variableDomainsDict)
for variable in variables:
currentFactor = bayesNet.Factor([variable], problemBayesNet.inEdges()[variable], variableDomainsDict)
for line in testDict[variable + 'FactorTable'].split('\n'):
assignments, probability = line.split(" = ")
assignmentList = [assignment for assignment in assignments.split(', ')]
assignmentsDict = {}
for assignment in assignmentList:
var, value = assignment.split(' : ')
assignmentsDict[var] = value
currentFactor.setProbability(assignmentsDict, float(probability))
problemBayesNet.setCPT(variable, currentFactor)
#print currentFactor
elif testDict['constructRandomly'] == 'True':
problemBayesNet = bayesNet.constructRandomlyFilledBayesNet(variables, edges, variableDomainsDict)
parseDict['problemBayesNet'] = problemBayesNet
if testDict['alg'] == 'inferenceByVariableElimination':
variableEliminationOrder = testDict['variableEliminationOrder'].split(' ')
parseDict['variableEliminationOrder'] = variableEliminationOrder
elif testDict['alg'] == 'inferenceByLikelihoodWeightingSampling':
numSamples = int(testDict['numSamples'])
parseDict['numSamples'] = numSamples
return parseDict
def parseBayesNetProblem(testDict):
# needs to be able to parse in a bayes net,
# and figure out what type of operation to perform and on what
parseDict = {}
variableDomainsDict = {}
for line in testDict['variableDomainsDict'].split('\n'):
variable, domain = line.split(' : ')
variableDomainsDict[variable] = domain.split(' ')
parseDict['variableDomainsDict'] = variableDomainsDict
variables = []
for line in testDict["variables"].split('\n'):
variable = line.strip()
variables.append(variable)
edges = []
for line in testDict["edges"].split('\n'):
tokens = line.strip().split()
if len(tokens) == 2:
edges.append((tokens[0], tokens[1]))
else:
raise Exception(
"[parseBayesNetProblem] Bad evaluation line: |%s|" % (line, ))
# inference query args
queryVariables = testDict['queryVariables'].split(' ')
parseDict['queryVariables'] = queryVariables
evidenceDict = {}
for line in testDict['evidenceDict'].split('\n'):
if (line.count(' : ')
): #so we can pass empty dicts for unnormalized variables
(evidenceVariable, evidenceValue) = line.split(' : ')
evidenceDict[evidenceVariable] = evidenceValue
parseDict['evidenceDict'] = evidenceDict
if testDict['constructRandomly'] == 'False':
# load from test file
problemBayesNet = bayesNet.constructEmptyBayesNet(
variables, edges, variableDomainsDict)
for variable in variables:
currentFactor = bayesNet.Factor(
[variable],
problemBayesNet.inEdges()[variable], variableDomainsDict)
for line in testDict[variable + 'FactorTable'].split('\n'):
assignments, probability = line.split(" = ")
assignmentList = [
assignment for assignment in assignments.split(', ')
]
assignmentsDict = {}
for assignment in assignmentList:
var, value = assignment.split(' : ')
assignmentsDict[var] = value
currentFactor.setProbability(assignmentsDict,
float(probability))
problemBayesNet.setCPT(variable, currentFactor)
#print currentFactor
elif testDict['constructRandomly'] == 'True':
problemBayesNet = bayesNet.constructRandomlyFilledBayesNet(
variables, edges, variableDomainsDict)
parseDict['problemBayesNet'] = problemBayesNet
if testDict['alg'] == 'inferenceByVariableElimination':
variableEliminationOrder = testDict['variableEliminationOrder'].split(
' ')
parseDict['variableEliminationOrder'] = variableEliminationOrder
elif testDict['alg'] == 'inferenceByLikelihoodWeightingSampling':
numSamples = int(testDict['numSamples'])
parseDict['numSamples'] = numSamples
return parseDict