def parseFactorFromFileDict(fileDict, variableDomainsDict=None, prefix=None):
if prefix is None:
prefix = ''
if variableDomainsDict is None:
variableDomainsDict = {}
for line in fileDict['variableDomainsDict'].split('\n'):
variable, domain = line.split(' : ')
variableDomainsDict[variable] = domain.split(' ')
# construct a dict from names to factors and
# load a factor from the test file for each
unconditionedVariables = []
for variable in fileDict[prefix + "unconditionedVariables"].split(' '):
unconditionedVariable = variable.strip()
unconditionedVariables.append(unconditionedVariable)
conditionedVariables = []
for variable in fileDict[prefix + "conditionedVariables"].split(' '):
conditionedVariable = variable.strip()
if variable != '':
conditionedVariables.append(conditionedVariable)
if 'constructRandomly' not in fileDict or fileDict[
'constructRandomly'] == 'False':
currentFactor = bayesNet.Factor(unconditionedVariables,
conditionedVariables,
variableDomainsDict)
for line in fileDict[prefix + '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))
elif fileDict['constructRandomly'] == 'True':
currentFactor = bayesNet.constructAndFillFactorRandomly(
unconditionedVariables, conditionedVariables, variableDomainsDict)
return currentFactor
def parseFactorFromFileDict(fileDict, variableDomainsDict=None, prefix=None):
if prefix is None:
prefix = ''
if variableDomainsDict is None:
variableDomainsDict = {}
for line in fileDict['variableDomainsDict'].split('\n'):
variable, domain = line.split(' : ')
variableDomainsDict[variable] = domain.split(' ')
# construct a dict from names to factors and
# load a factor from the test file for each
unconditionedVariables = []
for variable in fileDict[prefix + "unconditionedVariables"].split(' '):
unconditionedVariable = variable.strip()
unconditionedVariables.append(unconditionedVariable)
conditionedVariables = []
for variable in fileDict[prefix + "conditionedVariables"].split(' '):
conditionedVariable = variable.strip()
if variable != '':
conditionedVariables.append(conditionedVariable)
if 'constructRandomly' not in fileDict or fileDict['constructRandomly'] == 'False':
currentFactor = bayesNet.Factor(unconditionedVariables, conditionedVariables,
variableDomainsDict)
for line in fileDict[prefix + '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))
elif fileDict['constructRandomly'] == 'True':
currentFactor = bayesNet.constructAndFillFactorRandomly(unconditionedVariables, conditionedVariables, variableDomainsDict)
return currentFactor