#add the attributes
mySet.addDefinition(att1) # wifi
mySet.addDefinition(att2) # time
mySet.addDefinition(att3) # location
parameters = [att1, att2, att3]
#add 10 random attributes
random_factory = RandomAttributeFactory(1000)
for idx in range(1, 10):
rndatt = random_factory.getNext()
parameters.append(rndatt)
mySet.addDefinition(rndatt)
#select classifier
learner = RulesLearner('tree_learner', mySet, 'on')
learner.setTemplateRuleLearnerName("[WiFi] <= When [Location] ")
learner.setFieldNames(map(str, parameters))
print map(str, parameters)
# [on] <= When [morning, home]
for a in range(1,300):
learner.addSample(['on', 'morning', 'home'] + map( lambda x: x.sample(), parameters[3:]), parameters)
# [off] <= When [morning, work]
for a in range(1,200):
learner.addSample(['off', 'morning', 'work'] + map( lambda x: x.sample(), parameters[3:]), parameters)
# [on] <= When [afternoon, work]
for a in range(1,200):
learner.addSample(['on', 'afternoon', 'work'] + map( lambda x: x.sample(), parameters[3:]), parameters)
