#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)