def test_forward_chaining():
def condition1(subject):
return subject['animal'] == 'dog'
def condition2(subject):
return subject['legsQuantity'] == '4'
def consequence1(subject):
subject['legsQuantity'] = '4'
def consequence2(subject):
subject['locomotion'] = 'quadruped'
rule1 = Rule('all dogs have 4 legs', condition1, consequence1, {'animal': 'dog'}, ('animal',))
rule2 = Rule('Anything with 4 legs is a quadruped', condition2, consequence2, {'legsQuantity': '4'},
('legsQuantity',))
rs = RuleSet()
rs.add_rule(rule1)
rs.add_rule(rule2)
kb = KnowledgeBase()
kb.add_knowledge({'animal': 'dog'})
ie = ForwardChainingInferenceEngine(kb, rs)
ie.run_engine()
assert ie.knowledge_base.knowledge == {'animal': 'dog', 'legsQuantity': '4', 'locomotion': 'quadruped'}
def forward_chaining_method(rules, init_knowledge):
rules_set = RuleSet()
map(rules_set.add_rule, rules)
knowledge_base = KnowledgeBase()
knowledge_base.add_knowledge(yaml.safe_load(init_knowledge))
inference_engine = ForwardChainingInferenceEngine(knowledge_base, rules_set)
inference_engine.run_engine()
logger.info('Final Knowledge')
logger.info(knowledge_base.knowledge)