def output(train, test, result, k):
popularity = Evaluation.Popularity(train, test, result)
coverage = Evaluation.Coverage(train, test, result) * 100
print("K is:", k)
print("popularity:{0}".format(popularity))
print("coverage:{0}".format(coverage))
return popularity, coverage
# result = UserCF.Recommendation(test.keys(), train, W)
# W = UserCF_IIF.UserSimilarity(train)
# rank = UserCF_IIF.Recommend('1',train,W)
# result = UserCF_IIF.Recommendation(test.keys(), train, W)
# W = ItemCF.ItemSimilarity(train)
# rank = ItemCF.Recommend('1',train,W)
# result = ItemCF_IUF.Recommendation(test.keys(),train, W)
# W = ItemCF_IUF.ItemSimilarity(train)
# rank = ItemCF_IUF.Recommend('1',train,W)
# result = ItemCF_IUF.Recommendation(test.keys(),train, W)
[P, Q] = LFM.LatentFactorModel(train, 10, 30, 0.02, 0.01)
# rank = LFM.Recommend('2',train,P,Q)
result = LFM.Recommendation(test.keys(), train, P, Q)
N = 30
precision += Evaluation.Precision(train, test, result, N)
recall += Evaluation.Recall(train, test, result, N)
coverage += Evaluation.Coverage(train, test, result, N)
popularity += Evaluation.Popularity(train, test, result, N)
precision /= numFlod
recall /= numFlod
coverage /= numFlod
popularity /= numFlod
#运行完标志
print('Done!')
# W = ItemCF_IUF.ItemSimilarity(train)
# rank = ItemCF_IUF.Recommend('1',train,W)
# result = ItemCF_IUF.Recommendation(test.keys(),train, W)
# [P,Q] = LFM.LatentFactorModel(train, 10,30, 0.02, 0.01)
# rank = LFM.Recommend('2',train,P,Q)
# result = LFM.Recommendation(test.keys(), train,P,Q)
N = 5
# 用户-物品训练集，用户-物品测试集，用户推荐字典集，推荐要N个
precision += Evaluation.Precision(train,test, result,N)
recall += Evaluation.Recall(train,test,result,N)
coverage += Evaluation.Coverage(train, test, result,N)
# popularity += Evaluation.Popularity(train, test, result,N)
precision_transE += Evaluation.Precision(train, test, result_transE, N)
recall_transE += Evaluation.Recall(train, test, result_transE, N)
coverage_transE += Evaluation.Coverage(train, test, result_transE, N)
print('========================')
# 除以五次的平均
precision /= numFlod
recall /= numFlod
coverage /= numFlod
# popularity /= numFlod
precision_transE /= numFlod
recall_transE /= numFlod
coverage_transE /= numFlod
