package mp05.com;
import java.io.File;
import java.io.IOException;
import java.util.List;

import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.eval.RecommenderBuilder;
import org.apache.mahout.cf.taste.eval.RecommenderEvaluator;
import org.apache.mahout.cf.taste
 
.impl.eval.AverageAbsoluteDifferenceRecommenderEvaluator;
import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
import org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood;
import org.apache.mahout.cf.taste.impl.neighborhood.ThresholdUserNeighborhood;
import org.apache.mahout
 
.cf.taste.impl.recommender.GenericItemBasedRecommender;
import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.EuclideanDistanceSimilarity;
import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
import org.apache
 
.mahout.cf.taste.impl.similarity.TanimotoCoefficientSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.cf.taste.similarity.ItemSimilarity;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;
public class RecommenderIntro {
    //下面是一個基於使用者的簡單的推薦
    //探究使用者與使用者之間的相似性，簡單的說就是你有一個好基友，他喜歡這首歌，那麼你喜歡這首歌的可能性很大。
    public static void main(String[] args) throws TasteException, Exception {
        try {
            DataModel model=new FileDataModel(new File("/home/xuyao/mahout/test_data/intro.csv"));
            //UserSimilarity封裝了使用者間相似性的概念
            UserSimilarity similarity=new PearsonCorrelationSimilarity(model);
            //UserNeighborhood封裝了最相似使用者組的概念.  2是使用者的鄰域，指的是最相似的幾個使用者
            UserNeighborhood neighborhood=new NearestNUserNeighborhood(2,similarity,model);
            //Recommender推薦引擎
            Recommender recommender=new GenericUserBasedRecommender(model,neighborhood,similarity);
            List<RecommendedItem> recommendations=recommender.recommend(1,1);
            for(RecommendedItem recommendation : recommendations)
                System.out.println(recommendation);
        } catch (IOException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
        evaluator();

    }
    //配置並評估一個推薦程式，這裡也是基於使用者的推薦
    public static void evaluator() throws IOException, TasteException{
        DataModel model=new FileDataModel(new File("/home/xuyao/mahout/ua.base"));
        RecommenderEvaluator evaluator=new AverageAbsoluteDifferenceRecommenderEvaluator();
        RecommenderBuilder builder =new RecommenderBuilder() {

            public Recommender buildRecommender(DataModel model) throws TasteException {

                //PearsonCorrelationSimilarity：相似性度量標準--皮爾遜相關係數
                UserSimilarity similarity=new PearsonCorrelationSimilarity(model);

                //EuclideanDistanceSimilarity: 相似性度量標準--歐式距離
                UserSimilarity similarity_2=new EuclideanDistanceSimilarity(model);

                //TanimotoCoefficientSimilarity: 相似性度量標準--谷本系數--完全拋開偏好值
                UserSimilarity similarity_3=new TanimotoCoefficientSimilarity(model);

                //NearestNUserNeighborhood ：固定大小的鄰域。。改變這個100可以得到不同的打分，所以這個是可以用來調優的
                UserNeighborhood neighborhood=new NearestNUserNeighborhood(100,similarity,model);

                //下面是另一個表示鄰域的，用的是基於閾值的鄰域。。其中0.5為可調優。
                UserNeighborhood neighborhood_2=new ThresholdUserNeighborhood(0.5, similarity, model);

                return new GenericUserBasedRecommender(model, neighborhood, similarity);
            }
        };
        //0.9指的是訓練90%的資料，測試10%的資料。 而1.0指的是輸入的資料的比例。  這裡表示資料集全部輸入，其中90%用來訓練，另外10%用來測試。
        double socre =evaluator.evaluate(builder, null, model, 0.9, 1.0);
        //這個socre表示這個模型的打分，分數越小表示這個模型越好。
        System.out.println(socre);
    }
    //下面是基於物品的推薦，簡單的說就是你的電腦有360安全衛士，360防毒，360瀏覽器，於是說你比較喜歡360的產品，就給你推薦360WIFI。
    public static void evaluator_2() throws IOException{
        DataModel model=new FileDataModel(new File("/home/xuyao/mahout/ua.base"));
        RecommenderBuilder builder =new RecommenderBuilder() {

            public Recommender buildRecommender(DataModel model) throws TasteException {
                ItemSimilarity similarity =new PearsonCorrelationSimilarity(model);
                return new GenericItemBasedRecommender(model, similarity);
            }
        };
    }
}

1,101,5  

1,102,3  

1,103,2.5  

2,101,2  

2,102,2.5  

2,103,5  

2,104,2  

3,101,2.5  

3,104,4  

3,105,4.5  

3,107,5  

4,101,5  

4,103,3  

4,104,4.5  

4,106,4  

5,101,4  

5,102,3  

5,103,2  

5,104,4  

5,105,3.5  

5,106,4