1.HelloMahout.java
2.DistanceTest.java
3.MahoutDemo.java
1.HelloMahout.java
package cn.crxy.mahout; import java.io.File;
import java.util.List; import org.apache.log4j.Logger;
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.recommender.GenericUserBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
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.UserSimilarity; public class HelloMahout { public static void main(String[] args) { Logger logger=Logger.getLogger(HelloMahout.class);
try {
//读取用户评分数据 封装成一个model
DataModel model = new FileDataModel(new File("F:\\360Downloads\\超人学院\\第14期视频\\2016-09-12【mahout】\\样本数据\\info.csv"));
// 根据相似度找出对应的好朋友的标准 物以类聚,人以群分
UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model);
// 邻域 选择两个好朋友帮我推荐
UserNeighborhood userNeighborhood = new NearestNUserNeighborhood(2,userSimilarity, model);
// 构建推荐引擎
Recommender recommender = new GenericUserBasedRecommender(model,userNeighborhood, userSimilarity);
// 进行推荐
List<RecommendedItem> recommend = recommender.recommend(1, 5);
for (RecommendedItem item : recommend) {
logger.info(item);
}
} catch (Exception e) {
logger.error(e.getMessage());
}
}
}
2.DistanceTest.java
package cn.crxy.mahout; import org.junit.Before;
import org.junit.Test; public class DistanceTest { // 水果维度依次为：苹果、梨、桃子、栗子、香蕉、橘子
// 小明：5,4,2,1,5,5
// 小丽：5,3,1,2,1,1
// 小王：5,3,4,1,4,3
private int[] a;
private int[] b;
private int[] c; @Before
public void initData(){
a=new int[]{5,4,2,1,5,5};
b=new int[]{5,3,1,2,1,1};
c=new int[]{5,3,4,1,4,3};
} @Test
public void Distance(){
// a-b:5.916079783099616
// a-c:3.1622776601683795
// c-b:4.795831523312719 System.out.println(String.format("a-b:%s", 1.0/(1.0+Man(a, b))));
System.out.println(String.format("a-c:%s", 1.0/(1.0+Man(a, c))));
System.out.println(String.format("c-b:%s", 1.0/(1.0+Man(c, b))));
// a-b:0.08333333333333333
// a-c:0.14285714285714285
// c-b:0.1 }
//欧式距离
private double ErluD(int[] a_array,int[] b_array){
double result=0;
for (int i = 0; i < a_array.length; i++) {
result+=Math.pow(a_array[i]-b_array[i],2);
}
return Math.sqrt(result);
}
//曼哈顿距离
private double Man(int[] a_array,int[] b_array){
double result=0;
for (int i = 0; i < a_array.length; i++) {
result+=Math.abs(a_array[i]-b_array[i]);
}
return result;
}
//min式距离
private double Min(int[] a_array,int[] b_array,int p){
double result=0;
for (int i = 0; i < a_array.length; i++) {
result+=Math.pow(Math.abs(a_array[i]-b_array[i]),p);
}
return Math.pow(result,1.0/p);
} }
3.MahoutDemo.java
package cn.crxy.mahout; import java.io.File;
import java.util.List; import org.apache.mahout.cf.taste.impl.common.FastByIDMap;
import org.apache.mahout.cf.taste.impl.common.FastIDSet;
import org.apache.mahout.cf.taste.impl.model.GenericPreference;
import org.apache.mahout.cf.taste.impl.model.GenericUserPreferenceArray;
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.GenericBooleanPrefItemBasedRecommender;
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.CachingItemSimilarity;
import org.apache.mahout.cf.taste.impl.similarity.CachingUserSimilarity;
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.model.PreferenceArray;
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;
import org.junit.Before;
import org.junit.Ignore;
import org.junit.Test; public class MahoutDemo { //组装datamodel // userid itemid score
// 101 102 103 104
// 1(5,4,2,)
// 2(,2,4,1)
// 3(4,3,1,)
DataModel dataModel; @Before
public void initData() throws Exception{
//每一个用户的喜好列表 key：用户id value：该用户的偏好列表
FastByIDMap<PreferenceArray> data=new FastByIDMap<PreferenceArray>();
//组装第一个用户 偏好列表
PreferenceArray array1=new GenericUserPreferenceArray(3);
//PreferenceArray index 指：偏好列表的index 序号。
array1.setUserID(0, 1);
array1.setItemID(0, 101);
array1.setValue(0, 5); array1.setUserID(1, 1);
array1.setItemID(1, 102);
array1.setValue(1, 4); array1.setUserID(2, 1);
array1.setItemID(2, 103);
array1.setValue(2, 2); data.put(1, array1); //组装第二个喜好
PreferenceArray array2=new GenericUserPreferenceArray(3);
//2(,2,4,1)
array2.set(0, new GenericPreference(2,102,2));
array2.set(1, new GenericPreference(2,103,4));
array2.set(2, new GenericPreference(2,104,1));
data.put(2, array2);
//组装第三个喜好
PreferenceArray array3=new GenericUserPreferenceArray(3);
//3(4,3,1,)
array3.set(0, new GenericPreference(3,101,4));
array3.set(1, new GenericPreference(3,102,3));
array3.set(2, new GenericPreference(3,103,1));
data.put(3, array3); //dataModel=new GenericDataModel(data);
// dataModel=new GenericBooleanPrefDataModel(userData);
// System.out.println(dataModel.getPreferenceValue(1, 102));//获得1用户对102的评分
// System.out.println(dataModel.getItemIDsFromUser(1));
// System.out.println(dataModel.getUserIDs()); //1 101 102 103
//2 102 103
// key为userid value:物品的集合 set
FastByIDMap<FastIDSet> userData=new FastByIDMap<FastIDSet>(); FastIDSet userSet1=new FastIDSet(3);
userSet1.add(101);
userSet1.add(102);
userSet1.add(103);
userData.put(1,userSet1); FastIDSet userSet2=new FastIDSet(2);
userSet2.add(102);
userSet2.add(103);
userData.put(2,userSet2); //无偏好的构建
// dataModel=new GenericBooleanPrefDataModel(userData); //读取文件 有偏好的
dataModel=new FileDataModel(new File("F:\\360Downloads\\超人学院\\第14期视频\\2016-09-12【mahout】\\样本数据\\info.csv"));
//读取文件 无偏好的 无偏好的数据只有用户和其关联的商品 没有对应商品的评分
// dataModel=new FileDataModel(new File("F:\\360Downloads\\超人学院\\第14期视频\\2016-09-12【mahout】\\样本数据\\ubool.data")); // 对于无偏好数据：getvalue：如果存在记录则是1.0；否则为null。
// System.out.println(dataModel.getPreferenceValue(1, 103));
// System.out.println(dataModel.getItemIDsFromUser(1));
// System.out.println(dataModel.getUserIDs()); }
@Ignore
public void testUserSimi() throws Exception{ //利用model和相似度函数 计算用户相似度
// UserSimilarity userSimilarity=new TanimotoCoefficientSimilarity(dataModel);
UserSimilarity userSimilarity=new PearsonCorrelationSimilarity(dataModel);
userSimilarity=new CachingUserSimilarity(userSimilarity, dataModel);
//查询用户之间的相似度 0.9999999999999998 0.944911182523068
//如果使用CachingUserSimilarity userSimilarity(1,5) 第二次不会再次计算了
System.out.println(userSimilarity.userSimilarity(1, 5));
System.out.println(userSimilarity.userSimilarity(1, 5));
}
@Ignore
public void testItemSimi() throws Exception{ //利用model和相似度函数 计算物品相似度
ItemSimilarity itemSimilarity=new PearsonCorrelationSimilarity(dataModel);
itemSimilarity =new CachingItemSimilarity(itemSimilarity,dataModel);
//查询物品之间的相似度 0.9449111825230729
System.out.println(itemSimilarity.itemSimilarity(101, 102));
}
@Test
public void testuserNeighborhood() throws Exception{
//相似度 有相似度才能算邻居是谁
UserSimilarity userSimilarity=new PearsonCorrelationSimilarity(dataModel);
//1.固定数目的邻居 如果取邻居 只取前三个
UserNeighborhood userNeighborhood=new NearestNUserNeighborhood(3,userSimilarity,dataModel);
long[] userNeighborhoods = userNeighborhood.getUserNeighborhood(1);//为1用户取得用户
for (long l : userNeighborhoods) {
System.out.println(l+"NearestNUserNeighborhoodsimi---"+userSimilarity.userSimilarity(1, l));
}
// 4NearestNUserNeighborhoodsimi---0.9999999999999998
// 5NearestNUserNeighborhoodsimi---0.944911182523068
// 2NearestNUserNeighborhoodsimi--- -0.7642652566278799这个是负0.7 //2.固定阈值的邻居 只要0.8以上的
userNeighborhood=new ThresholdUserNeighborhood(0.7,userSimilarity,dataModel);
long[] userNeighborhoodsnew = userNeighborhood.getUserNeighborhood(1);
System.out.println(userSimilarity.userSimilarity(1, 2)); //查看1和2的相似度
for (long l : userNeighborhoodsnew) {
System.out.println(l+"ThresholdUserNeighborhoodsimi---"+userSimilarity.userSimilarity(1, l));
} }
@Test
public void testItemCmd() throws Exception{
//1.基于物品的有偏好的推荐 基于物品的不需要邻居
// ItemSimilarity itemSimilarity=new PearsonCorrelationSimilarity(dataModel);
// Recommender recommender=new GenericItemBasedRecommender(dataModel,itemSimilarity); //2.基于物品的无偏好推荐
ItemSimilarity itemSimilarity=new TanimotoCoefficientSimilarity(dataModel);
Recommender recommender=new GenericBooleanPrefItemBasedRecommender(dataModel,itemSimilarity); List<RecommendedItem> recommend = recommender.recommend(1, 3);//给用户1推荐3个.
for (RecommendedItem recommendedItem : recommend) {
System.out.println(recommendedItem);
//1.基于物品的有偏好的推荐RecommendedItem[item:104, value:5.0]其他的推荐不出来了....所以只推荐出了1个 //2.基于物品的无偏好的推荐
//RecommendedItem[item:104, value:1.8]
//RecommendedItem[item:106, value:1.15]
//RecommendedItem[item:105, value:0.85]
}
}
@Test
public void testUserCmd() throws Exception{
//1.基于用户的有偏好的推荐
//UserSimilarity userSimilarity=new PearsonCorrelationSimilarity(dataModel);
//2.基于用户的无偏好的推荐
UserSimilarity userSimilarity=new TanimotoCoefficientSimilarity(dataModel); UserNeighborhood userNeighborhood=new NearestNUserNeighborhood(3,userSimilarity,dataModel);//Top 3
//构建推荐对象
Recommender recommender=new GenericUserBasedRecommender(dataModel,userNeighborhood,userSimilarity);
List<RecommendedItem> recommend = recommender.recommend(1, 3);
for (RecommendedItem recommendedItem : recommend) {
System.out.println(recommendedItem);
//1.基于用户的有偏好推荐
//RecommendedItem[item:104, value:5.0]
//RecommendedItem[item:106, value:4.0]
//2.基于用户的无偏好推荐
//RecommendedItem[item:106, value:4.0]
//RecommendedItem[item:104, value:3.2121212] }
} }
